Atherogenic index of plasma and non-traditional lipid parameters in coronary artery calcification: a Chinese health checkup study

Atherogenic index of plasma and non-traditional lipid parameters in coronary artery calcification: a Chinese health checkup study | 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 Atherogenic index of plasma and non-traditional lipid parameters in coronary artery calcification: a Chinese health checkup study Ying Zhou, Rui Zhang, Ya Huang, Wenji Ni, Dandan Li, Xiangwei Bo, and 4 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-8788359/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 11 You are reading this latest preprint version Abstract Background Coronary artery calcification (CAC) is a reliable marker of subclinical atherosclerosis. Non-traditional lipid parameters, such as the atherogenic index of plasma (AIP), non-high density lipoprotein cholesterol (non-HDL-C), total cholesterol to high-density lipoprotein cholesterol (TC/HDL-C) ratio and remnant cholesterol (RC) are gaining attention for their association with cardiovascular risk. This study aimed to investigate the association of these non-traditional lipid parameters with CAC and their predictive value in a Chinese health checkup cohort. Methods In this retrospective cross-sectional study, 4196 adults who underwent health checkups at Jinling Hospital, Afliated Hospital of Medical School, Nanjing University, from January to December 2024 were enrolled. Participants were categorized into a non-calcification group (n = 3548) and a calcification group (n = 648) based on the presence of CAC detected by chest computed tomography. Demographic, clinical data, and lipid profiles were collected. Multivariable logistic regression analyses were used to evaluate the independent association between lipid parameters (including AIP, non-HDL-C, TC/HDL-C, RC, low-density lipoprotein cholesterol [LDL-C], and high-density lipoprotein cholesterol [HDL-C]) and CAC. Receiver operating characteristic (ROC) curve analysis and the area under the curve (AUC) were used to assess the discriminative ability of each parameter. Results Participants with CAC were older, more likely to be male, and had a higher burden of traditional cardiovascular risk factors. In multivariable logistic regression analyses adjusted for age, sex, blood pressure, body mass index, smoking, medical histories, and medications, AIP (odds ratio [OR] = 1.51, 95% confidence interval [CI]: 1.05–2.16, P = 0.025), non-HDL-C (OR = 1.23, 95% CI: 1.10–1.38, P < 0.001), and TC/HDL-C (OR = 1.09, 95% CI: 1.02–1.16, P = 0.006) remained significantly and positively associated with CAC. The association for RC became non-significant after full adjustment. ROC analysis revealed that AIP had the highest discriminative ability (AUC = 0.612, 95% CI: 0.589–0.635), followed by TC/HDL-C (0.597) and non-HDL-C (0.576), all of which performed better than traditional LDL-C (0.555) and HDL-C (0.574). Conclusion Among a Chinese health checkup cohort, the non-traditional lipid parameters AIP, non-HDL-C, and TC/HDL-C are independently associated with the presence of CAC. AIP demonstrates the best discriminative performance, suggesting it may be a promising biomarker for identifying individuals at high risk for coronary artery calcification. Coronary artery calcification atherogenic index of plasma non-high density lipoprotein cholesterol total cholesterol to high-density lipoprotein cholesterol ratio remnant cholesterol Figures Figure 1 Introduction Cardiovascular diseases remain the leading cause of global disease burden. According to World Health Organization statistics, cardiovascular diseases account for approximately 18.6 million deaths annually worldwide, with atherosclerotic cardiovascular disease (ASCVD) being a predominant component[ 1 ]. This grave epidemiological situation underscores the urgent need for more precise and efficient risk assessment tools to facilitate early identification and primary prevention of ASCVD. In the evaluation of subclinical atherosclerosis, coronary artery calcification (CAC) has emerged as a pivotal imaging biomarker with well-established prognostic value. First developed in 1990, the Agatston CAC score is an internationally endorsed quantitative method for CAC assessment and serves as a guideline-recommended decision-making tool for refined risk stratification and personalized management in ASCVD primary prevention [ 2 , 3 ]. CAC represents the deposition of hydroxyapatite calcium within coronary atherosclerotic plaques, serving as a direct marker of coronary atherosclerotic burden. Landmark prospective cohort studies, such as the Multi-Ethnic Study of Atherosclerosis (MESA), have consistently demonstrated that the CAC score is a strong independent predictor of future myocardial infarction, stroke, and cardiovascular death, providing incremental predictive value beyond traditional clinical risk scores[ 4 ]. Crucially, the CAC score offers critical clinical value by enabling precise risk re-stratification among individuals classified as intermediate or borderline risk by traditional risk assessment models, thereby informing statin treatment decisions[ 5 ]. In adults without established ASCVD, it serves as a robust decision‑support tool when uncertainty exists regarding the initiation of preventive statin therapy, particularly for those at intermediate risk[ 6 ]. Of particular note, a CAC score of zero (CAC = 0) has been validated to possess a high negative predictive value, effectively identifying individuals with very low risk of cardiovascular events over a 10-year period[ 7 , 8 ]. Conversely, a significantly elevated CAC score (≥ 100) definitively indicates a substantial atherosclerotic burden, which warrants the initiation or intensification of statin therapy[ 5 ]. Traditional lipid measures, particularly low-density lipoprotein cholesterol (LDL-C), serve as the cornerstone for ASCVD risk assessment and management. However, their capacity to predict residual risk remains limited. In recent years, a series of non-traditional lipid parameters—which are more integrative or reflective of distinct atherogenic pathways—have attracted significant attention. The atherogenic index of plasma (AIP), calculated as the base-10 logarithm of the ratio of triglycerides to high-density lipoprotein cholesterol (TG/HDL-C), serves as a composite biomarker that reflects lipid metabolism dysregulation. It has been consistently associated with an increased risk of cerebrovascular and cardiovascular diseases, including stroke in middle-aged and older adults, higher cardiovascular mortality in individuals with diabetes, and a spectrum of other cardiovascular and metabolic disorders [ 9 – 11 ]. Notably, AIP functions as an independent predictor of vulnerable plaques beyond traditional risk factors and offers incremental prognostic value in symptomatic patients with suspected coronary artery disease (CAD) [ 12 ]. Meta-analytic evidence further confirms that elevated AIP is strongly linked to heightened CAD risk, more severe disease progression, and poorer clinical outcomes, both in populations with and without established CAD[ 13 ]. Non-high-density lipoprotein cholesterol (non-HDL-C), encompassing the cholesterol content of all apolipoprotein B-containing atherogenic lipoproteins, has been recommended in several guidelines as a superior primary intervention target compared to LDL-C[ 14 , 15 ]. Clinical studies have shown that in patients with well-managed LDL-C, non-HDL-C serves as an easily accessible marker for identifying those at high residual risk of ASCVD and death[ 16 ]. The total cholesterol to high density lipoprotein cholesterol (HDL-C) ratio (TC/HDL-C) is a composite indicator of lipid balance. Remnant cholesterol (RC) represents the cholesterol content within triglyceride-rich lipoprotein remnants. It is closely associated with insulin resistance and chronic inflammatory states and has been identified as an independent cardiovascular risk factor beyond traditional risk factors[ 17 ]. Although these non-traditional lipid parameters have been linked to cardiovascular outcomes in Western populations, studies directly comparing their independent associations with CAC-the gold standard for subclinical disease-particularly in large-scale, relatively healthy screening populations, remain insufficient. Therefore, this study aims to utilize a large health examination cohort to systematically evaluate the strength of association between AIP, non-HDL-C, TC/HDL-C, RC, and the presence of CAC. Furthermore, after comprehensive adjustment for traditional risk factors, we will compare their independent predictive value and discriminative ability against LDL-C and HDL-C. The findings aim to provide novel evidence for optimizing early ASCVD risk stratification using readily accessible lipid parameters in routine health management. Methods Study participants A total of 4196 adult participants who underwent health checkups at the Medical Examination Center of Jinling Hospital, Afliated Hospital of Medical School, Nanjing University, from January 2024 to December 2024, were enrolled. Based on the presence of CAC detected by chest computed tomography (CT), participants were categorized into a non-calcification group (n = 3548) and a calcification group (n = 648). Inclusion criteria: Age ≥ 18 years; No history or clinical symptoms of atherosclerotic cardiovascular disease (ASCVD); Complete clinical data, including blood biochemical indicators and chest CT results. Exclusion criteria: Incomplete baseline data; History of angina pectoris, ASCVD, or prior coronary stent implantation or coronary artery bypass grafting; Comorbidities such as malignant tumors or severe hepatic/renal insufficiency. This study was approved by Jinling Hospital, Afliated Hospital of Medical School, Nanjing University (Approval No.: 2024DZKY-015-01). The requirement for informed consent was waived due to the retrospective nature of the study. Data collection Demographic and clinical data, including age, sex, smoking history, alcohol consumption history, medical history, medication use, body mass index (BMI), systolic blood pressure (SBP), and diastolic blood pressure (DBP), were collected from the hospital information system. Smoking history was defined as smoking at least one cigarette per day for a continuous or cumulative period of ≥ 6 months. Alcohol consumption history was defined as consuming alcoholic beverages at least once per month on average over the past 12 months. Height and weight were measured using a stadiometer and weighing scale (model SH-200; Shanghe, China). BMI was calculated as weight (kg) divided by height squared (m²). Blood pressure was measured using an Omron automated electronic sphygmomanometer (model HBP-9030; Omron, Japan). The average of three consecutive readings taken at rest was recorded. Medical histories were defined as follows: Hypertension: a documented history of hypertension or current use of antihypertensive medication; Dyslipidemia: a documented history of dyslipidemia or current use of lipid-lowering medication; Diabetes mellitus: a documented history of type 2 diabetes mellitus or current use of insulin or oral hypoglycemic agents. Fasting venous blood samples (4 mL) were collected in yellow gel-separator tubes in the morning. All samples were processed within 2 hours. Serum levels of fasting plasma glucose (FPG), total cholesterol (TC), triglycerides (TG), HDL-C, and LDL-C, alanine aminotransferase (ALT), aspartate aminotransferase (AST), blood urea nitrogen (BUN), and creatinine (Cr) were measured using an automated biochemical analyzer (Model 7600; Hitachi, Japan). All participants underwent non-contrast chest CT scanning in the supine position using a 64-slice multi-detector CT scanner (SIEMENS SOMATOM Definition Flash, Siemens, Germany). Scanning parameters were as follows: tube voltage 120 kV, tube current 20–50 mAs, reconstructed slice thickness 1 mm, slice interval 1 mm. The scanning range extended from the lung apex to the lung base. Acquired images were transferred to a picture archiving and communication system (PACS) workstation. Two experienced radiologists independently evaluated the coronary artery territory on mediastinal window settings using an overall visual assessment method. CAC was diagnosed if focal high-density lesions (CT value > 130 Hounsfield Units) were identified along the course of the coronary arteries. In cases of disagreement, a consensus was reached through discussion. Calculation of Derived Lipid Parameters Nontraditional lipid parameters were calculated using the following formula: $$\:\text{A}\text{I}\text{P}=\text{log}\left(\frac{\text{T}\text{G}}{\text{H}\text{D}\text{L}-\text{C}}\right)$$ $$\:\text{N}\text{o}\text{n}-\text{H}\text{D}\text{L}-\text{C}=\text{T}\text{C}-\text{H}\text{D}\text{L}-\text{C}$$ $$\:\text{T}\text{C}/\text{H}\text{D}\text{L}-\text{C}=\frac{\text{T}\text{C}}{\text{H}\text{D}\text{L}-\text{C}}$$ $$\:\text{R}\text{C}=\text{T}\text{C}-\text{L}\text{D}\text{L}-\text{C}-\text{H}\text{D}\text{L}-\text{C}$$ Statistical Analysis All statistical analyses were performed using R software (version 4.5.2; R Foundation for Statistical Computing, Vienna, Austria). All variables included in the analysis were complete, with no missing values; no data imputation or exclusion was necessary. Continuous variables were assessed for normality using the Shapiro-Wilk test and visual inspection of Q-Q plots. Normally distributed variables were presented as mean ± standard deviation (SD) and compared between groups using the independent samples t-test. Non-normally distributed continuous variables were presented as median (interquartile range, IQR) and compared using the Mann-Whitney U test. Categorical variables were expressed as numbers (percentages) and compared using the chi-square test or Fisher’s exact test, as appropriate. To examine the independent association between lipid parameters and the presence of CAC, multivariable logistic regression analysis was performed. Variables that showed a univariate association with CAC (P < 0.05) or were considered clinically relevant (e.g., age, sex, blood pressure, BMI, diabetes status, smoking, drinking) were included in the multivariable models. Multicollinearity among independent variables