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This research explores how metabolic score for visceral fat relates to the progression and regression of prediabetes.

March 15, 2026Open Access

Predictive Role of Metabolic Score for Visceral Fat for Prediabetes Progression and Regression: A Nationwide Cohort Study

Key Points

This research explores how metabolic score for visceral fat relates to the progression and regression of prediabetes.
Included 2066 participants from a large Chinese cohort
Measured METS‐VF using anthropometric and biochemical indicators
Employed multinomial logistic regression models for analysis
Calculated cumulative METS‐VF from different data waves
Conducted subgroup analyses to examine interactions among variables.
METS‐VF and cumulative METS‐VF were higher in participants moving from prediabetes to diabetes.
Significant correlations were found between METS‐VF, cumulative METS‐VF, and prediabetes outcomes.
Participants in the high-increasing METS‐VF cluster had a stronger connection to prediabetes outcomes compared to lower stable clusters.
Nonlinear relationships were noted in the study findings.
Predictive performance of METS‐VF surpassed that of other standard indicators.

Abstract

Background Metabolic score for visceral fat (METS‐VF), a novel index which represents visceral fat, is able to predict the incidence of prediabetes and diabetes. Our study aimed to investigate the association between METS‐VF, cumulative METS‐VF (CumMETS‐VF), dynamic changes in METS‐VF, and outcomes of prediabetes among middle‐aged and older adults. Methods This study included 2066 participants from a large Chinese cohort. METS‐VF was measured based on anthropometric and biochemical indicators, including waist circumference, height, sex, age, and the metabolic insulin resistance score, which consisted of triglyceride, fasting glucose, body mass index, and high‐density lipoprotein cholesterol. Multinomial logistic regression models were utilized to present associations between METS‐VF and progression and regression of prediabetes as a continuous variable and as a categorical variable based on tertiles. CumMETS‐VF was calculated using Wave 1 and Wave 3 of data. The predictive value of dynamic changes in METS‐VF (3 clusters) in participants with prediabetes was also assessed. Furthermore, subgroup analyses were conducted to better stratify analyses and show the interaction of variables. The relationships were visualized by restricted cubic splines with 4 knots. Results Of 2066 participants, the mean age was 59.36±(8.60), and 46.22% were males. METS‐VF and CumMETS‐VF were prominently elevated in people from prediabetes progression to diabetes. Also, METS‐VF and CumMETS‐VF were correlated significantly with progression and regression of prediabetes. People in the high‐increasing (Class 3) METS‐VF cluster levels showed greater connection with outcomes of prediabetes compared with low‐stable (Class 1) cluster. The associations remained robust after all covariates adjustment. Besides, nonlinear relationships were observed between METS‐VF, CumMETS‐VF, and prediabetes progression and regression. The results of subgroup analyses were basically consistent with the main findings. The predictive performance of METS‐VF outperformed other common indicators. Conclusions Our study highlighted that METS‐VF, CumMETS‐VF, and the high trajectory of METS‐VF significantly connected with prediabetes progression and regression. Monitoring of METS‐VF is important for individuals with prediabetes in clinical practice.

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