Abstract
BACKGROUND: The relationship between A Body Shape Index (ABSI) and female infertility is not well understood. ABSI, a novel anthropometric measure, is gaining recognition for its ability to more accurately capture visceral fat characteristics than traditional metrics like BMI. This study aims to explore the association between ABSI and female infertility, considering its potential applications in medical screening and risk assessment. METHODS: This cross-sectional study analyzed data from the NHANES from 2013 to 2020. Female infertility was assessed through reproductive health questionnaires, and ABSI was calculated using waist circumference, BMI, and height. Weighted logistic regression models and trend tests were used to evaluate the association between ABSI and female infertility. Restricted cubic splines (RCS) were employed to explore potential nonlinear relationships. Subgroup analyses were conducted to examine the consistency of the association across various demographic and health-related factors. Sensitivity analyses were also performed, including the exclusion of participants with missing covariate data, the application of propensity score matching, and restricting the analysis to women aged 20-45 years. RESULTS: The study included 3,718 participants, 433 of whom were diagnosed with infertility. Higher ABSI was associated with an increased risk of female infertility (OR = 1.56, 95% CI: 1.21-2.00, P = 0.001), as demonstrated by weighted logistic regression and trend tests. Women in the highest ABSI quartile had a significantly higher prevalence of infertility compared to those in the lowest quartile (OR = 1.73, 95% CI: 1.27-2.37, P = 0.001). RCS curves indicated a linear positive relationship between ABSI and infertility risk, with a critical value at 0.079. Subgroup and sensitivity analyses confirmed the stability of these findings. CONCLUSION: This study demonstrates a positive linear relationship between ABSI and the risk of female infertility. The use of a simple, non-invasive ABSI measurement could facilitate the early identification of high-risk individuals in large-scale screenings, potentially helping to prevent or reduce the incidence of infertility.