current research on dementia in australia

Making Alzheimer's a distant memory for future generations

A leading medical research institute specialising in alzheimer’s disease and related dementias..

We are working together to change the future of Alzheimer’s disease and make it a distant memory for future generations. With the vital assistance of supporters, we can do just that and more.

Without a medical breakthrough, these numbers are projected to escalate rapidly:

Australians currently living with dementia.

People involved in their care. The burden on individuals and families is overwhelming.

Australians received a dementia diagnosis in 2023.

Hear from Prof Ralph Martins, AO

Ralph Martins is the Director of Research at Alzheimer’s Research Australia.

Essential research

With Alzheimer’s Research Australia leading the way, we can look forward to a more promising tomorrow. Together, we can change the future.

At Alzheimer's Research Australia, a passionate and dedicated team is working tirelessly to make a profound impact in the fight against Alzheimer's disease.

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Research studies gather valuable information that serves as a cornerstone for improving health outcomes for society.

Evolution of a research foundation to a medical research institute.

Transforming the Future of Alzheimer’s Disease: Empowering Early Detection

The time to act is now, as early diagnosis can pave the way for timely interventions, improving outcomes for individuals and reducing the long-term impact of Alzheimer’s disease.

Alzheimer’s Research Australia is a leading medical research institute dedicated to Alzheimer’s disease and other dementias. With decades of experience and a deep commitment to reducing the impact of these diseases, our team of experts, researchers, and healthcare professionals is at the forefront of collaborative discoveries, working towards early diagnosis, proactive prevention measures, and innovative approaches that significantly improve lives.

Meet Mel, whose life took an unexpected turn when her mother was diagnosed with Alzheimer’s disease.

Witnessing the gradual decline of her mother’s memories and the impact it had on their family, Mel became a passionate advocate for Alzheimer’s Research Australia.

Making lifestyle changes will help to reduce your risk of developing Alzheimer’s. Your diet, exercise, and sleep are just some of the important ways to reduce your risk. It’s never too late to start.

Improving the ability to diagnose Alzheimer’s earlier is a key research theme at Alzheimer’s Research Australia. An early diagnosis enables actions to be taken to potentially slow disease progression.

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New look, same cause

The Australian Alzheimer’s Research Foundation has a new name, Alzheimer’s Research Australia. We remain resolutely focused on changing the future of Alzheimer’s disease through leading-edge medical research.

At the Australian Alzheimer’s Research Foundation, we currently support research in four areas of Alzheimer’s disease:

• understanding the pathology of the disease
• developing treatments
• identifying factors to defer or prevent the onset of the disease
• discovering an early diagnosis

Our current research utilises memory tests, medical and neuropsychological assessments; brain imaging; and highly specialised blood tests to help find an early diagnosis. We are also developing lifestyle interventions to delay or prevent the onset of symptoms, and are working to develop better treatments for those already diagnosed.

As part of our research into a diagnosis, we undertake the latest, highly specialised brain imaging, known as PET amyloid imaging. This allows detection of the accumulation of beta amyloid in the living brain.

While beta amyloid is a diagnostic marker, it is not sufficiently reliable on its own. We are therefore collaborating with researchers in Melbourne, the USA, Germany and other parts of the world to develop a suite of bio-markers and an accurate and reliable diagnostic tool for Alzheimer’s disease.

Another major study relates to the preventative value of specific nutritional supplements for Alzheimer’s disease. This study will focus on the potential of antioxidants and poly phenols to defer the onset of the disease.

The major research projects currently supported by the Foundation include:

• Blood-Based Protein and Lipid Biomarkers for Diagnosis of Alzheimer Disease
• Programs investigating the role of genetics in Alzheimer’s disease
• Developing agents that selectively target the enzyme responsible for beta amyloid generation
• The role of diet in the prevention of Alzheimer’s Disease
• Identification and validation of peptide agents that neutralise beta amyloid toxicity
• Molecular and neuropsychological predictive markers of cognitive decline
• The role of testosterone in Alzheimer’s Disease

If you would like further information about any of these projects please contact us

WA Memory Study (WAMS)

Memory changes are common among the general adult population as we age. Dementia, which is primarily caused by Alzheimer’s disease, is not a normal part of ageing and currently there is no cure. Current medications only treat symptoms but do not halt progression of the disease.

It is vital to differentiate early changes that are consistent with dementia from those we see in normal ageing so we can identify those at risk or diagnosed with dementia as early as possible, start preventive measures or treatment when such interventions become available. However, early diagnosis is difficult as early symptoms associated with dementia are not well established. Doctors do not have a reliable means of establishing which individuals with early memory loss will develop dementia due to Alzheimer’s disease.

The WAMS commenced in 1996 and involves baseline measurements and follow-up assessments at 18-month intervals. Currently, 326 participants are enrolled as at June 2019 and over 500 assessments have been made including 18 month and 36 month assessments.

The aims of the WAMS are:

• To identify those factors that may influence memory changes
• To follow the longitudinal trajectory of cognitive change and see who will or will not develop dementia
• To identify amongst individuals with memory loss what characteristics are specifically associated with Alzheimer’s disease, to enable identification of individuals at a higher than average risk of developing dementia

The WAMS baseline measurements include:

• Medical history
• Tests of cognition (e.g., memory, language abilities, thinking skills and so on), mental and psychological health and quality of life
• Blood tests
• Body composition x-ray looking for association between body fat and Alzheimer’s disease-related proteins
• Assessment of peripheral and central hearing, as hearing loss has been associated with decline in memory and other mental abilities and social activities
• Olfactory “smell” examination to examine the link between the loss of smell and Alzheimer’s disease

In addition to the research procedures described above there are other optional sub-studies which includes the following:

• Neuroimaging to identify the changes in the brain caused by Alzheimer’s disease
• Donation of a cerebrospinal fluid (CSF) sample
• Clinical Assessment by a medical doctor specialised in identifying dementia using clinical methods
• Eye imaging

WAMS Outcomes:

Over the next few years, the WAMS is aiming to develop 3 novel assessments of Alzheimer’s disease that have the potential to be used in clinical practice to identify those at higher risk of dementia.

These 3 novel measurements in development are:

• The McCusker Subjective Cognitive Impairment Inventory (McSCI): the primary aim of this test is to be used in research and clinical practice to screen those at risk of future dementia.
• Prospective Memory Test (WA-PROM): This measure will inform research on how forgetting future tasks and events (e.g., taking medication, future medical appointment, and etc.) may indicate higher risk of dementia.
• Olfactory Memory Test (WA-OMT): The WA Olfactory Memory Test assesses ability to remember odours correctly. Impaired olfactory ability is seen in different dementia-related conditions including Parkinson disease and Alzheimer’s. As memory impairment is a common sign of dementia due to Alzheimer’s disease, the WA-OMT is believed differentiate those who will progress to Alzheimer’s disease form other types of dementia.

Current PhD and Master Students:

WAMS provides important support for our future scientists in Alzheimer’s disease research. There are currently 4 PhD Candidates and Master Students working on the WAMS:

• Rasingi Seneviratne (UWA): Olfactory Memory
• Rachal Mumme (UWA): Early detection of Alzheimer’s disease
• Pamela Lam (ECU): Depression and risk of dementia
• Hadeel Tarawneh (UWA): Age-related hearing and dementia

If you would like to be involved in the WA Memory study, please contact Jo Shaw on [email protected]

  Why Research is Essential

With an aging population a far larger proportion of our community will be affected by Alzheimer’s and dementia in the coming years. Ongoing and committed research will play a vital role in the continuing journey towards an Alzheimer’s free world for the benefit of our whole community.

Alzheimer’s disease is occurring at an increased pace. The Australian Alzheimer’s Research Foundation is dedicated to ensuring research continues on an international level. Millions will be condemned to a demeaning and frightening end to their lives if treatments are not discovered.

The reality is that despite currently being the second largest cause of death, research into Alzheimer’s disease in Australia is underfunded relative to the current and projected costs and the scope for huge savings from investment in research for cause, prevention and treatment. Urgent action is essential.

Identifying a means of early intervention is a priority as the effectiveness of any treatments will be limited by the current inability to diagnose Alzheimer’s disease until significant neurological damage has already been sustained.

As a result of past research we are now aware of a number of mechanisms implicated in the body developing abnormal levels of beta amyloid in the blood and its deposition on the brain.

Our knowledge of beta amyloid is increasing all the time. We now know that beta amyloid is a commonly occurring protein which has a beneficial role in normal bodily functioning. There are different forms of beta amyloid, some being beneficial, others destructive. We know that in some people there is an increased production of the destructive forms.

We also know that deposition of beta amyloid is widespread among the population, even for those who do not develop the condition. With some people, there is increased production of beta amyloid which in itself may contribute to increased deposition. The problem could also be due to a reduced ability of the body to remove the amyloid from the brain.

New look, same cause

The Australian Alzheimer’s Research Foundation has a new name, Alzheimer’s Research Australia.

We remain resolutely focused on changing the future of Alzheimer’s disease through leading-edge medical research.

Click the links below to visit our new website:

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Dementia Research Centre

The dementia research centre builds an environment that enables cutting edge discovery research with a focus on translating findings into treatments for patients..

In 2018, Macquarie University launched the first dementia research centre in New South Wales that is fully dedicated to discovery-based research and drug development for Alzheimer’s disease. The Director, Professor Lars Ittner , is an internationally renown expert and opinion leader in Alzheimer’s disease research. The Dementia Research Centre (DRC) brings together international and national leaders in translational dementia research and strengthen the University’s current investment into neuroscience research. The multidisciplinary team of the DRC strives to accelerate today’s discoveries into tomorrow’s therapies. The DRC currently includes eight research groups.

The DRC resides within the Department of Biomedical Sciences and the Faculty of Medicine, Health and Human Sciences in MQ Health. Besides its world-class research program for dementia research, the DRC contributes to the teaching endeavours of the department and the faculty by delivering state-of-the-art training in applied neuroscience to both undergraduate and postgraduate students as well as MQ’s MD Program.

Read more about the Dementia Research Centre .

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Australian Dementia Research Forum 2024

The  Australian Dementia Research Forum (ADRF2024) is the premier annual event that brings together dementia researchers, health professionals and policy makers, as well as people living with dementia and their families and carers, to discuss the latest research, innovations, and best practices in dementia. Following the success of last year’s conference, we are pleased to announce that this year’s ADRF2024 will again be held in person on the sunny Gold Coast, Queensland from  3rd to 5th June.

The theme for the ADRF2024 is  Turning the Tide On Dementia , with particular focus on exciting new innovations in the field. The Forum is a meeting place for national and global experts to share the latest developments in dementia research, care and policy and to challenge perceptions of living with, or caring for someone with a dementia diagnosis.

For the second consecutive year, we will be holding Continuing Professional Development (CPD) sessions on Day 1 of the Forum. Adding to your CPD points, these sessions are vital for healthcare professionals to keep up with advancements in dementia diagnosis, treatment, prevention, and post-diagnostic care. Our speakers will cover a broad range of topics, including dementia risk reduction, deprescribing, anti-amyloid therapies, support for care partners and more.

Early bird registrations are now open, so be sure to register today to secure your seat at the premier dementia research event in Australia.

Register here or visit the ADRF website for more details.

You can also follow the ADRF on Twitter for more updates.

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• Open access
• Published: 25 August 2024

Improving genetic risk modeling of dementia from real-world data in underrepresented populations

• Mingzhou Fu   ORCID: orcid.org/0000-0001-8584-4314 1 , 2 ,
• Leopoldo Valiente-Banuet 1 ,
• Satpal S. Wadhwa 1 ,
• Bogdan Pasaniuc   ORCID: orcid.org/0000-0002-0227-2056 3 ,
• Keith Vossel 1 &
• Timothy S. Chang   ORCID: orcid.org/0000-0002-9225-9874 1  

Communications Biology volume  7 , Article number:  1049 ( 2024 ) Cite this article

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Metrics details

• Population genetics

Genetic risk modeling for dementia offers significant benefits, but studies based on real-world data, particularly for underrepresented populations, are limited. We employ an Elastic Net model for dementia risk prediction using single-nucleotide polymorphisms prioritized by functional genomic data from multiple neurodegenerative disease genome-wide association studies. We compare this model with APOE and polygenic risk score models across genetic ancestry groups (Hispanic Latino American sample: 610 patients with 126 cases; African American sample: 440 patients with 84 cases; East Asian American sample: 673 patients with 75 cases), using electronic health records from UCLA Health for discovery and the All of Us cohort for validation. Our model significantly outperforms other models across multiple ancestries, improving the area-under-precision-recall curve by 31–84% (Wilcoxon signed-rank test p -value <0.05) and the area-under-the-receiver-operating characteristic by 11–17% (DeLong test p -value <0.05) compared to the APOE and the polygenic risk score models. We identify shared and ancestry-specific risk genes and biological pathways, reinforcing and adding to existing knowledge. Our study highlights the benefits of integrating functional mapping, multiple neurodegenerative diseases, and machine learning for genetic risk models in diverse populations. Our findings hold potential for refining precision medicine strategies in dementia diagnosis.

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Improving genetic risk prediction across diverse population by disentangling ancestry representations

Deep learning-based polygenic risk analysis for Alzheimer’s disease prediction

Novel Alzheimer’s disease genes and epistasis identified using machine learning GWAS platform

Introduction.

Dementia is a progressive syndrome marked by cognitive decline beyond what is expected from normal aging 1 . Globally, it affects about 36 million people and incurs costs of approximately $594 billion annually 2 , 3 . The primary etiologies of dementia include Alzheimer’s disease (AD), vascular dementia, Lewy body dementia (LBD), Frontotemporal dementia (FTD), and Parkinson’s disease dementia (PDD), among others 4 . Genetic predisposition plays a significant role in dementia, with numerous significant variants identified through Genome-Wide Association Studies (GWASs). For example, the Apolipoprotein E ( APOE) gene, which encodes a protein responsible for binding and transporting low-density lipids, significantly influences the risk of late-onset AD, the most prevalent form of dementia 5 , 6 .

Polygenic risk scores (PRSs) aggregate the effects of multiple genetic variants to quantify an individual’s genetic predisposition for complex diseases like dementia 7 . A growing number of studies have underscored the robust links between AD PRS and dementia related phenotypes in the non-Hispanic white populations 8 , 9 , 10 , 11 . However, further research is needed to refine personal dementia genetic risk models and understand their potential limitations.

PRS performance is suboptimal in non-European ancestries, as weights for single nucleotide polymorphisms (SNPs) are mostly derived from European ancestry GWASs, limiting their generalizability 12 , 13 , 14 , 15 . Including causal variants like APOE in risk models due to their independent risk contribution is recommended, while non-causal variants can introduce noise 16 , 17 . Moreover, few genetic studies on dementia have been conducted within the context of Electronic Health Records (EHRs), and have predominantly focused on AD 9 , 18 . While AD accounts for a significant portion, many dementia cases display mixed pathologies 19 , 20 , with mixed dementia being a common occurrence in real-world scenarios 21 . Addressing all-cause dementia could better reflect the clinical landscape and lead to advances in precision medicine that benefit a larger cohort 22 .

Dementia remains significantly underdiagnosed in real-world community settings 23 , 24 , 25 , 26 , 27 , 28 . Early detection through genetic modeling can help healthcare providers improve diagnosis, manage symptoms effectively, and initiate appropriate treatments. The need for more refined methodologies to develop accurate genetic risk models across diverse populations is imperative.

In the present study, we hypothesized that the risk SNPs associated with dementia and their corresponding weights vary across diverse populations, specifically Amerindian, African, and East Asian genetic ancestries. We further proposed that the predictive performance for dementia phenotypes in non-European populations could be enhanced by identifying biologically meaningful SNPs and applying sparse machine learning models tailored to each genetic ancestry group. Thus, we present a novel approach for assessing individual dementia genetic risks across diverse populations.

To address previous limitations, we implemented several innovative measures. Firstly, we prioritized SNPs using functional and biological information based on GWAS results, focusing on causal SNPs most likely to contribute to dementia risk. Secondly, we utilized machine learning algorithms to select significant genetic variants, allowing us to fine-tune models for different ancestry groups. This method provides a notable advantage for non-European populations, which are often underrepresented in GWAS studies. Finally, we developed and validated our models within real-world EHR settings, targeting dementia as a comprehensive condition. This innovative approach holds promise for improving our understanding of individual dementia genetic risks for dementia and promoting health equity in genetic research.

Sample description

The primary dataset for model development was derived from EHR linked to the biobank of the UCLA Health System 29 . Fig.  1 illustrates the finalized UCLA ATLAS samples, stratified by Genetic Inferred Ancestry (GIA) groups. The Hispanic Latino American (HLA) sample included 610 patients with 126 dementia cases, while the AA sample consisted of 440 patients with 84 dementia cases. The distribution of International Classification of Diseases, 10th Revision (ICD-10) diagnosis codes was relatively consistent across the two GIA samples, with Alzheimer’s disease (G30) and unspecified dementia (F03) being the most prevalent diagnoses. However, the African American (AA) group exhibited a higher proportion of vascular dementia (F01) diagnoses compared to the HLA group. The East Asian American (EAA) group, with a limited case count ( N  = 75), was excluded from primary analyses but included in sensitivity analyses.

Distribution of diagnosis in ICD-10 codes by genetic inferred ancestry groups. AA African Americans, HLA Hispanic Latino Americans. ICD-10 codes descriptions: G30, Alzheimer’s disease; F03, Unspecified dementia; F02, Dementia in other diseases classified elsewhere; F01, Vascular dementia; G31, Other degenerative diseases of nervous system, not elsewhere classified.

Within each GIA group, eligible controls, due to the stringent inclusion criteria, had longer spans of records and more encounters. There were no significant differences in other EHR features between dementia cases and controls (Table  1 ).

Performance comparison for dementia phenotype prediction task

We developed and evaluated machine learning models to predict the binary dementia phenotype within the UCLA ATLAS sample, stratified by GIA groups. After accounting for age, sex, and ancestry-specific genetic variations (represented by principal components (PCs)), we constructed genetic risk models for dementia, incorporating offset corrections within a linearized framework. The predictive capabilities of these models were assessed using four distinct sets of genetic markers: (1) APOE-ε4 counts, (2) AD PRS, (3) a composite of multiple PRSs, and (4) select SNPs refined through Elastic Net regularization 30 . For SNP selection, we utilized the Functional Mapping and Annotation of Genome-Wide Association Studies (FUMA) tool 31 to prioritize independent genome-wide-significant SNPs or independent gene-annotated SNPs.

Table  2 presents the overall performance of models for predicting dementia phenotypes. No discernible differences were observed among APOE-ε4 and all PRS models (AD-PRS and Multi-PRS), regardless of the SNP set used for PRS construction—whether derived from ancestry-specific GWASs, genome-wide-significant SNPs, or gene-annotated SNPs. Notably, the predictive performance in the AA GIA sample of all PRS models was inferior to APOE-ε4 , particularly evident in the Area Under the Precision-Recall Curve (AUPRC).

