A nomogram to predict the risk of insulin resistance in Chinese women with polycystic ovary syndrome
BackgroundInsulin 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 wom...
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| Language: | English |
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Frontiers Media S.A.
2024-11-01
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| Series: | Frontiers in Endocrinology |
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| Online Access: | https://www.frontiersin.org/articles/10.3389/fendo.2024.1446827/full |
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| author | Benjie Guo Yuting Shen Ziying Dai Kalibinuer Yimamu Jianhua Sun Lixia Pei |
| author_facet | Benjie Guo Yuting Shen Ziying Dai Kalibinuer Yimamu Jianhua Sun Lixia Pei |
| author_sort | Benjie Guo |
| collection | DOAJ |
| description | BackgroundInsulin 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.MethodsPatients 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.ResultsA 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.ConclusionsIndependent 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. |
| format | Article |
| id | doaj-art-c9076d741541485a9e4c120a248e4a49 |
| institution | DOAJ |
| issn | 1664-2392 |
| language | English |
| publishDate | 2024-11-01 |
| publisher | Frontiers Media S.A. |
| record_format | Article |
| series | Frontiers in Endocrinology |
| spelling | doaj-art-c9076d741541485a9e4c120a248e4a492025-08-20T02:48:38ZengFrontiers Media S.A.Frontiers in Endocrinology1664-23922024-11-011510.3389/fendo.2024.14468271446827A nomogram to predict the risk of insulin resistance in Chinese women with polycystic ovary syndromeBenjie GuoYuting ShenZiying DaiKalibinuer YimamuJianhua SunLixia PeiBackgroundInsulin 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.MethodsPatients 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.ResultsA 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.ConclusionsIndependent 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.https://www.frontiersin.org/articles/10.3389/fendo.2024.1446827/fullpolycystic ovary syndromeinsulin resistancePCOS combined with IRnomogramrisk stratification |
| spellingShingle | Benjie Guo Yuting Shen Ziying Dai Kalibinuer Yimamu Jianhua Sun Lixia Pei A nomogram to predict the risk of insulin resistance in Chinese women with polycystic ovary syndrome Frontiers in Endocrinology polycystic ovary syndrome insulin resistance PCOS combined with IR nomogram risk stratification |
| title | A nomogram to predict the risk of insulin resistance in Chinese women with polycystic ovary syndrome |
| title_full | A nomogram to predict the risk of insulin resistance in Chinese women with polycystic ovary syndrome |
| title_fullStr | A nomogram to predict the risk of insulin resistance in Chinese women with polycystic ovary syndrome |
| title_full_unstemmed | A nomogram to predict the risk of insulin resistance in Chinese women with polycystic ovary syndrome |
| title_short | A nomogram to predict the risk of insulin resistance in Chinese women with polycystic ovary syndrome |
| title_sort | nomogram to predict the risk of insulin resistance in chinese women with polycystic ovary syndrome |
| topic | polycystic ovary syndrome insulin resistance PCOS combined with IR nomogram risk stratification |
| url | https://www.frontiersin.org/articles/10.3389/fendo.2024.1446827/full |
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