Abstract
Objectives: To investigate the prevalence of coronary artery calcification (CAC) in a Chinese population undergoinghealth examinations and to evaluate the associations of cardiometabolic, hepatic, and renal markers with CAC positivity and CAC burden. Methods: This single-center cross-sectional study included 2,957 Chinese adults who underwent routine health examinations and coronary artery calcium score (CACS) assessment at the Health Management Center of Shanghai Electric Power Hospital between January 1 and December 31, 2025. CAC positivity was defined as CACS > 0. Univariable and multivariable logistic regression analyses were used to identify factors associated with CAC positivity. CAC burden was further evaluated using ordinal logistic regression, linear regression for ln(CACS+1), and logistic regression for CACS ≥ 100. Results: Among the 2,957 participants, 737 (24.9%) were CAC-positive. In multivariable analyses, older age (adjusted OR=1.133, 95% CI: 1.113-1.153, P < 0.001), male sex (adjusted OR=7.990, 95% CI: 4.381-14.573, P < 0.001), higher body mass index (BMI) (adjusted OR=1.077, 95% CI: 1.036-1.119, P < 0.001), and higher HbA1c levels (adjusted OR=1.338, 95% CI: 1.202-1.491, P < 0.001) were independently associated with CAC positivity. These associations were consistent in sensitivity analyses and across CAC burden models. Conclusion: CAC is not unusual in the Chinese population undergoing health examinations. Old age, male sex, higher BMI, and higher HbA1c levels were consistently associated with both CAC positivity and higher CAC burden. Age, adiposity, and continuing glycemic exposure are among the routine clinical indicators that may help identify asymptomatic individuals at a high risk of CAC and support additional risk stratification.
Keywords: Coronary artery calcification, health check-up population, metabolic markers, hepatic markers, renal markers
Introduction
Coronary artery disease (CAD) is one of the major clinical manifestations of cardiovascular disease, which continues to be the leading cause of mortality and disease burden worldwide [1,2]. Cardiovascular disease remains the primary cause of death in China. The prevalence of CAD among adults reached 758 per 100,000 in 2021, and the crude mortality of cardiovascular disease in urban China was 305.39 per 100,000, underscoring its substantial burden [3].
CAD is primarily driven by atherosclerosis, a chronic process that involves endothelial dysfunction, lipid retention, inflammation, plaque formation, and subsequent calcification [4,5]. Traditional and metabolic risk factors, including age, sex, obesity, dysglycemia, dyslipidemia, and renal-metabolic dysfunction, contribute to CAD development [4,6]. For primary prevention and cardiovascular risk stratification, early identification of high-risk individuals in asymptomatic or health examination groups is crucial.
Coronary artery calcification (CAC) is a direct imaging marker of coronary atherosclerotic burden and a key indicator of subclinical coronary atherosclerosis [7]. CAC reflects the calcified component of atherosclerotic plaque and provides a quantitative estimate of the cumulative coronary atherosclerotic burden [7]. The coronary artery calcium score (CACS), which is calculated using the Agatston method, is the most popular and validated CAC measure in clinical practice. It has been widely used for cardiovascular risk stratification in asymptomatic adults [7-10].
Metabolic syndrome and dysglycemia are associated with both CAC prevalence and calcification burden [11]. In Chinese populations, high hemoglobin A1c (HbA1c) levels have been associated with a greater likelihood of CAC [12]. Furthermore, hepatic and renal dysfunction have been related to CAC, and the severity of liver fibrosis and chronic kidney diseases may be associated with CAC progression or a substantial coronary atherosclerotic burden [13,14]. Nevertheless, there remains a lack of information that integrates cardiometabolic, hepatic, and renal indicators to evaluate both CAC positivity and burden in Chinese health examination populations.
Therefore, the purpose of this study was to investigate CAC prevalence and its burden distribution, as determined by CACS, in a Chinese population undergoing health examinations. Moreover, the study aimed to analyze the associations between cardiometabolic, hepatic, and renal markers and both CAC positivity and its burden.
Methods
Study design and population
This single-center cross-sectional study was conducted at the Health Management Center of Shanghai Electric Power Hospital, Shanghai, China. The study population consisted of Chinese adults who underwent routine health examinations and CACS assessments between January 01, 2025, and December 31, 2025.
Eligibility criteria
The inclusion criteria were as follows: (1) Participation in routine health examinations at the Health Management Center during the study; (2) Completing a coronary artery calcium assessment with an available CACS; (3) Availability of anthropometric measurements, such as height, weight, and body mass index (BMI), as well as core laboratory measurements, such as cardiometabolic, hepatic, and renal markers; and (4) Age ≥ 18 years.
The exclusion criteria were as follows: (1) Missing or uninterpretable CACS; (2) Missing key research variables, implausible measures, or unverifiable measurements; and (3) Duplicate examination records, of which only the first or most complete record was retained.
Data collection and variable definitions
Demographic, anthropometric, cardiometabolic, hepatic, and renal markers
Demographic and anthropometric data, including age, sex, height, weight, and BMI, were extracted from the health examination database. Laboratory variables were classified as cardiometabolic, hepatic, and renal markers. Cardiometabolic markers included fasting plasma glucose (FPG), HbA1c, triglycerides (TG), total cholesterol (TC), high-density lipoprotein cholesterol (HDL-C), low-density lipoprotein cholesterol (LDL-C), and uric acid. Hepatic markers consisted of alanine aminotransferase (ALT), aspartate aminotransferase (AST), gamma-glutamyl transferase (GGT), and alkaline phosphatase (ALP). Renal markers comprised urea, creatinine, and estimated glomerular filtration rate (eGFR).