was assessed using variance inflation factors (VIFs), with a VIF < 5 considered acceptable. The goodness-of-fit of the logistic regression models was evaluated using the Hosmer-Lemeshow test. Receiver operating characteristic (ROC) curve analysis was conducted, and the area under the ROC curve (AUC) along with its 95% confidence interval (CI) was calculated to assess the discriminative ability of each lipid parameter for predicting CAC. Differences in AUCs were compared using DeLong’s test. A two-tailed P-value < 0.05 was considered statistically significant for all analyses. Results Baseline characteristics A total of 4,196 participants were included and stratified by coronary artery calcification (CAC) status into a CAC=0 group (n=3,548) and a CAC>0 group (n=648). Baseline characteristics are summarized in Table 1. Participants in the CAC>0 group were significantly older than those in the CAC=0 group (56.42 ± 10.79 vs. 43.03 ± 10.80 years; P <0.001) and had a higher proportion of males (72.53% vs. 49.44%; P 0 group (20.99% vs. 10.60%, P < 0.001), while no significant difference was observed in alcohol consumption (7.56% vs. 5.98%, P = 0.150). Comparisons of cardiovascular risk factors revealed significantly higher prevalences of hypertension (38.43% vs. 10.57%), hyperlipidemia (10.34% vs. 3.75%), and diabetes mellitus (15.28% vs. 2.82%) in the CAC>0 group (all P 0 group. (all P <0.001). Physiological parameters, including systolic blood pressure (131.40 ± 18.43 vs. 119.43 ± 15.84 mmHg), diastolic blood pressure (78.89 ± 11.36 vs. 73.96 ± 10.93 mmHg), and body mass index (25.60 ± 3.34 vs. 24.25 ± 3.55 kg/m²), were all elevated in the CAC>0 group (all P 0 group had significantly higher levels of liver function markers (ALT, AST), renal function parameters (BUN, CR), and fasting plasma glucose (all P 0 group, and HDL-C levels were significantly lower (all P 0 (n = 648) P Age (years) 45.10 ± 11.83 43.03 ± 10.80 56.42 ± 10.79 <0.001 Sex(male), n (%) 2,224 (53.00%) 1,754 (49.44%) 470 (72.53%) <0.001 Smoking, n (%) 512 (12.20%) 376 (10.60%) 136 (20.99%) <0.001 Drinking, n (%) 261 (6.22%) 212 (5.98%) 49 (7.56%) 0.150 Hypertension, n (%) 624 (14.87%) 375 (10.57%) 249 (38.43%) <0.001 Antihypertensive medication, n (%) 361 (8.60%) 203 (5.72%) 158 (24.38%) <0.001 Hyperlipidemia, n (%) 200 (4.77%) 133 (3.75%) 67 (10.34%) <0.001 Lipid-lowering medication, n (%) 93 (2.22%) 51 (1.44%) 42 (6.48%) <0.001 Diabetes mellitus, n (%) 199 (4.74%) 100 (2.82%) 99 (15.28%) <0.001 Antidiabetic medication, n (%) 100 (2.38%) 47 (1.32%) 53 (8.18%) <0.001 SBP (mmHg) 121.28 ± 16.83 119.43 ± 15.84 131.40 ± 18.43 <0.001 DBP (mmHg) 74.73 ± 11.14 73.96 ± 10.93 78.89 ± 11.36 <0.001 BMI (kg/m²) 24.46 ± 3.55 24.25 ± 3.55 25.60 ± 3.34 <0.001 ALT (U/L) 18.00 (13.00, 28.00) 18.00 (12.00, 27.00) 20.00 (14.00, 29.00) <0.001 AST (U/L) 19.00 (16.00, 23.00) 18.00 (15.00, 23.00) 20.00 (17.00, 24.00) <0.001 BUN (mmol/L) 5.19 ± 1.40 5.11 ± 1.32 5.68 ± 1.67 <0.001 Cr (μmol/L) 64.90 (54.60, 77.00) 63.80 (53.85, 76.00) 69.60 (60.10, 79.00) <0.001 FPG (mmol/L) 5.36 ± 1.19 5.26 ± 1.05 5.90 ± 1.67 <0.001 TC (mmol/L) 4.98 ± 0.92 4.95 ± 0.89 5.12 ± 1.06 <0.001 TG (mmol/L) 1.28 (0.91, 2.00) 1.24 (0.88, 2.00) 1.56 (1.08, 2.00) <0.001 LDL-C (mmol/L) 2.94 ± 0.76 2.92 ± 0.74 3.07 ± 0.85 <0.001 HDL-C (mmol/L) 1.25 ± 0.30 1.26 ± 0.30 1.19 ± 0.29 <0.001 AIP 0.05 ± 0.33 0.03 ± 0.32 0.16 ± 0.32 <0.001 TC/HDL-C 4.02 (3.30, 5.00) 3.95 (3.26, 5.00) 4.34 (3.60, 5.00) <0.001 Non-HDL-C (mmol/L) 3.73 ± 0.92 3.69 ± 0.89 3.93 ± 1.03 <0.001 RC (mmol/L) 0.78 ± 0.40 0.77 ± 0.38 0.86 ± 0.49 <0.001 Association of lipid parameters with coronary artery calcification Multivariable logistic regression models were used to assess the associations between various lipid parameters and the presence of CAC. The results are summarized in Table 2. AIP: After adjusting for age and sex (Model 1), AIP as a continuous variable was significantly associated with the risk of CAC (OR = 2.29, 95% CI: 1.69-3.10, P <0.001). This association remained independent after further adjustment for multiple traditional cardiovascular risk factors and clinical indicators (Models 2 and 3) (Model 3: OR = 1.51, 95% CI: 1.05-2.16, P =0.025). Analysis by AIP quartiles showed that compared to the lowest quartile (Q1), subjects in the highest quartile (Q4) had a 57% significantly increased odds of CAC in the fully adjusted Model 3 (OR = 1.57, 95% CI: 1.11-2.22, P =0.011). Non-HDL-C: Non-HDL-C as a continuous variable showed a significant positive association with CAC risk across all three models (all P <0.001). In the fully adjusted Model 3, each 1 mmol/L increase in non-HDL-C was associated with a 23% increase in the odds of CAC (OR = 1.23, 95% CI: 1.10-1.38). Compared with the lowest quartile, subjects in the highest quartile had a 62% increased odds of CAC (OR = 1.62, 95% CI: 1.19-2.21, P =0.002). TC/HDL-C: The TC/HDL-C ratio as a continuous variable was significantly and positively associated with CAC risk in all models (Model 3: OR = 1.09, 95% CI: 1.02-1.16, P =0.006). Quartile analysis indicated that compared to Q1, the Q4 group had a 53% increased odds of CAC in Model 3 (OR = 1.53, 95% CI: 1.09-2.17, P =0.015). RC: In Model 1 adjusted only for age and sex, RC as a continuous variable was associated with CAC risk (OR = 1.37, P =0.002). However, this association lost statistical significance after further multivariable adjustments (Models 2 and 3) (Model 3: OR = 1.13, P =0.474). Quartile analysis showed a similar trend, with no significant association observed in the fully adjusted models. LDL-C: In contrast to RC, LDL-C as a continuous variable remained significantly associated with CAC risk after multivariable adjustment (Model 3: OR = 1.28, 95% CI: 1.13-1.45, P <0.001). The Q4 group had a 62% increased odds compared to the Q1 group (OR = 1.62, P =0.001). HDL-C: In Model 1, HDL-C as a continuous variable was significantly inversely associated with CAC risk (OR = 0.52, P <0.001). However, this association was attenuated and became non-significant after including more covariates (Models 2 and 3). Table 2 Association between non-traditional lipid parameters and coronary artery calcification Model 1 Model 2 Model 3 Variable OR (95% CI) P OR (95% CI) P OR (95% CI) P AIP (continuous) 2.29 (1.69-3.10) <0.001 1.49 (1.06-2.08) 0.021 1.51 (1.05-2.16) 0.025 Q1 (<-0.18) 1 (reference) 1 (reference) 1 (reference) Q2 (-0.18-0.03) 1.37 (0.99-1.90) 0.056 1.24 (0.89-1.73) 0.216 1.18 (0.84-1.66) 0.344 Q3 (0.03-0.26) 1.37 (0.99-1.89) 0.056 1.10 (0.79-1.55) 0.564 1.01 (0.71-1.43) 0.961 Q4 (≥0.26) 2.33 (1.71-3.19) <0.001 1.63 (1.17-2.29) 0.004 1.57 (1.11-2.22) 0.011 Non-HDL-C (continuous) 1.22 (1.11-1.35) <0.001 1.23 (1.11-1.37) <0.001 1.23 (1.10-1.38) <0.001 Q1 (<3.09) 1 (reference) 1 (reference) 1 (reference) Q2 (3.09-3.67) 1.07 (0.79-1.44) 0.683 1.21 (0.88-1.66) 0.250 1.20 (0.87-1.66) 0.261 Q3 (3.67-4.28) 1.11 (0.83-1.49) 0.466 1.20 (0.88-1.64) 0.255 1.19 (0.87-1.63) 0.284 Q4 (≥4.28) 1.54 (1.17-2.04) 0.002 1.64 (1.22-2.22) 0.001 1.62 (1.19-2.21) 0.002 TC/HDL-C (continuous) 1.11 (1.05-1.17) <0.001 1.09 (1.03-1.15) 0.002 1.09 (1.02-1.16) 0.006 Q1 (<3.30) 1 (reference) 1 (reference) 1 (reference) Q2 (3.30-4.01) 1.06 (0.78-1.45) 0.698 0.99 (0.72-1.38) 0.956 0.99 (0.72-1.38) 0.958 Q3 (4.01-4.82) 1.29 (0.96-1.75) 0.094 1.13 (0.82-1.55) 0.471 1.13 (0.82-1.57) 0.447 Q4 (≥4.82) 1.80 (1.35-2.43) <0.001 1.52 (1.11-2.09) 0.010 1.53 (1.09-2.17) 0.015 RC-C (continuous) 1.37 (1.11-1.67) 0.002 1.21 (0.97-1.49) 0.090 1.13 (0.80-1.59) 0.474 Q1 (<0.55) 1 (reference) 1 (reference) 1 (reference) Q2 (0.55-0.72) 1.00 (0.74-1.34) 0.988 0.99 (0.73-1.34) 0.957 0.98 (0.72-1.32) 0.884 Q3 (0.72-0.92) 1.04 (0.78-1.38) 0.807 0.99 (0.74-1.33) 0.963 0.98 (0.73-1.31) 0.868 Q4 (≥0.92) 1.31 (1.01-1.71) 0.046 1.19 (0.90-1.56) 0.220 1.11 (0.82-1.51) 0.491 LDL-C (continuous) 1.20 (1.07-1.36) 0.003 1.27 (1.12-1.44) <0.001 1.28 (1.13-1.45) <0.001 Q1 (<2.41) 1 (reference) 1 (reference) 1 (reference) Q2 (2.41-2.91) 1.06 (0.79-1.42) 0.689 1.27 (0.93-1.73) 0.134 1.26 (0.93-1.72) 0.143 Q3 (2.91-3.42) 1.09 (0.82-1.46) 0.541 1.25 (0.92-1.70) 0.153 1.25 (0.92-1.70) 0.152 Q4 (≥3.42) 1.38 (1.05-1.81) 0.021 1.60 (1.20-2.15) 0.002 1.62 (1.21-2.18) 0.001 HDL-C (continuous) 0.52 (0.36-0.73) <0.001 0.76 (0.52-1.10) 0.144 0.79 (0.53-1.18) 0.255 Q1 (<1.04) 1 (reference) 1 (reference) 1 (reference) Q2 (1.04-1.22) 0.88 (0.68-1.13) 0.318 0.98 (0.76-1.27) 0.878 1.01 (0.77-1.32) 0.961 Q3 (1.22-1.44) 0.72 (0.55-0.94) 0.017 0.88 (0.66-1.17) 0.373 0.92 (0.68-1.24) 0.591 Q4 (≥1.44) 0.61 (0.46-0.82) 0.001 0.84 (0.61-1.15) 0.281 0.87 (0.62-1.22) 0.420 Note: Model 1: Adjusted for age and sex. Model 2: Additionally adjusted for SBP, DBP, BMI, smoking, drinking, hypertension, antihypertensive medication, hyperlipidemia, lipid-lowering medication, Diabetes mellitus, and antidiabetic medication. Model 3: Additionally adjusted for ALT, AST, BUN, CR, FPG. To avoid multicollinearity in models containing derived lipid parameters, specific component lipids were additionally adjusted as follows: For AIP (log[TG/HDL-C]), TC and LDL-C were adjusted for. For non-HDL-C, TC/HDL-C, LDL-C, and HDL-C, TG were adjusted for. RC was modeled without further adjustment for its components. Predictive performance of lipid parameters for coronary artery calcification To further evaluate the discriminative ability of different lipid parameters for the presence of coronary artery calcification (CAC>0), ROC curve analysis was performed, and AUC was calculated. The results are shown in Figure 1. Among all the lipid parameters assessed, AIP demonstrated the highest discriminative performance, with an AUC of 0.612 (95% CI: 0.589–0.635). TC/HDL-C showed the next best discriminative ability, with an AUC of 0.597 (95% CI: 0.573–0.620). The AUC for non-HDL-C was 0.576 (95% CI: 0.551–0.600). HDL-C showed a similar AUC of 0.574 (95% CI: 0.550–0.597). RC and LDL-C showed relatively weaker discriminative abilities, with AUCs of 0.556 (95% CI: 0.530–0.581) and 0.555 (95% CI: 0.530–0.580), respectively. Although the 95% confidence intervals of the AUCs for these indices partially overlapped, AIP consistently presented the highest numerical value among all compared parameters, suggesting its potential relative advantage as a marker for identifying individuals at risk for coronary artery calcification. Discussion This cross-sectional study aimed to investigate the associations of AIP and other non-traditional lipid parameters with the risk of CAC in a Chinese health checkup cohort. The main findings are as follows: after adjusting for multiple traditional cardiovascular risk factors, AIP, non-HDL-C, and TC/HDL-C remained independently associated with CAC risk. The association between RC and CAC lost statistical significance after comprehensive adjustment for multiple confounders. Among these, AIP demonstrated the best discriminative performance for predicting the presence of CAC (AUC = 0.612), outperforming other lipid parameters. These results suggest that AIP, as a composite index reflecting lipid metabolism disorders, may hold value in the early identification and risk assessment of CAC within a health checkup setting. First, we found a significant positive association between AIP and CAC risk. Even after full adjustment for confounders, subjects in the highest quartile had a 57% increased risk of CAC (OR = 1.57). This finding aligns with several recent studies. For instance, research by Li et al. has similarly demonstrated the significant value of AIP in identifying subclinical coronary and carotid atherosclerosis in the Chinese population[ 18 ]. AIP serves not only as an independent predictor of coronary artery disease severity [ 19 ] but also as a sensitive marker for identifying vulnerable plaques in patients with acute coronary syndrome [ 12 ], highlighting its potential clinical utility across the spectrum of atherosclerotic progression. The present study further confirms that, in a health checkup population, the association between AIP and CAC is independent of traditional risk factors such as age, sex, hypertension, and diabetes, highlighting its potential value as a complementary tool for lipid assessment. Second, non-HDL-C and the TC/HDL-C ratio also showed independent associations with CAC. Non-HDL-C represents the cholesterol content of all atherogenic lipoprotein particles and has been recommended as a primary target for cardiovascular risk assessment in several guidelines[ 14 , 15 ]. In this study, each 1 mmol/L increase in non-HDL-C was associated with a 23% increased risk of CAC (OR = 1.23), which aligns with the established role of non-HDL-C as a key marker of subclinical atherosclerosis, as demonstrated in earlier studies such as that by Orakzai et al.