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

Publication ,  Journal Article
Arbeev, KG; Bagley, O; Yashkin, AP; Duan, H; Akushevich, I; Ukraintseva, SV; Yashin, AI
Published in: Mechanisms of Ageing and Development
April 2023

There is growing literature on applications of biodemographic models, including stochastic process models (SPM), to studying regularities of age dynamics of biological variables in relation to aging and disease development. Alzheimer's disease (AD) is especially good candidate for SPM applications because age is a major risk factor for this heterogeneous complex trait. However, such applications are largely lacking. This paper starts filling this gap and applies SPM to data on onset of AD and longitudinal trajectories of body mass index (BMI) constructed from the Health and Retirement Study surveys and Medicare-linked data. We found that APOE e4 carriers are less robust to deviations of trajectories of BMI from the optimal levels compared to non-carriers. We also observed age-related decline in adaptive response (resilience) related to deviations of BMI from optimal levels as well as APOE- and age-dependence in other components related to variability of BMI around the mean allostatic values and accumulation of allostatic load. SPM applications thus allow revealing novel connections between age, genetic factors and longitudinal trajectories of risk factors in the context of AD and aging creating new opportunities for understanding AD development, forecasting trends in AD incidence and prevalence in populations, and studying disparities in those.

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Published In

Mechanisms of Ageing and Development

DOI

EISSN

1872-6216

ISSN

0047-6374

Publication Date

April 2023

Volume

211

Start / End Page

111791

Related Subject Headings

  • United States
  • Retirement
  • Medicare
  • Humans
  • Gerontology
  • Apolipoproteins E
  • Alzheimer Disease
  • Aging
  • Aged
  • 1103 Clinical Sciences
 

Citation

APA
Chicago
ICMJE
MLA
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Arbeev, K. G., Bagley, O., Yashkin, A. P., Duan, H., Akushevich, I., Ukraintseva, S. V., & Yashin, A. I. (2023). Understanding Alzheimer's disease in the context of aging: Findings from applications of stochastic process models to the Health and Retirement Study. Mechanisms of Ageing and Development, 211, 111791. https://doi.org/10.1016/j.mad.2023.111791
Arbeev, Konstantin G., Olivia Bagley, Arseniy P. Yashkin, Hongzhe Duan, Igor Akushevich, Svetlana V. Ukraintseva, and Anatoliy I. Yashin. “Understanding Alzheimer's disease in the context of aging: Findings from applications of stochastic process models to the Health and Retirement Study.Mechanisms of Ageing and Development 211 (April 2023): 111791. https://doi.org/10.1016/j.mad.2023.111791.
Arbeev KG, Bagley O, Yashkin AP, Duan H, Akushevich I, Ukraintseva SV, et al. Understanding Alzheimer's disease in the context of aging: Findings from applications of stochastic process models to the Health and Retirement Study. Mechanisms of Ageing and Development. 2023 Apr;211:111791.
Arbeev, Konstantin G., et al. “Understanding Alzheimer's disease in the context of aging: Findings from applications of stochastic process models to the Health and Retirement Study.Mechanisms of Ageing and Development, vol. 211, Apr. 2023, p. 111791. Epmc, doi:10.1016/j.mad.2023.111791.
Arbeev KG, Bagley O, Yashkin AP, Duan H, Akushevich I, Ukraintseva SV, Yashin AI. Understanding Alzheimer's disease in the context of aging: Findings from applications of stochastic process models to the Health and Retirement Study. Mechanisms of Ageing and Development. 2023 Apr;211:111791.
Journal cover image

Published In

Mechanisms of Ageing and Development

DOI

EISSN

1872-6216

ISSN

0047-6374

Publication Date

April 2023

Volume

211

Start / End Page

111791

Related Subject Headings

  • United States
  • Retirement
  • Medicare
  • Humans
  • Gerontology
  • Apolipoproteins E
  • Alzheimer Disease
  • Aging
  • Aged
  • 1103 Clinical Sciences