Changes in androstenedione, dehydroepiandrosterone, testosterone, estradiol, and estrone over the menopausal transition

Abstract Background Previous reports have noted that dehydroepiandrosterone-sulfate (DHEAS) increases prior to the final menstrual period (FMP) and remains stable beyond the FMP. How DHEAS concentrations correspond with other sex hormones across the menopausal transition (MT) including androstenedione (A4), testosterone (T), estrone (E1), and estradiol (E2) is not known. Our objective was to examine how DHEAS, A4, T, E1, and E2 changed across the MT by White vs. African-American (AA) race/ethnicity. Methods We conducted a longitudinal observational analysis of a subgroup of women from the Study of Women’s Health Across the Nation observed over 4 visits prior to and 4 visits after the FMP (n = 110 women over 9 years for 990 observations). The main outcome measures were DHEAS, A4, T, E1, and E2. Results Compared to the decline in E2 concentrations, androgen concentrations declined minimally over the MT. T (β 9.180, p < 0.0001) and E1 (β 11.365, p < 0.0001) were higher in Whites than in AAs, while elevations in DHEAS (β 28.80, p = 0.061) and A4 (β 0.2556, p = 0.052) were borderline. Log-transformed E2 was similar between Whites and AAs (β 0.0764, p = 0.272). Body mass index (BMI) was not significantly associated with concentrations of androgens or E1 over time. Conclusion This report suggests that the declines in E2 during the 4 years before and after the FMP are accompanied by minimal changes in DHEAS, A4, T, and E1. There are modest differences between Whites and AAs and minimal differences by BMI.https://deepblue.lib.umich.edu/bitstream/2027.42/138836/1/40695_2017_Article_28.pd

Comparison of SWAN and WISE Menopausal Status Classification Algorithms

Background: Classification of menopausal status is important for epidemiological and clinical studies as well as for clinicians treating midlife women. Most epidemiological studies, including the Study of Women's Health Across the Nation (SWAN), classify women based on self-reported bleeding history. Methods: The Women's Ischemia Syndrome Evaluation (WISE) study developed an algorithm using menstrual and reproductive history and serum hormone levels to reproduce the menopausal status classifications assigned by the WISE hormone committee. We applied that algorithm to women participating in SWAN and examined characteristics of women with concordant and discordant SWAN and WISE classifications. Results: Of the 3215 SWAN women with complete information at baseline (1995–1997), 2466 (76.7%) received concordant classifications (kappa = 0.52); at the fifth annual follow-up visit, of the 1623 women with complete information, 1154 (72.7%) received concordant classifications (kappa = 0.57). At each time point, we identified subgroups of women with discordant SWAN and WISE classifications. These subgroups, ordered by chronological age, showed increasing trends for menopausal symptoms and follicle-stimulating hormone (FSH) and a decreasing trend for estrogen (p < 0.001). Conclusions: The WISE algorithm is a useful tool for studies that have access to blood samples for hormone data unrelated to menstrual cycle phase, with or without an intact uterus, and no resources for adjudication. Future studies may want to combine aspects of the SWAN and WISE algorithms by adding hormonal measures to the series of bleeding questions in order to determine more precisely where women are in the perimenopausal continuum.Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/63232/1/jwh.2006.15.1184.pd

Semiparametric Mixed Effect Model with Application to the Longitudinal Knee Osteoarthritis (OAK) Data

Motivated by the study of the longitudinal development and progression of knee osteoarthritis (OA) over a 15-year period, this study developed non-parametric mixed-effect models for ordinal outcomes. A stochastic mixed-effect model was used to evaluate the similarity of trajectories associated with increasing disease severity of OA in both knees. Then, a non-parametric mixed-effects model, based on cubic B-splnes, was developed to characterize the unknown nonlinear trend of logits as a function of time1-order. A Markov Transition Model was developed to characterize the transitions among multi-states of knee OA. This newly developed approach allows more flexible functional dependence of the ordinal outcome, levels of increasing knee OA severity, on the covariates