[ 20 ]. The TC/HDL-C ratio reflects the overall balance of cholesterol. Evidence suggests that the total cholesterol to high-density lipoprotein cholesterol ratio serves as a significant predictor of first major cardiovascular events in the general population, while also exhibiting a nonlinear association with all-cause mortality, where both excessively high and low ratios may elevate mortality risk[ 21 , 22 ]. Notably, in this study, the association between RC and CAC lost statistical significance after multivariable adjustment. This may suggest that, in this relatively healthy Chinese checkup cohort, the independent contribution of RC to CAC is weak or confounded by other metabolic factors, warranting further investigation. This finding appears to differ from observations in general Western populations, such as those from the CARDIA and MESA studies[ 23 ], which concluded that elevated RC levels were associated with an increased risk of CAC progression independent of traditional cardiovascular risk factors. The disparity highlights that the predictive value of RC may vary depending on population characteristics (e.g., baseline risk, health status) and disease stage (early calcification vs. established progression). Future studies are needed to validate the association between RC and subclinical atherosclerosis across diverse ethnic and clinical spectra. The main strengths of this study include: 1) a large sample size from a health checkup cohort (n = 4196), enhancing statistical power and result reliability; 2) standardized CT examinations for CAC assessment, independently evaluated by two experienced radiologists, ensuring accuracy in outcome determination; 3) comprehensive adjustment for multiple potential confounders, including traditional risk factors, comorbidities, and medication use, strengthening the reliability of the associations; and 4) simultaneous evaluation of multiple non-traditional lipid parameters with comparative analysis of their predictive performance. However, this study also has several limitations. First, the cross-sectional design precludes determination of causal direction, necessitating validation through prospective cohort studies. Second, the study participants were recruited from a single medical center’s health checkup population, which may introduce selection bias and limit the generalizability of the findings; multicenter studies are needed for broader validation. Third, although we adjusted for several important confounders, unmeasured potential confounders (such as dietary patterns, physical activity levels, and genetic factors) may still influence the results. Finally, the study population consisted of relatively healthy individuals undergoing routine checkups, with a low prevalence of CAC, which may restrict analyses in high-risk subgroups. Conclusion Among a Chinese health checkup cohort, the non-traditional lipid parameters AIP, non-HDL-C, and TC/HDL-C are independently associated with the presence of CAC. AIP demonstrates the best discriminative performance, suggesting it may be a promising biomarker for identifying individuals at high risk for coronary artery calcification. Abbreviations CAC: coronary artery calcification AIP: atherogenic index of plasma Non-HDL-C: non-high density lipoprotein cholesterol TC/HDL-C: total cholesterol to high-density lipoprotein cholesterol RC: remnant cholesterol LDL-C: low density lipoprotein cholesterol HDL-C: high density lipoprotein cholesterol BMI: body mass index SBP: systolic blood pressure DBP: diastolic blood pressure FPG: fasting plasma glucose TC: total cholesterol TG: triglycerides ALT: alanine aminotransferase AST: aspartate aminotransferase BUN: blood urea nitrogen CR: creatinine ROC: receiver operating characteristic AUC: curve analysis and the area under the curve ASCVD: Atherosclerotic cardiovascular disease CAD : coronary artery disease Declarations Ethics approval and consent to participate This study was approved by Jinling Hospital, Afliated Hospital of Medical School, Nanjing University (Approval No.: 2024DZKY-015-01). The requirement for informed consent was waived due to the retrospective nature of the study. Consent for publication Not applicable. Availability of data and materials The datasets supporting the findings of this study were provided by Jinling Hospital, Afliated Hospital of Medical School, Nanjing University. Due to institutional security regulations, access to these data is restricted. The data are available from the corresponding author upon reasonable request. Competing interests The authors declare that they have no competing interests. Funding This work was supported by scientific research project of Jiangsu Provincial Health Commission (BJ2407). Authors' contributions Ying Zhou, Rui Zhang and Ya Huang: Conceptualization, Methodology, Formal analysis, Writing – Original Draft. Wenji Ni, Dandan Li, Xiangwei Bo, Tao Jin and Yanhui Wan: Data Curation. Weimin Jiang and Yong Zhong: Funding acquisition, Supervision, Writing – Review & Editing. Ying Zhou, Rui Zhang and Ya Huang contributed equally to this article and share first authorship. Both Weimin Jiang and Yong Zhong served as co-corresponding authors, responsible for the overall supervision, project administration, and the final version of the manuscript. Acknowledgements Not applicable. Clinical trial number Not applicable. References Roth GA, Mensah GA, Johnson CO, Addolorato G, Ammirati E, Baddour LM, Barengo NC, Beaton AZ, Benjamin EJ, Benziger CP et al : Global Burden of Cardiovascular Diseases and Risk Factors, 1990-2019: Update From the GBD 2019 Study . Journal of the American College of Cardiology 2020, 76 (25):2982-3021. Nasir K, Cainzos-Achirica M: Role of coronary artery calcium score in the primary prevention of cardiovascular disease . BMJ (Clinical research ed) 2021, 373 :n776. Pavlović J, Bos D, Ikram MK, Ikram MA, Kavousi M, Leening MJG: Guideline-Directed Application of Coronary Artery Calcium Scores for Primary Prevention of Atherosclerotic Cardiovascular Disease . JACC Cardiovascular imaging 2025, 18 (4):465-475. Detrano R, Guerci AD, Carr JJ, Bild DE, Burke G, Folsom AR, Liu K, Shea S, Szklo M, Bluemke DA et al : Coronary calcium as a predictor of coronary events in four racial or ethnic groups . The New England journal of medicine 2008, 358 (13):1336-1345. Glynn P, Khan SS, Greenland P: Cardiac CT Calcium Score . Jama 2025, 333 (16):1447-1448. Blaha MJ: Filling the Evidence Gaps Toward a Coronary Artery Calcium-Guided Primary Prevention Strategy . JAMA cardiology 2025, 10 (5):411-413. Agha AM, Pacor J, Grandhi GR, Mszar R, Khan SU, Parikh R, Agrawal T, Burt J, Blankstein R, Blaha MJ et al : The Prognostic Value of CAC Zero Among Individuals Presenting With Chest Pain: A Meta-Analysis . JACC Cardiovascular imaging 2022, 15 (10):1745-1757. Shah NS, Huang X, Cameron NA, Petito LC, Zhou B, Allen NB, Carnethon MR, Greenland P, Lloyd-Jones DM, Khan SS: Association of Cardiovascular Health and Time Lived With Zero Coronary Artery Calcium . JACC Cardiovascular imaging 2025, 18 (9):985-993. Qu L, Fang S, Lan Z, Xu S, Jiang J, Pan Y, Xu Y, Zhu X, Jin J: Association between atherogenic index of plasma and new-onset stroke in individuals with different glucose metabolism status: insights from CHARLS . Cardiovascular diabetology 2024, 23 (1):215. You FF, Gao J, Gao YN, Li ZH, Shen D, Zhong WF, Yang J, Wang XM, Song WQ, Yan H et al : Association between atherogenic index of plasma and all-cause mortality and specific-mortality: a nationwide population ‑based cohort study . Cardiovascular diabetology 2024, 23 (1):276. Rashidian P, Mirchandani M, Somu KP, Jala SRA, Mahapatro A, Hashemi SM, Nasrollahizadeh A, Joukar F, Eshraghi R, Letafatkar N et al : Association between atherogenic index of plasma and various metabolic conditions: an umbrella review on meta-analyses . BMC cardiovascular disorders 2025, 26 (1):41. Wu S, Gao Y, Liu W, Wang R, Ma Q, Sun J, Han W, Jia S, Du Y, Zhao Z et al : The relationship between atherogenic index of plasma and plaque vulnerabilities: an optical coherence tomography study . Cardiovascular diabetology 2024, 23 (1):442. Assempoor R, Daneshvar MS, Taghvaei A, Abroy AS, Azimi A, Nelson JR, Hosseini K: Atherogenic index of plasma and coronary artery disease: a systematic review and meta-analysis of observational studies . Cardiovascular diabetology 2025, 24 (1):35. Mach F, Baigent C, Catapano AL, Koskinas KC, Casula M, Badimon L, Chapman MJ, De Backer GG, Delgado V, Ference BA et al : 2019 ESC/EAS Guidelines for the management of dyslipidaemias: lipid modification to reduce cardiovascular risk . European heart journal 2020, 41 (1):111-188. Grundy SM, Stone NJ, Bailey AL, Beam C, Birtcher KK, Blumenthal RS, Braun LT, de Ferranti S, Faiella-Tommasino J, Forman DE et al : 2018 AHA/ACC/AACVPR/AAPA/ABC/ACPM/ADA/AGS/APhA/ASPC/NLA/PCNA Guideline on the Management of Blood Cholesterol: A Report of the American College of Cardiology/American Heart Association Task Force on Clinical Practice Guidelines . Circulation 2019, 139 (25):e1082-e1143. Hansen MK, Mortensen MB, Warnakula Olesen KK, Thrane PG, Maeng M: Non-HDL cholesterol and residual risk of cardiovascular events in patients with ischemic heart disease and well-controlled LDL cholesterol: a cohort study . The Lancet regional health Europe 2024, 36 :100774. Varbo A, Benn M, Tybjærg-Hansen A, Jørgensen AB, Frikke-Schmidt R, Nordestgaard BG: Remnant cholesterol as a causal risk factor for ischemic heart disease . Journal of the American College of Cardiology 2013, 61 (4):427-436. Li XW, Shuai P, Huang XC, Mou Y, He PY: Atherogenic index of plasma and subclinical vascular disease: predictive value for coronary and carotid atherosclerosis in a health screening population . Lipids in health and disease 2025, 24 (1):369. Li Y, Feng Y, Li S, Ma Y, Lin J, Wan J, Zhao M: The atherogenic index of plasma (AIP) is a predictor for the severity of coronary artery disease . Frontiers in cardiovascular medicine 2023, 10 :1140215. Orakzai SH, Nasir K, Blaha M, Blumenthal RS, Raggi P: Non-HDL cholesterol is strongly associated with coronary artery calcification in asymptomatic individuals . Atherosclerosis 2009, 202 (1):289-295. Zhou D, Liu X, Lo K, Huang Y, Feng Y: The effect of total cholesterol/high-density lipoprotein cholesterol ratio on mortality risk in the general population . Frontiers in endocrinology 2022, 13 :1012383. Kappelle PJ, Gansevoort RT, Hillege JL, Wolffenbuttel BH, Dullaart RP: Apolipoprotein B/A-I and total cholesterol/high-density lipoprotein cholesterol ratios both predict cardiovascular events in the general population independently of nonlipid risk factors, albuminuria and C-reactive protein . Journal of internal medicine 2011, 269 (2):232-242. Hao QY, Gao JW, Yuan ZM, Gao M, Wang JF, Schiele F, Zhang SL, Liu PM: Remnant Cholesterol and the Risk of Coronary Artery Calcium Progression: Insights From the CARDIA and MESA Study . Circulation Cardiovascular imaging 2022, 15 (7):e014116. Additional Declarations No competing interests reported. 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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-8788359","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":594918313,"identity":"84b666d0-55aa-48df-bba2-ff0f2ae0447e","order_by":0,"name":"Ying Zhou","email":"","orcid":"","institution":"Nanjing University of Chinese Medicine","correspondingAuthor":false,"prefix":"","firstName":"Ying","middleName":"","lastName":"Zhou","suffix":""},{"id":594918315,"identity":"1fc032ed-d632-4ea5-abd5-f70d7079ed58","order_by":1,"name":"Rui Zhang","email":"","orcid":"","institution":"Jinling Hospital, Afliated Hospital of Medical School, Nanjing University","correspondingAuthor":false,"prefix":"","firstName":"Rui","middleName":"","lastName":"Zhang","suffix":""},{"id":594918317,"identity":"3c7dd6be-07c9-40c7-9d67-fd861d3bfd68","order_by":2,"name":"Ya Huang","email":"","orcid":"","institution":"Jinling Hospital, Afliated Hospital of Medical School, Nanjing University","correspondingAuthor":false,"prefix":"","firstName":"Ya","middleName":"","lastName":"Huang","suffix":""},{"id":594918319,"identity":"4f7dcffe-1b46-4271-a82e-ebf6fdd07cfe","order_by":3,"name":"Wenji Ni","email":"","orcid":"","institution":"Jinling Hospital, Afliated Hospital of Medical School, Nanjing University","correspondingAuthor":false,"prefix":"","firstName":"Wenji","middleName":"","lastName":"Ni","suffix":""},{"id":594918321,"identity":"0f285154-02ea-47c4-950f-20c5350b8b43","order_by":4,"name":"Dandan Li","email":"","orcid":"","institution":"Jinling Hospital, Afliated Hospital of Medical School, Nanjing University","correspondingAuthor":false,"prefix":"","firstName":"Dandan","middleName":"","lastName":"Li","suffix":""},{"id":594918324,"identity":"dd5142fb-4fdc-4ae7-803e-64f0f89f2ab8","order_by":5,"name":"Xiangwei Bo","email":"","orcid":"","institution":"Jinling Hospital, Afliated Hospital of Medical School, Nanjing University","correspondingAuthor":false,"prefix":"","firstName":"Xiangwei","middleName":"","lastName":"Bo","suffix":""},{"id":594918327,"identity":"a35bc176-3659-484b-aa73-5ec5104056c3","order_by":6,"name":"Tao Jin","email":"","orcid":"","institution":"Jinling Hospital, Afliated Hospital of Medical School, Nanjing University","correspondingAuthor":false,"prefix":"","firstName":"Tao","middleName":"","lastName":"Jin","suffix":""},{"id":594918330,"identity":"6af27acc-14ad-4f8d-9157-735809b83374","order_by":7,"name":"Yanhui Wan","email":"","orcid":"","institution":"Jinling Hospital, Afliated Hospital of Medical School, Nanjing University","correspondingAuthor":false,"prefix":"","firstName":"Yanhui","middleName":"","lastName":"Wan","suffix":""},{"id":594918334,"identity":"d912a9b6-e859-42be-bcd2-7e12804952dc","order_by":8,"name":"Yong Zhong","email":"","orcid":"","institution":"Jinling Hospital, Afliated Hospital of Medical School, Nanjing University","correspondingAuthor":false,"prefix":"","firstName":"Yong","middleName":"","lastName":"Zhong","suffix":""},{"id":594918339,"identity":"6fca6321-11b5-4d8a-817e-d66508667ada","order_by":9,"name":"Weimin Jiang","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA5UlEQVRIiWNgGAWjYBAC9gYGhgMSBhJyQAoswNhASAtQBeMDiwIbY5K0MBtUfEhLbEAIENIyIztN4obB4fTtjGcMP91gsJHdcID52QP8WnK3Sc4wOJy7s+GMsXQOQ5rxhgNs5gZ4tczO3SYtAdSy4cAZM+YchsOJGw7wsEkQ1PIH6DADiJb/hLUIzs7dbCBhkJYA1XKAsBZp+bcbH0gY2BhuOHCsWDrHINl45mE2M7xa+HjObjgg8UdC3uDG4Y2fcyrsZPuONz/DqwUBJA4ACVBQMROnHgj4G4hWOgpGwSgYBSMMAABkjk7htlM28gAAAABJRU5ErkJggg==","orcid":"","institution":"Jiangsu Provincial Hospital of Traditional Chinese Medicine","correspondingAuthor":true,"prefix":"","firstName":"Weimin","middleName":"","lastName":"Jiang","suffix":""}],"badges":[],"createdAt":"2026-02-04 15:38:59","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-8788359/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-8788359/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":103199865,"identity":"d2f4202e-8a6c-4f85-897b-4a2658372af5","added_by":"auto","created_at":"2026-02-23 05:40:03","extension":"jpeg","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":111696,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eDiscriminative performance of lipid parameters for coronary artery calcification (CAC).\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e(A) Receiver operating characteristic (ROC) curves for traditional and non-traditional lipid parameters in predicting the presence of CAC. (B) Bar graph comparing the area under the ROC curve (AUC) values for each lipid parameter.\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"floatimage1.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-8788359/v1/ddf18b6d92bccc3316c11e53.jpeg"},{"id":103199971,"identity":"1d71f9cc-650f-471e-9cee-65f93e2b990a","added_by":"auto","created_at":"2026-02-23 05:40:36","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":2182089,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-8788359/v1/89219a82-279f-4e6c-af13-078f0b192197.