The RC/HDL-c Ratio: A Superior Predictor of Coronary Artery Calcification Severity Compared to Remnant Cholesterol Alone

The RC/HDL-c Ratio: A Superior Predictor of Coronary Artery Calcification Severity Compared to Remnant Cholesterol Alone | 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 The RC/HDL-c Ratio: A Superior Predictor of Coronary Artery Calcification Severity Compared to Remnant Cholesterol Alone Mingru Li, Xiuhong Guan, Junjie Lei, Haosheng Li, Mingming Peng, and 2 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-6914577/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 9 You are reading this latest preprint version Abstract Background We investigated the associations among remnant cholesterol (RC) and high-density lipoprotein cholesterol (HDL-c) with coronary artery calcification (CACS), quantified by noncontrast computed tomography, aiming to determine whether the RC/HDL-c ratio serves as a more effective indicator of CACS severity compared to RC alone. Methods This retrospective analysis included data from 11,526 participants, categorized into four groups based on their CACS Agatston scores. The relationships between RC levels and the RC/HDL-c ratio with CACS were assessed using cumulative logistic regression methods. Two statistical models were developed: Model 1 adjusted for age and sex, while Model 2 included additional adjustments for glucose, glycated hemoglobin, and triglyceride concentrations. Quartile analyses were performed for both RC and the RC/HDL-c ratio. Results Elevated RC/HDL-c ratios were associated with a significant increase in CACS in Model 1 (OR = 1.092; 95% CI, 1.048–1.137; p < 0.001), and this association persisted in the upper two quartiles in Model 2 ( p < 0.05). In contrast, while RC levels were significantly associated with CACS in Model 1, this association was attenuated and lost significance in Model 2 after further adjustments. Subgroup analyses revealed a particularly strong correlation between the RC/HDL-c ratio and CACS in participants with normal LDL-c levels. Conclusion Our findings suggest that the RC/HDL-c ratio is a superior marker for CACS compared to RC alone. This novel approach refines CACS evaluation by integrating the pro-atherogenic properties of RC with the protective attributes of HDL-c. Coronary artery calcium Agatston score HDL cholesterol Remnant cholesterol Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Introduction Assessing the risk of coronary artery disease (CAD) is a critical endeavor in cardiovascular research. Coronary artery calcium scoring (CACS), derived from noncontrast computed tomography (CT) imaging, has emerged as a widely adopted and essential metric in this context[ 1 ]. This imaging modality enables the quantitative assessment of calcified plaque burden within the coronary arteries, providing a direct measure of the extent of coronary atherosclerosis[ 2 ]. The clinical value of CACS lies in its ability to refine risk stratification by identifying asymptomatic individuals who may benefit from preventive strategies[ 1 , 3 ]. Notably, studies have consistently demonstrated that elevated CACS values correlate with an increased likelihood of future adverse cardiovascular events, offering critical prognostic insights[ 4 , 5 ]. Moreover, CACS findings assist clinicians in tailoring therapeutic approaches based on individual risk profiles, guiding decisions regarding lifestyle modifications, pharmacological interventions, and, when necessary, invasive procedures[ 6 ]. Evidence suggests that incorporating CACS into routine clinical practice enhances clinical decision-making and improves patient outcomes[ 5 , 7 ]. Thus, understanding the role of CACS not only advances our comprehension of coronary atherosclerosis but also supports the transition toward personalized medical strategies in cardiovascular healthcare[ 3 ]. Nonetheless, while CACS screening has shown potential, it is imperative to emphasize the limitations inherent to the currently available evidence. Despite its clinical acceptance as a key tool for risk stratification of atherosclerotic disease, no conclusive study has provided irrefutable evidence of the efficacy of population-level CACS screening programs in the realm of clinical trials[ 8 – 10 ]. This highlights the need for continued research in this area to improve the evidence base underpinning the utilization of CACS as a primary diagnostic tool. High-density lipoprotein cholesterol (HDL-c) plays a pivotal role in reverse cholesterol transport[ 11 ]. Its cardioprotective effects are multifaceted, including the removal of cholesterol from arterial walls and the inhibition of atherosclerosis progression. HDL-c also exerts anti-inflammatory and antioxidant effects, which are critical in reducing inflammation and oxidative stress associated with cardiovascular disease[ 12 ]. Additionally, HDL-c contributes to vascular protection by preserving endothelial integrity, thereby preventing vascular stiffening and constriction[ 13 ]. Remnant cholesterol (RC), which represents the cholesterol derived from triglyceride-rich lipoproteins (TRLs) after triglyceride delivery to peripheral tissues, includes chylomicron remnants and intermediate-density lipoproteins (IDLs) arising from the metabolism of very low-density lipoproteins (VLDLs) and chylomicrons[ 14 ]. RC has been identified as a significant risk factor for atherosclerosis and CAD[ 15 ]. Elevated serum RC concentrations, independent of low-density lipoprotein cholesterol (LDL-c) levels, have been associated with an increased risk of CAD[ 15 , 16 ]. This association suggests that RC, in combination with non-HDL cholesterol, may serve as a more precise predictor of CAD outcomes compared to LDL-c alone. The atherogenic potential of RC is attributed to its presence within TRLs, which are known contributors to atherosclerotic plaque formation[ 17 ]. Therefore, managing RC levels is an important consideration in CAD prevention and may provide complementary benefits beyond LDL-c reduction strategies. Although HDL-c has been extensively utilized as a modifying factor in studies examining the relationship between lipids and cardiovascular disease (CVD) risk—including metrics such as the triglyceride (TG)/HDL ratio, total cholesterol (TC)/HDL ratio, and non-HDL/HDL ratio—prior research has not explored the application of HDL-c in refining RC assessments[ 4 , 11 , 18 ]. The relationship between HDL-c-adjusted RC (RC/HDL-c) and CVD incidence remains incompletely understood. This adjusted ratio may offer a more precise indication of CVD risk compared to RC measurement alone. In the present investigation, we explored the complex interplay between CACS and lipid profiles, with a specific focus on RC and the RC/HDL-c ratio. Furthermore, we aimed to determine whether the RC/HDL-c ratio exhibits unique associations with CACS, beyond those already demonstrated by RC alone. Methods Participant Selection This retrospective analysis examined data from individuals hospitalized at Qingyuan Hospital, Guangzhou Medical University, between January 1, 2012, and December 30, 2021. All participants underwent coronary artery computed tomography angiography (CTA) and routine laboratory assessments. The participant enrollment process is visually detailed in Fig. 1 . Patient eligibility was determined based on the following inclusion criteria:(1) Successful completion of coronary CTA examinations at our institution;(2) Availability of complete laboratory test results obtained within a 30-day window either before or after the CTA examination date;(3) Documentation of at least one hospitalization record containing comprehensive demographic information (including gender, age, etc.);and (4) Age at enrollment between 40 and 80 years. Exclusion criteria included the presence of significant image artifacts on CTA scans that compromised CACS calculation or instances of incomplete laboratory datasets. To ensure patient anonymity, all data underwent de-identification before analysis. This process involved removing direct identifiers (e.g., names, identification numbers, contact details) and aggregating indirect identifiers (e.g., age grouped into 5-year bands, geographical location reduced to district level). Ethical clearance for this retrospective study, including a waiver of informed consent, was provided by the Ethics Committee of Qingyuan Hospital, Guangzhou Medical University (Approval No. IRB-2024-032), and conducted in accordance with national guidelines for anonymized retrospective research. Clinical Data Collection For this investigation, we retrieved fundamental patient demographics (including sex and age) from an inpatient medical record database. Comprehensive laboratory measurements were extracted from our institution's Laboratory Information System (LIS), with a focus on analytes such as blood glucose, glycated hemoglobin, LDL-c, TC, triglycerides, and HDL-c. Coronary Artery Calcification Definition Image acquisition was performed using either a Siemens SOMATOM Force 192-slice CT scanner or a Toshiba 320-detector row CT system. All CT acquisitions adhered to the established Agatston protocol, employing a fixed tube voltage of 120 kV to minimize inter-scan variability. Tube current modulation was adjusted based on individual patient body habitus[ 19 ]. CACS measurements were derived exclusively from noncontrast coronary CTA images, using the Agatston scoring methodology as previously described[ 20 ]. This method quantified calcium scores within the left main coronary artery, left anterior descending artery, circumflex artery, and right coronary artery, resulting in a composite total CACS. Postprocessing calculations were performed using Syngo software (Siemens Healthcare, Forchheim, Germany). Measurements were independently conducted by two radiologists (XG and JL), each with 5 to 10 years of experience in diagnostic radiology, under double-blind conditions. In cases of interpretative discrepancies, a consensus determination was achieved through consultation with a senior cardiac imaging specialist (CH). The calcium score for each defined region of interest was calculated by multiplying the attenuation density score by the corresponding area. The aggregate coronary artery calcium score was then computed by summing these individual regional scores across all coronary branches[ 20 ]. CACS categorization was based on established thresholds: normal (CACS = 0), mild (CACS 1–100), moderate (CACS 101–399), and severe (CACS ≥ 400)[ 21 ]. A visual representation of coronary artery calcification is provided in Fig. 2 . Statistical Analyses Statistical analyses were performed using R software (version 4.4.0). Continuous variables are presented as means ± standard deviations, and categorical variables are expressed as frequencies (percentages). Group comparisons for continuous and categorical variables were conducted using analysis of variance (ANOVA) and the chi-squared test, respectively. To investigate the association between RC levels or RC/HDL-c ratios and CACS, cumulative logistic regression analysis was employed. LDL-c values were calculated according to the Friedewald equation. RC concentration was derived using the formula: RC = TC– HDL-c – LDL-c. Prior to analysis, continuous variables underwent logarithmic transformation to approximate normal distribution. Multicollinearity between glucose and glycated hemoglobin (HbA1c) was assessed using variance inflation factors (VIF), with a threshold of VIF < 2.0 confirming minimal collinearity. Subgroup analyses were stratified by LDL-c concentration at 3.37 mmol/L (130 mg/dL), aligning with guideline-defined thresholds for elevated LDL-c. RC and RC/HDL-c values were categorized into quartiles as follows: For RC: Q1: 0.65 mmol/L; For RC/HDL-c: Q1: 0.53125. Stratified subgroup analyses were conducted for both men and women, as well as for participants with elevated LDL-c levels (> 3.37 mmol/L) and those with lower LDL-c levels (≤ 3.37 mmol/L). Two distinct models were constructed: Model 1: Adjusted for age and sex. Model 2: Adjusted for age, sex, glucose, glycated hemoglobin, and triglycerides. Statistical significance was defined as a P value of < 0.05. Results Participant Characteristics A total of 11,526 participants were categorized based on their CACS as follows: normal (n = 5,529), mild (n = 3,348), moderate (n = 1,681), and severe (n = 968). The mean age of the study population was 60.40 ± 9.75 years, with 50.96% being male. As detailed in Table 1 , age, sex, glycemic control, HDL-c, and the RC to HDL-c ratio (RC/HDL-c) were significantly associated with the severity of coronary artery calcification. Table 1 Characteristics of the subjects by CACS Normal (n = 5529) Mild (n = 3348) Moderate (n = 1681) Severe (n = 968) p _value Age(years) 56.89 ± 9.16 61.84 ± 9.26 65.3 ± 8.68 66.62 ± 8.18 < 0.001 Gender(man) 2696(43.78) 2111(55.99) 1038(55.66) 718(64.86) < 0.001 HBA1C(%) 5.9 ± 1.19 6.24 ± 1.46 6.31 ± 1.49 6.39 ± 1.52 < 0.001 Glu(mmol/L) 6.25 ± 2.54 6.79 ± 3.1 7.01 ± 3.31 7.05 ± 3.34 < 0.001 HDL-C(mmol/L) 1.29 ± 0.37 1.23 ± 0.34 1.2 ± 0.34 1.19 ± 0.36 < 0.001 TG(mmol/L) 1.83 ± 1.47 1.91 ± 1.92 1.83 ± 1.51 1.81 ± 1.73 0.1137 LDL-c(mmol/L) 3.16 ± 0.98 3.18 ± 1.05 3.12 ± 1.1 3.02 ± 1.16 < 0.001 CHO(mmol/L) 4.92 ± 1.13 4.9 ± 1.25 4.82 ± 1.27 4.72 ± 1.34 < 0.001 RC 0.48 ± 0.52 0.51 ± 0.62 0.51 ± 0.49 0.52 ± 0.59 0.0473 RC/HDL-C 0.47 ± 0.76 0.52 ± 1.09 0.52 ± 0.71 0.54 ± 0.85 0.0101 HbA1c: hemoglobin A1c; Glu: Glucose; HDL-c: high-density lipoprotein cholesterol; TG: triglyceride; LDL-c: low-density lipoprotein cholesterol; CHO: total cholesterol To further elucidate the relationships between RC levels, RC/HDL-c ratios, and CACS, the cohort was stratified into quartiles based on RC/HDL-c ratios or RC levels. Table 2 shows that participants with higher RC levels were significantly older and exhibited elevated levels of HbA1c, blood glucose, HDL-c, TG, TC, LDL-c, and CHO compared to those with lower RC levels ( p < 0.05). Similar trends were observed for RC/HDL-c ratios, with detailed data presented in Table 3 . Table 2 Characteristics of the subjects according to the RC level quartile RC Q1(n = 2931) Q2(n = 2856) Q3(n = 2858) Q4(n = 2881) p _value Age(years) 60.38 ± 9.54 60.63 ± 9.86 60.68 ± 9.89 59.51 ± 9.56 < 0.001 Gender(man) 1386(47.29) 1479(51.79) 1517(53.08) 1492(51.79) < 0.001 HbA1c (%) 5.96 ± 1.14 6.0 ± 1.16 6.14 ± 1.41 6.31 ± 1.63 < 0.001 Glu(mmol/L) 6.19 ± 2.44 6.27 ± 2.49 6.61 ± 2.84 7.04 ± 3.34 < 0.001 HDL-C(mmol/L) 1.46 ± 0.37 1.27 ± 0.32 1.18 ± 0.3 1.08 ± 0.32 < 0.001 TG(mmol/L) 1.08 ± 0.43 1.35 ± 0.53 1.76 ± 0.7 3.22 ± 2.67 < 0.001 LDL-c(mmol/L) 3.16 ± 1.02 3.02 ± 0.96 3.2 ± 1.0 3.21 ± 1.14 < 0.001 CHO(mmol/L) 4.64 ± 1.07 4.58 ± 1.08 4.9 ± 1.11 5.42 ± 1.35 < 0.001 CACS Normal 1490 (51.7) 1374 (47.69) 1321 (45.85) 1344 (46.63) Mild 832 (28.87) 820 (28.46) 826 (28.67) 870 (30.19) Moderate 388 (13.46) 400 (13.88) 456 (15.83) 437 (15.16) Severe 221 (7.67) 262 (9.09) 255 (8.85) 230 (7.98) HbA1c: hemoglobin A1c; Glu: Glucose; HDL-c: high-density lipoprotein cholesterol; TG: triglyceride; LDL-c: low-density lipoprotein cholesterol; CHO: total cholesterol Table 3 Characteristics of the subjects by RC/HDL-c ratio quartile RC/HDL-C Q1(n = 2882) Q2(n = 2881) Q3(n = 2881) Q4(n = 2882) p _value Age(years) 60.33 ± 9.45 60.62 ± 9.91 60.57 ± 9.82 59.66 ± 9.68 < 0.001 Gender(man) 1277(44.31) 1431(49.67) 1518(52.69) 1648(57.18) < 0.001 HbA1c (%) 5.94 ± 1.14 5.99 ± 1.16 6.11 ± 1.37 6.36 ± 1.64 < 0.001 Glu(mmol/L) 6.16 ± 2.43 6.24 ± 2.4 6.58 ± 2.82 7.13 ± 3.4 < 0.001 HDL-C(mmol/L) 1.52 ± 0.37 1.32 ± 0.3 1.18 ± 0.27 0.97 ± 0.24 < 0.001 TG(mmol/L) 1.08 ± 0.43 1.36 ± 0.58 1.74 ± 0.7 3.24 ± 2.65 < 0.001 LDL-c(mmol/L) 3.19 ± 1.02 3.08 ± 0.98 3.23 ± 1.0 3.09 ± 1.13 < 0.001 CHO(mmol/L) 4.73 ± 1.08 4.7 ± 1.11 4.94 ± 1.17 5.16 ± 1.38 < 0.001 CACS Normal 1498 (51.98) 1415 (49.11) 1326 (46.03) 1290 (44.76) Mild 821 (28.49) 782 (27.14) 865 (30.02) 880 (30.53) Moderate 357 (12.39) 419 (14.54) 444 (15.41) 461 (16.0) Severe 206 (7.15) 265 (9.2) 246 (8.54) 251 (8.71) HbA1c: hemoglobin A1c; Glu: Glucose; HDL-c: high-density lipoprotein cholesterol; TG: triglyceride; LDL-c: low-density lipoprotein cholesterol; CHO: total cholesterol The frequency of coronary artery calcification (CAC) occurrence across the entire participant group, as well as within both sexes, is visually depicted in Fig. 3 . A positive correlation was identified between CAC frequency and increasing age. 3.2. Association of the RC-to-HDL-c Ratio with CACS in Conditional Logistic Regression Analysis Conditional logistic regression analysis was used to assess the associations of HDL-c (Table 4 ). Restricted cubic spline analysis demonstrated a graded positive association between both the RC/HDL-c and levels with CAC risk, as illustrated in Fig. 4 . Table 4 Correlation analysis of HDL-c with CACS Model 1 p _value Model 2 p _value OR (X95.CI) OR (X95.CI) HDL-c (Continuous) 0.677(0.610–0.752) 0 0.764(0.668–0.874) 0.0001 HDL-c (categorical) 2.0 0.697(0.563–0.863) 0.0009 0.785(0.594–1.038) 0.0895 In Model 1 (adjusted for age and sex), an increase in the RC/HDL-c ratio was significantly associated with an elevated risk of CACS (Table 5 ; OR = 1.092; 95% CI, 1.048–1.137; p < 0.001). Categorical analysis further revealed that Quartiles 2, 3, and 4 of the RC/HDL-c ratio demonstrated significant associations with higher CACS compared to Quartile 1 as the reference group (p for trend < 0.001). In Model 2 (adjusted for age, sex, glucose, glycated hemoglobin, and triglycerides), this trend remained statistically significant for both Quartile 3 and Quartile 4 ( p < 0.05). Table 5 Association between the RC level and the RC/HDL-c ratio and the risk of CACS Model 1 p _value Model 2 p _value OR (95%) OR (95%) RC(Continuous) 1.179(1.107–1.257) < 0.001 1.029(0.884–1.197) 0.7162 RC_Q1 1 1 RC_Q2 1.09(0.986-1.1.205) 0.0932 1.065(0.938–1.208) 0.3319 RC_Q3 1.162(1.058–1.292) 0.0022 1.096(0.966–1.244) 0.1536 RC_Q4 1.241(1.123–1.371) < 0.001 1.083(0.939–1.249) 0.274 p for trend < 0.001 0.216 RC/HDL-C(Continuous) 1.092(1.048–1.137) < 0.001 0.979(0.902–1.062) 0.6058 RC/HDL-C_Q1 1 1 RC/HDL-C_Q2 1.137(1.027–1.258) 0.0131 1.142(1.005–1.297) 0.0421 RC/HDL-C_Q3 1.210(1.094–1.338) 0.0002 1.158(1.02–1.315) 0.0238 RC/HDL-C_Q4 1.336(1.208–1.477) < 0.001 1.161(1.006–1.34) 0.0418 p for trend < 0.001 0.0381 A statistically significant positive correlation between levels and CACS was identified in Model 1 (OR = 1.179; 95% CI, 1.107–1.257; p < 0.001), and trend analysis across RC quartiles also yielded statistically significant results. However, this association was not sustained as statistically significant in Model 2 ( p = 0.716 and p = 0.216). To evaluate the incremental predictive utility of the RC/HDL-c beyond RC alone, net reclassification improvement (NRI) was calculated. The RC/HDL-c ratio facilitated the reclassification of 12.3% (95% CI: 9.8–14.7%) of participants into elevated-risk CACS categories (NRI = 0.123, p < 0.001), particularly in the subgroup of individuals with normal LDL-c levels (NRI = 0.158, p < 0.001). Subgroup Analysis Subgroup analysis was conducted to explore the interrelationships between RC levels and the RC/HDL-c with CACS in participant subgroups categorized by LDL-c levels (Fig. 5 ). For participants with LDL-c ≤ 3.37 mmol/L, both RC levels (Model 1: OR, 1.1542; 95% CI, 1.0745–1.2398; p = 0.0001) and the RC to HDL-c ratio (RC/HDL-c) (Model 1: OR, 1.0776; 95% CI, 1.0335–1.1235; p = 0.0005) demonstrated statistically significant associations with CACS (Table 6 , Model 1 p for trend < 0.001). These associations persisted after adjustments for additional covariates (Model 2 p for trend < 0.05). Conversely, these associations were not statistically significant among participants with elevated LDL-c in Model 2 (Table 7 ). Table 6 Association of RC level and RC/HDL-c ratio with CACS risk for LDL-c ≤ 3.37. Model1 Model2 OR X95.CI p _value OR X95.CI p _value RC(Continuous) 1.1542 [1.0745,1.2398] 0.0001 1.0061 [0.827,1.224] 0.9516 RC_Q1 1 1 RC_Q2 1.1122 [0.9788,1.2638] 0.1027 1.0855 [0.9277,1.2701] 0.3061 RC_Q3 1.1541 [1.0156,1.3116] 0.0281 1.1114 [0.949,1.3016] 0.1901 RC_Q4 1.2347 [1.0865,1.4032] 0.0012 1.0763 [0.8981,1.2899] 0.4257 p for trend 0.0012 0.344 RC/HDL-C(Continuous) 1.0776 [1.0335,1.1235] 0.0005 0.9769 [0.8884,1.0742] 0.6292 RC/HDL-C_Q1 1 1 RC/HDL-C_Q2 1.1947 [1.0501,1.3592] 0.0069 1.1794 [1.0064,1.3821] 0.0414 RC/HDL-C_Q3 1.2175 [1.0708,1.3843] 0.0027 1.1547 [0.9851,1.3535] 0.076 RC/HDL-C_Q4 1.3839 [1.2175,1.5731] 0 1.2502 [1.0431,1.4985] 0.0157 p for trend 0 0.0253 Table 7 Association of RC level and RC/HDL-c ratio with CACS risk for LDL-c > 3.37. Model1 Model2 OR X95.CI p _value OR X95.CI p _value RC(Continuous) 1.2906 [1.1289,1.4756] 0.0002 1.1384 [0.8914,1.4538] 0.299 RC_Q1 1 1 RC_Q2 1.1463 [0.9743,1.3487] 0.0998 1.1287 [0.9115,1.3976] 0.2669 RC_Q3 1.149 [0.976,1.3526] 0.0954 1.0158 [0.8192,1.2597] 0.8862 RC_Q4 1.2775 [1.086,1.5028] 0.0031 1.0628 [0.8365,1.3504] 0.618 p for trend 0.0049 0.862 RC/HDL-C(Continuous) 1.3014 [1.1381,1.4881] 0.0001 1.1371 [0.9206,1.4045] 0.2332 RC/HDL-C_Q1 1 1 RC/HDL-C_Q2 1.148 [0.9749,1.3519] 0.0978 1.1693 [0.9417,1.4519] 0.1568 RC/HDL-C_Q3 1.1972 [1.0168,1.4095] 0.0307 1.1496 [0.9278,1.4243] 0.2024 RC/HDL-C_Q4 1.3133 [1.116,1.5454] 0.001 1.0613 [0.8322,1.3535] 0.6314 p for trend 0.0011 0.632 Additionally, subgroup analysis was performed in male and female participants (Fig. 6 ). In Model 1, both RC levels and the RC/HDL-c were significantly associated with CACS in both male (Table 8 ) and female (Table 9 ) subgroups. However, these associations were not sustained in Model 2 after adjustment for additional covariates. Table 8 Association of RC level and RC/HDL-c ratio with CACS in males. Male Model1 Model2 OR X95.CI p _value OR X95.CI p _value RC(Continuous) 1.1262 [1.042,1.2171] 0.0027 1.0126 [0.8258,1.2415] 0.9045 RC_Q1 1 1 RC_Q2 1.1346 [0.9907,1.2994] 0.0679 1.1261 [0.9489,1.3364] 0.174 RC_Q3 1.1523 [1.0062,1.3196] 0.0404 1.1321 [0.954,1.3434] 0.1555 RC_Q4 1.2003 [1.0472,1.3758] 0.0087 1.0356 [0.8527,1.2578] 0.724 p for trend 0.0101 0.6051 RC/HDL-C(Continuous) 1.0587 [1.013,1.1064] 0.0113 0.9964 [0.8974,1.1063] 0.9457 RC/HDL-C_Q1 1 1 RC/HDL-C_Q2 1.2419 [1.0841,1.4226] 0.0018 1.2901 [1.0864,1.532] 0.0037 RC/HDL-C_Q3 1.1563 [1.0088,1.3254] 0.037 1.1938 [1.0046,1.4187] 0.0443 RC/HDL-C_Q4 1.3417 [1.1705,1.5379] 0 1.2096 [0.9946,1.4709] 0.0566 p for trend 0.0002 0.0965 Table 9 Association of RC level and RC/HDL-c ratio with CACS in females. Female Model1 Model2 OR X95.CI p _value OR X95.CI p _value RC(Continuous) 1.2412 [1.1149,1.3819] 0.0001 1.0397 [0.8271,1.3069] 0.7389 RC_Q1 1 1 RC_Q2 1.0909 [0.9389,1.2676] 0.2556 1.0486 [0.8681,1.2667] 0.6222 RC_Q3 1.1389 [0.9792,1.3246] 0.0914 0.9936 [0.8207,1.203] 0.9474 RC_Q4 1.2604 [1.0856,1.4633] 0.0024 1.0963 [0.8812,1.3639] 0.4094 p for trend 0.0022 0.577 RC/HDL-C(Continuous) 1.1703 [1.0785,1.2699] 0.0002 0.976 [0.8494,1.1216] 0.7323 RC/HDL-C_Q1 1 1 RC/HDL-C_Q2 1.1101 [0.9542,1.2914] 0.1762 1.0571 [0.8734,1.2794] 0.5688 RC/HDL-C_Q3 1.151 [0.9902,1.3379] 0.0671 0.9965 [0.8232,1.2064] 0.9715 RC/HDL-C_Q4 1.316 [1.134,1.5273] 0.0003 1.0525 [0.8438,1.3129] 0.6499 p for trend 0.0003 0.828 Discussion CVD remains the leading cause of mortality globally, with CACS serving as a salient predictor of CVD risk[ 22 – 24 ]. HDL-c, recognized for its protective role in reducing coronary atherosclerosis, is widely utilized as an adjustment factor for TC, triglycerides, and non-HDL-c[ 25 , 26 ]. However, to the best of our knowledge, limited research has explored the modification of these indices through the incorporation of HDL-c levels, and the relationship between the RC/HDL-c and CACS remains incompletely understood[ 22 , 27 ]. Our findings contribute novel insights by highlighting the potential role of the RC/HDL-c as an emerging risk marker for CACS, which may enhance the precision of CVD risk assessment protocols. Notably, compared to RC alone, the RC/HDL-c ratio demonstrated a more robust and consistent association with CACS. Our results position the RC/HDL-c ratio as a complementary marker to established lipid ratios such as TG/HDL-c or LDL-c/HDL-c. While the TG/HDL-c ratio has been extensively studied in atherosclerosis research[ 28 – 30 ], the RC/HDL-c ratio uniquely integrates atherogenic RC with the protective properties of HDL-c. This dual consideration may explain its superior performance in individuals with controlled LDL-c, a population in whom residual risk often persists despite statin therapy. Contemporary genetic and interventional studies have challenged the simplistic "good cholesterol" paradigm of HDL-c, emphasizing its functional heterogeneity[ 31 , 32 ]. While our study operationalizes HDL-c as a quantitative metric, future research should explore HDL particle subfractions (e.g., HDL2/HDL3) or cholesterol efflux capacity to better capture its role in modulating RC-driven atherogenesis. The RC/HDL-c ratio is particularly significant as it integrates both the atherogenic effects of RC and the protective effects of HDL-c. This dual consideration provides a more comprehensive evaluation of cardiovascular risk, especially in individuals with controlled LDL-c levels. While LDL-c has traditionally been the primary focus of lipid management, the RC/HDL-c ratio offers a distinct perspective by addressing residual risk factors that persist even after LDL-c normalization. This is particularly relevant in the context of statin therapy, where patients may still experience cardiovascular events despite achieving target LDL-c levels[ 33 ]. The RC/HDL-c ratio thus functions as a complementary marker, reflecting the balance between atherogenic and protective lipid components, and may identify individuals who could benefit from additional therapeutic interventions, such as fibrates or proprotein convertase subtilisin/kexin type 9 (PCSK9) inhibitors, to further reduce RC burden[ 34 ]. Both RC and the RC/HDL-c ratio exhibited statistically significant positive correlations with CACS before adjustment for other covariates; however, this relationship lost statistical significance for RC after adjustment for glucose, glycated hemoglobin, and triglycerides. This observation may be attributed to the multifaceted protective effects of HDL-c on the cardiovascular system. One of the key functions of HDL-c is cholesterol efflux, which involves the transportation of cholesterol from peripheral tissues back to the liver for metabolic processing and excretion, thereby reducing cholesterol deposition in arterial walls[ 31 , 32 ]. Additionally, HDL-c possesses anti-inflammatory and antioxidant properties, which can prevent the oxidation of LDL and the occurrence of inflammatory responses. These effects contribute to the attenuation of atherosclerosis development and the reduction of CVD risk[ 35 , 36 ]. Consequently, the protective actions of HDL-c may mask the independent impact of RC levels on CVD risk, resulting in the loss of statistical significance for RC after adjustment for relevant factors. Moreover, our study revealed that the association between the RC/HDL-c ratio and CACS is particularly pronounced among individuals with normal LDL levels, aligning with prior research emphasizing the importance of considering supplementary lipid metrics beyond LDL-c alone for a comprehensive evaluation of cardiovascular risk[ 37 ]. Our findings highlight the potential of the RC/HDL-c ratio as a complementary tool in CACS stratification, especially in populations with controlled LDL-c levels. This study also opens avenues for further investigation into the utility of this ratio in tailoring preventive strategies for individuals with varying lipid profiles. The results underscore the clinical significance of the RC/HDL-c ratio in CVD risk assessment. Compared to traditional lipid indicators, the RC/HDL-c ratio offers a more comprehensive cardiovascular risk assessment by concurrently considering the atherogenic effects of RC and the protective effects of HDL-c. This finding may influence CVD prevention strategies, particularly in individuals with elevated RC/HDL-c ratios, who require more vigilant monitoring and earlier intervention to prevent coronary artery calcification and subsequent cardiovascular events. The implementation of the RC/HDL-c ratio in clinical practice could facilitate more personalized and targeted therapeutic approaches, ultimately improving patient outcomes and reducing the burden of CACS. Our study has several notable strengths. It pioneered the approach of adjusting RC with HDL-c levels and investigating the association between the RC/HDL-c ratio and CACS. Furthermore, our study benefited from a large sample size, enabling multiple subgroup analyses. However, limitations exist. First, we did not stratify CACS by single and multivessel involvement. Second, the analysis did not account for certain confounding factors, such as body mass index (BMI), smoking status, alcohol consumption, and statin use. Third, the cross-sectional design limited our ability to establish a causal relationship between the RC/HDL-c ratio and CACS; thus, longitudinal studies are warranted. Additionally, this study compared the RC/HDL-c ratio only with RC levels; further research is needed to compare this ratio with other modified indices. The generalizability of our results may be constrained by the demographic characteristics of our study population, and future studies should aim to replicate these findings in diverse and international cohorts. Notwithstanding these limitations, our study provides a robust foundation for future research and clinical applications. Future research should explore the applicability of the RC/HDL-c ratio across diverse populations, including individuals of different sexes, ages, ethnicities, and geographical origins. More longitudinal studies are needed to establish a causal link between the RC/HDL-c ratio and cardiovascular events and to evaluate the practical utility of this ratio in CVD prevention. Future studies should also investigate the interplay between the RC/HDL-c ratio and other CVD risk factors, such as inflammatory markers and components of metabolic syndrome, and how these factors collectively influence CVD risk. In conclusion, our study provides novel insights into the association between the RC/HDL-c ratio and CACS and underscores the importance of considering RC and HDL-c levels in CACS assessment. These findings lay the groundwork for future research aimed at refining and validating the RC/HDL-c ratio as a prognostic marker in diverse clinical settings and populations. Declarations Conflict of interest The authors declare that they have no competing interests. Ethics approval and consent to participate The Institutional Review Board (IRB) of Qingyuan Hospital, Guangzhou Medical University granted ethical clearance (Approval No. IRB-2024-032). The study was conducted in accordance with the Declaration of Helsinki, and informed consent has been waived by the ethics committee of Qingyuan Hospital, Guangzhou Medical University due to the retrospective nature of the study. A scanned copy of the approval letter can be provided by the corresponding author upon request. Clinical trial number Not applicable. Funding This study was funded by the National Beijing Medical Award Foundation (YXJL-2023--0227--0097), the Guangzhou Medical University Scientific Research Enhancement Program (2023--00154), Guangdong Basic and Applied Basic Research Fund Enterprise Joint fund-surface project (2024A1515220139) and the High-Level Talents Program of the Affiliated Qingyuan Hospital, Guangzhou Medical University (2022--11038). Author’s contributions CH and XG contributed to the design of the study. CH, ML and KW wrote the manuscript. XG, ML, JL, MP, HL and KW collected and/or analysed the data. All the authors have read and approved the final manuscript. Acknowledgements None. References Golub IS, et al. Major Global Coronary Artery Calcium Guidelines. JACC Cardiovasc Imaging. 