Association Between Cardiometabolic Index and Coronary Artery Calcification in Young and Middle-Aged Adults With Coronary Heart Disease

Association Between Cardiometabolic Index and Coronary Artery Calcification in Young and Middle-Aged Adults With Coronary Heart Disease | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Association Between Cardiometabolic Index and Coronary Artery Calcification in Young and Middle-Aged Adults With Coronary Heart Disease Xi Wu, Mingxing Wu, Haobo Huang, Zhe Liu, He Huang, Lei Wang This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-6649906/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 05 Jul, 2025 Read the published version in BMC Cardiovascular Disorders → Version 1 posted 10 You are reading this latest preprint version Abstract Background: Coronary artery calcification (CAC) serves as a well-established indicator of subclinical atherosclerosis and is a robust predictor of future cardiovascular complications. The cardiometabolic index (CMI), a recently proposed composite metric combining waist-to-height ratio (WHtR) and the triglyceride-to-HDL cholesterol (TG/HDL-C) ratio, has gained attention as a proxy for both visceral fat accumulation and atherogenic dyslipidemia. Nevertheless, the relationship between CMI and CAC in young and middle-aged individuals diagnosed with coronary heart disease (CHD) remains insufficiently explored. Methods: This retrospective analysis included CHD patients aged 18–59 years who were admitted to the hospital and underwent coronary computed tomography angiography (CTA) between January 2018 and August 2022. Based on CTA findings, participants were stratified into CAC-positive and CAC-negative groups. Data on demographics, clinical characteristics, and biochemical parameters were obtained. CMI was computed using the formula WHtR × (TG/HDL-C). Multivariate logistic regression models and receiver operating characteristic (ROC) curve analysis were employed to assess both the association and diagnostic value of CMI for CAC. Results: A total of 439 patients were analyzed, of whom 128 were classified as having CAC. The CAC group exhibited a significantly elevated CMI compared to their counterparts without CAC (P < 0.001). After adjusting for potential confounders, CMI emerged as an independent predictor of CAC (OR = 2.436, 95% CI: 1.694–3.502, P < 0.001), whereas BMI did not reach statistical significance. ROC curve analysis revealed that CMI had superior predictive accuracy relative to BMI (AUC: 0.753 vs. 0.606). The optimal CMI threshold for predicting CAC was 0.742, yielding a sensitivity of 72.81% and a specificity of 70.95%. Conclusion: CMI shows a significant independent association with CAC and outperforms BMI in predicting its presence among young and middle-aged adults with CHD. Given its simplicity, non-invasiveness, and low cost, CMI may represent a practical screening tool for early detection of asymptomatic coronary atherosclerosis in clinical settings. Cardiometabolic Index Coronary Artery Calcification Coronary Heart Disease Young and Middle-Aged Adults Cardiovascular Risk Prediction Figures Figure 1 Figure 2 Introduction Coronary artery calcification (CAC) is widely recognized as a hallmark of subclinical atherosclerosis and serves as a robust prognostic indicator for future coronary artery disease (CAD) events. It reflects the cumulative progression of atherosclerotic plaque formation and is closely linked with adverse cardiovascular outcomes( 1 ). The detection of CAC through imaging is considered a dependable surrogate for underlying coronary atherosclerosis and is regarded as a potent marker of poor clinical prognosis in patients with coronary atherosclerotic heart disease (CHD)( 2 ). Although CAC has traditionally been associated with older populations, emerging evidence indicates an increasing prevalence among younger and middle-aged adults, drawing attention to the asymptomatic and concealed progression of atherosclerosis in these groups( 3 ). Early CAC identified in younger individuals has been correlated with markedly elevated risks for future cardiovascular incidents and overall mortality( 3 , 4 ). Furthermore, young individuals with conventional or hereditary cardiovascular risk factors are reported to have a two- to three-fold greater probability of developing CAC when compared with age-matched counterparts lacking such risks( 5 ). Nevertheless, current CAC-related research predominantly focuses on older populations, with relatively little attention given to early-onset CHD despite its rising incidence in younger cohorts( 6 ). The pathophysiological development of CHD is multifaceted and heavily influenced by metabolic factors. Although obesity is recognized as an independent contributor to CHD, the body mass index (BMI) does not adequately reflect the complexity of fat distribution, particularly visceral adiposity, which plays a more pivotal role in cardiovascular risk. Notably, some individuals classified as obese exhibit a metabolically healthy phenotype, whereas others with a normal BMI may present with a metabolically unhealthy normal weight profile( 7 , 8 ). This paradox accentuates the limitations of BMI and underscores the need for more sophisticated and precise metabolic risk markers. Recently, the cardiometabolic index (CMI)—defined as the product of the triglyceride to high-density lipoprotein cholesterol (TG/HDL-C) ratio and the waist-to-height ratio (WHtR)—has gained attention as a promising indicator of visceral adiposity and dysregulated lipid metabolism( 9 ). CMI has demonstrated strong associations with metabolic syndrome, early-stage atherosclerosis, carotid plaque formation, peripheral arterial ischemia, and ischemic cardiovascular events( 10 – 12 ). A meta-analysis has further validated the TG/HDL-C ratio, a component of CMI, as an independent risk factor for CHD( 13 ), while WHtR has been shown to outperform traditional metrics like BMI and waist circumference (WC) in predicting cardiovascular risk( 14 ). Despite these findings, there remains a lack of systematic investigations examining whether CMI is associated with the presence or severity of CAC in younger and middle-aged adults. Most existing models that assess CAC focus on parameters such as total calcium score, lesion density, or arc length. In contrast, relatively few have evaluated the predictive potential of CMI concerning the binary presence of CAC—an early, accessible, and clinically relevant marker of subclinical coronary atherosclerosis( 6 ). Therefore, the present study aims to investigate the relationship between CMI and both the occurrence and extent of CAC in young and middle-aged individuals with CHD. The goal is to determine whether CMI can serve as a valuable clinical tool for the early identification and stratification of subclinical atherosclerosis and premature coronary calcification( 15 ). Materials and methods Study Participants This retrospective and observational study was conducted at a single tertiary-care hospital. From January 2018 to August 2022, patients who were admitted to the Department of Cardiology at Xiangtan Central Hospital and underwent coronary computed tomography angiography (CTA), followed by their first coronary angiography (CAG) and, when clinically indicated, percutaneous coronary intervention (PCI), were consecutively screened for eligibility. Based on CTA findings, patients were classified into two groups: those with CAC and those without. CAC was defined as the presence of high-attenuation lesions (> 130 Hounsfield units [HU]) within the coronary artery wall. All CTA images were independently reviewed and validated by experienced radiologists. Individuals with any visible calcified plaque were assigned to the CAC group, while patients without calcification were categorized as non-CAC controls( 16 ). Inclusion criteria: 1) Underwent CTA and subsequent initial CAG; 2) Diagnosed with coronary heart disease (CHD), defined as ≥ 50% stenosis in at least one major coronary artery on CAG( 17 ); 3) Complete clinical and imaging records available; 4) Age between 18 and 59 years. Exclusion criteria: 1) Diagnosis of myocarditis, cardiomyopathy, congenital heart defects, or severe valvular disease; 2) Hepatic dysfunction, defined as alanine aminotransferase (ALT) or aspartate aminotransferase (AST) > 2 times the upper limit of normal; 3) Renal insufficiency with estimated glomerular filtration rate (eGFR) < 60 mL/min/1.73 m²; 4) Other exclusion factors included thyroid dysfunction, acute cerebrovascular disease, severe infections, autoimmune disorders, or malignancy; 5) History of coronary stent implantation, coronary artery bypass grafting (CABG), heart valve surgery, catheter ablation, or pacemaker implantation; 6) Incomplete clinical or imaging documentation (Fig. 1 ). Demographic information, medical histories, laboratory findings, and imaging data were retrieved from the hospital’s electronic medical records and image archiving systems. The study complied with the ethical principles outlined in the Declaration of Helsinki (2013 revision) and was approved by the institutional ethics committee (Approval No. X2022325). Written informed consent was obtained from all participants; verbal consent was documented when permitted by institutional policy. Data Collection and Measurements Collected baseline information included age, sex, presenting symptoms, smoking and alcohol use history, current medications, and comorbidities such as diabetes mellitus (DM), hypertension, and other cardiovascular or metabolic disorders. Anthropometric assessments were performed by trained personnel following standardized procedures. Height and body weight were measured using calibrated instruments while participants wore light clothing and no shoes. BMI was calculated as weight (kg) divided by height squared (m²). WC was measured using a non-elastic measuring tape at the midpoint between the lower costal margin and the iliac crest, with patients standing upright. Each measurement was performed twice, and the average to two decimal places was recorded. WHtR was calculated as WC (cm) divided by height (cm). After overnight fasting (≥ 8 hours), venous blood samples were drawn in the early morning and processed within two hours. Biochemical analyses were conducted using the VITROS 950/5600 automated analyzer (Ortho Clinical Diagnostics, USA). Parameters measured included: Lipid profile: total cholesterol (TC), triglycerides (TG), high-density lipoprotein cholesterol (HDL-C), low-density lipoprotein cholesterol (LDL-C); Apolipoproteins: apolipoprotein A1 (ApoA1), apolipoprotein B (ApoB); Metabolic/inflammatory markers: uric acid (UA), high-sensitivity C-reactive protein (hs-CRP), D-dimer, glycated hemoglobin A1c (HbA1c), alkaline phosphatase (ALP), homocysteine (HCY); Cardiac biomarkers: creatine kinase-MB (CK-MB), cardiac troponin I (cTnI). All laboratory assessments were conducted by certified technicians under internal quality control protocols. Results were reported in SI units. The CMI was calculated using the formula: CMI = WHtR × (TG / HDL-C)( 9 ), integrating central obesity and lipid imbalance as a proxy for visceral adiposity and metabolic dysregulation. CTA was performed using a SOMATOM Force dual-source CT scanner (Siemens Healthineers, Germany), with iopromide administered intravenously as contrast agent. CAC was defined as a high-density plaque exceeding 130 HU within coronary arteries( 16 ). All images were reviewed by two radiologists independently. CAG was performed using the Judkins technique via radial or femoral access, employing the Artis zee III floor-mounted digital subtraction angiography (DSA) system (Siemens Healthineers, Germany). Procedures were conducted and interpreted by two senior interventional cardiologists. A diagnosis of CHD was confirmed based on ≥ 50% luminal stenosis in any major coronary artery( 17 ). Statistical Analysis All statistical procedures were carried out using SPSS version 26.0 (IBM Corp., Armonk, NY, USA). Normality of continuous variables was assessed using the Shapiro–Wilk test. Variables with normal distribution were expressed as mean ± standard deviation (SD) and compared using the independent-samples t-test. Non-normally distributed variables were reported as median (interquartile range [IQR]) and compared using the Mann–Whitney U test. Categorical variables were presented as frequencies and percentages, and compared using the chi-square (χ²) test. Significant variables identified in univariate analysis were entered