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Atherogenic index of plasma and non-traditional lipid parameters in coronary artery calcification: a Chinese health checkup study","fulltext":[{"header":"Introduction","content":"\u003cp\u003eCardiovascular diseases remain the leading cause of global disease burden. According to World Health Organization statistics, cardiovascular diseases account for approximately 18.6\u0026nbsp;million deaths annually worldwide, with atherosclerotic cardiovascular disease (ASCVD) being a predominant component[\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e]. This grave epidemiological situation underscores the urgent need for more precise and efficient risk assessment tools to facilitate early identification and primary prevention of ASCVD. In the evaluation of subclinical atherosclerosis, coronary artery calcification (CAC) has emerged as a pivotal imaging biomarker with well-established prognostic value. First developed in 1990, the Agatston CAC score is an internationally endorsed quantitative method for CAC assessment and serves as a guideline-recommended decision-making tool for refined risk stratification and personalized management in ASCVD primary prevention [\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e, \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eCAC represents the deposition of hydroxyapatite calcium within coronary atherosclerotic plaques, serving as a direct marker of coronary atherosclerotic burden. Landmark prospective cohort studies, such as the Multi-Ethnic Study of Atherosclerosis (MESA), have consistently demonstrated that the CAC score is a strong independent predictor of future myocardial infarction, stroke, and cardiovascular death, providing incremental predictive value beyond traditional clinical risk scores[\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e]. Crucially, the CAC score offers critical clinical value by enabling precise risk re-stratification among individuals classified as intermediate or borderline risk by traditional risk assessment models, thereby informing statin treatment decisions[\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e]. In adults without established ASCVD, it serves as a robust decision‑support tool when uncertainty exists regarding the initiation of preventive statin therapy, particularly for those at intermediate risk[\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e]. Of particular note, a CAC score of zero (CAC\u0026thinsp;=\u0026thinsp;0) has been validated to possess a high negative predictive value, effectively identifying individuals with very low risk of cardiovascular events over a 10-year period[\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e, \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e]. Conversely, a significantly elevated CAC score (\u0026ge;\u0026thinsp;100) definitively indicates a substantial atherosclerotic burden, which warrants the initiation or intensification of statin therapy[\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eTraditional lipid measures, particularly low-density lipoprotein cholesterol (LDL-C), serve as the cornerstone for ASCVD risk assessment and management. However, their capacity to predict residual risk remains limited. In recent years, a series of non-traditional lipid parameters\u0026mdash;which are more integrative or reflective of distinct atherogenic pathways\u0026mdash;have attracted significant attention. The atherogenic index of plasma (AIP), calculated as the base-10 logarithm of the ratio of triglycerides to high-density lipoprotein cholesterol (TG/HDL-C), serves as a composite biomarker that reflects lipid metabolism dysregulation. It has been consistently associated with an increased risk of cerebrovascular and cardiovascular diseases, including stroke in middle-aged and older adults, higher cardiovascular mortality in individuals with diabetes, and a spectrum of other cardiovascular and metabolic disorders [\u003cspan additionalcitationids=\"CR10\" citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e]. Notably, AIP functions as an independent predictor of vulnerable plaques beyond traditional risk factors and offers incremental prognostic value in symptomatic patients with suspected coronary artery disease (CAD) [\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e]. Meta-analytic evidence further confirms that elevated AIP is strongly linked to heightened CAD risk, more severe disease progression, and poorer clinical outcomes, both in populations with and without established CAD[\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e]. Non-high-density lipoprotein cholesterol (non-HDL-C), encompassing the cholesterol content of all apolipoprotein B-containing atherogenic lipoproteins, has been recommended in several guidelines as a superior primary intervention target compared to LDL-C[\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e, \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e]. Clinical studies have shown that in patients with well-managed LDL-C, non-HDL-C serves as an easily accessible marker for identifying those at high residual risk of ASCVD and death[\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e]. The total cholesterol to high density lipoprotein cholesterol (HDL-C) ratio (TC/HDL-C) is a composite indicator of lipid balance. Remnant cholesterol (RC) represents the cholesterol content within triglyceride-rich lipoprotein remnants. It is closely associated with insulin resistance and chronic inflammatory states and has been identified as an independent cardiovascular risk factor beyond traditional risk factors[\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eAlthough these non-traditional lipid parameters have been linked to cardiovascular outcomes in Western populations, studies directly comparing their independent associations with CAC-the gold standard for subclinical disease-particularly in large-scale, relatively healthy screening populations, remain insufficient. Therefore, this study aims to utilize a large health examination cohort to systematically evaluate the strength of association between AIP, non-HDL-C, TC/HDL-C, RC, and the presence of CAC. Furthermore, after comprehensive adjustment for traditional risk factors, we will compare their independent predictive value and discriminative ability against LDL-C and HDL-C. The findings aim to provide novel evidence for optimizing early ASCVD risk stratification using readily accessible lipid parameters in routine health management.\u003c/p\u003e"},{"header":"Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003eStudy participants\u003c/h2\u003e \u003cp\u003e A total of 4196 adult participants who underwent health checkups at the Medical Examination Center of Jinling Hospital, Afliated Hospital of Medical School, Nanjing University, from January 2024 to December 2024, were enrolled. Based on the presence of CAC detected by chest computed tomography (CT), participants were categorized into a non-calcification group (n\u0026thinsp;=\u0026thinsp;3548) and a calcification group (n\u0026thinsp;=\u0026thinsp;648). Inclusion criteria: Age\u0026thinsp;\u0026ge;\u0026thinsp;18 years; No history or clinical symptoms of atherosclerotic cardiovascular disease (ASCVD); Complete clinical data, including blood biochemical indicators and chest CT results. Exclusion criteria: Incomplete baseline data; History of angina pectoris, ASCVD, or prior coronary stent implantation or coronary artery bypass grafting; Comorbidities such as malignant tumors or severe hepatic/renal insufficiency.\u003c/p\u003e \u003cp\u003e This study was approved by Jinling Hospital, Afliated Hospital of Medical School, Nanjing University (Approval No.: 2024DZKY-015-01). The requirement for informed consent was waived due to the retrospective nature of the study.\u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eData collection\u003c/h3\u003e\n\u003cp\u003eDemographic and clinical data, including age, sex, smoking history, alcohol consumption history, medical history, medication use, body mass index (BMI), systolic blood pressure (SBP), and diastolic blood pressure (DBP), were collected from the hospital information system. Smoking history was defined as smoking at least one cigarette per day for a continuous or cumulative period of \u0026ge;\u0026thinsp;6 months. Alcohol consumption history was defined as consuming alcoholic beverages at least once per month on average over the past 12 months. Height and weight were measured using a stadiometer and weighing scale (model SH-200; Shanghe, China). BMI was calculated as weight (kg) divided by height squared (m\u0026sup2;). Blood pressure was measured using an Omron automated electronic sphygmomanometer (model HBP-9030; Omron, Japan). The average of three consecutive readings taken at rest was recorded. Medical histories were defined as follows: Hypertension: a documented history of hypertension or current use of antihypertensive medication; Dyslipidemia: a documented history of dyslipidemia or current use of lipid-lowering medication; Diabetes mellitus: a documented history of type 2 diabetes mellitus or current use of insulin or oral hypoglycemic agents.\u003c/p\u003e \u003cp\u003eFasting venous blood samples (4 mL) were collected in yellow gel-separator tubes in the morning. All samples were processed within 2 hours. Serum levels of fasting plasma glucose (FPG), total cholesterol (TC), triglycerides (TG), HDL-C, and LDL-C, alanine aminotransferase (ALT), aspartate aminotransferase (AST), blood urea nitrogen (BUN), and creatinine (Cr) were measured using an automated biochemical analyzer (Model 7600; Hitachi, Japan).\u003c/p\u003e \u003cp\u003eAll participants underwent non-contrast chest CT scanning in the supine position using a 64-slice multi-detector CT scanner (SIEMENS SOMATOM Definition Flash, Siemens, Germany). Scanning parameters were as follows: tube voltage 120 kV, tube current 20\u0026ndash;50 mAs, reconstructed slice thickness 1 mm, slice interval 1 mm. The scanning range extended from the lung apex to the lung base. Acquired images were transferred to a picture archiving and communication system (PACS) workstation. Two experienced radiologists independently evaluated the coronary artery territory on mediastinal window settings using an overall visual assessment method. CAC was diagnosed if focal high-density lesions (CT value\u0026thinsp;\u0026gt;\u0026thinsp;130 Hounsfield Units) were identified along the course of the coronary arteries. In cases of disagreement, a consensus was reached through discussion.\u003c/p\u003e\n\u003ch3\u003eCalculation of Derived Lipid Parameters\u003c/h3\u003e\n\u003cp\u003eNontraditional lipid parameters were calculated using the following formula:\u003cdiv id=\"Equa\" class=\"Equation\"\u003e\u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equa\" name=\"EquationSource\"\u003e\n$$\\:\\text{A}\\text{I}\\text{P}=\\text{log}\\left(\\frac{\\text{T}\\text{G}}{\\text{H}\\text{D}\\text{L}-\\text{C}}\\right)$$\u003c/div\u003e\u003c/div\u003e\u003cdiv id=\"Equb\" class=\"Equation\"\u003e\u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equb\" name=\"EquationSource\"\u003e\n$$\\:\\text{N}\\text{o}\\text{n}-\\text{H}\\text{D}\\text{L}-\\text{C}=\\text{T}\\text{C}-\\text{H}\\text{D}\\text{L}-\\text{C}$$\u003c/div\u003e\u003c/div\u003e\u003cdiv id=\"Equc\" class=\"Equation\"\u003e\u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equc\" name=\"EquationSource\"\u003e\n$$\\:\\text{T}\\text{C}/\\text{H}\\text{D}\\text{L}-\\text{C}=\\frac{\\text{T}\\text{C}}{\\text{H}\\text{D}\\text{L}-\\text{C}}$$\u003c/div\u003e\u003c/div\u003e\u003cdiv id=\"Equd\" class=\"Equation\"\u003e\u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equd\" name=\"EquationSource\"\u003e\n$$\\:\\text{R}\\text{C}=\\text{T}\\text{C}-\\text{L}\\text{D}\\text{L}-\\text{C}-\\text{H}\\text{D}\\text{L}-\\text{C}$$\u003c/div\u003e\u003c/div\u003e\u003c/p\u003e \u003cdiv id=\"Sec6\" class=\"Section2\"\u003e \u003ch2\u003eStatistical Analysis\u003c/h2\u003e \u003cp\u003eAll statistical analyses were performed using R software (version 4.5.2; R Foundation for Statistical Computing, Vienna, Austria). All variables included in the analysis were complete, with no missing values; no data imputation or exclusion was necessary. Continuous variables were assessed for normality using the Shapiro-Wilk test and visual inspection of Q-Q plots. Normally distributed variables were presented as mean\u0026thinsp;\u0026plusmn;\u0026thinsp;standard deviation (SD) and compared between groups using the independent samples t-test. Non-normally distributed continuous variables were presented as median (interquartile range, IQR) and compared using the Mann-Whitney U test. Categorical variables were expressed as numbers (percentages) and compared using the chi-square test or Fisher\u0026rsquo;s exact test, as appropriate.\u003c/p\u003e \u003cp\u003eTo examine the independent association between lipid parameters and the presence of CAC, multivariable logistic regression analysis was performed. Variables that showed a univariate association with CAC (P\u0026thinsp;\u0026lt;\u0026thinsp;0.05) or were considered clinically relevant (e.g., age, sex, blood pressure, BMI, diabetes status, smoking, drinking) were included in the multivariable models. Multicollinearity among independent variables was assessed using variance inflation factors (VIFs), with a VIF\u0026thinsp;\u0026lt;\u0026thinsp;5 considered acceptable. The goodness-of-fit of the logistic regression models was evaluated using the Hosmer-Lemeshow test.\u003c/p\u003e \u003cp\u003eReceiver operating characteristic (ROC) curve analysis was conducted, and the area under the ROC curve (AUC) along with its 95% confidence interval (CI) was calculated to assess the discriminative ability of each lipid parameter for predicting CAC. Differences in AUCs were compared using DeLong\u0026rsquo;s test. A two-tailed P-value\u0026thinsp;\u0026lt;\u0026thinsp;0.05 was considered statistically significant for all analyses.