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• Published: 20 June 2017

International Society of Sports Nutrition Position Stand: protein and exercise

• Ralf Jäger 1 ,
• Chad M. Kerksick 2 ,
• Bill I. Campbell 3 ,
• Paul J. Cribb 4 ,
• Shawn D. Wells 5 ,
• Tim M. Skwiat 5 ,
• Martin Purpura 1 ,
• Tim N. Ziegenfuss 6 ,
• Arny A. Ferrando 7 ,
• Shawn M. Arent 8 ,
• Abbie E. Smith-Ryan 9 ,
• Jeffrey R. Stout 10 ,
• Paul J. Arciero 11 ,
• Michael J. Ormsbee 12 , 13 ,
• Lem W. Taylor 14 ,
• Colin D. Wilborn 14 ,
• Doug S. Kalman 15 ,
• Richard B. Kreider 16 ,
• Darryn S. Willoughby 17 ,
• Jay R. Hoffman 10 ,
• Jamie L. Krzykowski 18 &
• Jose Antonio   ORCID: orcid.org/0000-0002-8930-1058 19  

Journal of the International Society of Sports Nutrition volume  14 , Article number:  20 ( 2017 ) Cite this article

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Position statement

The International Society of Sports Nutrition (ISSN) provides an objective and critical review related to the intake of protein for healthy, exercising individuals. Based on the current available literature, the position of the Society is as follows:

An acute exercise stimulus, particularly resistance exercise, and protein ingestion both stimulate muscle protein synthesis (MPS) and are synergistic when protein consumption occurs before or after resistance exercise.

For building muscle mass and for maintaining muscle mass through a positive muscle protein balance, an overall daily protein intake in the range of 1.4–2.0 g protein/kg body weight/day (g/kg/d) is sufficient for most exercising individuals, a value that falls in line within the Acceptable Macronutrient Distribution Range published by the Institute of Medicine for protein.

There is novel evidence that suggests higher protein intakes (>3.0 g/kg/d) may have positive effects on body composition in resistance-trained individuals (i.e., promote loss of fat mass).

Recommendations regarding the optimal protein intake per serving for athletes to maximize MPS are mixed and are dependent upon age and recent resistance exercise stimuli. General recommendations are 0.25 g of a high-quality protein per kg of body weight, or an absolute dose of 20–40 g.

Acute protein doses should strive to contain 700–3000 mg of leucine and/or a higher relative leucine content, in addition to a balanced array of the essential amino acids (EAAs).

These protein doses should ideally be evenly distributed, every 3–4 h, across the day.

The optimal time period during which to ingest protein is likely a matter of individual tolerance, since benefits are derived from pre- or post-workout ingestion; however, the anabolic effect of exercise is long-lasting (at least 24 h), but likely diminishes with increasing time post-exercise.

While it is possible for physically active individuals to obtain their daily protein requirements through the consumption of whole foods, supplementation is a practical way of ensuring intake of adequate protein quality and quantity, while minimizing caloric intake, particularly for athletes who typically complete high volumes of training.

Rapidly digested proteins that contain high proportions of essential amino acids (EAAs) and adequate leucine, are most effective in stimulating MPS.

Different types and quality of protein can affect amino acid bioavailability following protein supplementation.

Athletes should consider focusing on whole food sources of protein that contain all of the EAAs (i.e., it is the EAAs that are required to stimulate MPS).

Endurance athletes should focus on achieving adequate carbohydrate intake to promote optimal performance; the addition of protein may help to offset muscle damage and promote recovery.

Pre-sleep casein protein intake (30–40 g) provides increases in overnight MPS and metabolic rate without influencing lipolysis.

In 2007, the International Society of Sports Nutrition (ISSN) published its first position stand devoted to the science and application of dietary protein intake [ 1 ]. Subsequently, this paper has been accessed more than 200,000 times and continues to serve as a key reference on the topic. In the past ten years, there have been continued efforts to advance the science and application of dietary protein intake for the benefit of athletes and fitness-minded individuals. This updated position stand includes new information and addresses the most important dietary protein categories that affect physically active individuals across domains such as exercise performance, body composition, protein timing, recommended intakes, protein sources and quality, and the preparation methods of various proteins.

Benefits on exercise performance

Most of the scientific research investigating the effects of protein intake on exercise performance has focused on supplemental protein intake. From a broad perspective, the dependent measures of these studies can be categorized into two domains:

Endurance exercise performance

Resistance exercise performance (increases in maximal strength)

Very few studies have investigated the effects of prolonged periods (one week or more) of dietary protein manipulation on endurance performance. Macdermid and colleagues [ 2 ] compared the influence of an isoenergetic, high-protein/moderate-carbohydrate diet (3.3 and 5.9 g of protein and carbohydrate/kg body weight per day, respectively) with a diet that was more typical of an endurance athlete (1.3 and 7.9 g of protein and carbohydrate/kg body weight per day, respectively) in endurance-trained cyclists. The trained cyclists ingested each diet for a 7-day period in a randomized, crossover fashion. Before and following the 7-day diet intervention, a self-paced cycling endurance time trial was conducted as the primary measure of exercise performance. At the end of the treatment period, it took cyclists on the higher protein diet 20% more time to complete the self-paced time trial - significantly longer than for those on the lower protein/higher carbohydrate diet. This finding is not surprising given that dietary protein is not a preferred energy source and the dietary carbohydrate intakes in the higher protein treatment were below recommended intakes for endurance athletes (6–10 g of carbohydrate/kg/d) [ 3 ]. It should be noted however that a 7-day treatment period is exceedingly brief. It is unknown what the effect of a higher protein diet would be over the course of several weeks or months.

In another study [ 4 ] utilizing highly trained cyclists during a period of increased training intensity, it was observed that 3 g of protein/kg/d offered no improvements in a simulated time trial as compared to 1.5 g of protein/kg body weight/day. Carbohydrate intake was kept constant (6 g/kg/d) in both the moderate and high protein treatments during this three-week intervention. Although the number of investigations is limited, it appears as if increasing protein intakes above recommended intakes does not enhance endurance performance [ 2 , 4 , 5 ].

In addition to these studies that spanned one to three weeks, several acute-response (single feeding and exercise sessions) studies exist, during which protein was added to a carbohydrate beverage prior to or during endurance exercise. Similarly, most of these interventions also reported no added improvements in endurance performance when protein was added to a carbohydrate beverage as compared to carbohydrate alone [ 6 , 7 , 8 , 9 ]. An important research design note, however, is that those studies which reported improvements in endurance performance when protein was added to a carbohydrate beverage before and during exercise all used a time-to-exhaustion test [ 10 , 11 , 12 ]. When specifically interested in performance outcomes, a time trial is preferred as it better mimics competition and pacing demands.

In conclusion, added protein does not appear to improve endurance performance when given for several days, weeks, or immediately prior to and during endurance exercise. While no ergogenic outcomes may be evident, the scientific literature is consistent in reporting that adding protein to a carbohydrate beverage/gel during exhaustive endurance exercise suppresses markers of muscle damage (creatine kinase) 12 to 24 h post-exercise [ 8 , 11 , 12 , 13 ] and decreases the endurance athletes’ feelings of muscular soreness [ 6 , 7 , 8 , 13 ]. For these reasons, it seems prudent to recommend for endurance athletes to ingest approximately 0.25 g of protein/kg body weight per hour of endurance exercise (in addition to the athlete’s regular carbohydrate intake) to suppress markers of muscle damage and improve subjective feelings of muscular soreness [ 11 , 12 ]. Another important consideration relates to the impact of ingesting protein along with carbohydrate on rates of protein synthesis and balance during prolonged bouts of endurance exercise. Beelen and colleagues [ 14 ] determined that adding protein to carbohydrate consumption throughout a prolonged bout of endurance exercise promotes a higher whole body net protein balance, but the added protein does not exert any further impact on rates of MPS. While performance outcomes were not measured, these results shift the focus of nutrient ingestion during prolonged bouts of endurance exercise to the ingestion of carbohydrate.

When adequate carbohydrate is delivered, adding protein to carbohydrate does not appear to improve endurance performance over the course of a few days or weeks.

Adding protein during or after an intensive bout of endurance exercise may suppress the rise in plasma proteins linked to myofibrillar damage and reduce feelings of muscle soreness.

There are relatively few investigations on the effects of protein supplementation on endurance performance.

Resistance exercise performance

The extent to which protein supplementation, in conjunction with resistance training, enhances maximal strength is contingent upon many factors, including:

Resistance-training program variables (such as intensity, volume, and progression)

Length of the resistance-training program/intervention

Training status of the participants engaging in the resistance-training program

Energy intake in the diet

Quality and quantity of protein intake (with an emphasis on leucine content of the protein)

Co-ingestion of additional dietary ingredients that may favorably impact strength (e.g. creatine, HMB)

Taking each of these variables into consideration, the effects of supplemental protein consumption has on maximal strength enhancement are varied, with a majority of the investigations reporting no benefit [ 15 , 16 , 17 , 18 , 19 , 20 , 21 , 22 , 23 , 24 , 25 ] and a few reporting improvements in maximal strength [ 26 , 27 , 28 , 29 ]. With limited exceptions [ 16 , 18 , 23 , 27 ], most of the studies utilized young, healthy, untrained males as participants. In one investigation examining college football athletes supplementing with a proprietary milk protein supplement (two servings of 42 g per day) for 12 weeks, a 14.5% increase in maximal squat strength was observed compared to a 6.9% increase in the placebo group [ 28 ]. These differences were statistically significant. When females were the only sex investigated, the outcomes consistently indicated that supplemental protein does not appear to enhance maximal strength at magnitudes that reach statistical significance. Hida et al. [ 30 ] reported that females supplementing with 15 g of egg white protein (which raised daily protein intake to 1.23 g of protein/kg body weight/day) experienced no improvements in maximal upper and lower body strength as compared to a carbohydrate placebo (ingesting one gram of protein/kg body weight/day) over an 8-week period. An important note for this study is that 15 g of egg protein is considered by many to be a sub-optimal dose [ 31 ]. However, others have advocated that the total daily intake of protein might be as important or more important [ 32 ]. In another study, Josse et al. [ 33 ] reported that non-resistance trained females supplementing with one liter of skimmed bovine milk (providing 36 g of protein) after resistance exercise improved maximal strength in seven of nine measures as compared to a carbohydrate placebo group, but only the improvements to maximal bench press strength attained statistical significance compared to the placebo. In contrast, Taylor and colleagues [ 34 ] reported that pre- and post-exercise whey protein ingestion significantly increased maximal upper-body strength (+4.9 kg bench press one repetition maximum) in comparison to changes seen when a maltodextrin placebo (+2.3 kg) was ingested in a group of female collegiate basketball players over an 8-week period.

In summary, while research investigating the addition of supplemental protein to a diet with adequate energy and nutrient intakes is inconclusive in regards to stimulating strength gains in conjunction with a resistance-training program to a statistically significant degree, greater protein intakes that are achieved from both dietary and supplemental sources do appear to have some advantage. Hoffman and colleagues [ 29 ] reported that in athletes consuming daily protein intakes above 2.0 g/kg/d which included protein intakes from both diet and supplements, a 22% and 42% increase in strength was noted in both the squat and bench press exercises during off-season conditioning in college football players compared to athletes that consumed only the recommended levels (1.6–1.8 g/kg/d) for strength/power athletes. Further, it is important to highlight that in most studies cited, protein intervention resulted in greater but non-statistically significant strength improvements as compared to the placebo/control condition. Cermak and colleagues [ 35 ] pooled the outcomes from 22 separate clinical trials to yield 680 subjects in their statistical analysis and found that protein supplementation with resistance training resulted in a 13.5 kg increase (95% Confidence Interval: 6.4–20.7 kg) in lower-body strength when compared to changes seen when a placebo was provided. A similar conclusion was also drawn by Pasiakos et al. [ 36 ] in a meta-analysis where they reported that in untrained participants, protein supplementation might exert very little benefit on strength during the initial weeks of a resistance training program, but as duration, frequency and volume of resistance training increased, protein supplementation may favorably impact skeletal muscle hypertrophy and strength.

Key points:

Results from many single investigations indicate that in both men and women protein supplementation exerts a small to modest impact on strength development.

Pooled results of multiple studies using meta-analytic and other systematic approaches consistently indicate that protein supplementation (15 to 25 g over 4 to 21 weeks) exerts a positive impact on performance.

Body composition

Improving one’s body composition through the loss of fat mass and increasing fat-free mass is often associated with improvements in physical performance. In this respect, many published investigations report that protein supplementation results in significant improvements in lean body weight/cross-sectional areas as compared to placebo treatments [ 15 , 17 , 21 , 22 , 23 , 26 , 27 , 33 , 37 ]. Andersen et al. [ 15 ] examined 22 healthy men that completed a 14-week resistance-training program (3 days/week consisting of 3–4 sets of lower body exercises) while supplementing with either 25 g of a high-quality protein blend or 25 g of carbohydrate. When the blend of milk proteins was provided, significantly greater increases in fat-free mass, muscle cross-sectional area in both the Type I and Type II muscle fibers occurred when compared to changes seen with carbohydrate consumption. Collectively, a meta-analysis by Cermak and colleagues [ 35 ] reported a mean increase in fat-free mass of 0.69 kg (95% Confidence Interval: 0.47–0.91 kg) when protein supplementation was provided versus a placebo during a resistance-training program. Other reviews by Tipton, Phillips and Pasiakos, respectively, [ 36 , 38 , 39 ] provide further support that protein supplementation (15–25 g over 4–14 weeks) augments lean mass accretion when combined with completion of a resistance training program.

Beyond accretion of fat-free mass, increasing daily protein intake through a combination of food and supplementation to levels above the recommended daily allowance (RDA) (RDA 0.8 g/kg/day, increasing to 1.2–2.4 g/kg/day for the endurance and strength/power athletes) while restricting energy intake (30–40% reduction in energy intake) has been demonstrated to maximize the loss of fat tissue while also promoting the maintenance of fat-free mass [ 40 , 41 , 42 , 43 , 44 , 45 ]. The majority of this work has been conducted using overweight and obese individuals who were prescribed an energy-restricted diet that delivered a greater ratio of protein relative to carbohydrate. As a classic example, Layman and investigators [ 40 ] randomized obese women to consume one of two restricted energy diets (1600–1700 kcals/day) that were either higher in carbohydrates (>3.5: carbohydrate-to-protein ratio) or protein (<1.5: carbohydrate-to-protein ratio). Groups were further divided into those that followed a five-day per week exercise program (walking + resistance training, 20–50 min/workout) and a control group that performed light walking of less than 100 min per week. Greater amounts of fat were lost when higher amounts of protein were ingested, but even greater amounts of fat loss occurred when the exercise program was added to the high-protein diet group, resulting in significant decreases in body fat. Using an active population that ranged from normal weight to overweight (BMI: 22–29 kg/m 2 ), Pasiakos and colleagues [ 42 ] examined the impact of progressively increasing dietary protein over a 21-day study period. An aggressive energy reduction model was employed that resulted in each participant reducing their caloric intake by 30% and increasing their energy expenditure by 10%. Each person was randomly assigned to consume a diet that contained either 1× (0.8 g/kg), 2× (1.6 g/kg) or 3× (2.4 g/kg) the RDA for protein. Participants were measured for changes in body weight and body composition. While the greatest body weight loss occurred in the 1× RDA group, this group also lost the highest percentage of fat-free mass and lowest percentage of fat mass. The 2× and 3× RDA groups lost significant amounts of body weight that consisted of 70% and 64% fat mass, respectively.

Collectively, these results indicate that increasing dietary protein can promote favorable adaptations in body composition through the promotion of fat-free mass accretion when combined with a hyperenergetic diet and a heavy resistance training program and can also promote the loss of fat mass when higher intakes of daily protein (2-3× the RDA) are combined with an exercise program and a hypoenergetic diet.

When combined with a hyperenergetic diet and a heavy resistance-training program, protein supplementation may promote increases in skeletal muscle cross-sectional area and lean body mass.

When combined with a resistance-training program and a hypoenergetic diet, an elevated daily intake of protein (2 – 3× the RDA) can promote greater losses of fat mass and greater overall improvements in body composition.

Protein timing

Thanks to seminal work by pioneering research groups [ 37 , 46 , 47 ], by the 1990’s it was clear that exercise and macronutrient consumption interact synergistically to provide a net anabolic effect far greater than either feeding or exercise alone. In the absence of feeding, muscle protein balance remains negative in response to an acute bout of resistance exercise [ 48 ]. Tipton et al. [ 49 ] were one of the first groups to illustrate that an acute feeding of amino acids significantly increases rates of muscle protein synthesis (MPS). Later, Burd et al. [ 50 ] indicated that the combination of acute, exhaustive resistance exercise increases the muscle’s anabolic responsiveness to whey protein provision for up to 24 h. In addition to heightened anabolic sensitivity that stems from the combination of resistance exercise and protein/amino acid feeding, the importance of the EAAs with respect to muscle protein growth has also been elucidated. Tipton et al. [ 51 ] first indicated that nonessential amino acids were not necessary to stimulate MPS. Subsequently, these conclusions were supported by Borsheim [ 52 ] and Volpi [ 53 ]. The study by Borsheim also documented a dose-response outcome characterized by a near doubling of net protein balance in response to a three to six gram dose of the EAAs [ 52 ]. Building on this work, Tipton et al. [ 54 ] reported that EAAs (9–15 g dose) before and after resistance exercise promoted higher net protein accretion, not just 3 or 4 h post exercise but also over a 24-h period [ 55 ]. These findings formed the theoretical concept of protein timing for resistance exercise that has since been transferred to not only other short-duration, high-intensity activities [ 56 ] but also endurance-based sports [ 57 ] and subsequent performance outcomes [ 58 ]. The strategic consumption of nutrition, namely protein or various forms of amino acids, in the hours immediately before and during exercise (i.e., peri-workout nutrition) has been shown to maximize muscle repair and optimize strength- and hypertrophy-related adaptations [ 59 , 60 ]. While earlier investigations reported positive effects from consumption of amino acids [ 37 , 46 , 61 ], it is now clear that intact protein supplements such as egg, whey, casein, beef, soy and even whole milk can evoke an anabolic response that can be similar or greater in magnitude to free form amino acids, assuming ingestion of equal EAA amounts [ 62 , 63 , 64 ].

For instance, whey protein ingested close to resistance exercise, promotes a higher activation (phosphorylation) of mTOR (a key signaling protein found in myocytes that is linked to the synthesis of muscle proteins) and its downstream mRNA translational signaling proteins (i.e., p70s6 kinase and eIF4BP) that further suggests timed ingestion of protein may favorably promote heightened muscle hypertrophy [ 21 , 62 ]. Moreover, it was found that the increased mTOR signaling corresponded with significantly greater muscle hypertrophy after 10 weeks of training [ 65 ]. However, the hypertrophic differences between protein consumption and a non-caloric placebo appeared to plateau by week 21, despite a persistently greater activation of this molecular signaling pathway from supplementation. Results from other research groups [ 56 , 57 , 58 , 66 ] show that timing of protein near (± 2 h) aerobic and anaerobic exercise training appears to provide a greater activation of the molecular signalling pathways that regulate myofibrillar and mitochondrial protein synthesis as well as glycogen synthesis.

It is widely reported that protein consumption directly after resistance exercise is an effective way to acutely promote a positive muscle protein balance [ 31 , 55 , 67 ], which if repeated over time should translate into a net gain or hypertrophy of muscle [ 68 ]. Pennings and colleagues [ 69 ] reported an increase in both the delivery and incorporation of dietary proteins into the skeletal muscle of young and older adults when protein was ingested shortly after completion of exercise. These findings and others add to the theoretical basis for consumption of post-protein sooner rather than later after exercise, since post workout MPS rates peak within three hours and remain elevated for an additional 24–72 h [ 50 , 70 ]. This extended time frame also provides a rationale for both immediate and sustained (i.e., every 3–4 h) feedings to optimize impact. These temporal considerations would also capture the peak elevation in signalling proteins shown to be pivotal for increasing the initiation of translation of muscle proteins, which for the most part appears to peak between 30 and 60 min after exercise [ 71 ]. Finally, while some investigations have shown that a rapid increase in amino acids (aminoacidemia) from a protein dose immediately after or surrounding exercise stimulates increased adaptations to resistance training [ 72 , 73 ], others examining competitive strength/power athletes reported no advantage from pre/post supplement feedings compared to similar feedings in morning and evening hours [ 74 ]. However, these differences may be related to the type of protein used between the studies. The studies showing positive effects of protein timing used milk proteins, whereas the latter study used a collagen based protein supplement.

While a great deal of work has focused on post-exercise protein ingestion, other studies have suggested that pre-exercise and even intra-exercise ingestion may also support favorable changes in MPS and muscle protein breakdown [ 14 , 54 , 75 , 76 , 77 , 78 ]. Initially, Tipton and colleagues [ 54 ] directly compared immediate pre-exercise and immediate post-exercise ingestion of a mixture of carbohydrate (35 g) and EAAs (6 g) combination on changes in MPS. They reported that pre-exercise ingestion promoted higher rates of MPS while also demonstrating that nutrient ingestion prior to exercise increased nutrient delivery to a much greater extent than other (immediate or one hour post-exercise) time points. These results were later challenged by Fujita in 2009 who employed an identical study design with a different tracer incorporation approach and concluded there was no difference between pre- or post-exercise ingestion [ 75 ]. Subsequent work by Tipton [ 79 ] also found that similar elevated rates of MPS were achieved when ingesting 20 g of a whey protein isolate immediately before or immediately after resistance exercise.

At this point, whether any particular time of protein ingestion confers any unique advantage over other time points throughout a 24-h day to improve strength and hypertrophy has yet to be adequately investigated. To date, although a substantial amount of literature discusses this concept [ 60 , 80 ], a limited number of training studies have assessed whether immediate pre- and post-exercise protein consumption provides unique advantages compared to other time points [ 72 , 73 , 81 ]. Each study differed in population, training program, environment and nutrition utilized, with each reporting a different result. What is becoming clear is that the subject population, nutrition habits, dosing protocols on both training and non-training days, energy and macronutrient intake, as well as the exercise bout or training program itself should be carefully considered alongside the results. In particular, the daily amount of protein intake seems to operate as a key consideration because the benefits of protein timing in relation to the peri-workout period seem to be lessened for people who are already ingesting appropriate amounts of protein (e.g. ≥1.6 g/kg/day). This observation can be seen when comparing the initial results of Cribb [ 72 ], Hoffman [ 74 ] and most recently with Schoenfeld [ 82 ]; however, one must also consider that the participants in the Hoffman study may have been hypocaloric as they reported consuming approximately 30 kcal/kg in all groups across the entire study. A literature review by Aragon and Schoenfeld [ 83 ] determined that while compelling evidence exists showing muscle is sensitized to protein ingestion following training, the increased sensitivity to protein ingestion might be greatest in the first five to six hours following exercise. Thus, the importance of timing may be largely dependent on when a pre-workout meal was consumed, the size and composition of that meal and the total daily protein in the diet. In this respect, a pre-exercise meal will provide amino acids during and after exercise and therefore it stands to reason there is less need for immediate post-exercise protein ingestion if a pre-exercise meal is consumed less than five hours before the anticipated completion of a workout. A meta-analysis by Schoenfeld et al. [ 84 ] found that consuming protein within one-hour post resistance exercise had a small but significant effect on increasing muscle hypertrophy compared to delaying consumption by at least two hours. However, sub-analysis of these results revealed the effect all but disappeared after controlling for the total intake of protein, indicating that favorable effects were due to unequal protein intake between the experimental and control groups ( ∼ 1.7 g/kg versus 1.3 g/kg, respectively) as opposed to temporal aspects of feeding. The authors concluded that total protein intake was the strongest predictor of muscular hypertrophy and that protein timing likely influences hypertrophy to a lesser degree. However, the conclusions from this meta-analysis may be questioned because the majority of the studies analyzed were not protein timing studies but rather protein supplementation studies. In that respect, the meta-analysis provides evidence that protein supplementation (i.e., greater total daily protein intake) may indeed confer an anabolic effect. While a strong rationale remains to support the concept that the hours immediately before or after resistance exercise represents an opportune time to deliver key nutrients that will drive the accretion of fat-free mass and possibly other favorable adaptations, the majority of available literature suggests that other factors may indeed be operating to a similar degree that ultimately impact the observed adaptations. In this respect, a key variable that must be accounted for is the absolute need for energy and protein required to appropriately set the body up to accumulate fat-free mass.

A review by Bosse and Dixon [ 84 ] critically summarized the available literature on protein supplementation during resistance exercise and hypothesized that protein intake may need to increase by as much as 59% above baseline levels for significant changes in fat-free mass to occur. Finally, it should be noted that for many athletes, consuming a post- or pre-workout protein-containing meal represents a feeding opportunity with little downside, since there is no benefit from not consuming protein pre- and/or post-exercise. In other words, not consuming protein-containing foods/supplements post-exercise is a strategy that provides no benefit whatsoever. Thus, the most practical recommendation is to have athletes consume a meal during the post-workout (or pre-workout) time period since it may either help or have a neutral effect.

In younger subjects, the ingestion of 20–30 g of any high biological value protein before or after resistance exercise appears to be sufficient to maximally stimulate MPS [ 21 , 64 ]. More recently, Macnaughton and colleagues [ 85 ] reported that 40 g of whey protein ingestion significantly increased the MPS responses compared to a 20 g feeding after an acute bout of whole-body resistance exercise, and that the absolute protein dose may operate as a more important consideration than providing a protein dose that is normalized to lean mass. Free form EAAs, soy, milk, whey, caseinate, and other protein hydrolysates are all capable of activating MPS [ 86 ]. However, maximal stimulation of MPS, which results in higher net muscle protein accretion, is the product of the total amount of EAA in circulation as well as the pattern and appearance rate of aminoacidemia that modulates the MPS response [ 86 ]. Recent work has clarified that whey protein provides a distinct advantage over other protein sources including soy (considered another fast absorbing protein) and casein (a slower acting protein source) on acute stimulation of MPS [ 86 , 87 ]. Importantly, an elegant study by West and investigators [ 87 ] sought to match the delivery of EAAs in feeding patterns that replicated how whey and casein are digested. The authors reported that a 25 g dose of whey protein that promoted rapid aminoacidemia further enhanced MPS and anabolic signaling when compared to an identical total dose of whey protein when delivered as ten separate 2.5 g doses intended to replicate a slower digesting protein. The advantages of whey protein are important to consider, particularly as all three sources rank similarly in assessments of protein quality [ 88 ]. In addition to soy, other plant sources (e.g., pea, rice, hemp, etc.) have garnered interest as potential protein sources to consider. Unfortunately, research that examines the ability of these protein sources to modulate exercise performance and training adaptations is limited at this time. One study conducted by Joy and investigators [ 89 ] compared the effect of supplementing a high-dose (48 g/day) of whey or rice protein in experienced resistance-trained subjects during an 8-week resistance training program. The investigators concluded that gains in strength, muscle thickness and body composition were similar between the two protein groups, suggesting that rice protein may be a suitable alternative to whey protein at promoting resistance training adaptations. Furthermore, differences in absorption kinetics, and the subsequent impact on muscle protein metabolism appear to extend beyond the degree of hydrolysis and amino acid profiles [ 69 , 86 , 90 , 91 , 92 , 92 ]. For instance, unlike soy more of the EAAs from whey proteins (hydrolysates and isolates) survive splanchnic uptake and travel to the periphery to activate a higher net gain in muscle [ 86 ]. Whey proteins (hydrolysates and isolates) appear to be the most extensively researched for pre/post resistance exercise supplementation, possibly because of their higher EAA and leucine content [ 93 , 94 ], solubility, and optimal digestion kinetics [ 69 ]. These characteristics yield a high concentration of amino acids in the blood (aminoacidemia) [ 69 , 87 ] that facilitates greater activation of MPS and net muscle protein accretion, in direct comparison to other protein choices [ 50 , 69 , 91 ]. The addition of creatine to whey protein supplementation appears to further augment these adaptations [ 27 , 72 , 95 ]; however, an optimal timing strategy for this combination remains unclear.

The timing of protein-rich meals consumed throughout a day has the potential to influence adaptations to exercise. Using similar methods, other studies over recent decades [ 53 , 62 , 87 , 91 , 96 , 97 , 98 , 99 , 100 ] have established the following:

MPS increases approximately 30–100% in response to a protein-containing meal to promote a positive net protein balance, and the major contributing factor to this response is the EAA content.

The anabolic response to feeding is pronounced but transient. During the post-prandial phase (1–4 h after a meal) MPS is elevated, resulting in a positive muscle protein balance. In contrast, MPS rates are lower in a fasted state and muscle protein balance is negative. Protein accretion only occurs in the fed state. The concentration of EAA in the blood (plasma) regulates protein synthesis rates within muscle at rest and post exercise. More recent work has established that protein-carbohydrate supplementation after strenuous endurance exercise stimulates contractile MPS via similar signaling pathways as resistance exercise [ 56 , 57 ]. Most importantly, and as mentioned initially in this section, muscle appears to be “sensitized” to protein feeding for at least 24 h after exercise [ 50 ]. That is, the consumption of a protein-containing meal up to 24 h after a single bout of resistance exercise results in a higher net stimulation of MPS and protein accretion than the same meal consumed after 24 h of inactivity [ 50 ].

The effect of insulin on MPS is dependent on its ability to increase amino acid availability, which does not occur when insulin is systematically increased (e.g., following feeding) [ 101 ]. In particular, insulin’s impact on net protein balance seems to operate most powerfully in an anti-catabolic manner on muscle [ 102 ]. However, insulin-mediated effects that reduce muscle protein breakdown peaks at low to moderate levels of insulin (~15–30 μIU/mL) [ 103 , 104 ] that can be achieved by consumption of a 45-g dose of whey protein isolate alone [ 105 ]. Taken together, these results seem to indicate that post-workout carbohydrate supplementation offers very little contribution from a muscle development standpoint provided adequate protein is consumed. For example, Staples and colleagues [ 106 ] compared the impact of a carbohydrate + protein combination on rates of MPS and reported no further increases in MPS beyond what was seen with protein ingestion alone. Importantly, these results are not to be interpreted to mean that carbohydrate administration offers no potential effect for an athlete engaging in moderate to high volumes of training, but rather that benefits derived from carbohydrate administration appear to more favorably impact aspects of muscle glycogen recovery as opposed to stimulating muscle protein accretion.

Pre-sleep protein intake

Eating before sleep has long been controversial [ 107 , 108 , 109 ]. However, a methodological consideration in the original studies such as the population used, time of feeding, and size of the pre-sleep meal confounds firm conclusions about benefits or drawbacks. Recent work using protein-rich beverages 30-min prior to sleep and two hours after the last meal (dinner) have identified pre-sleep protein consumption/ingestion as advantageous to MPS, muscle recovery, and overall metabolism in both acute and long-term studies [ 110 , 111 ]. Results from several investigations indicate that 30–40 g of casein protein ingested 30-min prior to sleep [ 112 ] or via nasogastric tubing [ 113 ] increased overnight MPS in both young and old men, respectively. Likewise, in an acute setting, 30 g of whey protein, 30 g of casein protein, and 33 g of carbohydrate consumed 30-min prior to sleep resulted in an elevated morning resting metabolic rate in young fit men compared to a non-caloric placebo [ 114 ]. Similarly, although not statistically significant, morning increases in resting metabolic rate were reported in young overweight and/or obese women [ 115 ]. Interestingly, Madzima et al. [ 114 ] reported that subjects’ respiratory quotient measured during the morning after pre-sleep nutrient intake was unchanged only for the placebo and casein protein trials, while both carbohydrate and whey protein were increased compared to placebo. This infers that casein protein consumed pre-sleep maintains overnight lipolysis and fat oxidation. This finding was further supported by Kinsey et al. [ 116 ] using a microdialysis technique to measure interstitial glycerol concentrations overnight from the subcutaneous abdominal adipose tissue, reporting greater fat oxidation following consumption of 30 g of casein compared to a flavor and sensory-matched noncaloric placebo in obese men. Similar to Madzima et al. [ 114 ], Kinsey et al. [ 116 ] concluded that pre-sleep casein did not blunt overnight lipolysis or fat oxidation. Interestingly, the pre-sleep protein and carbohydrate ingestion resulted in elevated insulin concentrations the next morning and decreased hunger in this overweight population. Of note, it appears that exercise training completely ameliorates any rise in insulin when eating at night before sleep [ 117 ], while the combination of pre-sleep protein and exercise has been shown to reduce blood pressure and arterial stiffness in young obese women with prehypertension and hypertension [ 118 ]. In athletes, evening chocolate milk consumption has also been shown to influence carbohydrate metabolism in the morning, but not running performance [ 108 ]. In addition, data supports that exercise performed in the evening augments the overnight MPS response in both younger and older men [ 119 , 120 , 121 ].

To date, only a few studies involving nighttime protein ingestion have been carried out for longer than four weeks. Snijders et al. [ 122 ] randomly assigned young men (average age of 22 years) to consume a protein-centric supplement (27.5 g of casein protein, 15 g of carbohydrate, and 0.1 g of fat) or a noncaloric placebo every night before sleep while also completing a 12-week progressive resistance exercise training program (3 times per week). The group receiving the protein-centric supplement each night before sleep had greater improvements in muscle mass and strength over the 12-week study. Of note, this study was non-nitrogen balanced and the protein group received approximately 1.9 g/kg/day of protein compared to 1.3 g/kg/day in the placebo group. More recently, in a study in which total protein intake was equal, Antonio et al. [ 123 ] studied young healthy men and women that supplemented with casein protein (54 g) for 8 weeks either in the morning (any time before 12 pm) or the evening supplementation (90 min or less prior to sleep). They examined the effects on body composition and performance [ 123 ]. All subjects maintained their usual exercise program. The authors reported no differences in body composition or performance between the morning and evening casein supplementation groups. However, it is worth noting that, although not statistically significant, the morning group added 0.4 kg of fat free mass while the evening protein group added 1.2 kg of fat free mass, even though the habitual diet of the trained subjects in this study consumed 1.7 to 1.9 g/kg/day of protein. Although this finding was not statistically significant, it supports data from Burk et al. [ 81 ] indicating that casein-based protein consumed in the morning (10 am) and evening (10:30 pm) was more beneficial for increasing fat-free mass than consuming the protein supplement in the morning (10 am) and afternoon (~3:50 pm). It should be noted that the subjects in the Burk et al. study were resistance training. A retrospective epidemiological study by Buckner et al. [ 124 ] using NHANES data (1999–2002) showed that participants consuming 20, 25, or 30 g of protein in the evening had greater leg lean mass compared to subjects consuming protein in the afternoon. Thus, it appears that protein consumption in the evening before sleep might be an underutilized time to take advantage of a protein feeding opportunity that can potentially improve body composition and performance.