into a multivariate logistic regression model to determine independent predictors of CAC. The results were expressed as odds ratios (ORs) with corresponding 95% confidence intervals (CIs). Ordinal data were assessed using Spearman’s rank correlation. The predictive performance of CMI for CAC was evaluated using receiver operating characteristic (ROC) curve analysis, and the area under the curve (AUC) was computed using MedCalc version 20.0 (MedCalc Software Ltd., Ostend, Belgium). The optimal cutoff value for CMI was identified using the Youden index, and its sensitivity and specificity were reported. A two-tailed P-value < 0.05 was considered statistically significant. Results A total of 439 patients aged 18 to 59 years were enrolled in the study, comprising 128 individuals (29.2%) in the CAC group and 311 (70.8%) in the non-CAC group. Patients in the CAC group were significantly older (median age: 55 vs. 52 years, P < 0.001) and had a higher prevalence of male sex (69.5% vs. 43.4%, P < 0.001), smoking history (71.1% vs. 29.9%, P < 0.001), hypertension (68.0% vs. 14.5%, P < 0.001), and DM (22.7% vs. 7.4%, P < 0.001) compared to those without calcification. In addition, anthropometric measures such as BMI, WC, and WHtR were all significantly elevated in the CAC group (P < 0.001 for all comparisons) (Table 1 ). Table 1 Baseline clinical characteristics All (n = 439) Calcification group (n = 128) Non-calcification group (n = 311) P value Age(years) 53 (51,55.08) 55 (53,57.04) 52 (50,54.05) < 0.001 Male, n% 224 (51.0%) 89 (69.5%) 135 (43.4%) < 0.001 Smoking history, n% 184 (41.9%) 91 (71.1%) 93 (29.9%) < 0.001 Drinking history, n% 129 (29.4%) 42 (32.8%) 87 (28.0%) 0.3702 Hypertension, n% 132 (30.1%) 87 (68.0%) 45 (14.5%) < 0.001 Diabetes, n% 52 (11.8%) 29 (22.7%) 23 (7.4%) < 0.001 BMI, kg/m² 24.17 (23.18,25.21) 25.15 (24.37,25.85) 23.83 (22.85,24.65) < 0.001 WC, cm 91.21 (86.30,96.93) 97.54 (92.71,102.39) 88.85 (84.20,93.49) < 0.001 WHtR 0.54 (0.52,0.61) 0.64 (0.62,0.66) 0.53 (0.52,0.55) < 0.001 Continuous variables were expressed as mean ± SD, or median (interquartile range). Abbreviations: Categorical variables were expressed as number (percentage). BMI, body mass index; WHtR, waist-to-height ratio; WC, waist circumference; Lipid profile analysis revealed that patients with CAC had significantly higher levels of TC, TG, and LDL-C, along with lower concentrations of ApoA1 (P < 0.001 for all). Inflammatory and metabolic markers, including hs-CRP, D-dimer, HCY, and HbA1c, were also significantly elevated in the CAC group (P < 0.001 for each). CK-MB exhibited a borderline difference (P = 0.069), while no statistically significant differences were observed in cTnI, UA, serum electrolytes, or eGFR. Notably, the CMI was substantially higher among patients in the CAC group (median: 0.99 vs. 0.54, P < 0.001) (Table 2 ). Table 2 Lab findings All (n = 439) Calcification group (n = 128) Non-calcification group (n = 311) P value TC, mmol/L 4.77 (4.37,5.08) 5.06 (4.80,5.35) 4.65 (4.26,4.94) < 0.001 TG, mmol/L 1.58 (1.23,1.92) 1.97 (1.64,2.27) 1.40 (1.13,1.70) < 0.001 HDL-C, mmol/L 1.21 (1.06,1.35) 1.19 (1.08,1.33) 1.22 (1.06,1.35) 0.541 LDL-C, mmol/L 2.36 (2.00,2.74) 2.58 (2.22,2.87) 2.28 (1.91,2.64) < 0.001 ApoA1, g/L 1.44 (1.32,1.57) 1.38 (1.25,1.48) 1.47 (1.35,1.59) < 0.001 ApoB, g/L 0.85 ± 0.23 0.90 ± 0.26 0.83 ± 0.22 0.004 Uric Acid, µmol/L 329.62 ± 90.05 333.43 ± 100.26 328.05 ± 85.62 0.569 HbA1c, % 5.84 (5.64,6.23) 6.46 (6.25,6.62) 5.73 (5.54,5.89) < 0.001 hs-CRP, mg/L 1.25 (0.71,1.87) 1.50 (0.83,2.16) 1.16 (0.68,1.72) 0.001 D-dimer, mg/L 0.080 (0.066,0.099) 0.104 (0.083,0.117) 0.073 (0.063,0.086) < 0.001 HCY, µmol/L 12.00 (10.49,13.45) 13.64 (12.04,14.94) 11.39 (10.03,12.75) < 0.001 CK-MB, U/L 15.78 ± 4.92 16.45 ± 5.11 15.51 ± 4.82 0.069 cTnI, ng/mL 0.03 ± 0.01 0.03 ± 0.02 0.03 ± 0.01 0.844 Na, mmol/L 141.81 ± 2.13 141.48 ± 1.97 141.94 ± 2.19 0.036 K, mmol/L 3.99 ± 0.36 3.94 ± 0.39 4.01 ± 0.35 0.060 Cl, mmol/L 105.13 ± 2.30 105.16 ± 2.24 105.11 ± 2.32 0.837 P, mmol/L 1.21 ± 2.04 1.30 ± 2.06 1.17 ± 2.04 0.553 Ca, mmol/L 2.30 ± 0.13 2.32 ± 0.13 2.30 ± 0.13 0.196 GLU, mmol/L 5.30 (4.97,5.62) 5.34 (5.05,5.64) 5.27 (4.93,5.60) 0.210 ALP, U/L 70.66 (64.75,76.94) 76.98 (71.31,81.55) 68.50 (63.46,73.19) < 0.001 Cr, umol/L 68.50 (61.53,75.91) 71.55 (61.97,77.17) 67.93 (61.37,75.34) 0.079 e-GFR, ml/min/1.73 m 2 99.33 (91.56,108.06) 97.55 (90.68,106.38) 100.16 (92.12,108.53) 0.061 CMI 0.64 (0.40,0.89) 0.99 (0.73,1.26) 0.54 (0.35,0.74) < 0.001 Continuous variables were expressed as mean ± SD, or median (interquartile range). Categorical variables were expressed as number (percentage). Abbreviations: TC, total cholesterol; TG, triglyceride; HDL-C, high-density lipoprotein cholesterol; LDL-C, low-density lipoprotein cholesterol; ApoA1, apolipoprotein A1; ApoB, apolipoprotein B; UA, uric acid; HbA1c, glycated hemoglobin A1c; hs-CRP, high-sensitivity C-reactive protein; D-dimer, D-dimer; HCY, homocysteine; CK-MB, creatine kinase–MB isoenzyme; cTnI, cardiac troponin I; Na, sodium; K, potassium; Cl, chloride; P, phosphorus; Ca, calcium; GLU, glucose; ALP, alkaline phosphatase; Cr, creatinine; eGFR, estimated glomerular filtration rate; CMI, cardiometabolic index. Variables that reached statistical significance in univariate analysis were subsequently included in a multivariate logistic regression model to determine independent predictors of CAC. The final model identified older age (odds ratio [OR] = 1.059, 95% confidence interval [CI]: 1.038–1.081, P < 0.001), smoking (OR = 1.749, 95% CI: 1.072–2.853, P = 0.025), CMI (OR = 2.436, 95% CI: 1.694–3.502, P < 0.001), hs-CRP (OR = 1.157, 95% CI: 1.058–1.266, P = 0.001), and HCY (OR = 1.044, 95% CI: 1.002–1.082, P = 0.042) as independent risk factors. Other variables—including male sex, hypertension, BMI, TC, LDL-C, UA, and HbA1c—did not retain statistical significance in the multivariate model (Table 3 ). Table 3 Multivariable logistic analyses to predict CAC β SE Waldχ² P OR 95% CI Male 0.353 0.232 2.318 0.128 1.423 0.904–2.241 Age 0.058 0.010 32.337 < 0.001 1.059 1.038–1.081 Smoking 0.559 0.250 5.015 0.025 1.749 1.072–2.853 Hypertension 0.231 0.195 1.406 0.236 1.259 0.860–1.844 Diabetes 0.302 0.333 0.827 0.363 1.353 0.705–2.596 BMI 0.021 0.033 0.398 0.528 1.021 0.957–1.090 CMI 0.890 0.185 23.079 < 0.001 2.436 1.694–3.502 TC 0.324 0.241 2.304 0.129 1.383 0.910–2.102 LDL-C −0.350 0.314 1.244 0.265 0.705 0.381–1.304 hs-CRP 0.146 0.046 10.085 0.001 1.157 1.058–1.266 UA 0.000 0.001 0.172 0.678 1.000 0.997–1.002 HCY 0.043 0.021 4.129 0.042 1.044 1.002–1.082 HbA1c 0.244 0.148 2.722 0.099 1.276 0.955–1.704 Abbreviations: BMI, body mass index; CMI, cardiometabolic index; TC, total cholesterol; LDL-C, low-density lipoprotein cholesterol; hs-CRP, high-sensitivity C-reactive protein; UA, uric acid; HCY, homocysteine; HbA1c, glycated hemoglobin A1c; OR, odds ratio; CI, confidence interval. ROC curve analysis was performed to evaluate and compare the predictive ability of CMI and BMI for identifying CAC. The ROC curve for CMI yielded an AUC of 0.753 (95% CI: 0.721–0.783, P < 0.001), indicating good discrimination. The optimal CMI threshold was determined to be 0.742, which corresponded to a sensitivity of 72.81% and a specificity of 70.95%, with a Youden index of 0.4376. In contrast, BMI demonstrated lower predictive value, with an AUC of 0.606 (95% CI: 0.570–0.640, P < 0.001). The best cutoff point for BMI was 23.67, yielding a sensitivity of 71.75% and specificity of 52.38%, and a Youden index of 0.2414. These findings suggest that CMI outperforms BMI in predicting the risk of CAC in this population (Fig. 2 ). Discussion In this retrospective observational study of young and middle-aged adults with CHD, we assessed the relationship between the CMI and the presence of CAC as detected by coronary CTA. A total of 439 patients aged 18–59 years were analyzed and stratified into CAC and non-CAC groups based on CTA findings. Our findings are the first to demonstrate that elevated CMI is significantly and independently associated with the presence of CAC in this relatively younger CHD population. Multivariate logistic regression confirmed CMI as an independent risk factor for CAC, alongside established risk variables including age, smoking, hs-CRP, and HCY. Importantly, CMI demonstrated superior predictive performance compared to BMI, an anthropometric indicator traditionally used in cardiovascular risk assessment. ROC curve analysis supported this, showing a higher AUC for CMI (0.753) than for BMI (0.606), indicating greater clinical utility in detecting early subclinical atherosclerosis. Association of CAC with Traditional Risk Factors Our data reaffirm the association between increasing age and CAC, a finding consistent with previous literature identifying age as a key determinant of both prevalence and extent of vascular calcification( 18 ). Age-related calcification is largely driven by disordered calcium-phosphate metabolism and phenotypic transition of vascular smooth muscle cells toward an osteogenic lineage. Although a greater proportion of males was observed in the CAC group, sex was not an independent predictor in multivariate analysis, suggesting that gender disparities may be mediated by metabolic risk factors or sample size limitations( 3 ). Smoking was identified as an independent risk factor for CAC. This is supported by mechanistic evidence implicating cigarette smoke in the promotion of oxidative stress, endothelial dysfunction, and chronic inflammation—all of which contribute to the calcific progression of atherosclerotic plaques( 19 ). Nicotine may directly induce osteogenic transformation of vascular smooth muscle cells via nicotinic acetylcholine receptor pathways, and long-term exposure has been positively correlated with CAC severity, whereas smoking cessation is linked to stabilization or regression of calcified lesions( 20 ). Our study also highlighted hypertension and diabetes mellitus as modifiable risk factors associated with CAC. Hyperglycemia stimulates bone morphogenetic protein-2 expression, increases oxidative stress, and promotes endothelial injury, facilitating vascular calcification( 21 , 22 ). Diabetic patients are at heightened risk of developing CAC and face increased cardiovascular morbidity and mortality. Similarly, elevated blood pressure contributes to endothelial damage and upregulates osteogenic markers such as osteopontin, further accelerating arterial calcification( 23 ). Elevated serum UA was also associated with CAC and remained significant in regression analysis, suggesting its role in vascular mineralization. Although serum creatinine and eGFR did not reach statistical significance, trends toward increased calcification among patients with impaired renal function align with prior studies linking chronic kidney disease to vascular calcification( 24 , 25 ). Additionally, serum ALP levels were higher in patients with CAC, reinforcing its proposed function in degrading pyrophosphate—a physiological inhibitor of vascular calcification( 26 ). Overall, our findings underscore that CAC in younger adults is primarily influenced by modifiable cardiometabolic risk factors rather than demographic variables alone. Pathophysiological Relevance and Predictive Value of CMI CMI, defined as the product of the WHtR and the TG/HDL-C ratio, was originally developed as a surrogate marker of visceral adiposity and atherogenic dyslipidemia( 9 ). It captures two critical risk dimensions—central obesity and lipid metabolic disturbance—that contribute to vascular inflammation and calcification. Unlike BMI, which lacks specificity in distinguishing visceral from subcutaneous fat, WHtR offers a more accurate assessment of abdominal fat distribution. Evidence indicates that WHtR outperforms both BMI and WC in predicting cardiovascular events, particularly in Asian populations where central obesity may be underestimated( 14 ). The TG/HDL-C ratio component adds metabolic insight, as it reflects insulin resistance (IR), small dense low-density lipoprotein particles, and endothelial dysfunction—all recognized contributors to vascular calcification( 13 , 27 ). Several mechanisms may explain the observed link between CMI and CAC. First, increased visceral fat secretes pro-inflammatory cytokines such as tumor necrosis factor-alpha, interleukin-6, and resistin, while decreasing protective adipokines like adiponectin( 28 ). This imbalance promotes vascular injury and osteogenic transformation of vascular smooth muscle cells. Second, visceral