\u003c/p\u003e \u003c/div\u003e"},{"header":"Results","content":"\u003cp\u003e\u003cstrong\u003eBaseline characteristics\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eA total of 4,196 participants were included and stratified by coronary artery calcification (CAC) status into a CAC=0 group (n=3,548) and a CAC\u0026gt;0 group (n=648). Baseline characteristics are summarized in Table 1.\u003c/p\u003e\n\u003cp\u003eParticipants in the CAC\u0026gt;0 group were significantly older than those in the CAC=0 group (56.42 \u0026plusmn; 10.79 vs. 43.03 \u0026plusmn; 10.80 years; \u003cem\u003eP\u003c/em\u003e\u0026lt;0.001) and had a higher proportion of males (72.53% vs. 49.44%; \u003cem\u003eP\u003c/em\u003e\u0026lt;0.001). The prevalence of current smoking was also higher in the CAC\u0026gt;0 group (20.99% vs. 10.60%, P \u0026lt; 0.001), while no significant difference was observed in alcohol consumption (7.56% vs. 5.98%, \u003cem\u003eP\u003c/em\u003e = 0.150).\u003c/p\u003e\n\u003cp\u003eComparisons of cardiovascular risk factors revealed significantly higher prevalences of hypertension (38.43% vs. 10.57%), hyperlipidemia (10.34% vs. 3.75%), and diabetes mellitus (15.28% vs. 2.82%) in the CAC\u0026gt;0 group (all \u003cem\u003eP\u003c/em\u003e\u0026lt;0.001). Correspondingly, use of antihypertensive, lipid-lowering, and antidiabetic medications was also more prevalent in the CAC\u0026gt;0 group. (all \u003cem\u003eP\u003c/em\u003e\u0026lt;0.001). Physiological parameters, including systolic blood pressure (131.40 \u0026plusmn; 18.43 vs. 119.43 \u0026plusmn; 15.84 mmHg), diastolic blood pressure (78.89 \u0026plusmn; 11.36 vs. 73.96 \u0026plusmn; 10.93 mmHg), and body mass index (25.60 \u0026plusmn; 3.34 vs. 24.25 \u0026plusmn; 3.55 kg/m\u0026sup2;), were all elevated in the CAC\u0026gt;0 group (all \u003cem\u003eP\u003c/em\u003e\u0026lt;0.001).\u003c/p\u003e\n\u003cp\u003eLaboratory analyses demonstrated that the CAC\u0026gt;0 group had significantly higher levels of liver function markers (ALT, AST), renal function parameters (BUN, CR), and fasting plasma glucose (all \u003cem\u003eP\u003c/em\u003e\u0026lt;0.001). The lipid profile also differed markedly: total cholesterol, triglycerides, LDL-C, non-HDL-C, RC, AIP, and TC/HDL-C were all significantly higher in the CAC\u0026gt;0 group, and HDL-C levels were significantly lower (all \u003cem\u003eP\u003c/em\u003e\u0026lt;0.001).\u003c/p\u003e\n\u003ctable border=\"0\" cellspacing=\"0\" cellpadding=\"0\" width=\"581\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"5\" style=\"width: 581px;\"\u003e\n \u003cp\u003eTable 1 Characteristics of the subjects divided by coronary artery calcification\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 191px;\"\u003e\n \u003cp\u003eCharacteristic\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 109px;\"\u003e\n \u003cp\u003eOverall N = 4196\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 109px;\"\u003e\n \u003cp\u003eCAC=0 (n = 3548)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 109px;\"\u003e\n \u003cp\u003eCAC\u0026gt;0 (n = 648)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 64px;\"\u003e\n \u003cp\u003e\u003cem\u003eP\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 191px;\"\u003e\n \u003cp\u003eAge (years)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 109px;\"\u003e\n \u003cp\u003e45.10 \u0026plusmn; 11.83\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 109px;\"\u003e\n \u003cp\u003e43.03 \u0026plusmn; 10.80\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 109px;\"\u003e\n \u003cp\u003e56.42 \u0026plusmn; 10.79\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 64px;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 191px;\"\u003e\n \u003cp\u003eSex(male), n (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 109px;\"\u003e\n \u003cp\u003e2,224 (53.00%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 109px;\"\u003e\n \u003cp\u003e1,754 (49.44%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 109px;\"\u003e\n \u003cp\u003e470 (72.53%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 64px;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 191px;\"\u003e\n \u003cp\u003eSmoking, n (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 109px;\"\u003e\n \u003cp\u003e512 (12.20%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 109px;\"\u003e\n \u003cp\u003e376 (10.60%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 109px;\"\u003e\n \u003cp\u003e136 (20.99%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 64px;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 191px;\"\u003e\n \u003cp\u003eDrinking, n (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 109px;\"\u003e\n \u003cp\u003e261 (6.22%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 109px;\"\u003e\n \u003cp\u003e212 (5.98%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 109px;\"\u003e\n \u003cp\u003e49 (7.56%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 64px;\"\u003e\n \u003cp\u003e0.150\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 191px;\"\u003e\n \u003cp\u003eHypertension, n (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 109px;\"\u003e\n \u003cp\u003e624 (14.87%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 109px;\"\u003e\n \u003cp\u003e375 (10.57%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 109px;\"\u003e\n \u003cp\u003e249 (38.43%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 64px;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 191px;\"\u003e\n \u003cp\u003eAntihypertensive medication, n (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 109px;\"\u003e\n \u003cp\u003e361 (8.60%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 109px;\"\u003e\n \u003cp\u003e203 (5.72%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 109px;\"\u003e\n \u003cp\u003e158 (24.38%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 64px;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 191px;\"\u003e\n \u003cp\u003eHyperlipidemia, n (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 109px;\"\u003e\n \u003cp\u003e200 (4.77%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 109px;\"\u003e\n \u003cp\u003e133 (3.75%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 109px;\"\u003e\n \u003cp\u003e67 (10.34%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 64px;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 191px;\"\u003e\n \u003cp\u003eLipid-lowering medication, n (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 109px;\"\u003e\n \u003cp\u003e93 (2.22%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 109px;\"\u003e\n \u003cp\u003e51 (1.44%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 109px;\"\u003e\n \u003cp\u003e42 (6.48%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 64px;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 191px;\"\u003e\n \u003cp\u003eDiabetes mellitus, n (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 109px;\"\u003e\n \u003cp\u003e199 (4.74%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 109px;\"\u003e\n \u003cp\u003e100 (2.82%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 109px;\"\u003e\n \u003cp\u003e99 (15.28%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 64px;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 191px;\"\u003e\n \u003cp\u003eAntidiabetic medication, n (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 109px;\"\u003e\n \u003cp\u003e100 (2.38%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 109px;\"\u003e\n \u003cp\u003e47 (1.32%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 109px;\"\u003e\n \u003cp\u003e53 (8.18%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 64px;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 191px;\"\u003e\n \u003cp\u003eSBP (mmHg)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 109px;\"\u003e\n \u003cp\u003e121.28 \u0026plusmn; 16.83\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 109px;\"\u003e\n \u003cp\u003e119.43 \u0026plusmn; 15.84\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 109px;\"\u003e\n \u003cp\u003e131.40 \u0026plusmn; 18.43\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 64px;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 191px;\"\u003e\n \u003cp\u003eDBP (mmHg)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 109px;\"\u003e\n \u003cp\u003e74.73 \u0026plusmn; 11.14\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 109px;\"\u003e\n \u003cp\u003e73.96 \u0026plusmn; 10.93\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 109px;\"\u003e\n \u003cp\u003e78.89 \u0026plusmn; 11.36\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 64px;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 191px;\"\u003e\n \u003cp\u003eBMI (kg/m\u0026sup2;)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 109px;\"\u003e\n \u003cp\u003e24.46 \u0026plusmn; 3.55\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 109px;\"\u003e\n \u003cp\u003e24.25 \u0026plusmn; 3.55\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 109px;\"\u003e\n \u003cp\u003e25.60 \u0026plusmn; 3.34\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 64px;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 191px;\"\u003e\n \u003cp\u003eALT (U/L)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 109px;\"\u003e\n \u003cp\u003e18.00 (13.00, 28.00)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 109px;\"\u003e\n \u003cp\u003e18.00 (12.00, 27.00)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 109px;\"\u003e\n \u003cp\u003e20.00 (14.00, 29.00)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 64px;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 191px;\"\u003e\n \u003cp\u003eAST (U/L)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 109px;\"\u003e\n \u003cp\u003e19.00 (16.00, 23.00)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 109px;\"\u003e\n \u003cp\u003e18.00 (15.00, 23.00)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 109px;\"\u003e\n \u003cp\u003e20.00 (17.00, 24.00)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 64px;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 191px;\"\u003e\n \u003cp\u003eBUN (mmol/L)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 109px;\"\u003e\n \u003cp\u003e5.19 \u0026plusmn; 1.40\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 109px;\"\u003e\n \u003cp\u003e5.11 \u0026plusmn; 1.32\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 109px;\"\u003e\n \u003cp\u003e5.68 \u0026plusmn; 1.67\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 64px;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 191px;\"\u003e\n \u003cp\u003eCr (\u0026mu;mol/L)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 109px;\"\u003e\n \u003cp\u003e64.90 (54.60, 77.00)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 109px;\"\u003e\n \u003cp\u003e63.80 (53.85, 76.00)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 109px;\"\u003e\n \u003cp\u003e69.60 (60.10, 79.00)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 64px;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 191px;\"\u003e\n \u003cp\u003eFPG (mmol/L)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 109px;\"\u003e\n \u003cp\u003e5.36 \u0026plusmn; 1.19\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 109px;\"\u003e\n \u003cp\u003e5.26 \u0026plusmn; 1.05\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 109px;\"\u003e\n \u003cp\u003e5.90 \u0026plusmn; 1.67\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 64px;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 191px;\"\u003e\n \u003cp\u003eTC (mmol/L)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 109px;\"\u003e\n \u003cp\u003e4.98 \u0026plusmn; 0.92\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 109px;\"\u003e\n \u003cp\u003e4.95 \u0026plusmn; 0.89\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 109px;\"\u003e\n \u003cp\u003e5.12 \u0026plusmn; 1.06\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 64px;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 191px;\"\u003e\n \u003cp\u003eTG (mmol/L)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 109px;\"\u003e\n \u003cp\u003e1.28 (0.91, 2.00)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 109px;\"\u003e\n \u003cp\u003e1.24 (0.88, 2.00)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 109px;\"\u003e\n \u003cp\u003e1.56 (1.08, 2.00)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 64px;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 191px;\"\u003e\n \u003cp\u003eLDL-C (mmol/L)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 109px;\"\u003e\n \u003cp\u003e2.94 \u0026plusmn; 0.76\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 109px;\"\u003e\n \u003cp\u003e2.92 \u0026plusmn; 0.74\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 109px;\"\u003e\n \u003cp\u003e3.07 \u0026plusmn; 0.85\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 64px;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 191px;\"\u003e\n \u003cp\u003eHDL-C (mmol/L)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 109px;\"\u003e\n \u003cp\u003e1.25 \u0026plusmn; 0.30\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 109px;\"\u003e\n \u003cp\u003e1.26 \u0026plusmn; 0.30\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 109px;\"\u003e\n \u003cp\u003e1.19 \u0026plusmn; 0.29\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 64px;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 191px;\"\u003e\n \u003cp\u003eAIP\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 109px;\"\u003e\n \u003cp\u003e0.05 \u0026plusmn; 0.33\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 109px;\"\u003e\n \u003cp\u003e0.03 \u0026plusmn; 0.32\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 109px;\"\u003e\n \u003cp\u003e0.16 \u0026plusmn; 0.32\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 64px;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 191px;\"\u003e\n \u003cp\u003eTC/HDL-C\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 109px;\"\u003e\n \u003cp\u003e4.02 (3.30, 5.00)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 109px;\"\u003e\n \u003cp\u003e3.95 (3.26, 5.00)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 109px;\"\u003e\n \u003cp\u003e4.34 (3.60, 5.00)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 64px;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 191px;\"\u003e\n \u003cp\u003eNon-HDL-C (mmol/L)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 109px;\"\u003e\n \u003cp\u003e3.73 \u0026plusmn; 0.92\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 109px;\"\u003e\n \u003cp\u003e3.69 \u0026plusmn; 0.89\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 109px;\"\u003e\n \u003cp\u003e3.93 \u0026plusmn; 1.03\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 64px;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 191px;\"\u003e\n \u003cp\u003eRC (mmol/L)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 109px;\"\u003e\n \u003cp\u003e0.78 \u0026plusmn; 0.40\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 109px;\"\u003e\n \u003cp\u003e0.77 \u0026plusmn; 0.38\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 109px;\"\u003e\n \u003cp\u003e0.86 \u0026plusmn; 0.49\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 64px;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u003cstrong\u003eAssociation of lipid parameters with coronary artery calcification\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eMultivariable logistic regression models were used to assess the associations between various lipid parameters and the presence of CAC. The results are summarized in Table 2.