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

**Authors:** Lin Zhu, Xiaodie Fan, Jiangang Lu, Yutao He, Youyuan Gao, Sirong He, Longbin Lai, Ruobei Zhao, Rui Cheng, Xi Li, Fengning Chuan, Bin Wang

PMC · DOI: 10.3390/metabo16020111 · 2026-02-03

## TL;DR

This study finds that a protein called CCDC3 in the blood may indicate visceral fat levels in people with type 2 diabetes, offering a new way to assess health risks.

## Contribution

The study identifies CCDC3 as a novel blood-based biomarker for visceral fat accumulation in type 2 diabetes patients.

## Key findings

- Circulating CCDC3 levels were significantly associated with visceral fat area after adjusting for demographic and metabolic factors.
- Adding CCDC3 to traditional models improved the accuracy of predicting abdominal obesity.
- SHAP analysis confirmed CCDC3's incremental importance over standard measures like waist circumference.

## Abstract

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.

## Linked entities

- **Genes:** CCDC3 (coiled-coil domain containing 3) [NCBI Gene 83643]
- **Diseases:** type 2 diabetes mellitus (MONDO:0005148)

## Full-text entities

- **Genes:** CCDC3 (coiled-coil domain containing 3) [NCBI Gene 83643], LEP (leptin) [NCBI Gene 3952] {aka LEPD, OB, OBS}, TP63 (tumor protein p63) [NCBI Gene 8626] {aka AIS, B(p51A), B(p51B), EEC3, KET, LMS}, CRP (C-reactive protein) [NCBI Gene 1401] {aka PTX1}, ALB (albumin) [NCBI Gene 213] {aka FDAHT, HSA, PRO0883, PRO0903, PRO1341}, NFKB1 (nuclear factor kappa B subunit 1) [NCBI Gene 4790] {aka CVID12, EBP-1, KBF1, NF-kB, NF-kB1, NF-kappa-B1}, ADIPOQ (adiponectin, C1Q and collagen domain containing) [NCBI Gene 9370] {aka ACDC, ACRP30, ADIPQTL1, ADPN, APM-1, APM1}, INS (insulin) [NCBI Gene 3630] {aka IDDM, IDDM1, IDDM2, ILPR, IRDN, MODY10}
- **Diseases:** CAD (MESH:D003324), beta-cell dysfunction (MESH:D007340), peripheral neuropathy (MESH:D010523), VAT (MESH:D018205), kidney disease (MESH:D007674), thyroid dysfunction (MESH:D013959), T2DM (MESH:D003924), illnesses (MESH:D002908), diseases of the cardiovascular, cerebrovascular, liver, kidney, or pulmonary systems (MESH:D018376), AO (MESH:D056128), ischemic or hemorrhagic stroke (MESH:D002543), diabetic complications (MESH:D048909), Hypertension (MESH:D006973), carotid atherosclerosis (MESH:D002340), insulin resistance (MESH:D007333), endocrine diseases (MESH:D004700), immune system diseases (MESH:D007154), infections (MESH:D007239), myocardial infarction (MESH:D009203), Stroke (MESH:D020521), retinopathy (MESH:D058437), Obesity (MESH:D009765), visceral adiposity (MESH:D007418), Cushing's syndrome (MESH:D003480), metabolic disease (MESH:D008659), hyperparathyroidism (MESH:D006961), inflammation (MESH:D007249), injury to (MESH:D014947), Chronic Kidney Disease (MESH:D051436), angina (MESH:D000787), malignancies (MESH:D009369), Diabetes (MESH:D003920)
- **Chemicals:** lipid (MESH:D008055), glucose (MESH:D005947), alcohol (MESH:D000438), VFA (-), TG (MESH:D014280), UA (MESH:D014527), EDTA (MESH:D004492)
- **Species:** Homo sapiens (human, species) [taxon 9606], Mus musculus (house mouse, species) [taxon 10090]

## Figures

4 figures with captions in the complete paper: https://tomesphere.com/paper/PMC12942216/full.md

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Source: https://tomesphere.com/paper/PMC12942216