2023;16(1):98–117. Chen X et al. Relationship between Coronary Artery Calcium Score and Coronary Stenosis. Cardiol Res Pract, 2023. 2023: p. 5538111. Kawaguchi YO et al. Current status and future perspective of coronary artery calcium score in asymptomatic individuals. J Cardiol, 2024. Radford NB, et al. Cardiorespiratory Fitness, Coronary Artery Calcium, and Cardiovascular Disease Events in a Cohort of Generally Healthy Middle-Age Men: Results From the Cooper Center Longitudinal Study. Circulation. 2018;137(18):1888–95. Elnagar B, et al. The value of coronary calcium score in predicting clinical outcomes in patients with chronic coronary syndrome. BMC Cardiovasc Disord. 2024;24(1):567. Ghodeshwar GK, Dube A, Khobragade D. Impact of Lifestyle Modifications on Cardiovascular Health: A Narrative Review. Cureus. 2023;15(7):e42616. Cury RC, et al. Document of the Society of Cardiovascular Computed Tomography (SCCT), the American College of Cardiology (ACC), the American College of Radiology (ACR), and the North America Society of Cardiovascular Imaging (NASCI). JACC Cardiovasc Imaging. 2022;15(11):1974–2001. CAD-RADS 2.0–2022 Coronary Artery Disease-Reporting and Data System: An Expert Consensus. Curry SJ, et al. Risk Assessment for Cardiovascular Disease With Nontraditional Risk Factors: US Preventive Services Task Force Recommendation Statement. JAMA. 2018;320(3):272–80. Elnagar B, et al. The value of coronary calcium score in predicting clinical outcomes in patients with chronic coronary syndrome. BMC Cardiovasc Disord. 2024;24(1):567. Pinto-Sietsma SJ, et al. Computed tomography and coronary artery calcium score for screening of coronary artery disease and cardiovascular risk management in asymptomatic individuals. Neth Heart J. 2024;32(11):371–7. Ouimet M, Barrett TJ, Fisher EA. HDL and Reverse Cholesterol Transport. Circ Res. 2019;124(10):1505–18. Barter PJ, et al. Antiinflammatory properties of HDL. Circ Res. 2004;95(8):764–72. Kosmas CE, et al. High-density lipoprotein (HDL) functionality and its relevance to atherosclerotic cardiovascular disease. Drugs Context. 2018;7:212525. Varbo A, et al. Remnant cholesterol as a causal risk factor for ischemic heart disease. J Am Coll Cardiol. 2013;61(4):427–36. Wilson P, Remaley AT. Ischemic Heart Disease Risk and Remnant Cholesterol Levels. J Am Coll Cardiol. 2022;79(24):2398–400. Doi T, Langsted A, Nordestgaard BG. Elevated Remnant Cholesterol Reclassifies Risk of Ischemic Heart Disease and Myocardial Infarction. J Am Coll Cardiol. 2022;79(24):2383–97. Pinto X, et al. Remnant cholesterol, vascular risk, and prevention of atherosclerosis. Clin Investig Arterioscler. 2023;35(4):206–17. Paunica I, et al. Comparative evaluation of LDL-CT, non-HDL/HDL ratio, and ApoB/ApoA1 in assessing CHD risk among patients with type 2 diabetes mellitus. J Diabetes Complications. 2023;37(12):108634. Blaha MJ et al. Coronary Artery Calcium Scoring: Is It Time for a Change in Methodology? JACC Cardiovasc Imaging, 2017. 10(8): pp. 923–37. Agatston AS, et al. Quantification of coronary artery calcium using ultrafast computed tomography. J Am Coll Cardiol. 1990;15(4):827–32. Czaja-Ziolkowska MZ, et al. An update on the coronary calcium score: a review for clinicians. Postepy Kardiol Interwencyjnej. 2022;18(3):201–5. Tsao CW, et al. Heart Disease and Stroke Statistics-2022 Update: A Report From the American Heart Association. Circulation. 2022;145(8):e153–639. Mensah GA, et al. Global Burden of Cardiovascular Diseases and Risks, 1990–2022. J Am Coll Cardiol. 2023;82(25):2350–473. Chong B et al. Global burden of cardiovascular diseases: projections from 2025 to 2050. Eur J Prev Cardiol, 2024. Sionis A, et al. Improving lipid management in patients with acute coronary syndrome: The ACS Lipid EuroPath tool. Atheroscler Suppl. 2020;42:e65–71. Kaminski M et al. Therapeutic inertia in lipid management among Polish adults with type 1 diabetes - results from the cross-sectional PARADISE T1DM study. Nutr Metab Cardiovasc Dis, 2025: p. 103853. Pan L, et al. Association between the remnant cholesterol to high-density lipoprotein cholesterol ratio and the risk of coronary artery disease. Coron Artery Dis. 2024;35(2):114–21. Rader DJ, Tall AR. The not-so-simple HDL story: Is it time to revise the HDL cholesterol hypothesis? Nat Med. 2012;18(9):1344–6. Zou Y, et al. Remnant cholesterol/high-density lipoprotein cholesterol ratio is a new powerful tool for identifying non-alcoholic fatty liver disease. BMC Gastroenterol. 2022;22(1):134. Huangfu G, et al. Triglyceride to High-Density Lipoprotein Cholesterol Ratio as a Marker of Subclinical Coronary Atherosclerosis and Hepatic Steatosis in Familial Hypercholesterolemia. Endocr Pract; 2025. Fazio S, Pamir N. HDL Particle Size and Functional Heterogeneity. Circ Res. 2016;119(6):704–7. Kosmas CE, et al. High-density lipoprotein (HDL) functionality and its relevance to atherosclerotic cardiovascular disease. Drugs Context. 2018;7:212525. Cannon CP, et al. Ezetimibe Added to Statin Therapy after Acute Coronary Syndromes. N Engl J Med. 2015;372(25):2387–97. Quispe R, et al. Remnant cholesterol predicts cardiovascular disease beyond LDL and ApoB: a primary prevention study. Eur Heart J. 2021;42(42):4324–32. Kexin W, et al. Association of Increased Remnant Cholesterol and the Risk of Coronary Artery Disease: A Retrospective Study. Front Cardiovasc Med. 2021;8:740596. Hao QY, et al. Remnant Cholesterol and the Risk of Coronary Artery Calcium Progression: Insights From the CARDIA and MESA Study. Circ Cardiovasc Imaging. 2022;15(7):e014116. Lee DY, et al. Effects of Low-density Lipoprotein Cholesterol on Coronary Artery Calcification Progression According to High-density Lipoprotein Cholesterol Levels. Arch Med Res. 2017;48(3):284–91. Additional Declarations No competing interests reported. Cite Share Download PDF Status: Under Review Version 1 posted Reviews received at journal 13 May, 2026 Reviewers agreed at journal 10 May, 2026 Reviews received at journal 08 Aug, 2025 Reviewers agreed at journal 07 Aug, 2025 Reviewers invited by journal 28 Jul, 2025 Editor invited by journal 30 Jun, 2025 Editor assigned by journal 27 Jun, 2025 Submission checks completed at journal 27 Jun, 2025 First submitted to journal 17 Jun, 2025 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. 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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-6914577","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":494762996,"identity":"3c3e4e27-5b4b-45a5-9749-5ef7cdb71a1d","order_by":0,"name":"Mingru Li","email":"","orcid":"","institution":"Guangzhou Medical University","correspondingAuthor":false,"prefix":"","firstName":"Mingru","middleName":"","lastName":"Li","suffix":""},{"id":494762997,"identity":"22160e2d-034c-4368-83b2-25a66e0ad348","order_by":1,"name":"Xiuhong Guan","email":"","orcid":"","institution":"Guangzhou Medical University","correspondingAuthor":false,"prefix":"","firstName":"Xiuhong","middleName":"","lastName":"Guan","suffix":""},{"id":494762998,"identity":"4c26e917-feb8-43b9-b796-56e45ec0b0de","order_by":2,"name":"Junjie Lei","email":"","orcid":"","institution":"Guangzhou Medical University","correspondingAuthor":false,"prefix":"","firstName":"Junjie","middleName":"","lastName":"Lei","suffix":""},{"id":494762999,"identity":"1557df98-9e3a-47d8-b81c-576870f9c48d","order_by":3,"name":"Haosheng Li","email":"","orcid":"","institution":"Guangzhou Medical University","correspondingAuthor":false,"prefix":"","firstName":"Haosheng","middleName":"","lastName":"Li","suffix":""},{"id":494763000,"identity":"dd05da94-cad7-4c0e-baf2-0f45ac6a4bb4","order_by":4,"name":"Mingming Peng","email":"","orcid":"","institution":"Guangzhou Medical University","correspondingAuthor":false,"prefix":"","firstName":"Mingming","middleName":"","lastName":"Peng","suffix":""},{"id":494763001,"identity":"06cd72d4-c84b-4993-8a8c-38acad9584df","order_by":5,"name":"Kejian Wang","email":"","orcid":"","institution":"Guangxi Academy of Sciences","correspondingAuthor":false,"prefix":"","firstName":"Kejian","middleName":"","lastName":"Wang","suffix":""},{"id":494763002,"identity":"f802cfbf-4f9a-4150-bcbb-0dab20ff6dbf","order_by":6,"name":"Ci He","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA0UlEQVRIiWNgGAWjYBACAyA+wMBmw8AgAeYzE60ljUQtDAxsh0nQYs7e/PDAj7LzdvNnd6dJMFRYJzawnz2AV4tlzzGDgz3nbic3zjm7TYLhTHpiA09eAn6H3chhOMzYdjuZWSJ3mwRj2+HEBgkeA/xa7r8BaTmXzAbW8o8YLTd4QFoO2PGAtTQQo+VMGsgvyQkSErmbLRKOpRu38eQQ0HL88OMPP8rs7OVn5G688aHGWraf/Qx+LTCQ2AAiE4CYjSj1QGBPrMJRMApGwSgYgQAAbAxGjRjcvmYAAAAASUVORK5CYII=","orcid":"","institution":"Guangzhou Medical University","correspondingAuthor":true,"prefix":"","firstName":"Ci","middleName":"","lastName":"He","suffix":""}],"badges":[],"createdAt":"2025-06-17 12:53:19","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-6914577/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-6914577/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":88231560,"identity":"64d3bbeb-daa9-482e-bfb9-c68dd7f7135b","added_by":"auto","created_at":"2025-08-04 09:35:50","extension":"jpg","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":126473,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eFlowchart of participant selection\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"Picture1.jpg","url":"https://assets-eu.researchsquare.com/files/rs-6914577/v1/8647d682d1cbcea885f9a384.jpg"},{"id":88231559,"identity":"cfccb45e-c508-4892-95f0-104ccfa3275c","added_by":"auto","created_at":"2025-08-04 09:35:50","extension":"jpg","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":60924,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eIllustration of CAC via computed tomography angiography. A: 69-year-old woman; B: 80-year-old man; C: 71-year-old man; D: 88-year-old man\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"Picture2.jpg","url":"https://assets-eu.researchsquare.com/files/rs-6914577/v1/12327fe7b5999cab3b524837.jpg"},{"id":88231561,"identity":"2fcb2d19-f30d-4e74-893c-832396a869ad","added_by":"auto","created_at":"2025-08-04 09:35:50","extension":"jpg","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":88448,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eThe occurrence rate of coronary artery calcification across all, male, and female participants categorized by various age ranges.\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"Picture3.jpg","url":"https://assets-eu.researchsquare.com/files/rs-6914577/v1/2b39ee09da4a550d56dc497a.jpg"},{"id":88232906,"identity":"47f3f94a-f281-486c-ab33-8df0d32426a1","added_by":"auto","created_at":"2025-08-04 09:43:50","extension":"jpg","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":43720,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eRestricted cubic splines derived from cumulative logistic regression analysis demonstrate the adjusted associations between coronary artery calcification (CACS) and (A) remnant cholesterol (RC) levels or (B) RC/HDL-C ratio. All continuous variables were log-transformed to approximate normal distribution. The RC/HDL-C ratio showed a stronger dose-response relationship with CACS than RC alone. Models were adjusted for age, sex, glucose, glycated haemoglobin, and triglycerides.\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"Picture4.jpg","url":"https://assets-eu.researchsquare.com/files/rs-6914577/v1/a769fc1d71ab3cb35ebfdfec.jpg"},{"id":88232907,"identity":"cb5f4296-3704-4564-b3b4-917d3db3bbc4","added_by":"auto","created_at":"2025-08-04 09:43:50","extension":"jpg","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":40942,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eSubgroup analyses explored RC level and RC/HDL-c ratio associations with CACS across different LDL-c levels.\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"Picture5.jpg","url":"https://assets-eu.researchsquare.com/files/rs-6914577/v1/a0e63714419b72ad4ef5dc7d.jpg"},{"id":88231573,"identity":"04d6d996-dfa7-44b1-a250-59b67a8ecb39","added_by":"auto","created_at":"2025-08-04 09:35:51","extension":"jpg","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":41739,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eSubgroup analyses determined RC/HDL-c ratio's association with CACS by sex.\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"Picture6.jpg","url":"https://assets-eu.researchsquare.com/files/rs-6914577/v1/f3bb868b55cac296d7b06932.jpg"},{"id":88234286,"identity":"6e88edd6-87f3-44be-9968-6ee460f701ba","added_by":"auto","created_at":"2025-08-04 09:59:51","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":2065789,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-6914577/v1/ded5f41a-f1f4-48a8-b4f7-7c94c9aea8a7.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"The RC/HDL-c Ratio: A Superior Predictor of Coronary Artery Calcification Severity Compared to Remnant Cholesterol Alone","fulltext":[{"header":"Introduction","content":"\u003cp\u003eAssessing the risk of coronary artery disease (CAD) is a critical endeavor in cardiovascular research. Coronary artery calcium scoring (CACS), derived from noncontrast computed tomography (CT) imaging, has emerged as a widely adopted and essential metric in this context[\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e]. This imaging modality enables the quantitative assessment of calcified plaque burden within the coronary arteries, providing a direct measure of the extent of coronary atherosclerosis[\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e]. The clinical value of CACS lies in its ability to refine risk stratification by identifying asymptomatic individuals who may benefit from preventive strategies[\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e, \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e]. Notably, studies have consistently demonstrated that elevated CACS values correlate with an increased likelihood of future adverse cardiovascular events, offering critical prognostic insights[\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e, \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eMoreover, CACS findings assist clinicians in tailoring therapeutic approaches based on individual risk profiles, guiding decisions regarding lifestyle modifications, pharmacological interventions, and, when necessary, invasive procedures[\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e]. Evidence suggests that incorporating CACS into routine clinical practice enhances clinical decision-making and improves patient outcomes[\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e, \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e]. Thus, understanding the role of CACS not only advances our comprehension of coronary atherosclerosis but also supports the transition toward personalized medical strategies in cardiovascular healthcare[\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eNonetheless, while CACS screening has shown potential, it is imperative to emphasize the limitations inherent to the currently available evidence. Despite its clinical acceptance as a key tool for risk stratification of atherosclerotic disease, no conclusive study has provided irrefutable evidence of the efficacy of population-level CACS screening programs in the realm of clinical trials[\u003cspan additionalcitationids=\"CR9\" citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e]. This highlights the need for continued research in this area to improve the evidence base underpinning the utilization of CACS as a primary diagnostic tool.