Elastic Net SNP models demonstrated overall improvement in dementia prediction across both GIA groups. The model incorporating gene-annotated SNPs from AD and other dementia-related disease GWASs (SNPs from AD + Neuro GWASs) emerged as the most effective, indicating a collective contribution from SNPs associated with various dementia-related diseases. Specifically, the leading Elastic Net SNP model for HLA GIA sample significantly enhanced the AUPRC by 31% (0.41 [95%CI: 0.27, 0.52] vs. 0.31 [95%CI: 0.21, 0.41], Wilcoxon signed-rank test p -value < 0.001), and the area under the receiver operating characteristic (AUROC) by 12% (0.73 [95%CI: 0.66, 0.79] vs. 0.65 [95%CI: 0.56, 0.72], DeLong test p -value = 0.01) compared to the best PRS model. Furthermore, this model outperformed the APOE-ε4 count model, with an increase of 33% in AUPRC (0.41 vs. 0.31 [95%CI: 0.21, 0.41], Wilcoxon test p -value < 0.001) and 12% in AUROC (0.73 vs. 0.65 [95%CI: 0.56, 0.71], DeLong test p -value = 0.01).

This model’s efficacy was even more pronounced within the AA GIA sample, with an increase in AUPRC by 84% (0.45 [95%CI: 0.31, 0.58] vs. 0.24 [95%CI: 0.11, 0.41], Wilcoxon test p -value < 0.001) and the AUROC by 16% (0.71 [95%CI: 0.63, 0.78] vs. 0.60 [95%CI: 0.46, 0.74], DeLong test p -value = 0.004) in comparison to the best PRS model. Relative to the APOE-ε4 count model, the improvements were 65% in AUPRC (0.45 vs. 0.27 [95%CI: 0.16, 0.44], Wilcoxon test p -value < 0.001) and 17% in AUROC (0.71 vs. 0.61 [95%CI: 0.50, 0.74], DeLong test p -value = 0.01).

We also noted a substantial enhancement in the other performance metrics (based on the threshold that maximized the Matthews Correlation Coefficient (MCC)) of the Elastic Net SNPs models compared to other models across both GIA samples. This was evidenced by marked improvements in accuracy, precision, and the F1 score. In our sensitivity analysis, other non-linear models using gene-annotated SNPs from AD and other dementia-related disease GWASs, including Gradient Boosting Machine (GBM) and XGBoost, did not perform as well as the linear Elastic Net SNP models. Results from bootstrapping showed similar trends in model performances, as shown in Supplementary Fig.  1 . Applying a more stringent r 2 cut-off (<0.1) for defining independent genome-wide-significant SNPs yielded results consistent with our initial findings, as detailed in Supplementary Table  1 .

In summary, models leveraging SNPs as features identified through machine learning methods possess the potential to surpass those relying solely on summary scores such as PRSs in HLA and AA GIA. Furthermore, selecting SNPs mapped to genes using functional genomic data holds promise for further refining predictive performance.

Featured risk variants and mapped genes

In our analysis of the best-performing Elastic Net SNPs models, we examined the features selected by each model. According to results from bootstrapping (at least 95% of the 1000 iterations), the HLA and AA models identified 28 and 31 risk SNPs, respectively. The top 10 risk SNPs in variable importance selected by each model were shown in Table  3 , with a detailed list, including related information, provided in Supplementary Table  2 .

By assessing the feature importance of the SNPs chosen by the models, we found that for the HLA GIA group, the top three important predictors were rs429358 (chr19:44908684, nearest gene: APOE ), rs2075650 (chr19:44892362, nearest gene: TOMM40 ), and rs483082 (chr19: 44912921, nearest gene: APOC1 ), which together accounted for ~25% of the total predictive importance. For the AA GIA group, the most influential predictors were rs2627641 (chr19:45205500, nearest gene: BLOC1S3 ), rs8073976 (chr17:44955857, nearest gene: C1QL1 ), and rs77283277 (chr7: 143386852, nearest gene: ZYX ).

Eight risk SNPs were identified by both GIA Elastic Net SNPs models, including two AD-associated SNPs (rs429358 and rs2075650) from the top 10 features in both GIA groups, though their relative importance varied slightly. Both models also identified several unique risk SNPs associated with Parkinson’s disease (PD), Progressive Supranuclear Palsy (PSP), and stroke as significant predictors of dementia. Notably, the AA GIA model highlighted the significance of a PSP-associated risk SNP, rs8073976, located on chromosome 17, underscoring the distinct genetic underpinnings influencing these different ancestry groups. These findings suggest that while there are common genetic markers associated with dementia across different ancestry groups, there are also unique genetic risk factors that could provide insights into the specific genetic architecture and risk profiles of dementia in diverse populations.

To better understand the biological functions and pathways associated with the identified risk variants, we mapped these risk SNPs to genes using FUMA, which integrates positional, eQTL, and 3D chromatin mapping 31 .

Notably, 13 genes were identified by both non-European GIA models (Fig.  2 and Supplementary Table  3 ). Most shared genes were located near chr19q13 , which includes the well-established AD risk gene cluster, APOE-TOMM40-APOC1 32 . According to the enrichment analysis, these shared genes are predominantly involved in biological pathways associated with lipid metabolism. These pathways encompass processes such as the assembly and organization of protein-lipid complexes, as indicated by the Gene Ontology (GO) terms. Additionally, these genes play an essential role in regulating cholesterol, triglyceride, amyloid proteins, and lipoprotein particles, highlighting the importance of lipid metabolic processes in dementia. There are also shared genes located near chr3p22 ( SLC25A38 and RPSA , PSP risk genes), chr11q25 ( IGSF9B and JAM3 , PD risk genes) and chr17q21 ( CCDC43 , PSP risk gene).

Shared and ancestry-specific risk genes identified by the best-performing Elastic Net SNP models, UCLA ATLAS sample.

In addition, we investigated ancestry-specific genes. For instance, genes near the chr4p16 (e.g., PCGF3 and RP11-67M1.1 ) were uniquely pinpointed by the HLA GIA model, while genes near the chr7q34 region (e.g., ZYX and ARHGEF5 ) were uniquely identified by the AA GIA model. This differentiation underscores the unique genetic architecture influencing dementia risk across different ancestry groups and suggests potential pathways for tailored interventions.

In the sensitivity analyses, we performed dementia risk modeling in the EAA GIA sample ( N  = 673). Similar to other GIA groups, the model incorporating gene-annotated SNPs from AD and other dementia-related disease GWASs performed the best compared to all other models. This model enhanced the AUPRC by 43% (0.34 [95%CI: 0.24, 0.43] vs. 0.24 [95%CI: 0.19, 0.29], Wilcoxon test p -value < 0.001) and the AUROC by 12% (0.80 [95%CI: 0.73, 0.86] vs. 0.71 [95%CI: 0.68, 0.74], DeLong test p -value = 0.001) compared to the best PRS model. It also outperformed the APOE-ε4 count model, with increments of 42% in AUPRC (0.34 vs. 0.24 [95%CI: 0.19, 0.30], Wilcoxon test p -value < 0.001) and 11% in AUROC (0.80 vs. 0.71 [95%CI: 0.68, 0.73], DeLong test p -value = 0.004).

Among the featured 16 risk SNPs, rs429358 (chr19:44908684, nearest gene: APOE ), rs66626994 (chr19:44924977, nearest gene: APOC1P1 ), and rs6857 (chr19:44888997, nearest gene: NECTIN2 ) were the most significant predictors for the EAA GIA group, collectively accounting for ~45% of the overall predictive importance. After mapping these SNPs to gene, we identified the AD-risk gene cluster, APOE-TOMM40-APOC1 , as well as the gene region near chr17q21 (e.g., FMNL1 and SPPL2C ) (Supplementary Table  4A-D ).

Validations in the All of Us sample

We conducted a validation study using the All of Us cohort to evaluate the broad applicability of our findings obtained from the UCLA ATLAS sample. A comparable sample was selected from the All of Us Research Hub, employing the same selection scheme to their corresponding GIA groups in the UCLA ATLAS sample. However, due to the limited number of eligible dementia cases (N case = 8) in the All of Us EAA GIA sample, we could only validate our models and findings in the HLA (N_case = 68, N_control = 390) and AA (N_case = 129, N_control = 516) samples. In contrast to the UCLA ATLAS samples, participants in the All of Us cohort had shorter durations of EHR documentation and fewer recorded healthcare visits. The prevalence of dementia was also lower in the All of Us cohort in the HLA GIA group. Within each GIA sample, we found similar distributions of demographics and EHR features between dementia cases and eligible controls (Supplementary Tables  5 – 6 ).

We applied the model weights trained from the UCLA ATLAS sample to the All of Us sample, stratified by GIA groups. In comparing three representative models – (1) the APOE-ε4 model; (2) the best-performing PRS model; and (3) the best-performing Elastic Net SNP model – and accounting for demographic variables (age and sex) and genetic population structure (ancestry-specific PCs), our results mirrored those from the UCLA ATLAS sample. The Elastic Net SNP model, which included gene-annotated SNPs from GWASs of AD and other dementia-related diseases, outperformed all other models in terms of the AUPRC and AUROC in both the HLA and AA GIA samples (Table  4 ).

In particular, the Elastic Net SNP model demonstrated significant improvements over the other two models. In the HLA GIA sample, it outperformed both the APOE-ε4 and the best AD PRS model (AD AFR PRS.psig ) by 17% and 4% in AUPRC (both Wilcoxon test p -value < 0.001), and by 2.7% and 4% in AUROC (DeLong test p -value = 0.56 and 0.25), respectively. Similarly, in the AA GIA sample, the Elastic Net SNP model showed a 35% and 13% enhancement in AUPRC (both Wilcoxon test p -value < 0.001), and 9% and 22% in AUROC (both DeLong test p -value < 0.001) over the APOE-ε4 and best AD PRS model, respectively.

Traditional genetic risk models have faced limitations in effectively capturing causal disease risk variants and accurately assessing genetic risks across diverse populations. To address these challenges, our present study introduces a novel approach to predicting dementia risks by leveraging functional mapping of genetic data in conjunction with machine learning methods in the real-world EHR setting. Our proposed method shows remarkable improvements in prediction performance compared to well-known approaches like APOE gene and PRS models. We successfully identified shared and ancestry-specific risk genes and biological pathways contributing to dementia risks for each non-European GIA group. Finally, we bolstered the reliability and generalizability of our findings by validating our models using a comparable EHR sample from the All of Us cohort.

Our study highlights the significance of prioritizing biologically meaningful SNPs in genetic prediction. GWASs often identify genomic regions with multiple correlated SNPs, which may encompass several closely located genes. However, not all of these genes are relevant to the disease 33 . Functional annotation of genetic variants enabled us to target potential causal SNPs by considering various factors, such as regional linkage disequilibrium (LD) patterns, functional consequences of variants, their impact on gene expression, and their involvement in chromatin interaction sites 31 . In our models developed on UCLA ATLAS samples, we achieved significant improvements in model performance by prioritizing biologically meaningful SNPs, ranging from 31–84% in AUPRC and 11–17% in AUROC across different GIA groups, compared to the APOE-ε4 count and the best-performing PRS models. These results underscore the critical role of considering functional and biological information in enhancing the performance of genetic prediction models, especially in diverse populations.

It is worth highlighting that no discernible performance differences were observed between PRSs constructed using genome-wide-significant and gene-annotated SNPs. This can be attributed to the strong LD between genome-wide-significant and gene-annotated SNPs within the same genomic region. As a result, these SNPs tend to have similar effect estimates in the GWASs. Thus, it is expected that the PRSs built with these two sets of SNPs would exhibit a high correlation (Supplementary Table  7 ), which further supports the notion that the choice of genome-wide-significant or gene-annotated SNPs does not significantly impact the predictive performance of the PRSs in our study.

Moreover, our study emphasizes the significance of incorporating risk factors from multiple dementia-related diseases when developing predictive models for complex conditions like dementia. Both ancestry-specific Elastic Net SNP models highlighted several PD and PSP risk variants as significant predictors of dementia. This finding aligns with the well-known complexity of dementia as a multifactorial disorder that shares common features with these related conditions 34 . However, it is worth noting that including PRSs of those diseases did not significantly improve the overall performance (Table  2 ). This result is consistent with research conducted by Clark et al. 35 , in which they demonstrated that a combined genetic score, which incorporated risk variants for AD and 24 other traits, had an equivalent predictive power as the AD PRS on its own.

Our proposed Elastic Net SNPs models identified several shared risk factors across different ancestries. Notably, a substantial proportion of the identified shared genes were found near the chr19q13 region, which is well-known for the AD risk gene cluster comprising APOE-TOMM40-APOC1 . These findings align with previous research 6 , 36 , 37 , further supporting the significance of this genomic region in contributing to the genetic risks associated with dementia.

At the same time, we have discovered compelling evidence supporting our hypothesis that risk SNPs associated with dementia, along with their corresponding weights, exhibit significant variations across diverse populations. Notably, our analysis of PRS models revealed that the performance of PRS built with the European population GWAS was worse when predicting a non-European GIA group. This is consistent with other previous studies. Using PRSs for 245 curated traits from the UK Biobank data, Privé et al. 38 revealed notable disparities in the phenotypic variance explained by PRSs across different populations. Specifically, compared to individuals of Northwestern European ancestry, the PRS-driven phenotypic variance is only 64.7% in South Asians, 48.6% in East Asians, and 18% in West Africans. Similarly, using a population from the Health and Retirement Study, Marden et al. demonstrated that the estimated effect of the AD PRS was notably smaller for non-Hispanic black compared to non-Hispanic white in both dementia probability score and memory score 39 . On the other hand, we also observed that the APOE-ε4 count model performed better than most PRS models in HLA and AA GIA samples. These finding further reinforces the limitations of standard PRS when applied to non-European populations, in which attempting to transfer GWAS effect size from one GIA to another GIA, or when using matched genetic ancestry GWAS with smaller sample size, as demonstrated in several AD and other phenotype studies 40 , 41 , 42 , 43 .

In addition, we observed notable differences in the feature importance of various SNPs within the best-performing Elastic Net models across distinct GIA groups. Consequently, this led us to identify ancestry-specific genes and distinct biological pathways implicated in the genetic predisposition to dementia in diverse ancestral samples. These findings highlight the uniqueness of genetic risk factors and functional pathways in diverse population groups.

Finally, we validated our models using samples from separate EHR linked with genetic data (All of Us). Our proposed Elastic Net SNP model consistently outperformed the APOE-ε4 and the best PRS models. While the Elastic Net SNP model demonstrated improved performance in both HLA and AA populations, we observed a decrease in the general performance and significance (AUPRC and AUROC) in the All of Us sample compared to the UCLA ATLAS sample. One potential explanation for this discrepancy is the distinct population structure within each sample, as revealed by comparing patient characteristics (Supplementary Table  6 ). These findings underscore the influence of population-specific factors within GIA groups on the generalizability of genetic risk models, highlighting the critical need to account for population diversity in predictive models for complex diseases.

Our study boasts several notable strengths that contribute to its significance and impact. Firstly, we conducted our research with EHRs that mirror the practicalities of real-world community settings. Most current studies used longitudinal cohorts, which performed extensive testing and consensus criteria 44 applied by clinicians with expertize in dementias to diagnosis dementia. However, in real-world clinical care, the expertize in dementia may vary, and the criteria used for diagnosis may not always align with the stringent standards of research cohorts. Diagnoses documented in the EHRs capture these real-world data and, by routinely capturing patient data over extended periods, form an expansive longitudinal cohort ideal for real-world research. Compared to traditional cohorts, EHR cohorts offer additional benefits, such as vast sample sizes, diverse phenotypes, and a more inclusive representation of often underrepresented groups, like minority groups and older adults 45 . Secondly, machine learning techniques applied in our study allowed us to infer crucial dementia risk factors for underrepresented populations, such as HLA and AA, with GWAS summary statistics from extensively studied populations like Europeans. This approach enabled a deeper understanding of the genetic landscape of dementia in underrepresented populations, particularly valuable given the current limitations in large-sample-size GWASs specific to these groups. Thirdly, we fortified the robustness and generalizability of our findings through the validation of our model on an independent dataset from the All of Us cohort. Furthermore, our innovative approach, which incorporated biologically relevant genetic markers and functional annotations, significantly enhanced the accuracy of disease prediction. This approach can be readily adapted to predict other complex diseases, extending the scope of its applications and enriching our understanding of diverse human populations’ genetic traits.

However, we acknowledge certain limitations. Firstly, we observed variations in the composition of dementia subtypes among different GIA groups’ case samples. Consequently, the distinct genes and biological pathways identified by different ancestry models should be interpreted with this consideration. Secondly, although our study identified potential risk SNPs and genes associated with dementia, additional experimentation is necessary to understand the precise mechanisms underlying the association of these factors. Thirdly, the limited number of dementia cases in our non-European GIA samples, after applying inclusion criteria, constrains the generalizability of our findings. Future studies should aim to replicate these findings in larger samples for each GIA to enhance their robustness. Finally, although detailed clinical guidelines for disease diagnoses exist 46 , 47 , clinical providers may adapt these criteria to fit specific research focuses or populations. This adaptation can lead to variations in diagnostic criteria across different studies or clinical practices. Consequently, the precision of dementia diagnoses based on ICD-10 codes may vary compared to a gold standard of research criteria or autopsy findings.

In light of these limitations, further research with more extensive and diverse datasets, encompassing a broader range of dementia subtypes and GIA groups is imperative to strengthen the validity and applicability of our study’s outcomes. Such efforts will contribute to a more comprehensive understanding of the genetic complexities underlying dementia across diverse populations.

In conclusion, our study introduces a novel and robust approach to assessing individual genetic risks for dementia across diverse populations in a real-world setting. Our study demonstrates the importance of considering functional and biological information and population diversity when developing predictive models for complex diseases like dementia. The findings from our research provide valuable insights into the intricate genetic factors underlying dementia. Moreover, this work opens up promising avenues for developing more accurate and efficient predictive models for complex genetic traits in diverse human populations. Such advancements can potentially be paired with the development of targeted treatments tailored to the specific genetic profiles of individuals affected by dementia and related conditions.

Data source

Our discovery cohort for model development was derived from the biobank-linked EHR of the UCLA Health System 29 . The UCLA ATLAS Community Health Initiative collects biosamples during routine lab work at UCLA Health labs from a diverse population, which undergoes genotyping using a customized Illumina Global Screening Array 48 . Participants watch a short video explaining the initiative’s goals and record their consent decision. Detailed information regarding biobanking and consenting procedures is available in our previous publications 49 , 50 . After the genotype quality control, there were 54,935 individuals with both genotype and UCLA EHR data. All ethical regulations relevant to human research participants were followed. As the genetic data and EHRs were de-identified, the study was exempt from human subject research regulations (UCLA IRB# 21-000435).