Protein ingestion and meal timing

In addition to direct assessments of timed administration of nutrients, other studies have explored questions that center upon the pattern of when certain protein-containing meals are consumed. Paddon-Jones et al. [ 97 ] reported a correlation between acute stimulation of MPS via protein consumption and chronic changes in muscle mass. In this study, participants were given an EAA supplement three times a day for 28 days. Results indicated that acute stimulation of MPS provided by the supplement on day 1 resulted in a net gain of ~7.5 g of muscle over a 24-h period [ 97 ]. When extrapolated over the entire 28-day study, the predicted change in muscle mass corresponded to the actual change in muscle mass (~210 g) measured by dual-energy x-ray absorptiometry (DEXA) [ 97 ]. While these findings are important, it is vital to highlight that this study incorporated a bed rest model with no acute exercise stimulus while other work by Mitchell et al. [ 125 ] reported a lack of correlation between measures of acute MPS and the accretion of skeletal muscle mass.

Interestingly, supplementation with 15 g of EAAs and 30 g of carbohydrate produced a greater anabolic effect (increase in net phenylalanine balance) than the ingestion of a mixed macronutrient meal, despite the fact that both interventions contained a similar dose of EAAs [ 96 ]. Most importantly, the consumption of the supplement did not interfere with the normal anabolic response to the meal consumed three hours later [ 96 ]. The results of these investigations suggest that protein supplement timing between the regular “three square meals a day” may provide an additive effect on net protein accretion due to a more frequent stimulation of MPS. Areta et al. [ 126 ] were the first to examine the anabolic response in human skeletal muscle to various protein feeding strategies for a day after a single bout of resistance exercise. The researchers compared the anabolic responses of three different patterns of ingestion (a total of 80 g of protein) throughout a 12-h recovery period after resistance exercise. Using a group of healthy young adult males, the protein feeding strategies consisted of small pulsed (8 × 10 g), intermediate (4 × 20 g), or bolus (2 × 40 g) administration of whey protein over the 12-h measurement window. Results showed that the intermediate dosing (4 × 20 g) was superior for stimulating MPS for the 12-h experimental period. Specifically, the rates of myofibrillar protein synthesis were optimized throughout the day of recovery by the consumption of 20 g protein every three hours compared to large (2 × 40 g), less frequent servings or smaller but more frequent (8 × 10 g) patterns of protein intake [ 67 ]. Previously, the effect of various protein feeding strategies on skeletal MPS during an entire day was unknown. This study provided novel information demonstrating that the regulation of MPS can be modulated by the timing and distribution of protein over 12 h after a single bout of resistance exercise. However, it should be noted that an 80 g dose of protein over a 12-h period is quite low.

The logical next step for researchers is to extend these findings into longitudinal training studies to see if these patterns can significantly affect resistance-training adaptations. Indeed, published studies by Arnal [ 127 ] and Tinsley [ 128 ] have all made some attempt to examine the impact of adjusting the pattern of protein consumption across the day in combination with various forms of exercise. Collective results from these studies are mixed. Thus, future studies in young adults should be designed to compare a balanced vs. skewed distribution pattern of daily protein intake on the daytime stimulation of MPS (under resting and post-exercise conditions) and training-induced changes in muscle mass, while taking into consideration the established optimal dose of protein contained in a single serving for young adults. Without more conclusive evidence spanning several weeks, it seems pragmatic to recommend the consumption of at least 20-25 g of protein (~0.25 g/kg/meal) with each main meal with no more than 3–4 h between meals [ 126 ].

In the absence of feeding and in response to resistance exercise, muscle protein balance remains negative.

Skeletal muscle is sensitized to the effects of protein and amino acids for up to 24 h after completion of a bout of resistance exercise.

A protein dose of 20–40 g of protein (10–12 g of EAAs, 1–3 g of leucine) stimulates MPS, which can help to promote a positive nitrogen balance.

The EAAs are critically needed for achieving maximal rates of MPS making high-quality, protein sources that are rich in EAAs and leucine the preferred sources of protein.

Studies have suggested that pre-exercise feedings of amino acids in combination with carbohydrate can achieve maximal rates of MPS, but protein and amino acid feedings during this time are not clearly documented to increase exercise performance.

Ingestion of carbohydrate + protein or EAAs during endurance and resistance exercise can help to maintain a favorable anabolic hormone profile, minimize increases in muscle damage, promote increases in muscle cross-sectional area, and increase time to exhaustion during prolonged running and cycling.

Post-exercise administration of protein when combined with suboptimal intake of carbohydrates (<1.2 g/kg/day) can heighten muscle glycogen recovery, and may help mitigate changes in muscle damage markers.

Total protein and calorie intake appears to be the most important consideration when it comes to promoting positive adaptations to resistance training, and the impact of timing strategies (immediately before or immediately after) to heighten these adaptations in non-athletic populations appears to be minimal.

Recommended intake

Proteins provide the building blocks of all tissues via their constituent amino acids. Athletes consume dietary protein to repair and rebuild skeletal muscle and connective tissues following intense training bouts or athletic events. During in the 1980s and early 1990’s Tarnopolsky [ 129 ], Phillips [ 130 ], and Lemon [ 131 ] first demonstrated that total protein needs were 50 to 175% greater in athletes than sedentary controls. A report in 2004 by Phillips [ 132 ] summarized the findings surrounding protein requirements in resistance-trained athletes. Using a regression approach, he concluded that a protein intake of 1.2 g of protein per kg of body weight per day (g/kg/day) should be recommended, and when the upper limit of a 95% confidence interval was included the amount approached 1.33 g/kg/day. A key consideration regarding these recommended values is that all generated data were obtained using the nitrogen balance technique, which is known to underestimate protein requirements. Interestingly, two of the included papers had prescribed protein intakes of 2.4 and 2.5 g/kg/day, respectively [ 129 , 133 ]. All data points from these two studies also had the highest levels of positive nitrogen balance. For an athlete seeking to ensure an anabolic environment, higher daily protein intakes might be needed. Another challenge that underpins the ability to universally and successfully recommend daily protein amounts are factors related to the volume of the exercise program, age, body composition and training status of the athlete; as well as the total energy intake in the diet, particularly for athletes who desire to lose fat and are restricting calories to accomplish this goal [ 134 ]. For these reasons, and due to an increase of published studies in areas related to optimal protein dosing, timing and composition, protein needs are being recommended within this position stand on a per meal basis.

For example, Moore [ 31 ] found that muscle and albumin protein synthesis was optimized at approximately 20 g of egg protein at rest. Witard et al. [ 135 ] provided incremental doses of whey protein (0, 10, 20 and 40 g) in conjunction with an acute bout of resistance exercise and concluded that a minimum protein dose of 20 g optimally promoted MPS rates. Finally, Yang and colleagues [ 136 ] had 37 elderly men (average age of 71 years) consume incremental doses of whey protein isolate (0, 10, 20 and 40 g/dose) in combination with a single bout of lower body resistance exercise and concluded that a 40 g dose of whey protein isolate is needed in this population to maximize rates of MPS. Furthermore, while results from these studies offer indications of what optimal absolute dosing amounts may be, Phillips [ 134 ] concluded that a relative dose of 0.25 g of protein per kg of body weight per dose might operate as an optimal supply of high-quality protein. Once a total daily target protein intake has been achieved, the frequency and pattern with which optimal doses are ingested may serve as a key determinant of overall changes in protein synthetic rates.

Research indicates that rates of MPS rapidly rise to peak levels within 30 min of protein ingestion and are maintained for up to three hours before rapidly beginning to lower to basal rates of MPS even though amino acids are still elevated in the blood [ 137 ]. Using an oral ingestion model of 48 g of whey protein in healthy young men, rates of myofibrillar protein synthesis increased three-fold within 45–90 min before slowly declining to basal rates of MPS all while plasma concentration of EAAs remained significantly elevated [ 138 ]. While human models have not fully explored the mechanistic basis of this ‘muscle-full’ phenomenon, an energy deficit theory has been proposed which hypothesizes that rates of MPS were blunted even though plasma concentrations of amino acids remained elevated because a relative lack of cellular ATP was available to drive the synthetic process [ 139 ]. While largely unexplored in a human model, these authors relied upon an animal model and were able to reinstate increases in MPS using the consumption of leucine and carbohydrate 135 min after ingestion of the first meal. As such, it is suggested that individuals attempting to restrict caloric intake should consume three to four whole meals consisting of 20–40 g of protein per meal. While this recommendation stems primarily from initial work that indicated protein doses of 20–40 g favorably promote increased rates of MPS [ 31 , 135 , 136 ], Kim and colleagues [ 140 ] recently reported that a 70 g dose of protein promoted a more favorable net balance of protein when compared to a 40 g dose due to a stronger attenuation of rates of muscle protein breakdown.

For those attempting to increase their calories, we suggest consuming small snacks between meals consisting of both a complete protein and a carbohydrate source. This contention is supported by research from Paddon-Jones et al. [ 97 ] that used a 28-day bed rest model. These researchers compared three 850-cal mixed macronutrient meals to three 850-cal meals combined with three 180-cal amino acid-carbohydrate snacks between meals. Results demonstrated that subjects, who also consumed the small snacks, experienced a 23% increase in muscle protein fractional synthesis and successful maintenance of strength throughout the bed rest trial. Additionally, using a protein distribution pattern of 20–25 g doses every three hours in response to a single bout of lower body resistance exercise appears to promote the greatest increase in MPS rates and phosphorylation of key intramuscular proteins linked to muscle hypertrophy [ 126 ]. Finally, in a series of experiments, Arciero and colleagues [ 116 , 141 ] employed a protein pacing strategy involving equitable distribution of effective doses of protein (4–6 meals/day of 20–40 g per meal) alone and combined with multicomponent exercise training. Using this approach, their results consistently demonstrate positive changes in body composition [ 116 , 142 ] and physical performance outcomes in both lean [ 143 , 144 ] and overweight/obese populations [ 142 , 143 , 145 ]. This simple addition could provide benefits for individuals looking to increase muscle mass and improve body composition in general while also striving to maintain or improve health and performance.

The current RDA for protein is 0.8 g/kg/day with multiple lines of evidence indicating this value is not an appropriate amount for a training athlete to meet their daily needs.

While previous recommendations have suggested a daily intake of 1.2–1.3 g/kg/day is an appropriate amount, most of this work was completed using the nitrogen balance technique, which is known to systematically underestimate protein needs.

Daily and per dose needs are combinations of many factors including volume of exercise, age, body composition, total energy intake and training status of the athlete.

Daily intakes of 1.4 to 2.0 g/kg/day operate as a minimum recommended amount while greater amounts may be needed for people attempting to restrict energy intake while maintaining fat-free mass.

Recommendations regarding the optimal protein intake per serving for athletes to maximize MPS are mixed and are dependent upon age and recent resistance exercise stimuli. General recommendations are 0.25 g of a high-quality protein per kg of body weight, or an absolute dose of 20–40 g.

Higher doses (~40 g) are likely needed to maximize MPS responses in elderly individuals.

Even higher amounts (~70 g) appear to be necessary to promote attenuation of muscle protein breakdown.

Pacing or spreading these feeding episodes approximately three hours apart has been consistently reported to promote sustained, increased levels of MPS and performance benefits.

Protein quality

There are 20 total amino acids, comprised of 9 EAAs and 11 non-essential amino acids (NEAAs). EAAs cannot be produced in the body and therefore must be consumed in the diet. Several methods exist to determine protein quality such as Chemical Score, Protein Efficiency Ratio, Biological Value, Protein Digestibility-Corrected Amino Acid Score (PDCAAS) and most recently, the Indicator Amino Acid Oxidation (IAAO) technique. Ultimately, in vivo protein quality is typically defined as how effective a protein is at stimulating MPS and promoting muscle hypertrophy [ 146 ]. Overall, research has shown that products containing animal and dairy-based proteins contain the highest percentage of EAAs and result in greater hypertrophy and protein synthesis following resistance training when compared to a vegetarian protein-matched control, which typically lacks one or more EAAs [ 86 , 93 , 147 ].

Several studies, but not all, [ 148 ] have indicated that EAAs alone stimulate protein synthesis in the same magnitude as a whole protein with the same EAA content [ 98 ]. For example, Borsheim et al. [ 52 ] found that 6 g of EAAs stimulated protein synthesis twice as much as a mixture of 3 g of NEAAs combined with 3 g of EAAs. Moreover, Paddon-Jones and colleagues [ 96 ] found that a 180-cal supplement containing 15 g of EAAs stimulated greater rates of protein synthesis than an 850-cal meal with the same EAA content from a whole protein source. While important, the impact of a larger meal on changes in circulation and the subsequent delivery of the relevant amino acids to the muscle might operate as important considerations when interpreting this data. In contrast, Katsanos and colleagues [ 148 ] had 15 elderly subjects consume either 15 g of whey protein or individual doses of the essential and nonessential amino acids that were identical to what is found in a 15-g whey protein dose on separate occasions. Whey protein ingestion significantly increased leg phenylalanine balance, an index of muscle protein accrual, while EAA and NEAA ingestion exerted no significant impact on leg phenylalanine balance. This study, and the results reported by others [ 149 ] have led to the suggestion that an approximate 10 g dose of EAAs might serve as an optimal dose to maximally stimulate MPS and that intact protein feedings of appropriate amounts (as opposed to free amino acids) to elderly individuals may stimulate greater improvements in leg muscle protein accrual.

Based on this research, scientists have also attempted to determine which of the EAAs are primarily responsible for modulating protein balance. The three branched-chain amino acids (BCAAs), leucine, isoleucine, and valine are unique among the EAAs for their roles in protein metabolism [ 150 ], neural function [ 151 , 152 , 153 ], and blood glucose and insulin regulation [ 154 ]. Additionally, enzymes responsible for the degradation of BCAAs operate in a rate-limiting fashion and are found in low levels in splanchnic tissues [ 155 ]. Thus, orally ingested BCAAs appear rapidly in the bloodstream and expose muscle to high concentrations ultimately making them key components of skeletal MPS [ 156 ]. Furthermore, Wilson and colleagues [ 157 ] have recently demonstrated, in an animal model, that leucine ingestion (alone and with carbohydrate) consumed between meals (135 min post-consumption) extends protein synthesis by increasing the energy status of the muscle fiber. Multiple human studies have supported the contention that leucine drives protein synthesis [ 158 , 159 ]. Moreover, this response may occur in a dose-dependent fashion, plateauing at approximately two g at rest [ 31 , 157 ], and increasing up to 3.5 g when ingestion occurs after completion of a 60-min bout of moderate intensity cycling [ 159 ]. However, it is important to realize that the duration of protein synthesis after resistance exercise appears to be limited by both the signal (leucine concentrations), ATP status, as well as the availability of substrate (i.e., additional EAAs found in a whole protein source) [ 160 ]. As such, increasing leucine concentration may stimulate increases in muscle protein, but a higher total dose of all EAAs (as free form amino acids or intact protein sources) seems to be most suited for sustaining the increased rates of MPS [ 160 ].

It is well known that exercise improves net muscle protein balance and in the absence of protein feeding, this balance becomes more negative. When combined with protein feeding, net muscle protein balance after exercise becomes positive [ 161 ]. Norton and Layman [ 150 ] proposed that consumption of leucine, could turn a negative protein balance to a positive balance following an intense exercise bout by prolonging the MPS response to feeding. In support, the ingestion of a protein or essential amino acid complex that contains sufficient amounts of leucine has been shown to shift protein balance to a net positive state after intense exercise training [ 46 , 150 ]. Even though leucine has been demonstrated to independently stimulate protein synthesis, it is important to recognize that supplementation should not be with just leucine alone. For instance, Wilson et al. [ 139 ] demonstrated in an animal model that leucine consumption resulted in a lower duration of protein synthesis compared to a whole meal. In summary, athletes should focus on consuming adequate leucine content in each of their meals through selection of high-quality protein sources [ 139 ].

Protein sources containing higher levels of the EAAs are considered to be higher quality sources of protein.

The body uses 20 amino acids to make proteins, seven of which are essential (nine conditionally), requiring their ingestion to meet daily needs.

EAAs appear to be uniquely responsible for increasing MPS with doses ranging from 6 to 15 g all exerting stimulatory effects. In addition, doses of approximately one to three g of leucine per meal appear to be needed to stimulate protein translation machinery.

The BCAAs (i.e., isoleucine, leucine, and valine) appear to exhibit individual and collective abilities to stimulate protein translation. However, the extent to which these changes are aligned with changes in MPS remains to be fully explored.

While greater doses of leucine have been shown to independently stimulate increases in protein synthesis, a balanced consumption of the EAAs promotes the greatest increases.

The prioritization of feedings of protein with adequate levels of leucine/BCAAs will best promote increases in MPS.

Protein sources

Milk proteins.

Milk proteins have undergone extensive research related to their potential roles in augmenting adaptations from exercise training [ 86 , 93 ]. For example, consuming milk following exercise has been demonstrated to accelerate recovery from muscle damaging exercise [ 162 ], increase glycogen replenishment [ 163 ], improve hydration status [ 162 , 164 ], and improve protein balance to favor synthesis [ 86 , 93 ], ultimately resulting in increased gains in both neuromuscular strength and skeletal muscle hypertrophy [ 93 ]. Moreover, milk protein contains the highest score on the PDCAAS rating system, and in general contains the greatest density of leucine [ 156 ]. Milk can be fractionated into two protein classes, casein and whey.

Comparison of the quality of whey and casein reveal that these two proteins routinely contain the highest leucine content of all other protein sources at 11% and 9.3%, respectively. While both are high in quality, the two differ in the rate at which they digest as well as the impact they have on protein metabolism [ 165 , 166 , 167 ]. Whey protein is water soluble, mixes easily, and is rapidly digested [ 168 ]. In contrast, casein is water insoluble, coagulates in the gut and is digested more slowly than whey protein [ 168 ]. Casein also has intrinsic properties such as opioid peptides, which effectively slow gastric motility [ 168 ]. Original research investigating the effects of digestion rate was conducted by Boirie, Dangin and colleagues [ 165 , 166 , 167 ]. These researchers gave a 30 g bolus of whey protein and a 43 g bolus of casein protein to subjects on separate occasions and measured amino acid levels for several hours after ingestion. They reported that the whey protein condition displayed robust hyperaminoacidemia 100 min after administration. However, by 300 min, amino acid concentrations had returned to baseline. In contrast, the casein condition resulted in a slow increase in amino acid concentrations, which remained elevated above baseline after 300 min. Over the study duration, casein produced a greater whole body leucine balance than the whey protein condition, leading the researcher to suggest that prolonged, moderate hyperaminoacidemia is more effective at stimulating increases in whole body protein anabolism than a robust, short lasting hyperaminoacidemia.

While this research appears to support the efficacy of slower digesting proteins, subsequent work has questioned its validity in athletes. The first major criticism is that Boire and colleagues investigated whole body (non-muscle and muscle) protein balance instead of skeletal (myofibrillar) MPS. This is important considering that skeletal muscle protein turnover occurs at a much slower rate than protein turnover of both plasma and gut proteins; as a result, MPS has been suggested to contribute anywhere from 25 to 50% of total whole body protein synthesis [ 169 ]. These findings suggest that changes in whole body protein turnover may poorly reflect the level of skeletal muscle protein metabolism that may be taking place. Trommelen and investigators [ 121 ] examined 24 young men ingesting 30 g of casein protein with or without completion of a single bout of resistance exercise, and concluded that rates of MPS were increased, but whole-body protein synthesis rates were not impacted.

More recently, Tang and colleagues [ 86 ] investigated the effects of administering 22 g of hydrolyzed whey isolate and micellar casein (10 g of EAAs) at both rest and following a single bout of resistance training in young males. The area under the curve calculations demonstrated a 200% greater increase in leucine concentrations in the blood following whey versus casein ingestion. Moreover, these researchers reported that whey protein ingestion stimulated greater MPS at both rest and following exercise when compared to casein. Tipton et al. [ 79 ] used an acute study design involving a single bout of lower body resistance exercise and 20-g doses of casein or whey after completing the exercise session. In comparison to the control group, both whey and casein significantly increased leucine balance, but no differences were found between the two protein sources for amino acid uptake and muscle protein balance. Additional research has also demonstrated that 10 weeks of whey protein supplementation in trained bodybuilders resulted in greater gains in lean mass (5.0 vs. 0.8 kg) and strength compared to casein [ 170 ]. These findings suggest that the faster-digesting whey proteins may be more beneficial for skeletal muscle adaptations than the slower digesting casein.

Effects of milk proteins on glycogen replenishment and skeletal muscle damage

Skeletal muscle glycogen stores are a critical element to both prolonged and high-intensity exercise. In skeletal muscle, glycogen synthase activity is considered one of the key regulatory factors for glycogen synthesis. Research has demonstrated that the addition of protein in the form of milk and whey protein isolate (0.4 g/kg) to a moderate (0.8 g/kg), but not high (1.2 g/kg) carbohydrate-containing (dextrose-maltodextrin) beverage promotes increased rates of muscle glycogen replenishment following hard training [ 47 ]. Further, the addition of protein facilitates repair and recovery of the exercised muscle [ 12 ]. These effects are thought to be related to a greater insulin response following the exercise bout. Intriguingly, it has also been demonstrated that whey protein enhances glycogen synthesis in the liver and skeletal muscle more than casein in an insulin-independent fashion that appears to be due to its capacity to upregulate glycogen synthase activity [ 171 ]. Therefore, the addition of milk protein to a post-workout meal may augment recovery, improve protein balance, and speed glycogen replenishment.

Health benefits of milk-based proteins

While athletes tend to view whey as the ideal protein for skeletal muscle repair and function it also has several health benefits. In particular, whey protein contains an array of biologically active peptides whose amino acids sequences give them specific signaling effects when liberated in the gut. Not only is whey protein high in β-Lactoglobulin and α-lactalbumin (75% of total bovine whey proteins), but it is also rich in EAAs (approximately 50% by weight). Furthermore, whey protein appears to play a role in enhancing lymphatic and immune system responses [ 106 ]. In addition, α-lactalbumin contains an ample supply of tryptophan which increases cognitive performance under stress [ 172 ], improves the quality of sleep [ 172 , 173 ], and may also speed wound healing [ 172 ], properties which could be vital for recovery from combat and contact sporting events. In addition, lactoferrin is also found in both milk and in whey protein, and has been demonstrated to have antibacterial, antiviral, and antioxidant properties [ 174 ]. Moreover, there is some evidence that whey protein can bind iron and therefore increase its absorption and retention [ 175 ].

Egg proteins

Egg protein is often thought of as an ideal protein because its amino acid profile has been used as the standard for comparing other dietary proteins [ 168 ]. Due to their excellent digestibility and amino acid content, eggs are an excellent source of protein for athletes. While the consumption of eggs has been criticized due to their cholesterol content, a growing body of evidence demonstrates the lack of a relationship between egg consumption and coronary heart disease, making egg-based products more appealing [ 176 ]. One large egg has 75 kcal and 6 g of protein, but only 1.5 g of saturated fat while one large egg white has 16 kcal with 3.5 g of protein and is fat-free. Research using eggs as the protein source for athletic performance and body composition is lacking, perhaps due to less funding opportunities relative to funding for dairy. Egg protein may be particularly important for athletes, as this protein source has been demonstrated to significantly increase protein synthesis of both skeletal muscle and plasma proteins after resistance exercise at both 20 and 40 g doses. Leucine oxidation rates were found to increase following the 40 g dose, suggesting that this amount exceeds an optimal dose [ 31 ]. In addition to providing a cost effective, high-quality source of protein rich in leucine (0.5 g of leucine per serving), eggs have also been identified as a functional food [ 177 ]. Functional foods are defined as foods that, by the presence of physiologically active components, provide a health benefit beyond basic nutrition [ 178 ]. According to the Academy of Nutrition and Dietetics, functional foods should be consumed as part of a varied diet on a regular basis, at effective levels [ 179 ]. Thus, it is essential that athletes select foods that meet protein requirements and also optimize health and prevent decrements in immune function following intense training. Important nutrients provided by eggs include riboflavin (15% RDA), selenium (17% RDA) and vitamin K (31% RDA) [ 177 ]. Eggs are also rich in choline, a nutrient which may have positive effects on cognitive function [ 180 ]. Moreover, eggs provide an excellent source of the carotenoid-based antioxidants lutein and zeaxanthin [ 181 ]. Also, eggs can be prepared with most meal choices, whether at breakfast, lunch, or dinner. Such positive properties increase the probability of the athletes adhering to a diet rich in egg protein.

Beef and other flesh proteins

Meat proteins are a major staple in the American diet and, depending on the cut of meat, contain varying amounts of fat and cholesterol. Meat proteins are well known to be rich sources of the EAAs [ 182 ]. Beef is a common source of dietary protein and is considered to be of high biological value because it contains the full balance of EAAs in a fraction similar to that found in human skeletal muscle [ 182 ]. A standard serving of 113.4 g lean beef provides 10 g of the EAAs (3.5 g of leucine) and 30 g of total amino acids. Moreover, this 30 g dose of beef protein has been shown to stimulate protein synthesis in both young and elderly subjects [ 182 ]. In addition to its rich content of amino acids, beef and other flesh proteins can serve as important sources of micronutrients such as iron, selenium, vitamins A, B12 and folic acid. For the most part, these quality minerals and micronutrients cannot be as easily obtained through plant-based proteins and/or the bioavailability of these macronutrients from plants is limited. This is a particularly important consideration for pregnant and breastfeeding women. Ultimately, as an essential part of a mixed diet, meat helps to ensure adequate distribution of essential micronutrients and amino acids to the body.

Research has shown that significant differences in skeletal muscle mass and body composition between older men who resistance train and either consume meat-based or lactoovovegetarian diet [ 147 ]. Over a 12-week period, whole-body density, fat-free mass, and whole-body muscle mass (as measured by urinary creatinine excretion) increased in the meat-sourced diet group but decreased in the lactoovovegetarian diet group. These results indicate that not only do meat-based diets increase fat-free mass, but also they may specifically increase muscle mass, thus supporting the many benefits of meat-based diets. A diet high in meat protein in older adults may provide an important resource in reducing the risk of sarcopenia.

Positive results have also been seen in elite athletes that consume meat-based proteins, as opposed to vegetarian diets [ 183 ]. For example, carnitine is a molecule that transports long-chain fatty acids into mitochondria for oxidation and is found in high amounts in meat. While evidence is lacking to support an increase in fat oxidation with increased carnitine availability, carnitine has been linked to the sparing of muscle glycogen, and decreases in exercise-induced muscle damage [ 184 ]. Certainly, more research is needed to support these assertions. Creatine is a naturally occurring compound found mainly in muscle. The concentration of creatine in uncooked chicken and beef is approximately 30 mmol/kg (4–5 g/kg), meaning that one serving of beef contains approximately 0.4 g of creatine [ 185 ]. Vegetarians have lower total body creatine stores than omnivores, which demonstrates that regular meat eating has a significant effect on human creatine status [ 186 ]. Moreover, creatine supplementation studies with vegetarians indicate that increased creatine uptake levels do exist in people who practice various forms of vegetarianism [ 187 ]. Sharp and investigators [ 188 ] published the only study known to compare different supplemental (powdered) forms of animal proteins on adaptations to resistance training such as increases in strength and improvements in body composition. Forty-one men and women performed a standardized resistance-training program over eight weeks and consumed a daily 46 g dose of either hydrolyzed chicken protein, beef protein isolate, or whey protein concentrate in comparison to a control group. All groups experienced similar increases in upper and lower-body strength, but all protein-supplemented groups reported significant increases in lean mass and decreases in fat mass.

Meat-based diets have been shown to include additional overall health benefits. Some studies have found that meat, as a protein source, is associated with higher serum levels of IGF-1 [ 189 ], which in turn is related to increased bone mineralization and fewer fractures [ 190 ].

Meat vs. plant based proteins: Is one better than the other?

A highly debated topic in nutrition and epidemiology is whether vegetarian diets are a healthier choice than omnivorous diets. One key difference is the fact that vegetarian diets often lack equivalent amounts of protein when compared to omnivorous diets [ 147 ]. However, with proper supplementation and careful nutritional choices, it is possible to have complete proteins in a vegetarian diet. Generally by consuming high-quality, animal-based products (meat, milk, eggs, and cheese) an individual will achieve optimal growth as compared to ingesting only plant proteins [ 147 ]. Research has shown that soy is considered a lower quality complete protein. Hartman et al. [ 93 ] had participants consume a mixture of sucrose and either 30 g of milk or soy proteins during 12-weeks of resistance training. They found that the participants that consumed the milk protein increased lean mass and decreased fat mass more than the control and soy groups. Moreover, the soy group was not significantly different from the control group. Similarly, a study by Tang and colleagues [ 86 ] directly compared the abilities of hydrolyzed whey isolate, soy isolate, and micellar casein to stimulate rates of MPS both at rest and in response to a single bout of lower body resistance training. These authors reported that the ability of soy to stimulate MPS was greater than casein, but less than whey, at rest and in response to an acute resistance exercise stimulus. While soy is considered a complete protein, it contains lower amounts of BCAAs than bovine milk [ 168 ]. Additionally, research has found that dietary soy phytoestrogens inhibit mTOR expression in skeletal muscle through activation of AMPK [ 191 ]. Thus, not only does soy contain lower amounts of the EAAs and leucine, but soy protein may also be responsible for inhibiting growth factors and protein synthesis via its negative regulation of mTOR. When considering the multitude of plant sources of protein, soy overwhelmingly has the most research. Limited evidence using wheat protein in older men has suggested that wheat protein stimulates significantly lower levels of MPS when compared to an identical dose (35 g) of casein protein, but when this dose is increased nearly two fold (60 g) this protein source is able to significantly increase rates of myofibrillar protein synthesis [ 192 ]. Rice protein is a medium to slow absorbing protein, which is in line with other non-meat/non-dairy proteins, however, leucine from rice protein shows unique absorption kinetics, peaking faster than leucine from whey protein [ 193 ]. As mentioned earlier, a study by Joy and colleagues [ 89 ] in which participants participated in resistance training program for eight weeks while taking identical, high doses of either rice or whey protein, demonstrated that rice protein stimulated similar increases in body composition adaptations to whey protein.

Protein blends

The majority of available science has explored the efficacy of ingesting single protein sources, but evidence continues to mount that combining protein sources may afford additional benefits [ 194 ]. For example, a 10-week resistance training study by Kerksick and colleagues [ 22 ] demonstrated that a combination of whey (40 g) and casein (8 g) yielded the greatest increase in fat-free mass (determined by DEXA) when compared to both a combination of 40 g of whey, 5 g of glutamine, and 3 g of BCAAs and a placebo consisting of 48 g of a maltodextrin carbohydrate. Later, Kerksick et al. [ 95 ] demonstrated various combinations of whey, casein, and colostrum proteins with and without creatine can also yield positive improvements in strength and body composition over a 12-week resistance training and supplementation regimen. Similarly, Hartman and investigators [ 93 ] had 56 healthy young men train for 12 weeks while either ingesting isocaloric and isonitrogenous doses of fat-free milk (a blend of whey and casein), soy protein or a carbohydrate placebo and concluded that fat-free milk stimulated the greatest increases in Type I and II muscle fiber area as well as fat-free mass; however, strength outcomes were not affected. Moreover, Wilkinson and colleagues [ 94 ] demonstrated that ingestion of fat-free milk (vs. soy or carbohydrate) led to a greater area under the curve for net balance of protein and that the fractional synthesis rate of muscle protein was greatest after milk ingestion. In 2013, Reidy et al. [ 195 ] indicated that a mixture of whey and soy protein over a four-hour measurement window similarly increased MPS rates during the early (0–2 h) time-period versus whey protein, but only the protein blend was able to stimulate significantly increased MPS rates during the later (2–4 h) measurement window. However, when the entire four-hour measurement period was considered, no difference in MPS rates were found. A follow-up publication from the same clinical trial also reported that ingestion of the protein blend resulted in a positive and prolonged amino acid balance when compared to ingestion of whey protein alone, while post-exercise rates of myofibrillar protein synthesis were similar between the two conditions [ 196 ]. Reidy et al. [ 197 ] reported that in 68 healthy young men who were participating in a supervised resistance-training program over 12 weeks, there were increases in whole body lean mass with either whey protein or a whey protein and soy protein blend compared to a maltodextrin placebo. No differences were found between whey and the whey and soy blend.

Criteria for comparing protein sources

Some valid criteria exist to compare protein sources and provide an objective method of how to include them in a diet. As previously mentioned, common means of assessing protein quality include Biological Value, Protein Efficiency Ratio, PDCAAS and IAAO. The derivation of each technique is different with all having distinct advantages and disadvantages. For nearly all populations, ideal methods should be linked to the capacity of the protein to positively affect protein balance in the short term, and facilitate increases and decreases in lean and fat-mass, respectively, over the long term. In addition, the protein’s ability to enhance immune function and promote an anti-oxidative environment should also be considered. To this point, dairy, egg, meat, and plant-based proteins have been discussed. Two critical variables exist that determine a protein’s impact on overall protein accretion and protein turnover: a) the protein’s leucine content and b) the rate at which the protein is digested. In general, the proteins with the greatest leucine content include dairy (9–11%), egg (8.6%), and meat (8%), while sources low in leucine include plant-based proteins. Faster digesting sources of protein include whey and egg whites, soy, and very lean cuts of meat (>95% lean). In contrast, casein and fatty cuts of meat (<80% lean) act as slowly digested sources of protein. As mentioned previously, initial research by Boirie and Dangin has highlighted the impact of protein digestion rate on net protein balance with the two milk proteins: whey and casein [ 165 , 166 , 167 ]. Subsequent follow-up work has used this premise as a reference point for the digestion rates of other protein sources.

Using the criteria of leucine content, Norton and Wilson et al. [ 198 , 199 ] used animal models to compare the potential to activate initiation factors and MPS between four different protein sources: wheat (supplemented with leucine), soy, egg, and whey, (containing 6.8, 8.0, 8.8, and 11% leucine, respectively) using a diet consisting of three meals per day. Macronutrient intake was 16/54/30% for protein, carbohydrates and fat, respectively. Wheat and soy did not stimulate MPS above fasted levels, whereas egg and whey proteins significantly increased MPS rates, with MPS for whey protein being greater than egg protein. MPS responses were closely related to changes in plasma leucine and phosphorylation of 4E–BP1 and S6 K protein signaling molecules. More importantly, following 2- and 11-weeks of ingestion, it was demonstrated that the leucine content of the meals increased muscle mass and was inversely correlated with body fat.