\u003c/p\u003e\u003cp\u003eHigh-density lipoprotein cholesterol (HDL-c) plays a pivotal role in reverse cholesterol transport[\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e]. Its cardioprotective effects are multifaceted, including the removal of cholesterol from arterial walls and the inhibition of atherosclerosis progression. HDL-c also exerts anti-inflammatory and antioxidant effects, which are critical in reducing inflammation and oxidative stress associated with cardiovascular disease[\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e]. Additionally, HDL-c contributes to vascular protection by preserving endothelial integrity, thereby preventing vascular stiffening and constriction[\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eRemnant cholesterol (RC), which represents the cholesterol derived from triglyceride-rich lipoproteins (TRLs) after triglyceride delivery to peripheral tissues, includes chylomicron remnants and intermediate-density lipoproteins (IDLs) arising from the metabolism of very low-density lipoproteins (VLDLs) and chylomicrons[\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e]. RC has been identified as a significant risk factor for atherosclerosis and CAD[\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e]. Elevated serum RC concentrations, independent of low-density lipoprotein cholesterol (LDL-c) levels, have been associated with an increased risk of CAD[\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e, \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e]. This association suggests that RC, in combination with non-HDL cholesterol, may serve as a more precise predictor of CAD outcomes compared to LDL-c alone. The atherogenic potential of RC is attributed to its presence within TRLs, which are known contributors to atherosclerotic plaque formation[\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e]. Therefore, managing RC levels is an important consideration in CAD prevention and may provide complementary benefits beyond LDL-c reduction strategies.\u003c/p\u003e\u003cp\u003eAlthough HDL-c has been extensively utilized as a modifying factor in studies examining the relationship between lipids and cardiovascular disease (CVD) risk\u0026mdash;including metrics such as the triglyceride (TG)/HDL ratio, total cholesterol (TC)/HDL ratio, and non-HDL/HDL ratio\u0026mdash;prior research has not explored the application of HDL-c in refining RC assessments[\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e, \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e, \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e]. The relationship between HDL-c-adjusted RC (RC/HDL-c) and CVD incidence remains incompletely understood. This adjusted ratio may offer a more precise indication of CVD risk compared to RC measurement alone.\u003c/p\u003e\u003cp\u003eIn the present investigation, we explored the complex interplay between CACS and lipid profiles, with a specific focus on RC and the RC/HDL-c ratio. Furthermore, we aimed to determine whether the RC/HDL-c ratio exhibits unique associations with CACS, beyond those already demonstrated by RC alone.\u003c/p\u003e"},{"header":"Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e\u003ch2\u003eParticipant Selection\u003c/h2\u003e\u003cp\u003eThis retrospective analysis examined data from individuals hospitalized at Qingyuan Hospital, Guangzhou Medical University, between January 1, 2012, and December 30, 2021. All participants underwent coronary artery computed tomography angiography (CTA) and routine laboratory assessments. The participant enrollment process is visually detailed in Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e. Patient eligibility was determined based on the following inclusion criteria:(1) Successful completion of coronary CTA examinations at our institution;(2) Availability of complete laboratory test results obtained within a 30-day window either before or after the CTA examination date;(3) Documentation of at least one hospitalization record containing comprehensive demographic information (including gender, age, etc.);and (4) Age at enrollment between 40 and 80 years.\u003c/p\u003e\u003cp\u003eExclusion criteria included the presence of significant image artifacts on CTA scans that compromised CACS calculation or instances of incomplete laboratory datasets.\u003c/p\u003e\u003cp\u003eTo ensure patient anonymity, all data underwent de-identification before analysis. This process involved removing direct identifiers (e.g., names, identification numbers, contact details) and aggregating indirect identifiers (e.g., age grouped into 5-year bands, geographical location reduced to district level). Ethical clearance for this retrospective study, including a waiver of informed consent, was provided by the Ethics Committee of Qingyuan Hospital, Guangzhou Medical University (Approval No. IRB-2024-032), and conducted in accordance with national guidelines for anonymized retrospective research.\u003c/p\u003e\u003c/div\u003e\n\u003ch3\u003eClinical Data Collection\u003c/h3\u003e\n\u003cp\u003eFor this investigation, we retrieved fundamental patient demographics (including sex and age) from an inpatient medical record database. Comprehensive laboratory measurements were extracted from our institution's Laboratory Information System (LIS), with a focus on analytes such as blood glucose, glycated hemoglobin, LDL-c, TC, triglycerides, and HDL-c.\u003c/p\u003e\n\u003ch3\u003eCoronary Artery Calcification Definition\u003c/h3\u003e\n\u003cp\u003eImage acquisition was performed using either a Siemens SOMATOM Force 192-slice CT scanner or a Toshiba 320-detector row CT system. All CT acquisitions adhered to the established Agatston protocol, employing a fixed tube voltage of 120 kV to minimize inter-scan variability. Tube current modulation was adjusted based on individual patient body habitus[\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eCACS measurements were derived exclusively from noncontrast coronary CTA images, using the Agatston scoring methodology as previously described[\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e]. This method quantified calcium scores within the left main coronary artery, left anterior descending artery, circumflex artery, and right coronary artery, resulting in a composite total CACS. Postprocessing calculations were performed using Syngo software (Siemens Healthcare, Forchheim, Germany).\u003c/p\u003e\u003cp\u003eMeasurements were independently conducted by two radiologists (XG and JL), each with 5 to 10 years of experience in diagnostic radiology, under double-blind conditions. In cases of interpretative discrepancies, a consensus determination was achieved through consultation with a senior cardiac imaging specialist (CH). The calcium score for each defined region of interest was calculated by multiplying the attenuation density score by the corresponding area. The aggregate coronary artery calcium score was then computed by summing these individual regional scores across all coronary branches[\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eCACS categorization was based on established thresholds: normal (CACS\u0026thinsp;=\u0026thinsp;0), mild (CACS 1\u0026ndash;100), moderate (CACS 101\u0026ndash;399), and severe (CACS\u0026thinsp;\u0026ge;\u0026thinsp;400)[\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e]. A visual representation of coronary artery calcification is provided in Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e.\u003c/p\u003e\n\u003ch3\u003eStatistical Analyses\u003c/h3\u003e\n\u003cp\u003eStatistical analyses were performed using R software (version 4.4.0). Continuous variables are presented as means\u0026thinsp;\u0026plusmn;\u0026thinsp;standard deviations, and categorical variables are expressed as frequencies (percentages). Group comparisons for continuous and categorical variables were conducted using analysis of variance (ANOVA) and the chi-squared test, respectively. To investigate the association between RC levels or RC/HDL-c ratios and CACS, cumulative logistic regression analysis was employed. LDL-c values were calculated according to the Friedewald equation. RC concentration was derived using the formula: RC\u0026thinsp;=\u0026thinsp;TC\u0026ndash; HDL-c \u0026ndash; LDL-c. Prior to analysis, continuous variables underwent logarithmic transformation to approximate normal distribution. Multicollinearity between glucose and glycated hemoglobin (HbA1c) was assessed using variance inflation factors (VIF), with a threshold of VIF\u0026thinsp;\u0026lt;\u0026thinsp;2.0 confirming minimal collinearity. Subgroup analyses were stratified by LDL-c concentration at 3.37 mmol/L (130 mg/dL), aligning with guideline-defined thresholds for elevated LDL-c. RC and RC/HDL-c values were categorized into quartiles as follows: For RC: Q1: \u0026lt; 0.18 mmol/L, Q2: 0.18\u0026ndash;0.39 mmol/L, Q3: 0.39\u0026ndash;0.65 mmol/L, Q4: \u0026gt;0.65 mmol/L; For RC/HDL-c: Q1: \u0026lt; 0.1343, Q2: 0.1343\u0026ndash;0.2559, Q3: 0.2559\u0026ndash;0.53125, Q4: \u0026gt;0.53125. Stratified subgroup analyses were conducted for both men and women, as well as for participants with elevated LDL-c levels (\u0026gt;\u0026thinsp;3.37 mmol/L) and those with lower LDL-c levels (\u0026le;\u0026thinsp;3.37 mmol/L). Two distinct models were constructed: Model 1: Adjusted for age and sex. Model 2: Adjusted for age, sex, glucose, glycated hemoglobin, and triglycerides. Statistical significance was defined as a P value of \u0026lt;\u0026thinsp;0.05.\u003c/p\u003e"},{"header":"Results","content":"\u003cdiv id=\"Sec8\" class=\"Section2\"\u003e\u003ch2\u003eParticipant Characteristics\u003c/h2\u003e\u003cp\u003eA total of 11,526 participants were categorized based on their CACS as follows: normal (n\u0026thinsp;=\u0026thinsp;5,529), mild (n\u0026thinsp;=\u0026thinsp;3,348), moderate (n\u0026thinsp;=\u0026thinsp;1,681), and severe (n\u0026thinsp;=\u0026thinsp;968). The mean age of the study population was 60.40\u0026thinsp;\u0026plusmn;\u0026thinsp;9.75 years, with 50.96% being male. As detailed in Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e, age, sex, glycemic control, HDL-c, and the RC to HDL-c ratio (RC/HDL-c) were significantly associated with the severity of coronary artery calcification.\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eCharacteristics of the subjects by CACS\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"6\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eNormal\u003c/p\u003e\u003cp\u003e(n\u0026thinsp;=\u0026thinsp;5529)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eMild\u003c/p\u003e\u003cp\u003e(n\u0026thinsp;=\u0026thinsp;3348)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003eModerate\u003c/p\u003e\u003cp\u003e(n\u0026thinsp;=\u0026thinsp;1681)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u003cp\u003eSevere\u003c/p\u003e\u003cp\u003e(n\u0026thinsp;=\u0026thinsp;968)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c6\"\u003e\u003cp\u003e\u003cem\u003ep\u003c/em\u003e_value\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eAge(years)\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e56.89\u0026thinsp;\u0026plusmn;\u0026thinsp;9.16\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e61.84\u0026thinsp;\u0026plusmn;\u0026thinsp;9.26\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e65.3\u0026thinsp;\u0026plusmn;\u0026thinsp;8.68\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e66.62\u0026thinsp;\u0026plusmn;\u0026thinsp;8.18\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eGender(man)\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e2696(43.78)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e2111(55.99)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e1038(55.66)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e718(64.86)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eHBA1C(%)\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e5.9\u0026thinsp;\u0026plusmn;\u0026thinsp;1.19\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e6.24\u0026thinsp;\u0026plusmn;\u0026thinsp;1.46\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e6.31\u0026thinsp;\u0026plusmn;\u0026thinsp;1.49\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e6.39\u0026thinsp;\u0026plusmn;\u0026thinsp;1.52\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eGlu(mmol/L)\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e6.25\u0026thinsp;\u0026plusmn;\u0026thinsp;2.54\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e6.79\u0026thinsp;\u0026plusmn;\u0026thinsp;3.1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e7.01\u0026thinsp;\u0026plusmn;\u0026thinsp;3.31\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e7.05\u0026thinsp;\u0026plusmn;\u0026thinsp;3.34\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eHDL-C(mmol/L)\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1.29\u0026thinsp;\u0026plusmn;\u0026thinsp;0.37\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1.23\u0026thinsp;\u0026plusmn;\u0026thinsp;0.34\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e1.2\u0026thinsp;\u0026plusmn;\u0026thinsp;0.34\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e1.19\u0026thinsp;\u0026plusmn;\u0026thinsp;0.36\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eTG(mmol/L)\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1.83\u0026thinsp;\u0026plusmn;\u0026thinsp;1.47\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1.91\u0026thinsp;\u0026plusmn;\u0026thinsp;1.92\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e1.83\u0026thinsp;\u0026plusmn;\u0026thinsp;1.51\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e1.81\u0026thinsp;\u0026plusmn;\u0026thinsp;1.73\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.1137\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eLDL-c(mmol/L)\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e3.16\u0026thinsp;\u0026plusmn;\u0026thinsp;0.98\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e3.18\u0026thinsp;\u0026plusmn;\u0026thinsp;1.05\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e3.12\u0026thinsp;\u0026plusmn;\u0026thinsp;1.1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e3.02\u0026thinsp;\u0026plusmn;\u0026thinsp;1.16\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eCHO(mmol/L)\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e4.92\u0026thinsp;\u0026plusmn;\u0026thinsp;1.13\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e4.9\u0026thinsp;\u0026plusmn;\u0026thinsp;1.25\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e4.82\u0026thinsp;\u0026plusmn;\u0026thinsp;1.27\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e4.72\u0026thinsp;\u0026plusmn;\u0026thinsp;1.34\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eRC\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.48\u0026thinsp;\u0026plusmn;\u0026thinsp;0.52\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.51\u0026thinsp;\u0026plusmn;\u0026thinsp;0.62\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.51\u0026thinsp;\u0026plusmn;\u0026thinsp;0.49\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.52\u0026thinsp;\u0026plusmn;\u0026thinsp;0.59\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.0473\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eRC/HDL-C\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.47\u0026thinsp;\u0026plusmn;\u0026thinsp;0.76\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.52\u0026thinsp;\u0026plusmn;\u0026thinsp;1.09\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.52\u0026thinsp;\u0026plusmn;\u0026thinsp;0.71\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.54\u0026thinsp;\u0026plusmn;\u0026thinsp;0.85\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.0101\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003ctfoot\u003e\u003ctr\u003e\u003ctd colspan=\"6\"\u003eHbA1c: hemoglobin A1c; Glu: Glucose; HDL-c: high-density lipoprotein cholesterol; TG: triglyceride; LDL-c: low-density lipoprotein cholesterol; CHO: total cholesterol\u003c/td\u003e\u003c/tr\u003e\u003c/tfoot\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003eTo further elucidate the relationships between RC levels, RC/HDL-c ratios, and CACS, the cohort was stratified into quartiles based on RC/HDL-c ratios or RC levels. Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e shows that participants with higher RC levels were significantly older and exhibited elevated levels of HbA1c, blood glucose, HDL-c, TG, TC, LDL-c, and CHO compared to those with lower RC levels (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05). Similar trends were observed for RC/HDL-c ratios, with detailed data presented in Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e.\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eCharacteristics of the subjects according to the RC level quartile\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"6\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eRC\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/th\u003e\u003cth align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u003cb\u003eQ1(n\u0026thinsp;=\u0026thinsp;2931)\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e\u003cb\u003eQ2(n\u0026thinsp;=\u0026thinsp;2856)\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e\u003cb\u003eQ3(n\u0026thinsp;=\u0026thinsp;2858)\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e\u003cb\u003eQ4(n\u0026thinsp;=\u0026thinsp;2881)\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e\u003cem\u003ep\u003c/em\u003e\u003cb\u003e_value\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eAge(years)\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e60.38\u0026thinsp;\u0026plusmn;\u0026thinsp;9.54\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e60.63\u0026thinsp;\u0026plusmn;\u0026thinsp;9.86\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e60.68\u0026thinsp;\u0026plusmn;\u0026thinsp;9.89\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e59.51\u0026thinsp;\u0026plusmn;\u0026thinsp;9.56\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eGender(man)\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1386(47.29)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1479(51.79)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e1517(53.08)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e1492(51.79)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eHbA1c (%)\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e5.96\u0026thinsp;\u0026plusmn;\u0026thinsp;1.14\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e6.0\u0026thinsp;\u0026plusmn;\u0026thinsp;1.16\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e6.14\u0026thinsp;\u0026plusmn;\u0026thinsp;1.41\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e6.31\u0026thinsp;\u0026plusmn;\u0026thinsp;1.63\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eGlu(mmol/L)\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e6.19\u0026thinsp;\u0026plusmn;\u0026thinsp;2.44\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e6.27\u0026thinsp;\u0026plusmn;\u0026thinsp;2.49\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e6.61\u0026thinsp;\u0026plusmn;\u0026thinsp;2.84\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e7.04\u0026thinsp;\u0026plusmn;\u0026thinsp;3.34\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eHDL-C(mmol/L)\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1.46\u0026thinsp;\u0026plusmn;\u0026thinsp;0.37\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1.27\u0026thinsp;\u0026plusmn;\u0026thinsp;0.32\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e1.18\u0026thinsp;\u0026plusmn;\u0026thinsp;0.3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e1.08\u0026thinsp;\u0026plusmn;\u0026thinsp;0.32\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eTG(mmol/L)\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1.08\u0026thinsp;\u0026plusmn;\u0026thinsp;0.43\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1.35\u0026thinsp;\u0026plusmn;\u0026thinsp;0.53\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e1.76\u0026thinsp;\u0026plusmn;\u0026thinsp;0.7\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e3.22\u0026thinsp;\u0026plusmn;\u0026thinsp;2.67\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eLDL-c(mmol/L)\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e3.16\u0026thinsp;\u0026plusmn;\u0026thinsp;1.02\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e3.02\u0026thinsp;\u0026plusmn;\u0026thinsp;0.96\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e3.2\u0026thinsp;\u0026plusmn;\u0026thinsp;1.0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e3.21\u0026thinsp;\u0026plusmn;\u0026thinsp;1.14\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eCHO(mmol/L)\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e4.64\u0026thinsp;\u0026plusmn;\u0026thinsp;1.07\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e4.58\u0026thinsp;\u0026plusmn;\u0026thinsp;1.08\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e4.9\u0026thinsp;\u0026plusmn;\u0026thinsp;1.11\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e5.42\u0026thinsp;\u0026plusmn;\u0026thinsp;1.35\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eCACS\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eNormal\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1490 (51.7)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1374 (47.69)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e1321 (45.85)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e1344 (46.63)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eMild\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e832 (28.87)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e820 (28.46)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e826 (28.67)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e870 (30.19)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eModerate\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e388 (13.46)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e400 (13.88)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e456 (15.83)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e437 (15.16)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eSevere\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e221 (7.67)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e262 (9.09)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e255 (8.85)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e230 (7.98)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003ctfoot\u003e\u003ctr\u003e\u003ctd colspan=\"6\"\u003eHbA1c: hemoglobin A1c; Glu: Glucose; HDL-c: high-density lipoprotein cholesterol; TG: triglyceride; LDL-c: low-density lipoprotein cholesterol; CHO: total cholesterol\u003c/td\u003e\u003c/tr\u003e\u003c/tfoot\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab3\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eCharacteristics of the subjects by RC/HDL-c ratio quartile\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"6\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eRC/HDL-C\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/th\u003e\u003cth align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u003cb\u003eQ1(n\u0026thinsp;=\u0026thinsp;2882)\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e\u003cb\u003eQ2(n\u0026thinsp;=\u0026thinsp;2881)\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e\u003cb\u003eQ3(n\u0026thinsp;=\u0026thinsp;2881)\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e\u003cb\u003eQ4(n\u0026thinsp;=\u0026thinsp;2882)\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e\u003cem\u003ep\u003c/em\u003e\u003cb\u003e_value\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eAge(years)\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e60.33\u0026thinsp;\u0026plusmn;\u0026thinsp;9.45\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e60.62\u0026thinsp;\u0026plusmn;\u0026thinsp;9.91\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e60.57\u0026thinsp;\u0026plusmn;\u0026thinsp;9.82\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e59.66\u0026thinsp;\u0026plusmn;\u0026thinsp;9.68\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eGender(man)\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1277(44.31)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1431(49.67)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e1518(52.69)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e1648(57.18)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eHbA1c (%)\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e5.94\u0026thinsp;\u0026plusmn;\u0026thinsp;1.14\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e5.99\u0026thinsp;\u0026plusmn;\u0026thinsp;1.16\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e6.11\u0026thinsp;\u0026plusmn;\u0026thinsp;1.37\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e6.36\u0026thinsp;\u0026plusmn;\u0026thinsp;1.64\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eGlu(mmol/L)\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e6.16\u0026thinsp;\u0026plusmn;\u0026thinsp;2.43\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e6.24\u0026thinsp;\u0026plusmn;\u0026thinsp;2.4\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e6.58\u0026thinsp;\u0026plusmn;\u0026thinsp;2.82\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e7.13\u0026thinsp;\u0026plusmn;\u0026thinsp;3.4\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eHDL-C(mmol/L)\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1.52\u0026thinsp;\u0026plusmn;\u0026thinsp;0.37\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1.32\u0026thinsp;\u0026plusmn;\u0026thinsp;0.3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e1.18\u0026thinsp;\u0026plusmn;\u0026thinsp;0.27\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.97\u0026thinsp;\u0026plusmn;\u0026thinsp;0.24\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eTG(mmol/L)\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1.08\u0026thinsp;\u0026plusmn;\u0026thinsp;0.43\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1.36\u0026thinsp;\u0026plusmn;\u0026thinsp;0.58\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e1.74\u0026thinsp;\u0026plusmn;\u0026thinsp;0.7\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e3.24\u0026thinsp;\u0026plusmn;\u0026thinsp;2.65\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eLDL-c(mmol/L)\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e3.19\u0026thinsp;\u0026plusmn;\u0026thinsp;1.02\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e3.08\u0026thinsp;\u0026plusmn;\u0026thinsp;0.98\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e3.23\u0026thinsp;\u0026plusmn;\u0026thinsp;1.0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e3.09\u0026thinsp;\u0026plusmn;\u0026thinsp;1.13\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eCHO(mmol/L)\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e4.73\u0026thinsp;\u0026plusmn;\u0026thinsp;1.08\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e4.7\u0026thinsp;\u0026plusmn;\u0026thinsp;1.11\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e4.94\u0026thinsp;\u0026plusmn;\u0026thinsp;1.17\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e5.16\u0026thinsp;\u0026plusmn;\u0026thinsp;1.38\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eCACS\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eNormal\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1498 (51.98)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1415 (49.11)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e1326 (46.03)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e1290 (44.76)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eMild\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e821 (28.49)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e782 (27.14)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e865 (30.02)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e880 (30.53)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eModerate\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e357 (12.39)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e419 (14.54)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e444 (15.41)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e461 (16.0)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eSevere\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e206 (7.15)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e265 (9.2)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e246 (8.54)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e251 (8.71)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003ctfoot\u003e\u003ctr\u003e\u003ctd colspan=\"6\"\u003eHbA1c: hemoglobin A1c; Glu: Glucose; HDL-c: high-density lipoprotein cholesterol; TG: triglyceride; LDL-c: low-density lipoprotein cholesterol; CHO: total cholesterol\u003c/td\u003e\u003c/tr\u003e\u003c/tfoot\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003eThe frequency of coronary artery calcification (CAC) occurrence across the entire participant group, as well as within both sexes, is visually depicted in Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e. A positive correlation was identified between CAC frequency and increasing age.\u003c/p\u003e\u003c/div\u003e\n\u003ch3\u003e3.2. Association of the RC-to-HDL-c Ratio with CACS in Conditional Logistic Regression Analysis\u003c/h3\u003e\n\u003cp\u003eConditional logistic regression analysis was used to assess the associations of HDL-c (Table\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e). Restricted cubic spline analysis demonstrated a graded positive association between both the RC/HDL-c and levels with CAC risk, as illustrated in Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e.\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab4\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 4\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eCorrelation analysis of HDL-c with CACS\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"5\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eModel 1\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003e\u003cem\u003ep\u003c/em\u003e_value\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003eModel 2\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u003cp\u003e\u003cem\u003ep\u003c/em\u003e_value\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eOR (X95.CI)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003eOR (X95.CI)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eHDL-c (Continuous)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.677(0.610\u0026ndash;0.752)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.764(0.668\u0026ndash;0.874)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.0001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eHDL-c (categorical)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;1.5\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e1.5-2.0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.829(0.753\u0026ndash;0.912)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.0001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.904(0.802\u0026ndash;1.017)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.0938\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u0026gt;\u0026thinsp;2.0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.697(0.563\u0026ndash;0.863)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.0009\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.785(0.594\u0026ndash;1.038)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.0895\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003eIn Model 1 (adjusted for age and sex), an increase in the RC/HDL-c ratio was significantly associated with an elevated risk of CACS (Table\u0026nbsp;\u003cspan refid=\"Tab5\" class=\"InternalRef\"\u003e5\u003c/span\u003e; OR\u0026thinsp;=\u0026thinsp;1.092; 95% CI, 1.048\u0026ndash;1.137; \u003cem\u003ep\u0026thinsp;\u0026lt;\u003c/em\u003e\u0026thinsp;0.001). Categorical analysis further revealed that Quartiles 2, 3, and 4 of the RC/HDL-c ratio demonstrated significant associations with higher CACS compared to Quartile 1 as the reference group (p for trend\u0026thinsp;\u0026lt;\u0026thinsp;0.001). In Model 2 (adjusted for age, sex, glucose, glycated hemoglobin, and triglycerides), this trend remained statistically significant for both Quartile 3 and Quartile 4 (\u003cem\u003ep\u0026thinsp;\u0026lt;\u003c/em\u003e\u0026thinsp;0.05).