adiposity is linked to excess free fatty acids, mitochondrial stress, and oxidative injury, all of which upregulate calcification-promoting signaling cascades( 29 ). Third, IR exacerbates inflammation through downregulation of endothelial nitric oxide synthase, activation of nuclear factor-kappa B, and increased vascular cell adhesion molecule expression, accelerating vascular stiffening and calcific plaque development( 30 ). Our findings support these mechanisms: CMI remained an independent predictor of CAC after adjusting for key covariates including age, BMI, hs-CRP, and HCY, whereas BMI lacked predictive value in multivariate models. These results reinforce CMI’s superior utility as a risk stratification tool and its relevance in detecting early metabolic disturbances that contribute to subclinical atherosclerosis. Clinical Implications and Practical Considerations The clinical relevance of this study lies in its potential to improve early risk identification for subclinical CAD in younger adults. Given that CAC is a strong predictor of future cardiovascular events, timely identification of high-risk individuals is essential( 3 , 4 ). CMI represents a low-cost, noninvasive, and easily accessible metric that leverages routine anthropometric and lipid measurements. In contrast to imaging techniques such as CTA or invasive angiography—which are expensive, involve radiation exposure, and are impractical for mass screening—CMI is ideally suited for integration into outpatient clinics, primary care, and preventive health programs. CMI enables the early detection of cardiometabolic dysregulation, even in patients with normal BMI or isolated lipid abnormalities( 31 ). In resource-limited settings, it could serve as a preliminary screening tool to guide more definitive diagnostics such as coronary calcium scoring. Moreover, CMI can be integrated into existing risk prediction models such as the Framingham, Atherosclerotic Cardiovascular Disease (ASCVD) risk calculator or the China-Prediction for ASCVD Risk (China-PAR) model- to enhance predictive performance in younger populations, where traditional models often underestimate risk( 32 – 34 ). Notably, the elevated CMI levels observed in younger males in our study suggest that age- and sex-specific CMI thresholds may enhance clinical applicability. CMI could also inform therapeutic decisions, such as initiating statins, prescribing lifestyle interventions, or considering aspirin therapy in appropriately selected individuals based on their CMI-defined risk category. While our findings provide novel insights, further research is warranted. Prospective, multicenter studies with diverse populations are needed to validate our results and establish optimal CMI thresholds. Longitudinal monitoring of CMI may help clarify whether changes in CMI precede the onset or progression of CAC or predict major adverse cardiovascular events (MACE). Additionally, combining CMI with other emerging markers—such as the Visceral Adiposity Index or Lipid Accumulation Product—may enhance risk prediction. Finally, it remains to be seen whether CMI can forecast not only the presence but also the progression of CAC, procedural complexity of PCI, and long-term clinical outcomes. Limitations Despite its valuable contributions, this study has several limitations that warrant consideration. First, the retrospective nature and single-center design restrict the external validity of the findings and may introduce selection bias. Second, CAC was assessed at a single time point without longitudinal follow-up, limiting the ability to assess the progression of calcification or the occurrence of clinical outcomes such as myocardial infarction or cardiovascular mortality. Third, although high-resolution coronary CTA was used for CAC assessment, calcification was dichotomized (present vs. absent) rather than quantified using standardized scoring systems such as the Agatston score, which could provide more granular insights into calcific burden. Fourth, although multivariate logistic regression was employed to adjust for potential confounders, residual confounding due to unmeasured variables—such as dietary habits, physical activity levels, genetic factors, or medication compliance—cannot be entirely excluded. Lastly, the study did not examine the prognostic capacity of the CMI for predicting MACE, nor did it investigate its utility in stratifying procedural complexity or clinical outcomes among patients undergoing PCI. Conclusion In conclusion, this study is the first to establish an independent association between the CMI—a simple, noninvasive metric integrating waist-to-height ratio and the TG/HDL-C ratio—and the presence of CAC in young and middle-aged adults with CHD. CMI demonstrated stronger predictive capability for subclinical atherosclerosis than traditional indices such as BMI, as evidenced by superior performance in ROC curve analysis. These findings suggest that CMI could be implemented as an accessible early screening tool to identify asymptomatic high-risk individuals for targeted cardiovascular evaluation. To confirm these results and better understand the clinical utility of CMI, future large-scale, prospective studies involving multi-ethnic populations and extended follow-up are essential. Such investigations should also explore whether dynamic changes in CMI over time are predictive of CAC progression, treatment efficacy, or long-term cardiovascular outcomes. Declarations Ethics approval and consent to participate The present research was carried out in accordance with the tenets mentioned in the Helsinki Declaration and was approved by the Ethical Board of Xiangtan Central Hospital (approval number: X2022325). Prior to the commencement of the research, our team obtained written informed consent from each patient. Consent for publication Not applicable. No individual patient data will be reported. Availability of data and materials The datasets generated during and/or analyzed during the current study are available from the corresponding author on reasonable request. Competing interests The authors declare that they have no competing interests. Funding No funding was received for this study. Authors' contributions X.W. and L.W. had the idea for the paper, reviewed and edited it critically for important intellectual content. H.B.H. and H.H. performed the literature search and analysis. X.W., M.X.W., L.W., Z.L. and H.H. substantially contributed to the conception of the paper, drafted and critically revised the manuscript. All authors contributed to editorial changes in the manuscript. All authors read and approved the final manuscript. All authors have participated sufficiently in the work and agreed to be accountable for all aspects of the work. Acknowledgment Not applicable. Clinical trial number Not applicable. References Greenland P, Blaha MJ, Budoff MJ, Erbel R, Watson KE. Coronary Calcium Score and Cardiovascular Risk. Journal of the American College of Cardiology. 2018;72(4):434-47. Strauss HW, Nakahara T, Narula N, Narula J. 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Obesity reviews : an official journal of the International Association for the Study of Obesity. 2016;17(10):989-1000. Wakabayashi I, Daimon T. The "cardiometabolic index" as a new marker determined by adiposity and blood lipids for discrimination of diabetes mellitus. Clinica chimica acta; international journal of clinical chemistry. 2015;438:274-8. Cai X, Hu J, Wen W, Wang J, Wang M, Liu S, et al. Associations of the Cardiometabolic Index with the Risk of Cardiovascular Disease in Patients with Hypertension and Obstructive Sleep Apnea: Results of a Longitudinal Cohort Study. Oxidative medicine and cellular longevity. 2022;2022:4914791. Wakabayashi I, Sotoda Y, Hirooka S, Orita H. Association between cardiometabolic index and atherosclerotic progression in patients with peripheral arterial disease. Clinica chimica acta; international journal of clinical chemistry. 2015;446:231-6. Wakabayashi I, Marumo M, Kubota Y, Higashiyama A, Miyamoto Y, Okamura T. Cardiometabolic index as a useful discriminator for the risk of increased arterial stiffness. Clinica chimica acta; international journal of clinical chemistry. 2018;486:42-3. Guan CL, Liu HT, Chen DH, Quan XQ, Gao WL, Duan XY. Is elevated triglyceride/high-density lipoprotein cholesterol ratio associated with poor prognosis of coronary heart disease? A meta-analysis of prospective studies. Medicine. 2022;101(45):e31123. Zhang S, Fu X, Du Z, Guo X, Li Z, Sun G, et al. Is waist-to-height ratio the best predictive indicator of cardiovascular disease incidence in hypertensive adults? A cohort study. BMC cardiovascular disorders. 2022;22(1):214. Michos ED, Choi AD. Coronary Artery Disease in Young Adults: A Hard Lesson But a Good Teacher. Journal of the American College of Cardiology. 2019;74(15):1879-82. Kaur M, Rahimi R, Razali F, Mohd Noor N, Omar E, Abdul Manaf Z, et al. Association of coronary artery calcium score with calcification and degree of stenosis: An autopsy study. The Malaysian journal of pathology. 2019;41(2):177-83. Knuuti J, Wijns W, Saraste A, Capodanno D, Barbato E, Funck-Brentano C, et al. 2019 ESC Guidelines for the diagnosis and management of chronic coronary syndromes. European heart journal. 2020;41(3):407-77. Budoff MJ, Young R, Burke G, Jeffrey Carr J, Detrano RC, Folsom AR, et al. Ten-year association of coronary artery calcium with atherosclerotic cardiovascular disease (ASCVD) events: the multi-ethnic study of atherosclerosis (MESA). European heart journal. 2018;39(25):2401-8. Kim BJ, Han JM, Kang JG, Kim BS, Kang JH. The association between self-reported versus nicotine metabolite-confirmed smoking status and coronary artery calcification. Coronary artery disease. 2018;29(3):254-61. Lee MJ, Park JT, Chang TI, Joo YS, Yoo TH, Park SK, et al. Smoking Cessation and Coronary Artery Calcification in CKD. Clinical journal of the American Society of Nephrology : CJASN. 2021;16(6):870-9. Zhang M, Zhou SH, Zhao S, Li XP, Liu LP, Shen XQ. Pioglitazone can downregulate bone morphogenetic protein-2 expression induced by high glucose in human umbilical vein endothelial cells. Pharmacology. 2008;81(4):312-6. Syvänne M, Pajunen P, Kahri J, Lahdenperä S, Ehnholm C, Nieminen MS, et al. Determinants of the severity and extent of coronary artery disease in patients with type-2 diabetes and in nondiabetic subjects. Coronary artery disease. 2001;12(2):99-106. Zhang Y, Schwartz JE, Jaeger BC, An J, Bellows BK, Clark D, 3rd, et al. Association Between Ambulatory Blood Pressure and Coronary Artery Calcification: The JHS. Hypertension (Dallas, Tex : 1979). 2021;77(6):1886-94. Baber U, Stone GW, Weisz G, Moreno P, Dangas G, Maehara A, et al. Coronary plaque composition, morphology, and outcomes in patients with and without chronic kidney disease presenting with acute coronary syndromes. JACC Cardiovascular imaging. 2012;5(3 Suppl):S53-61. Liang L, Hou X, Bainey KR, Zhang Y, Tymchak W, Qi Z, et al. The association between hyperuricemia and coronary artery calcification development: A systematic review and meta-analysis. Clinical cardiology. 2019;42(11):1079-86. Park JB, Kang DY, Yang HM, Cho HJ, Park KW, Lee HY, et al. Serum alkaline phosphatase is a predictor of mortality, myocardial infarction, or stent thrombosis after implantation of coronary drug-eluting stent. European heart journal. 2013;34(12):920-31. Natali A, Baldi S, Bonnet F, Petrie J, Trifirò S, Tricò D, et al. Plasma HDL-cholesterol and triglycerides, but not LDL-cholesterol, are associated with insulin secretion in non-diabetic subjects. Metabolism: clinical and experimental. 2017;69:33-42. Ragino YI, Stakhneva EM, Polonskaya YV, Kashtanova EV. The Role of Secretory Activity Molecules of Visceral Adipocytes in Abdominal Obesity in the Development of Cardiovascular Disease: A Review. Biomolecules. 2020;10(3). Ibrahim MM. Subcutaneous and visceral adipose tissue: structural and functional differences. Obesity reviews : an official journal of the International Association for the Study of Obesity. 2010;11(1):11-8. Zhao Q, Zhang TY, Cheng YJ, Ma Y, Xu YK, Yang JQ, et al. Impacts of triglyceride-glucose index on prognosis of patients with type 2 diabetes mellitus and non-ST-segment elevation acute coronary syndrome: results from an observational cohort study in China. Cardiovascular diabetology. 2020;19(1):108. Shi WR, Wang HY, Chen S, Guo XF, Li Z, Sun YX. Estimate of prevalent diabetes from cardiometabolic index in general Chinese population: a community-based study. Lipids in health and disease. 