Abstract
The process of ovarian aging is influenced by a complex and poorly understood interplay of endocrine, metabolic, and environmental factors. The purpose of this study was to explore the feasibility of using latent class analysis to identify subgroups based on cardiometabolic, psychological, and reproductive parameters of health and to describe patterns of anti-Müllerian hormone levels, a biomarker of the ovarian reserve, within these subgroups. Sixty-nine lean (body mass index [BMI] ⩽ 25 kg/m(2)) and severely obese (BMI ⩾ 40 kg/m(2)) postpartum women in Edinburgh, Scotland, were included in this exploratory study. The best fitting model included three classes: Class 1, n = 23 (33.5%); Class 2, n = 30 (42.2%); Class 3, n = 16 (24.3%). Postpartum women with lower ovarian reserve had less favorable cardiometabolic and psychological profiles. Examining the ovarian reserve within distinct subgroups based on parameters of health that affect ovarian aging may facilitate risk stratification in the context of ovarian aging.