We validated our models and findings using data from the All of Us Research Hub, one of the most diverse biomedical data resources in the United States. The All of Us Research Program serves as a centralized data repository, offering secure access to de-identified data from program participants 51 . For validation, we utilized data release version 7, encompassing 409,420 individuals, of which 245,400 have undergone whole genome sequencing.

Patient genetic data preprocessing

Quality control was conducted using PLINK v1.9 52 , adhering to established guidelines 29 . Samples with a missingness rate exceeding 5% were removed. Low-quality SNPs with >5% missingness, monomorphic SNPs, and strand-ambiguous SNPs were excluded. Post-quality control, genotype imputation was performed via the Michigan Imputation Server 53 to enhance the coverage of genetic variants and facilitate comparison across diverse genotyping platforms. SNPs with imputation r 2  < 0.90 or minor allele frequency <1 % were pruned. After these measures, 21,220,668 genotyped SNPs were retained across the 54,935 individuals. Finally, we restricted our analyses to SNPs that overlapped between UCLA ATLAS and All of Us, resulting in a total of 8,705,988 SNPs, ensuring consistency in genetic variables across datasets.

Genetic ancestry refers to the geographic origins of an individual’s genome, tracing back to their most recent biological ancestors 54 . GIA employs genetic data, a reference population, and inferential methodologies to categorize individuals within groups likely sharing common geographical ancestors 55 . In our UCLA ATLAS sample, we used the reference panel from the 1000 Genomes Project 56 and principal component analysis 57 to infer genetic ancestry. GIA groups included European American (EA), African American (AA), Hispanic Latino American (HLA), East Asian American (EAA), and South Asian American (SAA). For instance, individuals in the United States with recent biological ancestors inferred to be of Amerindian ancestry were designated as “HLA GIA” 58 . In addition, we calculated ancestry-specific principal components within each GIA group using principal component analysis.

Genetic predictors

The initial step in our study involved identifying potential risk SNPs as candidate predictors for dementia using GWASs. A summary of the GWASs used and the steps to select candidate SNPs is provided in Supplementary Table  8 and Supplementary Fig.  2 .

We selected GWASs for AD 5 , 36 , 59 , Parkinson’s disease (PD) 60 , Progressive Supranuclear Palsy (PSP) 61 , Lewy Body Dementia (LBD) 62 , and stroke 63 phenotypes. For AD, we included three GWASs conducted on diverse populations, including European 5 , African American 36 , and multi-ancestries 59 . Summary statistics from these GWAS are publicly available, with detailed recruitment procedures and diagnostic criteria available in the original publications.

A significant proportion of GWAS hits are located in non-coding or intergenic regions 64 . Due to the correlated nature of genetic variants in LD, distinguishing causal from non-causal variants based solely on association P -values from GWASs is challenging 31 . Identifying the most likely causal variants involves understanding the regional LD patterns and assessing the functional consequences of correlated SNPs, such as those affecting protein-coding, regulatory, and structural sequences 65 . Several functionally validated variants have been clinically relevant to diseases pathogenesis, confirmed through experimental validation 66 . To address this, we utilized the FUMA, a tool that leverages information from biological data repositories to annotate and prioritize SNPs 31 .

For each GWAS summary statistic, we first identified genomic risk loci using a P -value threshold (<5e-8) and a pre-calculated LD structure ( r 2  < 0.2) based on the relevant reference population from the 1000 Genomes Project 56 . Subsequently, we identified two distinct sets of SNPs:

Independent genome-wide-significant SNPs : We selected the SNP with the most significant GWAS P -value within each genomic risk locus. This process was iterated until all SNPs were assigned to a risk locus cluster or considered independent.

Independent gene-annotated SNPs : We prioritized SNPs based on their functional consequences on genes. Using FUMA, the mapping from SNPs to genes was achieved by performing ANNOVAR 67 using Ensembl genes (build 85). SNPs were mapped to genes through positional mapping, eQTL associations, and 3D chromatin interactions. The Combined Annotation-Dependent Depletion (CADD) score 68 was used to select potential causal SNPs, with the SNP possessing the highest CADD score within each genomic risk locus being chosen, indicating a higher probability of the variant being deleterious.

The identified independent genome-wide-significant SNPs and independent gene-annotated SNPs were subsequently used in constructing the disease PRSs and as candidate features in dementia prediction models. To ensure robustness in a sensitivity analysis, we also adopted a stringent r 2 cut-off ( < 0.1) to define independent genome-wide-significant SNPs, ensuring the selected SNPs were independent.

We computed the disease-specific PRS as the sum of an individual’s risk allele dosages, each weighted by its corresponding risk allele effect size from the GWAS summary statistics, as shown in the PRS equation \({PR}{S}_{i}={\sum }_{j}^{M}{\hat{\beta }}_{j}\times {dosag}{e}_{{ij}}\) . All PRSs were standardized to a mean of 0 and a standard deviation of 1. The standardization process used the 1000 Genome European genetic ancestry as the reference population, ensuring the scores’ range and values are comparable across different GWASs. For each phenotype, we employed two distinct sets of SNPs identified by FUMA, namely the independent genome-wide-significant SNPs and independent gene-annotated SNPs, to calculate two respective PRSs: PRS.psig and PRS.map .

The APOE gene has two variants, rs7412 and rs429358, which determine the three common isoforms of the apoE protein: E2, E3, and E4, encoded by the ε2, ε3, and ε4 alleles 37 . Previous research has demonstrated that carriers of APOE-ε4 are at a higher risk of developing AD, exhibiting a dose-dependent effect 69 . Therefore, to quantify the APOE genotype in our study, we created a numerical variable, “ APOE - ε4count ”, representing the number of ε4 alleles (0, 1, or 2) carried by each individual.

Dementia definition and demographic features

The primary outcome of interest was dementia, defined using the ICD-10 codes (Supplementary Table  9 ). Demographic variables considered included self-reported age and sex. The age of each participant, measured in years, was calculated based on their birth date and encounters dates. For individuals diagnosed with dementia, we determined the age at dementia onset.

Analytical sample selection

To focus on patients with longitudinal records, our analyses included patients with complete demographic data (age and sex) who had at least two medical encounters after age 55. We restricted the age at the last recorded encounter to <90, as patients in the UCLA EHR dataset are censored at this age.

Eligible dementia cases were identified as patients with at least one encounter with a recorded dementia diagnosis, provided the initial onset occurred after age 55. Eligible controls were required to meet the following criteria: (1) no recorded dementia or related diagnoses, as determined by predefined exclusion phenotypes 70 ; (2) age at the last recorded visit ≥ 70, to exclude younger patients who may not have manifested signs of dementia yet; and (3) a minimum of 5 years of records with an average of at least one encounter per year, minimizing potential bias from misdiagnosis (Fig.  3 ).

Sample selection steps and modeling steps description.

Prediction of dementia risk with machine learning models

In our discovery study, we developed machine learning models to predict the binary dementia phenotype in the UCLA ATLAS sample, stratified by GIA groups.

To distinctly assess genetic influences, our analysis began by mitigating the impact of demographic factors, including age, sex, and ancestry-specific PCs. We first employed a logistic regression model that utilized only these variables to predict dementia status. Subsequently, we derived the predicted values for each patient through this model. Applying an appropriate inverse link function (e.g., logit), we then subtracted these predicted values from the ultimate outcome (dementia status), generating an “offset” value. These offset values encapsulated the dementia status after regressing out the effects of demographic variables and genetic population structure.

Next, we trained genetic risk models to predict dementia status with offset corrections applied in the linearized space, expressed as: \({\hat{y}}_{i}={g}^{-1}({\beta }_{0}+{\beta }_{1}{x}_{i1}+\cdots +{\beta }_{p}{x}_{{ip}}+{{offset}}_{i})\) , where \({\hat{y}}_{i}\) represents the predicted dementia status, and \({g}^{-1}({\cdot })\) is the inverse of the link function 71 . We compared four different sets of predictors: (1) APOE status, (2) AD PRS, (3) multiple PRSs, and (4) smaller SNP sets with Elastic Net regularization. For the multiple PRS models, we crafted models utilizing diverse AD PRSs of varying ancestries or PRSs derived from other GWASs focused on neurodegenerative diseases. The (4) model involved the application of Elastic Net regularization, which combines the benefits of both Lasso (L1) and Ridge (L2) regression methods to enhance model stability and variance handling. This technique aids in variable selection by reducing the coefficients of less relevant variables to zero, simplifying the model, and improving its ability to manage multicollinearity 30 . The hyperparameter α, which balances L1 and L2 regularization, was optimized using a grid search to maximize the penalized likelihood within each training set.

As part of our sensitivity analysis, we assessed the performance of various non-linear models incorporating different regularization techniques, including GBM 72 and XGBoost 73 . Hyperparameter optimization was also performed using a grid search approach for each model within each training set.

We employed a 5-fold cross-validation methodology across all models to evaluate performance, with final results reported on the combined hold-out testing sets (Fig.  3 ). To enhance the robustness of our findings, we utilized bootstrapping 74 to determine feature importance, determine confidence intervals (CIs), and establish statistical significance. Specifically, we repeated the modeling process 1000 times using random sampling with replacement of all subjects (cases and controls) within the analytical sample set of each GIA group.

The primary assessment criterion was the AUPRC, chosen for its suitability in scenarios involving imbalanced datasets where the number of cases is significantly outnumbered by controls 75 . Additionally, the AUROC was reported as a comprehensive metric for model evaluation. To determine the optimal threshold, we selected the point that maximized the MCC 45 . Subsequent performance metrics, such as the F1 score, accuracy, precision, recall, and specificity, were computed based on this threshold.

To compare models, we calculated DeLong test p -values 76 , which are specifically tailored for comparing two AUROC values derived from identical observations. Given the lack of an equivalent test for AUPRC comparisons, we employed the paired Wilcoxon signed-rank test 77 to compare AUPRC using the bootstrapping results.

We conducted a validation study using the All of Us cohort to assess the generalizability of our findings derived from the UCLA ATLAS sample. A comparable sample was selected, adhering to the same criteria and sampling scheme for the GIA groups as in the UCLA ATLAS sample. We employed the same methodologies to define dementia cases and controls, extracting the same genetic risk loci from the All of Us Whole Genome Sequencing data for PRS construction or those identified through Elastic Net models in the UCLA ATLAS sample. Consistent methodologies were used to regress out demographic variables and genetic population structure (i.e., PCs) to derive offset corrections, ensuring statistical models accurately reflect intrinsic genetic associations without confounding from demographic or population genetic structure.

In the All of Us sample, we compared three models: (1) the APOE-ε4 model; (2) the best-performing PRS model; and (3) the best-performing Elastic Net SNP model. The same evaluation metrics were utilized for model comparisons.

Gene mapping and gene set analysis

We further examined the features selected from the Elastic Net SNP models. During bootstrapping, each iteration potentially identified a subset of SNPs as important features contributing to the dementia prediction. SNPs consistently identified in at least 95% of the 1000 bootstrap iterations were retained. To facilitate biological interpretations, we employed FUMA’s positional, eQTL, and chromatin interaction mapping to associate dementia risk SNPs from the top-performing Elastic Net SNP models with specific genes 31 . These mapped genes were tested against gene sets procured from MsigDB, including positional gene sets and GO gene sets, to assess the enrichment of biological functions through hypergeometric tests., The Benjamin-Hochberg adjustment was applied to correct for multiple testing 78 .

Statistics and reproducibility

The study included diverse genetic ancestry groups: Hispanic Latino American (610 patients, 126 cases), African American (440 patients, 84 cases), and East Asian American (673 patients, 75 cases). Sample sizes were chosen based on availability and representativeness from UCLA Health records and the All of Us cohort.

Each sample was treated as an independent replicate. Analyses were conducted with appropriate statistical methods to ensure validity and reproducibility. The robustness of the findings was further confirmed through cross-validation techniques and comparison with established models ( APOE and PRSs).

To ensure reproducibility, we adhered to rigorous data handling and processing standards, with detailed documentation of data sources, processing steps, and analysis pipelines. All codes and scripts used in the analysis are available online and upon request for verification and replication purposes.

Reporting summary

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

Data availability

The Genome-Wide Association Study summary statistics data analyzed in this study are publicly available. Individual electronic health record data are not publicly available due to patient confidentiality and security concerns. Collaboration with the study authors who have been approved by UCLA Health for Institutional Review Board-qualified studies are possible and encouraged. Individual data from All of Us are publicly available for qualified researchers per the National Institutes of Health.

Code availability

Codes are publicly available on GitHub: https://github.com/TSChang-Lab/Dementia-prediction ( https://doi.org/10.5281/zenodo.12754446 ) 79 . Requests for additional information can be directed to the Lead Contact: Timothy S. Chang ([email protected]).

Abbreviations

Description

African American

Alzheimer’s disease

Apolipoprotein E

area under the precision-recall curve

area under the receiver operating characteristic

Combined Annotation-Dependent Depletion

confidence intervals

European American

East Asian American

Electronic Health Record

Frontotemporal dementia

Functional Mapping and Annotation of Genome-Wide Association Studies

Genetic Inferred Ancestry

Gene Ontology

Genome-Wide Association Studies

Hispanic Latino American

Lewy body dementia

Linkage disequilibrium

Matthews Correlation Coefficient

principal components

Parkinson’s disease

Polygenic risk score

South Asian American

Single-Nucleotide Polymorphism

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Acknowledgements

M.F., L.V.B., S.S.W., and T.S.C. was supported by the National Institutes of Health (NIH) National Institute of Aging (NIA) grant K08AG065519-01A1, UH2AG083254, the Hillblom Foundation, and the Fineberg Foundation. T.S.C. was supported by U54NS123746 and the California Department of Public Health. K.V. was supported by NIH grants R01 NS033310, R01 AG058820, R01 AG075955, and R56 AG074473, and UH2 AG083254. B.P. was supported by NIH grants R01HG009120, R01MH115676, and R01HG006399. We gratefully acknowledge the resources provided by the Institute for Precision Health (IPH) and participating UCLA ATLAS Community Health Initiative patients. The UCLA ATLAS Community Health Initiative in collaboration with UCLA ATLAS Precision Health Biobank, is a program of IPH, which directs and supports the biobanking and genotyping of biospecimen samples from participating UCLA patients in collaboration with the David Geffen School of Medicine, UCLA CTSI and UCLA Health. We would also like to acknowledge all participants and researchers at the All of Us program. The All of Us Research Program is supported by the National Institutes of Health, Office of the Director: Regional Medical Centers: 1 OT2 OD026549; 1 OT2 OD026554; 1 OT2 OD026557; 1 OT2 OD026556; 1 OT2 OD026550; 1 OT2 OD 026552; 1 OT2 OD026553; 1 OT2 OD026548; 1 OT2 OD026551; 1 OT2 OD026555; IAA #: AOD 16037; Federally Qualified Health Centers: HHSN 263201600085U; Data and Research Center: 5 U2C OD023196; Biobank: 1 U24 OD023121; The Participant Center: U24 OD023176; Participant Technology Systems Center: 1 U24 OD023163; Communications and Engagement: 3 OT2 OD023205; 3 OT2 OD023206; and Community Partners: 1 OT2 OD025277; 3 OT2 OD025315; 1 OT2 OD025337; 1 OT2 OD025276.

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Fu, M., Valiente-Banuet, L., Wadhwa, S.S. et al. Improving genetic risk modeling of dementia from real-world data in underrepresented populations. Commun Biol 7 , 1049 (2024). https://doi.org/10.1038/s42003-024-06742-0

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Nearly Half of Dementia Cases Can Be Prevented or Delayed

Science suggests 14 ways to forestall cognitive decline..

Updated August 27, 2024 | Reviewed by Hara Estroff Marano

• What Is Dementia?
• Take our Memory Test
• Find counselling to help with dementia
• The vast majority of adults worry about cognitive decline.
• Vision loss and high cholesterol levels are newly added to the list of modifiable risk factors for dementia.
• Fourteen health modifications can prevent or delay 45% of all dementia cases.
• Education, exercise, social ties—all are key to protecting brain health from an early age.

Three-quarters of adults age 40 and older are concerned about their brain health declining in the future, according to an AARP survey of 1,563 adults.

Many older adults try strategies like doing crossword puzzles and taking supplements to stave off dementia , but do such approaches actually work?

Research shows that potentially 45% of dementia cases can be prevented or delayed through a series of personal and societal changes.

A new report in the journal Lancet , dated July 31, 2024, highlights two new modifiable risk factors for dementia, bringing the known total to 14.

14 Evidence-Based Modifiable Risk Factors for Dementia

According to the 2024 report, by the Lancet Commission on dementia, highlighling prevention, intervention, and care, there are 14 evidence-based modifiable risk factors for dementia. They include:

• Less education
• Head injury
• Physical inactivity
• Excessive alcohol consumption
• Hypertension
• Hearing loss
• Infrequent social contact
• Air pollution
• Vision loss (new)
• High cholesterol (new)

Modifying all 14 risk factors would potentially delay or prevent a remarkable 45% of all dementia cases whether or not a person has the APOE gene (the Alzheimer's gene).

​Now that we know the modifiable risk factors for preventing or delaying dementia, here's what the Lancet Commission recommends that you do to prevent or delay dementia.

Specific Recommendations to Prevent or Delay Dementia

• Ensure that good-quality education is available for all, and encourage cognitively stimulating activities in midlife to protect cognition .
• Make hearing aids accessible to people with hearing loss, and decrease harmful noise exposure to reduce hearing loss.
• Treat depression effectively.
• Encourage use of helmets and head protection in contact sports and on bicycles.
• Encourage exercise, because people who participate in sport and exercise are less likely to develop dementia.
• Reduce cigarette smoking through education, price control, and preventing smoking in public places, and make smoking cessation advice accessible.
• Prevent or reduce hypertension and maintain systolic blood pressure of 130 mm Hg or less from age 40 on.
• Detect and treat high LDL cholesterol from midlife.
• Maintain a healthy weight and treat obesity as early as possible, which also helps to prevent diabetes.
• Reduce high alcohol consumption through price control and increased awareness of levels and risks of overconsumption.
• Prioritize age-friendly and supportive community environments and housing, and reduce social isolation by facilitating participation in activities and living with others.
• Make screening and treatment for vision loss accessible for all.
• Reduce exposure to air pollution.

The Lancet Commission also recommends being ambitious about prevention starting early in life and continuing throughout life.

Did You Notice Something Missing from the List?

Sleep. Anxiety . PTSD . Severe mental illness. Diet . Infection. Menopause .

​The Lancet Commission cites these domains as potential risk factors as well, noting that each is correlated with dementia. However, at present, there is not enough research to prove that they are causal of dementia.

That still leaves many opportunities for action by mental health providers, senior care providers, friends, family, and individuals.​

Take the Challenge

Review the list of recommendations above and choose one domain to begin to make changes with in your own life. Then share this list with others so that each and every one of us has the best chance to have optimal cognitive health as we age.

