This study protocol was approved by the Ethics Committee of Xinxiang First Affiliated Hospital (approval number: EC-025-374). The requirement for individual informed consent was waived by the Ethics Committee due to the retrospective design and strict anonymization of all patient data, in accordance with the Declaration of Helsinki and the Council for International Organizations of Medical Sciences (CIOMS) International Ethical Guidelines for Biomedical Research Involving Human Subjects. All data collection and processing strictly adhered to institutional data protection protocols, with researchers having access only to de-identified data. This study was conducted and reported following the Strengthening the Reporting of Observational Studies in Epidemiology (STROBE) guidelines. All research personnel completed medical ethics training and signed confidentiality agreements.

A retrospective analysis was performed on the medical records of adult T2DM patients who underwent health examinations at Xinxiang First Affiliated Hospital between January 2020 and January 2025. The inclusion criteria were: (1) T2DM patients who had a low-dose chest CT scan assessing VFA; (2) aged 20 to 80 years; and (3) complete demographic and questionnaire data. Exclusion criteria were patients with a historical or current diagnosis of any cancer, severe liver or kidney disease, thyroid disease, recent significant weight fluctuations (≥5%), pregnancy or breastfeeding, mental disturbance, or extreme values in examination results.

Initially, 6,671 T2DM patients were enrolled. After applying the exclusion criteria, 3,173 patients were retained for analysis, with 865 excluded. Among the retained patients, 923 (29.1%) reported a history of physician-diagnosed CVD (verified through our quality control procedures) and 2,250 (70.9%) reported no history of CVD. General demographic information, medical history, and medication history were collected through face-to-face interviews conducted by trained researchers. A detailed flowchart of the case selection process is shown in Figure 1 .

The independent variable was VFA in T2DM, measured by quantitative CT (QCT). VFA was defined as the intraperitoneal fatty area enclosed by the peritoneal wall or fascia transversalis muscle at the level of the L2/3 intervertebral space and the umbilicus, measured in square centimeters (cm²). CVD served as the dependent variable in this study. CVD was defined as a composite endpoint including any of the following physician-diagnosed conditions: coronary artery disease, angina pectoris, stroke, congestive heart failure, or myocardial infarction. CVD diagnosis was determined based on patient self-report of previous physician diagnosis obtained through standardized face-to-face interviews using a structured medical questionnaire. During the interview, trained researchers specifically asked each T2DM patient: ‘Has a doctor or healthcare professional ever diagnosed you with any of the following cardiovascular conditions: (1) coronary artery disease, (2) angina pectoris, (3) stroke, (4) congestive heart failure, or (5) myocardial infarction?’ A positive response to any of these five conditions was classified as having CVD. To ensure data quality and accuracy, any discrepancies or missing information regarding CVD history were systematically reconfirmed with participants through additional in-person interviews or telephone follow-up. These physician-diagnosed cardiovascular disease histories were classified according to the International Statistical Classification of Diseases and Related Health Problems, 10th edition (ICD-10), using the following specific codes: coronary artery disease (I20-I25.9), angina pectoris (I20.0-I20.9), congestive heart failure (I50.0, I50.1, I50.9), myocardial infarction (I21-I23), and stroke (I60-I69) (24). All analyses were based on verified CVD status as defined above, with quality control measures implemented to ensure data accuracy.

The diagnosis of T2DM followed the American Diabetes Association criteria (25): a previous physician diagnosis of diabetes, current treatment with hypoglycemic medications, fasting plasma glucose (FPG) ≥ 7.0 mmol/L, glycosylated hemoglobin (HbA1c) level ≥ 6.5%, 2-hour oral glucose tolerance test (OGTT) blood glucose ≥ 11.1 mmol/L, or use of insulin or oral hypoglycemic agents.

Body mass index (BMI) was calculated as weight (kg)/height² (m²) and categorized according to Chinese standards (26): normal weight (< 24 kg/m²), overweight (≥ 24 kg/m², < 28 kg/m²), and obese (≥ 28 kg/m²).

Hypertension was defined as having two consecutive measurements of systolic blood pressure (SBP) ≥ 140 mmHg or diastolic blood pressure (DBP) ≥ 90 mmHg, self-reported hypertension, use of antihypertensive drugs, or current antihypertensive therapy (27).

