Elevated Remnant Cholesterol Predicts Poor Outcome in Patients with Premature Acute Coronary Syndrome：A Retrospective, Single-Center Study

Elevated Remnant Cholesterol Predicts Poor Outcome in Patients with Premature Acute Coronary Syndrome：A Retrospective, Single-Center Study | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Elevated Remnant Cholesterol Predicts Poor Outcome in Patients with Premature Acute Coronary Syndrome：A Retrospective, Single-Center Study Ming Zhang, Jibin Chen, Wei Xia, Zhen Yu, Chunquan Zhang, Wenying Wang, and 2 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-6510079/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 17 Jul, 2025 Read the published version in Journal of Thrombosis and Thrombolysis → Version 1 posted You are reading this latest preprint version Abstract Background The association of remnant cholesterol (RC) with recurrent cardiovascular events following acute coronary syndrome (ACS) is well documented. However, no RC-stratified analysis focused on patients with premature ACS (PACS). Objectives This study sought to elucidate the clinical characteristics and subsequent cardiovascular events in high RC and low RC with PACS. Methods In this retrospective cohort study, we consecutively recruited 820 PACS patients between January 2019 and January 2020. RC was calculated as total cholesterol minus high-density lipoprotein cholesterol minus low-density lipoprotein cholesterol. RC ≥ 66.6 percentile of the cohort was defined as high RC. The primary endpoint was major adverse cardiovascular and cerebrovascular event (MACCE), including cardiovascular death, myocardial infarction (MI), stroke, ischemia-driven revascularization, or hospitalization for unstable angina or heart failure. Results Among 820 patients enrolled, 277 (33.8%) were high RC and 543 (66.2%) were low RC. High RC had higher prevalence of traditional risk factors including diabetes (33.6% vs 27.3%, p = 0.04), hypertension (68.2% vs 61.3%, p = 0.04), and hyperlipidemia (43.3% vs 31.3%, p = 0.001). The levels of glucose (p < 0.001), hemoglobin A1C (p = 0.005), triglyceride (p < 0.001), total cholesterol (p < 0.001) and LDL-C (p = 0.017) in high RC group were significantly higher than those in low RC group, while the levels of HDL-C (p = 0.001) were lower. During 3 years of follow-up, high RC, compared with low RC, have a significantly higher cumulative incidence of MACCE (16.2% vs 10.9%; adjusted HR 1.68, 95% CI 1.10–2.59; p = 0.02). The increased risk of MACCE in high RC was primarily attributable to significantly higher rates of hospitalization for unstable angina (12.3% vs 7.9%; adjusted HR 1.69, 95% CI 1.03–2.75; p = 0.03) and composite for cardiac events including cardiovascular death, MI, ischemia-driven revascularization or hospitalization for unstable angina or heart failure (14.8% vs 9.8%; adjusted HR 1.75, 95% CI 1.12–2.73; p = 0.01). Conclusions In hospitalized PACS patients, the cumulative incidence of MACCE in high RC patients was significantly higher than that in low RC patients during a median follow-up of nearly 3 years. The incremental risk in high RC might be explained by significantly higher rates of hospitalization for unstable angina and composite for cardiac events. Therefore, we should pay close attention to the indicator of RC and pursue further exploration. premature acute coronary syndrome elevated remnant cholesterol outcomes Figures Figure 1 Figure 2 1 Introduction Acute coronary syndrome (ACS), representing a spectrum of clinical manifestations ranging from unstable angina (UA) to myocardial infarction (MI), constitutes a major global health challenge [ 1 , 2 ] . The emerging phenomenon of ACS onset in younger populations, clinically defined as premature ACS (PACS) with age cutoffs varying between < 45–55 years depending on study populations, is particularly concerning due to its strong association with elevated risks of recurrent cardiovascular events and accelerated mortality [ 3 – 6 ] . The identification of reliable biomarkers capable of precisely stratifying prognostic outcomes in this vulnerable population is crucial for