Using easy-to-collect indices to develop and validate models for identifying metabolic syndrome and pre-metabolic syndrome
BackgroundThis study aimed to develop and validate models for identifying individuals at high risk for metabolic syndrome (MetS) and pre-MetS using easily collectible indices.MethodsA cross-sectional analysis was conducted using data from the Ningxia Cardiovascular Disorders Survey (NCDS) in China,...
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Frontiers Media S.A.
2025-06-01
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| Series: | Frontiers in Endocrinology |
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| Online Access: | https://www.frontiersin.org/articles/10.3389/fendo.2025.1587354/full |
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| author | Chao Shi Chao Shi Yin Cheng Ling Ma Lanqiqi Wu Hongjuan Shi Yining Liu Yining Liu Jinyu Ma Jinyu Ma Huitian Tong Huitian Tong |
| author_facet | Chao Shi Chao Shi Yin Cheng Ling Ma Lanqiqi Wu Hongjuan Shi Yining Liu Yining Liu Jinyu Ma Jinyu Ma Huitian Tong Huitian Tong |
| author_sort | Chao Shi |
| collection | DOAJ |
| description | BackgroundThis study aimed to develop and validate models for identifying individuals at high risk for metabolic syndrome (MetS) and pre-MetS using easily collectible indices.MethodsA cross-sectional analysis was conducted using data from the Ningxia Cardiovascular Disorders Survey (NCDS) in China, collected between January 2020 and December 2021. The study population comprised 10,520 participants with complete demographic, anthropometric, and laboratory data. The diagnostic models for MetS were developed using five easily collectible indicators. The performance of the models was compared with that of Lipid Accumulation Product (LAP), Triglyceride-Glucose (TyG) Index, and Waist-to-Height Ratio (WHtR). These same models were subsequently applied to pre-MetS detection as a secondary analysis. Area under the receiver operating characteristic curve (AUC), Hosmer and Lemeshow test, bootstrap method, Brier score and Decision Curve Analysis were employed to evaluate the performance of the models.ResultsModel 1 comprised factors such as WC, SBP, DBP and gender. In contrast, Model 2 included all the variables from Model 1 while additionally incorporating FPG. In the training set, the AUC for Model 1 and Model 2 were 0.914 and 0.924, respectively. The AUC for Model 1 and Model 2 in identifying the presence of pre-MetS and MetS conditions were 0.883 and 0.902, respectively. In the external validation set, the AUC for Model 1 and Model 2 in identifying the presence of MetS were 0.929 and 0.934, respectively. For detecting pre-MetS and MetS conditions, the AUC for Model 1 and Model 2 were 0.885 and 0.902, respectively. Compared to TyG, LAP, and WHtR, model 1 and 2 exhibited a superior ability to identify MetS as well as pre-MetS and MetS conditions in both the training and validation sets.ConclusionsOur models offered an easy, accurate and efficient tool for identifying MetS and pre-MetS, which might be used in large-scale population screening or self-health management at home. |
| format | Article |
| id | doaj-art-d3d9cc257ca4459a8cfca764c285ab59 |
| institution | Kabale University |
| issn | 1664-2392 |
| language | English |
| publishDate | 2025-06-01 |
| publisher | Frontiers Media S.A. |
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| series | Frontiers in Endocrinology |
| spelling | doaj-art-d3d9cc257ca4459a8cfca764c285ab592025-08-20T03:46:42ZengFrontiers Media S.A.Frontiers in Endocrinology1664-23922025-06-011610.3389/fendo.2025.15873541587354Using easy-to-collect indices to develop and validate models for identifying metabolic syndrome and pre-metabolic syndromeChao Shi0Chao Shi1Yin Cheng2Ling Ma3Lanqiqi Wu4Hongjuan Shi5Yining Liu6Yining Liu7Jinyu Ma8Jinyu Ma9Huitian Tong10Huitian Tong11People’s Hospital of Ningxia Hui Autonomous Region, Ningxia Medical University, Yinchuan, Ningxia Hui Autonomous Region, ChinaNingxia Institute of Clinical Medicine, People’s Hospital of Ningxia Hui Autonomous Region, Yinchuan, Ningxia Hui Autonomous Region, ChinaSchool of Public Health, Ningxia Medical University, Yinchuan, Ningxia Hui Autonomous Region, ChinaSchool of Public Health, Ningxia Medical University, Yinchuan, Ningxia Hui Autonomous Region, ChinaSchool of Public Health, Ningxia Medical University, Yinchuan, Ningxia Hui Autonomous Region, ChinaSchool of Public Health, Ningxia Medical