# Genetic and Non-Genetic Modulators of Morbidity/Disability Compression in a Large Population-Based Study of Cognitive and Physical Impairment with Emphasis on Alzheimer's Disease and Related Dementias

> **NIH NIH R01** · DUKE UNIVERSITY · 2020 · $799,922

## Abstract

Project Summary/Abstract
Life expectancy at age 65 increased steadily in the United States over the past half-century. There is great
uncertainty, however, regarding the extent to which this increase was accompanied by the compression of
morbidity/disability due to Alzheimer’s disease (AD), AD-related dementias (ADRD), and stroke—the leading
causes of cognitive impairment (CI) among the elderly—or to heart disease, cancer, diabetes, obesity, arthritis,
and fractures—the leading causes of non-cognitive disablement in basic activities of daily living (ADL) among
the elderly. Given the aging of the U.S. population and the increasing costs of health care and long-term care
above age 65, addressing this uncertainty is of profound public health importance. The 1982–1994 National
Long Term Care Survey (NLTCS) produced the first reports of major improvements in ADL and instrumental
ADL (IADL) disability rates above age 65. While ADL/IADL improvements continued through 2004 in the NLTCS,
dramatically larger improvements occurred for severe cognitive impairment—including AD/ADRD—during 1984–
2004. Moreover, multiple reports from the Health and Retirement Study (HRS) indicated that the favorable trends
in severe cognitive impairment continued, but at a slower pace, through 2012; similar reports from the National
Health and Aging Trends Study (NHATS) provided additional independent evidence of continuing improvement
during 2011–2015. We propose to conduct comprehensive analyses of genetic and non-genetic modulators of
the compression of morbidity/disability in the NLTCS, HRS, NHATS, and Long Life Family Study (LLFS), using
morbidity/disability criteria consistent with the HIPAA ADL and CI triggers, to test two major hypotheses: (1) that
modifiable non-genetic risk factors account for the recent temporal changes in the incidence, prevalence, and
continuance of cognitive and physical impairments; and (2) that constitutional genetic and epigenetic factors
modulate individual differences in lifetime morbidity/disability incidence, prevalence, and continuance of
cognitive and physical impairments. We will analyze the roles of modifiable non-genetic risk factors in longevity,
co-morbidity, functional health (ADL/IADL), and severe cognitive impairment (Aim 1). We will complete the SNP
array analysis of 2,680 biospecimen samples (918 currently done) and conduct DNA methylation analysis of 639
blood samples in the NLTCS. De-identified NLTCS genetic and epigenetic data will be released using NIAGADS
protocols. We will use SNP and DNA methylation data to conduct genetic and epigenetic association analyses
with phenotypes of aging, health, longevity, physical disability, and severe cognitive impairment (Aim 2). We will
analyze associations of phenotypes of long healthy life with candidate polymorphisms within two highly relevant
coupled gene networks—Insulin/IGF1 signaling (incl. FOXO3A and IGFR) and mTOR pathways—linked to aging
and longevity across different species a...

## Key facts

- **NIH application ID:** 9913288
- **Project number:** 1R01AG063971-01A1
- **Recipient organization:** DUKE UNIVERSITY
- **Principal Investigator:** P.J. ERIC STALLARD
- **Activity code:** R01 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2020
- **Award amount:** $799,922
- **Award type:** 1
- **Project period:** 2020-03-15 → 2025-02-28

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/9913288

## Citation

> US National Institutes of Health, RePORTER application 9913288, Genetic and Non-Genetic Modulators of Morbidity/Disability Compression in a Large Population-Based Study of Cognitive and Physical Impairment with Emphasis on Alzheimer's Disease and Related Dementias (1R01AG063971-01A1). Retrieved via AI Analytics 2026-05-22 from https://api.ai-analytics.org/grant/nih/9913288. Licensed CC0.

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