CAC-related measures and definitions
Non-contrast electrocardiogram-gated cardiac computed tomography (CT) or standard non-contrast chest CT was used to measure CAC. Automated or semi-automatic image processing using the Agatston method was used to determine CACS. A weighting factor of 1, 2, 3, or 4 was allocated to each calcified lesion based on the peak attenuation value of 130 to 199, 200 to 299, 300 to 399, or ≥ 400 Hounsfield units, respectively. The score of each lesion was calculated by multiplying the calcified area by the corresponding weighting factor, and the total CACS was obtained by summing the scores of all calcified lesions.
CACS is classified as 0, 1 to 99, 100 to 399, and ≥ 400. Here, CAC positivity was defined as CACS > 0. CACS was categorized as 0, 1 to 99, and ≥ 100 for burden analysis. A CACS ≥ 100 suggested a high CAC burden. As a continuous measure of CAC burden, ln(CACS+1) was used because CACS displayed a right-skewed distribution with a high percentage of zero values.
Statistical analysis
Continuous variables are shown as median (interquartile range), and categorical variables as n (%). The Mann-Whitney U test for continuous variables and the chi-square test for categorical variables were used to compare participants with CACS=0 and CACS > 0.
Crude associations between candidate variables and CAC positivity (CACS > 0) were assessed using univariate logistic regression. Next, multivariable logistic regression was conducted to detect factors that were independently associated with CAC positivity. Clinical relevance, previous research, and univariable outcomes were used to choose candidate variables for multivariable analyses. Variables representing related biological domains were not added to the same model concurrently to minimize potential collinearity and overadjustment.
Sensitivity analyses were conducted using alternative-variable multivariable models. Multiple linear regression for ln(CACS+1), multivariable logistic regression for CACS < 100 versus CACS ≥ 100, and ordinal logistic regression for CACS categories of 0, 1 to 99, and ≥ 100 were used to further evaluate factors related to CAC burden.
R software (version 4.2.1) was used for all data analyses. All tests were two-sided, and a P-value < 0.05 was considered significant.
Ethics statement
The Ethics Committee of Shanghai Electric Power Hospital approved this research protocol (Approval No. 2026-J001). Before analysis, all data were de-identified, and informed consent was not required. The study was conducted in accordance with the tenets of the Declaration of Helsinki.
Results
Baseline characteristics based on CAC status
Demographic and anthropometric differences
The final analysis included 2,957 participants. With a median age of 50 years (IQR, 44-55 years) and a median BMI of 24.5 kg/m2 (IQR, 22.4-26.8 kg/m2), the study population predominantly comprised men (2,427/2,957, 82.1%). Overall, 2,220 participants (75.1%) had a CACS of 0, whereas 737 (24.9%) had a CACS > 0. Participants with CAC positivity were older, more likely to be men, and had a higher BMI than participants with a CACS of 0 (all P < 0.001; Table 1).
Table 1.
Baseline characteristics of the study population according to coronary artery calcification status
| Characteristics | Overall (n=2957) | CACS=0 (n=2220) | CACS > 0 (n=737) | P value |
|---|---|---|---|---|
| Age, years | 50 (44, 55) | 48 (43, 54) | 55 (50, 58) | < 0.001 |
| Sex, n (%) | < 0.001 | |||
| Female | 530 (17.9) | 517 (23.3) | 13 (1.8) | |
| Male | 2427 (82.1) | 1703 (76.7) | 724 (98.2) | |
| BMI, kg/m2 | 24.5 (22.4, 26.8) | 24.2 (22.0, 26.4) | 25.6 (23.7, 27.8) | < 0.001 |
| Cardiometabolic markers | ||||
| FPG, mmol/L | 5.1 (4.8, 5.7) | 5.1 (4.7, 5.5) | 5.5 (5, 6.4) | < 0.001 |
| HbA1c, % | 5.6 (5.4, 5.8) | 5.5 (5.4, 5.7) | 5.7 (5.5, 6.3) | < 0.001 |
| TG, mmol/L | 1.6 (1.1, 2.3) | 1.5 (1.0, 2.2) | 1.8 (1.3, 2.7) | < 0.001 |
| TC, mmol/L | 5.3 (4.7, 5.9) | 5.3 (4.7, 5.9) | 5.3 (4.6, 5.9) | 0.947 |
| HDL-C, mmol/L | 1.2 (1.0, 1.4) | 1.2 (1.1, 1.4) | 1.1 (1.0, 1.3) | < 0.001 |