2018;17(1):236. Zhou H, Mao Y, Ye M, Zuo Z. Exploring the nonlinear association between cardiometabolic index and hypertension in U.S. Adults: an NHANES-based study. BMC public health. 2025;25(1):1092. Zhou J, Fan J, Zhang X, You L, Lin D, Huang C, et al. Fatty Liver Index and Its Association with 10-Year Atherosclerotic Cardiovascular Disease Risk: Insights from a Population-Based Cross-Sectional Study in China. Metabolites. 2023;13(7). Goff DC, Jr., Lloyd-Jones DM, Bennett G, Coady S, D'Agostino RB, Gibbons R, et al. 2013 ACC/AHA guideline on the assessment of cardiovascular risk: a report of the American College of Cardiology/American Heart Association Task Force on Practice Guidelines. Circulation. 2014;129(25 Suppl 2):S49-73. Additional Declarations No competing interests reported. Cite Share Download PDF Status: Published Journal Publication published 05 Jul, 2025 Read the published version in BMC Cardiovascular Disorders → Version 1 posted Editorial decision: Revision requested 27 May, 2025 Reviews received at journal 23 May, 2025 Reviews received at journal 23 May, 2025 Reviewers agreed at journal 23 May, 2025 Reviewers agreed at journal 22 May, 2025 Reviewers invited by journal 22 May, 2025 Editor invited by journal 20 May, 2025 Editor assigned by journal 16 May, 2025 Submission checks completed at journal 16 May, 2025 First submitted to journal 12 May, 2025 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-6649906","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":461088332,"identity":"3f1b4c97-6b25-4535-aff7-fdf047b199c4","order_by":0,"name":"Xi Wu","email":"","orcid":"","institution":"Xiangtan Central Hospital (the affiliated hospital of Hunan University)","correspondingAuthor":false,"prefix":"","firstName":"Xi","middleName":"","lastName":"Wu","suffix":""},{"id":461088333,"identity":"89279c78-cc3b-4b57-ae04-8335c2254b97","order_by":1,"name":"Mingxing Wu","email":"","orcid":"","institution":"Xiangtan Central Hospital (the affiliated hospital of Hunan University)","correspondingAuthor":false,"prefix":"","firstName":"Mingxing","middleName":"","lastName":"Wu","suffix":""},{"id":461088334,"identity":"598b0787-a299-4b89-960a-cfcec78c7f1c","order_by":2,"name":"Haobo Huang","email":"","orcid":"","institution":"Xiangtan Central Hospital (the affiliated hospital of Hunan University)","correspondingAuthor":false,"prefix":"","firstName":"Haobo","middleName":"","lastName":"Huang","suffix":""},{"id":461088335,"identity":"94e8b431-900a-4511-ba9e-70b1e3422e52","order_by":3,"name":"Zhe Liu","email":"","orcid":"","institution":"Xiangtan Central Hospital (the affiliated hospital of Hunan University)","correspondingAuthor":false,"prefix":"","firstName":"Zhe","middleName":"","lastName":"Liu","suffix":""},{"id":461088336,"identity":"0d1bea1f-0632-44a5-ba81-360e3cdce295","order_by":4,"name":"He Huang","email":"","orcid":"","institution":"Xiangtan Central Hospital (the affiliated hospital of Hunan University)","correspondingAuthor":false,"prefix":"","firstName":"He","middleName":"","lastName":"Huang","suffix":""},{"id":461088337,"identity":"4a958c0f-65dd-41d9-a4e4-bbdc1b380bfd","order_by":5,"name":"Lei Wang","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAArklEQVRIiWNgGAWjYBACAwhlIcfG3nyABC0HGCSM+XiOJZCmJXGeRI4CcVrM2Zufff7YJpHexpDDwPCjYhthLZY9x4xnHGyTyG1jOHuAsefMbSIcdiPBmOHgNqAWxr4EZsY2YrTcf/4ZpCWdjZnHgEgtN3jAtiSwsRGt5UxOMcPZfxKGbTxsCQeJ88vx45sZKs7YyMvPf3zwwY8KIrSggAMkqh8Fo2AUjIJRgAsAAIJ2OyPELqyvAAAAAElFTkSuQmCC","orcid":"","institution":"Xiangtan Central Hospital (the affiliated hospital of Hunan University)","correspondingAuthor":true,"prefix":"","firstName":"Lei","middleName":"","lastName":"Wang","suffix":""}],"badges":[],"createdAt":"2025-05-12 22:53:05","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-6649906/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-6649906/v1","draftVersion":[],"editorialEvents":[{"content":"https://doi.org/10.1186/s12872-025-04950-y","type":"published","date":"2025-07-05T15:58:16+00:00"}],"editorialNote":"","failedWorkflow":false,"files":[{"id":83480213,"identity":"ae8119d8-8e98-4c3a-a404-9f83ca99b7b4","added_by":"auto","created_at":"2025-05-27 06:31:00","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":920150,"visible":true,"origin":"","legend":"\u003cp\u003eStudy flowchart.\u003c/p\u003e\n\u003cp\u003eCABG, coronary artery bypass grafting; CHD, coronary heart disease; ALT, alanine aminotransferase; AST, aspartate aminotransferase; eGFR, estimated glomerular filtration rate;\u003c/p\u003e","description":"","filename":"Figure1.png","url":"https://assets-eu.researchsquare.com/files/rs-6649906/v1/2ab53e57042e7bd7d6fa7e75.png"},{"id":83480842,"identity":"587b4e37-1b18-4fcd-b9a0-a67cfb7d2fb4","added_by":"auto","created_at":"2025-05-27 06:39:00","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":404414,"visible":true,"origin":"","legend":"\u003cp\u003eROC Curves for CMI and BMI in Predicting CHD\u003c/p\u003e\n\u003cp\u003eThe figure illustrates the ROC curves of CMI and BMI for predicting the presence of CHD. The area under the AUC for CMI was 0.753 (95% CI: 0.721–0.783), indicating a good discriminatory ability. The optimal cutoff value of CMI was 0.742, yielding a sensitivity of 72.81% and a specificity of 70.95%, with a Youden index of 0.4376. In contrast, the BMI ROC curve had a lower AUC of 0.606 (95% CI: 0.570–0.640), with a cutoff value of 23.67, sensitivity of 71.75%, specificity of 52.38%, and a Youden index of 0.2414. These results demonstrate that CMI outperforms BMI in predicting CHD risk.\u003c/p\u003e\n\u003cp\u003eAbbreviations: ROC, receiver operating characteristic; CMI, cardiometabolic index; BMI, body mass index; CHD, coronary heart disease; AUC, area under the curve; CI, confidence interval;\u003c/p\u003e","description":"","filename":"Figure2ROC.png","url":"https://assets-eu.researchsquare.com/files/rs-6649906/v1/2d7ea1103a969ccab008d68c.png"},{"id":86180285,"identity":"e5fd356a-64b2-4a51-8fc8-7c01f16f3b61","added_by":"auto","created_at":"2025-07-07 16:21:56","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":2998135,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-6649906/v1/62b6079d-91af-401f-ade5-b1108b204850.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Association Between Cardiometabolic Index and Coronary Artery Calcification in Young and Middle-Aged Adults With Coronary Heart Disease","fulltext":[{"header":"Introduction","content":"\u003cp\u003eCoronary artery calcification (CAC) is widely recognized as a hallmark of subclinical atherosclerosis and serves as a robust prognostic indicator for future coronary artery disease (CAD) events. It reflects the cumulative progression of atherosclerotic plaque formation and is closely linked with adverse cardiovascular outcomes(\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e). The detection of CAC through imaging is considered a dependable surrogate for underlying coronary atherosclerosis and is regarded as a potent marker of poor clinical prognosis in patients with coronary atherosclerotic heart disease (CHD)(\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eAlthough CAC has traditionally been associated with older populations, emerging evidence indicates an increasing prevalence among younger and middle-aged adults, drawing attention to the asymptomatic and concealed progression of atherosclerosis in these groups(\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e). Early CAC identified in younger individuals has been correlated with markedly elevated risks for future cardiovascular incidents and overall mortality(\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e, \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e). Furthermore, young individuals with conventional or hereditary cardiovascular risk factors are reported to have a two- to three-fold greater probability of developing CAC when compared with age-matched counterparts lacking such risks(\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e). Nevertheless, current CAC-related research predominantly focuses on older populations, with relatively little attention given to early-onset CHD despite its rising incidence in younger cohorts(\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eThe pathophysiological development of CHD is multifaceted and heavily influenced by metabolic factors. Although obesity is recognized as an independent contributor to CHD, the body mass index (BMI) does not adequately reflect the complexity of fat distribution, particularly visceral adiposity, which plays a more pivotal role in cardiovascular risk. Notably, some individuals classified as obese exhibit a metabolically healthy phenotype, whereas others with a normal BMI may present with a metabolically unhealthy normal weight profile(\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e, \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e). This paradox accentuates the limitations of BMI and underscores the need for more sophisticated and precise metabolic risk markers.\u003c/p\u003e \u003cp\u003eRecently, the cardiometabolic index (CMI)\u0026mdash;defined as the product of the triglyceride to high-density lipoprotein cholesterol (TG/HDL-C) ratio and the waist-to-height ratio (WHtR)\u0026mdash;has gained attention as a promising indicator of visceral adiposity and dysregulated lipid metabolism(\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e). CMI has demonstrated strong associations with metabolic syndrome, early-stage atherosclerosis, carotid plaque formation, peripheral arterial ischemia, and ischemic cardiovascular events(\u003cspan additionalcitationids=\"CR11\" citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e). A meta-analysis has further validated the TG/HDL-C ratio, a component of CMI, as an independent risk factor for CHD(\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e), while WHtR has been shown to outperform traditional metrics like BMI and waist circumference (WC) in predicting cardiovascular risk(\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eDespite these findings, there remains a lack of systematic investigations examining whether CMI is associated with the presence or severity of CAC in younger and middle-aged adults. Most existing models that assess CAC focus on parameters such as total calcium score, lesion density, or arc length. In contrast, relatively few have evaluated the predictive potential of CMI concerning the binary presence of CAC\u0026mdash;an early, accessible, and clinically relevant marker of subclinical coronary atherosclerosis(\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e). Therefore, the present study aims to investigate the relationship between CMI and both the occurrence and extent of CAC in young and middle-aged individuals with CHD. The goal is to determine whether CMI can serve as a valuable clinical tool for the early identification and stratification of subclinical atherosclerosis and premature coronary calcification(\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e).\u003c/p\u003e"},{"header":"Materials and methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003eStudy Participants\u003c/h2\u003e \u003cp\u003eThis retrospective and observational study was conducted at a single tertiary-care hospital. From January 2018 to August 2022, patients who were admitted to the Department of Cardiology at Xiangtan Central Hospital and underwent coronary computed tomography angiography (CTA), followed by their first coronary angiography (CAG) and, when clinically indicated, percutaneous coronary intervention (PCI), were consecutively screened for eligibility. Based on CTA findings, patients were classified into two groups: those with CAC and those without. CAC was defined as the presence of high-attenuation lesions (\u0026gt;\u0026thinsp;130 Hounsfield units [HU]) within the coronary artery wall. All CTA images were independently reviewed and validated by experienced radiologists. Individuals with any visible calcified plaque were assigned to the CAC group, while patients without calcification were categorized as non-CAC controls(\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e). Inclusion criteria: 1) Underwent CTA and subsequent initial CAG; 2) Diagnosed with coronary heart disease (CHD), defined as \u0026ge;\u0026thinsp;50% stenosis in at least one major coronary artery on CAG(\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e); 3) Complete clinical and imaging records available; 4) Age between 18 and 59 years. Exclusion criteria: 1) Diagnosis of myocarditis, cardiomyopathy, congenital heart defects, or severe valvular disease; 2) Hepatic dysfunction, defined as alanine aminotransferase (ALT) or aspartate aminotransferase (AST)\u0026thinsp;\u0026gt;\u0026thinsp;2 times the upper limit of normal; 3) Renal insufficiency with estimated glomerular filtration rate (eGFR)\u0026thinsp;\u0026lt;\u0026thinsp;60 mL/min/1.73 m\u0026sup2;; 4) Other exclusion factors included thyroid dysfunction, acute cerebrovascular disease, severe infections, autoimmune disorders, or malignancy; 5) History of coronary stent implantation, coronary artery bypass grafting (CABG), heart valve surgery, catheter ablation, or pacemaker implantation; 6) Incomplete clinical or imaging documentation (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eDemographic information, medical histories, laboratory findings, and imaging data were retrieved from the hospital\u0026rsquo;s electronic medical records and image archiving systems. The study complied with the ethical principles outlined in the Declaration of Helsinki (2013 revision) and was approved by the institutional ethics committee (Approval No. X2022325). Written informed consent was obtained from all participants; verbal consent was documented when permitted by institutional policy.