implementing targeted preventive measures and optimized therapeutic interventions. While low-density lipoprotein cholesterol (LDL-c) has long been considered the cornerstone of lipid management in ACS patients [ 7 – 9 ] , contemporary research reveals significant limitations in this conventional paradigm. Emerging data indicate that non-LDL lipid fractions - not adequately reflected in traditional lipid profiling-exert critical pathophysiological effects on the initiation and advancement of atherosclerotic lesions [ 10 ] . Notably, remnant cholesterol (RC) has garnered increasing attention as a clinically significant risk determinant for adverse cardiovascular outcomes [ 11 , 12 ] . This metabolically active fraction encompasses atherogenic lipoproteins including intermediate-density lipoproteins (IDL) and very-low-density lipoprotein (VLDL) remnants, which retain cholesterol-rich cores following triglyceride hydrolysis [ 13 ] . Accumulating evidence demonstrates remnant cholesterol (RC) exerts multifaceted atherogenic effects, being mechanistically implicated in endothelial dysfunction, oxidative stress-mediated inflammation, and accelerated progression of vulnerable atherosclerotic plaques [ 14 , 15 ] . Notably, elevated RC concentrations independently predict incident coronary heart disease, persisting as a residual cardiovascular risk factor even among normolipidemic individuals with optimal LDL-c control (LDL-c < 70 mg/dL) [ 10 , 16 ] . Importantly, the pathological lipid triad - characterized by concomitant dysregulation of triglyceride-rich lipoproteins, low HDL-c, and small dense LDL particles - is disproportionately prevalent in PACS populations, underscoring the imperative to delineate RC's specific contributions within this unique metabolic milieu. Therefore, based on our cohort of patients with PACS, our study aims to elucidate the clinical characteristics and subsequent cardiovascular events in patients with PACS who have high versus low levels of RC. 2 Methods 2.1 Study Design and Participants The project was a large-scale, retrospective cohort study to assess clinical characteristics and subsequent cardiovascular outcomes of hospitalized PACS patients in Qingdao Municipal Hospital, University of Health and Rehabilitation Sciences (Qingdao, China) between January 2019 and January 2020, with follow-up until December 2023. Patients with one of two forms of ACS, either ST-elevation myocardial infarction (STEMI) or non-ST-elevation ACS (NSTE-ACS), were included in this study. STEMI was diagnosed as symptoms characteristic of cardiac ischemia with persistent ST-segment elevation on electrocardiography. Patients with NSTE-ACS included those with non-ST-elevation myocardial infarction (NSTEMI) or unstable angina (UA). NSTEMI was diagnosed based on the presence of persistent ischemic symptoms with elevated cardiac troponin levels but no ST-segment elevation on electrocardiography. UA produced symptoms suggestive of cardiac ischemia without elevated cardiac troponin levels. According to the National Cholesterol Education Program Adult Treatment Panel III guidelines, PACS is defined as an initial disease onset occurring at ≤ 55 years of age in men or ≤ 65 years of age in women [ 17 ] . This study conformed to the Strengthening the Reporting of Observational Studies in Epidemiology guidelines [ 18 ] , and was conducted in accordance with the amended Declaration of Helsinki [ 19 ] . The protocol was approved by the ethics committee of Qingdao Municipal Hospital, University of Health and Rehabilitation Sciences (XS202311001). All patients provided written informed consent. 2.2 Procedures and management All patients received standard care during index ACS hospitalization included, the administration of aspirin, clopidogrel/ticagrelor, low molecular weight heparin, statins, isosorbide mononitrate tablets, angiotensin converting enzyme inhibitors/angiotensin II receptor blocker or metoprolol sustained-release tablets, depending on the heart rate and blood pressure of the individual patient, according to current guidelines [ 20 ] . Experienced senior interventional cardiologists performed the PCI procedures and visual estimation of lesion characteristics. Medical therapies for secondary prevention included dual Antiplatelet therapy (DAPT) at discharge, β-blockers at discharge, statins at discharge, angiotensin converting enzyme inhibitors/angiotensin receptor blockers at discharge, smoking cessation counseling, and cardiac rehabilitation counseling. 