| LDL-C, mmol/L | 2.9 (2.4, 3.5) | 2.9 (2.4, 3.4) | 2.9 (2.4, 3.6) | 0.424 |
| Uric acid, umol/L | 355.0 (298.0, 412.0) | 349.0 (291.0, 408.3) | 371.0 (324.0, 425.0) | < 0.001 |
| Hepatic markers | ||||
| ALT, u/L | 23.0 (17.0, 34.0) | 22.0 (16.0, 33.0) | 25.0 (18.0, 36.0) | < 0.001 |
| AST, u/L | 20.0 (17.0, 25.0) | 20 (17.0, 24.0) | 21.0 (18.0, 26.0) | < 0.001 |
| GGT, u/L | 29.0 (20.0, 46.0) | 27.0 (19.0, 43.0) | 35.0 (25.0, 55.0) | < 0.001 |
| ALP, u/L | 71.0 (60.0, 85.0) | 71.0 (59.0, 84.0) | 73.0 (62.0, 86.0) | 0.006 |
| Total bilirubin, umol/L | 11.8 (9.1, 15.3) | 11.9 (9.0, 15.4) | 11.8 (9.4, 15) | 0.832 |
| Direct bilirubin, umol/L | 3.6 (2.8, 4.6) | 3.6 (2.8, 4.6) | 3.6 (2.8, 4.6) | 0.900 |
| Total bile acid, umol/L | 3.6 (2.9, 4.5) | 3.6 (2.8, 4.3) | 3.6 (2.9, 4.9) | 0.011 |
| Total protein, g/L | 76.5 (73.2, 78.7) | 76.5 (73.2, 78.7) | 76.5 (73.2, 78.6) | 0.742 |
| Albumin, g/L | 46.6 (45.0, 47.8) | 46.6 (45.0, 47.8) | 46.6 (45.0, 47.4) | 0.574 |
| Globulin, g/L | 30.2 (27.6, 32.3) | 30.2 (27.6, 32.3) | 30.2 (27.6, 32.3) | 0.925 |
| A/G | 1.6 (1.4, 1.7) | 1.6 (1.4, 1.7) | 1.6 (1.4, 1.7) | 0.853 |
| Prealbumin, mg/L | 291.0 (260.0, 334.0) | 291.0 (260.0, 334.0) | 300 (262.0, 333.0) | 0.022 |
| Cholinesterase, u/L | 9246.0 (8115.0, 10250.0) | 9171.0 (7974.5, 10200.0) | 9476.0 (8507.0, 10380.0) | < 0.001 |
| α-HBDH, u/L | 126.0 (114.0, 140.0) | 125 (113.0, 139.0) | 127.0 (115.0, 141.0) | 0.081 |
| Renal markers | ||||
| Urea, mmol/L | 5.0 (4.3, 5.8) | 4.9 (4.2, 5.7) | 5.3 (4.5, 6.1) | < 0.001 |
| Creatinine, umol/L | 71.0 (62.0, 80.0) | 71.0 (61.0, 79.0) | 73.0 (65.0, 81.0) | < 0.001 |
| eGFR, mL/min | 98.9 (85.8, 115.4) | 100.2 (87.2, 116.7) | 95.4 (82.2, 110.5) | < 0.001 |
Abbreviations: CAC, coronary artery calcification; CACS, coronary artery calcification score; BMI, body mass index; FPG, fasting plasma glucose; HbA1c, glycated hemoglobin; TG, triglycerides; TC, total cholesterol; HDL-C, high-density lipoprotein cholesterol; LDL-C, low-density lipoprotein cholesterol; ALT, alanine aminotransferase; AST, aspartate aminotransferase; GGT, gamma-glutamyl transferase; ALP, alkaline phosphatase; A/G, albumin/globulin ratio; α-HBDH, alpha-hydroxybutyrate dehydrogenase; eGFR, estimated glomerular filtration rate. Note: Data are presented as median (interquartile range) for continuous variables and n (%) for categorical variables. Comparisons between the two groups were performed using the Mann-Whitney U test for continuous variables and the chi-square test for categorical variables.
Cardiometabolic differences
FPG, HbA1c, TG, and uric acid levels were higher in participants with CACS > 0, whereas HDL-C levels were lower than in participants with a CACS of 0 (all P < 0.001). No statistically significant differences were observed in TC or LDL-C between the two groups (P=0.947 and P=0.424, respectively; Table 1).
Hepatic and renal differences
ALT, AST, GGT, ALP, total bile acid, prealbumin, and cholinesterase levels were higher in participants with a CACS > 0 than in participants without CAC. In contrast, no statistically significant differences were observed in total bilirubin, direct bilirubin, total protein, albumin, globulin, albumin/globulin ratio, or α-HBDH between the two groups. Notably, participants with a CACS > 0 had higher urea and creatinine levels but lower eGFR values (all P < 0.001; Table 1).
Overall, several cardiometabolic, hepatic, and renal markers showed differences between participants with and without CAC.
Factors associated with CAC positivity in univariable analyses
Demographic and anthropometric factors
According to univariable logistic regression analyses, old age, male sex, and higher BMI were associated with CAC positivity (Table 2). Specifically, the odds of CAC positivity rose with age (OR 1.147, 95% CI 1.129-1.165, P < 0.001) and BMI (OR 1.136, 95% CI 1.108-1.165, P < 0.001). Furthermore, men were more likely to test positive for CAC than women (OR 16.907, 95% CI 9.685-29.516, P < 0.001).
Table 2.
