ArticleJournal of diabetes investigation2025
MetS-Z: A gender- and age-specific scoring system for predicting type 2 diabetes.
Article in Journal of diabetes investigation, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers, 1 of them a synthesis that pooled it.
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Who cites it
5 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Pooled it
- A non-invasive tool for the early identification of children at risk of cardiometabolic dysfunction: data from the PODiaCar project.Pediatric research · 2026Article
- An interpretable machine learning model for predicting postoperative hypotension in type 2 diabetes mellitus undergoing non‑cardiac surgery.Cardiovascular diabetology · 2026Article
- Utility of the US Metabolic Syndrome Severity Calculator for Group-Level Comparison in Estonia.Medicina (Kaunas, Lithuania) · 2026Article
- Quantifying Metabolic Syndrome Severity: Methodological Evolution, Clinical Validation, and Translational Perspectives.Diabetes, metabolic syndrome and obesity : targets and therapy · 2026Review
Corrections and comments
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Authors and funding
3 authors.
Funding
Abstract
aimsCurrent 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
methodsThe 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.
resultsThe 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).
conclusionsBy 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.
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