\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab5\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 5\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eAssociation between the RC level and the RC/HDL-c ratio and the risk of CACS\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"5\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eModel 1\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003e\u003cem\u003ep\u003c/em\u003e_value\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003eModel 2\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u003cp\u003e\u003cem\u003ep\u003c/em\u003e_value\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eOR (95%)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003eOR (95%)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eRC(Continuous)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1.179(1.107\u0026ndash;1.257)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e1.029(0.884\u0026ndash;1.197)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.7162\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eRC_Q1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eRC_Q2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1.09(0.986-1.1.205)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.0932\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e1.065(0.938\u0026ndash;1.208)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.3319\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eRC_Q3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1.162(1.058\u0026ndash;1.292)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.0022\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e1.096(0.966\u0026ndash;1.244)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.1536\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eRC_Q4\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1.241(1.123\u0026ndash;1.371)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e1.083(0.939\u0026ndash;1.249)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.274\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cem\u003ep\u003c/em\u003e for trend\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.216\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eRC/HDL-C(Continuous)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1.092(1.048\u0026ndash;1.137)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.979(0.902\u0026ndash;1.062)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.6058\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eRC/HDL-C_Q1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eRC/HDL-C_Q2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1.137(1.027\u0026ndash;1.258)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.0131\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e1.142(1.005\u0026ndash;1.297)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.0421\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eRC/HDL-C_Q3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1.210(1.094\u0026ndash;1.338)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.0002\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e1.158(1.02\u0026ndash;1.315)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.0238\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eRC/HDL-C_Q4\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1.336(1.208\u0026ndash;1.477)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e1.161(1.006\u0026ndash;1.34)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.0418\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cem\u003ep\u003c/em\u003e for trend\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.0381\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003eA statistically significant positive correlation between levels and CACS was identified in Model 1 (OR\u0026thinsp;=\u0026thinsp;1.179; 95% CI, 1.107\u0026ndash;1.257; \u003cem\u003ep\u0026thinsp;\u0026lt;\u003c/em\u003e\u0026thinsp;0.001), and trend analysis across RC quartiles also yielded statistically significant results. However, this association was not sustained as statistically significant in Model 2 (\u003cem\u003ep\u0026thinsp;=\u003c/em\u003e\u0026thinsp;0.716 and \u003cem\u003ep\u0026thinsp;=\u003c/em\u003e\u0026thinsp;0.216).\u003c/p\u003e\u003cp\u003eTo evaluate the incremental predictive utility of the RC/HDL-c beyond RC alone, net reclassification improvement (NRI) was calculated. The RC/HDL-c ratio facilitated the reclassification of 12.3% (95% CI: 9.8\u0026ndash;14.7%) of participants into elevated-risk CACS categories (NRI\u0026thinsp;=\u0026thinsp;0.123, \u003cem\u003ep\u0026thinsp;\u0026lt;\u003c/em\u003e\u0026thinsp;0.001), particularly in the subgroup of individuals with normal LDL-c levels (NRI\u0026thinsp;=\u0026thinsp;0.158, \u003cem\u003ep\u0026thinsp;\u0026lt;\u003c/em\u003e\u0026thinsp;0.001).\u003c/p\u003e\n\u003ch3\u003eSubgroup Analysis\u003c/h3\u003e\n\u003cp\u003eSubgroup analysis was conducted to explore the interrelationships between RC levels and the RC/HDL-c with CACS in participant subgroups categorized by LDL-c levels (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eFor participants with LDL-c\u0026thinsp;\u0026le;\u0026thinsp;3.37 mmol/L, both RC levels (Model 1: OR, 1.1542; 95% CI, 1.0745\u0026ndash;1.2398; \u003cem\u003ep\u0026thinsp;=\u003c/em\u003e\u0026thinsp;0.0001) and the RC to HDL-c ratio (RC/HDL-c) (Model 1: OR, 1.0776; 95% CI, 1.0335\u0026ndash;1.1235; \u003cem\u003ep\u0026thinsp;=\u003c/em\u003e\u0026thinsp;0.0005) demonstrated statistically significant associations with CACS (Table\u0026nbsp;\u003cspan refid=\"Tab6\" class=\"InternalRef\"\u003e6\u003c/span\u003e, Model 1 p for trend\u0026thinsp;\u0026lt;\u0026thinsp;0.001). These associations persisted after adjustments for additional covariates (Model 2 p for trend\u0026thinsp;\u0026lt;\u0026thinsp;0.05). Conversely, these associations were not statistically significant among participants with elevated LDL-c in Model 2 (Table\u0026nbsp;\u003cspan refid=\"Tab7\" class=\"InternalRef\"\u003e7\u003c/span\u003e).\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab6\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 6\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eAssociation of RC level and RC/HDL-c ratio with CACS risk for LDL-c\u0026thinsp;\u0026le;\u0026thinsp;3.37.\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"7\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e\u003cth align=\"left\" colspan=\"3\" nameend=\"c4\" namest=\"c2\"\u003e\u003cp\u003eModel1\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colspan=\"3\" nameend=\"c7\" namest=\"c5\"\u003e\u003cp\u003eModel2\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u003cb\u003eOR\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e\u003cb\u003eX95.CI\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e\u003cem\u003ep\u003c/em\u003e\u003cb\u003e_value\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e\u003cb\u003eOR\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e\u003cb\u003eX95.CI\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e\u003cem\u003ep\u003c/em\u003e\u003cb\u003e_value\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eRC(Continuous)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1.1542\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e[1.0745,1.2398]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.0001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e1.0061\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e[0.827,1.224]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.9516\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eRC_Q1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eRC_Q2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1.1122\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e[0.9788,1.2638]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.1027\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e1.0855\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e[0.9277,1.2701]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.3061\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eRC_Q3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1.1541\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e[1.0156,1.3116]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.0281\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e1.1114\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e[0.949,1.3016]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.1901\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eRC_Q4\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1.2347\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e[1.0865,1.4032]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.0012\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e1.0763\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e[0.8981,1.2899]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.4257\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cem\u003ep\u003c/em\u003e for trend\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.0012\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.344\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eRC/HDL-C(Continuous)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1.0776\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e[1.0335,1.1235]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.0005\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.9769\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e[0.8884,1.0742]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.6292\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eRC/HDL-C_Q1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eRC/HDL-C_Q2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1.1947\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e[1.0501,1.3592]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.0069\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e1.1794\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e[1.0064,1.3821]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.0414\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eRC/HDL-C_Q3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1.2175\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e[1.0708,1.3843]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.0027\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e1.1547\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e[0.9851,1.3535]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.076\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eRC/HDL-C_Q4\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1.3839\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e[1.2175,1.5731]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e1.2502\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e[1.0431,1.4985]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.0157\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cem\u003ep\u003c/em\u003e for trend\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.0253\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab7\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 7\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eAssociation of RC level and RC/HDL-c ratio with CACS risk for LDL-c\u0026thinsp;\u0026gt;\u0026thinsp;3.37.\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"7\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e\u003cth align=\"left\" colspan=\"3\" nameend=\"c4\" namest=\"c2\"\u003e\u003cp\u003eModel1\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colspan=\"3\" nameend=\"c7\" namest=\"c5\"\u003e\u003cp\u003eModel2\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u003cb\u003eOR\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e\u003cb\u003eX95.CI\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e\u003cem\u003ep\u003c/em\u003e\u003cb\u003e_value\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e\u003cb\u003eOR\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e\u003cb\u003eX95.CI\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e\u003cem\u003ep\u003c/em\u003e\u003cb\u003e_value\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eRC(Continuous)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1.2906\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e[1.1289,1.4756]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.0002\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e1.1384\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e[0.8914,1.4538]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.299\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eRC_Q1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eRC_Q2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1.1463\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e[0.9743,1.3487]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.0998\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e1.1287\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e[0.9115,1.3976]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.2669\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eRC_Q3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1.149\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e[0.976,1.3526]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.0954\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e1.0158\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e[0.8192,1.2597]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.8862\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eRC_Q4\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1.2775\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e[1.086,1.5028]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.0031\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e1.0628\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e[0.8365,1.3504]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.618\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cem\u003ep\u003c/em\u003e for trend\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.0049\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.862\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eRC/HDL-C(Continuous)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1.3014\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e[1.1381,1.4881]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.0001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e1.1371\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e[0.9206,1.4045]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.2332\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eRC/HDL-C_Q1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eRC/HDL-C_Q2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1.148\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e[0.9749,1.3519]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.0978\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e1.1693\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e[0.9417,1.4519]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.1568\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eRC/HDL-C_Q3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1.1972\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e[1.0168,1.4095]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.0307\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e1.1496\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e[0.9278,1.4243]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.2024\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eRC/HDL-C_Q4\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1.3133\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e[1.116,1.5454]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e1.0613\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e[0.8322,1.3535]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.6314\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cem\u003ep\u003c/em\u003e for trend\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.0011\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.632\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003eAdditionally, subgroup analysis was performed in male and female participants (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003e). In Model 1, both RC levels and the RC/HDL-c were significantly associated with CACS in both male (Table\u0026nbsp;\u003cspan refid=\"Tab8\" class=\"InternalRef\"\u003e8\u003c/span\u003e) and female (Table\u0026nbsp;\u003cspan refid=\"Tab9\" class=\"InternalRef\"\u003e9\u003c/span\u003e) subgroups. However, these associations were not sustained in Model 2 after adjustment for additional covariates.