\u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eData Collection and Measurements\u003c/h3\u003e\n\u003cp\u003eCollected baseline information included age, sex, presenting symptoms, smoking and alcohol use history, current medications, and comorbidities such as diabetes mellitus (DM), hypertension, and other cardiovascular or metabolic disorders.\u003c/p\u003e \u003cp\u003eAnthropometric assessments were performed by trained personnel following standardized procedures. Height and body weight were measured using calibrated instruments while participants wore light clothing and no shoes. BMI was calculated as weight (kg) divided by height squared (m\u0026sup2;). WC was measured using a non-elastic measuring tape at the midpoint between the lower costal margin and the iliac crest, with patients standing upright. Each measurement was performed twice, and the average to two decimal places was recorded. WHtR was calculated as WC (cm) divided by height (cm). After overnight fasting (\u0026ge;\u0026thinsp;8 hours), venous blood samples were drawn in the early morning and processed within two hours. Biochemical analyses were conducted using the VITROS 950/5600 automated analyzer (Ortho Clinical Diagnostics, USA). Parameters measured included: Lipid profile: total cholesterol (TC), triglycerides (TG), high-density lipoprotein cholesterol (HDL-C), low-density lipoprotein cholesterol (LDL-C); Apolipoproteins: apolipoprotein A1 (ApoA1), apolipoprotein B (ApoB); Metabolic/inflammatory markers: uric acid (UA), high-sensitivity C-reactive protein (hs-CRP), D-dimer, glycated hemoglobin A1c (HbA1c), alkaline phosphatase (ALP), homocysteine (HCY); Cardiac biomarkers: creatine kinase-MB (CK-MB), cardiac troponin I (cTnI). All laboratory assessments were conducted by certified technicians under internal quality control protocols. Results were reported in SI units. The CMI was calculated using the formula: CMI\u0026thinsp;=\u0026thinsp;WHtR \u0026times; (TG / HDL-C)(\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e), integrating central obesity and lipid imbalance as a proxy for visceral adiposity and metabolic dysregulation.\u003c/p\u003e \u003cp\u003eCTA was performed using a SOMATOM Force dual-source CT scanner (Siemens Healthineers, Germany), with iopromide administered intravenously as contrast agent. CAC was defined as a high-density plaque exceeding 130 HU within coronary arteries(\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e). All images were reviewed by two radiologists independently. CAG was performed using the Judkins technique via radial or femoral access, employing the Artis zee III floor-mounted digital subtraction angiography (DSA) system (Siemens Healthineers, Germany). Procedures were conducted and interpreted by two senior interventional cardiologists. A diagnosis of CHD was confirmed based on \u0026ge;\u0026thinsp;50% luminal stenosis in any major coronary artery(\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e).\u003c/p\u003e \u003cdiv id=\"Sec5\" class=\"Section2\"\u003e \u003ch2\u003eStatistical Analysis\u003c/h2\u003e \u003cp\u003eAll statistical procedures were carried out using SPSS version 26.0 (IBM Corp., Armonk, NY, USA). Normality of continuous variables was assessed using the Shapiro\u0026ndash;Wilk test. Variables with normal distribution were expressed as mean\u0026thinsp;\u0026plusmn;\u0026thinsp;standard deviation (SD) and compared using the independent-samples t-test. Non-normally distributed variables were reported as median (interquartile range [IQR]) and compared using the Mann\u0026ndash;Whitney U test. Categorical variables were presented as frequencies and percentages, and compared using the chi-square (χ\u0026sup2;) test. Significant variables identified in univariate analysis were entered into a multivariate logistic regression model to determine independent predictors of CAC. The results were expressed as odds ratios (ORs) with corresponding 95% confidence intervals (CIs). Ordinal data were assessed using Spearman\u0026rsquo;s rank correlation. The predictive performance of CMI for CAC was evaluated using receiver operating characteristic (ROC) curve analysis, and the area under the curve (AUC) was computed using MedCalc version 20.0 (MedCalc Software Ltd., Ostend, Belgium). The optimal cutoff value for CMI was identified using the Youden index, and its sensitivity and specificity were reported. A two-tailed P-value\u0026thinsp;\u0026lt;\u0026thinsp;0.05 was considered statistically significant.\u003c/p\u003e \u003c/div\u003e"},{"header":"Results","content":"\u003cp\u003eA total of 439 patients aged 18 to 59 years were enrolled in the study, comprising 128 individuals (29.2%) in the CAC group and 311 (70.8%) in the non-CAC group. Patients in the CAC group were significantly older (median age: 55 vs. 52 years, P\u0026thinsp;\u0026lt;\u0026thinsp;0.001) and had a higher prevalence of male sex (69.5% vs. 43.4%, P\u0026thinsp;\u0026lt;\u0026thinsp;0.001), smoking history (71.1% vs. 29.9%, P\u0026thinsp;\u0026lt;\u0026thinsp;0.001), hypertension (68.0% vs. 14.5%, P\u0026thinsp;\u0026lt;\u0026thinsp;0.001), and DM (22.7% vs. 7.4%, P\u0026thinsp;\u0026lt;\u0026thinsp;0.001) compared to those without calcification. In addition, anthropometric measures such as BMI, WC, and WHtR were all significantly elevated in the CAC group (P\u0026thinsp;\u0026lt;\u0026thinsp;0.001 for all comparisons) (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eBaseline clinical characteristics\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"5\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eAll (n\u0026thinsp;=\u0026thinsp;439)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eCalcification group (n\u0026thinsp;=\u0026thinsp;128)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eNon-calcification group (n\u0026thinsp;=\u0026thinsp;311)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eP value\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAge(years)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e53 (51,55.08)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e55 (53,57.04)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e52 (50,54.05)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMale, n%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e224 (51.0%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e89 (69.5%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e135 (43.4%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSmoking history, n%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e184 (41.9%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e91 (71.1%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e93 (29.9%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDrinking history, n%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e129 (29.4%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e42 (32.8%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e87 (28.0%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.3702\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHypertension, n%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e132 (30.1%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e87 (68.0%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e45 (14.5%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDiabetes, n%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e52 (11.8%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e29 (22.7%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e23 (7.4%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBMI, kg/m\u0026sup2;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e24.17 (23.18,25.21)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e25.15 (24.37,25.85)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e23.83 (22.85,24.65)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eWC, cm\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e91.21 (86.30,96.93)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e97.54 (92.71,102.39)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e88.85 (84.20,93.49)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eWHtR\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.54 (0.52,0.61)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.64 (0.62,0.66)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.53 (0.52,0.55)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"5\"\u003eContinuous variables were expressed as mean\u0026thinsp;\u0026plusmn;\u0026thinsp;SD, or median (interquartile range). Abbreviations: Categorical variables were expressed as number (percentage).\u003c/td\u003e\u003c/tr\u003e \u003ctr\u003e\u003ctd colspan=\"5\"\u003eBMI, body mass index; WHtR, waist-to-height ratio; WC, waist circumference;\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eLipid profile analysis revealed that patients with CAC had significantly higher levels of TC, TG, and LDL-C, along with lower concentrations of ApoA1 (P\u0026thinsp;\u0026lt;\u0026thinsp;0.001 for all). Inflammatory and metabolic markers, including hs-CRP, D-dimer, HCY, and HbA1c, were also significantly elevated in the CAC group (P\u0026thinsp;\u0026lt;\u0026thinsp;0.001 for each). CK-MB exhibited a borderline difference (P\u0026thinsp;=\u0026thinsp;0.069), while no statistically significant differences were observed in cTnI, UA, serum electrolytes, or eGFR. Notably, the CMI was substantially higher among patients in the CAC group (median: 0.99 vs. 0.54, P\u0026thinsp;\u0026lt;\u0026thinsp;0.001) (Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eLab findings\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"5\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eAll (n\u0026thinsp;=\u0026thinsp;439)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eCalcification group (n\u0026thinsp;=\u0026thinsp;128)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eNon-calcification group (n\u0026thinsp;=\u0026thinsp;311)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eP value\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTC, mmol/L\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e4.77 (4.37,5.08)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e5.06 (4.80,5.35)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e4.65 (4.26,4.94)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTG, mmol/L\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.58 (1.23,1.92)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.97 (1.64,2.27)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.40 (1.13,1.70)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHDL-C, mmol/L\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.21 (1.06,1.35)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.19 (1.08,1.33)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.22 (1.06,1.35)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.541\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLDL-C, mmol/L\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e2.36 (2.00,2.74)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e2.58 (2.22,2.87)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e2.28 (1.91,2.64)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eApoA1, g/L\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.44 (1.32,1.57)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.38 (1.25,1.48)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.47 (1.35,1.59)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eApoB, g/L\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.85\u0026thinsp;\u0026plusmn;\u0026thinsp;0.23\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.90\u0026thinsp;\u0026plusmn;\u0026thinsp;0.26\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.83\u0026thinsp;\u0026plusmn;\u0026thinsp;0.22\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.004\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eUric Acid, \u0026micro;mol/L\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e329.62\u0026thinsp;\u0026plusmn;\u0026thinsp;90.05\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e333.43\u0026thinsp;\u0026plusmn;\u0026thinsp;100.26\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e328.05\u0026thinsp;\u0026plusmn;\u0026thinsp;85.62\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.569\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHbA1c, %\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e5.84 (5.64,6.23)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e6.46 (6.25,6.62)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e5.73 (5.54,5.89)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ehs-CRP, mg/L\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.25 (0.71,1.87)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.50 (0.83,2.16)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.16 (0.68,1.72)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eD-dimer, mg/L\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.080 (0.066,0.099)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.104 (0.083,0.117)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.073 (0.063,0.086)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHCY, \u0026micro;mol/L\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e12.00 (10.49,13.45)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e13.64 (12.04,14.94)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e11.39 (10.03,12.75)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCK-MB, U/L\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e15.78\u0026thinsp;\u0026plusmn;\u0026thinsp;4.92\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e16.45\u0026thinsp;\u0026plusmn;\u0026thinsp;5.11\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e15.51\u0026thinsp;\u0026plusmn;\u0026thinsp;4.82\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.069\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ecTnI, ng/mL\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.03\u0026thinsp;\u0026plusmn;\u0026thinsp;0.01\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.03\u0026thinsp;\u0026plusmn;\u0026thinsp;0.02\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.03\u0026thinsp;\u0026plusmn;\u0026thinsp;0.01\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.844\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNa, mmol/L\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e141.81\u0026thinsp;\u0026plusmn;\u0026thinsp;2.13\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e141.48\u0026thinsp;\u0026plusmn;\u0026thinsp;1.97\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e141.94\u0026thinsp;\u0026plusmn;\u0026thinsp;2.19\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.036\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eK, mmol/L\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e3.99\u0026thinsp;\u0026plusmn;\u0026thinsp;0.36\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e3.94\u0026thinsp;\u0026plusmn;\u0026thinsp;0.39\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e4.01\u0026thinsp;\u0026plusmn;\u0026thinsp;0.35\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.060\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCl, mmol/L\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e105.13\u0026thinsp;\u0026plusmn;\u0026thinsp;2.30\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e105.16\u0026thinsp;\u0026plusmn;\u0026thinsp;2.24\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e105.11\u0026thinsp;\u0026plusmn;\u0026thinsp;2.32\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.837\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eP, mmol/L\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.21\u0026thinsp;\u0026plusmn;\u0026thinsp;2.04\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.30\u0026thinsp;\u0026plusmn;\u0026thinsp;2.06\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.17\u0026thinsp;\u0026plusmn;\u0026thinsp;2.04\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.553\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCa, mmol/L\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e2.30\u0026thinsp;\u0026plusmn;\u0026thinsp;0.13\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e2.32\u0026thinsp;\u0026plusmn;\u0026thinsp;0.13\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e2.30\u0026thinsp;\u0026plusmn;\u0026thinsp;0.13\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.196\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGLU, mmol/L\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e5.30 (4.97,5.62)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e5.34 (5.05,5.64)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e5.27 (4.93,5.60)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.210\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eALP, U/L\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e70.66 (64.75,76.94)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e76.98 (71.31,81.55)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e68.50 (63.46,73.19)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCr, umol/L\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e68.50 (61.53,75.91)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e71.55 (61.97,77.17)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e67.93 (61.37,75.34)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.079\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ee-GFR, ml/min/1.73 m\u003csup\u003e2\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e99.33 (91.56,108.06)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e97.55 (90.68,106.38)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e100.16 (92.12,108.53)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.061\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCMI\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.64 (0.40,0.89)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.99 (0.73,1.26)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.54 (0.35,0.74)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"5\"\u003eContinuous variables were expressed as mean\u0026thinsp;\u0026plusmn;\u0026thinsp;SD, or median (interquartile range). Categorical variables were expressed as number (percentage).\u003c/td\u003e\u003c/tr\u003e \u003ctr\u003e\u003ctd colspan=\"5\"\u003eAbbreviations: TC, total cholesterol; TG, triglyceride; HDL-C, high-density lipoprotein cholesterol; LDL-C, low-density lipoprotein cholesterol; ApoA1, apolipoprotein A1; ApoB, apolipoprotein B; UA, uric acid; HbA1c, glycated hemoglobin A1c; hs-CRP, high-sensitivity C-reactive protein; D-dimer, D-dimer; HCY, homocysteine; CK-MB, creatine kinase\u0026ndash;MB isoenzyme; cTnI, cardiac troponin I; Na, sodium; K, potassium; Cl, chloride; P, phosphorus; Ca, calcium; GLU, glucose; ALP, alkaline phosphatase; Cr, creatinine; eGFR, estimated glomerular filtration rate; CMI, cardiometabolic index.\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eVariables that reached statistical significance in univariate analysis were subsequently included in a multivariate logistic regression model to determine independent predictors of CAC. The final model identified older age (odds ratio [OR]\u0026thinsp;=\u0026thinsp;1.059, 95% confidence interval [CI]: 1.038\u0026ndash;1.081, P\u0026thinsp;\u0026lt;\u0026thinsp;0.001), smoking (OR\u0026thinsp;=\u0026thinsp;1.749, 95% CI: 1.072\u0026ndash;2.853, P\u0026thinsp;=\u0026thinsp;0.025), CMI (OR\u0026thinsp;=\u0026thinsp;2.436, 95% CI: 1.694\u0026ndash;3.502, P\u0026thinsp;\u0026lt;\u0026thinsp;0.001), hs-CRP (OR\u0026thinsp;=\u0026thinsp;1.157, 95% CI: 1.058\u0026ndash;1.266, P\u0026thinsp;=\u0026thinsp;0.001), and HCY (OR\u0026thinsp;=\u0026thinsp;1.044, 95% CI: 1.002\u0026ndash;1.082, P\u0026thinsp;=\u0026thinsp;0.042) as independent risk factors. Other variables\u0026mdash;including male sex, hypertension, BMI, TC, LDL-C, UA, and HbA1c\u0026mdash;did not retain statistical significance in the multivariate model (Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab3\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eMultivariable logistic analyses to predict CAC\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"7\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eβ\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eSE\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eWaldχ\u0026sup2;\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eP\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eOR\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003e95% CI\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMale\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.353\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.232\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e2.318\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.128\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e1.423\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.904\u0026ndash;2.241\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAge\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.058\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.010\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e32.337\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e1.059\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e1.038\u0026ndash;1.081\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSmoking\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.559\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.250\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e5.015\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.025\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e1.749\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e1.072\u0026ndash;2.853\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHypertension\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.231\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.195\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.406\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.236\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e1.259\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.860\u0026ndash;1.844\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDiabetes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.302\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.333\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.827\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.363\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e1.353\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.705\u0026ndash;2.596\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBMI\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.021\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.033\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.398\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.528\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e1.021\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.957\u0026ndash;1.090\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCMI\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.890\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.185\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e23.079\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e2.436\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e1.694\u0026ndash;3.502\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTC\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.324\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.241\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e2.304\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.129\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