Tang et al. [ 86 ] compared high leucine/fast-digesting (hydrolyzed whey isolate), lower leucine/intermediate digesting (soy isolate) and high leucine/slow-digesting (micellar casein) protein sources on MPS at rest and following exercise. The researchers demonstrated that MPS at rest was higher after ingestion of faster digesting proteins compared to slower digesting proteins (whey and soy > casein). Specifically, MPS after consumption of whey was approximately 93% and 18% greater than casein and soy, respectively. A similar pattern of results was observed after resistance exercise (whey > soy > casein) whereby protein synthesis following whey consumption was approximately 122% and 31% greater than casein and soy, respectively. MPS was also greater after soy consumption at rest (64%) and following resistance exercise (69%) compared with casein. These findings lead us to conclude that athletes should seek protein sources that are both fast-digesting and high in leucine content to maximally stimulate rates of MPS at rest and following training. Moreover, in consideration of the various additional attributes that high-quality protein sources deliver, it may be advantageous to consume a combination of higher quality protein sources (dairy, egg, and meat sources).

Multiple protein sources are available for an athlete to consider, and each has their own advantages and disadvantages.

Protein sources are commonly evaluated based upon the content of amino acids, particularly the EAAs, they provide. Beyond amino acid content, the fat, calorie, and micronutrient content, and presence of various bioactive peptides all contribute to a protein’s quality.

Leucine content and rate of digestion have also been demonstrated in multiple scientific studies to play an important role in an athlete’s ability to train, compete, and recover.

Blends of protein sources might afford a favorable combination of key nutrients such as leucine, EAAs, bioactive peptides, and antioxidants, but more research is needed to determine their ideal composition.

Preparation methods of various proteins

Nutrient density is defined as the amount of a particular nutrient (carbohydrate, protein, fat, etc.) per unit of energy in a given food. In many situations, the commercial preparation method of foods can affect the actual nutrient density of the resulting food. Using protein as an example, full-fat milk is approximately 150 cal a serving, and of this 8 g, or about 21% is from protein. Skim milk on the other hand contains approximately 9 g of protein in a 90-cal eight-ounce serving, making it approximately 40% protein. When producing milk protein supplements, special preparations must be made to separate the protein sources from the lactose and fat calories in milk. For example, the addition of acid to milk causes the casein to coagulate or collect at the bottom, while the whey is left on the top [ 200 ]. These proteins are then filtered to increase their purity. A concentrate is commonly defined as any protein product that is 29–80% protein by dry weight. Sport nutrition products generally use concentrates that are 70–80% protein [ 200 ]. As extra filtering steps are added, the purity of the final product increases and when a final protein product yields greater than 90% protein, it is considered an isolated protein [ 200 ].

Filtration processes

Filtration methods differ, and there are both benefits and disadvantages to each. The two most popular methods of filtration of a given protein are the use of ion exchange and micro/ultrafiltration methods. Ion exchange exposes a given protein source, such as whey, to hydrochloric acid and sodium hydroxide, thereby producing an electric charge on the proteins that can be used to separate them from lactose and fat [ 200 ]. The advantage of this method is that it is relatively cheap and produces the highest protein concentration [ 200 ]. The disadvantage is that ion exchange filtration typically denatures some of the valuable immune-boosting, anti-carcinogenic peptides found in whey [ 200 ]. Cross-flow microfiltration, and ultra-micro filtration are based on the premise that the molecular weight of whey protein is greater than lactose, and use 1 and 0.25-μm ceramic membranes, respectively, to separate the two. As a result, whey protein is trapped in the membranes but the lactose and other components pass through. The advantage is that these processes do not denature valuable proteins and peptides found in whey, so the protein itself is deemed to be of higher quality [ 200 ]. The main disadvantage is that this filtration process is typically costlier than the ion exchange method.

Hydrolyzed proteins

When consumed whole, proteins are digested through a series of steps beginning with homogenization by chewing, followed by partial digestion by pepsin in the stomach [ 201 ]. Following this, a combination of peptides, proteins, and negligible amounts of single amino acids are released into the small intestine and from there are either partially hydrolyzed into oligopeptides, 2–8 amino acids in length or are fully hydrolyzed into individual amino acids [ 201 ]. Absorption of individual amino acids and various small peptides (di, tri, and tetra) into the blood occurs inside the small intestine through separate transport mechanisms [ 201 ]. Oftentimes, products contain proteins that have been pre-exposed to specific digestive enzymes causing hydrolysis of the proteins into di, tri, and tetrapeptides. A plethora of studies have investigated the effects of the degree of protein fractionation (or degree of hydrolysis) on the absorption of amino acids and the subsequent hormonal response [ 202 , 203 , 204 , 205 , 206 , 207 ]. Research indicates that amino acids are absorbed more rapidly when they are consumed as di and/or tri peptides compared to free form amino acids or complete proteins [ 205 ]. Further, the rate of absorption may lead to a more favorable anabolic hormonal environment [ 202 , 203 , 206 ]. Calbet et al. [ 203 ] examined both amino acid appearance and insulin responses following consumption of protein solutions containing the same amount of protein, or pure carbohydrates. The treatments consisted of a pure glucose solution, whey peptide hydrolysates, and cow’s milk containing milk proteins, lactose and fat. Each of the nitrogen containing solutions contained 15 g of glucose and 30 g of protein. Results indicated that peptide hydrolysates produced a faster increase in venous plasma amino acids compared to milk proteins. Further, the peptide hydrolysates produced peak plasma insulin levels that were two- and four-times greater than that evoked by the milk and glucose solutions, respectively, with a correlation of 0.8 between plasma amino acids and the insulin response in the peptide hydrolysates. One of the inherent shortcomings of this study is that milk proteins are 80% casein and, therefore, are not ideal candidates to compare with hydrolyzed whey.

In a more appropriate comparison, Morifuji et al. [ 205 ] investigated the effects of 12.5 g of either hydrolyzed or non-hydrolyzed soy and whey proteins on changes in plasma levels of the EAAs, BCAAs, and insulin. Results indicated that protein hydrolysates produced greater responses than their non-hydrolyzed counterpart in plasma for each of the variables (Hydrolyzed whey > Non-hydrolyzed whey > hydrolyzed soy > Non-hydrolyzed soy). However, Calbet et al. [ 202 ] found that 36 g of hydrolyzed or non-hydrolyzed whey and casein led to no differences in the plasma amino acid/BCAA responses in the whey groups. The hydrolyzed casein, however, did result in a greater amino acid response than the nonhydrolyzed casein. Finally, both hydrolyzed groups resulted in greater gastric secretions, as well as greater plasma increases, in glucose-dependent insulinotropic polypeptides [ 208 ].

Buckley and colleagues [ 207 ] found that a ~ 30 g dose of a hydrolyzed whey protein isolate resulted in a more rapid recovery of muscle force-generating capacity following eccentric exercise, compared with a flavored water placebo or a non-hydrolyzed form of the same whey protein isolate. Indeed, the effect of this hydrolysate was such that complete recovery of muscle force-generating capacity had been achieved by six hours post supplementation, while the normal whey and placebo groups’ strength remained depressed 24 h later. In agreement with these findings, Cooke et al. [ 209 ] had 17 untrained men complete an eccentric-based resistance training bout to invoke muscle damage and supplemented with either carbohydrate or a hydrolyzed whey protein isolate. Three and seven days after completing the damaging exercise bout, maximal strength levels were higher in the hydrolyzed whey protein group compared to carbohydrate supplementation. Additionally, blood concentrations of muscle damage markers tended to be lower when four ~30-g doses of a hydrolyzed whey protein isolate were ingested for two weeks following the damaging bout. Beyond influencing strength recovery after damaging exercise, other benefits of hydrolyzed proteins have been suggested. For example, Morifuji et al. [ 210 ] using an animal model reported that the ability of whey hydrolysates to increase skeletal muscle glycogen replenishment after exercise was greater when compared to BCAA ingestion. Furthermore, Lockwood et al. [ 204 ] investigated the effects of ingesting either 30 g of hydrolyzed whey or two varying forms of whey protein concentrates during a linear resistance-training protocol over 8 weeks. Results indicated that strength and lean body mass (LBM) increased equally in all groups. However, fat mass decreased only in the hydrolyzed whey protein group. While more work needs to be completed to fully determine the potential impact of hydrolyzed proteins on strength and body composition changes, this initial study suggests that hydrolyzed whey may be efficacious for decreasing body fat. Finally, Saunders et al. [ 7 ] had thirteen trained male cyclists complete a simulated 60-km time trial where they ingested either carbohydrate or carbohydrate and protein hydrolysate at equal intervals throughout the race as well as at the conclusion of the race. The authors reported that co-ingestion of a carbohydrate and protein hydrolysate improved time-trial performance late in the exercise protocol and significantly reduced soreness and markers of muscle damage. Two excellent reviews on the topic of hydrolyzed proteins and their impact on performance and recovery have been published by Van Loon et al. [ 211 ] and Saunders [ 212 ].

Digestive enzymes in proteins

Digestion is the physiological process of rendering the food we eat into smaller components that allow key nutrients to be assimilated into our body’s tissues. The prevalence of digestive enzymes in sports nutrition products has increased during recent years with many products now containing a combination of proteases and lipases, with the addition of carbohydrates in plant proteins. Proteases can hydrolyze proteins into various peptide configurations and potentially single amino acids. It appears that digestive enzyme capabilities and production decrease with age [ 213 ], thus increasing the difficulty with which the body can break down and digest large meals. Digestive enzymes could potentially work to promote optimal digestion by allowing up-regulation of various metabolic enzymes that may be needed to allow for efficient bodily operation. Further, digestive enzymes have been shown to minimize quality differences between varying protein sources [ 214 ]. Individuals looking to increase plasma peak amino acid concentrations may benefit from hydrolyzed protein sources or protein supplemented with digestive enzymes. However, more work is needed before definitive conclusions can be drawn regarding the efficacy of digestive enzymes.

Protein safety

Despite a plethora of studies demonstrating safety, much concern still exists surrounding the clinical implications of consuming increased amounts of protein, particularly on renal and hepatic health. The majority of these concerns stem from renal failure patients and educational dogma that has not been rewritten as evidence mounts to the contrary. Certainly, it is clear that people in renal failure benefit from protein-restricted diets [ 215 ], but extending this pathophysiology to otherwise healthy exercise-trained individuals who are not clinically compromised is inappropriate. Published reviews on this topic consistently report that an increased intake of protein by competitive athletes and active individuals provides no indication of hepato-renal harm or damage [ 216 , 217 ]. This is supported by a recent commentary [ 134 ] which referenced recent reports from the World Health Organization [ 218 ] where they indicated a lack of evidence linking a high protein diet to renal disease. Likewise, the panel charged with establishing reference nutrient values for Australia and New Zealand also stated there was no published evidence that elevated intakes of protein exerted any negative impact on kidney function in athletes or in general [ 219 ].

Recently, Antonio and colleagues published a series of original investigations that prescribed extremely high amounts of protein (~3.4–4.4 g/kg/day) and have consistently reported no harmful effects [ 220 , 221 , 222 , 223 ]. The first study in 2014 had resistance-trained individuals consume an extremely high protein diet (4.4 g/kg/day) for eight weeks and reported no change in adverse outcomes [ 223 ]. A follow-up investigation [ 220 ] required participants to ingest up to 3.4 g/kg/day of protein for eight weeks while following a prescribed resistance training program and reported no changes in any of the blood parameters commonly used to assess clinical health (e.g., there was no effect on kidney or liver function). Their next study employed a crossover study design in twelve healthy resistance-trained men in which each participant was tested before and after for body composition as well as blood-markers of health and performance [ 221 ]. In one eight-week block, participants followed their normal (habitual) diet (2.6 g/kg/day) and in the other eight-week block, participants were prescribed to ingest greater than 3.0 g/kg/day resulting in an average protein intake of 2.9 g/kg/day over the entire 16-week study. No changes in body composition were reported, and importantly, no clinical side effects were observed throughout the study. Finally, the same group of authors published a one-year crossover study [ 222 ] in fourteen healthy resistance-trained men. When prescribed to a high protein diet, the participants were instructed to ingest 3 g/kg/day and achieved an average intake of 3.3 g/kg/day and when following their normal diet they consumed 2.5 g/kg/day. This investigation showed that the chronic consumption of a high protein diet (i.e., for 1 year) had no harmful effects on kidney or liver function. Furthermore, there were no alterations in clinical markers of metabolism and blood lipids.

Multiple review articles indicate that no controlled scientific evidence exists indicating that increased intakes of protein pose any health risks in healthy, exercising individuals.

Statements by large regulatory bodies have also indicated that concerns about one’s health secondary to ingesting high amounts of protein are unfounded.

A series of controlled investigations spanning up to one year in duration utilizing protein intakes of up to 2.5–3.3 g/kg/day in healthy resistance-trained individuals consistently indicate that increased intakes of protein exert no harmful effect on blood lipids or markers of kidney and liver function.

In alignment with our previous position stand, it is the position of the International Society of Sports Nutrition that the majority of exercising individuals should consume at minimum approximately 1.4 to 2.0 g of protein per kg of bodyweight per day to optimize exercise training induced adaptations. Importantly, this recommendation also falls within the Institute of Medicine’s Acceptable Macronutrient Distribution Range (AMDR) of 10–35% protein [ 224 ]. The amount is dependent upon the mode and intensity of the exercise, the quality of the protein ingested, as well as the energy and carbohydrate status of the individual. However, it should be noted that there is preliminary evidence that consuming much higher quantities of protein (> 3 g/kg/d) may confer a benefit as it relates to body composition. Concerns that protein intake within this range is unhealthy are unfounded in healthy, exercising individuals. An attempt should be made to consume whole foods that contain high-quality (e.g., complete) sources of protein; however, supplemental protein is a safe and convenient method of ingesting high-quality dietary protein. The timing of protein intake in the period encompassing the exercise session may offer several benefits including improved recovery and greater gains in lean body mass. However, perhaps the most important issue regarding protein intake during the peri-workout period is that it serves as an opportunity to eat thus elevating one’s total daily protein intake. In addition, consuming protein pre-sleep has been shown to increase overnight MPS and next-morning metabolism acutely along with improvements in muscle size and strength over 12 weeks of resistance training. Intact protein supplements, EAAs and leucine have been shown to be beneficial for the exercising individual by increasing the rates of MPS, decreasing muscle protein degradation, and possibly aiding in recovery from exercise. In summary, increasing protein intake using whole foods as well as high-quality supplemental protein sources can improve the adaptive response to training.

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Acknowledgements

The authors are particularly grateful for the thorough and excellent review by Jorn Trommelen (Maastricht University, The Netherlands) and Raza Bashir (Iovate Health Sciences International Inc., Canada). We would like to thank all the participants and researchers who contributed to the research studies and reviews described in this position stand.

No funding was provided.

Authors’ contributions

RJ, BIC, PJC, SDW & CMK prepared and compiled the draft for review and editing by coauthors. All other coauthors reviewed, edited, and approved the draft, and the final manuscript.

Competing interests

RJ has received grants to evaluate the efficacy and safety of proteins, serves on scientific advisory boards, and has served as an expert witness, legal and scientific consultant. CMK consults with and receives external funding from companies who sell supplemental protein, has received remuneration from companies for delivering scientific presentations at conferences and writes online, print and other media on topics related to exercise, nutrition and protein for related companies. Has served as an expert witness and provided testimonies related to exercise, supplementation and nutrition. BIC writes and is compensated for various media outlets on topics related to sports nutrition and fitness; has received funding for research related to dietary supplements; serves on an advisory board for a sports nutrition company and is compensated in product donations, and is a consistent expert witness and legal consultant in matters related to dietary supplement. PJC designs and sells exercise training and nutrition certifications to professionals in the fitness industry. SDW and TMS are employees of BioTRUST Nutrition. MP has received grants to evaluate the efficacy of dietary supplements, serves on a scientific advisory board for a sports nutrition company, and as a scientific consultant. TNZ has received external funding from companies who sell protein supplements, has received remuneration from companies for delivering scientific presentations at conferences, and authors online articles related to exercise, nutrition and protein. Has served as an expert witness and provided testimonies related to exercise, supplementation and nutrition. AAF has no conflicts to report. SMA serves on the advisory board for Dymatize Nutrition. ASR has received grants to evaluate the efficacy of dietary supplements and serves on the scientific advisory board for sports nutrition companies. JRS has received grants to evaluate the efficacy of dietary supplements and has previously served on scientific advisory boards for a sports nutrition companies. PJA serves on the American Heart Association Advisory Board (Capital Region); serves on the Scientific Advisory Boards for Dymatize Nutrition and Isagenix International LLC; serves as a paid consultant to Isagenix International LLC; Founder and CEO of PRISE LLC a health and wellness consultant company that owns the GenioFit App. MJO serves on the advisory board for Dymatize Nutrition and has received external funding from companies who sell supplemental protein. LWT has received external funding to evaluate the efficacy of dietary supplements and has previously served in a consulting role for a sports nutrition company. CDW has received external funding from supplement companies to do research, served on multiple advisory boards for supplement companies, and has served as a consultant, advisor, and spokesperson for various nutrition companies. DSK works for a contract research organization that does conduct clinical trials for dietary supplement and pharmaceutical companies. RBK has received externally funded grants from industry to conduct research on protein and protein-containing supplements, serves as a scientific and legal consultant, and is a university approved scientific advisor for Nutrabolt. DSW has received grants to evaluate the efficacy of dietary supplements, serves on a scientific advisory board for a sports nutrition company, and as scientific consultant, and has received remuneration from a company for delivering scientific presentations at conferences. JRH has received grants from various dietary supplement and pharmaceutical companies to investigate the efficacy of various supplements including protein. He has also been hired to serve as an expert witness on behalf of supplement companies in various legal proceedings. JK is an independent consultant for Isagenix. JA is the CEO and co-founder of the ISSN. The ISSN is supported in part by grants from raw good suppliers and branded companies that sell dietary protein supplements.

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Jäger, R., Kerksick, C.M., Campbell, B.I. et al. International Society of Sports Nutrition Position Stand: protein and exercise. J Int Soc Sports Nutr 14 , 20 (2017). https://doi.org/10.1186/s12970-017-0177-8

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Importance of energy, dietary protein sources, and amino acid composition in the regulation of metabolism: an indissoluble dynamic combination for life.

1. Introduction

2. energy metabolism, 3. protein synthesis and regulatory mechanisms, 3.1. energy sensors, 3.2. amino acid availability, 3.3. mtor signaling, 3.4. transcription factors, 4. dietary proteins: quality and sources, 4.1. the importance of protein quality, 4.2. plant or animal protein sources and intake, 4.3. insect proteins, 4.4. protein intake and utilization, 5. protein turnover and requirements, 5.1. physical activity and hypercatabolic syndrome, 5.2. the limits of protein intake, 6. the significance of eaa supplementation, 7. conclusions, author contributions, conflicts of interest.

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Corsetti, G.; Pasini, E.; Scarabelli, T.M.; Romano, C.; Singh, A.; Scarabelli, C.C.; Dioguardi, F.S. Importance of Energy, Dietary Protein Sources, and Amino Acid Composition in the Regulation of Metabolism: An Indissoluble Dynamic Combination for Life. Nutrients 2024 , 16 , 2417. https://doi.org/10.3390/nu16152417

Corsetti G, Pasini E, Scarabelli TM, Romano C, Singh A, Scarabelli CC, Dioguardi FS. Importance of Energy, Dietary Protein Sources, and Amino Acid Composition in the Regulation of Metabolism: An Indissoluble Dynamic Combination for Life. Nutrients . 2024; 16(15):2417. https://doi.org/10.3390/nu16152417

Corsetti, Giovanni, Evasio Pasini, Tiziano M. Scarabelli, Claudia Romano, Arashpreet Singh, Carol C. Scarabelli, and Francesco S. Dioguardi. 2024. "Importance of Energy, Dietary Protein Sources, and Amino Acid Composition in the Regulation of Metabolism: An Indissoluble Dynamic Combination for Life" Nutrients 16, no. 15: 2417. https://doi.org/10.3390/nu16152417

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Plant Proteins: Assessing Their Nutritional Quality and Effects on Health and Physical Function

Consumer demand for plant protein-based products is high and expected to grow considerably in the next decade. Factors contributing to the rise in popularity of plant proteins include: (1) potential health benefits associated with increased intake of plant-based diets; (2) consumer concerns regarding adverse health effects of consuming diets high in animal protein (e.g., increased saturated fat); (3) increased consumer recognition of the need to improve the environmental sustainability of food production; (4) ethical issues regarding the treatment of animals; and (5) general consumer view of protein as a “positive” nutrient (more is better). While there are health and physical function benefits of diets higher in plant-based protein, the nutritional quality of plant proteins may be inferior in some respects relative to animal proteins. This review highlights the nutritional quality of plant proteins and strategies for wisely using them to meet amino acid requirements. In addition, a summary of studies evaluating the potential benefits of plant proteins for both health and physical function is provided. Finally, potential safety issues associated with increased intake of plant proteins are addressed.

1. Introduction

Protein is a nutrient that has been trending increasingly positive in the minds of consumers, with demand rising for both plant and animal sources of protein [ 1 ]. In addition, there is a growing body of clinical evidence, especially in older adults, supporting health benefits associated with protein at or above current dietary protein intake recommendations. Among these health benefits are increases in lean body mass [ 2 , 3 , 4 , 5 , 6 ], functional benefits such as increased leg power [ 4 ] or gait speed [ 6 ], and improved bone density [ 7 , 8 , 9 ]. Thus, on the one hand, there is likely to be a continued push for protein-rich options in the food marketplace. On the other hand, the global production of an increased volume of food protein, especially high-quality animal protein, could present environmental sustainability challenges. The production of 1 kg of high-quality animal protein requires feeding 6 kg plant protein to livestock, which introduces the subsequent strain on land and water resources, as well as potential increases in greenhouse gas emissions, associated with livestock agriculture [ 1 , 10 ]. Wider and prudent use of plant proteins in the diet can help to supply adequate high-quality protein for the population and may reduce the potential for adverse environmental consequences. This review presents information on: (1) the nutritional quality of plant proteins; (2) strategies for wisely using plant proteins to meet indispensable amino acid requirements; (3) effects of plant proteins on health and physical function; and (4) potential health and safety concerns associated with plant proteins.

2. Determination of Protein Quality

Two requirements for a protein to be considered high quality, or complete, for humans are having adequate levels of indispensable amino acids (see Table 1 ) to support human growth and development and being readily digested and absorbed.

Indispensable, dispensable, and conditionally indispensable amino acids in the human diet. Adapted from [ 11 ].

Various methods for evaluating protein quality have been developed over the years, but amino acid scoring is currently the recommended method by the Food and Agricultural Organization of the United Nations (FAO) and the U.S. National Academy of Sciences [ 11 , 12 ]. The Protein Digestibility Corrected Amino Acid Score (PDCAAS) was developed in 1989 by a Joint FAO/WHO Expert Consultation on Protein Quality Evaluation [ 13 ] to compare the indispensable amino acid content of a test protein (mg/g protein) to a theoretical reference protein thought to meet indispensable amino acid requirements (mg/g protein) for a given age group, creating a ratio known as the amino acid or chemical score. The indispensable amino acid with the lowest ratio is referred to as the most limiting amino acid. The most limiting amino acid score is corrected for the fecal true digestibility of the protein. To determine fecal true protein digestibility, rats are fed a known amount of nitrogen from the test protein and then fecal nitrogen excretion is measured [ 14 ]. This measure represents apparent protein digestibility. The fecal nitrogen excretion from the rats on a protein-free diet is then subtracted from fecal nitrogen excretion on the test protein, which accounts for non-dietary protein nitrogen excretion from bacterial cells and digestive secretions. The result is referred to as true fecal protein digestibility. The calculation equation for the PDCAAS is shown in Figure 1 .

Calculation of the PDCAAS (adapted from [ 15 ]).

The results can be expressed as either decimals or multiplied by 100 to be expressed as a percent. A PDCAAS of <1.00 indicates that the protein is suboptimal and PDAAS scores >1.00 are truncated to 1.00.

In 2011, the FAO introduced an updated amino acid scoring system, the Digestible Indispensable Amino Acid Score (DIAAS) [ 16 ]. The DIAAS is calculated and interpreted similarly to the PDCAAS, but with a few important differences. First, the reference patterns for the indispensable amino acids were revised to reflect advances in the scientific knowledge regarding amino acid requirements. Second, a single estimate of fecal protein digestibility is no longer used. Rather, the concept of the ileal individual amino acid digestibility was incorporated. True fecal digestibility of protein, which is based on nitrogen excretion in the feces, is complicated by the considerable exchange of protein, amino acids, and urea between systemic pools and the lower gastrointestinal tract. In response to this limitation, it was recommended to measure ileal amino acid digestibility, which reflects the concentration of amino acids that reaches the ileum and would hence enter the colon, derived from ileostomy output studies conducted in animals or humans. As such, each indispensable amino acid from a given protein source will have an associated ileal digestibility value and its amino acid score will be corrected for that value. Finally, unlike the PDCAAS, the DIAAS method allows for scores >1.00 to acknowledge that there may be incremental health benefits associated with these higher DIAAS scores.

3. The Quality of Plant Proteins

In general, most animal-based protein sources, such as milk, whey, casein, eggs, and beef, have PDCAAS at or very near 1.00 [ 13 , 17 , 18 ]. As such, they are generally considered complete protein sources for supporting indispensable amino acid requirements for human growth and development. Plant proteins, however, may have insufficient levels of one or more indispensable amino acids. Legumes are frequently low in the sulfur-containing amino acids methionine and cysteine, while lysine is typically limiting in grains [ 19 ]. However, it should be noted that plant proteins differ regarding the amounts of limiting amino acids that are present. Table 2 shows the PDCAAS and DIAAS ratings for milk protein, whey, and several selected plant protein sources. Similar to milk protein and whey, soy protein essentially has a PDCAAS of 1.00, and four more proteins (canola, potato, pea, and quinoa) have a PDCAAS of at least 0.75.

Protein quality of whey and selected vegetable protein sources.

1 FAO FN Paper 51 1989, ages 2–5 year, AA ref standard (mg/g protein) [ 13 ]: His 19, Ile 28, Leu 66, Lys 58, SAA 25, AAA 63, Thr 34, Trp 11, Val 35. 2 IOM 2002/2005, ages 1+ year, AA ref standard (mg/g protein) [ 11 ]: His 18, Ile 25, Leu 55, Lys 51, SAA 25, AAA 47, Thr 27, Trp 7, Val 32. 3 FAO FN Paper 92 2011, ages 0.5–3 year, AA ref standard (mg/g protein) [ 16 ]: His 20, Ile 32, Leu 66, Lys 57, SAA 27, AAA 52, Thr 31, Trp 8.5, Val 43. 4 FAO FN Paper 92 2011, older child, adolescent, adult, AA ref standard (mg/g protein) [ 16 ]: His 16, Ile 30, Leu 61, Lys 48, SAA 23, AAA 41, Thr 25, Trp 6.6, Val 40. PDCAAS, Protein Digestibility Corrected Amino Acid Score; DIAAS, Digestible Indispensable Amino Acid Score; AA, amino acid; His, histidine; Ile, isoleucine; Leu, leucine; Lys, lysine; SAA, sulfur amino acids (methionine and cysteine); AAA, aromatic amino acids (phenylalanine and tyrosine); Thr, threonine; TRP, tryptophan; Val, valine; PI, protein isolate; PC, protein concentrate; bld, boiled; ckd, cooked; cnd, canned; drnd, drained. * Limiting amino acid by all four amino acid reference standards.

While the PDCAAS of most plant proteins may be less than 1.00, the individualized protein scoring system is only one way to evaluate the potential contributions of a protein to the diet. Canada uses a method based on the Protein Efficiency Ratio (PER), which is growth/weight gain assay on rats fed different protein sources. Health Canada provides a list of PER values for different protein foods on their website and suggests that the PER of a protein source can be estimated by multiplying the PDCAAS by 2.5 [ 58 ]. Several other factors can increase the potential contribution of plant-based proteins to meeting overall dietary protein and indispensable amino acid needs. One aspect to consider is the amount of dietary protein contributed by a specific plant protein source. In the case of plant versus animal proteins, simply consuming more of the plant protein can help to provide higher indispensable amino acid intakes. Given that many whole food sources of plant-protein are less calorie-dense than animal sources of protein, greater overall food intake is needed to meet energy requirements which, in turn, helps meet indispensable amino acid requirements. In addition, it has now become much easier for consumers to boost intake of plant proteins via the availability of multiple plant-based protein isolates and concentrates (soy, pea, canola, potato, fava, etc.) in the food industry. It was once difficult for individuals to take in relatively large amounts of protein from whole plant foods because they typically have a low percentage of protein. However, plant protein isolates and concentrates, which often contain 80% or more protein by weight, make it possible to consume 10–20 g or more of plant-based protein per one serving of a ready-to-drink shake or powder mix.

Dietary protein variety is also key for meeting indispensable amino acid requirements. While the PDCAAS of an individual protein is critical when evaluating the quality of a sole-source protein, it becomes less significant when the diet contains proteins from many sources. For example, lysine is often limiting in grain proteins, but such proteins are good sources of the sulfur-containing amino acids. On the other hand, legumes are often rich sources of lysine but are limiting in sulfur-containing amino acids. Consumption of these two protein sources over the course of the day allows them to “complement” one another, helping to meet requirements for both types of indispensable amino acids. A classic example would be a combination of pea and rice proteins. Protein blends of pea and rice ranging 40–90% pea protein can achieve a PDCAAS of 1.00, using the 2011 FAO amino acid reference pattern for adults [ 16 ]. Flexitarian approaches, in which persons consume increased amounts of plant-based proteins but also include some animal proteins, represent another strategy for helping to meet indispensable amino acid requirements. Thus, the quality of protein in the diet may be quite high if the plan is to consume a variety of plant proteins with differing amino acid profiles.

One question that has arisen for vegetarians is whether it is needed to combine complementary protein sources at the same meal. Young and Pellet [ 19 ] addressed this issue. They noted that the common limiting amino acid in grains, lysine, has a significant pool in the skeletal muscle. After a protein-rich meal, they estimated that 60% of the adult daily requirement for lysine could be stored in this pool within 3 h. If a person were to consume a lysine-poor meal within 3 h of a lysine-rich meal, there would still be adequate intracellular lysine available to promote protein synthesis. Thus, it is not necessary to consume complementary protein sources at the same meal if the gap between meals is relatively short, around 3 h; the complementary amino acids will be metabolically available for protein synthesis.

An often-neglected aspect of plant proteins is their high content of some important dispensable/conditionally indispensable amino acids. The PDCAAS method of evaluating protein quality focuses only on indispensable amino acids and generally on whole body protein requirements. However, since the development of the PDCAAS concept, the knowledge base around the health- or performance-related effects of individual amino acids, both indispensable and conditionally indispensable has grown dramatically. For example, whey protein has received much attention for muscle building due to its high level of leucine (see Figure 1 ), which serves as a nutrient signal for initiating the process of muscle protein synthesis [ 59 , 60 ]. However, it is important not to forget the vital physiologic functions of dispensable/conditionally indispensable amino acids found in large amounts in plant proteins. Soy protein, while not as high as whey in leucine, is nearly three times higher in arginine, 2–3 times higher in glutamine, and has double the glycine content ( Figure 2 and Table 3 ). Other plant proteins can be high in these amino acids as well. Arginine is necessary for the body’s synthesis of nitric oxide (vasodilator) and creatine, for urea cycle function, for regulating hormone secretion, and for immune function [ 61 , 62 ]. Glutamine is a primary fuel source for rapidly proliferating cells such as those in the immune system and gastrointestinal tract and functions in the synthesis of arginine, ornithine, and several other compounds [ 61 , 63 ]. Glycine is critical for collagen synthesis, comprising up to 1/3 of the amino acids in collagen and some studies suggest that its biosynthesis in humans may not be adequate to meet requirements [ 64 , 65 , 66 , 67 ]. Although amino acids such as arginine, glutamine, and glycine might not be classified all the time as indispensable amino acids, they perform many critical functions and plant proteins can be significant sources. Thus, the content of these dispensable/conditionally indispensable amino acids deserves to be taken into consideration when evaluating the value of plant proteins in the diet.

Comparisons of leucine and selected dispensable amino acid concentrations (mg/g protein): whey versus the Top 5 highest quality plant proteins in Table 2 .

Glutamine concentration of selected plant and dairy proteins. Sources of data: References [ 68 , 69 , 70 , 71 ] and unpublished data.

4. Importance of Plant Proteins in Health

The benefits of plant proteins on long-term health and chronic diseases have been a trending topic in recent years. This section summarizes some of the most recent evidence and analytical reviews for several target health areas, including cardiovascular health, metabolic syndrome, diabetes, cancer, renal function, lean body mass, and strength, as well as overall morbidity and mortality. This section is not meant to be a comprehensive review of the health effects of plant protein. Rather it is meant to highlight key recent studies and meta-analyses and open a dialogue to suggest future areas for research.

4.1. Plant Protein and Cardiovascular Disease and Metabolic Risk Factors

Numerous studies have explored the potential impact that dietary plant proteins have on reducing cardio-metabolic risk factors. One of the first reports to synthesize the results of plant protein intake as a substitution for animal protein was a study published in 2017. In this systematic review and meta-analysis of 112 randomized clinical trials across adults with and without hyperlipidemia, the authors demonstrated reduced markers of cardiovascular disease in favor of plant protein over animal protein consumption [ 72 ]. The authors reported a reduction in blood lipids across the studies, including lower low-density lipoprotein cholesterol, non-high-density lipoprotein cholesterol, and apolipoprotein B. While the authors called for higher quality randomized trials to confirm their results, this evidence supports plant protein as an effective substitution for animal protein in the diet to help reduce cardiovascular disease risk factors in adults. A more recent meta-analysis was performed on the impact of plant protein compared with animal protein across 32 intervention trials in hypercholesterolemic patients [ 73 ]. While there was evidence in favor of plant proteins to lower lipid profiles, most trials in this analysis examined soy products as the intervention compared with a variety of animal protein sources. Therefore, it may be difficult to draw a broad conclusion about all plant proteins based on the limited types of plant proteins studies and on potential confounding effects driven by other bioactive properties of soy products.

