Integrative regression modeling of insulin sensitivity, resistance, and beta-cell dysfunction in predicting female infertility: a cross-sectional NHANES study.

Insulin sensitivity, resistance, and pancreatic beta-cell function play key roles in metabolic and reproductive health. Despite the known role of glucose-insulin homeostasis in female reproductive health, the association and regression modeling of estimated glucose disposal rate (eGDR), homeostatic model assessment of insulin resistance (HOMA-IR), beta-cell function (HOMA-β), and insulin disposition index (Insulin-DI) with female infertility remain unclear. This cross-sectional study aimed to assess the associations between insulin-related indices and female infertility. This study examined data from 2832 women aged 18-45 years who participated in the National Health and Nutrition Examination Survey (NHANES) 2017-2023 cycles. To evaluate the associations between four insulin-related indices and infertility, multivariable logistic regression, generalized additive models, and threshold effect analyses were applied, while controlling for relevant sociodemographic, lifestyle, and metabolic confounders. Furthermore, integrated meta-analysis across models, distributional analysis, subgroup and interaction analyses were conducted to explore potential effect modifications. eGDR showed a strong inverse association with infertility across all models (OR range: 0.636-0.85, p < 0.001), with a significant nonlinear pattern (p = 0.003) and a threshold at 4.59. HOMA-IR was positively associated in unadjusted models (OR = 1.147, p < 0.001) but lost significance after full adjustment. HOMA-β showed a weak positive association (OR = 1.003, p = 0.0227), while Insulin-DI was inversely related to infertility only in unadjusted models. Subgroup analyses revealed significant effect modifications by BMI and age, especially for eGDR and HOMA-IR. Reduced insulin sensitivity, as indicated by lower eGDR, is strongly linked to higher infertility risk. HOMA-IR and Insulin-DI also show threshold-based nonlinear associations.