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

7项目摘要/摘要 种族/民族在预期寿命方面的差异是有据可查的。这些差距主要是由于过剩造成的。 黑人因高血压、心脏病和糖尿病等心脏代谢疾病而死亡。 在老龄化和健康恶化方面的种族/民族差异被认为源于“风化”或早期健康。 由于反复经历社会或经济问题的累积影响而造成的恶化 逆境和政治边缘化。为了证实这一理论，研究发现，种族/民族 死亡率的差异在中年和老年早期变得更加明显，当心脏疾病的风险 代谢性疾病增多，尤其是对女性而言。据推测，中年风险的增加 女性可能与荷尔蒙分泌的结束或生殖衰老有关，这一信号来自于 月经末期(FMP)。然而，目前还不清楚风险的增加是由于生殖老化还是 总体而言，年龄在增长。在生殖老龄化方面，种族/民族差异的证据是喜忧参半的。今年5月 部分原因是在老龄化队列中存在选择偏见，这是由于有系统地排除以下女性造成的 经历早衰(左截断)或采用可能无意中排除高年龄的选择标准 风险群体。因为设立队列是为了评估结果的发生率，如果潜在的变异性在 招募时不考虑平均老龄化速度，队列可能会受到选择偏差的影响。 此外，由于黑人女性有更高的手术闭经率，因此 子宫切除术/卵巢切除术由于早年的生殖并发症，通常是 有系统地被排除在生殖老龄化估计之外。女性健康的多种族/民族研究 全国(天鹅)招募年龄在42-52岁之间的女性，不包括有 外科闭经。Swan在队列选择的每个阶段都有丰富的数据，这使它成为 开发一种统计方法来处理因左截断而产生的选择偏差。因此，我们建议使用 开发和演示选择偏见对#年种族/民族差异估计的影响 生殖老龄化(目标1)和心脏代谢风险发生时间的种族/族裔差异(目标2a)。我们会 还要评估在心脏代谢风险方面的种族/民族差异是在之前还是之后更加明显 FMP(目标2b)。这项工作将通过开发一种新的统计方法来促进老龄化的研究 纠正老龄化队列中可能错过导致左截断的风险窗口的选择偏差 有排除标准，导致有系统地选择更健康的人。对这种偏见的修正是 在估计平均发病年龄和老龄化方面的健康差距时，这一点非常重要。这 研究-由该奖学金、密歇根大学和与 我尊敬的导师-将继续提高我在流行病学方法方面的应用知识，并将 作为一名专注于老龄化中种族/民族差异的独立学者，这对我的持续成长至关重要。

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 Michigan and collaboration with my distinguished mentors – will continue to advance my applied knowledge of epidemiologic methods and will be essential to my continued growth as an independent scholar focused on racial/ethnic disparities in aging.