Benefits of plant proteins and metabolic health have also been described for adolescent populations. Obesity is a growing problem worldwide among adolescents, and several studies have examined the potential benefits of plant protein intake in relation to obesity, weight management, or metabolic syndrome. One such study was the Healthy Lifestyle in Europe by Nutrition in Adolescence (HELENA) study, a cross-sectional study of European adolescents [ 74 ]. In this study, both total and animal protein intake were higher in obese adolescents. Adolescents consuming higher levels of plant protein exhibited lower body fat percentages and BMI compared with those adolescents with higher animal protein intake. However, protein is critical for many physiologic functions and facets of development, and adequate protein intake is important. The study suggested increasing plant protein in adolescent diets as a substitution for animal protein to help control obesity and for its potential positive benefits for cardio-metabolic factors [ 74 ]. Incorporating more plant proteins into the diet to take the place of excess calories and animal protein may be a useful strategy to assist with adolescent obesity.

Criticism has arisen from some researchers, however, regarding the attempt to make blanket statements about the superiority of the cardio-metabolic health benefits of plant proteins versus animal proteins. While advantages of plant food sources have been described, researchers advise not to indiscriminately consider all animal proteins as inferior to plant protein for cardiovascular health, citing limited and inconsistent evidence to support that type of conclusion [ 75 , 76 ]. In an editorial, Campbell cautioned that not all studies have shown a detrimental effect of red meat compared with plant protein on cardiovascular disease risk markers and suggested there is mixed evidence when evaluating white meat as compared to red meat as a healthier animal option [ 75 ]. For example, the randomized, crossover, controlled trial by Bergeron et al. [ 77 ] found a benefit of non-meat protein intake over animal protein intake, but no difference between white meat versus red meat in the animal protein dietary periods. The study authors concluded that more plant-based protein should be introduced into the diet to reduce cardiovascular disease risk but noted that their short intervention period and inability to show a difference between various animal protein diets may limit the interpretation of results. In summary, lumping all animal proteins together as being inferior to plant proteins regarding cardiovascular disease risk is not advised. Generalizing the health benefits of plant protein over animal protein is difficult due to trial inconsistencies and limited control of variables. The overall health composition of foods should be considered instead of creating competition between animal plant-based protein sources, and a wide variety of nutritious protein-rich foods from animal and plant sources should be incorporated into the diet along with healthy dietary habits [ 75 ].

4.2. Plant Protein and Diabetes

While vegetarian diets are associated with a substantial risk reduction for diabetes [ 78 ], it is unclear if substitution of plant protein for animal protein helps to drive this risk reduction. Malik et al. [ 79 ], analyzing data from the Nurses’ Health Study II, found that substituting 5% of energy intake from vegetable protein for animal protein was associated with a 23% reduced risk of type 2 diabetes. An acute feeding of 20 g yellow pea protein, served in a tomato soup 30 min in advance of an ad libitum pizza meal, reduced the glycemic response to the pizza meal and the energy intake from the pizza meal (when compared with tomato soup not containing pea protein) [ 80 ]. In a similar study, a 400-kcal breakfast comprising a meal replacement beverage containing about 29 g soy protein was compared with an isocaloric, higher glycemic index, lower protein breakfast. The soy protein beverage was associated with: (1) lower postprandial glycemic and ghrelin responses to the breakfast; and (2) decreased postprandial insulin secretion from a standardized lunch fed 4 h later [ 81 ].

In a 2015 meta-analysis of randomized control trials that replaced animal protein sources with plant protein for at least 35% of total dietary protein intake over a median study length of eight weeks, the authors reported significant, but modest, improvements in HbA1c, fasting glucose, and fasting insulin levels in individuals with diabetes [ 82 ]. These results were positive, but limitations were noted. The authors called for longer and larger clinical trials to confirm results as sample sizes were relatively small in the studies reviewed. It should also be noted that the meta-analysis included reported studies for both type 1 and type 2 diabetic populations.

Since this meta-analysis was conducted in 2015, a prospective clinical trial evaluated the potential benefits of high protein diets using either plant or animal protein sources in adult individuals (aged 64.3 ± 1 years) with type 2 diabetes. This randomized clinical study of 37 diabetic individuals placed on either a high animal protein diet (80.2% of total protein in intervention) or a high plant protein diet (72.3% of total protein in intervention) for six weeks found that both diets similarly reduced body weight, BMI, HbA1c, and blood lipid markers after the intervention [ 83 ]. The animal protein group experienced a decrease in fasting glucose and whole-body insulin sensitivity from baseline, but there was no difference between the protein groups. Further analyses of results from this trial revealed similar responses from both intervention groups for liver fat, markers of hepatic inflammation, and insulin resistance [ 84 ]; oxidative stress biomarkers [ 85 ]; and pro-inflammatory cytokines [ 83 ]. Other studies have also failed to show a benefit of a vegetarian diet over animal protein diets for individuals with diabetes. In a randomized controlled trial in patients with type 2 diabetes, no benefits or differences were observed in cardio-metabolic biomarkers across three groups randomized to a red meat protein diet, soy legume protein diet, or a non-soy legume protein diet after eight weeks [ 86 ]. The authors concluded that impact of whole diet could be more relevant than the impact of protein source, and that animal protein could be consumed as part of a balanced diet. Larger and longer-term studies in individuals with diabetes are warranted.

4.3. Plant Protein Intake and Incidence of Cancer

Another area of interest for examining benefits of increasing plant protein intake in place of animal protein is in cancer risk reduction. Certainly, the risk for developing cancer is influenced by multiple factors, such as genetic predisposition, environment, and dietary and other lifestyle habits. One group has focused on examining the risk of colorectal cancer in individuals using gene–environment interaction analyses, incorporating several lifestyle factors, genetic factors, and cancer risk [ 87 ]. In their examination of a large, prospective Danish cohort, the authors reported an association between certain genetic polymorphisms for fatty acid metabolism and colorectal cancer, which were further associated with high meat intake. They described that high meat intake was associated with high risk of colorectal cancer among some gene carriers compared with those having the same genetic polymorphism who consumed diets with lower meat intake [ 87 ]. Therefore, substituting plant protein for animal protein in the diet may be a strategy to lower the risk of colorectal cancer in individuals with certain gene variants. There have been mixed results, however, regarding whether shifting from animal protein to more plant protein will reduce colorectal cancer risk. For example, a recent study included 79 pre-diabetic adults on a one-year weight-loss dietary intervention [ 88 ]. This study examined total dietary protein intake, red meat intake, and animal to plant protein ratio. At baseline and after the one-year intervention, these dietary habits were compared with the level of fecal ammonia concentrations, a biomarker for colorectal cancer risk. While this study did report a dose-dependent association between fecal ammonia concentration and the amount of red meat intake, there was no associations between fecal ammonia and total protein intake or the ratio of animal to plant protein in these individuals [ 88 ].

In our review of the literature, there was limited evidence to confirm the benefits of plant protein above animal protein on its impact on cancer risk reduction. This will likely be a growing area of focus for future research to better understand if plant protein itself confers any benefits or whether the adoption of better dietary habits associated with increased plant protein intake helps to drive favorable health outcomes.

4.4. Plant Proteins as Functional Foods

Plant proteins have also been studied for their potential as functional foods. Numerous studies have been conducted to examine the impact on cardiovascular risk, glycemia, or satiety. Many studies have focused on the functional and bioactive properties of soy protein, especially for reducing cardiovascular disease risk, modulating inflammation, or modulating the immune system [ 89 ]. A recent systematic review examined the bioactive properties of plant protein sources other than soy, including protein from pea, lupin, fava bean, rice, oat, hemp, and lentil [ 90 ]. Most trials reported the benefits of plant protein ingredients by examining postprandial concentrations of blood glucose, insulin, and/or appetite regulating hormones. While there was heterogeneity in results, studies that compared animal to plant protein showed no benefit of plant protein on regulating postprandial glycemia. Similarly, the benefits of plant protein as a functional food for satiety showed mixed results, although there may be some benefit to pea protein. It is likely that the bioactive components of a plant diet are often attributed to whole food sources than isolated protein. It is well known that numerous components in plants, such as carotenoids and flavonoids, confer bioactive benefits for health. However, further research on plant proteins and bioactive peptides is needed.

4.5. Plant Protein Intake and Its Relationship to Mortality

Many studies have also linked sources of protein intake to mortality. A recent publication from the large, prospective cohort from the NIH-AARP Diet and Health Study also examined the impact of dietary protein choice on mortality [ 91 ]. In this study, 617,199 individuals aged 50–71 across the U.S. were followed from 1995 or 1996 until study follow-up in December 2011. Intake of plant protein was significantly inversely associated with all-cause mortality as well as cause-specific mortality from cardiovascular disease and stroke in both males and females. They reported that replacement of just 3% of protein intake with plant protein versus animal was associated with a 10% reduction in overall mortality across both men and women [ 91 ]. These results are consistent with a recent systematic review and meta-analysis on the impact of protein intake on mortality risk [ 92 ]. Aligned with other reports highlighting the importance of increased protein intake, especially as we age, higher total protein intake was associated with a reduced all-cause mortality risk. Stratifying data into animal protein intake versus plant protein intake, however, revealed a lower all-cause mortality risk for those consuming plant protein diets. Ten studies examining animal or plant protein intake were analyzed in the meta-analysis for the association with mortality from cardiovascular disease. While there was no clear association between animal protein intake and mortality, an inverse association was found between plant protein intake and cardiovascular disease risk. These studies support a benefit of substituting more plant protein into the diet in place of animal protein in terms of longevity and mortality.

4.6. Renoprotective Effect of Plant Proteins

The American diet is typically characterized as low in fruits, vegetables, dairy, and healthy oils and exceeds recommendations for total grains, total protein foods, added sugar, saturated fats, and sodium [ 93 ]. This diet, also characterized as the Western diet, has been under scrutiny to establish the metabolic differences that contribute to chronic disease, especially regarding chronic kidney disease (CKD) [ 94 ]. Recent epidemiological evidence suggests that not only the amount of protein, but also the origin of protein (e.g., plant vs. animal), may be a factor that influences kidney function [ 95 ]. The nuances of earlier experimentation with low versus normal recommended protein intake lent clues to the potential impact of protein origin. Viberti et al. [ 96 ] replaced the animal protein in an isocaloric diet with vegetable sources in a crossover study with healthy adults and observed a reduction in glomerular filtration rate (GFR) and renal plasma flow (RPF). A sub-study within a broader investigation that was designed to examine the effect of dietary protein on GFR compared healthy vegetarian subjects with those on an omnivorous diet. Both groups ate their normal diets ab libitum. The mean plasma creatinine level was not significantly different between groups, but the creatinine clearance was significantly lower in the vegetarian group [ 97 ]. A soy protein-rich diet was found to reduce glomerular hyperfiltration in a study of patients with type 1 diabetes with early stage nephropathy [ 98 ]. Increases in GFR and glomerular hyperfiltration contribute to the incidence of kidney injury and indicate how diet can have a negative impact on kidney function [ 99 ].

The effect of plant and animal protein intake on renal function continues to be explored. In a prospective analysis of a large cohort ( n = 15,055) from the Atherosclerosis Risk in Communities (ARIC) study [ 100 ], dietary renal acid load was positively associated with chronic kidney disease (CKD) incidence (defined by the authors as 25% reduction of estimated glomerular filtration rate (eGFR), CKD related hospitalization, end-stage renal disease, or mortality). This mirrors the findings of a 10-year longitudinal cohort study where the objective was to assess the source of protein intake in a cohort of older women and possible link to incidence for age-related rate of renal function decline. Greater consumption of plant protein was related to slower declines in eGFR, but intake of animal protein was not associated with kidney function decline [ 95 ]. In two one-year intervention studies, patients with stage 3 or 4 CKD were treated with either sodium bicarbonate or fruits and vegetables dosed to reduce renal acid load, a hypothetical metabolic risk factor for kidney damage, by 50% [ 101 , 102 ]. Both treatments ameliorated metabolic acidosis and indices of kidney injury and did so without producing hyperkalemia. In another trial, participants on diets with equivalent nutrient content had lower serum phosphorus and phosphorus excretion when the protein source was vegetarian as compared to animal-based [ 103 ]. The observational outcomes of the Chronic Renal Insufficiency Cohort Study supported these findings to indicate an association between plant protein consumption and reduction in metabolic risk factors for CKD exists [ 104 ]. The totality of this evidence points to the benefit of plant-proteins in the diet to lessen the impact of protein intake in patients with increased protein needs, due to wasting, from glomerular hyperfiltration.

The plant-based proteins from soybean and rice endosperm have demonstrated renal protective properties in diabetic rat models [ 105 ]. One potential mechanism of action for the renoprotective effect of plant protein is an indirect effect mediated by improved glucose homeostasis, with plant protein intake being associated with reduced fatty liver development. Another potential explanation is that a protein such as rice endosperm protein is high in arginine, a precursor of nitric oxide (NO), which is depleted in this rat model [ 105 , 106 ]. The improvement of renal hemodynamics which results from the supplementation of arginine could be the direct result of an increase in NO production [ 106 ]. These plant protein sources bring additional compounds into the mix that need to be considered as well, such as soy isoflavones, which might affect renal function through cell signaling actions and nitric oxide production affecting renal perfusion [ 107 ]. Soy consumption has also been associated with improvements in antioxidant status and systemic inflammation in CKD patients [ 107 ]. Put another way, the whole “protein package” should be considered in terms of health benefits. Soy’s effects on renal function could be the result of the whole food’s impact on risk factors for CKD such as dyslipidemia, hypertension, and hyperglycemia [ 108 ]. In summary, other factors such as fiber and phytochemicals may play a role in renal protection in whole food plant-based diets; however, these components cannot be fully responsible for the renal benefits seen in the studies using protein isolates. In diets high in whole plant foods, it is more likely that the positive effect on renal function is due to synergistic effects from plant-protein and from other plant components. This renoprotective effect is the basis for recommending the incorporation of high-quality plant proteins not only in the diet of those with renal insufficiency [ 109 ], but also the general population.

4.7. Plant Proteins for Lean Body Mass and Strength

Meeting total daily protein needs is important for persons engaging in either strength or endurance training. In addition, the concept of reaching meal total protein and leucine content thresholds of 20–40 and 2–4 g, respectively, several times per day to promote “maximal” muscle protein synthesis (MPS) [ 60 , 110 , 111 , 112 , 113 , 114 ] has become popular among active persons (young and old). Most studies examining the effects of meal protein dose on muscle protein synthesis, especially post-resistance training, fed high-quality animal proteins such as dairy (e.g., whey and casein) or egg protein. Tang et al. [ 115 ] studied the effect of feeding whey hydrolysate, soy protein, and casein, matched to provide 10 g indispensable amino acids, on mixed MPS at rest and over a 3-h period following unilateral leg resistance training. Postexercise, the whey hydrolysate promoted significantly greater MPS than did either soy or casein. However, the soy protein outperformed casein at rest and postexercise. Further, even though it was significantly lower, the postexercise MPS fractional synthetic rate (%/h) for soy protein was still about 80% that of whey. The authors attributed this finding to either differences in the rates of digestion of the three proteins or their leucine content. Because soy protein has a lower percentage of leucine (~8%) compared with whey protein (~12%), it is possible that simply providing a little bit more soy protein to reach the critical leucine threshold is all that is needed to promote comparable levels of postexercise MPS between the two proteins.

Studies with other plant proteins tend to bear this out. In a sample of young women also performing unilateral leg resistance training, increasing protein intake to double the RDA from potato protein elevated both resting and exercise-associated 24-h MPS above the baseline level [ 28 ]. Curiously, in this study, supplementation with an isocaloric carbohydrate placebo also caused comparable increases in MPS in both the resting and postexercise state, so the true benefits of the potato protein were unclear. In another study, ingestion of 35 g micellar casein by older men (non-exercising) versus 35 g wheat protein hydrolysate caused greater increases in MPS in the 4-h postingestion period [ 116 ]. However, upping the dose of wheat protein hydrolysate to 60 g resulted in rates of MPS that exceeded that of 35 g whey protein and were comparable to that of 35 g micellar casein.

Acute measures of MPS occurring a few hours after the ingestion of protein have questionable value in predicting longer-term gains in lean body mass with training [ 117 ]. Gaining muscle mass is a complex process affected by a variety of physiological factors, so actual training studies evaluating the influence of protein supplementation on muscle mass and strength gains over time are needed to better assess the value of plant proteins for muscle building. Some resistance training studies (12–36 weeks) in young adults have reported that fluid milk or whey protein is superior to soy milk or soy protein for muscle mass and strength [ 118 , 119 ]. However, a recent meta-analysis of nine resistance-training studies with a total of 266 participants [ 120 ] was conducted to evaluate the effects of matched protein doses from soy versus animal proteins on muscle mass and strength outcomes. Of the nine studies in the meta-analysis, five compared whey with soy, while four compared soy with other proteins (beef, milk, or dairy protein). Subjects included both young (18–38 years) and older (61–67 years) adults and the duration of training ranged 6–36 weeks (2–5 times per week). Amounts of protein supplemented to the diet ranged 18–85 g/day. There were no differences between soy protein and the animal proteins for improvements in bench press strength, squat/leg press strength, or lean body mass outcomes.

Training studies have also reported positive outcomes for other plant proteins than soy. Joy et al. [ 121 ] reported that 48 g/day of rice or whey protein isolate on training days during an eight-week resistance training program in college-aged adults caused similar improvements in body composition and bench and leg press strength. A study in elite mixed martial artists undergoing six weeks of intense training demonstrated no differences between 75 g/day of whey or rice protein isolate on body composition outcomes [ 122 ]. In addition, pea protein supplementation (25 g twice/day) was shown during 12 weeks of resistance training to increase biceps muscle thickness to the same degree as an equivalent amount of whey protein [ 123 ]. Likewise, Banaszek et al. [ 124 ] supplemented participants in a high-intensity functional training program over eight weeks with 48 g/day of either whey or pea protein and observed that both proteins resulted in similar body composition, muscle thickness, force production, workout performance, and strength. Finally, a meta-analysis of the effects of protein intake on resistance training outcomes concluded that the major considerations for protein intake were to achieve an intake of 1.6 g/kg body weight per day, separating it into 0.25 g/kg doses [ 125 ]. Of minor importance were factors such as timing of intake, postexercise protein dose, and protein source. Part of the explanation for differences in efficacy between plant and animal proteins may have to do with whether short-term (e.g., MPS) compared with long-term (e.g., increases in actual lean body mass) outcomes.

Whey protein is quite effective for promoting increases in both short-term measures of MPS and resistance-training induced gains in lean body mass and strength and, largely due to its high leucine content, can lead to these improvements in lower doses (<30 g/day) [ 119 ] than might be achieved with plant proteins. However, supplementing with larger doses of plant proteins (40 g/day or higher) can provide similar fitness outcomes to those achieved with whey protein. The wider availability of plant-based protein concentrates and isolates now makes it easier to achieve these higher plant protein intakes for those who wish to push the balance of their protein intake more heavily toward plant-based sources.

Another point of importance is the value that might be achieved by combining plant and animal proteins in a supplementation program to take advantage of the relative strengths of each kind of protein. For example, PER determinations in rats with 30:70 animal:plant protein ratios have shown that, for several animal and plant protein combinations, the 30:70 ratio resulted in equivalent or greater PER scores than did the animal protein at 100% [ 126 ]. Similarly, two studies of a protein blend (20 g) containing 25% whey protein isolate, 25% soy protein isolate, and 50% sodium caseinate can promote MPS to a level equivalent to whey protein alone and may be associated with more prolonged positive amino acid net balance (i.e., arteriovenous differences in the leg) compared with whey protein [ 127 , 128 ]. Thus, for those individuals who want to incorporate plant proteins but are still open to animal proteins as well, it is possible to put them together to achieve the desired results.

5. Health Concerns Associated with Plant Proteins

5.1. antinutrients.

One health concern associated with increased dietary intake of plant-based proteins is the presence of antinutrients in plant foods. Antinutrients are natural substances produced by plants that can interfere with the digestion, absorption or utilization of nutrients in food and may have other adverse effects as well [ 129 ]. Antinutrient adverse effects may include leaky gut and autoimmune effects (e.g., lectins and some saponins), protein maldigestion (trypsin and protease inhibitors), carbohydrate maldigestion (alpha-amylase inhibitors), mineral malabsorption (phytates, tannins, and oxalates), interference with thyroid iodine uptake (goitrogens), gut dysfunction, inflammation, and behavioral effects (conversion of cereal gliadins to exorphins) [ 129 ]. Often, the adverse effects of antinutrients have been observed in animals fed unprocessed plant proteins and these observations have triggered fears in people regarding the consumption of some plant foods. However, it is important to note that antinutrients are not always associated with adverse effects and, in some cases, their effects on the body may be positive. At low levels, phytates, lectins, phenolic compounds, enzyme inhibitors, and saponins may help to reduce blood glucose and/or plasma cholesterol and triglycerides [ 129 ]. Saponins may help liver function and reduce platelet agglutination and some saponins, as well as phytates, protease inhibitors, lignans, and phytoestrogens, may reduce cancer risk [ 129 ]. In addition, tannins may have antimicrobial effects [ 129 ]. As such, some of the health benefits of plant-based diets may be attributed to the presence of low levels of these “antinutrients”. Finally, multiple pathways exist for greatly reducing the concentration of antinutrients in plant proteins, including soaking, fermentation, sprouting (germination), heating, gamma irradiation, and genomic technologies [ 129 ]. Food processing techniques make it possible to largely remove antinutrients such as glucosinolates, phytates, erucic acid, and insoluble fiber from canola/rapeseed proteins, which dramatically improves their bioavailability [ 26 ]. Because plant protein concentrates and isolates typically undergo processing to mostly eliminate antinutrients, their digestibility is typically much higher than when the protein remains in the whole food matrix. For example, the protein digestibility of soy protein isolate is 96% or higher, while the protein digestibility of soy flour is only 84% [ 24 , 25 ].

5.2. Soy Protein and Isoflavones

Soy protein has been the target of both health promotion claims and potential adverse health effect concerns for some time due to its content of isoflavones. Isoflavones are compounds that have elements of their chemical structure similar to estrogen and some weakly bind with estrogen receptors [ 130 ]. The concern has been raised that soy isoflavones might have endocrine disrupting impacts on reproductive hormones, largely based on in vitro cell culture or rodent studies involving large doses of isoflavones [ 131 , 132 , 133 ]. The isoflavone content of various soy protein ingredients has been reported as follows (wet basis, expressed as aglycones): defatted and whole soy flours (120–340 mg/100 g), soy protein isolates (88–164 mg/100 g), commercial textured soy proteins (66–183 mg/100 g), and soy hypocotyl flours (542–851 mg/100 g) [ 134 ]. As a result, consumers may choose to avoid soy protein for fear of adverse effects on reproductive or thyroid hormones. However, multiple lines of research over the last 15 years have shown that concerns regarding adverse hormonal effects from physiological amounts of soy foods in the diet are largely unfounded. In 2015, the European Food Safety Authority conducted a comprehensive evaluation of the safety of isoflavone supplements for peri- and postmenopausal women. The evaluation showed that daily doses of 35–150 mg of isoflavones in this population resulted in no increase in breast cancer risk, no effects on endometrial thickness or histopathological changes in the uterus over 30 months (some nonmalignant histopathological changes at 60 months), and no changes in thyroid hormone status [ 135 ].

A meta-analysis of 15 placebo-controlled studies of men of varying ages have reported that soy protein intake up to 60 g/day has not been associated with significant alterations in testosterone, sex hormone-binding globulin, free testosterone, or free androgen index [ 136 ]. Similarly, Dillingham et al. [ 137 ] observed that the feeding of approximately 32 g protein/day for 57 days from either low or high isoflavone soy protein was associated with only minor changes in serum reproductive hormones in young healthy men. In another comparison of low versus high isoflavone soy protein supplementation, the protein supplementation, regardless of isoflavone content, did not influence semen quality parameters (semen volume, sperm concentration, sperm count, sperm mobility, sperm percent motility, total motile sperm count, or sperm morphology) in healthy young men [ 138 ].

Because some types of breast cancer may be estrogen-sensitive, the safety of soy for breast cancer patients has been questioned. The issue of whether soy protein/soy isoflavones affects the risk of breast cancer or its recurrence has also been addressed in multiple investigations and reviews. Messina [ 130 ] concluded that soy foods do not increase the risk of breast cancer and will not worsen cancer outcomes in women with breast cancer. A meta-analysis in 2016 and two more in 2019 reported similar conclusions and further suggested that soy food intake may be associated with a decrease in the risk of breast cancer and improved breast cancer survival [ 139 , 140 , 141 ]. A systematic review of 13 prospective cohort studies for primary breast cancer incidence and five prospective cohort studies examining risk of recurrence and mortality (4–7 years follow-up post first diagnosis) [ 142 , 143 ] stated that soy foods do not affect the risk of primary breast cancer, but, in patients with breast cancer, a diet high in soy is associated with a 25% decrease in cancer recurrence and a 15% decrease in mortality. The protective effect of soy was significant in both estrogen receptor-positive and -negative breast cancer types, but the reduction in recurrence was stronger in the estrogen receptor-negative (HR = 0.64; 95% CI 0.44–0.94) compared with estrogen receptor-positive (HR = 0.81; 95% CI 0.63–1.04) breast cancer type. The American Cancer Society supports the intake of soy foods in breast cancer survivors [ 144 ].

Potential concerns regarding the effects of soy foods on thyroid function may serve as a barrier to increased soy protein intake among consumers. These questions arose based on some cases of goiter in infants on soy infant formula 60 years ago [ 145 , 146 ] and on in vivo [ 147 ] and in vitro [ 148 ] research suggesting that isoflavones inhibit the activity of thyroid peroxidase, a key enzyme that, with iodine, helps the thyroid synthesize the hormones triiodothyronine (T3) and thyroxin (T4).

Despite early concerns regarding potential harmful effects of soy on thyroid function, the weight of current evidence points more strongly to the safety of soy. Recently, Otun et al. [ 149 ] conducted a systematic review and meta-analysis of 18 studies of the effects of soy foods/isoflavones on thyroid hormone function in adults. There were no overall effects of soy or isoflavones on thyroid function, although the authors did note a modest increase in TSH in some studies that was of unclear clinical relevance. Finally, the absence of an epidemiological association between soy food intake and thyroid function in countries where soy intake is high further argues for the safety of soy. While the possibility of adverse effects of soy on thyroid function cannot be ruled out in some sub-populations (e.g., those with marginal iodine status or sub-clinical hypothyroidism), individuals with normal thyroid function and iodine intake should be able to safely consume soy foods/protein [ 150 ].

With regard to hypothyroid individuals on thyroid replacement medication, there is limited case study evidence that soy foods may interfere to some degree with the absorption of levothyroxine in some hypothyroid individuals [ 151 ]. However, even in this situation, the reasonable intake of soy foods may still be acceptable if the dose of levothyroxine is either increased or timed such that it does not coincide with the soy intake [ 150 , 151 ].

5.3. Plant-Based Protein and Allergenicity

As mentioned above, the trend towards an increase in plant protein consumption stems from available evidence indicating that the source of protein (or, the protein “package”), not just the amount of protein, influences our health. Healthcare professionals are recommending adding different protein sources like soy, beans, nuts, or other plant-based proteins in place of red meat and processed meats to lower the risk of several diseases [ 152 ]. As the health food industry has grown, a focus for food manufacturers is the trend towards incorporating more plant-based foods to appeal to consumers. This is a trend not only in adults but also in the pediatric population. Increasingly, parents and caretakers are feeding infants and young children plant-based “milk” alternatives to cow milk [ 153 , 154 , 155 , 156 , 157 ] as well as providing more vegetarian options such as plant-based nuggets and burgers into their children’s daily meal plan. Such dietary choices may have unintended outcomes.

One of these outcomes is allergenicity. A food allergy is an adverse health effect resulting from a specific immune response that occurs reproducibly on exposure to a given food [ 158 ]. The health effect, called an allergic reaction, occurs because the immune system attacks food proteins that are normally harmless. Symptoms range from mild and transient to severe and life-threatening. According to Food Allergy Research and Education, 32 million Americans are living with potentially life-threatening food allergies. Based on a review of the literature, food allergy is estimated to affect more than 1–2% and less than 10% of the population [ 159 ].

In the U.S., more than 170 foods have been identified as triggers of food allergy [ 158 ]. The most common foods causing most of the significant allergic reactions include peanuts, tree nuts, fish, shellfish, milk, egg, wheat, and soy [ 158 ]. The most common food allergies in children and adults in the United States are allergies to peanut, milk, shellfish, and tree nut, with milk being most prevalent in children and shellfish most prevalent in adults [ 160 , 161 ]. Common food allergens from other countries include: sesame seeds in Canada, European Union (EU), Australia/New Zealand; mustard in EU and Canada; Buckwheat in Japan and Korea; and lupines in the EU [ 162 ]. The above have become common food allergens since they are frequently consumed, consumed in relatively large amounts, and consumed in early life stages. As plant protein consumption increases, so will the percentage of allergenic responses for these very reasons.

Take, for example, lupines. The Lupinus genus is closely related to other legumes, such as peanuts, soy, chickpeas, peas, lentils, and beans [ 163 ]. In the EU, lupine flour and other lupine protein ingredients were introduced in the 1990s as replacements for soy and wheat [ 164 ]. Since its introduction, allergic cross-reactions were noted in some peanut-allergic individuals. This was also observed in Australia, and now lupine is listed on the priority allergen lists by the International Union of Immunological Societies Allergen Nomenclature Subcommittee in the EU and Australia [ 164 ].

This is a very similar story to soy protein. Soy originated in southeast Asia and was first domesticated in China around 1100 BC, not being introduced in the U.S. until the 1760s [ 165 ]. Tofu and soy sauce were some of the first soy foods for humans. In 1930, soy infant formula was developed, but it was not widely used until the 1950s. In 1959, soy protein isolates were first introduced. From the 1950s, when some milk allergic infants transitioning to soy formula subsequently developed soy allergy, to the 1960s when higher intakes of soy protein in multiple different food sources became possible, the prevalence of soy allergies increased. Even so, soy has demonstrated value as a quality source of plant-based protein. Studies in children have demonstrated that soy supports normal growth and development [ 166 ] and improves growth when substituted for other legumes in malnourished children [ 167 , 168 ]. Overall, a wealth of evidence exists to demonstrate soy’s value as part of a healthy and varied diet [ 169 ]. All food proteins have the potential to cause allergic reactions, and children tend to be more sensitive to dietary proteins than adults [ 170 ]. While soy is a potential allergen in children, soy allergy in children is far less common than allergies to dairy [ 171 ], and soy allergy has a prevalence of only 0.4% among American children [ 172 , 173 ] and 0.32% in Canadian children [ 174 ]. This compares with prevalence rates of 2.0–3.0% for milk allergy [ 173 , 175 ], 2.0% for peanuts [ 173 ], 0.8–2.0% for eggs [ 176 , 177 ], and 1.0% for tree nuts [ 173 , 178 , 179 ]. Children also tend to outgrow soy allergies over time. One study reported that ~70% of infants with a soy allergy outgrew the allergy by the age of two years [ 180 ], and evidence suggests that, by the age of 10 years, only about 1 in 1000 children continue to have a soy allergy [ 178 ].

The chemical analysis of plant proteins has been happening for centuries, with the isolation of gluten proteins from wheat dating back over 250 years ago [ 181 ]. More recently, increasing emphasis has been placed on the role of plant proteins as allergens, particularly in Europe and the U.S.A., and in relation to novel and transgenic foods [ 181 ]. Plant-based food allergens fall into four main families: the prolamin superfamily, cupin superfamily, Bet v 1 family, and profilins. Over 50% of the plant protein allergens fall into two categories, the prolamin and cupin superfamilies [ 181 ]. The prolamin family is characterized based on the presence of a conserved eight cysteine amino acid residue pattern CXnCXnCCXnCXCXnCXnC. This stabilizes the protein structure which contributes to overall allergenicity of proteins in this class (highly resistant to heating, proteolysis, and digestion). The major allergens include cereal prolamins, 2S albumins, non-specific lipid transfer proteins, and α-amylase and trypsin inhibitor protein families [ 182 , 183 , 184 , 185 , 186 ].

The prolamin family are seed proteins which include but are not limited to wheat, barley, rye, soybean, rice, maize, and sunflower. Consequently, the prolamin superfamily currently forms the largest and most widely distributed group of plant food allergens [ 181 ]. One can visit the Food Allergy Research Resource Program (FARRP) database ( http://www.allergenonline.com/ ) to learn about more different types of allergens. FARRP database contains a comprehensive list of 2171 protein (amino acid) sequence entries that are categorized into 873 taxonomic-protein groups of unique proven or putative allergens (food, airway, venom/salivary, and contact) from 423 species [ 187 ].

All protein sources have the potential to have an allergenic effect. As novel plant-based sources of protein emerge into the market, they will inevitably elicit an allergenic response in someone. An example of this is pea protein. Peas are part of the legume family which also includes peanuts, beans, lentils, and soybeans. Due to other plant proteins, such as soy and wheat, having documented allergenic responses, pea protein has been viewed as a potentially less allergenic alternative. Pea protein use as a human food commodity has been steadily increasing in the U.S. Pea protein’s availability, physical and processing characteristics, nutritional value, and low cost have increased its use as a novel and effective alternative to substitute for soybean or animal proteins in functional foods [ 188 ]. It can be found in protein powders, medical formulas, and a variety of food substances such as milk, yogurt, cheese, and baked goods. While not common, there have been case studies documented of those with a proven peanut allergy having a reaction to pea protein [ 189 , 190 ]. What is interesting is that plain cooked yellow peas (e.g., entry of peas, split, mature seeds, cooked, boiled, and without salt) average approximately 8% protein by weight [ 37 ]. In comparison, current products include pea protein isolates (70–95% protein), concentrates (60–70% protein), and hydrolysates (90–95% protein) [ 189 ]. The products listed above provide much higher protein loads than someone eating a serving of cooked peas. It is not surprising, then, that someone not allergic to a serving of whole peas might experience an allergenic response to the much larger doses of pea protein found in products containing pea concentrates and isolates. Although some believe that pea and soy protein have a similar allergenic prevalence [ 191 ], pea protein allergenicity has not been extensively studied. While pea proteins are not required to be identified as a potential allergen on food labels in the U.S. or Canada, some have taken notice of pea as a “hidden allergen” [ 192 ].

While all dietary proteins are foreign proteins to the human immune system, only a few proteins from plant and animal origin cause an IgE-mediated immune response, typically in a small number of people [ 162 ]. Plant protein categories include legumes, nuts and seeds, whole grains, and other (mainly fruits and vegetables). At the time this article was written, there are insufficient data on all plant proteins, as some are novel and allergenic responses are just starting to emerge. This does not mean new protein sources should not be explored, but that labeling should be clear so those who do develop an allergy know what is in them.