Estimated glomerular filtration rate (eGFR) was calculated using the formula: eGFR = 175 * serum creatinine^ (-1.154) * age^ (-0.203) * 0.742 (if female) * 1.212 (if black) (28). eGFR is expressed in mL/min/1.73 m², with serum creatinine in mg/dL and age in years.

Current smoking was defined as self-reported smoking by the participant. Current alcohol consumption was defined as the intake of at least one alcoholic beverage per week in the 12 months preceding the health screening.

All T2DM patients were divided into quartiles based on VFA levels: Q1 (33.30–172.00 cm²), Q2 (172.10–219.90 cm²), Q3 (220.00–271.90 cm²), and Q4 (272.10–497.90 cm²).

All researchers underwent standardized training to ensure objectivity and accuracy. Before the examination, they collected basic information from participants using a standardized questionnaire. This included a history of cardiovascular disease, liver and kidney disease, various cancers, and the use of diabetes, hypertension, and lipid-lowering medications, as well as recent weight changes. After completing the questionnaires, the data was organized, summarized, and verified. Any discrepancies or missing information, particularly regarding cardiovascular disease history, were systematically reconfirmed with participants through additional in-person interviews or telephone follow-up to ensure data accuracy and completeness.

Height was measured using a stadiometer, and weight was recorded with a weighing scale. Fasting venous blood samples were collected from all participants at 8 a.m. after a 12-hour fast. These samples were analyzed for creatinine (Cre), blood urea nitrogen (BUN), total cholesterol (TC), low-density lipoprotein cholesterol (LDL-C), triglycerides (TG), high-density lipoprotein cholesterol (HDL-C), FBG, and HbA1c. FBG was measured using an Olympus^® AU 5800 fully automated biochemistry analyzer (Beckman Coulter Inc., Brea, CA, USA). Other biochemical parameters were measured following standard laboratory protocols. Blood pressure (SBP and DBP) was measured using an electronic sphygmomanometer (OMRON U30, Omron Corporation, Kyoto, Japan). Measurements were taken on the right arm of each T2DM patient, positioned semi-flexed at heart level.

VFA was measured using low-dose chest CT scan data, a routine examination for assessing pulmonary lesions during health check-ups. The scan range included the L3 vertebra, minimizing unnecessary radiation exposure. All participants were scanned using the same 64-detector row CT scanner with standardized parameters: slice thickness 5.0 mm, tube voltage 120 kVp, tube current 100–150 mAs (auto-adjusted based on patient habitus), reconstruction matrix 512×512, calibrated weekly with a phantom (Mindways, Austin, TX, USA) to ensure consistent data quality. After scanning, a trained radiologist measured VFA using the QCT Pro 6.1 supplemental tissue measurement application from Mindways Software. This software performs QCT measurements on two standardized anatomical levels: (1) L2/3 intervertebral space, identified using sagittal reformatted images; (2) umbilicus level, verified by external anatomical landmarks and coronal reformats based on chest CT scan data. The application automatically segmented adipose tissue using standardized Hounsfield unit thresholds (-190 to -30 HU) and calculated the VFA in these slices. The final VFA value represents the average of measurements from both anatomical levels. To minimize measurement errors, care was taken to avoid artifacts from lumbar internal fixation, intestinal gas, or high-density contents. As illustrated in Figure 2 . This measurement technique has been validated in the Chinese population (29). Further details on the measurement process can be found in previous studies (30, 31).

To ensure measurement reliability, intra-observer reproducibility was assessed by repeating measurements on 300 randomly selected cases (10% of total sample) after a 4-week interval, achieving an intraclass correlation coefficient (ICC) of 0.96 (95% CI: 0.94-0.98).

All statistical analyses were conducted using R version 4.2.0 (R Foundation) and EmpowerStats (http://www.empowerstats.com, X&Y Solutions, Inc., Boston, MA). All tests were two-tailed, with a significance level set at P < 0.05.

Normality tests were performed on all datasets to assess continuous variables. Normally distributed continuous variables were reported as mean ± standard deviation, and group differences were evaluated using t-tests or rank-sum tests. Categorical variables were presented as frequencies and percentages, with comparisons made using chi-square tests.