Univariable logistic regression analyses of factors associated with CAC positivity
| Variables | Total (N) | Odds Ratio (95% CI) | P value | |
|---|---|---|---|---|
| Age, years | 2,957 | 1.147 (1.129-1.165) | < 0.001 | |
| Sex | 2,957 | |||
| Female | 530 | Reference | ||
| Male | 2,427 | 16.907 (9.685-29.516) | < 0.001 | |
| BMI, kg/m2 | 2,957 | 1.136 (1.108-1.165) | < 0.001 | |
| Cardiometabolic markers | FPG, mmol/L | 2,957 | 1.425 (1.336-1.519) | < 0.001 |
| HbA1c, % | 2,957 | 1.815 (1.631-2.019) | < 0.001 | |
| TG, mmol/L | 2,957 | 1.201 (1.136-1.270) | < 0.001 | |
| TC, mmol/L | 2,957 | 0.961 (0.884-1.045) | 0.354 | |
| HDL-C, mmol/L | 2,957 | 0.261 (0.185-0.368) | < 0.001 | |
| LDL-C, mmol/L | 2,957 | 1.003 (0.904-1.112) | 0.961 | |
| Uric acid, umol/L | 2,957 | 1.003 (1.002-1.004) | < 0.001 | |
| Hepatic markers | ALT, u/L | 2,957 | 1.006 (1.003-1.010) | < 0.001 |
| AST, u/L | 2,957 | 1.012 (1.005-1.019) | < 0.001 | |
| GGT, u/L | 2,957 | 1.005 (1.003-1.007) | < 0.001 | |
| ALP, u/L | 2,957 | 1.005 (1.001-1.009) | 0.007 | |
| Total bilirubin, umol/L | 2,957 | 0.995 (0.980-1.010) | 0.507 | |
| Direct bilirubin, umol/L | 2,957 | 0.994 (0.943-1.048) | 0.825 | |
| Total bile acid, umol/L | 2,957 | 1.002 (0.988-1.017) | 0.736 | |
| Total protein, g/L | 2,957 | 0.999 (0.979-1.020) | 0.952 | |
| Albumin, g/L | 2,957 | 0.993 (0.954-1.033) | 0.729 | |
| A/G | 2,957 | 0.982 (0.657-1.468) | 0.930 | |
| Prealbumin, mg/L | 2,957 | 1.002 (1.001-1.004) | 0.008 | |
| Cholinesterase, u/L | 2,957 | 1.000 (1.000-1.000) | < 0.001 | |
| α-HBDH, u/L | 2,957 | 1.002 (0.999-1.006) | 0.157 | |
| Renal markers | Urea, mmol/L | 2,957 | 1.187 (1.117-1.262) | < 0.001 |
| Creatinine, umol/L | 2,957 | 1.016 (1.011-1.022) | < 0.001 | |
| eGFR, mL/min | 2,957 | 0.990 (0.986-0.994) | < 0.001 | |
Abbreviations: CAC, coronary artery calcification; CI, confidence interval; BMI, body mass index; FPG, fasting plasma glucose; HbA1c, glycated hemoglobin; TG, triglycerides; TC, total cholesterol; HDL-C, high-density lipoprotein cholesterol; LDL-C, low-density lipoprotein cholesterol; ALT, alanine aminotransferase; AST, aspartate aminotransferase; GGT, gamma-glutamyl transferase; ALP, alkaline phosphatase; α-HBDH, alpha-hydroxybutyrate dehydrogenase; eGFR, estimated glomerular filtration rate. Note: Univariable logistic regression analyses were performed with CAC positivity (CAC > 0) as the dependent variable. Continuous variables were entered as continuous terms, and sex was treated as a binary categorical variable with female as the reference category.
Cardiometabolic factors
Among cardiometabolic markers, high levels of FPG, HbA1c, TG, and uric acid were associated with greater odds of CAC positivity, whereas high HDL-C levels were associated with lower odds of CAC positivity (all P < 0.001; Table 2). In contrast, TC and LDL-C were not significantly associated with CAC positivity.
Hepatic and renal factors
High levels of hepatic markers, such as ALT, AST, GGT, ALP, prealbumin, and cholinesterase, were significantly associated with CAC positivity in univariable analyses (Table 2). However, there was no significant correlation between total bilirubin, direct bilirubin, total bile acid, total protein, albumin, albumin/globulin ratio, and α-HBDH and CAC positivity. Renal markers, such as higher urea and creatinine levels, were associated with greater odds of CAC positivity, whereas higher eGFR was associated with lower odds of CAC positivity (all P < 0.001; Table 2).
According to the univariable logistic regression analyses, CAC positivity was associated with negative cardiometabolic, hepatic, and renal markers.
Independent factors associated with CAC positivity
In the clinically driven multivariable logistic regression model, old age, male sex, high BMI, and high HbA1c levels were independently associated with CAC positivity (Table 3). Specifically, the odds of CAC positivity increased with age (adjusted OR 1.133, 95% CI 1.113-1.153, P < 0.001), BMI (adjusted OR 1.077, 95% CI 1.036-1.119, P < 0.001), and HbA1c levels (adjusted OR 1.338, 95% CI 1.202-1.491, P < 0.001). Men demonstrated a higher likelihood of CAC positivity than women (adjusted OR 7.990, 95% CI 4.381-14.573, P < 0.001).
Table 3.
Multivariable logistic regression analyses of factors associated with CAC positivity
| Variables | Total (N) | Odds Ratio (95% CI) | P value |
|---|---|---|---|
| Age, years | 2,957 | 1.133 (1.113-1.153) | < 0.001 |
| Sex | 2,957 | ||
| Female | 530 | Reference | |
| Male | 2,427 | 7.990 (4.381-14.573) | < 0.001 |
| BMI, kg/m2 | 2,957 | 1.077 (1.036-1.119) | < 0.001 |
| HbA1c, % | 2,957 | 1.338 (1.202-1.491) | < 0.001 |
| TG, mmol/L | 2,957 | 1.050 (0.990-1.114) | 0.105 |
| HDL-C, mmol/L | 2,957 | 1.074 (0.701-1.646) | 0.743 |
| Uric acid, umol/L | 2,957 | 1.000 (0.999-1.002) | 0.722 |
| GGT, u/L | 2,957 | 1.001 (0.999-1.003) | 0.362 |
| eGFR, mL/min | 2,957 | 1.003 (0.998-1.009) | 0.226 |
Abbreviations: CAC, coronary artery calcification; OR, odds ratio; CI, confidence interval; BMI, body mass index; HbA1c, glycated hemoglobin; TG, triglycerides; HDL-C, high-density lipoprotein cholesterol; GGT, gamma-glutamyl transferase; eGFR, estimated glomerular filtration rate. Note: Multivariable logistic regression was performed with CAC positivity (CACS > 0) as the dependent variable. The model included age, sex, BMI, HbA1c, TG, HDL-C, uric acid, GGT, and eGFR. Female sex was used as the reference category.