6. Conclusions

Products made with plant-based protein and plant-based whole food diets are growing in popularity. Plant protein has been associated with benefits regarding health and physical function. The trend toward increasing plant protein intake is likely to continue as consumers expand their knowledge of the nutritional benefits of protein and sustainability concerns about the food supply are raised. Plant proteins may also become more valuable if current public health protein recommendations are revised upward. However, plant proteins differ in nutritional quality and those who choose to largely emphasize plant versus animal proteins need to be aware of these differences when planning an appropriate diet, especially in more vulnerable populations. In addition, potential safety issues have come to light and may continue to emerge with the increased amount, variety, and forms of plant proteins that are incorporated into the diet. More research is needed on the best ways to incorporate plant proteins into the diet safely and effectively.

Author Contributions

All authors were involved in the design, preparation, and review of this manuscript and the decision to publish. J.C.L.-B. wrote Section 4.1 , Section 4.2 , Section 4.3 , Section 4.4 and Section 4.5 , M.W. wrote Section 4.6 , C.A. wrote Section 5.3 , and S.R.H. wrote the remainder. All authors have read and agreed to the published version of the manuscript.

This research received no external funding and was supported by Abbott Nutrition.

Conflicts of Interest

The authors are employed by Abbott Nutrition.

Publisher’s Note: MDPI stays neutral with regard to jurisdictional claims in published maps and institutional affiliations.

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Researchers have found a protein that seems to protect brain cells from Alzheimer's

Jon Hamilton

Alzheimer's resilience

A study of 48 post-mortem brains found a protein that appears to protect brain cells from Alzheimer's — even in people who had significant amounts of amyloid plaques in their brains.

Copyright © 2024 NPR. All rights reserved. Visit our website terms of use and permissions pages at www.npr.org for further information.

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a Inclusion criteria for tau pathology: low/medium or high tau indicated by standardized uptake value ratio >1.10 or positive visual read assessed by 18 F-flortaucipir positron emission tomography (PET) imaging.

b Inclusion criteria for amyloid pathology (≥37 Centiloids) assessed with 18 F-florbetapir or 18 F-florbetaben PET.

c Inclusion criteria for Mini-Mental State Examination: score of 20 to 28.

d Phosphorylated tau 181 (P-tau181) screening criterion was not implemented for the entire trial duration (eMethods in Supplement 3 ).

e Exclusion criteria for MRI include presence of amyloid-related imaging abnormalities of edema/effusion, >4 cerebral microhemorrhages, >1 area of superficial siderosis, and any intracerebral hemorrhage >1 cm or severe white matter disease.

f Summary of other screen failure can be found in eTable 3 in Supplement 3 (lists reason if ≥20 participants).

g Stratified by baseline tau categorization and enrolling sites.

h One additional death occurred after treatment completion and in the follow-up period.

i Alzheimer disease progression to a degree prompting study discontinuation, per investigator judgment.

j Treatment completion criteria: amyloid plaque level of 11 Centiloids on any single scan or 11 to <25 Centiloids on 2 consecutive scans.

k Participants who met treatment completion criteria are included in discontinuation and completion numbers.

l Percentage calculated as No./total No. of participants with a PET scan at visit: n = 761 at 24 wk, n = 672 at 52 wk, and n = 620 at 76 wk. Corresponding number of participants and percentages for the low/medium tau population were 20.3% (n = 106) at 24 wk, 51.9% (n = 241) at 52 wk, and 73.5% (n = 321) at 76 wk.

A, 35.1% slowing (95% CI, 19.90%-50.23%) of clinical progression. B, 22.3% slowing (95% CI, 11.38%-33.15%) of clinical progression. C, 36.0% slowing (95% CI, 20.76%-51.15%) of clinical progression. D, 28.9% slowing (95% CI, 18.41%-39.44%) of clinical progression. iADRS data were analyzed using the natural cubic spline model with 2 degrees of freedom (NCS2) and CDR-SB data were analyzed with mixed models for repeated measures (MMRM). For MMRM analyses, 95% CIs for least-squares mean changes were calculated with the normal approximation method. For the Alzheimer Disease Cooperative Study—Instrumental Activities of Daily Living, 13-item cognitive subscale of the Alzheimer Disease Assessment Scale, and CDR-SB clinical assessments analyzed with NCS2, see eFigure 1 (low/medium tau population) and eFigure 2 (combined population) in Supplement 3 and Table 2. For all clinical assessments analyzed with MMRM, see eFigure 3 (low/medium tau population) and 4 (combined population) in Supplement 3 and Table 2. P  < .001 for all 76 week time points.

Biomarker data shown were analyzed using mixed models for repeated measures (MMRM). For MMRM analyses, 95% CIs for the least-squares mean changes were calculated with the normal approximation method. P  < .001 for all time points in panels A-D. B, P value is from Fisher exact test comparing the percent amyloid negative by treatment groups at each visit. E and F, The analysis was conducted using a Cox proportional hazards model. There were 163 events among 573 participants in the placebo group and 100 events among 555 participants in the donanemab group in the low/medium tau population and 288 events among 844 participants in the placebo group and 186 events among 805 participants in the donanemab group in the combined population. CDR-G indicates Clinical Dementia Rating Global Score.

Trial protocol

Statistical analysis plan

Nonauthor collaborators

Data sharing statement

• Donanemab for Alzheimer Disease—Who Benefits and Who Is Harmed? JAMA Editorial August 8, 2023 Jennifer J. Manly, PhD; Kacie D. Deters, PhD
• Amyloid-Targeting Monoclonal Antibodies for Alzheimer Disease JAMA Editorial August 8, 2023 Gil D. Rabinovici, MD; Renaud La Joie, PhD
• Novel Alzheimer Disease Treatments and Reconsideration of US Pharmaceutical Reimbursement Policy JAMA Editorial August 8, 2023 Meredith B. Rosenthal, PhD
• Ushering in a New Era of Alzheimer Disease Therapy JAMA Editorial August 8, 2023 Eric W. Widera, MD; Sharon A. Brangman, MD; Nathaniel A. Chin, MD
• Role of Registries in Medicare Coverage of New Alzheimer Disease Drugs JAMA Viewpoint October 10, 2023 This Viewpoint discusses how the design of the Centers for Medicare & Medicaid Services (CMS) registry could impact Medicare’s ability to evaluate whether monoclonal antibodies are reasonable and necessary for patients with Alzheimer disease and help physicians understand when the drug is most beneficial. Ilina C. Odouard, MPH; Mariana P. Socal, MD, PhD; Gerard F. Anderson, PhD
• Who Should Get the New Alzheimer Disease Drug? JAMA Medical News & Perspectives October 17, 2023 This Medical News story examines the complexity of determining who to treat with lecanemab, the new Alzheimer disease drug. Rita Rubin, MA
• Use of Donanemab in Early Symptomatic Alzheimer Disease—Reply JAMA Comment & Response December 19, 2023 Cynthia D. Evans, PhD; John R. Sims, MD
• Use of Donanemab in Early Symptomatic Alzheimer Disease JAMA Comment & Response December 19, 2023 Nunzio Pomara, MD; Bruno Pietro Imbimbo, PhD
• Risks of Harm in Alzheimer Disease by Amyloid Lowering JAMA Viewpoint June 18, 2024 This Viewpoint discusses how data gaps in published research impede clinicians’ ability to clearly discuss the risks and benefits of amyloid-lowering drugs for treating Alzheimer disease. Madhav Thambisetty, MD, PhD; Robert Howard, MD
• FDA Greenlights Second Alzheimer Drug, Donanemab JAMA Medical News in Brief July 26, 2024 Emily Harris

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Sims JR , Zimmer JA , Evans CD, et al. Donanemab in Early Symptomatic Alzheimer Disease : The TRAILBLAZER-ALZ 2 Randomized Clinical Trial . JAMA. 2023;330(6):512–527. doi:10.1001/jama.2023.13239

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Donanemab in Early Symptomatic Alzheimer Disease : The TRAILBLAZER-ALZ 2 Randomized Clinical Trial

• 1 Eli Lilly and Company, Indianapolis, Indiana
• 2 Boston Center for Memory and Boston University Alzheimer’s Disease Center, Boston, Massachusetts
• 3 Department of Neurology and Department of Psychiatry, Alpert Medical School of Brown University, Providence, Rhode Island
• 4 Butler Hospital, Providence, Rhode Island
• 5 Department of Neurology, Indiana University School of Medicine, Indianapolis
• 6 Clinical Memory Research Unit, Department of Clinical Sciences Malmö, Lund University, Lund, Sweden; Memory Clinic, Skåne University Hospital, Lund, Sweden
• 7 Scottish Brain Sciences, Edinburgh, United Kingdom
• Editorial Donanemab for Alzheimer Disease—Who Benefits and Who Is Harmed? Jennifer J. Manly, PhD; Kacie D. Deters, PhD JAMA
• Editorial Amyloid-Targeting Monoclonal Antibodies for Alzheimer Disease Gil D. Rabinovici, MD; Renaud La Joie, PhD JAMA
• Editorial Novel Alzheimer Disease Treatments and Reconsideration of US Pharmaceutical Reimbursement Policy Meredith B. Rosenthal, PhD JAMA
• Editorial Ushering in a New Era of Alzheimer Disease Therapy Eric W. Widera, MD; Sharon A. Brangman, MD; Nathaniel A. Chin, MD JAMA
• Viewpoint Role of Registries in Medicare Coverage of New Alzheimer Disease Drugs Ilina C. Odouard, MPH; Mariana P. Socal, MD, PhD; Gerard F. Anderson, PhD JAMA
• Medical News & Perspectives Who Should Get the New Alzheimer Disease Drug? Rita Rubin, MA JAMA
• Comment & Response Use of Donanemab in Early Symptomatic Alzheimer Disease—Reply Cynthia D. Evans, PhD; John R. Sims, MD JAMA
• Comment & Response Use of Donanemab in Early Symptomatic Alzheimer Disease Nunzio Pomara, MD; Bruno Pietro Imbimbo, PhD JAMA
• Viewpoint Risks of Harm in Alzheimer Disease by Amyloid Lowering Madhav Thambisetty, MD, PhD; Robert Howard, MD JAMA
• Medical News in Brief FDA Greenlights Second Alzheimer Drug, Donanemab Emily Harris JAMA

Question   Does donanemab, a monoclonal antibody designed to clear brain amyloid plaque, provide clinical benefit in early symptomatic Alzheimer disease?

Findings   In this randomized clinical trial that included 1736 participants with early symptomatic Alzheimer disease and amyloid and tau pathology, the least-squares mean change in the integrated Alzheimer Disease Rating Scale score (range, 0-144; lower score indicates greater impairment) at 76 weeks was −6.02 in the donanemab group and −9.27 in the placebo group for the low/medium tau population and −10.19 in the donanemab group and −13.11 in the placebo group in the combined study population, both of which were significant differences.

Meaning   Among participants with early symptomatic Alzheimer disease and amyloid and tau pathology, donanemab treatment significantly slowed clinical progression at 76 weeks.

Importance   There are limited efficacious treatments for Alzheimer disease.

Objective   To assess efficacy and adverse events of donanemab, an antibody designed to clear brain amyloid plaque.

Design, Setting, and Participants   Multicenter (277 medical research centers/hospitals in 8 countries), randomized, double-blind, placebo-controlled, 18-month phase 3 trial that enrolled 1736 participants with early symptomatic Alzheimer disease (mild cognitive impairment/mild dementia) with amyloid and low/medium or high tau pathology based on positron emission tomography imaging from June 2020 to November 2021 (last patient visit for primary outcome in April 2023).

Interventions   Participants were randomized in a 1:1 ratio to receive donanemab (n = 860) or placebo (n = 876) intravenously every 4 weeks for 72 weeks. Participants in the donanemab group were switched to receive placebo in a blinded manner if dose completion criteria were met.

Main Outcomes and Measures   The primary outcome was change in integrated Alzheimer Disease Rating Scale (iADRS) score from baseline to 76 weeks (range, 0-144; lower scores indicate greater impairment). There were 24 gated outcomes (primary, secondary, and exploratory), including the secondary outcome of change in the sum of boxes of the Clinical Dementia Rating Scale (CDR-SB) score (range, 0-18; higher scores indicate greater impairment). Statistical testing allocated α of .04 to testing low/medium tau population outcomes, with the remainder (.01) for combined population outcomes.

Results   Among 1736 randomized participants (mean age, 73.0 years; 996 [57.4%] women; 1182 [68.1%] with low/medium tau pathology and 552 [31.8%] with high tau pathology), 1320 (76%) completed the trial. Of the 24 gated outcomes, 23 were statistically significant. The least-squares mean (LSM) change in iADRS score at 76 weeks was −6.02 (95% CI, −7.01 to −5.03) in the donanemab group and −9.27 (95% CI, −10.23 to −8.31) in the placebo group (difference, 3.25 [95% CI, 1.88-4.62]; P  < .001) in the low/medium tau population and −10.2 (95% CI, −11.22 to −9.16) with donanemab and −13.1 (95% CI, −14.10 to −12.13) with placebo (difference, 2.92 [95% CI, 1.51-4.33]; P  < .001) in the combined population. LSM change in CDR-SB score at 76 weeks was 1.20 (95% CI, 1.00-1.41) with donanemab and 1.88 (95% CI, 1.68-2.08) with placebo (difference, −0.67 [95% CI, −0.95 to −0.40]; P  < .001) in the low/medium tau population and 1.72 (95% CI, 1.53-1.91) with donanemab and 2.42 (95% CI, 2.24-2.60) with placebo (difference, −0.7 [95% CI, −0.95 to −0.45]; P  < .001) in the combined population. Amyloid-related imaging abnormalities of edema or effusion occurred in 205 participants (24.0%; 52 symptomatic) in the donanemab group and 18 (2.1%; 0 symptomatic during study) in the placebo group and infusion-related reactions occurred in 74 participants (8.7%) with donanemab and 4 (0.5%) with placebo. Three deaths in the donanemab group and 1 in the placebo group were considered treatment related.

Conclusions and Relevance   Among participants with early symptomatic Alzheimer disease and amyloid and tau pathology, donanemab significantly slowed clinical progression at 76 weeks in those with low/medium tau and in the combined low/medium and high tau pathology population.

Trial Registration   ClinicalTrials.gov Identifier: NCT04437511

Deposition of β-amyloid in the brain is an early event in Alzheimer disease that leads to neurofibrillary tangles composed of tau protein and other characteristic brain changes referred to as the amyloid cascade . 1 , 2 Abnormal β-amyloid is a key pathological hallmark of Alzheimer disease defined by the 2018 National Institute on Aging and the Alzheimer’s Association Research Framework 3 and is one of the major targets in Alzheimer disease research and drug development.

Over the past decade, considerable advances occurred in testing the amyloid cascade hypothesis in Alzheimer disease clinical trials. Numerous amyloid-targeting therapy trials failed to show appreciable slowing of clinical disease progression 4 - 7 ; however, aducanumab, lecanemab, and donanemab recently showed promising amyloid plaque clearance, potentially benefitting patients. 8 - 10

Donanemab is an immunoglobulin G1 monoclonal antibody directed against insoluble, modified, N-terminal truncated form of β-amyloid present only in brain amyloid plaques. Donanemab binds to N-terminal truncated form of β-amyloid and aids plaque removal through microglial-mediated phagocytosis. 11 In the phase 2 TRAILBLAZER-ALZ trial of donanemab vs placebo, the primary outcome was met, as measured by the integrated Alzheimer Disease Rating Scale (iADRS), an integrated assessment of cognition and daily function. 9 Adverse events of interest included amyloid-related imaging abnormalities and infusion-related reactions. 9 To confirm and expand results from TRAILBLAZER-ALZ, we report results from TRAILBLAZER-ALZ 2, a global phase 3 randomized clinical trial that assessed donanemab efficacy and adverse events in a larger group of participants with low/medium tau pathology (the population studied in the phase 2 trial) and in a combined population including those with high tau pathology, a population hypothesized to be more difficult to treat due to more advanced disease.

TRAILBLAZER-ALZ 2 was a 76-week, phase 3, randomized, double-blind, parallel, multicenter, placebo-controlled trial with participants screened at 277 sites in 8 countries (eTable 1 in Supplement 3 ). Enrollment began June 19, 2020, and ended November 5, 2021, and database lock/unblinding (double-blind phase) occurred on April 28, 2023. The trial was originally designed as a phase 2 trial but was subsequently amended to a larger phase 3 trial in February 2021 in an effort to confirm and expand the results of the previous TRAILBLAZER-ALZ trial. The trial was conducted according to the Declaration of Helsinki, the International Conference on Harmonization Good Clinical Practice Guideline, and local regulatory requirements. An independent ethics committee/institutional review board at each site approved the study protocol ( Supplement 1 ), which is provided alongside the statistical analysis plan ( Supplement 2 ). Participants and study partners provided written consent. An independent data and safety monitoring board provided trial oversight.

The trial included participants aged 60 to 85 years with early symptomatic Alzheimer disease (mild cognitive impairment [MCI] 12 or Alzheimer disease with mild dementia). 3 P-tau181 screening was removed in an early protocol amendment (eMethods in Supplement 3 ). Eligible participants had screening Mini-Mental State Examination (MMSE) scores of 20 to 28, amyloid pathology (≥37 Centiloids) assessed with 18 F-florbetapir 13 or 18 F-florbetaben 14 positron emission tomography (PET), and presence of tau pathology assessed by 18 F-flortaucipir PET imaging with central image evaluation. 13 , 15 Tau PET scans were categorized as low/medium or high tau by visual and quantitative reads as previously described 16 - 20 ( Supplements 1 and 2 ). Screening procedures also included magnetic resonance imaging (MRI), and key exclusion criteria included presence of amyloid-related imaging abnormalities of edema/effusion, more than 4 cerebral microhemorrhages, more than 1 area of superficial siderosis, and any intracerebral hemorrhage greater than 1 cm or severe white matter disease on MRI. For all eligibility criteria, see Supplement 1 . Demographic information, including race and ethnicity, was collected to potentially understand any differences in disease course, treatment effects, or adverse events. The participants self-reported race and ethnicity based on fixed categories.

Eligible participants were randomly assigned in a 1:1 ratio ( Figure 1 ) by a computer-generated sequence using interactive web response systems, with stratification by baseline tau categorization and enrolling sites; the randomization block size was 4. Randomized participants received either donanemab (700 mg for the first 3 doses and 1400 mg thereafter) or placebo, administered intravenously every 4 weeks for up to 72 weeks. If amyloid plaque level (assessed at 24 weeks and 52 weeks) was less than 11 Centiloids on any single PET scan or less than 25 but greater than or equal to 11 Centiloids on 2 consecutive PET scans (TRAILBLAZER-ALZ cutoffs 9 ), donanemab was switched to placebo in a blinded procedure. Final adverse events and efficacy assessments were performed at 76 weeks. Amyloid-related imaging abnormality monitoring occurred with scheduled MRIs at 4, 12, 24, 52, and 76 weeks and unscheduled MRIs at investigator discretion. Any participant with detected amyloid-related imaging abnormalities had imaging every 4 to 6 weeks until resolution or stabilization. Amyloid-related imaging abnormality management and treatment interruption guidelines (eTable 2 in Supplement 3 ) depended on severity and symptoms. If infusions were held, investigators were advised to await resolution of amyloid-related imaging abnormalities of edema/effusion on radiographic imaging and stabilization of amyloid-related imaging abnormalities of microhemorrhages and hemosiderin deposits before resuming infusions. Permanent discontinuation was advised for macrohemorrhages. Investigators made final amyloid-related imaging abnormality management decisions.

The primary outcome was change in the iADRS score from baseline to 76 weeks in either the low/medium tau population or combined (low/medium and high tau) population. The iADRS is an integrated assessment of cognition and daily function from the 13-item cognitive subscale of the Alzheimer Disease Assessment Scale (ADAS-Cog 13 ) and Alzheimer Disease Cooperative Study—Instrumental Activities of Daily Living (ADCS-iADL), measuring global disease severity across the Alzheimer disease continuum as a single summary score. The iADRS is validated and captures clinical progression from MCI due to Alzheimer disease through moderate dementia due to Alzheimer disease, and treatment effects have been demonstrated across MCI and Alzheimer disease with mild dementia. 6 , 9 , 21 - 27 The possible scores on the iADRS range from 0 to 144 (lower scores indicate greater impairment), and the meaningful within-patient change (MWPC) is a change of 5 points for those with Alzheimer disease with MCI and 9 points for those with Alzheimer disease with mild dementia. The MWPC, or minimal clinically important difference (MCID) as in Supplement 1 and 2 , is a threshold for outcome scores (either patient-reported or physician-measured) above which a patient or physician would consider the change meaningful. 28

Prespecified secondary outcomes included changes from baseline to 76 weeks by sum of boxes of the Clinical Dementia Rating Scale (CDR-SB), the ADAS-Cog 13 , the ADCS-iADL, and MMSE in the low/medium tau or combined population. Amyloid plaque reduction at 76 weeks, percentage of participants reaching amyloid clearance (<24.1 Centiloids measured by amyloid PET 9 , 29 ) at 24 weeks and 76 weeks, tau PET 1 (frontal cortical regions) change, volumetric MRI (vMRI; whole brain, hippocampus, and ventricles) change, and adverse events were additional secondary outcomes. Supplement 1 provides a complete listing and methodology of adverse events assessments. Amyloid-related imaging abnormalities of edema/effusion, amyloid-related imaging abnormalities of microhemorrhages and hemosiderin deposits, and infusion-related reactions were adverse events of special interest because they were considered class effects or observed in previous trials. 9 , 30 - 32 Secondary outcomes related to pharmacokinetics and antidrug antibodies were also prespecified and are planned for subsequent studies. Exploratory outcomes included change in plasma P-tau217 (C 2 N Diagnostics) at 76 weeks and time-based analyses: progression risk using the CDR Global score (CDR-G; progression defined as any increase from baseline in CDR-G at consecutive visits), participants with no progression at 1 year on the CDR-SB, and clinical progression delay (ie, months saved with treatment) on the iADRS and CDR-SB. Additional information about outcome measures, including score ranges and MWPCs, is provided in eMethods in Supplement 3 .

Prespecified primary and secondary outcomes were controlled for multiplicity (gated) at 76 weeks ( Supplement 2 and eMethods in Supplement 3 ) except for MMSE, changes in vMRI measurements, and adverse event assessments. Additional time points were gated for amyloid clearance and P-tau217. Nominal P values are reported for gated and nongated outcomes.

The trial was originally designed as a phase 2 trial with a plan to enroll 500 participants and assess CDR-SB as the primary outcome, but was subsequently amended to a phase 3 trial assessing the iADRS score as the primary outcome in February 2021 in an effort to confirm and expand the results of the TRAILBLAZER-ALZ trial. No unblinded data analysis of TRAILBLAZER-ALZ 2 was performed or used to inform design or analyses. Further details regarding major protocol or study adjustments are in eMethods in Supplement 3 and the trial protocol in Supplement 1 .

Revised study sample size and power calculations were based on the primary results from the TRAILBLAZER-ALZ trial, 9 where mean progression in the placebo and donanemab groups on iADRS was −10.06 and −6.86 (approximately 32% slowing of disease progression) over 76 weeks, respectively. Multiple longitudinal data sets were simulated and the natural cubic spline model with 2 degrees of freedom (NCS2) was fit to each sample to determine the power. The powering and sample size determination of the trial was based on the low/medium tau population. With a sample size of approximately 1000 randomized participants in the low/medium tau population and an assumed 30% discontinuation rate, the NCS model provided greater than 95% power to achieve statistical significance at a 2-sided α level of .05. The total planned enrollment (including both the low/medium and high tau populations) was 1800.

Most statistical analyses were done with SAS version 9.4 (SAS Institute). Some time-based progression analyses were analyzed with R Project version 4.3.0 (R Foundation).

The efficacy analyses were conducted by using the evaluable efficacy population (participants with a baseline and at least 1 postbaseline efficacy measurement based on randomized treatment). A prespecified gated testing scheme 33 , 34 was used to control for study-wise type I error rate at 2-sided α level of .05, with 80% of initial α spend (.04) for multiplicity control allocated to the low/medium tau population and 20% of initial α spend (.01) for multiplicity control allocated to the combined population (testing scheme in Supplement 2 ; eMethods in Supplement 3 also describes time-based analyses not described below).

Clinical outcomes (except for CDR-SB) were primarily analyzed using an NCS2 model. The protocol-specified week value for each participant was used as a continuous variable to create NCS basis functions with knot locations at 0 weeks, the median observation time, and 76 weeks. The model restricted baseline estimates to be the same for treatment and placebo groups. The baseline score and each scheduled postbaseline score were dependent variables in the model. The model’s independent variables included NCS basis expansion terms (2 terms), NCS basis expansion term × treatment interaction (2 terms), baseline age, concomitant acetylcholinesterase inhibitor and/or memantine use at baseline (yes/no), and randomization stratifying factors (pooled site and baseline tau category [baseline tau category in combined population only]). An unstructured variance covariance matrix was used to model the within-participant errors using restricted maximum likelihood. The Kenward-Roger approximation was used to estimate the denominator degrees of freedom.

The MMRM was used to primarily assess CDR-SB, plasma P-tau217, amyloid PET, and vMRI. The analysis model used change from baseline as the dependent variable. The model was adjusted for age, baseline value, visit as a categorical variable, treatment, baseline × visit interactions, treatment × visit interactions, concomitant acetylcholinesterase inhibitor/memantine use at baseline (CDR-SB only), and randomization stratifying factors of pooled site and, for combined population only, baseline tau category. For vMRI, only age and baseline brain volumes were covariates. The covariance matrix structure used was the same as NCS. Plasma P-tau 217 value was log 10 -transformed to meet the normality assumption.

Both the NCS2 and MMRM use the same protocol-specified time values for each participant in the analysis; the NCS2 model makes additional parametric assumptions for the shape of the longitudinal mean structure that can lead to increased efficiency.

The percent slowing relative to placebo was calculated by dividing the least-squares mean (LSM) change from baseline treatment differences at 76 weeks by the LSM change from baseline with placebo at 76 weeks and multiplying by 100.

ANCOVA analysis was conducted for tau PET standardized uptake value ratio (SUVR), with change from baseline to 76 weeks as the dependent variable and covariates of baseline tau SUVR, age, and, for the combined population, tau burden.

MMRM, NCS with 3 degrees of freedom model (NCS3), and bayesian disease progression model (DPM) were applied as sensitivity analyses for the primary outcome. DPM was applied to measure the proportion of disease progression in donanemab-treated participants relative to placebo-treated participants using a disease progression ratio, as previously described. 35 Details on sensitivity analyses for censoring after amyloid-related imaging abnormalities or infusion-related reactions, per-protocol analysis, and analysis of study completers are in eMethods in Supplement 3 . Details of subgroup analyses and time-based analyses are also described in eMethods in Supplement 3 .

Cox proportional hazard models were applied to CDR-G (gated), iADRS (nongated), and CDR-SB (nongated). Progression to next clinical stage was defined as any increase in CDR-G at 2 consecutive visits from baseline. MWPC was established as an iADRS change of greater than or equal to 5 for those with Alzheimer disease with MCI and greater than or equal to 9 points for those with Alzheimer disease with mild dementia and a CDR-SB change of greater than or equal to 1 point for those with Alzheimer disease with MCI and greater than or equal to 2 points for Alzheimer disease with mild dementia at 2 consecutive visits from baseline.

Analyses of the high tau population alone (ie, not combined with the low/medium tau population) for primary and secondary outcomes was performed post hoc.

Adverse events were evaluated in all participants exposed to study drug and were summarized according to event frequency by treatment assignment.

If less than 30% of the ADCS-iADL, 3 or fewer items of the ADAS-Cog 13, or 1 box of the CDR were missing, the total score for these assessments was imputed. If more items were missing than defined, the total score at that visit was considered missing ( Supplement 2 ). If either the ADCS-iADL or ADAS-Cog 13 scores were missing, the iADRS score was considered as missing. The missing data for NCS and MMRM analyses were handled by the likelihood-based mixed-effect model and the model parameters were estimated using restricted likelihood estimation incorporating all the observed data.

All presented primary, secondary, and exploratory outcomes were controlled for multiplicity (gated) in at least 1 population except for MMSE, vMRI measurements, and safety assessments. Of the 24 gated outcomes (eMethods in Supplement 3 ), 23 were statistically significant.

Of 8240 participants screened, 1736 were enrolled (mean age, 73.0 years; 996 [57.4%] women) and 76% completed the trial: 860 were assigned to receive donanemab and 876 were assigned to receive placebo ( Figure 1 ). Baseline characteristics are summarized by treatment groups in both low/medium tau (n = 1182) and combined populations (n = 1736) ( Table 1 ). As expected, the combined population had higher tau biomarkers at baseline due to the inclusion of participants with high tau pathology and showed greater impairment across baseline clinical assessments.

In the low/medium tau population, LSM change from baseline in the iADRS score at 76 weeks was −6.02 (95% CI, −7.01 to −5.03) in the donanemab group and −9.27 (95% CI, −10.23 to −8.31) in the placebo group (difference, 3.25 [95% CI, 1.88-4.62]; P  < .001), representing a 35.1% (95% CI, 19.90%-50.23%) slowing of disease progression ( Figure 2 , Table 2 ).

In the combined population, LSM change from baseline in the iADRS score at 76 weeks was −10.19 (95% CI, −11.22 to −9.16) in the donanemab group and −13.11 (95% CI, −14.10 to −12.13) in the placebo group (difference, 2.92 [95% CI, 1.51-4.33]; P  < .001), representing a 22.3% (95% CI, 11.38%-33.15%) slowing of disease progression ( Figure 2 , Table 2 ).

In the low/medium tau population, the differences between treatment groups in the LSM change from baseline at 76 weeks was −0.67 (95% CI, −0.95 to −0.40) (36.0% [95% CI, 20.76%-51.15%] slowing of clinical progression) for CDR-SB, 1.83 (95% CI, 0.91-2.75) (39.9% [95% CI, 19.15%-60.58%] slowing of clinical progression) for ADCS-iADL, and −1.52 (95% CI, −2.25 to −0.79) (32.4% [95% CI, 16.55%-48.35%] slowing of clinical progression) for ADAS-Cog 13 ( Figure 2 , Table 2 ; eFigure 1 and 3 in Supplement 3 ).

In the combined population, the differences in the LSM change from baseline to 76 weeks between the donanemab and placebo groups were −0.70 (95% CI, −0.95 to −0.45) (28.9% [95% CI, 18.26%-39.53%] slowing of clinical progression) for CDR-SB, 1.70 (95% CI, 0.84-2.57) (27.8% [95% CI, 13.48%-42.13%] slowing of clinical progression) for ADCS-iADL, and −1.33 (95% CI, −2.09 to −0.57) (19.5% [95% CI, 8.23%-30.83%] slowing of clinical progression) for ADAS-Cog13 ( Figure 2 , Table 2 ; eFigures 2 and 4 in Supplement 3 ).

At 76 weeks, brain amyloid plaque level decreased by 88.0 Centiloids (95% CI, −90.20 to −85.87) with donanemab treatment and increased by 0.2 Centiloids (95% CI, −1.91 to 2.26) in the placebo group in the low/medium tau population; in the combined population, amyloid plaque level decreased by 87.0 Centiloids (95% CI, −88.90 to −85.17) with donanemab treatment and decreased by 0.67 Centiloids (95% CI, −2.45 to 1.11) in the placebo group ( Figure 3 A). The percentages of donanemab-treated participants in the low/medium tau population who reached amyloid clearance 29 , 38 were 34.2% (95% CI, 30.22%-38.34%) at 24 weeks and 80.1% (95% CI, 76.12%-83.62%) at 76 weeks compared with 0.2% (95% CI, 0.03%-1.02%) at 24 weeks and 0% (95% CI, 0.00%-0.81%) at 76 weeks of placebo-treated participants. In the combined population, amyloid clearance was reached in 29.7% (95% CI, 26.56%-33.04%) of participants at 24 weeks and 76.4% (95% CI, 72.87%-79.57%) at 76 weeks of donanemab-treated participants compared with 0.2% (95% CI, 0.07%-0.90%) at 24 weeks and 0.3% (95% CI, 0.08%-1.05%) at 76 weeks of placebo-treated participants ( Figure 3 B).

Evaluation of the LSM change from baseline to 76 weeks in frontal tau SUVR (cerebellar gray reference) did not show a significant difference in the low/medium tau or in the combined population (eFigure 5 in Supplement 3 ). The difference in LSM change in tau SUVR from placebo in the frontal lobe at 76 weeks was −0.0002 (95% CI, −0.01 to 0.01; P  = .97) in the low/medium tau population and −0.0041 (95% CI, −0.01 to 0.01; P  = .45) in the combined population.

For both the low/medium tau and combined populations, at 76 weeks, vMRI (a non-gated secondary outcome) showed a greater decrease in whole brain volume, a lesser decrease in the hippocampal volume, and a greater increase in ventricular volume in the donanemab group than in the placebo group (eFigure 6 in Supplement 3 ).

P-tau217 was significantly reduced from baseline with donanemab treatment compared with placebo in the low/medium tau and combined population. The difference in LSM change in tau SUVR (log 10 -based) vs placebo was −0.25 (95% CI, −0.28 to −0.22; P  < .001) in the low/medium tau population and −0.22 (95% CI −0.24 to −0.20; P  < .001) in the combined population at 76 weeks ( Figure 3 C and D).

There was a 38.6% (CDR-G hazard ratio, 0.61 [95% CI, 0.47-0.80]; P  < .001) lower risk of disease progression in the low/medium tau population and a 37.4% (CDR-G hazard ratio, 0.63 [95% CI, 0.51-0.77; P  < .001) lower risk of disease progression in the combined population with donanemab treatment compared with placebo over the 18-month trial ( Figure 3 E and F; see eFigure 7 in Supplement 3 for nongated disease progression analyses of iADRS and CDR-SB). Substantial decline in the low/medium tau population occurred in 100 (18%) donanemab-treated participants and 163 (28%) placebo-treated participants and, in the combined population, occurred in 186 (23%) donanemab-treated and 288 (34%) placebo-treated participants. In addition, in the low/medium tau population, an estimated 47% of participants were stable (showed no decline in CDR-SB from baseline) with donanemab at 1 year compared with 29% of participants receiving placebo ( P  < .001) (eTable 6 in Supplement 3 ). At 76 weeks, disease progression with donanemab treatment in the low/medium tau population was delayed by 4.36 months (95% CI, 1.87-6.85) on the iADRS and 7.53 months (95% CI, 5.69-9.36) on the CDR-SB.