Livingston, G., Huntley, J., Liu, K. Y., Costafreda, S. G., Selbæk, G., Alladi, S., Ames, D., Banerjee, S., Burns, A., Brayne, C., Fox, N. C., Ferri, C. P., Gitlin, L. N., Howard, R., Kales, H. C., Kivimäki, M., Larson, E. B., Nakasujja, N., Rockwood, K., Samus, Q., … Mukadam, N. (2024). Dementia prevention, intervention, and care: 2024 report of the Lancet standing Commission. Lancet (London, England) , 404 (10452), 572–628. https://doi.org/10.1016/S0140-6736(24)01296-0

Skufca, Laura. 2015 Survey on Brain Health. Washington, DC: AARP Research, October 2015. https://doi.org/10.26419/res.00114.001

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Alzheimer’s disease: a review on the current trends of the effective diagnosis and therapeutics

Aimi syamima abdul manap.

1 Department of Biomedical Science, College of Veterinary Medicine, King Faisal University, Al-Ahsa, Saudi Arabia

Reema Almadodi

2 Faculty of Pharmacy and Biomedical Sciences, MAHSA University, Selangor, Malaysia

Shirin Sultana

Maheishinii grace sebastian, kenil sureshbhai kavani, vanessa elle lyenouq, aravind shankar.

Minhong Neenah Huang, Mayo Clinic, United States

The most prevalent cause of dementia is Alzheimer’s disease. Cognitive decline and accelerating memory loss characterize it. Alzheimer’s disease advances sequentially, starting with preclinical stages, followed by mild cognitive and/or behavioral impairment, and ultimately leading to Alzheimer’s disease dementia. In recent years, healthcare providers have been advised to make an earlier diagnosis of Alzheimer’s, prior to individuals developing Alzheimer’s disease dementia. Regrettably, the identification of early-stage Alzheimer’s disease in clinical settings can be arduous due to the tendency of patients and healthcare providers to disregard symptoms as typical signs of aging. Therefore, accurate and prompt diagnosis of Alzheimer’s disease is essential in order to facilitate the development of disease-modifying and secondary preventive therapies prior to the onset of symptoms. There has been a notable shift in the goal of the diagnosis process, transitioning from merely confirming the presence of symptomatic AD to recognizing the illness in its early, asymptomatic phases. Understanding the evolution of disease-modifying therapies and putting effective diagnostic and therapeutic management into practice requires an understanding of this concept. The outcomes of this study will enhance in-depth knowledge of the current status of Alzheimer’s disease’s diagnosis and treatment, justifying the necessity for the quest for potential novel biomarkers that can contribute to determining the stage of the disease, particularly in its earliest stages. Interestingly, latest clinical trial status on pharmacological agents, the nonpharmacological treatments such as behavior modification, exercise, and cognitive training as well as alternative approach on phytochemicals as neuroprotective agents have been covered in detailed.

Introduction

Alzheimer’s disease (AD) is a neurodegenerative disorder that leads to the deterioration of brain cells. It is the primary cause of dementia, which is marked by a decline in cognitive abilities and a loss of independence in daily tasks ( Porsteinsson et al., 2021 ). Over 35 million individuals worldwide suffer from AD, and by 2050, the disease’s incidence is predicted to quadruple ( Tiwari et al., 2019 ). Presently, China and the growing Western Pacific, Western Europe, and the United States are the countries or regions most affected by the situation ( Li et al., 2022 ). The World Health Organization (WHO) has designated AD, a condition that mostly affects the elderly and is frequently linked to dementia, as a global health public priority. Because AD progresses in the latent form of the neuropathological process, it presents one of the greatest difficulties to modern neuroscience and medical diagnosis ( Nasreddine et al., 2023 ). The accumulation of abnormal aggregates in the brain called amyloid plaques and tangles of fiber bundles called neurofibrillary (NFTs) are the hallmark of AD ( Zhao, 2020 ). The accumulation of aggregated amyloid beta (Aβ) plaques in the brain begins around 20 years before the onset of cognitive decline in AD, and this can be attributed to either defective clearance of Aβ or excessive production ( Serrano-Pozo et al., 2011 ). The accumulation of hyperphosphorylated tau protein leads to the formation of NFTs, which can be detected a decade to fifteen years before the onset of symptoms ( Bateman et al., 2012 ; Jack et al., 2018 ; Figure 1 ).

Alzheimer’s pathogenesis based on two classical hallmarks on amyloid beta and neurofibrillary tangles.

In 2018, the National Institute on Aging and Alzheimer’s Association (NIA-AA) revised their diagnostic criteria for AD and transitioned from a clinical to a biological perspective on the disease ( Jack et al., 2018 ). AD progresses along a continuum, starting with a phase when there are no symptoms but there is evidence of AD biomarkers (preclinical AD). It then progresses to a stage where there are minor cognitive abnormalities (mild cognitive impairment [MCI]) and/or neurobehavioral alterations (mild behavioral impairment [MBI]), and eventually leads to AD dementia. Several staging approaches have been devised to classify AD along this spectrum ( Dubois et al., 2010 ; Jack et al., 2018 ). Although the specific definitions of each stage may differ, all of these systems include the assessment of pathological Aβ and NFTs, as well as impairments in cognition, function, and behavior ( Dubois et al., 2010 ; Jack et al., 2018 ).

The terminology used to describe each stage may differ across different clinical and research classifications. Figure 2 presents a concise overview of the many naming standards employed in the AD community, along with the corresponding symptoms at each step of the continuum. MBI refers to the development of persistent and significant neuropsychiatric symptoms in individuals aged 50 years or older, before experiencing cognitive decline and dementia ( Ismail et al., 2017 ). Preclinical AD, which is the first stage in the AD progression, involves a prolonged period without symptoms, during which patients show signs of AD pathology but do not experience any cognitive or functional deterioration, and their everyday activities remain unchanged ( Dubois et al., 2010 ; Figure 2 ). The length of preclinical AD can vary among individuals but generally spans from 6 to 10 years, contingent upon the age at which symptoms first appear ( Insel et al., 2019 ; Vermunt et al., 2019 ). The likelihood of transitioning from preclinical AD to MCI caused by AD, with or without MBI, is influenced by various characteristics such as age, gender, and apolipoprotein E (ApoE) status ( Insel et al., 2019 ; Vermunt et al., 2019 ). However, it is important to note that not all persons with underlying AD pathology will eventually acquire MCI or AD dementia ( Knopman et al., 2003 ; Bennett et al., 2006 ). A recent meta-analysis of six longitudinal cohorts, with an average follow-up period of 3.8 years, revealed that 20% of individuals with preclinical AD developed MCI as a result of AD ( Vermunt et al., 2019 ). In a subsequent investigation conducted by Cho et al. (2021) , with an average rate of observation spanning 4 years, it was discovered that 29.1% of individuals diagnosed with preclinical AD experienced a progression to MCI as a result of AD.

The AD continuum can be categorized into various stages, ranging from preclinical AD to severe AD dementia. The terminology used to describe each stage can vary based on the specific clinical and scientific classifications. This diagram presents an overview of the naming standards employed in the AD community, along with the symptoms associated with each stage of the continuum. Aβ, amyloid beta; AD, Alzheimer’s disease; FDA, Food and Drug Administration; IWG, International Working Group; MCI, mild cognitive impairment; NIA-AA, National Institute on Aging—Alzheimer’s Association. Adaptation and modification from Porsteinsson et al. (2021) .

In individuals who develop MCI as a result of AD, the first noticeable symptoms usually involve difficulties with short-term memory. This is then followed by a gradual loss in other cognitive abilities in other areas ( Kazim and Iqbal, 2016 ; Figure 2 ). Individuals with MCI caused by AD may experience difficulties in daily activities such as finding appropriate words (language), remembering recent discussions (episodic memory), completing familiar tasks (executive function), or navigating familiar environments (visuospatial function) ( Kazim and Iqbal, 2016 ; Tolbert et al., 2019 ). Due to differences in coping techniques and cognitive reserve, patients have diverse experiences and symptoms. Nevertheless, patients generally maintain a reasonable level of independence during this stage, even though they may have minor impairments in function. The outlook for patients with MCI caused by AD can be unpredictable. A study that monitored individuals with MCI caused by AD for an average of 4 years discovered that 43.4% of them developed AD dementia ( Cho et al., 2021 ). Additional research findings indicate that 32.7% and 70.0% of persons diagnosed with MCI caused by AD develop AD dementia within 3.2 and 3.6 years of observation, respectively ( Roberts et al., 2018 ; Ye et al., 2018 ). Individuals who advance to AD dementia will experience significant cognitive impairments that hinder their ability to engage in social interactions and necessitate help with everyday tasks ( Jack et al., 2018 ). As the condition advances, more pronounced behavioral symptoms will arise, imposing a substantial load on both patients and their caretakers. Ultimately, the disease leads to a profound decline in independence and necessitates constant care.

Early diagnosis of AD is essential in order to facilitate the development of disease-modifying and secondary preventive therapies prior to the onset of symptoms ( Nasreddine et al., 2023 ). There has been a notable shift in the goal of the diagnosis process, transitioning from merely confirming the presence of symptomatic AD to recognizing the illness in its early, asymptomatic phases. Validating biomarkers as accurate indicators of AD pathology would allow them to be utilized as diagnostic tools, eliminating the need for brain samples or autopsies to confirm an accurate diagnosis ( Lee et al., 2019 ). The NIA-AA has classified diagnostic biomarkers for AD into three categories: Aβ-Aβ deposits (A), hyperphosphorylated tau aggregates (T), and neurodegeneration or neuronal damage (N). The ATN categorization based on NIA-AA research framework is displayed in Table 1 (Jack et al., 2018 ). The AD continuum is associated with one of the following biomarker profiles: A + T- N-, A + T+ N-, A + T+N +, or A + T-N +, regardless of any clinical symptoms ( Jack et al., 2018 ). Evaluation of the ATN profile is conducted using biofluids, such as cerebrospinal fluid (CSF), or imaging techniques, such as Positron Emission Tomography (PET). The objective of the present study is to provide extensive reviews on comprehensive diagnostic and therapeutic approaches grounded in a precisely defined ATN model that corresponds to the AD continuum. Additionally, the details on the most recent clinical trials involving pharmacological agents employed in therapeutic strategies have been presented. In the future, it will be crucial to investigate novel biomarkers that extend beyond the amyloid and tau pathologies, as well as the longitudinal evolution of these biomarkers throughout the course of AD.

Biomarker profiles and categories.

AD, Alzheimer’s disease.

Diagnostic process

The process of diagnosing AD can be categorized into the subsequent stages: identification, evaluation/differentiation, diagnosis, and treatment. Clinicians must employ suitable diagnostic techniques when examining a patient who is suspected of having AD in its initial phases.

Identification

In the context of dementia, the initial step of diagnosis does not involve executing tests but rather involves developing a suspicion that a dementia syndrome may be developing (referred to as the trigger phase). An issue that arises with dementia is the hesitancy of certain patients, families, and primary care physicians to make a diagnosis. Dementia is a severe and mostly unchangeable disease that is associated with a significant amount of social disgrace. Physicians may inadvertently hesitate to diagnose a patient with a specific condition ( Downs and Bowers, 2008 ), and family members may gradually assume the social responsibilities of the patient without being fully aware of their actions. This unintentionally shields the patient from worsening in their daily life, but also delays the conscious acknowledgment of the disorder by compensating for the impairments ( De Lepeleire et al., 1998 ; Iliffe et al., 2009 ). To confirm the presence of symptoms related to AD, the healthcare provider must perform an initial examination on patients who display even minor symptoms. This assessment should utilize a validated tool for detecting early-stage AD as discussed below.

Evaluation of a memory complaint

Clinical assessment tool.

Clinical assessment tools for evaluating memory complaints often include a combination of interviews, questionnaires, and cognitive tests. These tools help healthcare professionals gather information about a person’s memory concerns and assess their cognitive function. Nasreddine et al. (2023) reported a number of common and recent scales used in early diagnosis of AD include Mini-Cog, (MMSE) Mini-Mental State Examination, and MoCA. Those generally used in primary care and they are varying in sensitivity ( Nasreddine et al., 2023 ). MMSE is used primarily for assessing overall cognitive function including memory-related questions. It assesses orientation, attention, calculation, recall, and language, providing insights into memory and other cognitive domains. Therefore, it is low sensitivity compared to MoCA high sensitivity which assesses various cognitive domains, including memory. It includes tasks related to immediate and delayed recall, as well as other memory-related exercises ( Nasreddine et al., 2023 ). The summarization on the screening tools utilized in the early diagnosis of AD is demonstrated in Table 2 .

Summarisation of the screening tools utilized in the early diagnosis of AD.

Furthermore, a variety of neuropsychological tests have been used in previous research to assess AD. Recently, the Rey Auditory Verbal Learning Test (RAVLT) is a widely used neuropsychological assessment tool designed to evaluate various aspects of verbal memory and learning in AD and other forms of dementia ( Warren et al., 2023 ). Based on previous investigation studies, the RAVLT performance in individuals with memory complaints reflects well the underlying pathology caused by AD. As a result, RAVLT is an effective early marker for detecting AD in people who have memory problems ( Moradi et al., 2017 ). AD is commonly diagnosed with the clinical dementia rating (CDR) scale. Recently, interventional trials have emphasized the sum of boxes of the clinical dementia rating-sum of boxes (CDR-SB) to track the progression of cognitive impairment (CI) in the early stages of AD. As the stages of predementia progress, researchers have developed practical tools for measuring the deterioration of cognition or daily function. A recent study was conducted in Taiwan by Tzeng et al. (2022) have assessed the predictive value of the CDR-SB and CDR to widely employed AD staging tools, by investigating how they contribute to the process or reversion in individuals without dementia ( Tzeng et al., 2022 ). There were 1,827 participants observed, and during evaluation, a trained physician scored six cognitive or functional domains, including memory, orientation, judgment, community affairs, home hobbies, and personal care, following interviews with both participants and informants. Consequently, performance and function-based information are simultaneously acquired. This study’s significant results deserve special attention. Findings were found such as the CDR-SB tool has an excellent predictive value in detecting the onset of dementia in people without dementia. In addition, an increase in CDR-SB scores was associated with higher conversion rates, and the prediction power of CDR-SB levels was very good. CDR-SB is a reliable and global diagnostic tool. Additionally, it is very sensitive in detecting the disease progression among participants with different levels of disease severity ( Tzeng et al., 2022 ). However, in clinical trials, it is not capable of consistently detecting treatment effects ( Wessels et al., 2022 ).

Other cognitive scale used in clinical practice neuropsychological tests to detect AD is AD Assessment Scale–Cognitive (ADAS-Cog), which measures cognitive deficits, such as memory, language, and praxis. Cogo-Moreira et al. (2023) stated that the ADAS-Cog has been a prominent assessment tool and has been widely used in investigations of AD since its establishment in 1984 ( Cogo-Moreira et al., 2023 ). Due to its wide application, many studies have been conducted to evaluate, improve, and optimize ADAS-Cog for various uses (i.e., ADAS-11 and ADAS-13), as well as to serve as indicator of AD progression (ADAS-Cog) ( Kueper et al., 2018 ; Cogo-Moreira et al., 2023 ; Warren et al., 2023 ). Based on a study relating to ADAS-Cog, they hypothesized that different stages can be predicted of AD continuum with ADAS-13. This hypothesis was proven based on a research study that ADAS-11 could effectively distinguish between those with cognitive impairment and those with early AD ( Zainal et al., 2016 ). Similarly, some modifications have been made to ADAS-Cog-11, including assessments of executive function, improved scoring methodology, delayed recall and/or everyday functioning in order to detect early signs of cognitive decline preceding dementia ( Kueper et al., 2018 ). From all these findings shown, employing the ADAS-13 in clinical practice should be used to assess cognitive function when patients present with minor memory problems, as it distinguishes between the levels of cognitive function associated with different stages of AD. Basically, the scores can range from 0 to 70 for ADAS-Cog-11, with higher scores indicating greater cognitive impairment and scores are from 0 to 85 for ADAS-Cog-13, which takes approximately 30–45 min ( Clarke et al., 2022 ).

A comprehensive study done by Warren et al. (2023) which evaluating the ability of the most commonly used neuropsychological tests to screen AD. Moreover, it focuses on its ability to differentiate and distinguish of AD disease ( Warren et al., 2023 ) such as cognitively normal (CN), Subjective Memory Complaints (SMC), and MCI. Basically, the study included a total of 595 participants with AD. The screening tools include The Everyday Cognition Questionnaire (ECog), the Rey Auditory Verbal Learning Test (RAVLT), the Functional Abilities Questionnaire (FAQ), the AD Assessment Scale–Cognitive Subscale (ADAS-Cog), the Montreal Cognitive Assessment scale (MoCA), and the Trail Making test (TMT-B) as summarized in Table 3 . Interestingly, the study’s outcomes and results point out that screening tools such as ADAS-13, RAVLT (learning), FAQ, ECog, and MoCA all predicated the progression of AD. Furthermore, TMT-B and the RAVLT were not specific for predicting AD in contrast with ECog that showed a very strong predictor tool into screen AD progression. Finally, the author recommends and suggests using ECog (both versions), RAVLT (learning), ADAS-13, and the MoCA to screen AD in all stages.

Summarisation of advantages and disadvantages of screening tools employed in AD.

Structural imaging

Structural imaging techniques, such as MRI, offer valuable clinical insights when examining the underlying factors contributing to cognitive decline ( Harper et al., 2013 ). MRI is commonly performed to rule out other potential factors contributing to cognitive decline, rather than to confirm a diagnosis of AD ( Frisoni et al., 2017 ). Structural MRI utilizes powerful magnets and radio waves to generate detailed brain images. It enables the measurement of brain tissue volume and the identification of structural alterations associated with AD. Alzheimer’s patients often exhibit atrophy, or shrinkage, in key brain regions, particularly the hippocampus and the entorhinal cortex, both critical for memory and learning ( Vogel et al., 2019 ). MRI can detect Alzheimer’s by measuring brain tissue volume in these regions; a reduction in volume may indicate the disease. Additionally, MRI can identify Alzheimer’s by spotting changes in brain structure indicative of the presence of amyloid plaques and NFTs, the two key Alzheimer’s biomarkers. Trained radiologists use MRI to detect Alzheimer’s by observing signs such as atrophy in the hippocampus and entorhinal cortex, enlarged ventricles, white matter hyperintensities, microbleeds, amyloid plaques and neurofibrillary tangles.

