Quantitative CT Assessment of Skeletal Muscle and Abdominal Fat Distribution: Predictive Value for Type 2 Diabetes Mellitus.

OBJECTIVE

In this study, we aimed to investigate the association between skeletal muscle area (SMA), subcutaneous fat area (SFA), and visceral fat area (VFA), quantified using computed tomography (CT), and the risk of type 2 diabetes mellitus (T2DM). We also evaluated the predictive performance of these parameters for assessing T2DM risk.

METHODS

We used a retrospective case-control design, including 207 hospitalized patients who underwent abdominal quantitative CT (QCT) scans at Fushun Central Hospital from July 2021 to July 2022. Using QCT technology, SMA, SFA, and VFA were measured at the level of the third lumbar vertebra. Additionally, the skeletal muscle index (SMI=SMA/height2) and the visceral-to-subcutaneous fat ratio (VFA/SFA) were calculated. Univariate and multivariate logistic regression analyses were used to examine the association between skeletal muscle and abdominal fat parameters with T2DM, and receiver operating characteristic (ROC) curves evaluated the predictive performance of each indicator.

RESULTS

Body mass index, systolic blood pressure, diastolic blood pressure, fasting blood glucose, VFA, and SFA were significantly higher in the T2DM group compared with the control group, and SMA and SMI were significantly lower (all P<0.05). Multivariate logistic regression analysis showed that lower SMI (odds ratio [OR]=0.906, 95% confidence interval [CI]: 0.847-0.970, P=0.004) and greater VFA (OR=1.008, 95% CI: 1.004-1.012, P<0.001) were independent risk factors for T2DM. ROC curve analysis showed that SMI (area under the ROC curve [AUC]=0.634) and VFA (AUC=0.697) had moderate predictive performance for T2DM whereas the combined model (SMI+VFA) significantly improved predictive efficacy (AUC=0.816).

CONCLUSION

Visceral fat accumulation was an independent risk factor for T2DM, and increased skeletal muscle mass showed a protective effect. The combined SMI and VFA model showed significantly enhanced predictive ability for T2DM risk, suggesting its potential as a clinical biomarker.