Sensitivity analyses of the iADRS score (eFigure 8 in Supplement 3 ) using NCS3, MMRM, and DPM analyses, NCS2 in the completers and per protocol populations, and censoring change scores after amyloid-related imaging abnormalities edema/effusion and/or infusion-related reaction observations were consistent with the primary analysis (33.4%-39.6% slowing of clinical progression).

The findings as measured by iADRS and CDR-SB were generally consistent across baseline characteristic subgroups where the subgroup was sufficiently large (eFigure 9 in Supplement 3 ).

Analysis of the smaller (n = 552) high tau population alone (ie, not combined with the low/medium tau population) for all primary and secondary outcomes was completed post hoc. The difference between the donanemab and placebo groups in the LSM change from baseline at 76 weeks was 1.26 (95% CI, −1.77 to 4.28; P  = .42) for the iADRS score and −0.69 (95% CI −1.19 to −0.20; P  = .006) for the CDR-SB score. For additional assessments in the high tau population, see eTables 4, 5, and 10 and eFigures 10-13 in Supplement 3 .

The incidence of death was 1.9% in the donanemab group and 1.1% in the placebo group, while the incidence of serious adverse events was 17.4% in the donanemab group and 15.8% in the placebo group ( Table 3 ). In the donanemab group, 3 participants with serious amyloid-related imaging abnormalities subsequently died (2 APOE ε4 heterozygous carriers and one noncarrier; none were prescribed anticoagulant or anti-platelet medications; one resumed treatment after resolution of severe amyloid-related imaging abnormalities edema/effusion that was accompanied by severe amyloid-related imaging abnormalities microhemorrhages and hemosiderin deposits and one had superficial siderosis at baseline) (eTable 9 in Supplement 3 ). Treatment-emergent adverse events were reported by 759 of 853 participants (89.0%) receiving donanemab and 718 of 874 participants (82.2%) receiving placebo. Treatment discontinuation due to adverse events was reported in 112 participants receiving donanemab and 38 participants receiving placebo. The most common adverse events that led to treatment discontinuation were infusion-related reactions, either amyloid-related imaging abnormalities edema/effusion or microhemorrhages and hemosiderin deposits, and hypersensitivity (eTable 7 in Supplement 3 ).

Either amyloid-related imaging abnormalities of edema/effusion or microhemorrhages and hemosiderin deposits occurred in 314 participants (36.8%) receiving donanemab and 130 (14.9%) receiving placebo. Amyloid-related imaging abnormalities of edema/effusion, determined via MRI, occurred in 205 participants (24.0%) in the donanemab group and in 18 (2.1%) in the placebo group. Most amyloid-related imaging abnormalities of edema/effusion events were mild to moderate (see eTable 2 in Supplement 3 ) (n = 188 [93.1%] in the donanemab group; n = 17 [100%] in the placebo group). Symptomatic amyloid-related imaging abnormalities of edema/effusion were reported by 52 participants (6.1%) in the donanemab group (25.4% of those with amyloid-related imaging abnormalities of edema/effusion), with 45 participants (86.5%) having symptom resolution. Most cases (57.9%) of first amyloid-related imaging abnormalities of edema/effusion occurred after receiving up to 3 donanemab infusions. Serious amyloid-related imaging abnormalities of edema/effusion (see Table 3 ) occurred in 13 participants (1.5%) receiving donanemab. First events of amyloid-related imaging abnormalities of edema/effusion radiographically resolved in 198 (98.0%) donanemab-treated participants and 11 (64.7%) placebo-treated participants, with a mean amyloid-related imaging abnormalities of edema/effusion resolution time of 72.4 days for those receiving donanemab and 63.5 days for those receiving placebo. Edema/effusion were numerically less common among APOE ε4 noncarriers than carriers, with higher frequency among homozygotes than heterozygotes ( Table 3 ; further details in eTable 8 in Supplement 3 ).

The incidence of amyloid-related imaging abnormalities of microhemorrhages and hemosiderin deposits, determined via MRI, was higher in the donanemab group than the placebo group (268 participants [31.4%] vs 119 participants [13.6%]). Incidence of amyloid-related imaging abnormalities of microhemorrhages and hemosiderin deposits in the absence of amyloid-related imaging abnormalities of edema/effusion was not different between treatments (12.7% in the donanemab group vs 12.4% in the placebo group). The incidence of microhemorrhage and superficial siderosis was greater in the donanemab group than in the placebo group (microhemorrhage: 26.8% vs 12.5%; superficial siderosis: 15.7% vs 3.0%). Three intracerebral hemorrhages greater than 1 cm were recorded in the donanemab group and 2 were recorded in the placebo group ( Table 3 ).

Infusion-related reactions were reported by 74 participants (8.7%) in the donanemab group and 4 (0.5%) in the placebo group. Serious infusion-related reactions or hypersensitivity occurred in 3 participants (0.4%) in the donanemab group. Most infusion-related reactions were mild to moderate and occurred during or within 30 minutes of the end of the infusion and between the second and fifth infusion (73.6%). Anaphylactic reaction occurred in 3 participants (0.4%) in the donanemab group and were considered to be mild to moderate.

In this phase 3 trial, donanemab significantly slowed Alzheimer disease progression, based on the iADRS score, compared with placebo in the low/medium tau and combined tau populations and across secondary clinical outcomes of CDR-SB, ADAS-Cog 13 , and ADCS-iADL scores.

Donanemab treatment resulted in clinically meaningful benefit (considered to be >20% slowing of clinical progression 39 - 41 ) on the iADRS and CDR-SB scales for both the low/medium tau and combined populations, regardless of statistical model. Additional support for clinical relevance is the 38.6% risk reduction of disease progression as measured on the CDR-G score and the 4.4 to 7.5 months saved over the 18-month study (low/medium tau population). Furthermore, an estimated 47% of participants receiving donanemab had no change in the CDR-SB at 1 year (no disease progression), compared with 29% of participants receiving placebo.

This trial used a definition of a MWPC 28 based on any incremental change on the CDR-G scale (Alzheimer disease with MCI to mild Alzheimer disease or mild Alzheimer disease to moderate Alzheimer disease) or point changes of −5 on the iADRS and 1 on the CDR-SB for those with Alzheimer disease with MCI or −9 on the iADRS and 2 on the CDR-SB for those with Alzheimer disease with mild dementia at consecutive visits from baseline. In analyses assessing whether individual participants reached thresholds of clinically important progression over the course of the trial, donanemab resulted in significantly lower risk of meaningful change on the CDR-G as well as the prespecified nongated analyses of the iADRS and CDR-SB outcomes.

These clinical outcomes were achieved in 52% of low/medium tau participants completing donanemab treatment by 1 year, based on when a participant met amyloid clearance criteria. Limited-duration dosing was a distinct trial design feature reflecting donanemab binding specificity for amyloid plaque and implemented to decrease burden, cost, and potentially unnecessary treatments. 11 Early significant changes on both brain amyloid PET scans and P-tau217 blood test results suggest opportunities for clinical monitoring of therapy. Donanemab treatment resulted in significantly reduced brain amyloid plaque in participants at all time points assessed, with 80% (low/medium tau population) and 76% (combined population) of participants achieving amyloid clearance at 76 weeks. Clearance beyond 76 weeks, and associated Alzheimer disease biomarkers levels, are currently being studied in the ongoing extension phase. The lack of response in frontal tau-PET is inconsistent with the TRAILBLAZER-ALZ phase 2 results. 9 , 38 Additional regions have yet to be analyzed and reported. Factors resulting in this inconsistency will be examined. Changes in vMRI (including a greater decrease in whole brain volume in the donanemab group) were consistent with previous reports 9 , 42 and would benefit from further exploration.

The general belief is that treating Alzheimer disease at the earliest disease stage is likely to result in more clinically meaningful effects. 43 , 44 Post hoc evaluation in only high tau participants demonstrated no differences ( P < .05) on the primary outcome or on most secondary clinical outcomes in donanemab-treated compared with placebo-treated participants within the 18-month trial, with the exception of CDR-SB. Compared with significant differences in the low/medium tau population, this supports the hypothesis that a greater benefit from amyloid-lowering therapies may occur when initiated at an earlier disease stage.

Similar to other amyloid-lowering drugs, and the phase 2 TRAILBLAZER-ALZ trial, amyloid-related imaging abnormalities are an associated adverse event. When amyloid-related imaging abnormalities occur, they are mostly asymptomatic and resolve in approximately 10 weeks. When symptoms occur, they are usually mild, consisting of a headache or increase in confusion, but can have more severe symptoms such as seizures. In some instances, these events can be life-threatening and result in, or lead to, death. For 1.6% of participants in the donanemab treatment group, amyloid-related imaging abnormalities led to serious outcomes, such as hospitalization, and required supportive care and/or corticosteroid use. It is also important to note that 3 deaths in TRAILBLAZER-ALZ 2 occurred after serious amyloid-related imaging abnormalities. Further evaluation of the risks associated with serious and life-threatening amyloid-related imaging abnormalities will be important to identify the best approaches for managing risks and maximizing benefit, in addition to earlier treatment of the disease when less amyloid pathology is present and, theoretically, when amyloid-related imaging abnormalities risk is lower.

This study has several limitations. First, an inherent limitation to limited-duration dosing was variability in total donanemab doses received and/or duration of donanemab dosing. Second, data collection was for 76 weeks, limiting long-term understanding of donanemab; however, a study extension is ongoing. Third, the studied populations were primarily White (91.5%), which may limit generalizability to other populations due to a lack of racial and ethnic diversity. Fourth, although no related protocol amendments were necessary, this trial was conducted during the COVID-19 pandemic, and COVID-19 was the most commonly reported adverse event across treatment groups (see eMethods in Supplement 3 ). Fifth, direct comparison of results to other amyloid-targeting trials is not possible due to trial design differences such as stratification by baseline tau PET category. Sixth, amyloid-related imaging abnormality and infusion-related reaction occurrences may have caused participants and investigators to infer treatment assignment; attempts to minimize bias included blinding CDR raters to adverse event information and, based on sensitivity analyses, censoring change scores after the first observation of amyloid-related imaging abnormalities of edema/effusion and/or infusion-related reactions did not impact the results.

Among participants with early symptomatic Alzheimer disease and amyloid and tau pathology, donanemab significantly slowed clinical progression at 76 weeks in those with low/medium tau and in the combined low/medium and high tau pathology population.

Accepted for Publication: June 28, 2023.

Published Online: July 17, 2023. doi:10.1001/jama.2023.13239

Corresponding Author: John R. Sims, MD, Eli Lilly and Company, Lilly Corporate Center DC 1532, Indianapolis, IN 46285 ( [email protected] ).

Author Contributions: Dr Solomon had full access to all of the data in the study and takes responsibility for the integrity of the data and the accuracy of the data analysis.

Concept and design: Sims, Zimmer, Ardayfio, Sparks, Wessels, Wang, Collins, Salloway, Mintun, Skovronsky.

Acquisition, analysis, or interpretation of data: Sims, Zimmer, Evans, Lu, Ardayfio, Sparks, Wessels, Shcherbinin, Wang, Nery, Collins, Solomon, Apostolova, Hansson, Ritchie, Brooks, Mintun, Skovronsky.

Drafting of the manuscript: Sims, Evans, Ardayfio, Wang, Nery, Collins, Ritchie, Skovronsky.

Critical review of the manuscript for important intellectual content: Sims, Zimmer, Ardayfio, Wessels, Shcherbinin, Salloway, Apostolova, Ritchie, Mintun, Skovronsky.

Statistical analysis: Lu, Ardayfio, Sparks, Wang, Salloway, Skovronsky.

Obtained funding: Sims, Brooks, Mintun, Skovronsky.

Administrative, technical, or material support: Zimmer, Evans, Wessels, Shcherbinin, Collins, Salloway, Brooks, Mintun, Skovronsky.

Supervision: Sims, Wessels, Nery, Collins, Solomon, Brooks, Mintun, Skovronsky.

Other - imaging and biomarker analysis: Collins.

Other - suggested additional analyses: Apostolova.

Conflict of Interest Disclosures: Dr Sims reported being an employee of Eli Lilly and Company during the conduct of the study. Dr Zimmer reported receiving personal fees from and being a shareholder in Eli Lilly and Company during the conduct of the study. Dr Evans reported being an employee of and minority shareholder in Eli Lilly and Company during the conduct of the study. Dr Lu reported being an employee of and stockholder in Eli Lilly. Dr Ardayfio reported being an employee of and stockholder in Eli Lilly during the conduct of the study. Dr Wessels reported being a minor shareholder in Eli Lilly and Company outside the submitted work. Dr Shcherbinin reported being an employee of and stockholder in Eli Lilly and Company during the conduct of the study and Eli Lilly and Company having patents pending relevant to this research. Dr Nery reported being an employee of and shareholder in Eli Lilly and Company during the conduct of the study. Dr Collins reported being an employee of and stockholder in from Eli Lilly and Company during the conduct of the study. Dr Salloway reported receiving personal fees and grants from Biogen, Eli Lilly, Genentech, Avid, Roche, Eisai, Novartis, Acumen, NovoNordisk, and Prothena during the conduct of the study. Dr Apostolova reported receiving grants from NIA, Alzheimer Association, AVID Radiopharmaceuticals, Life Molecular Imaging, and Roche Diagnostics and personal fees from Eli Lilly, Biogen, Two Labs, IQVIA, Genentech, Siemens, Corium, GE Healthcare, Eisa, Roche Diagnostics, Alnylam, Alzheimer Association, and from the US Food And Drug Administration outside the submitted work. Dr Hansson reported personal fees from AC Immune, Amylyx, Alzpath, BioArtic, Biogen, Cerveau, Eisai, Eli Lilly, Fujirebio, Merk, Novartis, Novo Nordisk, Roche, Sanofi, and Siemens outside the submitted work. Dr Ritchie reported receiving personal fees from Actinogen, Biogen, Cogstate, Eisai, Eli Lilly, Janssen Cilag, Merck, Novo Nordisk, Roche Diagnostics, and Signant and being founder of and majority shareholder in Scottish Brain Sciences outside the submitted work. Dr Brooks reported being an employee of and shareholder in Eli Lilly and Company. Dr Mintun reported being an employee of and shareholder in Eli Lilly and Company and having a patent pending with Eli Lilly and Company. Dr Skovronsky reported being an employee of and shareholder in Eli Lilly and Company. No other disclosures were reported.

Funding/Support: This work was funded by Eli Lilly and Company.

Role of the Funder/Sponsor: Eli Lilly and Company was responsible for design and conduct of the study; collection, management, analysis, and interpretation of the data; preparation, review, or approval of the manuscript; and decision to submit the manuscript for publication.

Group Information: The TRAILBLAZER-ALZ 2 Investigators appear listed in Supplement 4 .

Data Sharing Statement: See Supplement 5 .

Additional Contributions: We thank all the trial participants and their families and caregivers who participated in the TRAILBLAZER-ALZ 2 trial as well as the site staff, raters, and site investigators (see list in Supplement 4 ); members of the data and safety monitoring board; vendor partners including BioAgilitix, Clario, Clinical Trial Media, Cogstate, C 2 N Diagnostics, Invicro, IQVIA, Labcorp and Quanterix. The authors would like to thank the following salaried employees of Eli Lilly and Company for their contributions to TRAILBLAZER-ALZ 2, for which they received no additional compensation: Andrea Abram, MBA; Hrideep Antony, BS; Anupa Arora, MD; Theresa Bauer, BS; Jude Burger, MS; Yang Dai, MS; Russell A Delgiacco, MS; Marybeth Devine, BS; Dawn East, BS; Tim Edison, PharmD; Naohisa Hatekeyama, MS; Jeremy T Hemiup, MS, MBA; Stacy A Huckins, BS; Blaire Iris Kaufman, BS; Rashna Khanna, MD; Min Jung Kim, MS; Albert Lo, MD, PhD; Dedeepya Masarapu, B Pharm; Shoichiro Sato, MD, PhD; Adam Schaum, MAS; Linda Shurzinske, MS; Andrea L Speas, RNN, BSN; LisaAnn Trembath, MS; Giulia Tronchin, PhD; Melissa Veenhuizen, DVM, MS; Wen Xu, PhD; and Wei Zhou, MS. The authors would like to acknowledge Paula Hauck, PhD; Deirdre Hoban, PhD; and Carmen Deveau, PhD, salaried employees of Eli Lilly and Company, for project management support, and strategic scientific communication expertise, for which they received no additional compensation.

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• Open access
• Published: 17 July 2024

Inhibition of IL-11 signalling extends mammalian healthspan and lifespan

• Anissa A. Widjaja   ORCID: orcid.org/0000-0001-9404-7608 1   na1   na2 ,
• Wei-Wen Lim   ORCID: orcid.org/0000-0003-0311-943X 1 , 2   na1 ,
• Sivakumar Viswanathan 1 ,
• Sonia Chothani 1 ,
• Ben Corden 2 , 3 ,
• Cibi Mary Dasan 1 ,
• Joyce Wei Ting Goh 1 ,
• Radiance Lim   ORCID: orcid.org/0000-0002-9865-7064 1 ,
• Brijesh K. Singh   ORCID: orcid.org/0000-0003-4615-3988 1 ,
• Jessie Tan 2 ,
• Chee Jian Pua   ORCID: orcid.org/0000-0003-4683-3043 2 ,
• Sze Yun Lim 1 ,
• Eleonora Adami 4 ,
• Sebastian Schafer   ORCID: orcid.org/0000-0002-6909-8275 1 ,
• Benjamin L. George 1 ,
• Mark Sweeney   ORCID: orcid.org/0000-0001-5098-0076 5 ,
• Chen Xie 2 ,
• Madhulika Tripathi 1 ,
• Natalie A. Sims   ORCID: orcid.org/0000-0003-1421-8468 6 , 7 ,
• Norbert Hübner   ORCID: orcid.org/0000-0002-1218-6223 4 , 8 , 9 ,
• Enrico Petretto   ORCID: orcid.org/0000-0003-2163-5921 1 , 10 ,
• Dominic J. Withers   ORCID: orcid.org/0000-0002-8009-7521 5 , 11 ,
• Lena Ho   ORCID: orcid.org/0000-0002-9358-621X 1 ,
• Jesus Gil   ORCID: orcid.org/0000-0002-4303-6260 5 , 11 ,
• David Carling   ORCID: orcid.org/0000-0002-2316-1830 5 , 11 &
• Stuart A. Cook   ORCID: orcid.org/0000-0001-6628-194X 1 , 2 , 5   na2  

Nature ( 2024 ) Cite this article

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• Fat metabolism
• Inflammation
• Interleukins

For healthspan and lifespan, ERK, AMPK and mTORC1 represent critical pathways and inflammation is a centrally important hallmark 1 , 2 , 3 , 4 , 5 , 6 , 7 . Here we examined whether IL-11, a pro-inflammatory cytokine of the IL-6 family, has a negative effect on age-associated disease and lifespan. As mice age, IL-11 is upregulated across cell types and tissues to regulate an ERK–AMPK–mTORC1 axis to modulate cellular, tissue- and organismal-level ageing pathologies. Deletion of Il11 or Il11ra1 protects against metabolic decline, multi-morbidity and frailty in old age. Administration of anti-IL-11 to 75-week-old mice for 25 weeks improves metabolism and muscle function, and reduces ageing biomarkers and frailty across sexes. In lifespan studies, genetic deletion of Il11 extended the lives of mice of both sexes, by 24.9% on average. Treatment with anti-IL-11 from 75 weeks of age until death extends the median lifespan of male mice by 22.5% and of female mice by 25%. Together, these results demonstrate a role for the pro-inflammatory factor IL-11 in mammalian healthspan and lifespan. We suggest that anti-IL-11 therapy, which is currently in early-stage clinical trials for fibrotic lung disease, may provide a translational opportunity to determine the effects of IL-11 inhibition on ageing pathologies in older people.

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Inflammation, epigenetics, and metabolism converge to cell senescence and ageing: the regulation and intervention

Inflammation and aging: signaling pathways and intervention therapies

The major signalling mechanisms that regulate lifespan across species include ERK, STK11 (also known as LKB1), AMPK, mTORC1 and IGF1–insulin modules 1 , 2 , 3 . These pathways are collectively perturbed in old age to activate hallmarks of ageing, which include mitochondrial dysfunction, inflammation and cellular senescence 1 . In aged organisms, the AMPK–mTORC1 axis is uniquely important for metabolic health, with notable effects in adipose tissue 8 , 9 , and therapeutic inhibition of mTOR extends lifespan in mice 10 , 11 .

Ageing studies to date have focused largely on lifespan extension, particularly in yeast, worms and fruit flies, but lifespan extension does not necessarily reflect longer healthspan 12 , 13 , 14 . There is a need for integrated studies to determine the effects of interventions on both healthspan and lifespan. Laboratory mice are particularly suited for such experiments, as ageing pathologies that are important for human wellbeing and function are apparent and lifespan studies are well established in mice 1 , 15 .

The importance of chronic sterile inflammation for ageing pathologies is increasingly recognized and inflammation itself is a central hallmark of ageing 7 , 16 , 17 . In simplified terms, ageing is associated with a dysfunctional adaptive immune system that is characterized by immunosenescence and thymic involution along with inappropriate activation of innate immune genes such as IL-6 7 , 16 , 18 , 19 . The pro-inflammatory signalling factors NF-κB and JAK–STAT3 are specifically implicated in ageing and JAK inhibitors can alleviate age-related dysfunction 2 , 20 , 21 .

We proposed that IL-11, a pro-inflammatory and pro-fibrotic member of the IL-6 family 22 , may promote age-associated pathologies and reduce lifespan. This premise was founded on studies showing that IL-11 can activate ERK–mTORC1 and/or JAK–STAT3 22 , 23 , 24 , 25 (Fig. 1a ), the observation that IL-11 is upregulated in older people 26 and the fact that IL-11 is increasingly recognized to have a role in senescence, a hallmark of ageing 27 . Here, using a range of genetic and pharmacological approaches, we tested the hypothesis that IL-11 signalling has a negative effect on healthspan and lifespan in mice.

a , Signalling pathway by which IL-11 induces canonical STAT3 activation and non-canonical ERK activation, LKB1–AMPK inactivation and mTOR activation. b , Western blots of the indicated liver phosphoproteins from male mice aged 12–110 weeks ( n  = 5 per group); total (phosphorylated plus unphosphorylated) proteins are shown in Extended Data Fig. 1a . c , Heat map showing densitometry of IL-11 protein expression normalized to GAPDH (immunoblots are shown in Extended Data Fig. 1c ) in gastrocnemius (gastroc) and vWAT from 12- to 110-week-old male mice ( n  = 5 per group). d , Representative immunofluorescence images (scale bars, 100 µm) of liver EGFP and SLC10A1 expression from 10 and 110-week-old Il11 - EGFP mice and age-matched wild-type (WT) controls ( n  = 3 per group). Scale bars, 100 μm. e , Western blot of liver extracts from 10- and 110-week-old male wild-type and Il11ra1 −/− mice ( n  = 5 per group); total proteins are shown in Extended Data Fig. 2a . f , g , Body weight ( f ) and percentages of fat and lean mass (male wild-type, n  = 12; male Il11ra1 −/− , n  = 16; female wild-type, n  = 15; female Il11ra1 −/− , n  = 13). h , i , Telomere length ( h ) and mtDNA copy number ( i ) in liver from young (10-week-old) and old (110-week-old) male and female wild-type and Il11ra1 −/− mice (young male wild-type, n  = 8; young male Il11ra1 −/− , n  = 7; old male wild-type, n  = 11; old male Il11ra1 −/− , n  = 17; young female wild-type, n  = 7; young female Il11ra1 −/− , n  = 8; old female wild-type, n  = 15; old female Il11ra1 −/− , n  = 13). FC, fold change. j , IL-11 and GAPDH immunoblots from the indicated organs of 12- and 105-week-old male wild-type and Il11 −/− mice (liver and soleus, n  = 4 per group; vWAT and gastrocnemius, n  = 6 per group). f – i , Data are mean ± s.d.; the table below each panel shows summary statistics from two-way ANOVA with Sidak’s correction. For gel source data, see Supplementary Fig. 1 .

Source Data

IL-11 is upregulated with age

We determined IL-11 expression in the liver, visceral gonadal white adipose tissue (vWAT) and skeletal muscle (gastrocnemius) in mice over a time course of ageing, which revealed progressive up-regulation of IL-11 in all tissues (Fig. 1b,c and Extended Data Fig. 1a,b ). With age, there was progressive activation of ERK–p90RSK, inactivation of LKB1–AMPK and mTOR–p70S6K activation, which comprise an IL-11 signalling module, in liver and muscle (Fig. 1b and Extended Data Fig. 1c ). IL-11 up-regulation was confirmed in livers, vWAT and skeletal muscle of two-year-old male and female mice (Extended Data Fig. 1d ).

To identify the cell types that express Il11 in old mice, we queried the Tabula Muris Senis 28 , which was uninformative, likely as Il11 is expressed at low levels and IL-11 is largely translationally regulated 25 . To better identify IL-11-expressing cells in tissues from old mice, we performed immunohistochemistry in tissues from aged Il11-EGFP reporter mice 29 . In two-year-old Il11-EGFP mice, IL-11 was apparent in parenchymal cells (hepatocytes in liver, adipocytes in vWAT and myocytes in skeletal muscle) and also seen in stromal, epithelial and endothelial cells across tissues and in nerves in skeletal muscle (Fig. 1d and Extended Data Fig. 1e–g ). Thus, IL-11 is expressed in diverse cell types in different tissues with ageing.

IL-11 is associated with senescence

To explore further the relationship between IL-11 up-regulation and ERK–mTOR activation in tissues of old mice, we studied ten-week-old and two-year-old Il11ra1 −/− mice and wild-type littermate controls 30 , 31 . On immunoblots of liver, vWAT and gastrocnemius extracts, old wild-type mice exhibited increased phosphorylated (p)-ERK and p-p90RSK, increased p-LKB1 leading to reduced p-AMPK, and increased levels of p-mTOR, p-p70S6K and p-S6RP (Fig. 1e and Extended Data Fig. 2a–c ). Levels of the canonical senescence markers p16 Inka4 and p21 Waf1/Cip1 were increased in tissues of old wild-type mice. By contrast, the phosphorylation status of these various kinases and S6RP, and levels of p16 and p21 were similar between old Il11ra1 −/− mice and young wild-type mice (Fig. 1e and Extended Data Fig. 2a–c ).

Deletion of Il11ra1 improves metabolism

Compared with wild-type littermate controls, two-year-old Il11ra1 −/− mice had lower body weights (Fig. 1f ), and female Il11ra1 −/− mice had decreased fat mass and increased lean mass (Fig. 1g ). Il11ra1 −/− mice of both sexes had slightly higher core body temperatures than wild-type controls (Extended Data Fig. 2d ). Old Il11ra1 −/− mice of both sexes had lower indexed vWAT mass (vWAT weight normalized to body weight) and increased indexed gastrocnemius mass (Extended Data Fig. 2e ). Liver indices were similar between genotypes, whereas liver triglyceride levels were lower in Il11ra1 −/− mice (Extended Data Fig. 2e,f ).

Serum cholesterol and triglyceride levels were higher in old wild-type mice than in Il11ra1 −/− littermates (Extended Data Fig. 2g,h ). Livers of old Il11ra1 −/− mice of both sexes exhibited reduced expression of pro-inflammatory ( Ccl2 , Ccl5 , Tnf and Il1b ) and fatty acid synthesis ( Acc , Fasn and Srebp1c) genes (Extended Data Fig. 2i,j ). Serum alanine transaminase (ALT) and aspartate aminotransferase (AST) levels, markers of hepatocyte damage, were increased in old wild-type mice but not in old Il11ra1 −/− mice (Extended Data Fig. 2k,l ).

We assessed telomere lengths and mitochondria DNA (mtDNA) copy numbers—biomarkers associated with biological age 1 , 32 —in liver and gastrocnemius and found significant preservation of these phenotypes in tissues of old Il11ra1 −/− mice (Fig. 1h,i and Extended Data Fig. 2m,n ).

IL-11 induces senescence in human cells

IL-11 is linked with senescence—we therefore explored the direct effects of IL-11 on senescence in human cell types corresponding to those found to express IL-11 in aged mice (Fig. 1d and Extended Data Fig. 1e–g ). Stimulation of human fibroblasts or hepatocytes with IL-11 activated ERK–mTOR, increased levels of p16 and p21, and reduced expression of PCNA and cyclin D1, which was prevented by U0126 or rapamycin (Extended Data Fig. 3a,b ). Supernatants of IL-11-stimulated fibroblasts had increased amounts of senescence-associated secretory phenotype factors, and this increase was inhibited by U0126 or rapamycin (Extended Data Fig. 3c,d ). Deeper profiling of mTORC1-dependent senescence-associated secretory phenotype factors (IL-6, IL-8, LIF, VEGFA, HGF, CCL2, CXCL1, CXCL5, CXCL6 and CCL20 33 ) in supernatants of hepatocytes revealed significant IL-11-stimulated ERK- and mTORC1-dependent regulation of the majority of these proteins (Extended Data Fig. 3e–g ).

Accumulation of senescent cells contributes to ageing pathologies. We modelled replicative senescence by serially passaging human fibroblasts in the presence of a neutralizing IL11RA antibody (X209) or an IgG control 29 , 34 . We observed passage-dependent phosphorylation of ERK–mTORC1, NF-κB and STAT3 along with increased amounts of senescence markers—these effects were IL-11-dependent with evidence of both autocrine and paracrine effects (Extended Data Fig. 4a–d ). Telomere lengths and mtDNA copy numbers were similar between early passage (passage 4 (P4)) cells and X209-treated late passage (P14) cells, whereas these phenotypes were reduced in IgG-treated P14 cells (Extended Data Fig. 4e,f ). Basal metabolic respiration was impaired in IgG-treated P14 fibroblasts, whereas X209-treated P14 cells were similar to P4 fibroblasts (Extended Data Fig. 4g,i ).

Deletion of Il11 in female mice

To support the data generated in Il11ra1 -deleted mice on a mixed C57BL6/129 genetic background 30 and to more deeply dissect age-related effects, we studied young (3-month-old) and aged (2-year-old) female mice with deletion of Il11 (Il11 −/− ) on a C57BL6/J background 31 .

Immunoblots confirmed IL-11 up-regulation across tissues in old age in this additional strain (Fig. 1m ). Old female Il11 −/− mice had lower body weights and fat mass and preserved lean mass (Fig. 2a–c ). The frailty score 15 of old female Il11 −/− mice was lower than that of old wild-type mice and their body temperatures were mildly increased (Fig. 2d and Extended Data Fig. 5a ). Lower frailty scores were largely driven by improvements in tremor, loss of fur colour, gait disorders and vestibular disturbance (Supplementary Table 1 ). Muscle strength was higher in both young and old Il11 −/− mice (a phenomenon that was observed for some other phenotypes) compared with age-matched controls (Fig. 2e and Extended Data Fig. 5b ).

a , Representative photograph of 105-week-old female wild-type and Il11 −/− mice. b – g , Body weight ( b ), percentage of fat and lean mass (normalized to body weight), frailty score ( d ), full body grip strength ( e ), serum cholesterol and triglycerides ( f ), and GTT and ITT ( g ) of young (12-week-old) and old (105-week-old) female wild-type and Il11 −/− mice. h – j , Indexed vWAT and scWAT weight ( h ), relative vWAT mRNA expression level of Acc1 , Fasn and Srebp1c ( i ), and western blot showing activation status of ERK1/2, p90RSK, LKB1, AMPK, mTOR, p70S6K and S6RP and protein expression levels of p16, p21 and GAPDH ( j ). n  = 6 per group. Western blots for the respective total proteins are shown in Extended Data Fig. 5k . k , l , Telomere length and mtDNA copy number ( l ) in vWAT from young and old female wild-type and Il11 −/− mice. b – i , k – l , Data are mean ± s.d. (young wild-type, n  = 8; young Il11 −/− , n  = 9; old wild-type, n  = 16; old Il11 −/− , n  = 18; except for h (scWAT): young wild-type, n  = 5; young Il11 −/− , n  = 7; old wild-type and Il11 −/− , n  = 16). Two-way ANOVA with Sidak’s correction ( b – f , h , i , k , l ); two-way repeated measures ANOVA with Sidak’s correction ( h ). For gel source data, see Supplementary Fig. 1 .

Chronic inhibition of mTORC1 with rapamycin can cause glucose intolerance owing to indirect inhibition of mTORC2 35 . It was therefore important to more fully assess the effects of IL-11 inhibition on liver function, metabolism and glucose utilization in old mice. As wild-type mice aged, there were increases in serum AST, ALT, cholesterol and triglycerides, which were collectively mitigated in old Il11 −/− mice (Fig. 2f and Extended Data Fig. 5c,d ). Glucose tolerance test (GTT) and insulin tolerance test (ITT) profiles of old Il11 −/− mice were similar to those of young wild-type mice, whereas GTTs and ITTs of old wild-type mice showed impairment (Fig. 2g and Extended Data Fig. 5e,f ). Indexed skeletal muscle mass was greater in both young and old Il11 −/− mice compared with the equivalent wild-type mice (Extended Data Fig. 5g ).

Compared with old wild-type controls, old Il11 −/− mice had reduced liver mass indices and liver triglyceride content (Extended Data Fig. 5h,i ). Indexed vWAT and inguinal subcutaneous white adipose tissue (scWAT) masses were reduced in old Il11 −/− mice, whereas brown adipose tissue (BAT) mass was unchanged (Fig. 2h and Extended Data Fig. 5j ). To examine the potential role of de novo lipogenesis in aged tissue, we profiled the expression of fatty acid synthesis genes in vWAT and found their expression to be increased with age in old wild-type mice but not in old Il11 −/− mice (Fig. 2i ). Similar to Il11ra1 −/− mice (Extended Data Fig. 2b,c ), activation of the ERK–mTORC1 axis and up-regulation of senescence markers were mitigated in vWAT and gastrocnemius of old Il11 −/− mice (Fig. 2j and Extended Data Fig. 5k,l ). Pro-inflammatory gene expression was increased in vWAT of old wild-type mice but not in that of old Il11 −/− mice (Extended Data Fig. 5m ).

Telomere lengths and mtDNA content in liver, skeletal muscle and vWAT were reduced in old wild-type mice and these effects were attenuated by Il11 deletion (Fig. 2k,l ). Serum IL-6 levels were increased in old wild-type mice but not in old Il11 −/− mice (Extended Data Fig. 2n ).

