Kelibinuer Mutailipu, Junwei Guo, Jiajing Yin, Yue Wang, Liesheng Lu, Xuyang Jia, Jie Zhang, Shen Qu, Haibing Chen, Le Bu
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Statistical analyses included univariate and multivariate logistic regression and receiver operating characteristic (ROC) curve analysis.</p>\n </section>\n \n <section>\n \n <h3> Results</h3>\n \n <p>The HUA group showed higher BMI, blood pressure and metabolic parameters. TyG, TyG-BMI and BMI were positively correlated with uric acid levels. ROC analysis revealed that TyG-BMI (AUC = 0.877) had better predictive power for HUA than TyG (AUC = 0.829) or BMI (AUC = 0.867). Multivariate analysis showed TyG-BMI and BMI as independent predictors, with women in the highest quartiles having a 3.111-fold and 2.779-fold higher risk for HUA, respectively.</p>\n </section>\n \n <section>\n \n <h3> Conclusion</h3>\n \n <p>TyG-BMI is the most effective predictor of HUA in women, surpassing TyG and BMI alone. It offers a practical tool for early identification and intervention in women at risk of HUA.</p>\n </section>\n </div>","PeriodicalId":36522,"journal":{"name":"Endocrinology, Diabetes and Metabolism","volume":"8 2","pages":""},"PeriodicalIF":2.7000,"publicationDate":"2025-02-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/edm2.70028","citationCount":"0","resultStr":"{\"title\":\"Association Between TyG-BMI Index and Hyperuricemia in Adult Women\",\"authors\":\"Kelibinuer Mutailipu, Junwei Guo, Jiajing Yin, Yue Wang, Liesheng Lu, Xuyang Jia, Jie Zhang, Shen Qu, Haibing Chen, Le Bu\",\"doi\":\"10.1002/edm2.70028\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<div>\\n \\n \\n <section>\\n \\n <h3> Purpose</h3>\\n \\n <p>This study aimed to explore the relationship between hyperuricemia (HUA), the triglyceride-glucose index (TyG) and its derivatives in adult women.</p>\\n </section>\\n \\n <section>\\n \\n <h3> Methods</h3>\\n \\n <p>A cross-sectional analysis was conducted on 1105 female patients from Shanghai Tenth People's Hospital. Participants were divided into HUA (<i>n</i> = 331) and non-HUA (<i>n</i> = 774) groups. Clinical and laboratory data were collected, and indices such as body mass index (BMI), TyG and TyG-BMI were calculated. Statistical analyses included univariate and multivariate logistic regression and receiver operating characteristic (ROC) curve analysis.</p>\\n </section>\\n \\n <section>\\n \\n <h3> Results</h3>\\n \\n <p>The HUA group showed higher BMI, blood pressure and metabolic parameters. TyG, TyG-BMI and BMI were positively correlated with uric acid levels. ROC analysis revealed that TyG-BMI (AUC = 0.877) had better predictive power for HUA than TyG (AUC = 0.829) or BMI (AUC = 0.867). Multivariate analysis showed TyG-BMI and BMI as independent predictors, with women in the highest quartiles having a 3.111-fold and 2.779-fold higher risk for HUA, respectively.</p>\\n </section>\\n \\n <section>\\n \\n <h3> Conclusion</h3>\\n \\n <p>TyG-BMI is the most effective predictor of HUA in women, surpassing TyG and BMI alone. It offers a practical tool for early identification and intervention in women at risk of HUA.</p>\\n </section>\\n </div>\",\"PeriodicalId\":36522,\"journal\":{\"name\":\"Endocrinology, Diabetes and Metabolism\",\"volume\":\"8 2\",\"pages\":\"\"},\"PeriodicalIF\":2.7000,\"publicationDate\":\"2025-02-03\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"https://onlinelibrary.wiley.com/doi/epdf/10.1002/edm2.70028\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Endocrinology, Diabetes and Metabolism\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://onlinelibrary.wiley.com/doi/10.1002/edm2.70028\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q3\",\"JCRName\":\"ENDOCRINOLOGY & METABOLISM\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Endocrinology, Diabetes and Metabolism","FirstCategoryId":"1085","ListUrlMain":"https://onlinelibrary.wiley.com/doi/10.1002/edm2.70028","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q3","JCRName":"ENDOCRINOLOGY & METABOLISM","Score":null,"Total":0}
Association Between TyG-BMI Index and Hyperuricemia in Adult Women
Purpose
This study aimed to explore the relationship between hyperuricemia (HUA), the triglyceride-glucose index (TyG) and its derivatives in adult women.
Methods
A cross-sectional analysis was conducted on 1105 female patients from Shanghai Tenth People's Hospital. Participants were divided into HUA (n = 331) and non-HUA (n = 774) groups. Clinical and laboratory data were collected, and indices such as body mass index (BMI), TyG and TyG-BMI were calculated. Statistical analyses included univariate and multivariate logistic regression and receiver operating characteristic (ROC) curve analysis.
Results
The HUA group showed higher BMI, blood pressure and metabolic parameters. TyG, TyG-BMI and BMI were positively correlated with uric acid levels. ROC analysis revealed that TyG-BMI (AUC = 0.877) had better predictive power for HUA than TyG (AUC = 0.829) or BMI (AUC = 0.867). Multivariate analysis showed TyG-BMI and BMI as independent predictors, with women in the highest quartiles having a 3.111-fold and 2.779-fold higher risk for HUA, respectively.
Conclusion
TyG-BMI is the most effective predictor of HUA in women, surpassing TyG and BMI alone. It offers a practical tool for early identification and intervention in women at risk of HUA.
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