e1.383\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.910\u0026ndash;2.102\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLDL-C\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e\u0026minus;0.350\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.314\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.244\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.265\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.705\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.381\u0026ndash;1.304\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ehs-CRP\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.146\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.046\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e10.085\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e1.157\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e1.058\u0026ndash;1.266\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eUA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.172\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.678\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e1.000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.997\u0026ndash;1.002\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHCY\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.043\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.021\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e4.129\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.042\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e1.044\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e1.002\u0026ndash;1.082\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHbA1c\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.244\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.148\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e2.722\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.099\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e1.276\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.955\u0026ndash;1.704\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"7\"\u003eAbbreviations: BMI, body mass index; CMI, cardiometabolic index; TC, total cholesterol; LDL-C, low-density lipoprotein cholesterol; hs-CRP, high-sensitivity C-reactive protein; UA, uric acid; HCY, homocysteine; HbA1c, glycated hemoglobin A1c; OR, odds ratio; CI, confidence interval.\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eROC curve analysis was performed to evaluate and compare the predictive ability of CMI and BMI for identifying CAC. The ROC curve for CMI yielded an AUC of 0.753 (95% CI: 0.721\u0026ndash;0.783, P\u0026thinsp;\u0026lt;\u0026thinsp;0.001), indicating good discrimination. The optimal CMI threshold was determined to be 0.742, which corresponded to a sensitivity of 72.81% and a specificity of 70.95%, with a Youden index of 0.4376. In contrast, BMI demonstrated lower predictive value, with an AUC of 0.606 (95% CI: 0.570\u0026ndash;0.640, P\u0026thinsp;\u0026lt;\u0026thinsp;0.001). The best cutoff point for BMI was 23.67, yielding a sensitivity of 71.75% and specificity of 52.38%, and a Youden index of 0.2414. These findings suggest that CMI outperforms BMI in predicting the risk of CAC in this population (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e"},{"header":"Discussion","content":"\u003cp\u003eIn this retrospective observational study of young and middle-aged adults with CHD, we assessed the relationship between the CMI and the presence of CAC as detected by coronary CTA. A total of 439 patients aged 18\u0026ndash;59 years were analyzed and stratified into CAC and non-CAC groups based on CTA findings. Our findings are the first to demonstrate that elevated CMI is significantly and independently associated with the presence of CAC in this relatively younger CHD population. Multivariate logistic regression confirmed CMI as an independent risk factor for CAC, alongside established risk variables including age, smoking, hs-CRP, and HCY. Importantly, CMI demonstrated superior predictive performance compared to BMI, an anthropometric indicator traditionally used in cardiovascular risk assessment. ROC curve analysis supported this, showing a higher AUC for CMI (0.753) than for BMI (0.606), indicating greater clinical utility in detecting early subclinical atherosclerosis.\u003c/p\u003e \u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003eAssociation of CAC with Traditional Risk Factors\u003c/h2\u003e \u003cp\u003eOur data reaffirm the association between increasing age and CAC, a finding consistent with previous literature identifying age as a key determinant of both prevalence and extent of vascular calcification(\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e). Age-related calcification is largely driven by disordered calcium-phosphate metabolism and phenotypic transition of vascular smooth muscle cells toward an osteogenic lineage. Although a greater proportion of males was observed in the CAC group, sex was not an independent predictor in multivariate analysis, suggesting that gender disparities may be mediated by metabolic risk factors or sample size limitations(\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eSmoking was identified as an independent risk factor for CAC. This is supported by mechanistic evidence implicating cigarette smoke in the promotion of oxidative stress, endothelial dysfunction, and chronic inflammation\u0026mdash;all of which contribute to the calcific progression of atherosclerotic plaques(\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e). Nicotine may directly induce osteogenic transformation of vascular smooth muscle cells via nicotinic acetylcholine receptor pathways, and long-term exposure has been positively correlated with CAC severity, whereas smoking cessation is linked to stabilization or regression of calcified lesions(\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eOur study also highlighted hypertension and diabetes mellitus as modifiable risk factors associated with CAC. Hyperglycemia stimulates bone morphogenetic protein-2 expression, increases oxidative stress, and promotes endothelial injury, facilitating vascular calcification(\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e, \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e). Diabetic patients are at heightened risk of developing CAC and face increased cardiovascular morbidity and mortality. Similarly, elevated blood pressure contributes to endothelial damage and upregulates osteogenic markers such as osteopontin, further accelerating arterial calcification(\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eElevated serum UA was also associated with CAC and remained significant in regression analysis, suggesting its role in vascular mineralization. Although serum creatinine and eGFR did not reach statistical significance, trends toward increased calcification among patients with impaired renal function align with prior studies linking chronic kidney disease to vascular calcification(\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e, \u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e). Additionally, serum ALP levels were higher in patients with CAC, reinforcing its proposed function in degrading pyrophosphate\u0026mdash;a physiological inhibitor of vascular calcification(\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e). Overall, our findings underscore that CAC in younger adults is primarily influenced by modifiable cardiometabolic risk factors rather than demographic variables alone.\u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003ePathophysiological Relevance and Predictive Value of CMI\u003c/h3\u003e\n\u003cp\u003eCMI, defined as the product of the WHtR and the TG/HDL-C ratio, was originally developed as a surrogate marker of visceral adiposity and atherogenic dyslipidemia(\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e). It captures two critical risk dimensions\u0026mdash;central obesity and lipid metabolic disturbance\u0026mdash;that contribute to vascular inflammation and calcification. Unlike BMI, which lacks specificity in distinguishing visceral from subcutaneous fat, WHtR offers a more accurate assessment of abdominal fat distribution. Evidence indicates that WHtR outperforms both BMI and WC in predicting cardiovascular events, particularly in Asian populations where central obesity may be underestimated(\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eThe TG/HDL-C ratio component adds metabolic insight, as it reflects insulin resistance (IR), small dense low-density lipoprotein particles, and endothelial dysfunction\u0026mdash;all recognized contributors to vascular calcification(\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e, \u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e). Several mechanisms may explain the observed link between CMI and CAC. First, increased visceral fat secretes pro-inflammatory cytokines such as tumor necrosis factor-alpha, interleukin-6, and resistin, while decreasing protective adipokines like adiponectin(\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e). This imbalance promotes vascular injury and osteogenic transformation of vascular smooth muscle cells. Second, visceral adiposity is linked to excess free fatty acids, mitochondrial stress, and oxidative injury, all of which upregulate calcification-promoting signaling cascades(\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e). Third, IR exacerbates inflammation through downregulation of endothelial nitric oxide synthase, activation of nuclear factor-kappa B, and increased vascular cell adhesion molecule expression, accelerating vascular stiffening and calcific plaque development(\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eOur findings support these mechanisms: CMI remained an independent predictor of CAC after adjusting for key covariates including age, BMI, hs-CRP, and HCY, whereas BMI lacked predictive value in multivariate models. These results reinforce CMI\u0026rsquo;s superior utility as a risk stratification tool and its relevance in detecting early metabolic disturbances that contribute to subclinical atherosclerosis.\u003c/p\u003e\n\u003ch3\u003eClinical Implications and Practical Considerations\u003c/h3\u003e\n\u003cp\u003eThe clinical relevance of this study lies in its potential to improve early risk identification for subclinical CAD in younger adults. Given that CAC is a strong predictor of future cardiovascular events, timely identification of high-risk individuals is essential(\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e, \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e). CMI represents a low-cost, noninvasive, and easily accessible metric that leverages routine anthropometric and lipid measurements. In contrast to imaging techniques such as CTA or invasive angiography\u0026mdash;which are expensive, involve radiation exposure, and are impractical for mass screening\u0026mdash;CMI is ideally suited for integration into outpatient clinics, primary care, and preventive health programs.\u003c/p\u003e \u003cp\u003eCMI enables the early detection of cardiometabolic dysregulation, even in patients with normal BMI or isolated lipid abnormalities(\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e). In resource-limited settings, it could serve as a preliminary screening tool to guide more definitive diagnostics such as coronary calcium scoring. Moreover, CMI can be integrated into existing risk prediction models such as the Framingham, Atherosclerotic Cardiovascular Disease (ASCVD) risk calculator or the China-Prediction for ASCVD Risk (China-PAR) model- to enhance predictive performance in younger populations, where traditional models often underestimate risk(\u003cspan additionalcitationids=\"CR33\" citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eNotably, the elevated CMI levels observed in younger males in our study suggest that age- and sex-specific CMI thresholds may enhance clinical applicability. CMI could also inform therapeutic decisions, such as initiating statins, prescribing lifestyle interventions, or considering aspirin therapy in appropriately selected individuals based on their CMI-defined risk category.