Diabetes, metabolic syndrome and obesity : targets and therapyDec 28, 2020

How Metabolic Syndrome Risk Prediction Models Are Reported and Developed: A Review and Evaluation

Longevity & Aging Weekly Brief ↗PubMed ↗DOI ↗OA ↗

Updated Mar 22, 2026

Abstract

Eleven studies were included in the review, identifying 48 predictors for metabolic syndrome risk assessment.

The for the prognostic models varied from 0.67 to 0.95, indicating differing levels of predictive accuracy.
All included studies exhibited a high risk of bias in methodological quality, particularly regarding outcome measurement and statistical analysis.
Six studies also demonstrated a high risk of bias related to the applicability of their findings.
Adherence to the TRIPOD statement is recommended for future model development and validation to enhance methodological quality.
Current models should not be used in clinical practice due to concerns over optimism and overfitting.

Simplified

PURPOSE: A for metabolic syndrome can calculate the probability of risk of experiencing metabolic syndrome within a specific period for individualized treatment decisions. We aimed to provide a systematic review and critical appraisal on prognostic models for metabolic syndrome.

MATERIALS AND METHODS: Studies were identified through searching in English databases (PubMed, EMBASE, CINAHL, and Web of Science) and Chinese databases (Sinomed, WANFANG, CNKI, and CQVIP). A checklist for critical appraisal and data extraction for systematic reviews of prediction modeling studies (CHARMS) and the prediction model risk of bias assessment tool (PROBAST) were used for the data extraction process and critical appraisal.

RESULTS: From the 29,668 retrieved articles, eleven studies meeting the selection criteria were included in this review. Forty-eight predictors were identified from prognostic prediction models. The ranged from 0.67 to 0.95. Critical appraisal has shown that all modeling studies were subject to a high risk of bias in methodological quality mainly driven by outcome and statistical analysis, and six modeling studies were subject to a high risk of bias in applicability.

CONCLUSION: Future model development and validation studies should adhere to the transparent reporting of a multivariable prediction model for individual prognosis or diagnosis (TRIPOD) statement to improve methodological quality and applicability, thus increasing the transparency of the reporting of a prediction model study. It is not appropriate to adopt any of the identified models in this study for clinical practice since all models are prone to optimism and overfitting.

Key numbers

Identified Predictors

Total predictors identified across the 11 studies.

0.67 to 0.95

Range

values indicating the models' predictive accuracy.

High Risk of Bias

All 11 studies were assessed to have a high risk of bias.

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How Metabolic Syndrome Risk Prediction Models Are Reported and Developed: A Review and Evaluation

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