University, Yinchuan, Ningxia Hui Autonomous Region, ChinaPeople’s Hospital of Ningxia Hui Autonomous Region, Ningxia Medical University, Yinchuan, Ningxia Hui Autonomous Region, ChinaNingxia Institute of Clinical Medicine, People’s Hospital of Ningxia Hui Autonomous Region, Yinchuan, Ningxia Hui Autonomous Region, ChinaPeople’s Hospital of Ningxia Hui Autonomous Region, Ningxia Medical University, Yinchuan, Ningxia Hui Autonomous Region, ChinaNingxia Institute of Clinical Medicine, People’s Hospital of Ningxia Hui Autonomous Region, Yinchuan, Ningxia Hui Autonomous Region, ChinaPeople’s Hospital of Ningxia Hui Autonomous Region, Ningxia Medical University, Yinchuan, Ningxia Hui Autonomous Region, ChinaNingxia Institute of Clinical Medicine, People’s Hospital of Ningxia Hui Autonomous Region, Yinchuan, Ningxia Hui Autonomous Region, ChinaBackgroundThis study aimed to develop and validate models for identifying individuals at high risk for metabolic syndrome (MetS) and pre-MetS using easily collectible indices.MethodsA cross-sectional analysis was conducted using data from the Ningxia Cardiovascular Disorders Survey (NCDS) in China, collected between January 2020 and December 2021. The study population comprised 10,520 participants with complete demographic, anthropometric, and laboratory data. The diagnostic models for MetS were developed using five easily collectible indicators. The performance of the models was compared with that of Lipid Accumulation Product (LAP), Triglyceride-Glucose (TyG) Index, and Waist-to-Height Ratio (WHtR). These same models were subsequently applied to pre-MetS detection as a secondary analysis. Area under the receiver operating characteristic curve (AUC), Hosmer and Lemeshow test, bootstrap method, Brier score and Decision Curve Analysis were employed to evaluate the performance of the models.ResultsModel 1 comprised factors such as WC, SBP, DBP and gender. In contrast, Model 2 included all the variables from Model 1 while additionally incorporating FPG. In the training set, the AUC for Model 1 and Model 2 were 0.914 and 0.924, respectively. The AUC for Model 1 and Model 2 in identifying the presence of pre-MetS and MetS conditions were 0.883 and 0.902, respectively. In the external validation set, the AUC for Model 1 and Model 2 in identifying the presence of MetS were 0.929 and 0.934, respectively. For detecting pre-MetS and MetS conditions, the AUC for Model 1 and Model 2 were 0.885 and 0.902, respectively. Compared to TyG, LAP, and WHtR, model 1 and 2 exhibited a superior ability to identify MetS as well as pre-MetS and MetS conditions in both the training and validation sets.ConclusionsOur models offered an easy, accurate and efficient tool for identifying MetS and pre-MetS, which might be used in large-scale population screening or self-health management at home.https://www.frontiersin.org/articles/10.3389/fendo.2025.1587354/fullmetabolic syndromepre-metabolic syndromeeasy-to-collect indicesdiagnostic modelidentifying |
| spellingShingle | Chao Shi Chao Shi Yin Cheng Ling Ma Lanqiqi Wu Hongjuan Shi Yining Liu Yining Liu Jinyu Ma Jinyu Ma Huitian Tong Huitian Tong Using easy-to-collect indices to develop and validate models for identifying metabolic syndrome and pre-metabolic syndrome Frontiers in Endocrinology metabolic syndrome pre-metabolic syndrome easy-to-collect indices diagnostic model identifying |
| title | Using easy-to-collect indices to develop and validate models for identifying metabolic syndrome and pre-metabolic syndrome |
| title_full | Using easy-to-collect indices to develop and validate models for identifying metabolic syndrome and pre-metabolic syndrome |
| title_fullStr | Using easy-to-collect indices to develop and validate models for identifying metabolic syndrome and pre-metabolic syndrome |
| title_full_unstemmed | Using easy-to-collect indices to develop and validate models for identifying metabolic syndrome and pre-metabolic syndrome |
| title_short | Using easy-to-collect indices to develop and validate models for identifying metabolic syndrome and pre-metabolic syndrome |
| title_sort | using easy to collect indices to develop and validate models for identifying metabolic syndrome and pre metabolic syndrome |
| topic | metabolic syndrome pre-metabolic syndrome easy-to-collect indices diagnostic model identifying |
| url | https://www.frontiersin.org/articles/10.3389/fendo.2025.1587354/full |
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