# Racial/Ethnic Differences in Reproductive Aging and Onset of Cardio-metabolic Risk in the Study of Women’s Health Across the Nation: Methodological Challenges in Aging Cohorts > **NIH NIH F31** · UNIVERSITY OF MICHIGAN AT ANN ARBOR · 2020 · $38,028 ## Abstract 7 PROJECT SUMMARY/ABSTRACT Racial/ethnic disparities in life expectancy are well documented. These disparities are mainly due to excess mortality among Blacks from cardio-metabolic conditions such as hypertension, heart disease and diabetes. Racial/ethnic disparities in aging and health deterioration are posited to stem from “weathering” or early health deterioration as a consequence of the cumulative impact of repeated experience with social or economic adversity and political marginalization. Corroborating this theory, studies have observed that the racial/ethnic differences in mortality become more pronounced in midlife and early old age when the risk for cardio- metabolic disease increases, particularly for women. It is hypothesized that the increase in risk in midlife women could be related to the end of hormonal production or reproductive aging, signaled by the timing of the final menstrual period (FMP). However, it is unclear if the increase in risk is due to reproductive aging or aging in general. Evidence of racial/ethnic differences in reproductive aging has been mixed. This may be partially due to selection bias into cohorts of aging, caused by systematically excluding women who experience early aging (left-truncation) or employing selection criteria that may unintentionally exclude high- risk groups. Because cohorts are set up to assess the incidence of an outcome, if the potential variability in average rate of aging is not considered upon recruitment, cohorts could be subject to selection bias. Furthermore, as Black women have a higher rate of surgical amenorrhea due to higher rates of hysterectomies/oophorectomies from reproductive complications earlier in life, they are often systematically excluded from estimates of reproductive aging. The multi-racial/ethnic Study of Women's Health Across the Nation (SWAN) recruited women at 42-52 years of age and does not include women who had surgical amenorrhea. SWAN has a wealth of data at each stage of selection for the cohort making it ideal for developing a statistical approach to handling selection bias due to left-truncation. Therefore, we propose to use SWAN to develop and demonstrate the impact of selection bias on estimates of racial/ethnic differences in reproductive aging (Aim 1) and on racial/ethnic differences in timing of cardio-metabolic risk (Aim 2a). We will also assess whether racial/ethnic differences in cardio-metabolic risk are more pronounced before or after the FMP (Aim 2b). This work will contribute to research on aging by developing a novel statistical approach to correct for selection bias in aging cohorts that may miss the window of risk leading to left truncation or that have exclusion criteria that leads to systematic selection of healthier persons. Correction for such bias is principally important when estimating average age of disease onset and health disparities in aging. This research – made possible by the support of this fellowship, the University of Michiga... ## Key facts - **NIH application ID:** 10016991 - **Project number:** 5F31AG064856-02 - **Recipient organization:** UNIVERSITY OF MICHIGAN AT ANN ARBOR - **Principal Investigator:** Alexis Reeves - **Activity code:** F31 (R01, R21, SBIR, etc.) - **Funding institute:** NIH - **Fiscal year:** 2020 - **Award amount:** $38,028 - **Award type:** 5 - **Project period:** 2019-09-01 → 2021-08-31 ## Primary source NIH RePORTER: https://reporter.nih.gov/project-details/10016991 ## Citation > US National Institutes of Health, RePORTER application 10016991, Racial/Ethnic Differences in Reproductive Aging and Onset of Cardio-metabolic Risk in the Study of Women’s Health Across the Nation: Methodological Challenges in Aging Cohorts (5F31AG064856-02). Retrieved via AI Analytics 2026-05-27 from https://api.ai-analytics.org/grant/nih/10016991. Licensed CC0. --- *[NIH grants dataset](/datasets/nih-grants) · CC0 1.0*