By contrast, after multivariable adjustment, TG, HDL-C, uric acid, GGT, and eGFR were not independently associated with CAC positivity.
Overall, age, male sex, BMI, and HbA1c were independent variables associated with CAC positivity.
Sensitivity analyses
Results from sensitivity analyses using alternative-variable multivariable logistic regression models were in line with the primary model’s findings (Table 4). Age, male sex, BMI, and HbA1c continued to be independently associated with CAC positivity in both alternative models, supporting the robustness of the key findings.
Table 4.
Sensitivity analyses using alternative-variable multivariable logistic regression models for factors associated with CAC positivity
| Variables | Total (N) | Odds Ratio (95% CI) | P value |
|
| |||
| Model 1: ALT and creatinine as alternative hepatic and renal markers | |||
|
| |||
| Age, years | 2,957 | 1.127 (1.108-1.145) | < 0.001 |
| Sex | 2,957 | ||
| Female | 530 | Reference | |
| Male | 2,427 | 8.185 (4.464-15.005) | < 0.001 |
| BMI, kg/m2 | 2,957 | 1.096 (1.061-1.132) | < 0.001 |
| HbA1c, % | 2,957 | 1.352 (1.214-1.505) | < 0.001 |
| TG, mmol/L | 2,957 | 1.057 (0.996-1.121) | 0.068 |
| HDL-C, mmol/L | 2,957 | 1.085 (0.709-1.661) | 0.706 |
| Uric acid, umol/L | 2,957 | 1.000 (0.999-1.002) | 0.595 |
| ALT, u/L | 2,957 | 0.998 (0.994-1.003) | 0.442 |
| Creatinine, umol/L | 2,957 | 0.996 (0.989-1.003) | 0.264 |
|
| |||
| Model 2: AST and urea as alternative hepatic and renal markers | |||
|
| |||
| Age, years | 2,957 | 1.127 (1.109-1.145) | < 0.001 |
| Sex | 2,957 | ||
| Female | 530 | Reference | |
| Male | 2,427 | 7.549 (4.170-13.665) | < 0.001 |
| BMI, kg/m2 | 2,957 | 1.093 (1.059-1.129) | < 0.001 |
| HbA1c, % | 2,957 | 1.349 (1.211-1.503) | < 0.001 |
| TG, mmol/L | 2,957 | 1.053 (0.993-1.117) | 0.085 |
| HDL-C, mmol/L | 2,957 | 1.084 (0.708-1.660) | 0.709 |
| Uric acid, umol/L | 2,957 | 1.000 (0.999-1.001) | 0.865 |
| AST, u/L | 2,957 | 0.999 (0.991-1.007) | 0.877 |
| Urea, mmol/L | 2,957 | 1.013 (0.946-1.084) | 0.712 |
Abbreviations: CAC, coronary artery calcification; OR, odds ratio; CI, confidence interval; BMI, body mass index; HbA1c, glycated hemoglobin; TG, triglycerides; HDL-C, high-density lipoprotein cholesterol; ALT, alanine aminotransferase; AST, aspartate aminotransferase. Note: Sensitivity analyses were performed using alternative-variable multivariable logistic regression models with CAC positivity (CACS > 0) as the dependent variable. Model 1 replaced GGT and eGFR in the main model with ALT and creatinine, respectively. Model 2 replaced GGT and eGFR in the main model with AST and urea, respectively. Female sex was used as the reference category.
Factors associated with CAC burden
Ordinal logistic regression for CACS categories (0, 1-99, and ≥ 100), linear regression for ln(CACS+1), and logistic regression for CACS ≥ 100 were used for multivariable analyses of CAC burden (Table 5). All three models produced results that were consistent. Old age, male sex, high BMI, and high HbA1c levels remained independently associated with greater CAC burden. In contrast, TG, HDL-C, uric acid, GGT, and eGFR levels did not show significant independent associations after adjustment. Taken together, age, male sex, BMI, and HbA1c were consistently identified as independent predictors of a high CAC burden.
Table 5.