2.3 Follow-Up and End-points All patients were followed-up and Clinical events were collected via clinic visit, medical records or telephone calls by research staff who were blinded to the patients’ clinical characteristics. The primary end-point was major adverse cardiovascular and cerebrovascular events (MACCE), defined as a composite of cardiovascular death, MI, stroke, ischemia-driven revascularization or hospitalization for UA or heart failure. Secondary end-points included the individual components of the primary end-point, a composite of cardiovascular death, MI or ischemic stroke, a composite of cardiac event (cardiovascular death, MI, ischemia-driven revascularization or hospitalization for UA or heart failure), all-cause death and repeat revascularization. 2.4 Statistical analyses We used counts and percentages (%) to describe qualitative variables and mean ± SD or medians with interquartile ranges (IQRs) for continuous variables. Categorical variables were analyzed by χ2 or Fisher’s exact tests, as appropriate. Continuous variables, where appropriate, were compared by an unpaired t-test or Mann-Whitney U test. Kaplan-Meier survival analysis was performed by log-rank test. Hazard ratios (HR) and 95% confidence interval (95% CI) were calculated with the fully adjusted Cox proportional hazard regression models, which including age, body mass index, smoking, hypertension, diabetes mellitus, hyperlipidemia, prior MI, prior stroke, left ventricular ejection fraction, multi-vessel disease and clinical presentation (acute MI vs unstable angina). For patient who experienced more than one adverse outcome, only the first adverse event was considered for this analysis. All statistical analyses were calculated by SPSS V.26.0 (IBM SPSS, Armonk, New York, USA). A two-sided P < 0.05 was considered statistically significant. 3 Results 3.1 Baseline clinical and procedural characteristics Among 820 patients enrolled, 277 (33.8%) were high RC and 543 (66.2%) were low RC. High RC had higher prevalence of traditional risk factors including diabetes (33.6% vs 27.3%, p = 0.04), hypertension (68.2% vs 61.3%, p = 0.04), and hyperlipidemia (43.3% vs 31.3%, p = 0.001). The levels of glucose (p < 0.001), hemoglobin A1C (p = 0.005), triglyceride (p < 0.001), total cholesterol (p < 0.001) and LDL-C (p = 0.017) in high RC group were significantly higher than those in low RC group, while the levels of HDL-C (p = 0.001) were lower. There were no differences in clinical characteristics and discharge medications between the two groups (Table 2 ). Table 1 Baseline clinical characteristics by 2 groups Subjects All High RC Low RC p-value (n = 820) (n = 277) (n = 543) Demographics Age, years 49.5 ± 7.7 49.2 ± 8.2 49.6 ± 7.4 0.42 BMI, kg·m − 2 27.6 ± 3.8 28.0 ± 3.6 27.4 ± 3.9 0.005 Waist, cm 100 (94–106) 101 (96–107) 98 (92–105) 0.001 Waist-to-hip ratio 0.98 (0.94–1.01) 0.98 (0.95–1.02) 0.97 (0.94-1.00) 0.001 Systolic BP, mm Hg 125 (116–137) 127 (118–137) 125 (116–137) 0.42 Diastolic BP, mm Hg 78 (70–86) 79 (70–88) 77 (70–85) 0.10 Medical history Diabetes mellitus 241 (29.4) 93 (33.6) 148 (27.3) 0.04 Hypertension 522 (63.7) 189 (68.2) 333 (61.3) 0.04 Hyperlipidemia 290 (35.4) 120 (43.3) 170 (31.3) 0.001 Family history of PCAD 49 (6.0) 23 (8.3) 26 (4.8) 0.04 Prior myocardial infarction 121 (14.8) 41 (14.8) 80 (14.7) 0.97 Prior stroke 58 (7.1) 21 (7.6) 37 (6.8) 0.69 Previous PCI 161 (19.6) 52 (18.8) 109 (20.1) 0.66 Previous CABG 8 (1.0) 3 (1.1) 5 (0.9) 0.82 Smoking 0.57 Yes 405 (49.4) 133 (48.0) 272 (50.1) No 415 (50.6) 144 (52.0) 271 (49.9) Drinking 0.29 Yes 270 (32.9) 98 (35.4) 172 (31.7) No 550 (67.1) 179 (64.6) 371 (68.3) Baseline tests Glucose, mmol/L 5.87 (5.28–7.55) 6.13 (5.35–8.48) 5.76 (5.23–7.05) <0.001 