Univariate analysis was used to evaluate the impact of various variables on CVD. Subsequently, multivariate logistic regression was conducted to assess the relationship between VFA and CVD, adjusting for covariates including sex, age, ethnicity, hypertensive medication, diabetes medication, lipid-lowering drugs, current smoking, current alcohol consumption, hypertension, BMI, BUN, creatinine, eGFR, LDL-C, TG, and HDL-C. Covariates with a variance inflation factor (VIF) >10 were excluded. Four models were developed in this study: a crude model with no adjustments, Model I adjust for demographic variables (sex, age, and ethnicity), Model II adjusting for demographic factors along with hypertensive drugs, diabetes drugs, lipid-lowering drugs, current smoking, alcohol consumption, hypertension, and BMI, and Model III adjusting for all previously mentioned confounders. The results from Model III were used for subsequent analyses. VFA was categorized into quartiles, with the lowest quartile serving as the reference group, to assess the relationship between VFA and CVD. A generalized additive model (GAM) with smooth curve fitting was employed to explore the dose-response relationship between VFA and CVD. Finally, stratified analyses and interaction tests based on Model III were performed to determine whether the relationship between VFA and CVD was consistent across different subgroups.

This study included 3,173 T2DM participants, comprising 2,254 men and 919 women. Participants were divided into four groups based on visceral fat area (VFA) quartiles: Q1 (33.30–172.00 cm², n = 793), Q2 (172.10–219.90 cm², n = 793), Q3 (220.00–271.90 cm², n = 792), and Q4 (272.10–497.90 cm², n = 795). As shown in Figure 3 , the prevalence of CVD increased progressively across the VFA quartiles. Compared to the Q1 group, participants in the Q4 group (highest VFA) were more likely to be male, have a higher BMI, smoke and drink, have hypertension, use antihypertensive drugs, and have higher levels of Cre, TC, LDL-C, TG, FBG, and CVD prevalence (all P < 0.05). They also had lower HDL-C levels (P < 0.05). There were no significant differences in other variables (all P > 0.05), as shown in Table 1 .

Univariate logistic regression analysis was used to evaluate the impact of traditional variables on CVD and to select covariates for subsequent multivariate analysis. As shown in Table 2 , male sex, older age, higher BMI, current smoking, current drinking, hypertension, eGFR, LDL-C, and TG were identified as risk factors for CVD (all P < 0.05). In contrast, the use of hypertensive drugs, diabetic drugs, lipid-lowering drugs, TC, and HDL-C levels were protective factors against CVD (all P < 0.05). Ethnic group, creatinine (Cre), and BUN did not show significant correlations (all P > 0.05).

Multivariate regression analysis was conducted to account for confounding variables, and four models were developed. As shown in Table 3 , the crude model, which did not adjust for any covariates, revealed a positive correlation between VFA and CVD (OR = 1.01, 95% CI: 1.00 - 1.02, P < 0.001). After adjusting for demographic variables such as sex, age, and ethnic group (Model I), a positive correlation between VFA and CVD was confirmed (OR = 1.02, 95% CI: 1.01– 1.06, P < 0.001). In Model II and Model III, VFA remained independently associated with an increased risk of CVD (OR = 1.43, 95% CI: 1.12 – 1.65, P < 0.001 in Model III). Specifically, for each unit increase in VFA, the odds of having prevalent CVD were 1.43 times higher. Additionally, when the VFA was divided into quartiles, after adjusting for confounding variables, the odds of having prevalent CVD in the Q4 group was 2.04 times higher than in the Q1 group (P < 0.001). Moreover, the association between VFA and CVD risk in T2DM patients was further examined through GAM smoothing curve fitting, which indicated a linear association (P-nonlinearity > 0.05). Each 10 cm² increase in VFA was associated with an odds ratio of 1.43 (95% CI: 1.12–1.65) for CVD risk ( Figure 4 ).

As illustrated in Figure 5 , the subgroup analyses revealed consistent findings. No significant interactions were observed when stratified by sex (female/male), ethnic group (non-Han/Han), age (<60 years/≥60 years), BMI (<24 kg/m²/≥24, <28 kg/m²/≥28 kg/m²), current smoking (yes/no), current drinking (yes/no), hypertension (yes/no), use of antihypertensive drugs (yes/no), diabetic drugs (yes/no), or lipid-lowering drugs (yes/no) (P for interaction > 0.05).