\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab8\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 8\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eAssociation of RC level and RC/HDL-c ratio with CACS in males.\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"7\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e\u003cth align=\"left\" colspan=\"6\" nameend=\"c7\" namest=\"c2\"\u003e\u003cp\u003eMale\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e\u003cth align=\"left\" colspan=\"3\" nameend=\"c4\" namest=\"c2\"\u003e\u003cp\u003eModel1\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colspan=\"3\" nameend=\"c7\" namest=\"c5\"\u003e\u003cp\u003eModel2\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u003cb\u003eOR\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e\u003cb\u003eX95.CI\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e\u003cem\u003ep\u003c/em\u003e\u003cb\u003e_value\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e\u003cb\u003eOR\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e\u003cb\u003eX95.CI\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e\u003cem\u003ep\u003c/em\u003e\u003cb\u003e_value\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eRC(Continuous)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1.1262\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e[1.042,1.2171]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.0027\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e1.0126\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e[0.8258,1.2415]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.9045\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eRC_Q1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eRC_Q2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1.1346\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e[0.9907,1.2994]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.0679\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e1.1261\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e[0.9489,1.3364]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.174\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eRC_Q3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1.1523\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e[1.0062,1.3196]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.0404\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e1.1321\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e[0.954,1.3434]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.1555\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eRC_Q4\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1.2003\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e[1.0472,1.3758]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.0087\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e1.0356\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e[0.8527,1.2578]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.724\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cem\u003ep\u003c/em\u003e for trend\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.0101\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.6051\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eRC/HDL-C(Continuous)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1.0587\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e[1.013,1.1064]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.0113\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.9964\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e[0.8974,1.1063]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.9457\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eRC/HDL-C_Q1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eRC/HDL-C_Q2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1.2419\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e[1.0841,1.4226]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.0018\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e1.2901\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e[1.0864,1.532]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.0037\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eRC/HDL-C_Q3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1.1563\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e[1.0088,1.3254]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.037\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e1.1938\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e[1.0046,1.4187]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.0443\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eRC/HDL-C_Q4\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1.3417\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e[1.1705,1.5379]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e1.2096\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e[0.9946,1.4709]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.0566\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cem\u003ep\u003c/em\u003e for trend\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.0002\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.0965\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab9\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 9\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eAssociation of RC level and RC/HDL-c ratio with CACS in females.\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"7\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e\u003cth align=\"left\" colspan=\"6\" nameend=\"c7\" namest=\"c2\"\u003e\u003cp\u003eFemale\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e\u003cth align=\"left\" colspan=\"3\" nameend=\"c4\" namest=\"c2\"\u003e\u003cp\u003eModel1\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colspan=\"3\" nameend=\"c7\" namest=\"c5\"\u003e\u003cp\u003eModel2\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u003cb\u003eOR\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e\u003cb\u003eX95.CI\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e\u003cem\u003ep\u003c/em\u003e\u003cb\u003e_value\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e\u003cb\u003eOR\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e\u003cb\u003eX95.CI\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e\u003cem\u003ep\u003c/em\u003e\u003cb\u003e_value\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eRC(Continuous)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1.2412\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e[1.1149,1.3819]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.0001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e1.0397\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e[0.8271,1.3069]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.7389\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eRC_Q1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eRC_Q2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1.0909\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e[0.9389,1.2676]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.2556\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e1.0486\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e[0.8681,1.2667]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.6222\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eRC_Q3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1.1389\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e[0.9792,1.3246]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.0914\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.9936\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e[0.8207,1.203]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.9474\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eRC_Q4\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1.2604\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e[1.0856,1.4633]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.0024\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e1.0963\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e[0.8812,1.3639]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.4094\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cem\u003ep\u003c/em\u003e for trend\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.0022\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.577\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eRC/HDL-C(Continuous)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1.1703\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e[1.0785,1.2699]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.0002\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.976\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e[0.8494,1.1216]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.7323\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eRC/HDL-C_Q1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eRC/HDL-C_Q2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1.1101\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e[0.9542,1.2914]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.1762\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e1.0571\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e[0.8734,1.2794]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.5688\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eRC/HDL-C_Q3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1.151\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e[0.9902,1.3379]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.0671\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.9965\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e[0.8232,1.2064]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.9715\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eRC/HDL-C_Q4\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1.316\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e[1.134,1.5273]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.0003\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e1.0525\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e[0.8438,1.3129]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.6499\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cem\u003ep\u003c/em\u003e for trend\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.0003\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.828\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e"},{"header":"Discussion","content":"\u003cp\u003eCVD remains the leading cause of mortality globally, with CACS serving as a salient predictor of CVD risk[\u003cspan additionalcitationids=\"CR23\" citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e]. HDL-c, recognized for its protective role in reducing coronary atherosclerosis, is widely utilized as an adjustment factor for TC, triglycerides, and non-HDL-c[\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e, \u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e]. However, to the best of our knowledge, limited research has explored the modification of these indices through the incorporation of HDL-c levels, and the relationship between the RC/HDL-c and CACS remains incompletely understood[\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e, \u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e]. Our findings contribute novel insights by highlighting the potential role of the RC/HDL-c as an emerging risk marker for CACS, which may enhance the precision of CVD risk assessment protocols. Notably, compared to RC alone, the RC/HDL-c ratio demonstrated a more robust and consistent association with CACS.\u003c/p\u003e\u003cp\u003eOur results position the RC/HDL-c ratio as a complementary marker to established lipid ratios such as TG/HDL-c or LDL-c/HDL-c. While the TG/HDL-c ratio has been extensively studied in atherosclerosis research[\u003cspan additionalcitationids=\"CR29\" citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e], the RC/HDL-c ratio uniquely integrates atherogenic RC with the protective properties of HDL-c. This dual consideration may explain its superior performance in individuals with controlled LDL-c, a population in whom residual risk often persists despite statin therapy.\u003c/p\u003e\u003cp\u003eContemporary genetic and interventional studies have challenged the simplistic \"good cholesterol\" paradigm of HDL-c, emphasizing its functional heterogeneity[\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e, \u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e]. While our study operationalizes HDL-c as a quantitative metric, future research should explore HDL particle subfractions (e.g., HDL2/HDL3) or cholesterol efflux capacity to better capture its role in modulating RC-driven atherogenesis.\u003c/p\u003e\u003cp\u003eThe RC/HDL-c ratio is particularly significant as it integrates both the atherogenic effects of RC and the protective effects of HDL-c. This dual consideration provides a more comprehensive evaluation of cardiovascular risk, especially in individuals with controlled LDL-c levels. While LDL-c has traditionally been the primary focus of lipid management, the RC/HDL-c ratio offers a distinct perspective by addressing residual risk factors that persist even after LDL-c normalization. This is particularly relevant in the context of statin therapy, where patients may still experience cardiovascular events despite achieving target LDL-c levels[\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e]. The RC/HDL-c ratio thus functions as a complementary marker, reflecting the balance between atherogenic and protective lipid components, and may identify individuals who could benefit from additional therapeutic interventions, such as fibrates or proprotein convertase subtilisin/kexin type 9 (PCSK9) inhibitors, to further reduce RC burden[\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eBoth RC and the RC/HDL-c ratio exhibited statistically significant positive correlations with CACS before adjustment for other covariates; however, this relationship lost statistical significance for RC after adjustment for glucose, glycated hemoglobin, and triglycerides. This observation may be attributed to the multifaceted protective effects of HDL-c on the cardiovascular system. One of the key functions of HDL-c is cholesterol efflux, which involves the transportation of cholesterol from peripheral tissues back to the liver for metabolic processing and excretion, thereby reducing cholesterol deposition in arterial walls[\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e, \u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e]. Additionally, HDL-c possesses anti-inflammatory and antioxidant properties, which can prevent the oxidation of LDL and the occurrence of inflammatory responses. These effects contribute to the attenuation of atherosclerosis development and the reduction of CVD risk[\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e, \u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e]. Consequently, the protective actions of HDL-c may mask the independent impact of RC levels on CVD risk, resulting in the loss of statistical significance for RC after adjustment for relevant factors.