\u003c/p\u003e \u003cp\u003eWhile our findings provide novel insights, further research is warranted. Prospective, multicenter studies with diverse populations are needed to validate our results and establish optimal CMI thresholds. Longitudinal monitoring of CMI may help clarify whether changes in CMI precede the onset or progression of CAC or predict major adverse cardiovascular events (MACE). Additionally, combining CMI with other emerging markers\u0026mdash;such as the Visceral Adiposity Index or Lipid Accumulation Product\u0026mdash;may enhance risk prediction. Finally, it remains to be seen whether CMI can forecast not only the presence but also the progression of CAC, procedural complexity of PCI, and long-term clinical outcomes.\u003c/p\u003e \u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003eLimitations\u003c/h2\u003e \u003cp\u003eDespite its valuable contributions, this study has several limitations that warrant consideration. First, the retrospective nature and single-center design restrict the external validity of the findings and may introduce selection bias. Second, CAC was assessed at a single time point without longitudinal follow-up, limiting the ability to assess the progression of calcification or the occurrence of clinical outcomes such as myocardial infarction or cardiovascular mortality. Third, although high-resolution coronary CTA was used for CAC assessment, calcification was dichotomized (present vs. absent) rather than quantified using standardized scoring systems such as the Agatston score, which could provide more granular insights into calcific burden. Fourth, although multivariate logistic regression was employed to adjust for potential confounders, residual confounding due to unmeasured variables\u0026mdash;such as dietary habits, physical activity levels, genetic factors, or medication compliance\u0026mdash;cannot be entirely excluded. Lastly, the study did not examine the prognostic capacity of the CMI for predicting MACE, nor did it investigate its utility in stratifying procedural complexity or clinical outcomes among patients undergoing PCI.\u003c/p\u003e \u003c/div\u003e"},{"header":"Conclusion","content":"\u003cp\u003eIn conclusion, this study is the first to establish an independent association between the CMI\u0026mdash;a simple, noninvasive metric integrating waist-to-height ratio and the TG/HDL-C ratio\u0026mdash;and the presence of CAC in young and middle-aged adults with CHD. CMI demonstrated stronger predictive capability for subclinical atherosclerosis than traditional indices such as BMI, as evidenced by superior performance in ROC curve analysis. These findings suggest that CMI could be implemented as an accessible early screening tool to identify asymptomatic high-risk individuals for targeted cardiovascular evaluation. To confirm these results and better understand the clinical utility of CMI, future large-scale, prospective studies involving multi-ethnic populations and extended follow-up are essential. Such investigations should also explore whether dynamic changes in CMI over time are predictive of CAC progression, treatment efficacy, or long-term cardiovascular outcomes.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eEthics approval and consent to participate\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe present research was carried out in accordance with the tenets mentioned in the Helsinki Declaration and was approved by the Ethical Board of Xiangtan Central Hospital (approval number: X2022325). Prior to the commencement of the research, our team obtained written informed consent from each patient.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent for publication\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable. No individual patient data will be reported.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAvailability of data and materials\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe datasets generated during and/or analyzed during the current study are available from the corresponding author on reasonable request.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting interests\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare that they have no competing interests.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNo funding was received for this study.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthors' contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eX.W. and L.W. had the idea for the paper, reviewed and edited it critically for important intellectual content. H.B.H. and H.H. performed the literature search and analysis. X.W., M.X.W., L.W., Z.L. and H.H. substantially contributed to the conception of the paper, drafted and critically revised the manuscript. All authors contributed to editorial changes in the manuscript. All authors read and approved the final manuscript. All authors have participated sufficiently in the work and agreed to be accountable for all aspects of the work.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAcknowledgment\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eClinical trial number\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n \u003cli\u003eGreenland P, Blaha MJ, Budoff MJ, Erbel R, Watson KE. Coronary Calcium Score and Cardiovascular Risk. Journal of the American College of Cardiology. 2018;72(4):434-47.\u003c/li\u003e\n \u003cli\u003eStrauss HW, Nakahara T, Narula N, Narula J. 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Subcutaneous and visceral adipose tissue: structural and functional differences. Obesity reviews : an official journal of the International Association for the Study of Obesity. 2010;11(1):11-8.\u003c/li\u003e\n \u003cli\u003eZhao Q, Zhang TY, Cheng YJ, Ma Y, Xu YK, Yang JQ, et al. Impacts of triglyceride-glucose index on prognosis of patients with type 2 diabetes mellitus and non-ST-segment elevation acute coronary syndrome: results from an observational cohort study in China. Cardiovascular diabetology. 2020;19(1):108.\u003c/li\u003e\n \u003cli\u003eShi WR, Wang HY, Chen S, Guo XF, Li Z, Sun YX. Estimate of prevalent diabetes from cardiometabolic index in general Chinese population: a community-based study. Lipids in health and disease. 2018;17(1):236.\u003c/li\u003e\n \u003cli\u003eZhou H, Mao Y, Ye M, Zuo Z. Exploring the nonlinear association between cardiometabolic index and hypertension in U.S. Adults: an NHANES-based study. BMC public health. 2025;25(1):1092.\u003c/li\u003e\n \u003cli\u003eZhou J, Fan J, Zhang X, You L, Lin D, Huang C, et al. Fatty Liver Index and Its Association with 10-Year Atherosclerotic Cardiovascular Disease Risk: Insights from a Population-Based Cross-Sectional Study in China. Metabolites. 2023;13(7).\u003c/li\u003e\n \u003cli\u003eGoff DC, Jr., Lloyd-Jones DM, Bennett G, Coady S, D\u0026apos;Agostino RB, Gibbons R, et al. 2013 ACC/AHA guideline on the assessment of cardiovascular risk: a report of the American College of Cardiology/American Heart Association Task Force on Practice Guidelines. Circulation. 2014;129(25 Suppl 2):S49-73.\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"bmc-cardiovascular-disorders","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"bcar","sideBox":"Learn more about [BMC Cardiovascular Disorders](http://bmccardiovascdisord.biomedcentral.com/)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/bcar/default.aspx","title":"BMC Cardiovascular Disorders","twitterHandle":"BMC_series","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"em","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"Cardiometabolic Index, Coronary Artery Calcification, Coronary Heart Disease, Young and Middle-Aged Adults, Cardiovascular Risk Prediction","lastPublishedDoi":"10.21203/rs.3.rs-6649906/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-6649906/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eBackground:\u003c/p\u003e\n\u003cp\u003eCoronary artery calcification (CAC) serves as a well-established indicator of subclinical atherosclerosis and is a robust predictor of future cardiovascular complications. The cardiometabolic index (CMI), a recently proposed composite metric combining waist-to-height ratio (WHtR) and the triglyceride-to-HDL cholesterol (TG/HDL-C) ratio, has gained attention as a proxy for both visceral fat accumulation and atherogenic dyslipidemia. Nevertheless, the relationship between CMI and CAC in young and middle-aged individuals diagnosed with coronary heart disease (CHD) remains insufficiently explored.\u003c/p\u003e\n\u003cp\u003eMethods:\u003c/p\u003e\n\u003cp\u003eThis retrospective analysis included CHD patients aged 18–59 years who were admitted to the hospital and underwent coronary computed tomography angiography (CTA) between January 2018 and August 2022. Based on CTA findings, participants were stratified into CAC-positive and CAC-negative groups. Data on demographics, clinical characteristics, and biochemical parameters were obtained. CMI was computed using the formula WHtR × (TG/HDL-C). Multivariate logistic regression models and receiver operating characteristic (ROC) curve analysis were employed to assess both the association and diagnostic value of CMI for CAC.\u003c/p\u003e\n\u003cp\u003eResults:\u003c/p\u003e\n\u003cp\u003eA total of 439 patients were analyzed, of whom 128 were classified as having CAC. The CAC group exhibited a significantly elevated CMI compared to their counterparts without CAC (P \u0026lt; 0.001). After adjusting for potential confounders, CMI emerged as an independent predictor of CAC (OR = 2.436, 95% CI: 1.694–3.502, P \u0026lt; 0.001), whereas BMI did not reach statistical significance. ROC curve analysis revealed that CMI had superior predictive accuracy relative to BMI (AUC: 0.753 vs. 0.606). The optimal CMI threshold for predicting CAC was 0.742, yielding a sensitivity of 72.81% and a specificity of 70.95%.\u003c/p\u003e\n\u003cp\u003eConclusion:\u003c/p\u003e\n\u003cp\u003eCMI shows a significant independent association with CAC and outperforms BMI in predicting its presence among young and middle-aged adults with CHD. Given its simplicity, non-invasiveness, and low cost, CMI may represent a practical screening tool for early detection of asymptomatic coronary atherosclerosis in clinical settings.\u003c/p\u003e","manuscriptTitle":"Association Between Cardiometabolic Index and Coronary Artery Calcification in Young and Middle-Aged Adults With Coronary Heart Disease","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-05-27 06:30:55","doi":"10.21203/rs.3.rs-6649906/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2025-05-27T17:48:09+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-05-23T16:48:16+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-05-23T15:45:31+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"229893561487570367106219850020319604501","date":"2025-05-23T09:52:18+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"295175047066423379118039287967164716521","date":"2025-05-22T14:26:05+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2025-05-22T11:49:22+00:00","index":"","fulltext":""},{"type":"editorInvited","content":"","date":"2025-05-20T06:50:11+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2025-05-16T10:56:33+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2025-05-16T10:52:03+00:00","index":"","fulltext":""},{"type":"submitted","content":"BMC Cardiovascular Disorders","date":"2025-05-12T22:37:17+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"bmc-cardiovascular-disorders","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"bcar","sideBox":"Learn more about [BMC Cardiovascular Disorders](http://bmccardiovascdisord.biomedcentral.com/)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/bcar/default.aspx","title":"BMC Cardiovascular Disorders","twitterHandle":"BMC_series","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"em","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"c5535b05-542d-4b0c-84d7-61dd8854dd7a","owner":[],"postedDate":"May 27th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"published-in-journal","subjectAreas":[],"tags":[],"updatedAt":"2025-07-07T16:16:37+00:00","versionOfRecord":{"articleIdentity":"rs-6649906","link":"https://doi.org/10.1186/s12872-025-04950-y","journal":{"identity":"bmc-cardiovascular-disorders","isVorOnly":false,"title":"BMC Cardiovascular Disorders"},"publishedOn":"2025-07-05 15:58:16","publishedOnDateReadable":"July 5th, 2025"},"versionCreatedAt":"2025-05-27 06:30:55","video":"","vorDoi":"10.1186/s12872-025-04950-y","vorDoiUrl":"https://doi.org/10.1186/s12872-025-04950-y","workflowStages":[]},"version":"v1","identity":"rs-6649906","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-6649906","identity":"rs-6649906","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}