Summary of multivariable analyses for factors associated with CAC burden
| Variables | Ordinal logistic regression | Linear regression | Logistic regression | |||
|---|---|---|---|---|---|---|
| CACS: 0, 1-99, ≥ 100 | ln(CACS+1) | CACS ≥ 100 | ||||
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| Adjusted OR (95% CI) | P value | Adjusted β (95% CI) | P value | Adjusted OR (95% CI) | P value | |
| Age, years | 1.130 (1.111-1.149) | < 0.001 | 0.059 (0.050 to 0.068) | < 0.001 | 1.134 (1.101-1.167) | < 0.001 |
| Sex | ||||||
| Female | Reference | Reference | Reference | |||
| Male | 7.887 (4.330-14.368) | < 0.001 | 0.427 (0.220 to 0.633) | < 0.001 | 6.733 (2.048-22.138) | 0.002 |
| BMI, kg/m2 | 1.079 (1.040-1.119) | < 0.001 | 0.040 (0.015 to 0.065) | 0.002 | 1.068 (1.008-1.131) | 0.025 |
| HbA1c, % | 1.382 (1.253-1.523) | < 0.001 | 0.355 (0.278 to 0.432) | < 0.001 | 1.440 (1.273-1.629) | < 0.001 |
| TG, mmol/L | 1.045 (0.989-1.104) | 0.115 | 0.025 (-0.017 to 0.068) | 0.239 | 1.045 (0.973-1.123) | 0.230 |
| HDL-C, mmol/L | 1.074 (0.707-1.632) | 0.738 | 0.046 (-0.208 to 0.301) | 0.722 | 1.129 (0.572-2.228) | 0.727 |
| Uric acid, μmol/L | 1.000 (0.999-1.001) | 0.850 | -0.000 (-0.001 to 0.001) | 0.536 | 1.000 (0.998-1.002) | 0.863 |
| GGT, U/L | 1.001 (0.999-1.003) | 0.338 | 0.000 (-0.001 to 0.002) | 0.631 | 1.001 (0.999-1.003) | 0.405 |
| eGFR, mL/min | 1.002 (0.997-1.008) | 0.409 | 0.000 (-0.003 to 0.004) | 0.933 | 0.999 (0.990-1.007) | 0.789 |
Abbreviations: CAC, coronary artery calcification; CACS, coronary artery calcification score; OR, odds ratio; CI, confidence interval; BMI, body mass index; HbA1c, glycated hemoglobin; TG, triglycerides; HDL-C, high-density lipoprotein cholesterol; GGT, gamma-glutamyl transferase; eGFR, estimated glomerular filtration rate. Note: All three models included age, sex, BMI, HbA1c, TG, HDL-C, uric acid, GGT, and eGFR as independent variables. The ordinal logistic regression used three CAC burden categories (0, 1-99, and ≥ 100). The linear regression used ln(CACS+1) as the dependent variable. The logistic regression used CACS < 100 versus CACS ≥ 100 as the dependent variable. Female sex was used as the reference category.
Discussion
The prevalence of CAC among asymptomatic adults in this Chinese population undergoing health examinations suggests a quantifiable burden of subclinical coronary atherosclerosis. Several cardiometabolic, hepatic, and renal markers were associated with CAC in univariable analyses. Nonetheless, only old age, male sex, high BMI, and high HbA1c levels were independently associated with CAC positivity in the clinically driven multivariable model. Sensitivity analyses and CAC burden models further supported these associations. Overall, these findings are in line with previous research that supports CAC as a direct imaging indicator of subclinical coronary atherosclerosis and its strong relationship with metabolic abnormalities, particularly glycemic dysfunction [7,8,11,12].
In this study, age, male sex, BMI, and HbA1c were most consistently associated with CAC. The associations of old age and male sex with a high risk of CAC and greater CAC burden are consistent with previous reports. Therefore, CAC reflects the prolonged cumulative process of atherosclerosis and may be influenced by sex-related differences in cardiovascular risk exposure [15]. In keeping with earlier findings that overweight (OR=1.13, 95% CI: 1.10-1.20) and obesity (OR=1.50, 95% CI: 1.40-1.60) are linked to a higher risk of CAC, BMI remained independently associated with both CAC positivity and a high CAC burden [6]. Notably, HbA1c remained significant in the primary, sensitivity, and burden analyses, making it the most stable cardiometabolic marker. Furthermore, this finding is consistent with the following previous findings: (I) The pooled prevalence of CAC in patients with metabolic syndrome was 39.8%, (II) Men had a two-fold higher risk of CAC than women, and (III) Metabolic syndrome was associated with a high risk of CAC (OR range, 1.34-1.50) [11]. Furthermore, on examining 8,955 Chinese adults, Huang et al. reported that HbA1c levels between 5.7% and 6.4% and ≥ 6.5% were associated with 28% (OR=1.28, 95% CI: 1.07-1.52) and 116% (OR=2.16, 95% CI: 1.48-3.16) higher risks of CAC, respectively, compared with HbA1c < 5.7% [12]. Together, continuing glycemic exposure and clustering of metabolic abnormalities may contribute to the development and progression of coronary calcification.
In univariable analysis, TG, HDL-C, uric acid, GGT, and eGFR levels were significantly associated with CAC. Nonetheless, after multivariable adjustment, these associations lost significance, suggesting a possible shared metabolic background rather than independent effects. Similarly, some metabolic and organ function markers may be associated with CAC or coronary atherosclerosis in crude analyses. However, their independent effects are not always stable after additional adjustment. For example, in 14,439 asymptomatic Korean adults, Lee et al. reported that the association between GGT and CAC was significant in men (OR=1.49, 95% CI: 1.21-1.85) but not in women (OR=1.33, 95% CI: 0.62-2.87). This relationship may be influenced by sex and other metabolic factors [16]. Likewise, Grossman et al. demonstrated that participants in the highest uric acid group had a greater risk of CAC than participants in the lowest uric acid group (OR=1.84, 95% CI: 1.10-3.07). In contrast, a meta-analysis showed substantial heterogeneity across studies, suggesting that the effect of uric acid may vary according to population characteristics and accompanying metabolic abnormalities [17,18]. Furthermore, hepatic and renal dysfunction have been related to CAC. In a meta-analysis involving 10,060 participants, nonalcoholic fatty liver disease (NAFLD) was associated with a high risk of CAC progression (OR=1.50, 95% CI: 1.34-1.68) [19]. In a longitudinal cohort of 10,153 asymptomatic adults, NAFLD was independently associated with CAC incidence (adjusted HR=1.348, 95% CI: 1.051-1.730) [20], suggesting that hepatic steatosis and its metabolic milieu may contribute to coronary calcification. Furthermore, CAC is prevalent in patients with chronic kidney disease (CKD), with a baseline prevalence of approximately 66% in adults with early CKD [21]. Its progression appears to be more pronounced in patients with advanced CKD [14]. This may explain why renal dysfunction-related markers showed crude associations with CAC in the cohort but did not remain independently significant after multivariable adjustment. Therefore, TG, HDL-C, uric acid, GGT, and eGFR levels may still indicate metabolic or organ dysfunction in the current Chinese population undergoing health examinations. However, their associations with CAC were at least partially explained by more dominant clinical factors, such as age, sex, BMI, and HbA1c.