PET is an imaging method that employs radioactive tracers to gauge the activity of specific molecules in the body. PET is instrumental in measuring the levels of Aβ and tau protein in the brain, both of which form plaques in the brains of individuals with AD ( Zhang et al., 2021 ). Fludeoxyglucose positron emission tomography (FDG-PET) is another non-invasive imaging technique that employs a radioactive tracer called fluorodeoxyglucose (FDG) to measure glucose metabolism levels in the brain ( Young et al., 2020 ). Glucose serves as the primary energy source for the brain, and FDG-PET assesses how efficiently the brain is functioning. FDG-PET is not advisable for diagnosing preclinical AD in patients due to the inability to determine if the hypometabolism is directly linked to AD pathology ( Dubois et al., 2016 ). However, clinicians may consider referring patients with more pronounced symptoms for an FDG-PET scan to detect areas of glucose hypometabolism and neurodegeneration that may suggest AD ( Frisoni et al., 2017 ).

Confirming AD pathology

In the field of contemporary healthcare, there have been significant advancements in the confirmation of AD pathology ( Hampel et al., 2018 ). AD, being a complex neurodegenerative condition, presents diagnostic challenges, underscoring the importance of early and precise detection. This multifaceted approach involves molecular investigations to identify genetic and protein markers, advanced imaging methods for observing structural and functional changes in the brain, and the examination of cerebrospinal fluid (CSF) for crucial biomarkers ( Bader et al., 2020 ). Although it is understood that pathological changes commence prior to the manifestation of symptoms, it is challenging to ascertain if the presence of biomarkers indicating pathophysiological changes in the preclinical phase definitively indicates the development of clinical disease in an individual’s lifetime. Single biomarkers do not offer solid prognostic data. In recent times, there have been efforts to enhance the precision of diagnoses and the capability to anticipate individuals who are prone to experiencing clinical symptoms by considering a combination of biomarker discovery.

Jack and colleagues suggested that diagnosis should consider the presence and absence of the biomarkers categorized as amyloid, tau, and neurodegeneration (A/T/N) ( Jack et al., 2016 ). This novel descriptive of ATN classification for AD has been recently developed to prioritize the pathological and physiological factors above traditional clinical measurements like cognitive test scores ( Jack et al., 2016 , 2018 ). In the ATN system, subjects are classified into three binary categories: amyloid burden, tau burden, and neurodegeneration. Each subject is assigned a rating of either normal (physiological, “−”) or abnormal (pathological, “+”). The resulting 8 groupings, each characterized by distinct combinations of biomarkers, span from A-T-N- (indicating the absence of pathology) to A+T+N+ (indicating the presence of pathology in all categories). There is a suggestion that any combination of ATN biomarkers with A+ indicates a pathogenic alteration associated with the AD continuum. Several recent research have investigated the potential of ATN to predict clinical progression and cognitive decline ( Altomare et al., 2019 ; Jack et al., 2019 ; Soldan et al., 2019 ; van Maurik et al., 2019 ; Yu et al., 2019 ). In this subsection, diagnostic biomarker based on the ATN model, along with emerging biomarkers will be discussed.

Biomarkers Aβ and pathologic tau (AT classification)

The biomarkers in the A+ group indicate the presence of aggregated Aβ ( Baldeiras et al., 2022 ). Aβ peptides are produced through the enzymatic cleavage of APP by β- and gamma-secretases. While there are many isoforms of Aβ, almost 90% of the Aβ peptides present in the brain are either Aβ (Aβ1-40) or Aβ (Aβ1-42). Aβ1-42 constitutes the primary constituent of senile plaques. An increased abundance of senile plaques is essential for a neuropathological diagnosis of AD. Senile plaques can be detected through the use of cortical amyloid PET ligand binding ( Beach, 2022 ). Additionally, cerebral Aβ aggregation can be identified by measuring Aβ1-42 and Aβ1-40 levels in CSF using non-radioactive, antibody-based techniques like ELISA ( Camporesi, 2021 ). An inherent trait of early AD is a decrease in CSF levels of Aβ1-42, likely caused by the accumulation of the peptide in senile plaques. However, certain studies have indicated that the ratio of CSF Aβ (Aβ1-42)/(Aβ1-40) may serve as a more reliable measure of Aβ production and aggregation, as opposed to solely examining Aβ1-42 levels ( Niemantsverdriet et al., 2017 ).

The biomarkers observed in the T+ group indicate the presence of aggregated Tau ( Baldeiras et al., 2022 ). Tau proteins are soluble microtubule-associated proteins (MAPs) that strongly stabilize axonal microtubules. Tau undergoes hyperphosphorylation in AD, resulting in its detachment from microtubules ( Alonso et al., 2018 ). Unconstrained, excessively phosphorylated Tau is susceptible to enzymatic breakdown, as well as self-assembly into harmful clusters and ultimately forming paired helical filaments (PHFs) and NFTs. Cortical Tau PET ligand binding can detect aggregated Tau, particularly PHFs ( Okamura et al., 2018 ). Nevertheless, research has demonstrated that the presence of phosphorylated Tau in CSF is indicative of Tau disease ( Wattmo et al., 2020 ). Neurons that have NFTs emit phosphorylated Tau, which can be quantified in the CSF by antibody-based immunoassays. More than 40 locations on Tau have been demonstrated to undergo phosphorylation in AD; yet Tau phosphorylated at threonine 181 (pTau181) is among the most extensively studied phosphorylated Tau indicators ( Suárez-Calvet et al., 2020 ). Studies have demonstrated that CSF levels of pTau181 are increased in persons with AD and are strongly associated with the extent of Tau pathology observed after death ( Shoji, 2019 ; Thijssen et al., 2020 ). Furthermore, this biomarker has demonstrated a high level of specificity for AD since elevated levels of CSF pTau181 are not observed in other tauopathies. The investigation of phosphorylated Tau at serine 199 (pTau199) and threonine 231 (pTau231) as possible biomarkers is ongoing ( Lewczuk et al., 2004 ). The levels of pTau199 and pTau231 in the CSF are strongly associated with pTau181 CSF levels and demonstrate comparable diagnostic accuracy ( Shoji, 2019 ). Apart from that, cross-sectional studies covering the entire clinical AD continuum have revealed that plasma isoforms p-tau181 and p-tau217 may distinguish amyloid-PET or tau-PET positive cases from amyloid-PET or tau-PET negative cases. These cross-sectional investigations have also demonstrated that plasma p-tau levels can identify patients with AD dementia from those with frontotemporal lobar degeneration ( Janelidze et al., 2020 ; Karikari et al., 2020 ; Palmqvist et al., 2020 ; Thijssen et al., 2020 ). Mattsson-Carlgren et al. (2020) enrolled 250 non-demented participants from the Swedish BioFINDER study and employed the “Meso Scale Discovery” (MSD) Eli Lilly immunoassay to quantify p-tau217 levels at baseline and during follow-up. The findings revealed that patients in the preclinical and early clinical stages of AD have higher levels of p-tau217 than cognitively healthy controls. Furthermore, higher p-tau217 levels were linked to an increased likelihood of developing AD dementia, as well as faster rates of cognitive decline and thinning of the temporal cortex and hippocampus ( Mattsson-Carlgren et al., 2020 ).

Recently, many studies have shown that CSF p-tau along with Aβ42, and t-tau together are the key biomarkers for AD. For instance, Suárez-Calvet et al. (2020) reported CSF p-tau is major prognosis marker in AD as it distinguishes dementia associated with AD from cognitively unimpaired (CU) and MCI, also CSF p-tau is useful for disease staging ( Suárez-Calvet et al., 2020 ). Furthermore, the biomarker’s robustness and reliability as an early diagnosis tool for AD is enhanced by the fact that Aβ plaque formation occurs years, if not decades, prior to the onset of symptoms ( Bălaşa et al., 2020 ). The measurement of Aβ and tau proteins in CSF continued to be a focus till now. In another study done by Teunissen et al. (2018) have been included an Aβ42 as standard AD diagnostic guideline ( Teunissen et al., 2018 ). It has been shown that a fluctuation in CSF Aβ42 level occurs 10–20 years before the beginning of visible symptoms which make it helpful tool ( Teunissen et al., 2018 ). Moreover, in the same study showing that Aβ42 biomarker concentration differs in plasma from CSF that showed reduced levels in CSF compared to plasma high levels ( Teunissen et al., 2018 ). This could be due to the blood-brain barrier (BBB) accessibility and transportation of all biomolecules, and high levels of Aβ42 found in plasma due to avoiding accumulating Aβ42 in in the brain (clearance system). In addition, integrating the Aβ42/Aβ40 and Aβ42/Aβ38 ratios with T-tau and P-tau levels is likely the most advantageous method to develop a diagnostic tool based on these two biomarkers. This technique offers a sensitivity and specificity of approximately 85–95% ( Jin et al., 2019 ). Recently, blood-based biomarkers for AD, like tau and Aβ, have been incorporated by Rissman et al. (2024) into screening algorithms in an attempt to increase screening precision. They used plasma samples from the first group of participants screened for AHEAD and used immunoprecipitation liquid chromatography-tandem mass spectrometry (LC-MS/MS) to measure phosphorylated and non-phosphorylated forms of tau181 and tau217 alongside Aβ42 and Aβ40 in order to further validate plasma p-tau species as early AD biomarkers. This work aimed to predict brain amyloid PET status in cognitively unimpaired patients using MS measured plasma p-tau217, np-tau217, p-tau181, concentration ratios, and Aβ42/Aβ40 ratio data. The results show improved performance for the identification of amyloid PET positive cognitively unimpaired individuals using plasma p-tau217/np-tau217; however, the combination of plasma p-tau217 and Aβ42/Aβ40 ratios in a model that predicted cerebral amyloid PET status yielded the best performance, as indicated by AUC ( Rissman et al., 2024 ). Figure 3 depicts the process of neurodegenerative decline in the brain and the corresponding markers that are connected with it.

Illustration of neurodegenerative deterioration in the brain and associated indicators. As portrayed, brain damage could be caused by the deposition of amyloid beta protein in the brain, resulting in amyloid plaques, as well as the creation of neurofibrillary tangles among neurons. These alterations cause the loss of neurons in the cortex, the brain’s outer layer. The results imply that brain traumas sustained in the NfL may raise the incidence of AD.

Biomarkers of neurodegeneration or neuronal injury (N classification)

The biomarkers observed in the N+ group indicate the presence of neurodegeneration. Axonal degeneration is a prominent characteristic of AD and is more strongly associated with the beginning of cognitive impairment compared to other clinical aspects. Neurodegeneration in brains affected by AD can be identified by the use of FDG PET hypometabolism and MRI. Nevertheless, studies have demonstrated that persons with AD exhibit elevated levels of t-tau in the CSF, and these levels are strongly associated with the extent of neurodegeneration. However, neurodegeneration is not exclusive to AD and can be observed in various other illnesses affecting the neurological system. Nevertheless, when employed alongside other indicators, t-tau can offer crucial insights into an individual’s placement on the AD spectrum and the extent of their cognitive decline ( Alcolea et al., 2021 ).

Other promising biomarkers

Blood-based approaches.

Blood, in contrast to CSF, which requires lumbar puncture for accessibility, comprises less invasive body fluids and is readily accessible for the purposes of diagnosing, evaluating, and monitoring the progression of AD ( Villa et al., 2020 ). The Alzheimer’s Association recommends that specialized memory clinics may employ blood biomarkers to aid in diagnosing patients with cognitive impairment. Several blood biomarkers, such as plasma Aβ42, Aβ42/40 ratio, p-tau, t-tau, neurofilament light polypeptide (NfL), glial fibrillary acidic protein (GFAP), and soluble triggering receptor expressed on myeloid cells 2 (sTREM2), have been identified as potential biomarkers for AD ( Tao et al., 2023 ). However, despite significant research activity, a complete and up-to-date summary of the key blood-based biomarker candidates remains insufficient.

Early investigations employed enzyme linked immunosorbent assays (ELISA) immunoassays to evaluate the concentration of Aβ40 and Aβ42 in plasma as predictors of conversion to AD in patients with MCI. In this investigation, the plasma samples were obtained at baseline from two independent cohorts of patients with MCI and age-matched controls (prodromal stage). The results demonstrated a negative correlation with AD and the authors concluded that the CSF biomarkers are better predictors of progression to AD than plasma Aβ isoforms ( Hansson et al., 2010 ). Following this, a study utilizing single-molecule array (Simoa) was conducted to analyze plasma levels of Aβ42 and Aβ40 in a cohort of 719 individuals, including patients with subjective cognitive decline (SCD), MCI, AD dementia and cognitively healthy elderly. Results revealed a decrease in plasma Aβ42 concentration in individuals with AD compared to the control group. This study concluded that during the dementia stage of AD, plasma Aβ is markedly reduced, suggesting that significant alterations in Aβ metabolism take place later in the peripheral rather than in the brain ( Janelidze et al., 2016 ). Currently, there is insufficient data to support the use of plasma Aβ42/40 as a reliable method for distinguishing between AD and other forms of dementia. Given their limited availability and somewhat high cost, both Simoa and IP/MS based assays require further optimization in several aspects before they may be effectively employed for screening AD in large populations.

In addition to the CSF, several emerging biomarkers from tears and saliva, are being found to predict AD. The non-invasive, convenient, and cost-effective collection of saliva makes it an attractive marker for monitoring diseases. Besides that, CSF shows a relationship with saliva which proteins are secreted into saliva ( Ashton et al., 2019 ). The diagnostic performance of AD-specific salivary biomarkers has been included Aβ1-40, Aβ1-42, p-tau, t-tau and lactoferrin in many research and studies ( Pawlik and Błochowiak, 2021 ). Aβ levels were found and deposited in many body tissues including nasal mucosa, skin, and other gland, in addition to its main build up in the brain. Moreover, APP and Aβ are the mostly expressed in epithelia cells in saliva ( Pawlik and Błochowiak, 2021 ). Saliva Aβ1-42 levels biomarkers are specific as they can differentiate patients with AD, but not patients with other neurological disease such as Parkinson’s disease (PD). More importantly, it can be used to diagnose early stages of the disease, cognitive difficulties, the severity and progression of AD, and not merely as an approach of identifying AD, but to distinguish it from other neurodegenerative diseases ( Pawlik and Błochowiak, 2021 ). Other than Aβ salivary biomarkers which is abundant in salivary, lactoferrin also shown to have Aβ-binding properties and thus could play an important role in the pathophysiology of AD ( Farah et al., 2018 ). Although saliva can serve as a valuable source of markers, its composition may be influenced by factors such as the circadian cycle, flow rate, and timing of sample collection ( Farah et al., 2018 ). In addition, the presence of degradative enzymes leads to the instability of biomarker levels, necessitating the process of normalization.

The eyes have a close relationship with the brain, which considers tears as a potential source of biomarker for AD. And interestingly, the presence of Aβ plaques and tau deposits in the retina and lens has been recognized at the cellular level. Moreover, certain investigations have demonstrated a correlation between the accumulation of protein deposits in the eyes of individuals with AD and the formation of such deposits in the brain ( Kaštelan et al., 2023 ). The discovery of potential AD biomarkers in tear samples could be exceptionally useful for conducting screenings among the general public ( Majeed et al., 2021 ). Del Prete et al. (2021) conducted a study where they found increased quantities of Aβ42 protein in the tears of two healthy persons with a family history of AD (pre-clinical stage). This was determined using an immunocytochemistry technique ( Del Prete et al., 2021 ). The study discovered a clear correlation between the presence of Aβ42 in tears and the development of retinal plaques. This correlation was not observed in the tear samples of a healthy participant without a family history of the condition. Given that the individuals being studied exhibited no apparent clinical symptoms of AD, the discovery of Aβ42 in tear samples has the potential to be utilized for early Alzheimer’s diagnosis and for screening purposes. Gharbiya et al. (2023) conducted an analysis of the amounts of Aβ peptide Aβ1-42, the C-terminal fragment of amyloid precursor protein (APP-CTF), and p-tau in the tears of individuals with MCI, mild to severe AD, and healthy volunteers ( Gharbiya et al., 2023 ). Their investigation demonstrated that the concentration of tears Aβ1-42 could effectively distinguish both MCI and AD patients with a high degree of specificity (93%) and sensitivity (81%). Moreover, the study found no significant variations in the abundance of APP-CTF and p-tau in tear samples. As per their findings, assessing the levels of Aβ1-42 in tears could offer a minimally invasive approach for the early detection and diagnosis of AD. The presence of reduced Aβ1-42 levels in tears may represent a specific, sensitive, non-invasive, and cost-effective biomarker for the early identification of AD. More importantly, tears biomarkers hold great promise for enhancing diagnostic precision, tracking disease advancement, and assessing the effectiveness of treatments. Also, they are easily accessible, non-invasive, less costly compared with other diagnostic tools, and can be performed by healthcare practitioners without the need for specialized training ( Chaitanuwong et al., 2023 ).

MicroRNAs (miRNAs)

Another genetic potential biomarker for AD is miRNA, a small non-coding RNA molecules that play a role in regulating gene expression ( Nikolac Perkovic et al., 2021 ). A miRNA is a single-stranded RNA that is 19 to 24 nucleotides long and plays a major role in post-transcriptional gene silencing. Also, it is a very effective tool in early diagnosis of AD since miRNAs has been investigated as marker of AD pathogenesis ( Nikolac Perkovic et al., 2021 ). A wide range of peripheral circulation (serum, plasma, exosomes, whole blood, peripheral blood mononuclear cells) and CSF miRNAs are commonly detected. More importantly, brain tissue has been linked to non-circulating miRNAs ( Nikolac Perkovic et al., 2021 ). Accordingly, in a study by Zhang et al. (2019) , a meta-analysis of ten different studies illustrated that miRNA as an AD diagnostic biomarker have overall and diagnostic odds ratio of 14 (95% CI: 11–19) sensitivity 0.80 (95% CI: 0.75–0.83) and specificity 0.83 (95% CI: 0.78–0.86) which represents an accurate and reliable biomarker ( Zhang et al., 2019 ). Furthermore, miRNAs are highly promising indicators for diagnosing diseases. More recently, studies have been conducted on diagnostic efficiency and accuracy on AD patients and can be characterized healthy people from AD ( Hu et al., 2016 ; Lusardi et al., 2017 ; Zhao et al., 2020 ).

Pharmacological approach

Fda-approved drugs.

Prior to recent developments, patients with AD had access to only symptomatic treatments, such as acetylcholinesterase inhibitors. The most recent addition to this class of drugs is galantamine, which was approved by the US Food and Drug Administration (FDA) in 2001 ( Cronin, 2001 ). Another treatment option is memantine, a noncompetitive N-methyl-D-aspartate receptor antagonist, which received FDA approval in 2003 ( Zarotsky et al., 2003 ).

