ArticleFrontiers in endocrinology2025
The association between obesity-related indicators and female infertility: the United States National Health and Nutrition Examination Survey, 2013-2018.
Article in Frontiers in endocrinology, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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Abstract
Background: Infertility is a public health issue closely related to obesity. However, the relationship between obesity-related indicators and infertility is currently uncertain. The present study aimed to explore the association between obesity-related indicators and female infertility. Methods: This cross-sectional study included data for 2,875 adult females aged 20-45 years from the National Health and Nutrition Examination Survey conducted between 2013 and 2018. Logistic regression models, restricted cubic spline (RCS), and receiver operating characteristic (ROC) analyses were used to evaluate the relationship between obesity-related indicators [body shape index (ABSI), weight-adjusted waist index (WWI), body roundness index (BRI), waist-to-height ratio (WHtR), non-HDL cholesterol to HDL cholesterol ratio (NHHR), relative fat mass (RFM), body mass index (BMI), and waist circumference (WC)] and female infertility. Results: Adult females were divided into five groups based on their ABSI, WWI, BRI, WHtR, NHHR, RFM, BMI, and WC. Individuals in the highest quintile for ABSI, WWI, BRI, WHtR, NHHR, RFM, BMI, and WC had a higher risk of infertility compared to those in the lowest quintile. The respective adjusted odds ratio values were 1.65 (95% confidence interval (CI), 1.14 to 2.42), 1.71 (95% CI, 1.15 to 2.57), 2.09 (95% CI, 1.39 to 3.19), 2.09 ( 95% CI, 1.39 to 3.19), 1.71 (95% CI, 1.14 to 2.59), 2.09 (95% CI, 1.39 to 3.19), 2.10 (95% CI, 1.40 to 3.18), and 2.28 (95% CI, 1.52 to 3.47). The Conclusions: The WHtR, RFM, WC, and BRI are superior to BMI in predicting and diagnosing infertility, particularly among individuals aged 20-35 years. Consequently, these indices show promise as more effective tools for identifying populations at an early risk of infertility. To confirm these findings, future studies, such as Mendelian randomization or cohort studies, are warranted.
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