Hemoglobin A1C, % 6.0 (5.5–6.9) 6.1 (5.6–7.4) 5.9 (5.5–6.7) 0.005 Triglyceride, mmol/L 1.57 (1.14–2.19) 2.45 (1.87–3.27) 1.33 (1.01–1.67) <0.001 Total Cholesterol, mmol/L 4.13 (3.45–4.91) 4.60 (3.55–5.42) 3.91 (3.26–4.57) <0.001 HDL-C, mmol/L 0.98 (0.86–1.15) 0.94 (0.83–1.10) 1.01 (0.87–1.17) 0.001 LDL-C, mmol/L 2.45 (1.91–3.17) 2.57 (2.00-3.33) 2.40 (1.83–3.07) 0.017 Hs-CRP, mmol/L 2.13 (0.8–6.5) 2.46 (1.05–6.28) 2.00 (0.69–6.60) 0.13 LVEF, % 62 (58–66) 62 (58–66) 62 (56–65) 0.13 Data are presented as n, mean ± SD, median (interquartile range) or n (%), unless otherwise stated. BMI: body mass index; BP: blood pressure; CABG: coronary artery bypass grafting; HDL-C: high density lipoprotein cholesterol; hs-CRP: high-sensitivity C-reactive protein; LDL-C: low-density lipoprotein cholesterol; LVEF: left ventricular ejection fraction; PCAD: premature coronary artery disease; PCI: percutaneous coronary intervention. Table 2 Clinical presentations and management by 2 groups Subjects All High RC Low RC p-value (n = 820) (n = 277) (n = 543) Diagnosis 0.05 STEMI 166 (20.2) 44 (15.9) 122 (22.5) NSTEMI 149 (18.2) 59 (21.3) 90 (16.6) Unstable angina 505 (61.6) 114 (62.8) 331 (61.0) Procedures Coronary angiography 797 (97.2) 268 (96.8) 529 (97.4) 0.58 LM 50 (6.1) 9 (5.6) 41 (6.2) 0.74 LAD 607 (74.0) 118 (73.3) 489 (74.2) 0.81 LCX 451 (55.0) 79 (49.1) 372 (56.4) 0.09 RCA 431 (52.6) 74 (46.0) 357 (54.2) 0.06 Number of vascular lesions 0.37 0 89 (10.9) 25 (9.0) 64 (11.8) 1 224 (27.3) 73 (26.4) 151 (27.8) ≥ 2 507 (61.8) 179 (64.6) 328 (60.4) PCI 516 (62.9) 182 (65.7) 334 (61.5) 0.24 CABG 38 (4.6) 11 (4.0) 27 (5.0) 0.52 Medications on discharge Aspirin 802 (97.8) 270 (97.5) 532 (98.0) 0.64 P 2 Y 12 inhibitors 746 (91.0) 249 (89.9) 497 (91.5) 0.44 β-blockers 656 (80.0) 224 (80.9) 432 (79.6) 0.66 ACEIs/ARBs 500 (61.0) 176 (63.5) 324 (59.7) 0.28 Statins 806 (98.3) 268 (96.8) 538 (99.1) 0.52 Data are presented as n, median (interquartile range), n (%) or n/N (%), unless otherwise stated. ACEI: angiotensin-converting enzymes inhibitor; ARB: angiotensin receptor blocker; CABG: coronary artery bypass grafting; LAD: left anterior descending artery; LCX: left circumflex artery; LM: left main artery; NSTEMI: non-ST-segment-elevation myocardial infarction; PCI: percutaneous coronary intervention; RCA: right coronary artery; STEMI: ST-segment-elevation myocardial infarction. Table 3 Clinical events up to 3 years by 2 groups Variables All High RC Low RC p-value (n = 820) (n = 277) (n = 543) MACCE 104 (12.7) 45 (16.2) 59 (10.9) 0.029 Cardiovascular death 5 (0.6) 3 (1.1) 2 (0.4) 0.21 Myocardial infarction 13 (1.6) 5 (1.8) 8 (1.5) 0.46 Stroke 12 (1.5) 4 (1.4) 8 (1.5) 0.62 Ischemia-driven revascularization 48 (5.9) 16 (5.8) 32 (5.9) 0.94 Hospitalization for unstable angina 77 (9.4) 34 (12.3) 43 (7.9) 0.031 Hospitalization for heart failure 2 (0.2) 1 (0.4) 1 (0.2) 0.63 Composite for cardiovascular death, MI, or ischemic stroke 30 (3.7) 12 (4.3) 18 (3.3) 0.46 Composite for cardiac events # 94 (11.5) 41 (14.8) 53 (9.8) 0.032 Allcause death 9 (1.1) 4 (1.4) 5 (0.9) 0.50 All repeat revascularization 81 (9.9) 31 (11.2) 50 (9.2) 0.37 MACCE: major adverse cardiovascular and cerebrovascular event; MI: myocardial infarction. #: includes cardiovascular death, myocardial infarction, ischemia-driven revascularization or hospitalization for unstable angina or heart failure. Table 4 Cox regression analyses evaluating the risk of cardiovascular events in the 2 groups Variables Unadjusted Fully adjusted HR (95% CI) p-value HR (95% CI) p-value MACCE 1.69 (1.14–2.49) 0.009 1.68 (1.10–2.59) 0.02 Cardiovascular death 3.30 (0.55–19.79) 0.19 5.77 (0.78–42.6) 0.09 Myocardial infarction 1.24 (0.40–3.81) 0.71 1.55 (0.43–5.63) 0.51 Stroke 1.06 (0.32–3.53) 0.92 0.51 (0.10–2.72) 0.43 Ischemia-driven revascularization 1.03 (0.56–1.87) 0.93 1.10 (0.57–2.10) 0.78 Hospitalization for unstable angina 1.72 (1.10–2.70) 0.02 1.69 (1.03–2.75) 0.03 Hospitalization for heart failure † 2.35 (0.15–37.57) 0.55 - - Composite for cardiovascular death, MI, or stroke 0.39 (0.67–2.89) 0.38 1.42 (0.60–3.37) 0.42 Composite for cardiac events # 