Circulating CCDC3 as an Indicator of Visceral Fat Accumulation in Patients with Type 2 Diabetes Mellitus.

Background: Visceral fat plays a central role in cardiometabolic risk among people with type 2 diabetes mellitus (T2DM), yet its assessment in routine clinical practice remains largely dependent on imaging techniques or indirect anthropometric measures. Identifying accessible blood-based markers that reflect visceral adiposity may facilitate improved phenotyping in this population. This study aimed to investigate whether circulating coiled-coil domain-containing protein 3 (CCDC3) reflects visceral fat accumulation in adults with T2DM. Methods: Public RNA-sequencing datasets and human adipose tissue samples were analyzed to identify CCDC3 as a visceral fat-enriched secretory gene. In this cross-sectional study of 160 adults with T2DM undergoing dual-energy X-ray absorptiometry, plasma CCDC3 was measured by ELISA. Associations between plasma CCDC3 and visceral fat area (VFA) were examined using multivariable regression. Logistic regression models for abdominal obesity (VFA ≥ 100 cm2), with and without CCDC3, were evaluated using receiver operating characteristic (ROC) analysis, calibration curves, decision curve analysis (DCA), and Shapley additive explanations (SHAP). Results: Circulating CCDC3 levels were positively associated with VFA (β = 3.11, p < 0.001), independent of demographic and metabolic factors. Incorporating CCDC3 into the baseline model significantly improved discrimination of abdominal obesity (AUC 0.820 vs. 0.663; p = 0.009). Calibration curves and DCA supported better model fit and higher net clinical benefit with CCDC3. SHAP analysis showed that CCDC3 contributed the greatest incremental importance beyond waist circumference, sex, and age. Conclusions: Circulating CCDC3 may serve as a blood-based biomarker reflecting visceral adiposity in adults with T2DM and provides complementary information beyond traditional anthropometric measures.
Diabetes
Diabetes type 2
Care/Management

Authors

Zhu Zhu, Fan Fan, Lu Lu, He He, Gao Gao, He He, Lai Lai, Zhao Zhao, Cheng Cheng, Li Li, Chuan Chuan, Wang Wang
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