\u003c/p\u003e\n\u003cp\u003eAIP:\u0026nbsp;After adjusting for age and sex (Model 1), AIP as a continuous variable was significantly associated with the risk of CAC (OR = 2.29, 95% CI: 1.69-3.10, \u003cem\u003eP\u003c/em\u003e \u0026lt;0.001). This association remained independent after further adjustment for multiple traditional cardiovascular risk factors and clinical indicators (Models 2 and 3) (Model 3: OR = 1.51, 95% CI: 1.05-2.16, \u003cem\u003eP\u003c/em\u003e=0.025). Analysis by AIP quartiles showed that compared to the lowest quartile (Q1), subjects in the highest quartile (Q4) had a 57% significantly increased odds of CAC in the fully adjusted Model 3 (OR = 1.57, 95% CI: 1.11-2.22, \u003cem\u003eP\u003c/em\u003e=0.011).\u003c/p\u003e\n\u003cp\u003eNon-HDL-C:\u0026nbsp;Non-HDL-C as a continuous variable showed a significant positive association with CAC risk across all three models (all \u003cem\u003eP\u003c/em\u003e \u0026lt;0.001). In the fully adjusted Model 3, each 1 mmol/L increase in non-HDL-C was associated with a 23% increase in the odds of CAC (OR = 1.23, 95% CI: 1.10-1.38). Compared with the lowest quartile, subjects in the highest quartile had a 62% increased odds of CAC (OR = 1.62, 95% CI: 1.19-2.21, \u003cem\u003eP\u003c/em\u003e=0.002).\u003c/p\u003e\n\u003cp\u003eTC/HDL-C:\u0026nbsp;The TC/HDL-C ratio as a continuous variable was significantly and positively associated with CAC risk in all models (Model 3: OR = 1.09, 95% CI: 1.02-1.16, \u003cem\u003eP\u003c/em\u003e=0.006). Quartile analysis indicated that compared to Q1, the Q4 group had a 53% increased odds of CAC in Model 3 (OR = 1.53, 95% CI: 1.09-2.17, \u003cem\u003eP\u003c/em\u003e=0.015).\u003c/p\u003e\n\u003cp\u003eRC:\u0026nbsp;In Model 1 adjusted only for age and sex, RC as a continuous variable was associated with CAC risk (OR = 1.37, \u003cem\u003eP\u003c/em\u003e=0.002). However, this association lost statistical significance after further multivariable adjustments (Models 2 and 3) (Model 3: OR = 1.13, \u003cem\u003eP\u003c/em\u003e=0.474). Quartile analysis showed a similar trend, with no significant association observed in the fully adjusted models.\u003c/p\u003e\n\u003cp\u003eLDL-C: In contrast to RC, LDL-C as a continuous variable remained significantly associated with CAC risk after multivariable adjustment (Model 3: OR = 1.28, 95% CI: 1.13-1.45, \u003cem\u003eP\u003c/em\u003e \u0026lt;0.001). The Q4 group had a 62% increased odds compared to the Q1 group (OR = 1.62, \u003cem\u003eP\u003c/em\u003e=0.001).\u003c/p\u003e\n\u003cp\u003eHDL-C: In Model 1, HDL-C as a continuous variable was significantly inversely associated with CAC risk (OR = 0.52, \u003cem\u003eP\u003c/em\u003e \u0026lt;0.001). However, this association was attenuated and became non-significant after including more covariates (Models 2 and 3).\u003c/p\u003e\n\u003ctable border=\"0\" cellspacing=\"0\" cellpadding=\"0\" width=\"582\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"7\" style=\"width: 582px;\"\u003e\n \u003cp\u003eTable 2 Association between non-traditional lipid parameters and coronary artery calcification\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 107px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"bottom\" style=\"width: 160px;\"\u003e\n \u003cp\u003eModel 1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"bottom\" style=\"width: 156px;\"\u003e\n \u003cp\u003eModel 2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"bottom\" style=\"width: 159px;\"\u003e\n \u003cp\u003eModel 3\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 107px;\"\u003e\n \u003cp\u003eVariable\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 95px;\"\u003e\n \u003cp\u003eOR (95% CI)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 65px;\"\u003e\n \u003cp\u003eP\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 99px;\"\u003e\n \u003cp\u003eOR (95% CI)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 57px;\"\u003e\n \u003cp\u003eP\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 99px;\"\u003e\n \u003cp\u003eOR (95% CI)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 59px;\"\u003e\n \u003cp\u003eP\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 107px;\"\u003e\n \u003cp\u003eAIP (continuous)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 95px;\"\u003e\n \u003cp\u003e2.29 (1.69-3.10)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 65px;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 99px;\"\u003e\n \u003cp\u003e1.49 (1.06-2.08)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 57px;\"\u003e\n \u003cp\u003e0.021\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 99px;\"\u003e\n \u003cp\u003e1.51 (1.05-2.16)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 59px;\"\u003e\n \u003cp\u003e0.025\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 107px;\"\u003e\n \u003cp\u003eQ1 (\u0026lt;-0.18)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 95px;\"\u003e\n \u003cp\u003e1 (reference)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 65px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 99px;\"\u003e\n \u003cp\u003e1 (reference)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 57px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 99px;\"\u003e\n \u003cp\u003e1 (reference)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 59px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 107px;\"\u003e\n \u003cp\u003eQ2 (-0.18-0.03)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 95px;\"\u003e\n \u003cp\u003e1.37 (0.99-1.90)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 65px;\"\u003e\n \u003cp\u003e0.056\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 99px;\"\u003e\n \u003cp\u003e1.24 (0.89-1.73)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 57px;\"\u003e\n \u003cp\u003e0.216\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 99px;\"\u003e\n \u003cp\u003e1.18 (0.84-1.66)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 59px;\"\u003e\n \u003cp\u003e0.344\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 107px;\"\u003e\n \u003cp\u003eQ3 (0.03-0.26)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 95px;\"\u003e\n \u003cp\u003e1.37 (0.99-1.89)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 65px;\"\u003e\n \u003cp\u003e0.056\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 99px;\"\u003e\n \u003cp\u003e1.10 (0.79-1.55)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 57px;\"\u003e\n \u003cp\u003e0.564\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 99px;\"\u003e\n \u003cp\u003e1.01 (0.71-1.43)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 59px;\"\u003e\n \u003cp\u003e0.961\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 107px;\"\u003e\n \u003cp\u003eQ4 (\u0026ge;0.26)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 95px;\"\u003e\n \u003cp\u003e2.33 (1.71-3.19)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 65px;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 99px;\"\u003e\n \u003cp\u003e1.63 (1.17-2.29)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 57px;\"\u003e\n \u003cp\u003e0.004\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 99px;\"\u003e\n \u003cp\u003e1.57 (1.11-2.22)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 59px;\"\u003e\n \u003cp\u003e0.011\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 107px;\"\u003e\n \u003cp\u003eNon-HDL-C (continuous)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 95px;\"\u003e\n \u003cp\u003e1.22 (1.11-1.35)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 65px;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 99px;\"\u003e\n \u003cp\u003e1.23 (1.11-1.37)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 57px;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 99px;\"\u003e\n \u003cp\u003e1.23 (1.10-1.38)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 59px;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 107px;\"\u003e\n \u003cp\u003eQ1 (\u0026lt;3.09)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 95px;\"\u003e\n \u003cp\u003e1 (reference)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 65px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 99px;\"\u003e\n \u003cp\u003e1 (reference)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 57px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 99px;\"\u003e\n \u003cp\u003e1 (reference)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 59px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 107px;\"\u003e\n \u003cp\u003eQ2 (3.09-3.67)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 95px;\"\u003e\n \u003cp\u003e1.07 (0.79-1.44)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 65px;\"\u003e\n \u003cp\u003e0.683\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 99px;\"\u003e\n \u003cp\u003e1.21 (0.88-1.66)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 57px;\"\u003e\n \u003cp\u003e0.250\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 99px;\"\u003e\n \u003cp\u003e1.20 (0.87-1.66)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 59px;\"\u003e\n \u003cp\u003e0.261\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 107px;\"\u003e\n \u003cp\u003eQ3 (3.67-4.28)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 95px;\"\u003e\n \u003cp\u003e1.11 (0.83-1.49)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 65px;\"\u003e\n \u003cp\u003e0.466\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 99px;\"\u003e\n \u003cp\u003e1.20 (0.88-1.64)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 57px;\"\u003e\n \u003cp\u003e0.255\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 99px;\"\u003e\n \u003cp\u003e1.19 (0.87-1.63)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 59px;\"\u003e\n \u003cp\u003e0.284\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 107px;\"\u003e\n \u003cp\u003eQ4 (\u0026ge;4.28)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 95px;\"\u003e\n \u003cp\u003e1.54 (1.17-2.04)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 65px;\"\u003e\n \u003cp\u003e0.002\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 99px;\"\u003e\n \u003cp\u003e1.64 (1.22-2.22)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 57px;\"\u003e\n \u003cp\u003e0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 99px;\"\u003e\n \u003cp\u003e1.62 (1.19-2.21)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 59px;\"\u003e\n \u003cp\u003e0.002\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 107px;\"\u003e\n \u003cp\u003eTC/HDL-C (continuous)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 95px;\"\u003e\n \u003cp\u003e1.11 (1.05-1.17)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 65px;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 99px;\"\u003e\n \u003cp\u003e1.09 (1.03-1.15)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 57px;\"\u003e\n \u003cp\u003e0.002\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 99px;\"\u003e\n \u003cp\u003e1.09 (1.02-1.16)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 59px;\"\u003e\n \u003cp\u003e0.006\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 107px;\"\u003e\n \u003cp\u003eQ1 (\u0026lt;3.30)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 95px;\"\u003e\n \u003cp\u003e1 (reference)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 65px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 99px;\"\u003e\n \u003cp\u003e1 (reference)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 57px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 99px;\"\u003e\n \u003cp\u003e1 (reference)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 59px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 107px;\"\u003e\n \u003cp\u003eQ2 (3.30-4.01)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 95px;\"\u003e\n \u003cp\u003e1.06 (0.78-1.45)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 65px;\"\u003e\n \u003cp\u003e0.698\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 99px;\"\u003e\n \u003cp\u003e0.99 (0.72-1.38)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 57px;\"\u003e\n \u003cp\u003e0.956\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 99px;\"\u003e\n \u003cp\u003e0.99 (0.72-1.38)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 59px;\"\u003e\n \u003cp\u003e0.958\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 107px;\"\u003e\n \u003cp\u003eQ3 (4.01-4.82)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 95px;\"\u003e\n \u003cp\u003e1.29 (0.96-1.75)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 65px;\"\u003e\n \u003cp\u003e0.094\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 99px;\"\u003e\n \u003cp\u003e1.13 (0.82-1.55)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 57px;\"\u003e\n \u003cp\u003e0.471\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 99px;\"\u003e\n \u003cp\u003e1.13 (0.82-1.57)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 59px;\"\u003e\n \u003cp\u003e0.447\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 107px;\"\u003e\n \u003cp\u003eQ4 (\u0026ge;4.82)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 95px;\"\u003e\n \u003cp\u003e1.80 (1.35-2.43)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 