ArticleFrontiers in endocrinology2024
The relationship between cardiometabolic index and infertility in American adults: a population-based study.
Article in Frontiers in endocrinology, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.
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Who cites it
5 citing papers in PubMed.
- Rising cardiovascular metabolic indices are associated with hypertension and stroke risk in individuals with abnormal glucose metabolism: A prospective cohort study.Aging clinical and experimental research · 2026Article
- Cigarette use, secondhand smoke exposure and age-related infertility among US women: A cross-sectional NHANES study.Tobacco prevention & cessation · 2026Article
- Association between cardiometabolic index and infertility among American women aged 20-45 years: a cross-sectional analysis from 2013-2020 NHANES data.BMC women's health · 2025Article
- Association between dietary index for gut microbiota and female infertility: a cross-sectional NHANES 2013-2020.Frontiers in nutrition · 2025Article
- Machine learning algorithm based on combined clinical indicators for the prediction of infertility and pregnancy loss.Frontiers in endocrinology · 2025Article
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Authors and funding
3 authors.
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Abstract
Background: Infertility is a fertility disorder caused by various factors, with lipid metabolism playing a crucial role in its development. The cardiometabolic index (CMI), which combines blood lipids (TG/HDL-C) and obesity-related parameters (WHtR), is a new quantitative indicator. This study used NHANES data to investigate the relationship between CMI and the incidence of infertility. Methods: We utilized data from women who took part in the National Health and Nutrition Examination Survey (NHANES) from 2015 to March 2020 to calculate the CMI index. Subsequently, we used multivariate logistic regression, smooth curve fitting, and subgroup analysis to investigate the relationship between the CMI index and infertility. Results: The logistic regression model revealed a significant positive correlation between the CMI index and infertility, even after adjusting for all confounding variables (OR=3.23, 95%CI: 1.55-6.73, p=0.0017). This association remained consistent across all subgroups (p>0.05 for all interactions). Smooth curve fitting demonstrated a positive nonlinear relationship between CMI and infertility. However, the CMI index had limited diagnostic power for infertility (AUC=0.60, 95%CI: 0.56-0.65). However, the CMI-BMI index combined with BMI had good predictive performance (AUC=0.722, 95%CI: 0.676-0.767). Conclusion: The CMI index shows a positive correlation with infertility, but its diagnostic value is restricted. The combination with BMI has good diagnostic value. Further investigation is required to determine the effectiveness of the CMI index as an early indicator of infertility.
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