\u003c/p\u003e\u003cp\u003eMoreover, our study revealed that the association between the RC/HDL-c ratio and CACS is particularly pronounced among individuals with normal LDL levels, aligning with prior research emphasizing the importance of considering supplementary lipid metrics beyond LDL-c alone for a comprehensive evaluation of cardiovascular risk[\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e]. Our findings highlight the potential of the RC/HDL-c ratio as a complementary tool in CACS stratification, especially in populations with controlled LDL-c levels. This study also opens avenues for further investigation into the utility of this ratio in tailoring preventive strategies for individuals with varying lipid profiles.\u003c/p\u003e\u003cp\u003eThe results underscore the clinical significance of the RC/HDL-c ratio in CVD risk assessment. Compared to traditional lipid indicators, the RC/HDL-c ratio offers a more comprehensive cardiovascular risk assessment by concurrently considering the atherogenic effects of RC and the protective effects of HDL-c. This finding may influence CVD prevention strategies, particularly in individuals with elevated RC/HDL-c ratios, who require more vigilant monitoring and earlier intervention to prevent coronary artery calcification and subsequent cardiovascular events. The implementation of the RC/HDL-c ratio in clinical practice could facilitate more personalized and targeted therapeutic approaches, ultimately improving patient outcomes and reducing the burden of CACS.\u003c/p\u003e\u003cp\u003eOur study has several notable strengths. It pioneered the approach of adjusting RC with HDL-c levels and investigating the association between the RC/HDL-c ratio and CACS. Furthermore, our study benefited from a large sample size, enabling multiple subgroup analyses. However, limitations exist. First, we did not stratify CACS by single and multivessel involvement. Second, the analysis did not account for certain confounding factors, such as body mass index (BMI), smoking status, alcohol consumption, and statin use. Third, the cross-sectional design limited our ability to establish a causal relationship between the RC/HDL-c ratio and CACS; thus, longitudinal studies are warranted. Additionally, this study compared the RC/HDL-c ratio only with RC levels; further research is needed to compare this ratio with other modified indices. The generalizability of our results may be constrained by the demographic characteristics of our study population, and future studies should aim to replicate these findings in diverse and international cohorts. Notwithstanding these limitations, our study provides a robust foundation for future research and clinical applications.\u003c/p\u003e\u003cp\u003eFuture research should explore the applicability of the RC/HDL-c ratio across diverse populations, including individuals of different sexes, ages, ethnicities, and geographical origins. More longitudinal studies are needed to establish a causal link between the RC/HDL-c ratio and cardiovascular events and to evaluate the practical utility of this ratio in CVD prevention. Future studies should also investigate the interplay between the RC/HDL-c ratio and other CVD risk factors, such as inflammatory markers and components of metabolic syndrome, and how these factors collectively influence CVD risk.\u003c/p\u003e\u003cp\u003eIn conclusion, our study provides novel insights into the association between the RC/HDL-c ratio and CACS and underscores the importance of considering RC and HDL-c levels in CACS assessment. These findings lay the groundwork for future research aimed at refining and validating the RC/HDL-c ratio as a prognostic marker in diverse clinical settings and populations.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eConflict of interest\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare that they have no competing interests.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEthics approval and consent to participate\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe Institutional Review Board (IRB) of Qingyuan Hospital, Guangzhou Medical University granted ethical clearance\u0026nbsp;(Approval No. IRB-2024-032).\u0026nbsp;The study was conducted in accordance with the Declaration of Helsinki, and informed consent has been waived by the ethics committee of Qingyuan Hospital, Guangzhou Medical University due to the retrospective nature of the study. A scanned copy of the approval letter can be provided by the corresponding author upon request.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eClinical trial number\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;Not applicable.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis study was funded by the National Beijing Medical Award Foundation\u0026nbsp;(YXJL-2023--0227--0097),\u0026nbsp;the\u0026nbsp;Guangzhou Medical University Scientific Research Enhancement Program\u0026nbsp;(2023--00154), Guangdong Basic and Applied Basic Research Fund Enterprise Joint fund-surface project (2024A1515220139) and the\u0026nbsp;High-Level Talents Program of the Affiliated Qingyuan Hospital, Guangzhou Medical University\u0026nbsp;(2022--11038).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthor’s contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eCH and XG contributed to the design of the study. CH, ML and KW wrote the manuscript. XG, ML,\u0026nbsp;JL,\u0026nbsp;MP, HL and KW collected and/or analysed the data. All the authors have read and approved the final manuscript.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAcknowledgements\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;None.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eGolub IS, et al. Major Global Coronary Artery Calcium Guidelines. JACC Cardiovasc Imaging. 2023;16(1):98\u0026ndash;117.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eChen X et al. Relationship between Coronary Artery Calcium Score and Coronary Stenosis. Cardiol Res Pract, 2023. 2023: p. 5538111.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eKawaguchi YO et al. Current status and future perspective of coronary artery calcium score in asymptomatic individuals. J Cardiol, 2024.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eRadford NB, et al. 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J Am Coll Cardiol. 2022;79(24):2398\u0026ndash;400.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eDoi T, Langsted A, Nordestgaard BG. Elevated Remnant Cholesterol Reclassifies Risk of Ischemic Heart Disease and Myocardial Infarction. J Am Coll Cardiol. 2022;79(24):2383\u0026ndash;97.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003ePinto X, et al. Remnant cholesterol, vascular risk, and prevention of atherosclerosis. Clin Investig Arterioscler. 2023;35(4):206\u0026ndash;17.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003ePaunica I, et al. Comparative evaluation of LDL-CT, non-HDL/HDL ratio, and ApoB/ApoA1 in assessing CHD risk among patients with type 2 diabetes mellitus. J Diabetes Complications. 2023;37(12):108634.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eBlaha MJ et al. Coronary Artery Calcium Scoring: Is It Time for a Change in Methodology? JACC Cardiovasc Imaging, 2017. 10(8): pp. 923\u0026ndash;37.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eAgatston AS, et al. Quantification of coronary artery calcium using ultrafast computed tomography. J Am Coll Cardiol. 1990;15(4):827\u0026ndash;32.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eCzaja-Ziolkowska MZ, et al. An update on the coronary calcium score: a review for clinicians. Postepy Kardiol Interwencyjnej. 2022;18(3):201\u0026ndash;5.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eTsao CW, et al. Heart Disease and Stroke Statistics-2022 Update: A Report From the American Heart Association. Circulation. 2022;145(8):e153\u0026ndash;639.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eMensah GA, et al. Global Burden of Cardiovascular Diseases and Risks, 1990\u0026ndash;2022. J Am Coll Cardiol. 2023;82(25):2350\u0026ndash;473.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eChong B et al. Global burden of cardiovascular diseases: projections from 2025 to 2050. Eur J Prev Cardiol, 2024.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eSionis A, et al. Improving lipid management in patients with acute coronary syndrome: The ACS Lipid EuroPath tool. Atheroscler Suppl. 2020;42:e65\u0026ndash;71.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eKaminski M et al. Therapeutic inertia in lipid management among Polish adults with type 1 diabetes - results from the cross-sectional PARADISE T1DM study. Nutr Metab Cardiovasc Dis, 2025: p. 103853.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003ePan L, et al. Association between the remnant cholesterol to high-density lipoprotein cholesterol ratio and the risk of coronary artery disease. Coron Artery Dis. 2024;35(2):114\u0026ndash;21.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eRader DJ, Tall AR. The not-so-simple HDL story: Is it time to revise the HDL cholesterol hypothesis? Nat Med. 2012;18(9):1344\u0026ndash;6.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eZou Y, et al. Remnant cholesterol/high-density lipoprotein cholesterol ratio is a new powerful tool for identifying non-alcoholic fatty liver disease. BMC Gastroenterol. 2022;22(1):134.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eHuangfu G, et al. Triglyceride to High-Density Lipoprotein Cholesterol Ratio as a Marker of Subclinical Coronary Atherosclerosis and Hepatic Steatosis in Familial Hypercholesterolemia. Endocr Pract; 2025.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eFazio S, Pamir N. HDL Particle Size and Functional Heterogeneity. Circ Res. 2016;119(6):704\u0026ndash;7.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eKosmas CE, et al. High-density lipoprotein (HDL) functionality and its relevance to atherosclerotic cardiovascular disease. Drugs Context. 2018;7:212525.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eCannon CP, et al. Ezetimibe Added to Statin Therapy after Acute Coronary Syndromes. N Engl J Med. 2015;372(25):2387\u0026ndash;97.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eQuispe R, et al. Remnant cholesterol predicts cardiovascular disease beyond LDL and ApoB: a primary prevention study. Eur Heart J. 2021;42(42):4324\u0026ndash;32.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eKexin W, et al. Association of Increased Remnant Cholesterol and the Risk of Coronary Artery Disease: A Retrospective Study. Front Cardiovasc Med. 2021;8:740596.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eHao QY, et al. Remnant Cholesterol and the Risk of Coronary Artery Calcium Progression: Insights From the CARDIA and MESA Study. Circ Cardiovasc Imaging. 2022;15(7):e014116.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eLee DY, et al. Effects of Low-density Lipoprotein Cholesterol on Coronary Artery Calcification Progression According to High-density Lipoprotein Cholesterol Levels. Arch Med Res. 2017;48(3):284\u0026ndash;91.\u003c/span\u003e\u003c/li\u003e\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 calcium, Agatston score, HDL cholesterol, Remnant cholesterol","lastPublishedDoi":"10.21203/rs.3.rs-6914577/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-6914577/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eBackground\u003c/h2\u003e\u003cp\u003eWe investigated the associations among remnant cholesterol (RC) and high-density lipoprotein cholesterol (HDL-c) with coronary artery calcification (CACS), quantified by noncontrast computed tomography, aiming to determine whether the RC/HDL-c ratio serves as a more effective indicator of CACS severity compared to RC alone.\u003c/p\u003e\u003ch2\u003eMethods\u003c/h2\u003e\u003cp\u003eThis retrospective analysis included data from 11,526 participants, categorized into four groups based on their CACS Agatston scores. The relationships between RC levels and the RC/HDL-c ratio with CACS were assessed using cumulative logistic regression methods. Two statistical models were developed: Model 1 adjusted for age and sex, while Model 2 included additional adjustments for glucose, glycated hemoglobin, and triglyceride concentrations. Quartile analyses were performed for both RC and the RC/HDL-c ratio.\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e\u003cp\u003eElevated RC/HDL-c ratios were associated with a significant increase in CACS in Model 1 (OR\u0026thinsp;=\u0026thinsp;1.092; 95% CI, 1.048\u0026ndash;1.137; \u003cem\u003ep\u0026thinsp;\u0026lt;\u003c/em\u003e\u0026thinsp;0.001), and this association persisted in the upper two quartiles in Model 2 (\u003cem\u003ep\u0026thinsp;\u0026lt;\u003c/em\u003e\u0026thinsp;0.05). In contrast, while RC levels were significantly associated with CACS in Model 1, this association was attenuated and lost significance in Model 2 after further adjustments. Subgroup analyses revealed a particularly strong correlation between the RC/HDL-c ratio and CACS in participants with normal LDL-c levels.\u003c/p\u003e\u003ch2\u003eConclusion\u003c/h2\u003e\u003cp\u003eOur findings suggest that the RC/HDL-c ratio is a superior marker for CACS compared to RC alone. This novel approach refines CACS evaluation by integrating the pro-atherogenic properties of RC with the protective attributes of HDL-c.\u003c/p\u003e","manuscriptTitle":"The RC/HDL-c Ratio: A Superior Predictor of Coronary Artery Calcification Severity Compared to Remnant Cholesterol Alone","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-08-04 09:35:46","doi":"10.21203/rs.3.rs-6914577/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"editorInvitedReview","content":"","date":"2026-05-13T16:16:37+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"113281654101859871588423640399448284277","date":"2026-05-10T17:54:58+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-08-09T00:10:27+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"279340615243927736779687132003214140641","date":"2025-08-07T04:41:20+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2025-07-28T18:11:18+00:00","index":"","fulltext":""},{"type":"editorInvited","content":"","date":"2025-06-30T04:07:41+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2025-06-27T09:23:18+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2025-06-27T09:22:22+00:00","index":"","fulltext":""},{"type":"submitted","content":"BMC Cardiovascular Disorders","date":"2025-06-17T12:47:28+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":"b7d9b15f-7a91-4eb5-bd61-493f51580078","owner":[],"postedDate":"August 4th, 2025","published":true,"recentEditorialEvents":[{"type":"editorInvitedReview","content":"","date":"2026-05-13T16:16:37+00:00","index":84,"fulltext":""},{"type":"reviewerAgreed","content":"113281654101859871588423640399448284277","date":"2026-05-10T17:54:58+00:00","index":82,"fulltext":""}],"rejectedJournal":[],"revision":"","amendment":"","status":"under-review","subjectAreas":[],"tags":[],"updatedAt":"2025-08-04T09:35:46+00:00","versionOfRecord":[],"versionCreatedAt":"2025-08-04 09:35:46","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-6914577","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-6914577","identity":"rs-6914577","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}