MetS-Z: A gender- and age-specific scoring system for predicting type 2 diabetes

Area of Science:

• Endocrinology
• Cardiovascular Health
• Metabolic Health

Background:

• Current metabolic syndrome (mets) criteria do not account for age and gender variations.
• This limitation impacts the accuracy of risk assessment and long-term outcome predictions.

Purpose of the Study:

• Introduce the mets-Z score, a novel tool for enhanced metabolic syndrome assessment.
• Improve the prediction of long-term health outcomes, specifically type 2 diabetes mellitus onset.
• Provide a more personalized and precise method for evaluating metabolic and diabetes risk.

Main Methods:

• Developed the mets-Z score using principal component analysis (PCA).
• Weighted five mets indicators (waist circumference, blood glucose, blood pressure, HDL cholesterol, triglycerides) by gender and age.
• Analyzed data from 188,739 Taiwan Biobank participants, stratified by gender and age groups.
• Assessed predictive performance for type 2 diabetes mellitus onset over a 4- to 5-year follow-up.

Main Results:

• The mets-Z score demonstrated superior accuracy in predicting type 2 diabetes mellitus onset.
• Achieved an Area Under the Curve (AUC) of 0.76 in men and 0.80 in women.
• Significantly outperformed conventional metabolic syndrome indices (P < 0.0001).

Conclusions:

• The mets-Z score offers a more personalized and precise tool for metabolic and diabetes risk assessment.
• Integrates age- and gender-specific variations, surpassing existing methods.
• The tool is publicly available, supporting applications in precision medicine.