Acetylcholinesterase (AChE) inhibitors are employed for patients with mild cognitive impairment or mild dementia stage disease (stage 4 based on FDA classification) to impede ACh degradation and, in turn, boost neural cell function by increasing ACh levels ( Akıncıoğlu and Gülçin, 2020 ). The cholinergic theory has garnered significant attention and has been the subject of much research, leading to the development of three authorized drugs for the treatment of AD. Tacrine, a type of medication known as a cholinesterase inhibitor (ChEI), was initially granted approval by the FDA as the first therapy for the treatment of AD. However, its administration was subsequently terminated due to the adverse effects it posed on liver function, known as hepatotoxicity. At present, the three ChEIs employed in the therapeutic management of people with AD are donepezil, rivastigmine and galantamine ( Liu et al., 2019 ) Figure 4 shows current treatments involving ChEIs with mechanism. In general, ChEIs are commonly perceived to possess poor therapeutic efficacy and are primarily acknowledged for their moderate capacity in managing the symptoms associated with AD ( Kepp, 2012 ). However, additional evidence is being uncovered that suggests a more intricate mechanism underneath the cholinergic system. This mechanism has the ability to interact with other pathological aspects of AD, such as aberrant Aβ and tau cascade, inflammation, apoptosis, and imbalances in neurotransmitter and neurohormonal systems ( Wang and Zhang, 2018 ).

(A) Visualization of current drugs (donepezil, galantamine, rivastigmine) (B) mechanism of action of memantine for Alzheimer’s disease (AD). Adaptation from Breijyeh and Karaman (2020) .

Memantine is an uncompetitive and NMDA receptor antagonist that is approved for the treatment of moderate to severe AD (stage 5 and 6 based on FDA classification). Memantine modulates glutamate activity, preventing excessive stimulation that can lead to neuronal damage ( Figure 5 ). This drug can help manage symptoms and slow cognitive decline in later stages of the disease ( Schmidt, 2022 ). Even so, it should be noted that memantine exhibits restricted clinical effectiveness ( Matsunaga et al., 2015 ). Given this perspective, there is a notable interest in exploring novel moderate-affinity NMDAR antagonists that possess similar yet distinguishable pharmacological characteristics. In the recent past, a new polycyclic amine called RL-208 has been synthesized ( Companys-Alemany et al., 2020 ). This compound acts as a voltage-dependent, moderate-affinity, uncompetitive blocker of NMDA receptors. Its pharmacological and electrophysiological properties have been thoroughly investigated using in vitro methods ( Companys-Alemany et al., 2020 ). However, memantine has a number of potential adverse effects. Common side effects include headaches, dizziness, elevated blood pressure, drowsiness, restlessness, and hallucinations. Less often occurring side effects include asthenia, constriction, diarrhea, nausea, anorexia, coughing, and breathing problems ( Shafiei-Irannejad et al., 2021 ).

Summary of pharmacological approaches for AD involving amyloid-related DMTs strategies, tau-related DMT strategies and other DMTs mechanisms.

Over the course of nearly two decades, despite multiple clinical studies, the prospects for advancing novel therapy were desolate and discouraging ( Cummings et al., 2022 ). In June 2021, the FDA granted expedited approval to aducanumab (AduhelmTM), a monoclonal antibody (mAb) called anti-amyloid-β (Aβ) that specifically targets “protofibrils” in patients who have MCI-AD or who are in the mild dementia stage of the disease (stage 3 and stage 4 based on FDA classification). These protofibrils were first characterized in the 1990s by Walsh et al. (1997) and have since been recognized as important neurotoxins. Aducanumab has obtained the initial approval as a drug that targets the fundamental cause of AD, despite the presence of significant negative consequences. The FDA clearance sparked significant controversy due to the adverse effects such as brain swelling, small brain bleeding, headache and falls, as well as limited effectiveness data ( Kuller and Lopez, 2021 ).

In January 2023, the FDA granted expedited approval to lecanemab (LeqembiTM), a monoclonal antibody that targets anti-Aβ protofibrils, for its similar mechanism of action and side effects (stage 3 and stage 4 based on FDA classification). However, this time, there was less controversy surrounding the approval due to the clinical trial data clearly showing a reduction in the progression of memory loss ( Van Dyck et al., 2023 ). The FDA’s Accelerated Approval Program provides support for medications that effectively treat severe medical problems and demonstrate a predictive marker indicating clinical benefit. This approach expedites the process of bringing a medicine to market compared to the conventional approval method, however, it relies on predicting rather than demonstrating the clinical advantages. In July 2023, the FDA awarded full authorisation to lecanemab for the treatment of early-stage AD after conducting further examination. Lecanemab and aducanumab effectively eliminate toxic Aβ protofibrils from the brain affected by AD. However, their usage is associated with notable adverse effects known as amyloid-related imaging abnormalities (ARIA), which may potentially induce symptoms such as headaches, exacerbation of cognitive impairment, dizziness, visual impairment, nausea, and seizures. Furthermore, a meta-analysis of clinical studies investigating possible treatments for AD, such as aducanumab, lecanemab, and donanemab, discovered that monoclonal antibodies (mAbs) that produce ARIA may lead to an increased rate of brain shrinkage ( Alves et al., 2023 ). Hence, the ongoing struggle against AD persists, necessitating patients and their caregivers to meticulously evaluate the advantages and disadvantages of these treatments.

Recently, the FDA has granted approval for the TRAILBLAZER-ALZ 2 Phase 3 study (donanemab-azbt, 350 mg/20 mL once-monthly injection for IV infusion) on 2 July 2024. This drug will be used to treat persons with early symptoms of Alzheimer’s disease, including those with moderate cognitive impairment (MCI) and mild dementia with confirmed amyloid plaques ( Wall, 2024 ). Those with a reduced risk of disease progression had the best outcomes with Kisunla in the TRAILBLAZER-ALZ 2 Phase 3 trial. Over the course of 18 months, trial participants were divided into two groups for analysis: the general population, which also included individuals with high tau levels, and a group of patients who were less advanced in their disease and had low to medium amounts of tau protein. In both groups, Kisunla treatment markedly reduced clinical deterioration. Those with less advanced disease who received treatment with Kisunla had a noteworthy 35% reduction in cognitive decline when compared to placebo on the integrated Alzheimer’s Disease Rating Scale (iADRS), which evaluates thinking, memory, and day-to-day functioning. Additionally, employing statistical significance, the response to treatment was observed in the entire population ( Bucci et al., 2021 ; Wall, 2024 ).

Present state of the landscape treatment

Ongoing research is primarily dedicated to the advancement of therapeutic strategies aimed at decelerating or halting the progression of the disease. This research considers the latest findings in the disease’s biology, diagnostic markers, accurate diagnosis of each individual’s disease state, and the design of clinical trials. Moreover, drug development research for AD has become increasingly complex due to the potential inclusion of preclinical and prodromal AD populations in current trials, in addition to the previously included groups representing all clinical phases of AD dementia ( Dubois et al., 2016 ). Molecular targets for treating AD are typically involved in Aβ or p-tau synthesis, as well as Aβ plaque and NFT development. The toxic proteinopathy theory implies that Aβ plays a role in a gain-of-function process. As Aβ deposition is linked to AD degenerative changes, reducing Aβ levels could prevent neurodegeneration and cognitive loss ( Ezzat et al., 2023 ). However, despite decades of research, the failing findings of current therapeutic studies aimed at counteracting Aβ formation or favoring Aβ clearance prompt a critical evaluation of the amyloid cascade concept ( Granzotto and Sensi, 2023 ). The primary objection to designating the Aβ pathway as the initiator of neurodegeneration is related to data showing that Aβ deposits are not predominantly correlated with cognitive function, that Aβ deposits can be found in people with normal cognitive function, and that neuronal injury and tau pathology markers can exist independently of Aβ deposition ( Perez-Nievas et al., 2013 ). The theory of a protein loss-of-function has been established in contrast to the gain-of-function mechanism, and it is likewise supported by translational and genetic investigations ( Ezzat et al., 2023 ).

The development of Aβ aggregates in the brain suggests a mechanism that goes beyond protein accumulation: the depletion of proteins in fluid. Since several studies have shown that Aβ-42 low CSF levels are associated with the longitudinal development of AD symptoms and with neurodegenerative markers, and that low Aβ levels better correlate with cognitive decline than the burden of the insoluble form, it is also possible to argue that the depletion of Aβ soluble forms is a crucial mechanism in neurodegeneration ( Villemagne and Chételat, 2016 ; McDade et al., 2018 ). The findings that both sporadic and hereditary types of AD are associated with normal cognition and high levels of soluble Aβ-42 in brain aberrant amyloid burden supports the loss-of-function hypothesis ( Sturchio et al., 2021 , 2022 ).

The unsatisfactory outcomes of anti-amyloid therapy strategies can be partially explained by the intricacy of the implicated pathways and the poor understanding of the amyloid cascade and its effects. There is substantial evidence to suggest that the primary toxic Aβ species in AD are oligomers ( Rinauro et al., 2024 ). The amount of soluble Aβ is correlated with the severity of neurodegenerative alterations rather than the burden of senile plaques, and oligomers are cytotoxic and break down synapses in vitro ( Kreiser et al., 2020 ). Targeting plaques, fibrils, protofibrils, and oligomers hence suggests more variation in the therapeutic response. Furthermore, the “amyloid cascade” is only one among numerous molecular modifications that define AD, including tau-mediated toxicity and neuroinflammation, and it starts decades before the onset of symptoms. Therefore, it’s possible that the anti-Aβ treatment strategies currently in practice will be insufficient to prevent AD ( Zhang et al., 2023 ).

This section will provide a discussion of the drugs that are currently being explored as potential disease-modifying therapies (DMTs). Additionally, it will briefly cover the ongoing clinical trials in AD that are in phases 1, 2, and 3 that currently being presented in the official clinical trial website ( clinicaltrials.gov ). However, the limitation of these studies were the ambiguity and lack of the study outcomes for certain intervention presented in the website. Due to these gaps, we couldn’t specify the projection of the completed study interventions as either being approved for further investigation or merely for research purpose. Figure 5 shows the overall summary of the pharmacological approaches for AD.

Current AD DMT research

Most molecules investigated as possible targets for AD-modifying therapy are involved in the formation of Aβ plaque and NFT, as well as in the generation of Aβ or p-tau ( Tondo et al., 2024 ).

Amyloid-related DMTs strategies

Anti-amyloid DMTs have primarily targeted three major mechanisms of action (MOAs): (i) decreasing the production of Aβ42 (through the use of γ-secretase inhibitors, β-secretase inhibitors, or α-secretase potentiation), (ii) reducing the accumulation of Aβ plaques (by employing aggregation inhibitors or drugs that interfere with metals), and (iii) enhancing the clearance of Aβ (via active or passive immunotherapy) ( Livingston et al., 2019 ). Table 4 summarized current clinical trial status employing all DMTs strategies including Aβ, tau and other mechanisms contribute to AD ( clinicaltrials.gov ).

Current clinical status of amyloid-related DMTs strategies, tau-related DMTs strategies and DMTs of other mechanisms.

Aβ, amyloid beta; AD, Alzheimer’s disease; MCI, Mild Cognitive Impairment; rrAD, risk reduction for Alzheimer’s disease; CDR-SB, clinical dementia rating-sum of boxes; Im, intramuscular; ADAS-Cog, Alzheimer’s disease assessment scale-cognitive subscale.

(i) Reduction of Aβ42 production

γ-secretase inhibitors.

As per the amyloid hypothesis, the amyloidogenic pathway is facilitated following the successive cleavage of APP by BACE1 and γ-secretase. Accordingly, the suppression of these enzymes has been regarded as a significant focus for therapeutic strategies. Unfortunately, with regards to γ-secretase, apart from APP, this specific enzyme interacts with numerous other substances and cleaves various transmembrane proteins. This fact likely accounts for the recent failures in clinical trials involving γ-secretase inhibitors. Semagacestat was linked to a deterioration in daily functioning and an increased incidence of infections and skin cancer ( Doody et al., 2013 ). Avagacestat was associated with a higher rate of cognitive decline and adverse effects that limited the dosage, such as skin cancer ( Coric et al., 2015 ). Tarenflurbil, on the other hand, exhibited poor ability to penetrate the brain ( Muntimadugu et al., 2016 ). The presence of significant safety issues surrounding γ-secretase inhibitors renders γ-secretase an unsuitable target for treating AD ( Penninkilampi et al., 2016 ). Thorough investigations on this crucial enzyme are necessary to enable the development of a safe therapeutic approach to target γ-secretase ( Steiner et al., 2018 ). There are presently no γ-secretase modulators being investigated in phase 1–3 clinical studies ( Huang et al., 2023 ).

BACE inhibitors

Within the amyloidogenic route, β-secretase cleaves APP, resulting in the production of Aβ peptides, which ultimately leads to neurodegeneration ( Das and Yan, 2017 ). Recently, several clinical trials have been conducted for BACE inhibitors. However, a significant number of these trials have been unsuccessful in demonstrating positive results in people with mild to moderate AD, despite using a rigorous research design that involved randomly assigning participants to either the treatment or placebo group.

The majority of BACE1 inhibitors, including LY2886721 [NCT01561430], Elenbecestat (E2609) [NCT02956486], CNP520 [NCT02565511], Verubecestat [NCT01953601], and Atabecestat [NCT02569398], have been discontinued from clinical trials ( Das and Yan, 2017 ; Egan et al., 2019 ; Imbimbo and Watling, 2019 ).

α-secretase modulators

APP undergoes processing by the α-secretase enzyme in the non-amyloidogenic route. α-secretase enzymatically breaks the peptide link between lysine 16 and leucine 17 in APP. This process generates two products: soluble amyloid precursor protein (sAPPα) and a membrane-bound fragment called C83. C83 is then subjected to additional processing by γ-secretase, resulting in the production of p3 and AICD ( Folch et al., 2018 ). Thus, α-secretase reduces the production of Aβ and also demonstrates neuroprotective effects ( Lichtenthaler and Haass, 2004 ). Therefore, α-secretase enhancers offer a compelling approach for the advancement of DMTs. Various substances have been examined to activate the non-amyloidogenic pathway. However, scientists are currently anticipating the development of a drug that can activate the non-amyloidogenic pathway in order to reduce the production of Aβ. The clinical trial stage is hindered by a lack of selectivity toward α-secretase and the presence of toxicities, resulting in a reduced number of compounds being reached.

Etazolate (EHT0202) functions as a selective modulator of GABA receptors and promotes the nonamyloidogenic α-secretase pathway. A prior phase 2 trial demonstrated that the drug was safe and well tolerated in patients with mild to moderate Alzheimer’s disease. Nevertheless, the advancement of etazolate in phase 3 trials has not continued ( Vellas et al., 2011 ).

(ii) Reduction of Aβ-plaque burden

Aggregation inhibitors.

Aggregation inhibitors directly interact with the Aβ peptide to prevent the development of Aβ42 fibers. As a result, they are seen as promising treatment agents for AD. Tramiprosate has undergone preclinical and clinical investigations to assess its effectiveness in treating AD ( Caltagirone et al., 2012 ; Hey et al., 2018 ). Tramiprosate is an oral medication that inhibits the aggregation of amyloid proteins. It has been studied in patients with mild to moderate AD ( Abushakra et al., 2016 ). Regrettably, tramiprosate proved unsuccessful in the phase 3 clinical study due to its adverse effects on the gastrointestinal system, including causing nausea and vomiting ( Abushakra et al., 2016 ). Following the unsuccessful phase 3 clinical studies, tramiprosate was subsequently marketed as a dietary supplement. In addition, a prodrug called ALZ-801, derived from tramiprosate, exhibits a unique ability to counteract amyloid oligomers. ALZ-801 is currently in phase-2 clinical trial at the moment the article is written ( clinicaltrials.gov ). It has been speculated that ALZ-801 be granted fast-track designation by the US FDA for the treatment of AD ( Gupta and Samant, 2021 ).

(iii) Enhancing Aβ clearance (active or passive immunotherapy)

The two primary immunotherapeutic strategies now being investigated in clinical and preclinical trials to enhance the removal of Aβ are active and passive immunization. Active immunization involves the activation of T and B cells, which in turn stimulates the phagocytic capacity of microglia, resulting in an immunological response. Passive immunization primarily focuses on stimulating the immune response against Aβ through the use of monoclonal or polyclonal antibodies ( Gupta and Samant, 2021 ).

Active Aβ immunotherapy

The fundamental advantage of active immunotherapy is that it stimulates the creation of endogenous antibodies without the need for repeated administration. However, no significant therapeutic benefit has been documented in AD patients, and due to the possibility of unpredictable immune response with potentially severe adverse effects, no vaccine has yet been approved for commercialization ( Kwan et al., 2020 ). CAD106 is a proactive Aβ immunotherapeutic drug that underwent phase 2 and 3 clinical trials to assess its potential in delaying the onset and advancement of clinical symptoms related to AD in individuals who are at risk of developing such symptoms based on their age and genotype. Nevertheless, CAD106 was discontinued as a result of unforeseen alterations in cognitive performance, reduction in brain capacity, and decreased in body weight ( clinicaltrials.gov ).

The efficacy of ABvac40 was assessed in a phase 2 clinical trial, making it the initial active immunization targeting the C-terminal region of Aβ40. A phase 1 clinical trial was undertaken including patients diagnosed with mild to moderate AD, ranging in age from 50 to 85 years. No signs of vasogenic oedema or microhaemorrhages were found. Anti-Aβ40 antibodies were specifically produced in 92% of those who had ABvac40 injections ( Lopez et al., 2019 ). A phase 2, double-blind, parallel-group, placebo-controlled and 6-month randomized clinical trial to evaluate the efficacy and safety of GV1001 in Alzheimer patients was completed in 2019. The findings demonstrate that 1.12 mg of GV1001 successfully achieved the primary objective of a statistically significant distinction. GV1001 demonstrated excellent tolerability without any safety issues ( Koh et al., 2021 ). The efficacy of ACC-001 (vanutide cridificar), a vaccine targeting Aβ, was evaluated in phase 2a extension trials involving individuals diagnosed with mild to moderate AD. The administration included the use of QS-21 adjuvant. Extended treatment with this combination was highly well-tolerated and resulted in the most elevated levels of anti-Aβ IgG antibodies in comparison to alternative therapy options ( Hull et al., 2017 ).

Passive Aβ immunotherapy

The use of monoclonal antibodies is limited by the development of dose-dependent side effects, which can be seen in one-third of individuals with “amyloid-related imaging abnormalities” (ARIAs) ( Piazza and Winblad, 2016 ). ARIAs can lead to the onset of vasogenic edema (ARIA-E) or cerebral micro-hemorrhages (ARIA-H), which are distinguished by neuroimaging evidence of hemosiderin deposits. ARIAs were identified in clinical trials assessing the safety and efficacy of practically all monoclonal antibodies, and were generally dose dependent ( Bateman et al., 2023 ).