Deletion of Il11 in male mice

Detrimental changes in body habitus, body weight, fat mass, lean mass and frailty scores (driven by tremor, coat condition and fur loss) were mitigated and body temperatures were mildly increased in old male Il11 −/− mice (Extended Data Fig. 6a–e and Supplementary Table 2 ). Muscle strength was higher in both young and old Il11 −/− mice compared with age-matched wild-type controls, as seen for female mice (Extended Data Fig. 6f ). GTT and ITT profiles of old Il11 −/− mice were similar to those of young mice, whereas those of old wild-type controls were impaired (Extended Data Fig. 6g ).

Metabolic cage analysis revealed that the respiratory exchange ratio (RER) was overall higher in old Il11 −/− mice compared with old wild-type mice (Extended Data Fig. 6h ). After a period of starvation, refeeding resulted in a greater increase of RER in old Il11 −/− mice, consistent with better metabolic flexibility 36 (Extended Data Fig. 6h ). Whereas old Il11 −/− mice were leaner and weighed less than wild-type controls, they consumed more food and had similar levels of locomotor activity (Extended Data Fig. 6h ). Calorie-losing enteropathy of Il11 −/− mice was excluded by bomb calorimetry (Extended Data Fig. 6i ).

Sarcopenia was apparent in the muscle of old wild-type mice, and this effect was less pronounced in old Il11 −/− mice, which exhibited greater indexed muscle mass even when young, as seen for female mice (Extended Data Fig. 6j,k ). Indexed liver weights were similar between mice of both genotypes (Extended Data Fig. 6l ). As for female mice, perhaps the most notable beneficial difference associated with loss of Il11 function in old male mice was seen for vWAT mass, whereas BAT mass was similar across genotypes and ages (Extended Data Fig. 6m ). An incidental finding of enlarged seminal vesicles, an idiosyncratic age-specific phenomenon in old male mice 37 , was more common in old wild-type mice compared with old Il11 −/− mice (wild-type: 11 out of 15, Il11 −/− : 1 out of 14; P  = 0.0003) (Supplementary Table 2 ).

Anti-IL-11 therapy in old male mice

Studies of germline genetic loss of function on healthspan and/or lifespan should be complemented by interventions in late life only to identify phenotypes that are carried through from younger animals and for translational relevance. This is pertinent to the current study, in which some beneficial effects of Il11 loss of function (such as increased muscle mass and strength) are apparent even in young Il11 -deleted mice (Fig. 2 and Extended Data Fig. 6 ). To achieve this goal, we administered a neutralizing IL-11 antibody (X203) or IgG control to aged mice and studied healthspan indices 29 , 38 (Fig. 3a ).

a , Schematic of anti-IL-11 (X203) therapeutic dosing experiment in old male mice for experiments shown in b – m . Mice were either aged naturally (untreated) or given either X203 or an IgG control antibody (40 mg kg −1 , every 3 weeks) starting from 75 weeks of age for a duration of 25 weeks. Created with BioRender.com. b , Body weights across time. c , d , Changes (Δ) in fat and lean mass percentage ( c ) and area under the curve (AUC) of GTT and ITT ( d ) (values at endpoint (100-week-old) − values at starting point (75-week-old)). a.u., arbitrary units. e , Frailty scores at start and endpoint. Data are shown as values recorded at starting and endpoint. f , Full body grip strength. g , RER in young (14-week-old) and IgG or X203-treated old (81-week-old) mice, 6 weeks after IgG or X203 administration was started ( n  = 10 per group). h – j , Body temperatures ( h ), serum ALT ( i ) and liver triglycerides ( j ). k , l , Indexed weights of ( k ) and total collagen content (by hydroxyproline assay) in ( l ) liver, gastrocnemius and vWAT. m , Western blot showing activation status of ERK1/2, p90RSK, LKB1, AMPK, mTOR, p70S6K, S6RP and protein expression levels of IL-11, p16, p21 and GAPDH in vWAT ( n  = 6 per group). Western blots of total protein are presented in Extended Data Fig. 7i . b – d , f , h – l , Data are mean ± s.d. 75-week-old control: n  = 10 ( f ), n  = 14 ( i – l ); untreated 100-week-old: n  = 6 (except for k (liver), n  = 5); IgG-treated 100-week-old: n  = 13; X203-treated 100-week-old: n  = 12. Two-way repeated measures ANOVA with Sidak’s correction ( b ); one-way ANOVA with Tukey’s correction ( c , d (GTT), e , f , h – l ); one-way ANOVA with Kruskal–Wallis correction ( d (ITT)). For gel source data, see Supplementary Fig. 1 .

Compared with controls, mice receiving X203 from 75 to 100 weeks of age progressively lost body weight that was defined by a reduction in indexed fat mass (Fig. 3b,c ). Impaired glucose metabolism was apparent across experimental groups at study start that was improved in mice receiving X203, whereas IgG had no effect (Fig. 3d ). Frailty scores were mildly increased across experimental groups at study initiation (Fig. 3e ). Over the study period, mice receiving no treatment or IgG exhibited frailty progression (for example, tremor and gait disorder), whereas those on X203 did not (Fig. 3e and Supplementary Table 3 ). Muscle strengths of 100-week-old mice receiving anti-IL-11 were higher than aged-matched controls receiving IgG or untreated, and also higher than those of 75-week-old mice at the start of the experiment (Fig. 3f and Extended Data Fig. 7a ).

After six weeks of antibody administration, mice were studied in metabolic cages. The RER of mice receiving X203 was higher than that of IgG-treated mice but lower than that of a cohort of young mice (Fig. 3g and Extended Data Fig. 7b ), suggesting that X203 slows age-associated metabolic inflexibility. Administration of X203 was associated with higher core temperatures and increased food intake, whereas locomotor activity levels and faecal caloric densities were similar between study groups (Fig. 3h and Extended Data Fig. 7b,c ).

Mice left untreated or given IgG had increased serum cholesterol, triglycerides and IL-6 by study end, which were collectively lowered by X203 therapy to below the levels at 75 weeks of age (Extended Data Fig. 7d,e ). Over the course of the experiment, markers of liver damage, hepatic triglyceride content and indexed liver mass increased in untreated and IgG control mice, whereas these phenotypes were either improved or reduced in mice receiving X203 (Fig. 3i–k and Extended Data Fig. 7f ).

There was a reduction in indexed vWAT and liver mass and an increase in indexed muscle mass in 100-week-old mice receiving X203, compared with both age-matched controls and 75-week-old mice (Fig. 3k and Extended Data Fig. 7g ). Mice receiving X203 had diminished scWAT and an increase in BAT (Extended Data Fig. 7h ). The age-specific phenotype of enlarged seminal vesicles in male mice 37 was again diminished by IL-11 loss of function (IgG: 8 out of 13, X203: 2 out of 12; P  = 0.022) (Supplementary Table 3 ).

Fibrosis is a canonical feature of ageing and a hallmark of senescence, and IL-11 is known to be pro-fibrotic in human cells and in young adult mice 22 , 39 . We quantified fibrosis in aged vWAT, skeletal muscle and livers of old mice across experimental groups, which showed reversal of tissue fibrosis across organs of mice receiving X203 (Fig. 3l ).

Compared with 75-week-old mice, vWAT from mice receiving IgG for 25 weeks had increased activation of the IL-11–mTORC1 axis and higher expression of senescence markers (Fig. 3m and Extended Data Fig. 7i ). By contrast, mice receiving X203 had reduced ERK–mTOR activity and decreased expression of p21 and p16 (Fig. 3m and Extended Data Fig. 7i ). One-hundred-week-old untreated and IgG-treated mice had telomere attrition and a reduction in mtDNA copy number, which were not seen in X203-treated mice (Extended Data Fig. 7j,k ).

Anti-IL-11 therapy in old female mice

We also examined the effects of anti-IL-11 therapy on ageing pathologies in old female mice (Extended Data Fig. 8a ). Old female mice receiving X203 lost body weight, whereas those administered with IgG gained weight (Extended Data Fig. 8b ). At the end of the study period, mice on X203 had lower fat mass, higher lean mass and better GTTs and ITTs than at the outset, whereas the opposite effect was observed in mice on IgG (Extended Data Fig. 8c,d ). Frailty scores were similar between study groups at the start of the experiment and these scores progressed in mice receiving IgG but not in mice receiving X203 (Extended Data Fig. 8e and Supplementary Table 4 ). Muscle strengths were greater than starting levels in female mice receiving X203 and core body temperatures were mildly increased (Extended Data Fig. 8f–h ).

Anti-IL-11 restores white adipose beiging

To further dissect molecular mechanisms, we performed bulk RNA sequencing (RNA-seq) of vWAT, gastrocnemius and liver from IgG-treated or anti-IL-11-treated 100-week-old mice (Supplementary Table 5 ). Across tissues, mice receiving anti-IL-11 had the most significant gene set enrichment scores for hallmarks of oxidative phosphorylation and metabolism, whereas scores for markers of inflammation, EMT and JAK–STAT3 signalling were reduced (Fig. 4a ).

a – e , g – j , Data for therapeutic experiments in old male mice as shown in Fig. 3a . a , Bubble map showing hallmark gene set enrichment analysis for differentially expressed genes in the vWAT, liver and gastrocnemius of mice receiving anti-IL-11 therapy compared with IgG. Colour represents normalized enrichment score (NES); black represents negative NES, indicating down-regulation of the gene set; yellow represents positive NES, suggesting up-regulation. Dot size indicates significance (the larger the dot, the smaller the adjusted P value). EMT, epithelial–mesenchymal transition. b , Heat map of row-wise scaled transcripts per million (TPM) values of senescence genes in vWAT, liver, gastrocnemius. c , Abundance of Ucp1 reads in vWAT. d , log 2 -transformed fold change heat map of beiging genes in vWAT from IgG- or anti-IL-11-treated 100-week-old mice, based on RNA-seq. e , Western blot of UCP1, PGC1α and GAPDH expression in vWAT ( n  = 6 per group). f , Relative expression levels of Ucp1 mRNA (young wild-type, n  = 8; young Il11 −/− , n  = 9; old wild-type, n  = 16; old Il11 −/− , n  = 18) as well as UCP1 and PGC1α protein expression ( n  = 6 per group) in vWAT isolated from young and old female wild-type and Il11 −/− mice. g , h , Abundance of Clstn3b and S100b reads ( g ) and log 2 -transformed fold change heat map of pro-inflammatory markers (from RNA-seq) ( h ) in vWAT. i , Haematoxylin and eosin-stained vWAT (scale bars, 100 µm) and quantification of lipid droplet size (mean of lipid droplet area, n  = 25 (5 fields per mouse from 5 mice per group)). j , Immunohistochemistry staining of CD68 in vWAT (scale bars, 50 µm). a – d , f – h , Liver and gastrocnemius ( n  = 8 per group), vWAT IgG, n  = 7; vWAT anti-IL-11, n  = 6. c , f , g , i , Data are mean ± s.d. Two-tailed Student’s t -test ( c , g , i ); two-way ANOVA with Sidak’s correction ( f ). For gel source data, see Supplementary Fig. 1 . Scale bars: 100 μm ( i ), 50 μm ( j ).

In aged vWAT, genes associated with senescence ( Cdkn2a , Tnf , Il10 , Il1b , Bst1 , Irg1 , Parp14 , Itgax and Itgam ) as identified by The Tabula Muris Senis 28 were upregulated, an effect that was mitigated by anti-IL-11 therapy (Fig. 4b and Extended Data Fig. 9a ). A similar, although less pronounced inhibition of senescence markers was seen in muscles and livers of mice receiving anti-IL-11.

More detailed study of the vWAT transcriptome revealed that the gene that was most upregulated by anti-IL-11 genome-wide was Ucp1 , which is important for the development of thermogenic ‘beige’ adipocytes in white adipose tissue (WAT) deposits 40 , 41 (Fig. 4c and Supplementary Table 5 ). On closer inspection, we found up-regulation of a larger beiging programme ( Acot2 , Cidea , Cox4i1 , Cox8b , Dio2 , Elovl3 , Eva1a , Fabp3 , Ppargc1a , Ppargc1b , Ppara and Prdm16 ) in vWAT of mice receiving anti-IL-11 (Fig. 4d ). Age-dependent up-regulation of UCP1 and PGC1α in male mice receiving anti-IL-11 was validated at the protein level (Fig. 4e )

To support our findings, we showed age-related suppression of UCP1 expression in vWAT of female control mice, which was mitigated in female mice lacking Il11 and in mice of both sexes lacking Il11ra1 (Fig. 4f and Extended Data Fig. 9b ). A targeted assessment of mitochondrial gene expression in vWAT revealed significant increases in terms associated with mitochondrial biogenesis and function in mice receiving anti-IL-11 (Extended Data Fig. 9c,d ).

In mice receiving X203, there was strong up-regulation of Clstn3b , a newly identified mammal-specific product of the 3′ end of the Clstn3 locus that promotes WAT triglyceride metabolism in partnership with S100b 42 , 43 , which was also upregulated (Fig. 4g and Extended Data Fig. 9e ). There was limited down-regulation of Ucp1 in BAT with age in wild-type mice, and Ucp1 expression was mildly increased in BAT of Il11 −/− mice but not in mice receiving anti-IL-11 (Extended Data Fig. 9f,g ).

Pro-inflammatory gene expression was higher in vWAT of mice receiving IgG compared with those receiving X203 (Fig. 4b ), mirroring findings seen in livers of Il11ra1 −/− mice and vWAT of Il11 −/− mice (Extended Data Figs. 2i and 5m ). Further analysis of young and old Il11ra1 −/− and wild-type mice confirmed age-dependent pro-inflammatory gene expression in the vWAT of wild-type mice that was decreased in mice of Il11ra1 −/− genotype across sexes (Extended Data Fig. 9h ).

Stromal inflammation is associated with immune cell infiltration, and we found that the immune cell surface marker genes Cd68 , Cd4 , Ly6C and Cd19 were downregulated in the vWAT of mice receiving X203 (Fig. 4h ). Histology studies revealed that vWAT of X203-treated mice exhibited an average 2.5-fold reduction in lipid droplet area, increased beige adipocyte foci and fewer resident CD68 + macrophages (Fig. 4i,j ).

Inhibition of IL-11 extends lifespan

In parallel to the healthspan experiments, we carried out lifespan studies in male and female Il11 −/− mice and wild-type littermate controls, which we observed until they were found dead or euthanized when moribund (Fig. 5a–c ). Pooled analysis showed that Il11 −/− mice had significantly longer lifespans than wild-type controls (median lifespan: wild-type, 120.9 weeks; Il11 −/− , 151 weeks). Sex-specific analyses revealed significant lifespan extension in female Il11 −/− (median lifespan: wild-type, 118.9 weeks; Il11 −/− , 148.3 weeks) as well as male Il11 −/− (median lifespan: wild-type, 128.7 weeks; Il11 −/− , to be determined) mice.

a – c , Kaplan–Meier survival curves (shading represents 95% confidence interval) showing the cumulative survival probabilities for male ( a ), female ( b ) and sex-pooled ( c ) wild-type and Il11 −/− mice. d – f , Kaplan–Meier survival curves showing the cumulative survival probabilities for male ( d ), female ( e ) and sex-pooled ( f ) mice, comparing those receiving monthly administration of IgG or X203 (40 mg kg −1 , intraperitoneal injection), starting from 75 weeks of age (red dotted line). Statistical significance (two-tailed P value) was assessed by means of the log-rank (Mantel–Cox) and Wilcoxon test for survival curve comparisons.

To progress our studies to a more translationally relevant approach, we examined the effects on lifespan following IL-11 inhibition in late life using 75-week-old male and female mice assigned to receive monthly injections of either anti-IL-11 or IgG until death (Fig. 5d–f and Extended Data Fig. 10 ). Pooled analysis showed that mice receiving anti-IL-11 have significantly longer lifespans (median lifespan: IgG, 120.9 weeks; X203, 155.6 weeks). Sex-specific analyses showed a significant extension of lifespan in females (median lifespan: IgG, 117.1 weeks; X203, 146.4 weeks), which was also apparent for males (median lifespan: wild-type, 130.3 weeks; X203, 159.6 weeks).

Cancers are a common cause of death in old mice 44 , and gross autopsy data revealed fewer macroscopic tumours in mice with Il11 deletion (pooled sexes: wild-type, 49 out of 84 mice had tumours; Il11 −/− , 3 out of 25 mice had tumours; P  < 0.0001) or on anti-IL-11 therapy (pooled sexes: IgG, 22 out of 36 mice had tumours; X203, 3 out of 19 mice had tumours; P  = 0.0013) (Supplementary Tables 6 and 7 ).

IL-11 is progressively upregulated across tissues with age, probably as an alarmin-type response to age-related pathogenic factors that include cytokines, proteotoxic stress, oxidative species and DNA damage, among others 22 . We propose that the pleiotropic benefits seen with inhibition of IL-11 reflect its modulation of multiple ageing pathways (such as ERK, AMPK, mTOR and JAK–STAT3), as seen using polypharmacy in flies 2 , 6 . IL-11 has not been extensively studied and was not previously thought to be important for ageing, however SNPs at the IL11 locus are associated with osteoarthritis 45 and menopause 46 , and IL-11 is linked with senescence and diseases that are common in older people 22 , 27 .

The metabolic effects seen with inhibition of IL-11 in old mice phenocopy those of young mice with WAT-specific deletion of Raptor 41 . Although we did not study mice at thermoneutrality, we surmise that inhibition of IL-11 prevents mTORC1 activation in fat, affording age-repressed WAT beiging that can be particularly prominent in mice 40 , 41 . We highlight that although we excluded food intake and enteric or locomotor-related energy expenditure and showed WAT beiging across genetic and therapeutic models, we did not pinpoint the specific physiology leading to weight loss with IL-11 inhibition.

Beyond metabolism, inhibition of IL-11 improved deterministic features of ageing that are common among vertebrates (such as frailty and sarcopenia), showing generic anti-ageing benefits at the organismal level. Intriguingly, some of the beneficial effects of germline Il11ra1 or Il11 deletion, notably in muscle and fat, were apparent even in young mice, perhaps suggesting primacy of metabolic benefits. We did not discern cell-type specificity but infer that tissue-localized IL-11 activity is important, given its known autocrine and paracrine activities 22 .

Inhibition of IL-11 increased lifespan in both male and female mice. The magnitude of lifespan extension remains to be fully determined but current data suggest that anti-IL-11 therapy given in late life increases median lifespan by more than 20% in both sexes. In these experiments, anti-IL-11 was injected in mice from 75 weeks of age (human equivalent to approximately 55 years of age) and it remains to be seen whether administration to older mice has similar effects and/or if short term anti-IL-11 therapy is effective for lifespan extension, as seen for rapamycin. Mouse mortality in old age is often cancer-related 44 and our end-of-life autopsy data support the notion that inhibition of IL-11 significantly reduces age-related cancers. Of note, IL-11 is important for tumorigenesis and tumour immune evasion and clinical trials of anti-IL-11 in combination with immunotherapy to treat cancer are planned 22 .

Chronic sterile inflammation is an important hallmark of ageing that is intimately linked with senescence and implicated in the pathogenesis of age-related frailty, metabolic dysfunction and multi-morbidity 7 , 16 , 17 , 39 . Studies of invertebrates have shown that innate immune signalling, notably Jak–Stat signalling in fly adipose tissue, can adversely affect metabolism and lifespan 47 , 48 . We show here that a pro-inflammatory cytokine can affect affect age-related decline and lifespan in a mammal. The relative contributions of canonical (JAK–STAT3) and non-canonical (MEK–ERK) IL-11 signalling, alone or in combination, for ageing phenotypes remain to be determined.

Inhibition of ERK or mTOR or activation of AMPK by trametinib, rapamycin or metformin, respectively, increase lifespan in model organisms and such drugs are advocated by some for use in humans. However, these agents have on- and off-target toxicities along with variable, and sometimes detrimental, effects on healthspan and inflammation 12 , 13 , 35 , 49 . Our data suggest that anti-IL-11 therapy, which has a reassuring safety profile and is currently in early-stage clinical trials for fibroinflammatory diseases, is a potentially translatable approach for extending human healthspan and lifespan 22 .

Commercial antibodies

Adiponectin (AdipoQ, 21613-1-AP, Proteintech), p-AMPK Thr172 (2535, clone 40H9, CST), AMPK (5832, clone D63G4, CST), CD31 (ab222783, clone EPR17260-263, abcam), CD68 (ab125212, abcam), cyclin D1 (55506, clone E3P5S, CST), p-ERK1/2 Thr202/Tyr204 (4370, clone D13.14.4E, CST), ERK1/2 (4695, clone 137F5, CST), GAPDH (2118, clone 14C10, CST), FHL1 (10991-1-AP, Proteintech), GFP (ab290 and ab6673, abcam), p-LKB1 Ser428 (3482, clone C67A3, CST), LKB1 (3047, clone D60C5, CST), p-mTOR Ser2448 (2971, CST), mTOR (2972,CST), p-NF-κB p65 Ser536 (3033, clone 93H1, CST), NF-κB p65 (8242, clone D14E12, CST), p16 (human, ab108349, clone EPR1473, abcam), p16 (mouse, ab232402, clone EPR20418, abcam), p21 (human, ab109520, clone EPR362, abcam), p21 (mouse, ab188224, clone EPR18021, abcam), p-p70S6K Thr389 (9234, clone 108D2, CST), p70S6K (2708, clone 49D7, CST), p-p90RSK Ser380 (11989, clone D3H11, CST), p90RSK (9355, clone 32D7, CST), PDGFRα (AF1062, R&D systems), p-S6 ribosomal protein Ser235/236 (4858, clone D57.2.2E, CST), S6 ribosomal protein (2217, clone 5G10, CST), PCNA (13110, clone D3H8P, CST), PGC1α (ab191838, abcam), SLC10A1 (MBS177905, MyBioSource), SM22α (ab14106, abcam), p-STAT3 Tyr705 (4113, clone M9C6, CST), STAT3 (4904, clone 79D7, CST), UCP1 (72298, clone E9Z2V, CST), anti-rabbit horseradish peroxidase (HRP) (7074, CST), anti-mouse HRP (7076, CST), anti-rabbit Alexa Fluor 488 (ab150077, abcam), anti-goat Alexa Fluor 488 (ab150129, abcam) and anti-rabbit Alexa Fluor 555 (ab150074, abcam). All commercially available antibodies have been validated by their manufacturer as indicated in their respective datasheet and/or website.

Custom-made antibodies

IgG (clone 11E10), anti-IL-11 (clone X203 for western blot and neutralizing studies), anti-IL11RA (clone X209 for neutralizing study) were manufactured by Genovac. The suitability of IgG (11E10) as a control antibody was validated previously 29 . X203 was validated for neutralization of human and mouse IL-11 29 , 38 and for western blot 38 , 50 . X209 was validated previously for neutralization of human and mouse IL11RA 38 and for western blot 38 .

Recombinant proteins

Recombinant human IL-11 (hIL11, Z03108, Genscript).

Bovine serum albumin (BSA, A7906, Sigma), 16% formaldehyde (w/v), methanol-free (28908, Thermo Fisher Scientific), DAPI (D1306, Thermo Fisher Scientific), DMSO (D2650, Sigma), rapamycin, (9904, CST), Triton X-100 (T8787, Sigma), Tween-20 (170-6531, Bio-Rad) and U0126 (9903, CST),

Ethics statements

All experimental protocols involving human subjects (commercial primary human cell lines) were performed in accordance with the ICH Guidelines for Good Clinical Practice. All participants provided written informed consent and ethical approvals have been obtained by the relevant parties as written in the datasheets provided by ScienCell from which primary human cardiac fibroblasts and primary human hepatocytes were commercially sourced.

Animal studies were carried out in compliance with the recommendations in the Guidelines on the Care and Use of Animals for Scientific Purposes of the National Advisory Committee for Laboratory Animal Research (NACLAR). All experimental procedures were approved (SHS/2019/1481 and SHS/2019/1483) and conducted in accordance with the SingHealth Institutional Animal Care and Use Committee (IACUC). Certified veterinarians were responsible for all animal experiment procedures according to the laws governing animal research in Singapore.

Cell culture

Cells were grown and maintained at 37 °C and 5% CO 2 .The growth medium was renewed every 2–3 days and cells were passaged at 80% confluence, using standard trypsinization techniques. All experiments were carried out at P3, unless otherwise specified. Cells were serum-starved overnight in basal media prior to stimulation with different treatment conditions (in the absence or presence of antibodies or inhibitors) and durations, as outlined in the main text or figure legends. All commercial cell lines were characterized by the company based on their morphology and by using immunofluorescence for cell-specific markers, as detailed in the respective product datasheet and certificate of analysis. Potential biological contaminants for HIV-1, HBV, HCV, mycoplasma, bacteria, yeast and fungi were confirmed negative as outlined in the certificate of analysis.

Primary human cardiac fibroblasts

Primary human cardiac fibroblasts (HCFs) (52-year-old male, 6330, lot 9580, ScienCell) were authenticated by their fibroblast morphology and phenotype, characterized by immunofluorescence staining for fibronectin and vimentin. Cell were grown and maintained in complete fibroblasts medium-2 (2331, ScienCell) supplemented with 5% foetal bovine serum (FBS, 0500, ScienCell), 1% fibroblasts growth supplement-2 (FGS-2, 2382, ScienCell) and 1% penicillin-streptomycin (P/S, 0513, ScienCell). For replicative senescence study, primary HCFs were serially passaged (from P4 to P14) in the absence or presence of a neutralizing IL11RA antibody (X209) or an IgG isotype control (11E10).

Primary human hepatocytes

Primary human hepatocytes were isolated from a 22-week-old foetus (5200, lot 34967, ScienCell) and authenticated by their hepatocyte morphology and phenotype, characterized by positive immunofluorescence for cytokeratin-18 and western blot for albumin. Following recovery from the initial thaw cycle, hepatocytes were seeded at a density 4 × 10 5 cells per well of a collagen-coated 6-well plate and maintained in hepatocyte medium (5201, ScienCell) which contains 2% FBS and 1% penicillin-streptomycin. Hepatocytes were then used directly for downstream experiments within 48 h of seeding.

Olink proximity extension assay

Human hepatocytes were seeded at a density of 2.5 × 10 5 cells per well into 6-well plates. The culture supernatants were collected following stimulation with IL-11 (0, 6 and 24 h) and were sent to Olink Proteomics for proximity extension assays using the 92-protein inflammation panel. Zero-hour time points refer to time-matched, unstimulated controls that were cultured and collected in parallel with the other stated time points. In this experiment, IL-11 was added at different times to stimulate cells; for instance, at 15:00 on day 1 for the 24-h time point and at 09:00 on day 2 for the 6-h time point. Supernatants from the unstimulated control, 6 and 24-h time points were collected at the same time. The protein concentrations were expressed as normalized protein expression (NPX; log 2 scale) and those proteins with concentrations below the limit of detection were excluded from analysis.

Operetta high throughput phenotyping assay

HCFs (P4, P7, P10 and P14) were seeded in 96-well black CellCarrier plates (PerkinElmer) at a density of 6 × 10 3 cells per well either untreated or in the presence of IgG or X209. After reaching ~80% confluence, cells were fixed in 4% formaldehyde, and permeabilized with 0.1% Triton X-100. Non-specific sites were blocked with blocking solutions (0.5% BSA and 0.1% Tween-20 in PBS). Cells were incubated overnight (4 °C) with primary antibodies (p16 and p21) at a dilution of 1:500, followed by incubation with the appropriate Alexa Fluor 488 secondary antibodies (1:1,000, 1 h, room temperature). Cells were then counterstained with 1 µg ml −1 DAPI in blocking solution. Antibodies and DAPI were diluted in blocking solutions. Each condition was imaged from duplicated wells and a minimum of seven fields per well using Operetta high-content imaging system 1483 (PerkinElmer). The measurement of p16 and p21 fluorescence intensity per area (normalized to the number of cells) was performed with Columbus 2.9 (PerkinElmer).

Seahorse assay

Primary HCFs were seeded into the Seahorse XF 96-well Cell Culture Microplate (40 × 10 3 cells per well) and serum-starved overnight prior to stimulations. Seahorse measurements were performed on Seahorse XFe96 Extracellular Flux Analyzer (Agilent). XF Cell Mito Stress Test kit (103015-100, Agilent) and Seahorse XF Mito Fuel Flex Test kit (103260-100, Agilent) were used according to the manufacturer’s protocol to measure the mitochondrial oxygen consumption rate and the percentage of fatty acid oxidation, respectively as described previously 51 . Seahorse Wave Desktop software (Ver 2.6.3) was used for report generation and data analysis.

Animal models

All mice were housed at 21–24 °C with 40–70% humidity on a 12-h light/dark cycle and provided food and water ad libitum. Our mouse colonies hold specific pathogen free (SPF) status and undergo quarterly and annual tests for common pathogens. The room housing our animals is positive for murine norovirus and Helicobacter , and these particular pathogens are deemed acceptable within our SPF facility. Sample sizes were determined based on the authors’ experience with the preliminary studies and by referencing a healthspan/lifespan study in mice 52 to detect a 20% change in phenotype between treatment groups or genotypes with 80% power ( α  = 0.05). Sample sizes for experiments involving Il11ra1 −/− and Il11 −/− mice (and their respective wild-type mice) varied depending on animal availability. Mice were randomly allocated to experimental groups on the day of the treatment except for Il11ra1 −/− and Il11 −/− in which randomization was not applicable. Treatments or genotypes were not disclosed to investigators generating quantitative readouts during data collection but were revealed during the analysis. The mouse strains used in our study are described below.

Il11ra1-deleted mice

Male and female Il11ra1 +/+ (wild-type) and Il11ra1 −/− mice 25 ( B6.129S1-Il11ra tm1Wehi /J, The Jackson Laboratory ) were euthanized at 110 weeks of age for blood and tissue collection; 10–12 weeks old male and female mice of the respective genotypes were used as controls.

Il11 -deleted mice

Male and female mice lacking functional alleles for Il11 ( Il11 −/− ), which were generated and characterized previously 31 , 50 , and their wild-type counterparts were euthanized at 10–12 weeks of age (young controls) and 104–108 weeks of age (old mice).

Il11-EGFP reporter mice

Young (10-week-old) and old (100-week-old) transgenic mice (C57BL/6 J background) with EGFP knocked into the Il11 gene ( Il11-EGFP mice, Cyagen Biosciences) 29 were euthanized for immunofluorescence staining studies of liver, gastrocnemius and vWAT. Old wild-type littermates were used as aged negative controls.

In vivo administration of anti-IL-11

Male and female C57BL/6 J mice (Jackson Laboratory) were randomized prior to receiving either no treatment, anti-IL-11 (X203) or IgG (11E10). X203 or 11E10 (40 mg kg −1 , every 3 weeks) were administered by intraperitoneal injection, starting from 75 weeks of age for a duration of 25 weeks; mice were then euthanized at 100 weeks of age.

Lifespan studies

Lifespan studies involved two distinct experimental groups (male and female) (1) C57BL/6 J mice (Jackson Laboratory) aged 75 weeks that received monthly injections of either anti-IL-11 (X203) or IgG (11E10) at a dosage of 40 mg kg −1 ; and (2) wild-type and Il11 −/− mice. Mice were inspected daily and medicated for non-life-threatening conditions by an experienced veterinarian, as needed. The principal experimental endpoint was age of death, which was recorded when mice were found dead or at the time of euthanasia if they were deemed severely moribund (or unlikely to survive longer than 48 h) at the time of inspection, according to previously described criteria 10 . Mice with gross tumours, when present, were monitored for tumour progression and euthanized when tumours developed >1.5 cm in size (at largest dimension) or when tumours become ulcerated, infected or interfered with mobility as permitted by the local SingHealth IACUC. These limits were not exceeded in any of the experiments. Gross examination was conducted at autopsy following natural death or euthanasia of mice to observe and document the presence of visible tumours in the larger body organs and to record any noticeable gross appearances.

GTT and ITT

Mice were fasted for 6 h prior to baseline blood glucose measurement. For GTT, mice were injected intraperitoneally with 20% glucose at 2 mg per g lean mass. For ITT, mice were injected intraperitoneally with recombinant human insulin at 1.2 mU per g body weight. Both glucose and insulin were diluted in sterile DPBS. Blood glucose concentrations were then measured at 15, 30, 60, 75 and 90 min after glucose or insulin administration for GTT or ITT, respectively. Blood was collected via tail snip and Accu-Chek blood glucometer was used for blood glucose measurements.

Mouse body composition (total body fat and lean mass measurements) was performed 1 day prior to GTT/ITT or euthanasia by Echo MRI analysis using 4in1 Composition Analyzer for live small animals (Echo Medical Systems).

Frailty scoring

Frailty scoring was performed, with observers blinded to treatment, at the start of the experiment or 1–2 days prior to euthanasia using a 27-point frailty scoring system 15 . Body temperatures were recorded by rectal thermometry using Kimo Thermocouple Thermometer (TK110, Kimo).

Grip strength assessment

A digital grip strength meter (BIO-GS3, BIOSEB) was used to measure full body (4 limbs) and forelimb (both forepaws) grip strengths, as per the manufacturer’s instruction. Mice were allowed to rest for at least 1 h between the two tests. The average of 3 readings of maximal average force exerted by each mouse on the grip strength meter was used for analysis.

Measurement of whole-body metabolic parameters

Whole-body metabolic parameters for IgG and X203-treated (antibodycohort), and wild-type and IL-11-knockout (KO cohort) mice were assessed by open-circuit indirect calorimetry. Animals were single-housed in the PhenoMaster automated home-cage system (TSE Systems) at a temperature of 22°C and in a humidity-controlled environment with a 12-h light/dark cycle. Parameters including oxygen consumption (VO 2 ), carbon dioxide production (VCO 2 ), food intake, and locomotor activity were measured simultaneously at 1-min time intervals. RER was calculated using the VCO 2 /VO 2 ratio. Locomotor activity was divided into horizontal plane locomotor activities, defined as the total number of infrared beam breaks in the x and y axis (counts). Mice were monitored for 5 consecutive overnight periods including an acclimatization period during the first light/dark cycle (day 0–1), which was not used for analysis. For both antibody and KO cohorts, the control (IgG or wild-type) group ( n  = 10) and intervention (X203 or IL-11-knockout) group ( n  = 10) were divided equally into two consecutive monitoring sessions. Baseline RER comparison was made using measurements from the second light/dark cycle (day 1–2). Animals were given ad libitum access to food and water except during test phases introduced after day 2 where food access was restricted to assess the resting metabolic rate (measured at thermoneutrality (28°C)) and adaptation to fasting (12 h).

Bomb calorimetry

To measure energy content in mouse stool, bomb calorimetry was performed by the core service at Department of Food Science and Technology, National University of Singapore. All faecal samples were collected and stored at −80 °C prior to measurement. Approximately 0.9 g of faecal samples were placed into a combustion bag in which a cotton thread and benzoic acid tablets were used as combustion aid. The gross calorie content was then determined using the IKA C5003 Control bomb calorimeter on the isoperibolic mode, with C5001 cooling system and oxygen gas supplied. Data was derived as a single point reading ( n  = 1). The average relative error ranges from 0.07%–0.59%.