Ordinal logistic regression, logistic regression for CACS ≥ 100, and linear regression for ln(CACS+1) were used to further evaluate CAC burden. Across all models, age, male sex, BMI, and HbA1c were consistently correlated with both CAC and a high calcification burden. This consistency implies that these factors may be involved in both the onset and progression of coronary calcification, strengthening the findings’ robustness. CAC and its burden categories are central to cardiovascular risk categorization in asymptomatic adults. Higher CACS categories suggest a greater risk of atherosclerotic cardiovascular events [7,8]. Therefore, standard clinical markers, such as age, sex, BMI, and HbA1c, may be used in Chinese populations undergoing health assessments to identify individuals who are at a high risk of CAC, direct further CACS assessments, and improve primary prevention strategies.
This study has several strengths. It was based on a sizeable Chinese population undergoing health examinations, which facilitated the assessment of CAC prevalence and related factors in asymptomatic adults. In addition to CAC positivity, three complementary models were used to determine the CAC burden, which strengthened the robustness of the findings. Simultaneously, cardiometabolic, hepatic, and renal markers were assessed, providing a comprehensive evaluation of their associations with CAC. Nonetheless, this study has several limitations. First, this study had a single-center and cross-sectional design, which precluded causal inference. Second, generalizability may be limited because the population was drawn from a health examination cohort that predominantly comprised men. Third, confounding cannot be ruled out because several confounders, including smoking, alcohol consumption, blood pressure, medication use, and history of cardiovascular diseases, were inaccessible.
Conclusion
CAC was not uncommon in this Chinese population undergoing health examinations. Old age, male sex, high BMI, and high HbA1c levels were associated with both CAC positivity and a high CAC burden. In contrast, several cardiometabolic, hepatic, and renal markers were not independently related to CAC after multivariable adjustment. Therefore, routine clinical indicators, particularly age, adiposity, and continuing glycemic exposure, may facilitate the identification of asymptomatic individuals at a high risk of CAC and allow for additional risk stratification.
Acknowledgements
During the preparation of this work, the authors used ChatGPT (OpenAI, GPT-5.5 Thinking model) solely for minor language polishing. The authors reviewed and edited the content as needed and take full responsibility for the integrity and accuracy of the manuscript.
Disclosure of conflict of interest
None.
References
- 1.Global Burden of Cardiovascular Diseases and Risks 2023 Collaborators. Global, regional, and national burden of cardiovascular diseases and risk factors in 204 countries and territories, 1990-2023. J Am Coll Cardiol. 2025;86:2167–2243. doi: 10.1016/j.jacc.2025.08.015. [DOI] [PubMed] [Google Scholar]
- 2.Wang Y, Wang X, Wang C, Zhou J. Global, regional, and national burden of cardiovascular disease, 1990-2021: results from the 2021 global burden of disease study. Cureus. 2024;16:e74333. doi: 10.7759/cureus.74333. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 3.Liu M, He X, Yang X, Wang Z. 2024 annual report on cardiovascular health and diseases in China: data and trend. Chin Med J (Engl) 2025;138:3037–3049. doi: 10.1097/CM9.0000000000003902. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 4.Libby P, Buring JE, Badimon L, Hansson GK, Deanfield J, Bittencourt MS, Tokgozoglu L, Lewis EF. Atherosclerosis. Nat Rev Dis Primers. 2019;5:56. doi: 10.1038/s41572-019-0106-z. [DOI] [PubMed] [Google Scholar]
- 5.Kong P, Cui ZY, Huang XF, Zhang DD, Guo RJ, Han M. Inflammation and atherosclerosis: signaling pathways and therapeutic intervention. Signal Transduct Target Ther. 2022;7:131. doi: 10.1038/s41392-022-00955-7. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 6.Jensen JC, Dardari ZA, Blaha MJ, White S, Shaw LJ, Rumberger J, Rozanski A, Berman DS, Budoff MJ, Nasir K, Miedema MD. Association of body mass index with coronary artery calcium and subsequent cardiovascular mortality: the coronary artery calcium consortium. Circ Cardiovasc Imaging. 2020;13:e009495. doi: 10.1161/CIRCIMAGING.119.009495. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 7.Pavlovic J, Bos D, Ikram MK, Ikram MA, Kavousi M, Leening MJG. Guideline-directed application of coronary artery calcium scores for primary prevention of atherosclerotic cardiovascular disease. JACC Cardiovasc Imaging. 2025;18:465–475. doi: 10.1016/j.jcmg.2024.12.008. [DOI] [PubMed] [Google Scholar]