Aducanumab, also known as Aduhelm, is a monoclonal antibody of the immunoglobulin gamma 1 (IgG1) class that has a strong attraction to and binds to the N-terminus of Aβ fibrils, preventing the aggregation of amyloid proteins ( Arndt et al., 2018 ). The initiation of two phase 3 clinical trials, ENGAGE and EMERGE investigations, began in August 2015. Aducanumab (BIIB037) has demonstrated substantial improvements in cognitive and functional domains, including memory, orientation, and language. Aducanumab (BIIB037) consistently and convincingly decreased the quantity of amyloid plaques in the brain. In June 2021, the FDA granted immediate approval for Aduhelm (aducanumab-avwa) to treat AD based on its observed effects. It was declared as a newly authorized drug for people with Alzheimer’s. Following approval, pharmaceutical companies are required to conduct Phase IV confirmatory trials to assess the efficacy of their medicines. If the drug fails to perform as expected, the FDA has the authority to withdraw it from the market. The controversial approval of Aducanumab, its disputed clinical impact, and subsequent decline all contribute to the anti-amyloid therapy debate. While Aβ accumulation is important for AD pathogenesis, it does not appear to be sufficient to trigger neurodegenerative alterations and cognitive impairment. Future clinical trials should not overlook the critical connection between amyloid, tau, and neuroinflammation to raise the likelihood of clinical efficacy ( Golde, 2023 ).

Lecanemab, also known as Leqembi, is a humanized IgG1 antibody that is generated from mAb158. It specifically attaches to soluble Aβ protofibrils ( Tucker et al., 2015 ). The US FDA granted permission on 6 January 2023, via an expedited approval process due to the presence of evidence indicating amyloid elimination in a phase 2 trial (NCT01767311) and the potential for clinical advantages ( Canady, 2023 ). An 856-patient double-blind, placebo-controlled phase 2 trial was conducted to study individuals with AD who had either MCI or mild dementia. The participants were confirmed to have amyloid pathology using amyloid PET or CSF Aβ1-42 testing. The findings demonstrated a notable and dosage-dependent decrease in amyloid plaques in the lecanemab group (10 mg/kg, administered through intravenous infusion every 2 weeks) from the initial measurement to week 79, in comparison to the placebo group. Currently, there are three ongoing phase 3 clinical trials for lecanemab ( clinicaltrials.gov ).

Donanemab is a monoclonal antibody that has been humanized from the mouse antibody mE8-IgG2a. It identifies Aβ (3–42), a clustered version of Aβ discovered in amyloid plaques ( Irizarry et al., 2016 ). Upon examination of postmortem brain samples from people with AD or Down syndrome, it was shown that the substance was attached to almost one-third of amyloid plaques. Furthermore, it exhibited a robust reaction with the central part of the plaque ( Bouter et al., 2022 ). Phase II TRAILBLAZER-ALZ research assessed the safety, tolerability, and effectiveness of donanemab, both as a standalone treatment and in conjunction with the Beta-Secretase 1 (BACE1) inhibitor LY3202626, which was produced by Eli Lilly and Company. The study spanned a duration of 18 months. The experiment successfully achieved its primary objective of significantly postponing the deterioration, as measured by iADRS scores, by 32% compared to the placebo. The decrease in amyloid accumulation was found to be associated solely with an improvement in iADRS scores in individuals who carry the ApoE4 gene ( Shcherbinin et al., 2022 ). Donanemab effectively decreased the accumulation of tau in the temporal, parietal, and frontal lobes, and resulted in a significant 24% reduction in plasma pTau217 levels in the treatment group. In contrast, the placebo group had a 6% increase in plasma pTau217 levels by the end of the study ( Pontecorvo et al., 2022 ). TRAILBLAZER-ALZ 2 Phase 3 study (donanemab-azbt, 350 mg/20 mL once-monthly injection for IV infusion) has been approved recently (2 July 2024) by FDA ( Wall, 2024 ). This drug will become a treatment option for adults with early symptomatic Alzheimer’s disease, including people with mild cognitive impairment (MCI) and mild dementia with confirmed amyloid plaques. Lilly is currently conducting several clinical trials with donanemab. These trials include TRAILBLAZER-ALZ 3 (currently recruiting), which aims to prevent symptomatic Alzheimer’s disease in participants with preclinical AD; TRAILBLAZER-ALZ 5 (currently recruiting), a registration trial for early symptomatic AD that is currently recruiting in China and Korea; and TRAILBLAZER-ALZ 6 (currently recruiting), which focuses on advancing our understanding of ARIA through novel MRI sequences, blood-based biomarkers, and various donanemab dosage regimens ( Wall, 2024 ). The TRAILBLAZER-ALZ 4 clinical study, which investigated the efficacy of donanemab compared to aducanumab in clearing brain amyloid plaques in individuals with early symptomatic AD, completed in 2023 ( clinicaltrials.gov ).

Solanezumab, a humanized monoclonal antibody, targeting the mid-domain of the Aβ peptide to enhance Aβ clearance ( Honig et al., 2018 ). The Phase III clinical trial of solanezumab (LY2062430) ended in October 2019 [NCT02760602] due to the failure of the EXPEDITION 3 study. Other Phase III studies with the same substance, solanezumab [NCT01900665; NCT01127633], also failed as a result of the EXPEDITION 3 study. Despite the disappointing outcomes of these studies, solanezumab is still being tested in patients with a genetic mutation that may put them at risk of developing AD in a Phase 2/3 clinical trial called DIAN-TU [NCT01760005] ( clinicaltrials.gov ). ALZ-801 is a pharmacologically inactive derivative of tramiprosate, a tiny molecule that can counteract Aβ oligomers and prevent their aggregation ( Hey et al., 2018 ). The APOLLOE4 (NCT04770220) phase 3 trial is assessing the safety and effectiveness of ALZ-801 in patients with early AD who have two copies of the ε4 allele on the apolipoprotein E gene (APOE4/4). A separate phase 2 clinical trial (NCT04693520) is currently examining the impact of oral ALZ-801 on individuals with early AD who possess the APOE4/4 or APOE3/4 genotype and have biomarkers indicating the presence of core AD pathology. The trial is evaluating the effectiveness, safety, and capacity to be tolerated of ALZ-801. ABBV-916 is a monoclonal antibody that targets Aβ. It identifies N-terminal truncated Aβ that has been changed with pyroglutamate at position 3 (N3), which is a variant of Aβ that forms aggregated amyloid plaques. The clinical trial for ABBV-916, consisting of two phases, is now in progress (NCT05291234) ( clinicaltrials.gov ).

Tau-related DMT strategies

The failure of multiple Phase II/III trials in AD that focused on reducing Aβ accumulation has led to a growing interest in alternate treatments for tau pathology ( Panza et al., 2016 ). Tau proteins, often referred to as axonal microtubule-associated protein (MAP), play a crucial role in controlling the assembly and arrangement of microtubules, as well as the transportation of organelles within axons. Excessive tau phosphorylation has been proposed as a possible factor in the development of neurofibrillary tangles in AD ( Götz et al., 2012 ). In individuals with AD, the process of hyperphosphorylation of tau proteins leads to the separation of tau proteins from the microtubules. This disruption of the axonal transport structure results in a lack of nutrients reaching the neurons, ultimately leading to their death ( Terwel et al., 2002 ).

Tau phosphorylation and aggregation inhibition

Tau phosphorylation and aggregation inhibitors are employed to mitigate tauopathy and hinder tau aggregation. TRx0237 is a second-generation inhibitor of tau protein aggregation that underwent Phase III clinical trials to assess the safety and effectiveness of TRx0237 at doses of 16 mg/day and 8 mg/day in the treatment of individuals with AD. The trial was completed in May 2023 [NCT03446001]. LY3303560 is another compound that acts as a tau phosphorylation inhibitor. It completed its Phase II clinical trial in October 2023. GSK3 inhibitors are utilized as a means to decrease tau hyperphosphorylation, which is primarily caused by the enzyme responsible for turning tau into hyperphosphorylated tau protein ( Hooper et al., 2008 ). Tideglusib, also known as NCT00948259, is a GSK3 inhibitor. It is a small-molecule medicine that can be taken orally and is designed to decrease the excessive phosphorylation of tau protein. Noscira SA is the company responsible for developing this therapy. Tideglusib commenced Phase II clinical trials and was administered to individuals with mild to moderate AD in December 2008. Nevertheless, tideglusib was determined to be safer in the trial. However, it did not meet its primary endpoint, and as a result, some of the secondary endpoints did not demonstrate any meaningful therapeutic advantages ( Serenó et al., 2009 ).

Various techniques have been employed to lower the amounts of various forms of tau protein (including monomers, oligomers, filaments, granules, fibrils, and insoluble aggregates) in AD. Considering tau aggregation inhibitors as a primary focus could be beneficial for managing AD ( Bulic et al., 2013 ). Tau-tau interactions play a crucial role in the development of neurofibrillary tangles (NFTs). The Phase III clinical trial evaluated the efficacy of low dose, 4 mg twice a day, Leuco-Methylthioninium Bis (Hydroxymethanesulfonate) monotherapy in treating mild AD patients. The modified primary outcome measure used in this trial was cohort analysis, which yielded favorable outcomes ( Wilcock et al., 2018 ).

Microtubule stabilizers

Tau hyperphosphorylation in AD is linked to the disruption of microtubules. AD treatment has been the subject of preclinical and clinical experiments with various microtubule stabilizers ( Brunden et al., 2011 ). Paclitaxel, an anti-mitotic drug, was discontinued from the trial because of its limited ability to pass through the BBB ( Fellner et al., 2002 ; Zhang et al., 2005 ; Zempel et al., 2010 ). The recruitment of individuals for the Phase I trial of TPI287, a synthetic epothilone derivative, focusing on safety, tolerability, pharmacokinetics, and pharmacodynamics, was completed in April 2020 ( Brunden et al., 2010 ).

Anti-tau immunotherapy

Recent evidence from multiple animal models indicates that focusing on p-tau epitopes is a viable strategy to stimulate antibody responses that can facilitate the removal of tau ( Wischik et al., 2015 ). Therefore, several immunotherapy efforts, both active and passive, have progressed to clinical trials for the treatment of AD ( Medina, 2018 ).

Active immunotherapy

AADvac1, which incorporates a synthetic tau peptide, underwent a phase 2 clinical trial for those with mild to severe AD. The clinical trial was completed in November 2019 (NCT02579252) ( Wischik et al., 2015 ).

Passive immunotherapy

ABBV-8E12, a humanized anti-tau monoclonal antibody, was evaluated in a phase 2 clinical trial including patients with early AD (NCT02880956) ( Budur et al., 2017 ). BIIB092 is a monoclonal antibody that has been humanized to target tau fragments. These fragments are obtained from the stem cells of a patient with familial AD ( Wilcock et al., 2018 ). A phase 2 clinical trial evaluates the safety and effectiveness of the drug in individuals with amnestic moderate cognitive impairment (AD MCI) and mild AD ( Cummings et al., 2022 ).

DMTs employing other pathways

Neuroprotection.

These group of drugs refers to preservation of neural tissue from damage or degeneration. AGB101, a low-dose extended-release form of levetiracetam, is a modulator of SV2A. It completed a phase 3 clinical trial in September 2023 as a repurposed medication. Originally approved for use in a different indication, namely MCI owing to AD, rather than epilepsy. The purpose is to decrease excessive neural activity caused by Aβ (NCT03486938) ( clinicaltrials.gov ).

BHV4157, also known as troriluzole, is a substance that modulates glutamate and decreases the amounts of glutamate in synapses. It has undergone a phase 2 clinical trial (NCT03605667) and the trial was finished in December 2023. The clinical trial aimed to test the efficacy and safety of BHV-4157 in patients diagnosed with mild to moderate AD ( clinicaltrials.gov ).

Icosapent ethyl is a refined version of eicosapentaenoic acid (EPA), which is an omega-3 fatty acid. The purpose of the phase 3 clinical trial (NCT02719327) was to determine whether icosapent ethyl, a medication, can protect neurons from disease pathology and positively impact intermediate physiological measures that are associated with the onset of AD. The trial aimed to evaluate whether larger, multi-site, longer-duration trials are necessary to assess more definitive clinical outcomes related to Alzheimer’s prevention ( clinicaltrials.gov ).

Anti-inflammatory effects

Neuroinflammation has been implied as a potential cause of AD for over 30 years. However, only recently the research into neuroinflammation gained momentum, likely due to two significant findings. Firstly, there is evidence indicating that activated glial cells play a role in the development of brain lesions in AD. Secondly, epidemiological studies have shown that patients with rheumatoid arthritis, who have been treated with anti-inflammatory drugs for many years, are protected from developing AD ( McGeer et al., 2016 ).

These are the anti-inflammatory drugs that have undergone completion in clinical trials:

ALZT-OP1a plus ALZT-OP1b is a combination of cromolyn, which is a mast cell stabilizer, and ibuprofen, which is an anti-inflammatory drug. The purpose of the phase 3 clinical trial (NCT02547818) was to analyze the safety and tolerability of the combination medication ALZT-OP1, as well as its effectiveness in slowing down, arresting, or reversing cognitive and functional deterioration in individuals with early-stage AD. The experiment also aimed to measure efficacy using the CDR-SB scale.

COR388 is a substance that specifically targets a type of bacteria that causes periodontal disease. The efficacy, safety, and tolerability of two dose levels of COR388 were evaluated in a Phase 2/3 clinical trial (NCT03823404). The study was conducted in a randomized, double-blind, placebo-controlled manner and included participants having a clinical diagnosis of mild to severe AD dementia.

Masitinib functions as a specific tyrosine kinase inhibitor and a regulator of neuroinflammation by targeting mast cells. The drug’s safety and efficacy in treating mild to moderate AD were evaluated in a phase 3 clinical trial (NCT01872598). The drug masitinib was given as an additional treatment to patients who had already been receiving treatment with a consistent dose of cholinesterase inhibitor (donepezil, rivastigmine, or galantamine) and/or memantine for at least 6 months.

Elderberry Juice enhances mitochondrial function by acting as a potent antioxidant, thanks to its high content of anthocyanins (NCT02414607). GRF6019, a fraction of human plasma protein, is administered through infusions with the aim of counteracting brain neuroinflammation through young blood parabiosis (NCT03520998, NCT03765762). These agents have successfully passed the phase 2 clinical trials ( Cummings et al., 2019 ).

In phase 1, anti-inflammatory drugs investigated included mAbs AL002 and AL003 (NCT03635047, NCT03822208) ( Cummings et al., 2019 ).

Metabolic effects

Utilizing a combination of losartan, amlodipine, atorvastatin, and exercise is a recommended treatment strategy for repurposing, aiming to significantly decrease vascular risk and preserve cognitive function. The assessment was carried out in a phase 3 clinical trial (NCT02913664) to ascertain the impact of aerobic exercise training and intense vascular risk reduction on cognitive performance in older persons who are at a high risk for AD ( Cummings et al., 2019 ).

Stem-cell approaches

AstroStem is a therapeutic procedure that utilizes stem cells obtained from a person’s adipose tissue. The treatment involves intravenous administration of these stem cells, which is repeated 10 times. AstroStem was evaluated in a phase 1/2 clinical trial (NCT03117738), while the treatment including human mesenchymal stem cells (hMSCs) was evaluated in a phase 1 clinical trial (NCT02600130) ( Cummings et al., 2019 ).

Phytochemical approaches

Ongoing research efforts are actively exploring the potential protective benefits of plant phytochemicals as nutraceutical agents against neuropathological conditions associated with AD. This strategy holds significant promise due to their therapeutic potential, minimal side effects, diverse molecular targets, potential for disease modification, dietary feasibility, and demonstrated neuroprotective effects in preclinical studies ( Abdul Manap et al., 2019a ; Ayaz et al., 2019 ; Rahman et al., 2021 ). Further research in this area may lead to the development of novel preventive and therapeutic strategies for AD. The summary of various phytochemicals undertaken by previous studies is demonstrated in the Table 5 below.

An overview of the several phytochemicals employed in earlier research as neuroprotective agents against AD.

Non-pharmacological approaches

Cognitive training.

Memory difficulties are a distinguishing hallmark of the early stages of AD and vascular dementia ( Karantzoulis and Galvin, 2011 ). Interventions that target these cognitive deficiencies and the concomitant difficulty with daily activities are gaining popularity. Cognitive training and cognitive rehabilitation are non-pharmacological interventions used to improve cognitive and non-cognitive outcomes ( Irazoki et al., 2020 ). Interventions that directly or indirectly target cognitive functioning are distinguished from those that primarily target behavioral (for example, roaming), emotional (for example, anxiety), or physical (for example, sedentary lifestyle) function ( Mandolesi et al., 2018 ). There are various forms of cognition-based therapies that have been described that are focusing on reasoning, speed of processing information and memory ( Harvey, 2019 ). The brain therapy and exercises can be as simple as doing thing with the non-dominant hands. This activity requires little to no cost and can be done anywhere anytime of the day which make it as an easy approach to the patient. New and challenging activity will stimulate the brain more rather than doing same thing every day ( Alzheimer’s & Dementia Resource Center, 2023 ). Hence, learning new language at the old age is very recommended as a form of exercises that will help to decrease the rate of brain declination. If the person is a person who likes to have fun and laidback, playing boardgames and card games within the Alzheimer’ community or together with their family members are highly recommended. Activities like this will not only help delaying the brain declines but making social connection with other people despite suffering from the disease ( Dementia and Alzheimer’s, 2022 ). Based on the research conducted by Trebbastoni et al. (2017) , the data for the usefulness of cognitive training in AD is still weak. This study demonstrates that a six-month intensive cognitive training program may aid in the preservation of cognition in people with mild-to-moderate AD. Indeed, post-intervention and six-month follow-up outcome tests show that this intervention is effective in improving numerous cognitive processes, including memory ( Trebbastoni et al., 2017 ). To corroborate findings, future randomized clinical trials should be designed as multicentre research trials with larger patient samples and longer intervention and post-intervention observation periods ( Nair, 2019 ). Furthermore, because cognitive training has no side effects, it is clearly preferred in circumstances where drug-drug interactions, drug-related side effects, or contraindications exclude a pharmaceutical therapy to the disease ( Giuli et al., 2016 ).

Physical exercise, ergotherapy and brain simulation

Physical exercise always been known for its benefit for human overall health. Alzheimer patient might also benefit from doing physical exercise ( Better Health Channel, 2023 ). According to a University of Wisconsin study, those over 60 who were at high risk of AD and who engaged in moderate exercise for 30 min five days a week experienced less memory and cognitive issues as well as a decreased chance of getting the condition ( Johnson et al., 2018 ). Study from the University of Kansas discovered that some participants with Alzheimer’s were able to improve their memory test scores and even increase the size of their brain’s hippocampus, an area of the brain important for learning and memory that is typically impacted early in the AD process, after routinely exercising ( Morris et al., 2017 ). To recently, exercise studies have either been too small-scale to be conclusive or have yielded mixed results in terms of their impact on memory and brain function ( Loprinzi et al., 2023 ). It is believed that the most beneficial aerobic exercise for the brain is low intensity aerobic activity, such as brisk walking or swimming. Though the precise mechanism of exercise’s benefits is unknown, many hypothesize that it stems from enhanced blood vessel health and an increase in oxygen-rich blood flow to the brain, both of which enhance brain function ( A Mental Workout, 2023 ). Ergotherapy is an occupational therapy to help people with dementia improve their self-care, productivity, and leisure/rest ( Korczak et al., 2013 ). This allows dementia patients to improve their functional abilities in activities of daily living, social participation, quality of life, and life happiness ( Ruthirakuhan et al., 2012 ).

