MetS‐Z: A gender‐ and age‐specific scoring system for predicting type 2 diabetes
ABSTRACT Aims Current metabolic syndrome (mets) criteria often lack consideration for age and gender differences. This study introduces the mets‐Z score, a novel tool designed to enhance mets assessment and improve long‐term outcome predictions. Materials and Methods The mets‐Z score was developed u...
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| Main Authors: | , , |
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| Format: | Article |
| Language: | English |
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Wiley
2025-05-01
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| Series: | Journal of Diabetes Investigation |
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| Online Access: | https://doi.org/10.1111/jdi.70004 |
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| _version_ | 1850187787831083008 |
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| author | Paul Wei‐Che Hsu Yi‐Rong Chen Wayne Huey‐Herng Sheu |
| author_facet | Paul Wei‐Che Hsu Yi‐Rong Chen Wayne Huey‐Herng Sheu |
| author_sort | Paul Wei‐Che Hsu |
| collection | DOAJ |
| description | ABSTRACT Aims Current metabolic syndrome (mets) criteria often lack consideration for age and gender differences. This study introduces the mets‐Z score, a novel tool designed to enhance mets assessment and improve long‐term outcome predictions. Materials and Methods The mets‐Z score was developed using principal component analysis (PCA) to weight five mets indicators—waist circumference, blood glucose, blood pressure, high‐density lipoprotein (HDL) cholesterol, and triglycerides—by gender and age. Data from 188,739 Taiwan Biobank participants, stratified by gender and age groups (20–39, 40–54, 55–64, 65+ years), were analyzed. Predictive performance for type 2 diabetes mellitus onset was assessed over a 4‐ to 5‐year follow‐up. Results The mets‐Z score achieved superior accuracy in predicting type 2 diabetes mellitus onset, with an AUC of 0.76 in men and 0.80 in women, significantly outperforming conventional indices (P < 0.0001). Conclusions By integrating age‐ and gender‐specific variations, the mets‐Z score provides a more personalized and precise tool for assessing metabolic and diabetes risk, surpassing existing methods. The tool is available for public use at http://bioinfolab.nhri.edu.tw/metsz/, supporting broader applications in precision medicine. |
| format | Article |
| id | doaj-art-4f0fa85e539b48d3b9249d07bce0ccaa |
| institution | OA Journals |
| issn | 2040-1116 2040-1124 |
| language | English |
| publishDate | 2025-05-01 |
| publisher | Wiley |
| record_format | Article |
| series | Journal of Diabetes Investigation |
| spelling | doaj-art-4f0fa85e539b48d3b9249d07bce0ccaa2025-08-20T02:16:02ZengWileyJournal of Diabetes Investigation2040-11162040-11242025-05-0116590791610.1111/jdi.70004MetS‐Z: A gender‐ and age‐specific scoring system for predicting type 2 diabetesPaul Wei‐Che Hsu0Yi‐Rong Chen1Wayne Huey‐Herng Sheu2Institute of Molecular and Genomic Medicine National Health Research Institutes Zhunan TaiwanInstitute of Molecular and Genomic Medicine National Health Research Institutes Zhunan TaiwanInstitute of Molecular and Genomic Medicine National Health Research Institutes Zhunan TaiwanABSTRACT Aims Current metabolic syndrome (mets) criteria often lack consideration for age and gender differences. This study introduces the mets‐Z score, a novel tool designed to enhance mets assessment and improve long‐term outcome predictions. Materials and Methods The mets‐Z score was developed using principal component analysis (PCA) to weight five mets indicators—waist circumference, blood glucose, blood pressure, high‐density lipoprotein (HDL) cholesterol, and triglycerides—by gender and age. Data from 188,739 Taiwan Biobank participants, stratified by gender and age groups (20–39, 40–54, 55–64, 65+ years), were analyzed. Predictive performance for type 2 diabetes mellitus onset was assessed over a 4‐ to 5‐year follow‐up. Results The mets‐Z score achieved superior accuracy in predicting type 2 diabetes mellitus onset, with an AUC of 0.76 in men and 0.80 in women, significantly outperforming conventional indices (P < 0.0001). Conclusions By integrating age‐ and gender‐specific variations, the mets‐Z score provides a more personalized and precise tool for assessing metabolic and diabetes risk, surpassing existing methods. The tool is available for public use at http://bioinfolab.nhri.edu.tw/metsz/, supporting broader applications in precision medicine.https://doi.org/10.1111/jdi.70004Metabolic syndromePredictionType 2 diabetes mellitus |
| spellingShingle | Paul Wei‐Che Hsu Yi‐Rong Chen Wayne Huey‐Herng Sheu MetS‐Z: A gender‐ and age‐specific scoring system for predicting type 2 diabetes Journal of Diabetes Investigation Metabolic syndrome Prediction Type 2 diabetes mellitus |
| title | MetS‐Z: A gender‐ and age‐specific scoring system for predicting type 2 diabetes |
| title_full | MetS‐Z: A gender‐ and age‐specific scoring system for predicting type 2 diabetes |
| title_fullStr | MetS‐Z: A gender‐ and age‐specific scoring system for predicting type 2 diabetes |
| title_full_unstemmed | MetS‐Z: A gender‐ and age‐specific scoring system for predicting type 2 diabetes |
| title_short | MetS‐Z: A gender‐ and age‐specific scoring system for predicting type 2 diabetes |
| title_sort | mets z a gender and age specific scoring system for predicting type 2 diabetes |
| topic | Metabolic syndrome Prediction Type 2 diabetes mellitus |
| url | https://doi.org/10.1111/jdi.70004 |
| work_keys_str_mv | AT paulweichehsu metszagenderandagespecificscoringsystemforpredictingtype2diabetes AT yirongchen metszagenderandagespecificscoringsystemforpredictingtype2diabetes AT waynehueyherngsheu metszagenderandagespecificscoringsystemforpredictingtype2diabetes |