Understanding Alzheimer's disease in the context of aging: Findings from applications of stochastic process models to the Health and Retirement Study

How are APOE4, changes in body weight, and longevity related? Insights from a causal mediation analysis

The ε4 allele of the APOE gene (APOE4) is known for its negative association with human longevity; however, the mechanism is unclear. APOE4 is also linked to changes in body weight, and the latter changes were associated with survival in some studies. Here, we explore the role of aging changes in weight in the connection between APOE4 and longevity using the causal mediation analysis (CMA) approach to uncover the mechanisms of genetic associations. Using the Health and Retirement Study (HRS) data, we tested a hypothesis of whether the association of APOE4 with reduced survival to age 85+ is mediated by key characteristics of age trajectories of weight, such as the age at reaching peak values and the slope of the decline in weight afterward. Mediation effects were evaluated by the total effect (TE), natural indirect effect, and percentage mediated. The controlled direct effect and natural direct effect are also reported. The CMA results suggest that APOE4 carriers have 19%-22% (TE p = 0.020-0.039) lower chances of surviving to age 85 and beyond, in part, because they reach peak values of weight at younger ages, and their weight declines faster afterward compared to non-carriers. This finding is in line with the idea that the detrimental effect of APOE4 on longevity is, in part, related to the accelerated physical aging of ε4 carriers.

Comprehensive allostatic load risk index is associated with increased frontal and left parietal white matter hyperintensities in mid-life cognitively healthy adults

To date, there is a considerable heterogeneity of methods to score Allostatic Load (AL). Here we propose a comprehensive algorithm (ALCS) that integrates commonly used approaches to generate AL risk categories and assess associations to brain structure deterioration. In a cohort of cognitively normal mid-life adults (n = 620, age 51.3 ± 5.48 years), we developed a comprehensive composite for AL scoring incorporating gender and age differences, high quartile approach, clinical reference values, and current medications, to then generate AL risk categories. Compared to the empirical approach (ALES), ALCS showed better model fit criteria and a strong association with age and sex. ALSC categories were regressed against brain and white matter hyperintensity (WMH) volumes. Higher AL risk categories were associated with increased total, periventricular, frontal, and left parietal WMH volumes, also showing better fit compared to ALES. When cardiovascular biomarkers were removed from the ALSC algorithm, only left-frontal WMHV remained associated with AL, revealing a strong vascular burden influencing the index. Our results agree with previous evidence and suggest that sustained stress exposure enhances brain deterioration in mid-life adults. Showing better fit than ALES, our comprehensive algorithm can provide a more accurate AL estimation to explore how stress exposure enhances age-related health decline.

The Construction of a Multidomain Risk Model of Alzheimer's Disease and Related Dementias

BACKGROUND

Alzheimer's disease (AD) and related dementia (ADRD) risk is affected by multiple dependent risk factors; however, there is no consensus about their relative impact in the development of these disorders.

OBJECTIVE

To rank the effects of potentially dependent risk factors and identify an optimal parsimonious set of measures for predicting AD/ADRD risk from a larger pool of potentially correlated predictors.

METHODS

We used diagnosis record, survey, and genetic data from the Health and Retirement Study to assess the relative predictive strength of AD/ADRD risk factors spanning several domains: comorbidities, demographics/socioeconomics, health-related behavior, genetics, and environmental exposure. A modified stepwise-AIC-best-subset blanket algorithm was then used to select an optimal set of predictors.

RESULTS

The final predictive model was reduced to 10 features for AD and 19 for ADRD; concordance statistics were about 0.85 for one-year and 0.70 for ten-year follow-up. Depression, arterial hypertension, traumatic brain injury, cerebrovascular diseases, and the APOE4 proxy SNP rs769449 had the strongest individual associations with AD/ADRD risk. AD/ADRD risk-related co-morbidities provide predictive power on par with key genetic vulnerabilities.

CONCLUSION

Results confirm the consensus that circulatory diseases are the main comorbidities associated with AD/ADRD risk and show that clinical diagnosis records outperform comparable self-reported measures in predicting AD/ADRD risk. Model construction algorithms combined with modern data allows researchers to conserve power (especially in the study of disparities where disadvantaged groups are often grossly underrepresented) while accounting for a high proportion of AD/ADRD-risk-related population heterogeneity stemming from multiple domains.