The efficacy of current symptomatic medications such as cholinesterase inhibitors and memantine for the treatment of AD is limited to delaying the progression of symptoms ( Hogan, 2014 ). However, some studies suggest that combining behavioral method and pharmacological treatment may optimize benefit for patient and caregiver, underlying the importance to develop nonpharmacological intervention programs ( Magierski et al., 2020 ). Physical therapy assists dementia patients with mental health issues such as anxiety and depression ( Orgeta et al., 2014 ). Regular exercise improves mood, reduces medication requirements, and aids the patient in controlling emotional symptoms of dementia such as restlessness, anger, and hostility. Physical therapy can provide major social benefits to dementia patients in addition to physical, cognitive, and emotional benefits ( Jia et al., 2019 ). It lessens social anxiety, promotes stronger social relationships, and aids dementia patients’ efforts to keep their independence for as long as possible ( Medical News Today, 2023 ).

Emerging treatments

Microbiota-gut-brain axis.

Emerging studies has shed light on how gut bacteria and astrocytes communicate in both health and illness. Astrocytes are the most common glial cells in the CNS, and their array of functions is expanding, making them an increasingly popular research topic. Astrocytes play an important role in maintaining CNS homeostasis, and any disturbances in their activity contribute to the development of neurological disorders. Importantly, emerging investigations have revealed that bidirectional signaling between astrocytes and microglia drives neuroinflammation and neurodegeneration ( Lee et al., 2022 ; Patani et al., 2023 ).

The effects of gut microbiota alteration on astrocytes in AD were very recently discovered. Perturbation of the gut microbiota has also been demonstrated to minimize reactive astrogliosis, promote astrocyte homeostasis, and protect against amyloidosis and tau-mediated neurodegeneration. Interestingly, these effects appear to be more prevalent in male mice ( Chandra et al., 2023 ; Seo et al., 2023 ).

Astrocytic responses to perturbation of the gut microbiota exhibit sexual dimorphism similar to that observed in microglia, highlighting the significance of incorporating gender effects into consideration in future studies. However, after microbiota restoration and SCFA supplementation, the neuroprotective effects of gut microbiota reduction were lessened. Specifically, in antibiotic-treated APP/PS1-21 mice, FMT from age-matched control mice recovered astrogliosis, but in GF TE4 mice, supplementation with SCFAs restored gliosis and tau pathology ( Chandra et al., 2023 ; Seo et al., 2023 ). These animal studies, however, preliminary, showed that the gut microbiota plays a role in promoting the onset and advancement of AD pathology, including the regulation of astrocytic responses.

40 Hz gamma frequency brain rhythms

Tactile stimulation adds to the body of evidence demonstrating that non-invasive sensory stimulation of 40 Hz gamma frequency brain rhythms can mitigate AD pathology and symptoms. This effect has previously been demonstrated with light and sound by several research groups in both humans and animals ( Peña-Ortega, 2019 ; Chan et al., 2021 ; Traikapi and Konstantinou, 2021 ). According to a recent study by MIT researchers, compared to untreated controls, Alzheimer’s model mice exposed to 40 Hz vibration for an hour a day for several weeks displayed better brain and motor performance ( Suk et al., 2023 ).

The MIT group is not the first to demonstrate that gamma frequency tactile stimulation can influence brain activity and enhance motor function; however, they are the first to demonstrate that the stimulation can also prevent neurons from dying or losing their synapse circuit connections, lessen neural DNA damage, and lower levels of phosphorylated tau, a protein that is characteristic of AD. A team led by Tsai’s lab has shown in a number of papers that light flickering and/or sound clicking at 40 Hz (a technique known as GENUS for Gamma Entrainment Using Sensory stimuli) can lower tau and amyloid-beta protein levels, preserve synapses and prevent neuron death, and even maintain learning and memory in a range of AD mouse models. The team’s most recent pilot clinical trials revealed that 40 Hz light and sound stimulation was safe, effectively boosted brain connection and activity, and looked to have a major positive clinical impact on a small group of human volunteers who were suffering from early-stage AD ( Suk et al., 2023 ).

In two widely used mouse models of Alzheimer’s neurodegeneration—the Tau P301S mouse, which mimics the disease’s tau pathology, and the CK-p25 mouse, which mimics the synapse loss and DNA damage seen in human disease—the new study examined whether whole-body 40 Hz tactile stimulation produced significant benefits. The primary motor cortex (MOp), where the brain generates movement commands for the body, and the primary somatosensory cortex (SSp), which processes touch sensations, were the focus of the team’s investigations.

The researchers vibrated mouse cages by placing them above speakers producing 40 Hz sound, which caused the cages to vibrate. The 40 Hz sound was played for all of the non-stimulated control mice since their cages were dispersed across the same space. Therefore, the addition of tactile stimulation was what caused the disparities between the stimulated and control mice to be measured. First, the scientists established that the 40 Hz vibration altered the neuronal activity in the brains of mice that were healthy—that is, animals without AD. Activity increased two-fold in the SSp and more than three-fold in the MOp, with a statistically significant increase in the latter case, as determined by the expression of the c-fos protein.

After learning that tactile stimulation at 40 Hz might raise brain activity, the researchers examined the effect on disease in the two mice models. The group employed female CK-p25 mice and male P301S mice to guarantee that both sexes were represented. When compared to unstimulated controls, P301S mice that were stimulated for three weeks demonstrated a notable preservation of neurons in both brain regions. Additionally, tau in the SSp by two measurements was significantly reduced in stimulated mice, and they also had comparable patterns in the MOp.

For six weeks, CK-p25 mice were subjected to vibration stimulation. In comparison to non-vibrated control mice, these mice exhibited increased levels of synaptic protein markers in both brain regions. Additionally, they displayed lower amounts of DNA damage. Lastly, the group evaluated the mice’s motor skills after exposing them to vibration vs. not. It was discovered that both mouse models could remain on a rotating rod for far longer. Additionally, P301S mice held onto a wire mesh for noticeably longer than control mice, but CK-p25 animals displayed a trend that was favorable but not statistically significant ( Suk et al., 2023 ).

Strategies to optimizing management for patient with AD

AD causes brain cells to die, causing the brain to function less effectively over time. This alters how a person behaves ( National Institute on Aging, 2023 ). Behavioral symptoms can be one of the initial signs of dementing diseases, occurring before cognitive changes ( Manni et al., 2023 ). These symptoms can occur at any point during the progression of the illness ( Leocadi et al., 2023 ; Manni et al., 2023 ) and can vary depending on the severity of the dementia ( Artaso-Irigoyen et al., 2004 ). Behavioral and psychological symptoms of dementia (BPSD) refer to non-cognitive symptoms that are frequently observed in individuals with AD ( Fernández et al., 2010 ). Timely identification of BPSD is highly crucial, as these symptoms not only cause significant impairment in individuals with dementia, but also contribute to heightened stress for caregivers ( Teixeira et al., 2024 ). Indeed, BPSD exacerbate difficulties in doing everyday tasks ( Bélanger-Dibblee et al., 2023 ), expedite the deterioration of cognitive function ( Honjo et al., 2020 ), and deteriorate the overall quality of life for patients ( Kim et al., 2021 ). In addition, behavioral disorders are the primary cause of and result in the early institutionalization of patients ( Gimeno et al., 2021 ), hence escalating the overall financial burden ( Boafo et al., 2023 ). Nevertheless, once accurately diagnosed, many illnesses can be effectively managed with pharmaceutical interventions ( López-Pousa et al., 2008 ), hence postponing the need for nursing home placement and enhancing the quality of life for both patients and caregivers.

A collaborative approach to tackling these complicated AD challenges is both realistic and effective. The team may also comprise professionals with knowledge of neurology, geriatric psychology, social work, clinical psychology, and elder law in addition to nurses and physician assistants ( Physiopedia, 2023 ). The neurologist provides support in managing later stage neurologic signs of AD, including seizures, as well as in the differential diagnosis of patients demonstrating with atypical dementia presentation ( Neugroschl and Wang, 2011 ). In addition to helping with the identification and psychopharmacologic treatment of behavioral issues like anxiety, psychosis, and depression, geriatric psychiatrists also aid in the differential diagnosis of difficult cases ( Targum, 2001 ). The social worker may offer psychotherapy to patients and caregivers in addition to helping to preserve the stability of the patient’s family and finding and utilizing community resources for care ( Ong et al., 2021 ). Expertise in behavioral responses to disorders like depression is provided by the clinical psychologist, who also helps with the identification of early-stage or dubious dementia. The elder law attorney can help with matters including guardianship and health-care financial planning ( JSTOR, 2023 ). Other disciplines, including as pharmacy, nutrition, physical therapy, and occupational therapy, can also contribute significantly to management. Referral to a geneticist or a genetic counselor for the entire family and the patient is recommended for patients with early-onset familial AD ( Grossberg and Desai, 2003 ).

Conclusion and future perspectives

We represent the most sophisticated and extensive reviews on current diagnosis biomarkers and therapeutic approach achieved thus so far. It is now acknowledged that pathological alterations commence several years before the onset of clinical symptoms in diseases, and AD encompasses a range from individuals who show no clinical signs to those who are seriously impaired. Defining AD only based on its clinical symptoms is considered artificial. Therefore, attempts have been made to identify the disease by considering both clinical manifestations and biomarker evidence. The development of biomarkers has led to a change in how the disease is seen as a clinical and physiological entity. There is now a growing recognition that AD should not be seen as having distinct and well-defined stages, but rather as a complex process that progresses along a continuous continuum. Recognizing this concept is crucial for comprehending the progression of disease-modifying treatments and for implementing efficient diagnostic and illness management alternatives. The ATN classification, which focuses on biomarkers of AD, has evolved due to several factors. One key aspect is the recognition that the capacity to assess the fundamental biological elements of AD is significantly superior to that of other neurodegenerative disorders. Nevertheless, if and when biomarkers for new proteinopathies emerge, they could be incorporated. In the future, the inclusion of synaptic dysfunction as a category could be beneficial. This category could encompass many techniques such as FDG-PET, task-free functional MRI, EEG, MEG (magnetoencephalography), and the measurement of synapse-specific proteins in CSF. Nevertheless, if we define neurodegeneration as a gradual deterioration and constriction of neurons and their processes, accompanied by a commensurate decline in neuronal function, then synaptic dysfunction falls within the umbrella of neurodegeneration. In the future, it will be crucial to investigate novel biomarkers that extend beyond the amyloid and tau pathologies, as well as the longitudinal evolution of these biomarkers throughout the course of AD.

Apart from that, our review also primarily examines the present state of therapies in clinical trials and offers insight into the potential and promising targets for the development of drugs for AD. Currently, no cure or treatment can affect the progression of AD. However, drugs approved by the FDA can only provide relief from the symptoms in those with the condition. A multitude of drug candidates progressed through several stages of clinical trials, nevertheless, owing to unfavorable effects and insufficient therapeutic effectiveness, the majority of these substances failed to achieve success in Phase II/III trials. Hence, it is imperative to have a thorough comprehension of the complete pathophysiology of AD prior to directing attention toward the creation of new drugs. Furthermore, there is a shift in focus within AD drug development from treatment to prevention. Recent approaches appear to prioritize reducing the generation, aggregation, and misfolding of Aβ proteins and tau, while also increasing the removal of toxic aggregate or misfolded versions of these proteins. These tactics are based on earlier clinical and nonclinical research which warrants further investigation and exploration.

Author contributions

AA: Conceptualization, Data curation, Formal analysis, Funding acquisition, Investigation, Resources, Validation, Writing – original draft, Writing – review & editing. RA: Formal analysis, Investigation, Methodology, Resources, Validation, Writing – original draft, Writing – review & editing. SS: Data curation, Formal analysis, Methodology, Project administration, Writing – original draft, Writing – review & editing. MS: Data curation, Formal analysis, Investigation, Methodology, Project administration, Writing – review & editing. KK: Data curation, Investigation, Methodology, Project administration, Visualization, Writing – review & editing. VL: Data curation, Formal analysis, Investigation, Project administration, Visualization, Writing – review & editing. AS: Formal analysis, Investigation, Methodology, Resources, Validation, Writing – review & editing.

Acknowledgments

The authors would like to thank the Deanship of Scientific Research, Vice Presidency for Graduate Studies and Scientific Research, King Faisal University, Saudi Arabia for funding this project (Grant No: GrantA268).

Funding Statement

The authors declare that financial support was received for the research, authorship, and/or publication of this article. This work was supported by the Deanship of Scientific Research, Vice Presidency for Graduate Studies and Scientific Research, King Faisal University, Saudi Arabia (Grant No: GrantA268).

Conflict of interest

The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

Publisher’s note

All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher.

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Dementia in Australia

Australian Institute of Health and Welfare (2024) Dementia in Australia , AIHW, Australian Government, accessed 28 August 2024.

Australian Institute of Health and Welfare. (2024). Dementia in Australia. Retrieved from https://www.aihw.gov.au/reports/dementia/dementia-in-aus

Dementia in Australia. Australian Institute of Health and Welfare, 28 March 2024, https://www.aihw.gov.au/reports/dementia/dementia-in-aus

Australian Institute of Health and Welfare. Dementia in Australia [Internet]. Canberra: Australian Institute of Health and Welfare, 2024 [cited 2024 Aug. 28]. Available from: https://www.aihw.gov.au/reports/dementia/dementia-in-aus

Australian Institute of Health and Welfare (AIHW) 2024, Dementia in Australia , viewed 28 August 2024, https://www.aihw.gov.au/reports/dementia/dementia-in-aus

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Prevalence of dementia

Key statistics.

It is estimated that in 2023 there were 411,100 Australians living with dementia. However, the exact number of people with dementia is currently not known as there is no single authoritative data source for deriving dementia prevalence in Australia.

This page presents dementia prevalence, as estimated by the Australian Institute of Health and Welfare (AIHW):

• by sex and age in 2023
• by place of residence in 2022 (that is, living in the community versus living in cared accommodation)
• for each year between 2024 and 2058
• by states/territories, remoteness, socioeconomic and small geographic areas in 2022 .

It also presents how the prevalence rate of dementia in Australia compares with other countries . 

Expand the headings below for information on the available data sources and methodologies to estimate dementia prevalence. Refer to the Prevalence data tables for the underlying data presented in this page.

See Population health impacts of dementia among First Nations people and  Dementia among people from culturally and linguistically diverse backgrounds for more information on the challenges involved in estimating dementia prevalence among these groups.

Australia’s dementia statistics are derived from a variety of data sources of varying quality, including administrative data (such as data on medications dispensed, hospital visits, aged care services, and causes of death), survey data (such as the Australian Bureau of Statistics (ABS) Survey of Disability, Ageing and Carers (SDAC)) and epidemiological studies (both Australian and international). As each data source has incomplete coverage of people with dementia, major studies have used a number of different approaches to estimate the prevalence of dementia in Australia. For example:

• The Economic cost of dementia report by the National Centre for Social and Economic Modelling estimated dementia prevalence using a pooled data set of Australian longitudinal studies for people aged 65 and over, which included cases of ‘probable dementia’ and mild cognitive impairment (Anstey et al. 2010; Brown et al. 2017). This resulted in an estimated 413,000 people living with dementia in Australia in 2017, higher than what was estimated by AIHW for the same year.
• The 2019 Global Burden of Disease Study estimated dementia prevalence through a systematic review of surveys and epidemiology studies, as well as administrative data. An updated literature review for the period 2016–2017 found 38 new studies were in scope for calculating prevalence of dementia at the global, regional, and country level (GBD 2019). This resulted in an estimated 301,000 people living with dementia in Australia in 2017, lower than what was estimated by AIHW for the same year.

Given the wide range of dementia prevalence estimates reported, improvements in dementia data are needed to truly understand the number of people with dementia in Australia.

Taking into consideration the strengths and limitations of available data sources and methodologies, the AIHW has produced revised dementia prevalence estimates for Australia. Our approach in this report is based on the methodology used in the AIHW 2012 Dementia in Australia   report to estimate prevalence but has incorporated new data. The prevalence of dementia among Australians aged 60 and over was estimated using data from a systematic review of worldwide dementia prevalence conducted by Alzheimer’s Disease International for the World Alzheimer report 2015  (ADI 2015). Prevalence estimates for those aged under 60 were derived from a recent Australian study (Withall et al. 2014). Therefore, the dementia prevalence estimates presented in this report supersede those published by AIHW in the 2012 Dementia in Australia report. See Methods  for more details on the methodology used to calculate dementia prevalence estimates.

There are ongoing efforts to improve the accuracy of dementia prevalence estimates in Australia. As a result, the approach used to estimate the prevalence of dementia in this report will likely be superseded in coming years as findings from these initiatives become available. See 3: Dementia prevalence and incidence of the National Dementia Data Improvement Plan 2023–2024 for information on current developments and future activities aimed at improving dementia prevalence data.

How many people have dementia in Australia?

The AIHW estimates that in 2023 there were around 411,100 people living with dementia in Australia, including nearly 257,500 women and 153,700 men. This is equivalent to 15 people with dementia per 1,000 Australians (19 per 1,000 women and 12 per 1,000 men).

New health condition question in the 2021 Census

In the 2021 Census, a new long term health condition question was asked, which captured whether a person had one or more of a select group of health conditions. One of these select conditions was dementia (including Alzheimer’s).

For the purposes of the census, long-term conditions are conditions that:

• the respondent has been told they have by a doctor or nurse
• have lasted, or are expected to last, for 6 months or more
• may recur from time to time
• are controlled by medication, or
• are in remission (ABS 2022).

The first results of the census were released on 28 June 2022, and they showed that 189,162 people living in Australia self-reported a dementia diagnosis (78,154 males and 111,003 females). While the ABS advises that their health surveys (the National Health Survey and the National Aboriginal and Torres Strait Islander Health Survey) continue to capture the prevalence of these conditions more accurately, work has commenced to better understand how the new census estimate aligns with other dementia diagnosis information, as well as how these data can be used to improve how we understand and monitor dementia prevalence in Australia.

The rate of dementia rises quickly with age – from less than one person with dementia per 1,000 Australians aged under 60, to 71 per 1,000 Australians aged 75–79, and then to 429 per 1,000 Australians aged 90 and over. Interestingly, the rates are similar for men and women in the younger age groups, but quickly diverge with increasing age. For the oldest age group, the rate of dementia among women is 1.4 times the rate of men (479 per 1,000 women and 337 per 1,000 men) (Figure 2.1).

Figure 2.1: Prevalence of dementia in 2023: estimated number and rate, by age and sex

Bar chart shows that the number of people with dementia and the rate of dementia both increase with age. The rate of dementia is higher in women than men in each age group, with the difference greatest among those aged 90 and over.