65px;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 99px;\"\u003e\n \u003cp\u003e1.52 (1.11-2.09)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 57px;\"\u003e\n \u003cp\u003e0.010\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 99px;\"\u003e\n \u003cp\u003e1.53 (1.09-2.17)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 59px;\"\u003e\n \u003cp\u003e0.015\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 107px;\"\u003e\n \u003cp\u003eRC-C (continuous)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 95px;\"\u003e\n \u003cp\u003e1.37 (1.11-1.67)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 65px;\"\u003e\n \u003cp\u003e0.002\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 99px;\"\u003e\n \u003cp\u003e1.21 (0.97-1.49)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 57px;\"\u003e\n \u003cp\u003e0.090\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 99px;\"\u003e\n \u003cp\u003e1.13 (0.80-1.59)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 59px;\"\u003e\n \u003cp\u003e0.474\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 107px;\"\u003e\n \u003cp\u003eQ1 (\u0026lt;0.55)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 95px;\"\u003e\n \u003cp\u003e1 (reference)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 65px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 99px;\"\u003e\n \u003cp\u003e1 (reference)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 57px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 99px;\"\u003e\n \u003cp\u003e1 (reference)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 59px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 107px;\"\u003e\n \u003cp\u003eQ2 (0.55-0.72)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 95px;\"\u003e\n \u003cp\u003e1.00 (0.74-1.34)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 65px;\"\u003e\n \u003cp\u003e0.988\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 99px;\"\u003e\n \u003cp\u003e0.99 (0.73-1.34)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 57px;\"\u003e\n \u003cp\u003e0.957\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 99px;\"\u003e\n \u003cp\u003e0.98 (0.72-1.32)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 59px;\"\u003e\n \u003cp\u003e0.884\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 107px;\"\u003e\n \u003cp\u003eQ3 (0.72-0.92)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 95px;\"\u003e\n \u003cp\u003e1.04 (0.78-1.38)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 65px;\"\u003e\n \u003cp\u003e0.807\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 99px;\"\u003e\n \u003cp\u003e0.99 (0.74-1.33)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 57px;\"\u003e\n \u003cp\u003e0.963\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 99px;\"\u003e\n \u003cp\u003e0.98 (0.73-1.31)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 59px;\"\u003e\n \u003cp\u003e0.868\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 107px;\"\u003e\n \u003cp\u003eQ4 (\u0026ge;0.92)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 95px;\"\u003e\n \u003cp\u003e1.31 (1.01-1.71)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 65px;\"\u003e\n \u003cp\u003e0.046\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 99px;\"\u003e\n \u003cp\u003e1.19 (0.90-1.56)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 57px;\"\u003e\n \u003cp\u003e0.220\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 99px;\"\u003e\n \u003cp\u003e1.11 (0.82-1.51)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 59px;\"\u003e\n \u003cp\u003e0.491\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 107px;\"\u003e\n \u003cp\u003eLDL-C (continuous)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 95px;\"\u003e\n \u003cp\u003e1.20 (1.07-1.36)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 65px;\"\u003e\n \u003cp\u003e0.003\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 99px;\"\u003e\n \u003cp\u003e1.27 (1.12-1.44)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 57px;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 99px;\"\u003e\n \u003cp\u003e1.28 (1.13-1.45)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 59px;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 107px;\"\u003e\n \u003cp\u003eQ1 (\u0026lt;2.41)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 95px;\"\u003e\n \u003cp\u003e1 (reference)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 65px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 99px;\"\u003e\n \u003cp\u003e1 (reference)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 57px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 99px;\"\u003e\n \u003cp\u003e1 (reference)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 59px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 107px;\"\u003e\n \u003cp\u003eQ2 (2.41-2.91)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 95px;\"\u003e\n \u003cp\u003e1.06 (0.79-1.42)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 65px;\"\u003e\n \u003cp\u003e0.689\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 99px;\"\u003e\n \u003cp\u003e1.27 (0.93-1.73)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 57px;\"\u003e\n \u003cp\u003e0.134\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 99px;\"\u003e\n \u003cp\u003e1.26 (0.93-1.72)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 59px;\"\u003e\n \u003cp\u003e0.143\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 107px;\"\u003e\n \u003cp\u003eQ3 (2.91-3.42)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 95px;\"\u003e\n \u003cp\u003e1.09 (0.82-1.46)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 65px;\"\u003e\n \u003cp\u003e0.541\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 99px;\"\u003e\n \u003cp\u003e1.25 (0.92-1.70)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 57px;\"\u003e\n \u003cp\u003e0.153\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 99px;\"\u003e\n \u003cp\u003e1.25 (0.92-1.70)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 59px;\"\u003e\n \u003cp\u003e0.152\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 107px;\"\u003e\n \u003cp\u003eQ4 (\u0026ge;3.42)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 95px;\"\u003e\n \u003cp\u003e1.38 (1.05-1.81)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 65px;\"\u003e\n \u003cp\u003e0.021\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 99px;\"\u003e\n \u003cp\u003e1.60 (1.20-2.15)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 57px;\"\u003e\n \u003cp\u003e0.002\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 99px;\"\u003e\n \u003cp\u003e1.62 (1.21-2.18)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 59px;\"\u003e\n \u003cp\u003e0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 107px;\"\u003e\n \u003cp\u003eHDL-C (continuous)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 95px;\"\u003e\n \u003cp\u003e0.52 (0.36-0.73)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 65px;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 99px;\"\u003e\n \u003cp\u003e0.76 (0.52-1.10)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 57px;\"\u003e\n \u003cp\u003e0.144\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 99px;\"\u003e\n \u003cp\u003e0.79 (0.53-1.18)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 59px;\"\u003e\n \u003cp\u003e0.255\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 107px;\"\u003e\n \u003cp\u003eQ1 (\u0026lt;1.04)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 95px;\"\u003e\n \u003cp\u003e1 (reference)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 65px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 99px;\"\u003e\n \u003cp\u003e1 (reference)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 57px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 99px;\"\u003e\n \u003cp\u003e1 (reference)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 59px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 107px;\"\u003e\n \u003cp\u003eQ2 (1.04-1.22)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 95px;\"\u003e\n \u003cp\u003e0.88 (0.68-1.13)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 65px;\"\u003e\n \u003cp\u003e0.318\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 99px;\"\u003e\n \u003cp\u003e0.98 (0.76-1.27)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 57px;\"\u003e\n \u003cp\u003e0.878\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 99px;\"\u003e\n \u003cp\u003e1.01 (0.77-1.32)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 59px;\"\u003e\n \u003cp\u003e0.961\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 107px;\"\u003e\n \u003cp\u003eQ3 (1.22-1.44)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 95px;\"\u003e\n \u003cp\u003e0.72 (0.55-0.94)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 65px;\"\u003e\n \u003cp\u003e0.017\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 99px;\"\u003e\n \u003cp\u003e0.88 (0.66-1.17)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 57px;\"\u003e\n \u003cp\u003e0.373\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 99px;\"\u003e\n \u003cp\u003e0.92 (0.68-1.24)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 59px;\"\u003e\n \u003cp\u003e0.591\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 107px;\"\u003e\n \u003cp\u003eQ4 (\u0026ge;1.44)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 95px;\"\u003e\n \u003cp\u003e0.61 (0.46-0.82)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 65px;\"\u003e\n \u003cp\u003e0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 99px;\"\u003e\n \u003cp\u003e0.84 (0.61-1.15)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 57px;\"\u003e\n \u003cp\u003e0.281\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 99px;\"\u003e\n \u003cp\u003e0.87 (0.62-1.22)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 59px;\"\u003e\n \u003cp\u003e0.420\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"7\" valign=\"top\" style=\"width: 582px;\"\u003e\n \u003cp\u003e\u003cem\u003eNote:\u003c/em\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003eModel 1: Adjusted for age and sex.\u0026nbsp;\u003c/p\u003e\n \u003cp\u003eModel 2: Additionally adjusted for SBP, DBP, BMI, smoking, drinking, hypertension, antihypertensive medication, hyperlipidemia, lipid-lowering medication, Diabetes mellitus, and antidiabetic medication.\u0026nbsp;\u003c/p\u003e\n \u003cp\u003eModel 3: Additionally adjusted for ALT, AST, BUN, CR, FPG. To avoid multicollinearity in models containing derived lipid parameters, specific component lipids were additionally adjusted as follows: For AIP (log[TG/HDL-C]), TC and LDL-C were adjusted for. For non-HDL-C, TC/HDL-C, LDL-C, and HDL-C, TG were adjusted for. RC was modeled without further adjustment for its components.\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u003cstrong\u003ePredictive performance of lipid parameters for coronary artery calcification\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eTo further evaluate the discriminative ability of different lipid parameters for the presence of coronary artery calcification (CAC\u0026gt;0), ROC curve analysis was performed, and AUC was calculated. The results are shown in Figure 1.\u003c/p\u003e\n\u003cp\u003eAmong all the lipid parameters assessed, AIP demonstrated the highest discriminative performance, with an AUC of 0.612 (95% CI: 0.589\u0026ndash;0.635). TC/HDL-C showed the next best discriminative ability, with an AUC of 0.597 (95% CI: 0.573\u0026ndash;0.620). The AUC for non-HDL-C was 0.576 (95% CI: 0.551\u0026ndash;0.600). HDL-C showed a similar AUC of 0.574 (95% CI: 0.550\u0026ndash;0.597).\u003c/p\u003e\n\u003cp\u003eRC and LDL-C showed relatively weaker discriminative abilities, with AUCs of 0.556 (95% CI: 0.530\u0026ndash;0.581) and 0.555 (95% CI: 0.530\u0026ndash;0.580), respectively. Although the 95% confidence intervals of the AUCs for these indices partially overlapped, AIP consistently presented the highest numerical value among all compared parameters, suggesting its potential relative advantage as a marker for identifying individuals at risk for coronary artery calcification.\u003c/p\u003e"},{"header":"Discussion","content":"\u003cp\u003eThis cross-sectional study aimed to investigate the associations of AIP and other non-traditional lipid parameters with the risk of CAC in a Chinese health checkup cohort. The main findings are as follows: after adjusting for multiple traditional cardiovascular risk factors, AIP, non-HDL-C, and TC/HDL-C remained independently associated with CAC risk. The association between RC and CAC lost statistical significance after comprehensive adjustment for multiple confounders. Among these, AIP demonstrated the best discriminative performance for predicting the presence of CAC (AUC\u0026thinsp;=\u0026thinsp;0.612), outperforming other lipid parameters. These results suggest that AIP, as a composite index reflecting lipid metabolism disorders, may hold value in the early identification and risk assessment of CAC within a health checkup setting.\u003c/p\u003e \u003cp\u003eFirst, we found a significant positive association between AIP and CAC risk. Even after full adjustment for confounders, subjects in the highest quartile had a 57% increased risk of CAC (OR\u0026thinsp;=\u0026thinsp;1.57). This finding aligns with several recent studies. For instance, research by Li et al. has similarly demonstrated the significant value of AIP in identifying subclinical coronary and carotid atherosclerosis in the Chinese population[\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e]. AIP serves not only as an independent predictor of coronary artery disease severity [\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e] but also as a sensitive marker for identifying vulnerable plaques in patients with acute coronary syndrome [\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e], highlighting its potential clinical utility across the spectrum of atherosclerotic progression. The present study further confirms that, in a health checkup population, the association between AIP and CAC is independent of traditional risk factors such as age, sex, hypertension, and diabetes, highlighting its potential value as a complementary tool for lipid assessment.