ArticleFrontiers in endocrinology2024
A nomogram to predict the risk of insulin resistance in Chinese women with polycystic ovary syndrome.
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 3 papers, 1 of them a synthesis that pooled it.
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Who cites it
3 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Artificial intelligence in polycystic ovarian syndrome management: past, present, and future.La Radiologia medica · 2025Pooled it
- Review
- A nomogram for predicting insulin resistance in polycystic ovary syndrome based on serum amino acid profiles and anti-Müllerian hormone.American journal of translational research · 2026Article
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Authors and funding
6 authors.
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No grant is acknowledged in the PubMed record.
Abstract
Background: Insulin resistance (IR) is considered a major driver of the pathophysiology of polycystic ovary syndrome (PCOS), mediating the progression of hyperandrogenism and metabolic and reproductive dysfunction in patients with PCOS. Early detection of the risk of concurrent IR is essential for women with PCOS. To address this need, this study developed a predictive nomogram for assessing the risk of IR in women with PCOS, aiming to provide a tool for risk stratification and assist in clinical decision-making. Methods: Patients with untreated PCOS-IR diagnosed in a single-center retrospective cohort study from January 2023 to December 2023 were included for nomogram construction and validation. The area under the ROC curve (AUC), calibration curve, Hosmer-Lemeshow (H-L) goodness-of-fit test, and decision curve analysis (DCA) were used to evaluate the nomogram's discrimination, calibration, and clinical decision performance. A risk stratification model based on the nomogram was then developed. Results: A total of 571 patients were included in the study; 400 patients enrolled before September 2023 were divided into the training and validation sets, and 171 patients enrolled later were used as the external validation set. The variables identified by logistic regression and the random forest algorithm-body mass index (BMI, OR 1.43), triglycerides (TG, OR 1.22), alanine aminotransferase (ALT, OR 1.03), and fasting plasma glucose (FPG, OR 5.19)-were used to build the nomogram. In the training, internal validation, and external validation sets, the AUCs were 0.911 (95% CI 0.878-0.911), 0.842 (95% CI 0.771-0.842), and 0.901 (95% CI 0.856-0.901), respectively. The nomogram showed good agreement between predicted and observed outcomes, and patients were categorized into low-, medium-, and high-risk groups based on their scores. Conclusions: Independent predictors of untreated PCOS-IR risk were incorporated into a nomogram that effectively classifies patients into risk groups, providing a practical tool for guiding clinical management and early intervention.
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