- 8.Parsa S, Saleh A, Raygor V, Hoeting N, Rao A, Navar AM, Rohatgi A, Kay F, Abbara S, Khera A, Joshi PH. Measurement and application of incidentally detected coronary calcium: JACC review topic of the week. J Am Coll Cardiol. 2024;83:1557–1567. doi: 10.1016/j.jacc.2024.01.039. [DOI] [PubMed] [Google Scholar]
- 9.Writing Committee, Lloyd-Jones DM, Morris PB, Ballantyne CM, Birtcher KK, Covington AM, DePalma SM, Minissian MB, Orringer CE, Smith SC Jr, Waring AA, Wilkins JT. 2022 ACC expert consensus decision pathway on the role of nonstatin therapies for LDL-cholesterol lowering in the management of atherosclerotic cardiovascular disease risk: a report of the American college of cardiology solution set oversight committee. J Am Coll Cardiol. 2022;80:1366–1418. doi: 10.1016/j.jacc.2022.07.006. [DOI] [PubMed] [Google Scholar]
- 10.Maron DJ, Budoff MJ, Sky JC, Bommer WJ, Epstein SD, Fisher DA, Stock EO, Taylor AJ, Wong ND, DeMaria AN. Coronary artery calcium staging to guide preventive interventions: a proposal and call to action. JACC Adv. 2024;3:101287. doi: 10.1016/j.jacadv.2024.101287. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 11.Ullah A, Jan A, Kareem HN, Babaresh WMA, Rahim A, Ali SS, Shah WA, Hajwal SK, Hermis AH, Jawad MK, Salman SS, Ajel MA, Alsuwayidi FS, Alobaidan FN, Kadhem AH. Prevalence and determinants of coronary artery calcification in adults with metabolic syndrome: a systematic review and meta-analysis. Clin Cardiol. 2025;48:e70156. doi: 10.1002/clc.70156. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 12.Huang Y, Ni W, Zhou Y, Li D, Zhang R, Jin T, Zhong Y. Association between glycated hemoglobin and coronary artery calcification in middle-aged and elderly Chinese checkup populations. Int J Endocrinol Metab. 2025;23:e158710. doi: 10.5812/ijem-158710. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 13.Lee Y, Lee W. Time-updated FIB-4 index predicts coronary artery calcification progression in individuals with metabolic dysfunction-associated steatotic liver disease. Sci Rep. 2025;16:1459. doi: 10.1038/s41598-025-31289-3. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 14.Tian L, Jaeger BC, Scialla JJ, Budoff MJ, Mehta RC, Jaar BG, Saab G, Dobre MA, Reilly MP, Rader DJ, Townsend RR, Lash JP, Greenland P, Isakova T, Bundy JD CRIC Study Investigators. Progression of coronary artery calcification and risk of clinical events in CKD: the chronic renal insufficiency cohort study. Am J Kidney Dis. 2025;85:67–77. e1. doi: 10.1053/j.ajkd.2024.06.018. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 15.Javaid A, Dardari ZA, Mitchell JD, Whelton SP, Dzaye O, Lima JAC, Lloyd-Jones DM, Budoff M, Nasir K, Berman DS, Rumberger J, Miedema MD, Villines TC, Blaha MJ. Distribution of coronary artery calcium by age, sex, and race among patients 30-45 years old. J Am Coll Cardiol. 2022;79:1873–1886. doi: 10.1016/j.jacc.2022.02.051. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 16.Lee W, Ryoo JH, Suh BS, Lee J, Kim J. Association of coronary artery calcification and serum gamma-glutamyl transferase in Korean. Atherosclerosis. 2013;226:269–274. doi: 10.1016/j.atherosclerosis.2012.10.059. [DOI] [PubMed] [Google Scholar]
- 17.Grossman C, Shemesh J, Koren-Morag N, Bornstein G, Ben-Zvi I, Grossman E. Serum uric acid is associated with coronary artery calcification. J Clin Hypertens (Greenwich) 2014;16:424–428. doi: 10.1111/jch.12313. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 18.Liang L, Hou X, Bainey KR, Zhang Y, Tymchak W, Qi Z, Li W, Banh HL. The association between hyperuricemia and coronary artery calcification development: a systematic review and meta-analysis. Clin Cardiol. 2019;42:1079–1086. doi: 10.1002/clc.23266. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 19.Koulaouzidis G, Charisopoulou D, Kukla M, Marlicz W, Rydzewska G, Koulaouzidis A, Skonieczna-Żydecka K. Association of non-alcoholic fatty liver disease with coronary artery calcification progression: a systematic review and meta-analysis. Prz Gastroenterol. 2021;16:196–206. doi: 10.5114/pg.2021.109063. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 20.Sinn DH, Kang D, Chang Y, Ryu S, Gu S, Kim H, Seong D, Cho SJ, Yi BK, Park HD, Paik SW, Song YB, Lazo M, Lima JA, Guallar E, Cho J, Gwak GY. Non-alcoholic fatty liver disease and progression of coronary artery calcium score: a retrospective cohort study. Gut. 2017;66:323–329. doi: 10.1136/gutjnl-2016-311854. [DOI] [PubMed] [Google Scholar]
- 21.Kestenbaum BR, Adeney KL, de Boer IH, Ix JH, Shlipak MG, Siscovick DS. Incidence and progression of coronary calcification in chronic kidney disease: the multi-ethnic study of atherosclerosis. Kidney Int. 2009;76:991–998. doi: 10.1038/ki.2009.298. [DOI] [PMC free article] [PubMed] [Google Scholar]