\u003c/p\u003e \u003cp\u003eSecond, non-HDL-C and the TC/HDL-C ratio also showed independent associations with CAC. Non-HDL-C represents the cholesterol content of all atherogenic lipoprotein particles and has been recommended as a primary target for cardiovascular risk assessment in several guidelines[\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e, \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e]. In this study, each 1 mmol/L increase in non-HDL-C was associated with a 23% increased risk of CAC (OR\u0026thinsp;=\u0026thinsp;1.23), which aligns with the established role of non-HDL-C as a key marker of subclinical atherosclerosis, as demonstrated in earlier studies such as that by Orakzai et al.[\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e]. The TC/HDL-C ratio reflects the overall balance of cholesterol. Evidence suggests that the total cholesterol to high-density lipoprotein cholesterol ratio serves as a significant predictor of first major cardiovascular events in the general population, while also exhibiting a nonlinear association with all-cause mortality, where both excessively high and low ratios may elevate mortality risk[\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e, \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eNotably, in this study, the association between RC and CAC lost statistical significance after multivariable adjustment. This may suggest that, in this relatively healthy Chinese checkup cohort, the independent contribution of RC to CAC is weak or confounded by other metabolic factors, warranting further investigation. This finding appears to differ from observations in general Western populations, such as those from the CARDIA and MESA studies[\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e], which concluded that elevated RC levels were associated with an increased risk of CAC progression independent of traditional cardiovascular risk factors. The disparity highlights that the predictive value of RC may vary depending on population characteristics (e.g., baseline risk, health status) and disease stage (early calcification vs. established progression). Future studies are needed to validate the association between RC and subclinical atherosclerosis across diverse ethnic and clinical spectra.\u003c/p\u003e \u003cp\u003eThe main strengths of this study include: 1) a large sample size from a health checkup cohort (n\u0026thinsp;=\u0026thinsp;4196), enhancing statistical power and result reliability; 2) standardized CT examinations for CAC assessment, independently evaluated by two experienced radiologists, ensuring accuracy in outcome determination; 3) comprehensive adjustment for multiple potential confounders, including traditional risk factors, comorbidities, and medication use, strengthening the reliability of the associations; and 4) simultaneous evaluation of multiple non-traditional lipid parameters with comparative analysis of their predictive performance.\u003c/p\u003e \u003cp\u003eHowever, this study also has several limitations. First, the cross-sectional design precludes determination of causal direction, necessitating validation through prospective cohort studies. Second, the study participants were recruited from a single medical center\u0026rsquo;s health checkup population, which may introduce selection bias and limit the generalizability of the findings; multicenter studies are needed for broader validation. Third, although we adjusted for several important confounders, unmeasured potential confounders (such as dietary patterns, physical activity levels, and genetic factors) may still influence the results. Finally, the study population consisted of relatively healthy individuals undergoing routine checkups, with a low prevalence of CAC, which may restrict analyses in high-risk subgroups.\u003c/p\u003e"},{"header":"Conclusion","content":"\u003cp\u003eAmong a Chinese health checkup cohort, the non-traditional lipid parameters AIP, non-HDL-C, and TC/HDL-C are independently associated with the presence of CAC. AIP demonstrates the best discriminative performance, suggesting it may be a promising biomarker for identifying individuals at high risk for coronary artery calcification.\u003c/p\u003e"},{"header":"Abbreviations","content":"\u003cp\u003eCAC: coronary artery calcification\u003c/p\u003e\n\u003cp\u003eAIP: atherogenic index of plasma\u003c/p\u003e\n\u003cp\u003eNon-HDL-C: non-high density lipoprotein cholesterol\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eTC/HDL-C: total cholesterol to high-density lipoprotein cholesterol\u003c/p\u003e\n\u003cp\u003eRC: remnant cholesterol\u003c/p\u003e\n\u003cp\u003eLDL-C: low density lipoprotein cholesterol\u003c/p\u003e\n\u003cp\u003eHDL-C: high density lipoprotein cholesterol\u003c/p\u003e\n\u003cp\u003eBMI: body mass index\u003c/p\u003e\n\u003cp\u003eSBP: systolic blood pressure\u003c/p\u003e\n\u003cp\u003eDBP: diastolic blood pressure\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eFPG: fasting plasma glucose\u003c/p\u003e\n\u003cp\u003eTC: total cholesterol\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eTG: triglycerides\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eALT: alanine aminotransferase\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eAST: aspartate aminotransferase\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eBUN: blood urea nitrogen\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eCR: creatinine \u0026nbsp;\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eROC: receiver operating characteristic\u003c/p\u003e\n\u003cp\u003eAUC: curve analysis and the area under the curve\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eASCVD: Atherosclerotic cardiovascular disease\u003c/p\u003e\n\u003cp\u003eCAD : coronary artery disease \u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eEthics approval and consent to participate\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis study was approved by Jinling Hospital, Afliated Hospital of Medical School, Nanjing University (Approval No.: 2024DZKY-015-01). The requirement for informed consent was waived due to the retrospective nature of the study.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent for publication\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAvailability of data and materials\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe datasets supporting the findings of this study were provided by Jinling Hospital, Afliated Hospital of Medical School, Nanjing University. Due to institutional security regulations, access to these data is restricted. The data are available from the corresponding author upon 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\u003eThis work was supported by scientific research project of Jiangsu Provincial Health Commission (BJ2407).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthors\u0026apos; contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eYing Zhou, Rui Zhang and Ya Huang: Conceptualization, Methodology, Formal analysis, Writing \u0026ndash; Original Draft. Wenji Ni, Dandan Li, Xiangwei Bo, Tao Jin and\u0026nbsp;Yanhui Wan: Data Curation. Weimin Jiang and Yong Zhong: Funding acquisition, Supervision, Writing \u0026ndash; Review \u0026amp; Editing.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eYing Zhou, Rui Zhang and Ya Huang contributed equally to this article and share first authorship. Both Weimin Jiang and Yong Zhong served as co-corresponding authors, responsible for the overall supervision, project administration, and the final version of the manuscript.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAcknowledgements\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\u003eRoth GA, Mensah GA, Johnson CO, Addolorato G, Ammirati E, Baddour LM, Barengo NC, Beaton AZ, Benjamin EJ, Benziger CP\u003cem\u003e et al\u003c/em\u003e: \u003cstrong\u003eGlobal Burden of Cardiovascular Diseases and Risk Factors, 1990-2019: Update From the GBD 2019 Study\u003c/strong\u003e. \u003cem\u003eJournal of the American College of Cardiology \u003c/em\u003e2020, \u003cstrong\u003e76\u003c/strong\u003e(25):2982-3021.\u003c/li\u003e\n\u003cli\u003eNasir K, Cainzos-Achirica M: 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lipoprotein cholesterol ratios both predict cardiovascular events in the general population independently of nonlipid risk factors, albuminuria and C-reactive protein\u003c/strong\u003e. \u003cem\u003eJournal of internal medicine \u003c/em\u003e2011, \u003cstrong\u003e269\u003c/strong\u003e(2):232-242.\u003c/li\u003e\n\u003cli\u003eHao QY, Gao JW, Yuan ZM, Gao M, Wang JF, Schiele F, Zhang SL, Liu PM: \u003cstrong\u003eRemnant Cholesterol and the Risk of Coronary Artery Calcium Progression: Insights From the CARDIA and MESA Study\u003c/strong\u003e. \u003cem\u003eCirculation Cardiovascular imaging \u003c/em\u003e2022, \u003cstrong\u003e15\u003c/strong\u003e(7):e014116.\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":false,"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":"Coronary artery calcification, atherogenic index of plasma, non-high density lipoprotein cholesterol, total cholesterol to high-density lipoprotein cholesterol ratio, remnant cholesterol","lastPublishedDoi":"10.21203/rs.3.rs-8788359/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-8788359/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eBackground\u003c/h2\u003e \u003cp\u003eCoronary artery calcification (CAC) is a reliable marker of subclinical atherosclerosis. Non-traditional lipid parameters, such as the atherogenic index of plasma (AIP), non-high density lipoprotein cholesterol (non-HDL-C), total cholesterol to high-density lipoprotein cholesterol (TC/HDL-C) ratio and remnant cholesterol (RC) are gaining attention for their association with cardiovascular risk. This study aimed to investigate the association of these non-traditional lipid parameters with CAC and their predictive value in a Chinese health checkup cohort.\u003c/p\u003e\u003ch2\u003eMethods\u003c/h2\u003e \u003cp\u003eIn this retrospective cross-sectional study, 4196 adults who underwent health checkups at Jinling Hospital, Afliated Hospital of Medical School, Nanjing University, from January to December 2024 were enrolled. Participants were categorized into a non-calcification group (n\u0026thinsp;=\u0026thinsp;3548) and a calcification group (n\u0026thinsp;=\u0026thinsp;648) based on the presence of CAC detected by chest computed tomography. Demographic, clinical data, and lipid profiles were collected. Multivariable logistic regression analyses were used to evaluate the independent association between lipid parameters (including AIP, non-HDL-C, TC/HDL-C, RC, low-density lipoprotein cholesterol [LDL-C], and high-density lipoprotein cholesterol [HDL-C]) and CAC. Receiver operating characteristic (ROC) curve analysis and the area under the curve (AUC) were used to assess the discriminative ability of each parameter.\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e \u003cp\u003eParticipants with CAC were older, more likely to be male, and had a higher burden of traditional cardiovascular risk factors. In multivariable logistic regression analyses adjusted for age, sex, blood pressure, body mass index, smoking, medical histories, and medications, AIP (odds ratio [OR]\u0026thinsp;=\u0026thinsp;1.51, 95% confidence interval [CI]: 1.05\u0026ndash;2.16, P\u0026thinsp;=\u0026thinsp;0.025), non-HDL-C (OR\u0026thinsp;=\u0026thinsp;1.23, 95% CI: 1.10\u0026ndash;1.38, P\u0026thinsp;\u0026lt;\u0026thinsp;0.001), and TC/HDL-C (OR\u0026thinsp;=\u0026thinsp;1.09, 95% CI: 1.02\u0026ndash;1.16, P\u0026thinsp;=\u0026thinsp;0.006) remained significantly and positively associated with CAC. The association for RC became non-significant after full adjustment. ROC analysis revealed that AIP had the highest discriminative ability (AUC\u0026thinsp;=\u0026thinsp;0.612, 95% CI: 0.589\u0026ndash;0.635), followed by TC/HDL-C (0.597) and non-HDL-C (0.576), all of which performed better than traditional LDL-C (0.555) and HDL-C (0.574).\u003c/p\u003e\u003ch2\u003eConclusion\u003c/h2\u003e \u003cp\u003eAmong a Chinese health checkup cohort, the non-traditional lipid parameters AIP, non-HDL-C, and TC/HDL-C are independently associated with the presence of CAC. AIP demonstrates the best discriminative performance, suggesting it may be a promising biomarker for identifying individuals at high risk for coronary artery calcification.\u003c/p\u003e","manuscriptTitle":"Atherogenic index of plasma and non-traditional lipid parameters in coronary artery calcification: a Chinese health checkup study","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2026-02-23 05:38:14","doi":"10.21203/rs.3.rs-8788359/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"editorInvitedReview","content":"","date":"2026-03-25T01:11:53+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"129637442398179497647454359618838212625","date":"2026-03-25T00:49:59+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2026-03-08T10:50:01+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"331764888689965275547240105961732784099","date":"2026-02-23T12:02:39+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2026-02-21T15:36:05+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"28079197343372409074266858911696117650","date":"2026-02-17T08:17:05+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2026-02-17T01:32:17+00:00","index":"","fulltext":""},{"type":"editorInvited","content":"","date":"2026-02-06T18:09:30+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2026-02-06T09:03:00+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2026-02-06T09:01:46+00:00","index":"","fulltext":""},{"type":"submitted","content":"BMC Cardiovascular Disorders","date":"2026-02-04T15:24:39+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":"392201c7-dd23-43e5-859d-994e8cdaae8c","owner":[],"postedDate":"February 23rd, 2026","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"under-review","subjectAreas":[],"tags":[],"updatedAt":"2026-02-23T05:38:14+00:00","versionOfRecord":[],"versionCreatedAt":"2026-02-23 05:38:14","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-8788359","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-8788359","identity":"rs-8788359","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}