Colorimetric and enzyme-linked immunosorbent assays (ELISA)

The levels of ALT, AST, cholesterol and IL-6 in mouse serum were measured using Alanine Transaminase Activity Assay Kit (ab105134, abcam), Aspartate Aminotransferase Activity Assay Kit (ab105135, abcam), Cholesterol Assay Kit (ab65390, Abcam), and Mouse IL-6 Quantikine ELISA Kit (M6000B, R&D Systems), respectively. The levels of triglyceride in mouse livers and serum were measured using Triglyceride Assay Kit (ab65336, Abcam). Total collagen content in mouse livers, gastrocnemius, and vWAT were measured using Quickzyme Total Collagen assay kit (QZBtotco15, Quickzyme Biosciences). The levels of IL-6, IL-8 and IL-11 in equal volumes of cell culture media collected from experiments with primary human cells were quantified using Human IL-8/CXCL8 Quantikine ELISA Kit (D8000C, R&D Systems), Human IL-6 Quantikine ELISA Kit (D6050, R&D Systems), Human IL-11 Quantikine ELISA kit (D1100, R&D Systems). All ELISA and colorimetric assays were performed according to the manufacturer’s protocol. Triglyceride Assay Kit (ab65336, Abcam),

Immunoblotting

Western blots were carried out on total protein extracts from liver, gastrocnemius, and vWAT tissues, which were homogenized in RIPA Lysis and Extraction Buffer (89901, Thermo Fisher Scientific) containing protease and phosphatase inhibitors (A32965 and A32957, Thermo Fisher Scientific). Protein lysates were separated by SDS–PAGE, transferred to PVDF membranes, blocked for 1 h with 3% BSA, and incubated overnight with primary antibodies (1:1,000 in TBST). This study was conducted over six years, and western blots were performed on many tissues, the smallest of which provided limited protein for blotting. To conserve antibody usage and maximize data output, membranes were often cut at the appropriate molecular weight markers and probed with different antibodies. In all instances, equal loading of protein lysates per membrane was ensured. Protein bands were visualized using SuperSignal West Femto Maximum Sensitivity Substrate detection system (34096, Thermo Fisher Scientific) with the appropriate HRP secondary antibodies (1:1,000 in TBST). Raw uncropped blots are provided in Supplementary Fig. 1 and semi-quantitative densitometry analyses are provided in Supplementary Fig. 2 .

Quantitative PCR with reverse transcription

Total RNA was extracted from cells or snap-frozen tissues using TRIzol Reagent (15596026, Thermo Fisher Scientific) and RNeasy Mini Kit (74104, Qiagen). PCR amplifications were performed using iScript cDNA Synthesis Kit (1708891, Bio-Rad). Gene expression analysis was performed with QuantiNova SYBR Green PCR Kit (208056, Qiagen) technology using StepOnePlus (Applied Biosystem). Expression data were normalized to GAPDH mRNA expression and fold change was calculated using 2 −∆∆Ct method. The primer sequences are provided in the Supplementary Table 8 .

Telomere length and mitochondrial copy number quantification

DNA from HCFs (P4 and P14) and snap-frozen liver, gastrocnemius, and vWAT was extracted with the E.Z.N.A. Tissue DNA Kit (D3396-02, Omega Bio-tek) according to the manufacturer’s protocol. Telomere length and mitochondrial copy number for HCFs were evaluated by quantitative PCR with reverse transcription (RT–qPCR) with the Relative Human Telomere Length Quantification qPCR Assay Kit (8908, ScienCell) and Relative Human Mitochondrial DNA copy number Length Quantification qPCR Assay Kit (8938, ScienCell), respectively. Similarly, the telomere length and mitochondrial copy number for mouse tissues were evaluated by RT–qPCR with the Relative Mouse Telomere Length Quantification qPCR Assay Kit (M8908, ScienCell) and Relative Human Mitochondrial DNA copy number Length Quantification qPCR Assay Kit (M8938, ScienCell), respectively.

Investigators performing histology and analysis were blinded to the genotype and treatment group.

Haematoxylin and eosin staining

Mouse vWAT were fixed in 10% neutral-buffered formalin (NBF) for 48 h, embedded in paraffin, cut into 4-μm sections followed by haematoxylin and eosin staining according to the standard protocol. Lipid droplet areas were quantified by ImageJ (version 1.53t, NIH) with the adipocytes tools plugin ( https://github.com/MontpellierRessourcesImagerie/imagej_macros_and_scripts/wiki/Adipocytes-Tools ) from 5 randomly selected fields at 200× magnification in vWAT images per mouse, and 5 mice per group were assessed. The mean value of lipid droplet areas per field was plotted for the final data presentation.

Immunohistochemistry

Four-micrometre mouse vWAT sections were dewaxed with histoclear and a gradient ethanol wash, followed by permeabilization using 1% Triton X-100 for 10 min and antigen retrieval process with Reveal Decloaker (RV1000M, Biocare Medical) using a double boiler method at 110 °C for 20 min. Slides were allowed to cool in the container together with the Reveal Decloaker solution for 10 min under running water. Double blocking was achieved with (1) H 2 O 2 for 10 min and (2) 2.5% normal horse serum for 1 h (S-2012, Vector Labs). vWAT sections were incubated overnight at 4 °C with primary antibody (CD68, 1:100 in PBST) and visualized by probing with Horse Anti-Rabbit IgG Polymer Kit (MP-7401, Vector Labs) for 1 h at 37 °C and ImmPACT DAB Peroxidase Substrate Kit (SK-4105, Vector Labs). Haematoxylin (H-3401, Vector Labs) was used to counterstain the nuclei prior to imaging by light microscopy (Olympus IX73).

Immunofluorescence

Young (10-week) and aged (100-week) Il11 EGFP/+ and aged wild-type Il11 +/+ mice underwent perfusion-fixation with PBS and 4% paraformaldehyde for multi-organ collection at terminal euthanasia. Mouse liver, vWAT and gastrocnemius were further fixed in 4% paraformaldehyde at 4 °C and serial 15–30% sucrose dehydration over 48 h before they were cryo-embedded in OCT medium. 5 µm sections were heat antigen retrieved using Reveal Decloaker (RV1000M, Biocare), permeabilized with 0.5% Triton X-100, and blocked with 5% normal horse serum before probing with primary antibodies diluted in 2.5% normal horse serum at 4 °C overnight. The antibody dilutions used for immunofluorescence studies are as follows: adiponectin, GFP, PDGFRα and SLC10A1 (1:100); CD31, FHL1 and SM22α (1:200). Alexa Fluor-conjugated secondary antibodies (1:300 in 2.5% normal horse serum) were incubated for 2 h at room temperature for visualization. Autofluorescence was quenched with 0.1% Sudan Black B for 20 min. DAPI was included for nuclear staining before mounting and sealed. Photomicrographs were randomly captured by researchers blinded to the strain and age groups.

RNA-seq libraries

Total RNA was isolated from liver, fat and skeletal muscle of mice receiving either IgG or X203 using RNeasy Mini Kit (74104, Qiagen) and quantified using Qubit RNA Broad Range Assay Kit (Q10210, Thermo Fisher Scientific). RNA quality scores (RQS) were assessed using the RNA Assay (CLS960010, PerkinElmer) and DNA 5 K/RNA/CZE HT Chip (760435, PerkinElmer) on a LabChip GX Touch HT Nucleic Acid Analyzer (CLS137031, PerkinElmer). TruSeq Stranded mRNA Library Prep kit (20020594, Illumina) was used to assess transcript abundance following the manufacturer’s instructions. In brief, poly(A) + RNA was purified from 1 µg of total RNA with RQS > 6, fragmented, and used for cDNA conversion, followed by 3′ adenylation, adapter ligation, and PCR amplification. The final libraries were quantified using Qubit DNA Broad Range Assay Kit (Q32853, Thermo Fisher Scientific) according to the manufacturer’s guide. The average fragment size of the final libraries was determined using DNA 1 K/12 K/Hi Sensitivity Assay LabChip (760517, PerkinElmer) and DNA High Sensitivity Reagent Kit (CLS760672, PerkinElmer). Libraries with unique dual indexes were pooled and sequenced on partial lanes targeting ~50 M reads per sample on a HiSeq or a NovaSeq 6000 sequencer (Illumina) using 150-bp paired-end sequencing chemistry.

Data processing and analysis for RNA-seq

Fastq files were generated by demultiplexing raw sequencing files (.bcl) with Illumina’s bcl2fastq v2.20.0.422 with the --no-lane-splitting option. Low quality read removal and adapter trimming was carried out using Trimmomatic V0.36 with the options ILLUMINACLIP: <keepBothReads > =TRUE MAXINFO:35:0.5 MINLEN:35. Reads were mapped to the Mus musculus GRCm39 using STAR v.2.7.9a with the options --outFilterType BySJout --outFilterMultimapNmax 20 --alignSJoverhangMin 8 --alignSJDBoverhangMin 1 --outFilterMismatchNmax 999 --alignIntronMin 20 --alignIntronMax 1000000 --alignMatesGapMax 1000000 in paired-end, single pass mode. Read counting at the gene-level was carried out using subread v.2.0.3: -t exon -g gene_id -O -s 2 -J -p -R -G. The Ensembl release 104 M. musculus GRCm39 GTF was used as annotation to prepare STAR indexes and for FeatureCounts. Principal component analysis clustered samples into tissue-types and conditions. Outlier samples that did not cluster with the expected group were removed. Differentially expressed genes were identified using R v4.2.0 using the Bioconductor package DESeq2 v1.36.0 using the Wald test for comparisons. IgG samples were used as the reference level for comparison with anti-IL-11 (X203) samples for vWAT, liver, and gastrocnemius. Mitocarta v3.0 gene list was downloaded and TPM values in Fat IgG and anti-IL-11 samples were plotted using pheatmap R package for genes which had TPM ≥ 5 in at least one condition. Gene set enrichment analysis was carried out using the fgsea v.1.22.0 R package for MSigDB Hallmark (msigdbr v.7.5.1) and MitoCarta v3.0 gene sets with 100,000 iterations. The ‘stat’ value quantified by DESeq2 was used to rank the genes, as an input for the enrichment analysis.

Statistical analysis

Statistical analyses were performed using GraphPad Prism software (version 10). Datasets were tested for normality with Shapiro–Wilk tests. For normally distributed data, two-tailed Student’s t -tests or one-way ANOVA were used for analysing experimental setups requiring testing of two conditions or more than two conditions, respectively. P values were corrected for multiple testing according to Dunnett (when several experimental groups were compared to a single control group) or Tukey (when several conditions were compared to each other within one experiment) tests. Non-parametric tests (Kruskal–Wallis with Dunn’s correction in place of ANOVA and Mann–Whitney U test in place of two-tailed Student’s t -tests) were conducted for non-normally distributed data. Comparison analysis for two parameters from two different groups were performed by two-way ANOVA and corrected with Sidak’s multiple comparisons. Two-way repeated measures ANOVA (Geisser–Greenhouse correction) with Sidak’s multiple comparisons was applied to temporal sampling in paired subjects for GTT, ITT and body weight. Individual endpoint frailty indices were ranked and compared using two-tailed Mann–Whitney test to compare (1) old Il11 −/− versus wild-type females; (2) old Il11 −/− versus wild-type males; or (3) IgG versus X203 groups in females, and the Kruskal–Wallis test with Dunn’s multiple comparisons of untreated, IgG and X203 treatment groups in males. The two-population proportions analysis (two-tailed) was used for comparing the difference in the proportion of cancer occurrence and seminal vesicle dilatation between two groups. The criterion for statistical significance was set at P   <  0.05. For the lifespan studies, differences in survival between the experimental groups ( Il11 −/− versus wild-type or X203 versus IgG) were compared using the Kaplan–Meier method implemented in IBM SPSS (release 29.0.1.0), and statistical significance ( P value) was assessed by means of the log-rank (Mantel–Cox) test. In addition to the log-rank test (that gives equal weight to all time points), we used the Wilcoxon test (that gives more weight to deaths at early time points), which provided significant results in all comparisons, therefore yielding a similar conclusion to reject the null hypothesis. Both survival comparison methods are non-parametric tests based on the chi-square statistic and provide two-tailed P values. The complete list of exact p-values and terms for supporting statistical information is provided in Supplementary Table 9 .

Reporting summary

Further information on research design is available in the  Nature Portfolio Reporting Summary linked to this article.

Data availability

All data are available within the Article or Supplementary Information . The RNA-seq data reported in this paper are available on the Short Read Archive with Bioproject ID: PRJNA939262 . Datasets used for analysis in this study are as follows: Ensembl release 104 M. musculus GRCm39 gene annotations (GRCm39, https://asia.ensembl.org/info/data/ftp/index.html ), MSigDB Hallmark (v.7.5.1, https://www.gsea-msigdb.org/gsea/msigdb/human/collections.jsp ) and MitoCarta (v3.0, https://www.broadinstitute.org/mitocarta/mitocarta30-inventory-mammalian-mitochondrial-proteins-and-pathways ).  Source data are provided with this paper.

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Acknowledgements

The authors thank M. M. Dy Varela, C. H. Lee and all other members of the Duke–NUS vivarium team for their invaluable assistance in monitoring and overseeing the care for our mice used in this study; and Enleofen and Boehringer Ingelheim for the permission to use X203 in this study. This research is supported by the National Medical Research Council (NMRC) MOH-STaR21nov-0003 (S.A.C.), NMRC Centre Grant to the NHCS (S.A.C.), MOH‐CIRG18nov‐0002 (S.A.C.), Tanoto Foundation (S.A.C.), Leducq Foundation (S.A.C.), NMRC/OFYIRG/0053/2017 (A.A.W.), NMRC MOH-OFIRG21nov-0006 (A.A.W.), NMRC MOHOFLCG22may-0003 (A.A.W.), Khoo Foundation (A.A.W. and E.A.), and Goh Foundation (S.A.C. and A.A.W.). N.H. is supported by Leducq Foundation 16CVD03, ERC advanced grant under the European Union Horizon 2020 Research and Innovation Program (AdG788970), and Deutsche Forschungsgemeinschaft (DFG-German Research Foundation) SFB 1470 HFpEF. W.-W.L. is supported by A*STAR AME YIRG (A2084c0157). B.K.S. is supported by MOH-OFIRG19may-0002. The D.J.W. lab is supported by the Wellcome Trust Grant 098565/Z/12/Z and the MRC grant MC-A654-5QB40. The J.G. lab is supported by MRC (MC-U120085810) and CRUK (C15075/A28647) grants. The D.C. lab is supported by the MRC grant MC-A654-5QB10. The S.A.C. laboratory receives core grant support from the MRC LMS and is also supported by the British Heart Foundation’s Big Beat Challenge award to CureHeart (BBC/F/21/220106).

Author information

These authors contributed equally: Anissa A. Widjaja, Wei-Wen Lim

These authors jointly supervised this work: Anissa A. Widjaja, Stuart A. Cook

Authors and Affiliations

Cardiovascular and Metabolic Disorders Program, Duke–National University of Singapore Medical School, Singapore, Singapore

Anissa A. Widjaja, Wei-Wen Lim, Sivakumar Viswanathan, Sonia Chothani, Cibi Mary Dasan, Joyce Wei Ting Goh, Radiance Lim, Brijesh K. Singh, Sze Yun Lim, Sebastian Schafer, Benjamin L. George, Madhulika Tripathi, Enrico Petretto, Lena Ho & Stuart A. Cook

National Heart Research Institute Singapore, National Heart Centre Singapore, Singapore, Singapore

Wei-Wen Lim, Ben Corden, Jessie Tan, Chee Jian Pua, Chen Xie & Stuart A. Cook

Barts Heart Centre, Barts Health NHS Trust, London, UK

Cardiovascular and Metabolic Sciences, Max Delbrück Center for Molecular Medicine in the Helmholtz Association (MDC), Berlin, Germany

Eleonora Adami & Norbert Hübner

MRC Laboratory of Medical Sciences, London, UK

Mark Sweeney, Dominic J. Withers, Jesus Gil, David Carling & Stuart A. Cook

Bone Biology and Disease Unit, St Vincent’s Institute of Medical Research, Melbourne, Victoria, Australia

Natalie A. Sims

Department of Medicine, St Vincent’s Hospital, The University of Melbourne, Melbourne, Victoria, Australia

DZHK (German Centre for Cardiovascular Research), Partner Site Berlin, Berlin, Germany

Norbert Hübner

Charité–Universitätsmedizin, Berlin, Germany

Institute for Big Data and Artificial Intelligence in Medicine, School of Science, China Pharmaceutical University, Nanjing, China

Enrico Petretto

Institute of Clinical Sciences, Faculty of Medicine, Imperial College, London, UK

Dominic J. Withers, Jesus Gil & David Carling

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Contributions

A.A.W. and S.A.C. conceived, designed, funded and provided supervision for the study. A.A.W., W.-W.L., S.V., B.C., D.M.C., J.W.T.G, B.K.S., J.T., S.S., E.A., B.L.G., M.S. and M.T. performed in vitro and in vivo studies, biochemistry and molecular biology experiments. W.-W.L., S.Y.L. and C.X. performed histology analysis. C.J.P. performed RNA-seq. R.L. and L.H performed the phenomaster study. A.A.W., W.-W.L., S.C., R.L., B.K.S., N.A.S., N.H., E.P., L.H. and S.A.C. analysed and interpreted the data. D.J.W., J.G. and D.C. provided intellectual input. A.A.W., W.-W.L. and S.A.C. prepared the manuscript with input from co-authors.

Corresponding authors

Correspondence to Anissa A. Widjaja or Stuart A. Cook .

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Competing interests.

A.A.W., B.C., B.K.S., S.S. and S.A.C. are co-inventors of a patent family that includes: WO2022090509A (methods to extend healthspan and treat age-related diseases) and WO2018109174 (IL-11 antibodies). S.S. and S.A.C. are co-founders and shareholders of Enleofen Bio Pte Ltd and VVB Bio Pte Ltd. A.A.W. had consulted for VVB Bio on work unrelated to the study presented here. J.G. has acted as a consultant for Unity Biotechnology, Geras Bio, Myricx Pharma and Merck KGaA. Pfizer and Unity Biotechnology have funded research in the J.G. laboratory unrelated to the work presented here. J.G. owns equity in Geras Bio. J.G. is a named inventor in MRC and Imperial College patents related to senolytic therapies (the patents are not related to the work presented here). The other authors declare no competing interests.

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Extended data figures and tables

Extended data fig. 1 age-dependent expression of il11 in varied cell types across tissues..

a Western blots (WB) of total ERK1/2, p90RSK, LKB1, AMPK, mTOR, p70S6K, and S6RP in livers from 12, 25, 50, 75, and 110-week-old male mice for the respective phosphoproteins shown in Fig. 1b . b WB of IL11 and GAPDH in visceral gonadal white adipose tissue (vWAT) and gastrocnemius from 12, 25, 50, 75, and 110-week-old male mice (n = 5/group). c WB of p-ERK1/2, p-p90RSK, p-LKB1, p-AMPK, p-mTOR, p-p70S6K, p-S6RP, and their respective total proteins in gastrocnemius from 12, 25, 50, 75, and 110-week-old male mice (n = 5/group). d WB of IL11 and GAPDH in the liver, vWAT and gastrocnemius from 12-week-old and 110-week-old male and female mice (n = 3/group). e - g Representative immunofluorescence images (scale bars, 100 µm) of EGFP expression in the livers, vWAT, and gastrocnemius, colocalized with parenchymal cell markers Adiponectin (AdipoQ) in vWAT and Four and a half LIM domains (FHL1) in gastrocnemius, endothelial cells (CD31), smooth muscle transgelin (SM22α), and pan-fibroblast marker (PDGFRα) of 10 and 110-week old Il11 - EGFP mice (representative dataset from n = 3/group).

Extended Data Fig. 2 Beneficial signalling, metabolic, inflammation and ageing biomarker effects associated with Il11ra1 deletion.

a WB of total proteins in livers for Fig. 1e . WB showing the activation status of ERK1/2, p90RSK, LKB1, AMPK, mTOR, p70S6K, S6RP, and protein expression levels of p16, p21 and GAPDH in b vWAT and c gastrocnemius from 10 and 110-week-old male WT and Il11ra1 −/− mice (n = 5/group). d body temperatures, e indexed weights of liver, vWAT and gastrocnemius, and f the levels of liver triglycerides (TG), g serum cholesterol, and h serum triglycerides of 110-week-old male and female WT and Il11ra1 −/− mice. Relative gene expression levels of i Ccl2 , Ccl5 , Tnfα , Il1β , Il6 , j Acc , Fasn and Srebp1c in livers, and serum levels of k ALT and l AST in young and old male and female WT and Il11ra1 −/− mice. Gastrocnemius m telomere length and n mitochondria DNA (mtDNA) copy number from young and old male and female WT and Il11ra1 −/− mice. d - l Data are shown as mean ± SD. d - n young male WT, n = 8 ( i - n , except for i ( Acc and Fasn ), n = 7); young male Il11ra1 −/− , n = 7 ( i - n , except for i ( Acc and Fasn ), n = 8); old male WT, n = 11 ( e (liver), f - n ), n = 12 ( d , e (vWAT and gastrocnemius); old male Il11ra1 −/− , n = 15 ( e (liver)), n = 16 ( d , f - l ), n = 17 ( e (vWAT and gastrocnemius), i ( Ccl5 ), m - n ); young female WT, n = 7; young female Il11ra1 −/− , n = 8; old female WT, n = 14 ( e (liver and vWAT)), n = 15 ( d , e (gastrocnemius), f - n ); old female Il11ra1 −/− , n = 12 ( m - n ), n = 13 ( d - l ); two-way ANOVA with Sidak’s correction. For gel source data, see Supplementary Fig. 1 . BW: body weight; FC: fold change.

Extended Data Fig. 3 IL11 causes ERK and mTORC1-dependent senescence and senescence-associated secretory phenotypes.

a - d , f , g Data for IL11 (24 h)-stimulated primary human cells in the presence of either DMSO, U0126, or rapamycin (n = 6/group). a - b WB showing the activation status of ERK1/2, mTOR, p16, p21, Cyclin D1, and PCNA protein expression by WB from IL11-stimulated a primary human cardiac fibroblasts (HCFs) and b hepatocytes. Levels of secreted c IL6 and d IL8 by ELISA from HCF supernatant. e Relative levels of IL6, IL8, LIF, VEGFA, HGF, CCL2, CXCL1, CXCL5, CXCL6, and CCL20 in the supernatant of IL11-stimulated primary human hepatocytes (6 and 24 h) as measured by Olink proximity extension assay (n = 4/group). Concentrations of f IL6 and g IL8 in the hepatocyte supernatant (as measured by ELISA). a - d , f - g IL11 (5 ng/ml for HCF, 10 ng/ml for hepatocytes), U0126 (10 µM), rapamycin (10 nM). c - g Data are shown as mean ± SD. c , d , f , g One-way ANOVA with Tukey’s correction; e one-way ANOVA with Dunnett’s correction. For gel source data, see Supplementary Fig. 1 .

Extended Data Fig. 4 Inhibition of IL11 signalling reduces replicative senescence, inflammation, ageing biomarkers, and metabolic decline in human cardiac fibroblasts.

a - c Data for HCF at passage 4 (P4), 7, 10, and 14 that had been passaged in the presence of either IgG or anti-IL11RA (X209; 2 µg/ml) from P2. a WB of total and p-ERK1/2, p-p90RSK, p-LKB1, p-AMPK, p-mTOR, p-p70S6K, p-S6RP, p-NFκB, p-STAT3, p16, p21, PCNA, Cyclin D, and GAPDH (n = 6/group). b Immunofluorescence images (scale bars, 100 μm; representative datasets from n = 7/group) and quantification of intensity/area (n = 14/group) for p16 and p21 staining. c IL11, IL6 and IL8 levels in the supernatant based on ELISA (n = 6/group). d WB showing the expression levels of p16, p21, and GAPDH from HCFs P4 that were stimulated for 8, 24, 48, and 72 h with media collected from HCFs P14 that had been grown and passaged in the presence of either IgG or anti-IL11RA (X209; 2 µg/ml) from P2 (n = 4/group). e Telomere length (n = 6/group) and f mtDNA copy number (n = 6/group) and seahorse assay (n = 8/group) showing g mitochondrial oxygen consumption rate (OCR), h changes in OCR during basal respiration and ATP production states, and i oxidative and glycolytic energy phenotypes at baseline in HCFs P4 and P14 either untreated or in the presence of either IgG or anti-IL11RA (X209; 2 µg/ml). b , c , e - i Data are shown as mean ± SD. b , c Two-way ANOVA with Sidak’s correction, e , f , h one-way ANOVA with Tukey’s correction. For gel source data, see Supplementary Fig. 1 .

Extended Data Fig. 5 Female Il11 −/− mice are protected from age-associated frailty and inflammation and have advantageous metabolic profiles.

a Body temperatures, b front paw grip strength, serum levels of c ALT, d AST, area under the curves (AUC) of e glucose tolerance tests (GTT) and f insulin tolerance tests (ITT), weights of g skeletal muscle (gastrocnemius and soleus) and h liver (normalised/indexed to BW), i liver triglyceride (TG) levels, j indexed brown adipose tissues (BAT) weight, k WB of total proteins for the respective phospho proteins in vWAT as shown in Fig. 2j,l WB showing ERK1/2, mTOR, p70S6K, and S6RP activation and p16, p21, and GAPDH protein expression levels (n = 6/group) in gastrocnemius, m relative pro-inflammatory gene expression ( Ccl2, Ccl5, Tnfα, Il1β and Il6 ) levels in vWAT, and n serum IL6 levels from young (12-week-old) and old (105-week-old) female WT and Il11 −/− mice. a - j , m - n Data are shown as mean ± SD, two-way ANOVA with Sidak’s correction (young WT, n = 5 ( j ), n = 8 ( a , c - i , m - n ), n = 10 ( b ); young Il11 −/− , n = 7 ( j ), n = 9 ( a - i , m - n ); old WT, n = 16; old Il11 −/− , n = 16 ( j ), n = 18 ( a - i , m - n ). For gel source data, see Supplementary Fig. 1 . AU: arbitrary units; BW: body weight; FC: fold change.

Extended Data Fig. 6 Old male Il11 −/− mice are protected from age-associated metabolic decline.

a Representative image of 108-week-old WT and Il11 −/− male mice. b Body weights, c percentages of fat and lean mass (normalised to BW), d frailty scores, e body temperatures, f full body and forepaw grip strength measurements, g glucose and insulin tolerance tests (GTT and ITT) from young (12-week-old) and old (105-week-old) male WT and Il11 −/− mice. h Respiratory exchange ratio (RER) measurement at day 2 (top; 24 h) and assessment of RER (second panel), cumulative food intake, and locomotive activities using the phenomaster system over a 5-day period in 68–70-week-old male WT and Il11 −/− mice (n = 10/group). i Faecal caloric density as measured by bomb calorimetry in 95–105-week-old male WT and Il11 −/− mice (n = 10/group). Indexed weight of j gastrocnemius k , soleus, l liver, m vWAT, subcutaneous WAT (scWAT), and BAT. b - g , i - m Data are shown as mean ± SD. b - g , j - m Two-way ANOVA with Sidak’s correction (young WT and Il11 −/− , n = 6 ( m (scWAT and BAT)), n = 9 ( b - g , j - l , m (vWAT)); old WT, n = 12 ( m (scWAT and BAT)), n = 15 ( b - g , j - l , m (vWAT)); old Il11 −/− , n = 12 ( f - g , m (scWAT and BAT)), n = 14 ( b - e , j - l , m (vWAT)); i two-tailed Mann Whitney test. AU: arbitrary units; BW: body weight; FC: fold change.

Extended Data Fig. 7 Anti-IL11 therapy improves muscle strength and metabolic health in old male mice.

a - k Data for anti-IL11 therapeutic dosing experiment as shown in Schematic Fig. 3a in which IgG or X203 were administered to male mice starting from the age of 75 weeks. a Forepaw grip strength, b RER measurements, cumulative food intake, and locomotive activities as measured by phenomaster for 5 days on IgG/X203-treated old (81-week-old) male mice − 6 weeks after IgG/X203 administration was started (n = 10/group). c Faecal caloric density as measured by bomb calorimetry in IgG and X203-treated 115-week-old male mice (IgG, n = 8; X203, n = 10). Serum levels of d cholesterol, TG, e IL6, and f AST, indexed weight of g soleus, h scWAT and BAT, and i WB of total proteins for the respective phospho-proteins in vWAT shown in Fig. 3m (n = 6/group). j telomere length and k mtDNA copy number. a , c - h , j - k Data are shown as mean ± SD. a , d - h , j - k One-way ANOVA with Tukey’s correction (75-week-old control, n = 6 ( h ), n = 10 ( a ), n = 14 ( d - g , j - k ); untreated 100-week-old, n = 6; IgG 100-week-old, n = 13; X203 100-week-old, n = 12); c two-tailed Mann Whitney test; j two-tailed Student’s t-test. For gel source data, see Supplementary Fig. 1 . BW: body weight; FC: fold change.

Extended Data Fig. 8 Therapeutic inhibition of IL11 reduces age-associated metabolic dysfunction, frailty and sarcopenia in female mice.

a Schematic of anti-IL11 (X203) therapeutic dosing experiment in old female mice for experiments shown in ( b - h ; IgG, n = 10 ( g ), n = 11 ( b - f , h ); X203, n = 13). Mice were given either X203 or an IgG control antibody (40 mg/kg, every 3 weeks) starting from 75 weeks of age for a duration of 25 weeks. Created with BioRender.com. b Body weights across time. c - d Changes (Δ; values at end-point (100-week-old) - values at starting point (75-week-old)) in c fat and lean mass percentage, and d area under the curve (AUC) of GTT and ITT. e Frailty scores at starting (75-week-old) and end-point (100-week-old). f - g Full body and front paw grip strength at end-point (100-week-old) and changes in full body and front paw grip strength over 25 weeks of treatment (values at end-point (100-week-old) - values at starting point (75-week-old)). h Body temperatures. b - d , f - h Data are shown as mean ± SD. b Two-way ANOVA, c - h two-tailed Student’s t-test except for d Δ AUC GTT, which was analysed by two-tailed Mann Whitney test. AU: arbitrary units; BW: body weight.

Extended Data Fig. 9 Beneficial effects of anti-IL11 in aged white adipose tissue.

a . Violin plot of Transcripts per million (TPM) values of senescence genes (based on Tabula Muris Senis consortium) in vWAT, liver, gastrocnemius samples from mice receiving either IgG or anti-IL11 as shown in schematic Fig. 3a . b . Relative Ucp1 mRNA from 10-week-old and 110-week-old male and female WT and Il11ra1 −/− mice (vWAT). c . Heatmap showing row-wise scaled TPM values for the gene-list in Mitocarta 3.0. (no. of genes = 1,019 with TPM > = 5 in at least one condition). d . A lollipop plot for top 50 significant Mitocarta 3.0 pathways (p-adj<0.05) in enrichment analysis using fgsea R package. No negative NES was found to be significant. e . Distribution of RNA-seq reads at the Clstn3 locus from IgG or anti-IL11-treated vWAT. Relative Ucp1 mRNA expression levels in BAT from f therapeutic dosing group (75-week-old, n = 6; untreated 100-week-old, n = 6; IgG-treated 100-week-old n = 13; X203-treated 100-week-old, n = 12) and g female WT and Il11 −/− mice (young WT, n = 5; young Il11 −/− , n = 7; old WT and Il11 −/− , n = 16/group). h Relative vWAT mRNA expression of pro-inflammatory markers ( Ccl2 , Ccl5 , Tnfα , Il1β , Il6 ) in young (10-week-old) and old (110-week-old) male and female WT and Il11ra1 −/− mice. a , c , d , e Liver and gastrocnemius (n = 8/group), vWAT IgG, n = 7; vWAT anti-IL11, n = 6; b , h young male WT, n = 8; young male Il11ra1 −/− , n = 7; old male WT, n = 11; old male Il11ra1 −/− , n = 14; young female WT, n = 7; young female Il11ra1 −/− , n = 8; old female WT, n = 15; old female Il11ra1 −/− , n = 12. a Data are shown as violin plots with median ± min-max; b , f - h data are shown as mean ± SD. b , g , h Two-way ANOVA with Sidak’s correction; e one-way ANOVA with Tukey’s correction.

Extended Data Fig. 10 Gross appearance of mice receiving anti-IL11 versus IgG in lifespan studies.

Representative images of 130-week-old male (top) and female (bottom) mice from the lifespan therapeutic dosing study where mice received either IgG (mice on the left) or anti-IL11 (X203; mice on the right) from 75 weeks of age until death.

Supplementary information

Supplementary information.

Supplementary Figs. 1 and 2, which include the raw blots and immunoblot densitometry analysis.

Reporting Summary

Supplementary table 1.

Frailty scores for young (12-week-old) and old (105-week-old) female wild-type and Il11 −/− mice.

Supplementary Table 2

Frailty scores for young (12-week-old) and old (105-week-old) male wild-type and Il11 −/− mice.

Supplementary Table 3

Frailty scores for male mice in anti-IL-11 therapeutic dosing groups.

Supplementary Table 4

Frailty scores for female mice in anti-IL-11 therapeutic dosing groups.

Supplementary Table 5

List of differentially expressed genes (DEGs) and their respective transcripts per million (TPM) levels in visceral vWAT, liver, and gastrocnemius muscle from the anti-IL-11 therapeutic dosing groups. Default parameters for differential expression testing using DESeq2 were used. Wald test was used for hypothesis testing and the P values obtained were corrected for multiple testing using the Benjamini–Hochberg method.

Supplementary Table 6

List of wild-type and Il11 −/− mice in the lifespan cohort.

Supplementary Table 7

List of mice in the therapeutic lifespan cohort.

Supplementary Table 8

Primer list.

Supplementary Table 9

Supporting statistical information.

Peer Review File

Source data, source data figs. 1–5 and source data extended data figs. 1–9, rights and permissions.

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Widjaja, A.A., Lim, WW., Viswanathan, S. et al. Inhibition of IL-11 signalling extends mammalian healthspan and lifespan. Nature (2024). https://doi.org/10.1038/s41586-024-07701-9

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Received : 18 April 2023

Accepted : 11 June 2024

Published : 17 July 2024

DOI : https://doi.org/10.1038/s41586-024-07701-9

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