Background: Mortality selection occurs when a non-random subset of a population of interest has died before data collection and is unobserved in the data. Mortality selection is of general concern in the social and health sciences, but has received little attention in genetic epidemiology. We tested the hypothesis that mortality selection may bias genetic association estimates, using data from the US-based Health and Retirement Study (HRS). Methods: We tested mortality selection into the HRS genetic database by comparing HRS respondents who survive until genetic data collection in 2006 with those who do not. We next modelled mortality selection on demographic, health and social characteristics to calculate mortality selection probability weights. We analysed polygenic score associations with several traits before and after applying inverse-probability weighting to account for mortality selection. We tested simple associations and time-varying genetic associations (i.e. gene-by-cohort interactions). Results: We observed mortality selection into the HRS genetic database on demographic, health and social characteristics. Correction for mortality selection using inverse probability weighting methods did not change simple association estimates. However, using these methods did change estimates of gene-by-cohort interaction effects. Correction for mortality selection changed gene-by-cohort interaction estimates in the opposite direction from increased mortality selection based on analysis of HRS respondents surviving through 2012.
Background-Active lifestyles are related to better cognitive aging outcomes, yet the unique role of different types of activity are unknown. Objective-To examine the independent contributions of physical (PA) versus cognitive (CA) leisure activities to brain and cognitive aging. Methods-Independent samples of non-demented older adults from University of California, San Francisco Hillblom Aging Network (UCSF; n = 344 typically aging) and University of California, Davis Diversity cohort (UCD; n = 485 normal to MCI) completed: 1) self-reported engagement in current PA and CA (UCSF: Physical Activity Scale for the Elderly and Cognitive Activity Scale; UCD: Life Experiences Assessment Form); 2) neuropsychological batteries; and 3) neuroimaging total gray matter volume, white matter hyperintensities, and/or global fractional anisotropy. PA and CA were simultaneously entered into multivariable linear regression models, adjusting for demographic characteristics and functional impairment severity.
Background: Mortality selection occurs when a non-random subset of a population of interest has died before data collection and is unobserved in the data. Mortality selection is of general concern in the social and health sciences, but has received little attention in genetic epidemiology. We tested the hypothesis that mortality selection may bias genetic association estimates, using data from the US-based Health and Retirement Study (HRS). Methods: We tested mortality selection into the HRS genetic database by comparing HRS respondents who survive until genetic data collection in 2006 with those who do not. We next modelled mortality selection on demographic, health and social characteristics to calculate mortality selection probability weights. We analysed polygenic score associations with several traits before and after applying inverse-probability weighting to account for mortality selection. We tested simple associations and time-varying genetic associations (i.e. gene-by-cohort interactions). Results: We observed mortality selection into the HRS genetic database on demographic, health and social characteristics. Correction for mortality selection using inverse probability weighting methods did not change simple association estimates. However, using these methods did change estimates of gene-by-cohort interaction effects. Correction for mortality selection changed gene-by-cohort interaction estimates in the opposite direction from increased mortality selection based on analysis of HRS respondents surviving through 2012.
Objectives Spousal death is a common late-life event with health-related sequelae. Evidence linking poor mental health to disease suggests the hypothesis that poor mental health following death of a spouse could be a harbinger of physical health decline. Thus, identification of bereavement-related mental health symptoms could provide an opportunity for prevention. Methods We analyzed data from N = 39,162 individuals followed from 1994 to 2016 in the U.S. Health and Retirement Study; N = 5,061 were widowed during follow-up. We tested change in mental and physical health from prebereavement through the 5 years following spousal death. Results Bereaved spouses experienced an increase in depressive symptoms following their spouses’ deaths but the depressive shock attenuated within 1 year. Bereaved spouses experienced increases in disability, chronic-disease morbidity, and hospitalization, which grew in magnitude over time, especially among older respondents. Bereaved spouses were at increased risk of death compared to nonbereaved respondents. The magnitude of depressive symptoms in the immediate aftermath of spousal death predicted physical-health decline and mortality risk over 5 years of follow-up. Discussion Bereavement-related depressive symptoms indicate a risk for physical health decline and death in older adults. Screening for depressive symptoms in bereaved older adults may represent an opportunity for intervention to preserve healthy life span.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.
hi@scite.ai
10624 S. Eastern Ave., Ste. A-614
Henderson, NV 89052, USA
Copyright © 2024 scite LLC. All rights reserved.
Made with 💙 for researchers
Part of the Research Solutions Family.