Cascading Pattern in Genetic Risk Research for Alzheimer’s Disease

Background and Research Motivation

Alzheimer’s disease (AD) is a slowly progressing neurodegenerative disorder characterized by the accumulation of two pathological features: amyloid plaques and phosphorylated tau neurofibrillary tangles. These pathological features typically exist for many years before memory loss and executive function decline. Amyloid plaques usually begin to accumulate about 20 years before the clinical symptoms of Alzheimer’s disease appear, while the spatial distribution of tau tangles is more closely related to reported cognitive deficits and neurodegenerative changes.

In recent years, a series of theoretical frameworks based on the progress of Alzheimer’s disease biomarkers have been proposed. Among these frameworks, the model based on the “Amyloid-Tau-Neurodegeneration (ATN)” framework is particularly noteworthy. In this model, the progression of AD occurs sequentially according to amyloid (A), tau protein (T), and neurodegeneration (N), with each biomarker being either positive (+) or negative (-).

Genome-wide association studies (GWAS) have revealed about 90 genetic loci involved in the development of late-onset Alzheimer’s disease. However, it remains unclear whether the contribution of these genes is equal across different disease stages or exhibits stage-dependent effects. Therefore, this study aims to explore whether the genetic risk of Alzheimer’s disease differs at various stages of disease progression.

Research Source

This study was jointly completed by scholars from multiple institutions, including: - Multiple scholars from University College London (UCL); - Researchers from the University of Southern California.

The article was published on May 31, 2024, in the journal “Brain” (DOI: https://doi.org/10.1093/brain/awae176).

Research Process and Methods

This study investigates the relationship between genetic risk and AD disease progression. To explore this relationship, researchers obtained longitudinal data containing amyloid and tau protein biomarker information from the ADNI database and applied Cox proportional hazards models for analysis.

The study includes two main steps:

Step 1: Analysis of conversion from A-T- to A+T-

This analysis is based on data from 312 participants who were initially A-T- and had genetic information and biomarkers. Among these 312 participants, 65 later converted to A+T-. Cox proportional hazards models were used to estimate the contributions of APOE and polygenic risk scores (PRS, excluding APOE).

Main findings:

• APOE-e4 allele burden significantly influences the conversion from A-T- to A+T- (HR = 2.88, 95% CI: 1.70–4.89, p < 0.001).
• PRS has no significant impact on this conversion (HR = 1.09, 95% CI: 0.84–1.42, p = 0.53).

Step 2: Analysis of conversion from A+T- to A+T+

This analysis includes data from 290 A+T- participants, of whom 45 converted to A+T+ in subsequent data collection. Cox models were similarly applied, adjusting for variables such as age, gender, and years of education, to estimate the contributions of APOE and PRS.

Main findings:

• APOE-e4 allele burden contributes less to the conversion from A+T- to A+T+ (HR = 1.62, 95% CI: 1.05–2.51, p = 0.031).
• PRS contributes more significantly to this process (HR = 1.73, 95% CI: 1.27–2.36, p < 0.001).

Research Conclusions

This study clearly demonstrates that the genetic risk of late-onset Alzheimer’s disease has differential effects across disease development stages. The APOE-e4 allele primarily promotes early accumulation of amyloid, while polygenic risk plays a more significant role in the development of tau pathology. This finding not only has the potential to advance understanding of the molecular mechanisms of Alzheimer’s disease but also provides a potential window for precision medical interventions.

Research Highlights

• Differential Genetic Influence: This study is the first to clearly demonstrate that the genetic risk of AD is not equal across all disease stages but exhibits stage dependency.
• Specific Contribution of Polygenic Risk: In previous studies, genetic risk was often viewed as a constant factor. This study breaks that view, showing the significant impact of polygenic risk in the tau-related pathological stage.
• Protective Effect of Educational Background: Data shows that higher education levels have a protective effect against progression from A+T- to A+T+ stage, suggesting the complex role of non-genetic factors in AD progression.

Other Valuable Information

This study also conducted multiple sensitivity analyses, including relaxing progression definitions, adjusting biomarker thresholds, using only PET or CSF biomarkers, and applying different PRS sources. Results show that despite reduced sample sizes, these sensitivity analyses are consistent with the main analysis, further validating the reliability of the study conclusions.

Research Significance

This study takes an important step forward in understanding the stage dependency of genetic risk. Based on these findings, it can be anticipated that personalized treatments and stage-specific interventions will provide new directions for the treatment of Alzheimer’s disease. This not only deepens the understanding of the disease mechanism but also helps to enhance the effectiveness of precision medicine.

Through the results of this paper, researchers call for further studies to expand the understanding of genetic and non-genetic factors acting at different pathological stages, especially the need for more longitudinal large-sample studies to confirm these key findings.

This work provides an exciting new perspective for exploring the complex pathology of Alzheimer’s disease, driving the research trend towards precise interventions for different disease stages.