1.71 (1.13–2.57) 0.01 1.75 (1.12–2.73) 0.01 Allcause death 1.74 (0.47–6.52) 0.41 4.32 (0.86–21.6) 0.08 All repeat revascularization 1.27 (0.81–1.99) 0.30 1.23 (0.75–2.02) 0.42 Data are presented as median (IQR). # : includes cardiovascular death, myocardial infarction, ischemia-driven revascularization or hospitalization for unstable angina or heart failure. Model adjusted for age, body mass index, smoking, hypertension, diabetes mellitus, hyperlipidemia, prior myocardial infarction, prior stroke, left ventricular ejection fraction, multi-vessel disease and clinical presentation (acute myocardial infarction vs unstable angina). † : Univariate and/or multivariate Cox regression was not done due to no or few number of events. CI: confidence interval; HR: hazard ratio; MACCE: major adverse cardiovascular and cerebrovascular event 3.2 Outcomes by RC During 3 years of follow-up, high RC, compared with low RC, have a significantly higher cumulative incidence of MACCE (16.2% vs 10.9%; adjusted HR 1.68, 95% CI 1.10–2.59; p = 0.02). The increased risk of MACCE in high RC was primarily attributable to significantly higher rates of hospitalization for UA (12.3% vs 7.9%; adjusted HR 1.69, 95% CI 1.03–2.75; p = 0.03) and composite for cardiac events including cardiovascular death, MI, ischemia-driven revascularization or hospitalization for UA or heart failure (14.8% vs 9.8%; adjusted HR 1.75, 95% CI 1.12–2.73; p = 0.01). 4 Discussion Our study provides novel insights into the prognostic implications of remnant cholesterol (RC) in patients with premature acute coronary syndrome (PACS), revealing three principal findings: First, elevated RC levels (≥ 66.6th percentile) were independently associated with a 68% increased risk of MACCE during medium-term follow-up. Second, this excess risk predominantly manifested as recurrent unstable angina requiring hospitalization and composite cardiac events. Third, the high-RC phenotype exhibited distinct metabolic derangements characterized by atherogenic dyslipidemia (elevated triglycerides, total cholesterol, and LDL-c with concomitant HDL-c depression), impaired glucose homeostasis, and clustering of traditional cardiovascular risk factors. The heightened MACCE risk aligns with RC's pathophysiological role in atherosclerosis progression. RC-rich lipoprotein remnants infiltrate arterial walls, undergoing oxidative modification to trigger endothelial dysfunction and foam cell formation through matrix metalloproteinase activation and fibrous cap destabilization [ 21 , 22 ] . This mechanism is particularly relevant in PACS populations where non-obstructive coronary disease and microvascular dysfunction prevail, as RC's small particle size (< 70 nm) facilitates endothelial penetration and promotes ischemia through both macrovascular and microvascular pathways [ 23 , 24 ] . The pro-inflammatory milieu in high-RC states, evidenced by elevated HbA1c and fasting glucose levels, may exacerbate endothelial progenitor cell dysfunction—a finding corroborated by studies linking RC to impaired vascular repair mechanisms in metabolic syndrome populations [ 22 , 24 ] . Notably, RC demonstrated prognostic discrimination beyond conventional lipid parameters, with 31.3% of low-RC patients meeting hyperlipidemia criteria yet exhibiting better outcomes. This residual risk phenomenon mirrors findings from large cohort studies where RC variability independently predicted metabolic dysfunction-associated steatoses liver disease (MASLD) and carotid intima-media thickening, even among normolipidemic individuals [ 21 , 25 ] . Our results extend recent Mendelian randomization evidence showing triglyceride-rich lipoproteins mediate cardiovascular risk independent of LDL-c, suggesting RC may serve as a superior therapeutic target in statin-treated PACS patients with residual dyslipidemia [ 22 ] . The strong RC-diabetes association (33.6% vs 27.3%, p = 0.04) unveils a bidirectional metabolic cycle: insulin resistance enhances hepatic VLDL secretion while impairing lipoprotein lipase activity, perpetuating RC accumulation - mechanism supported by NHANES data showing RC-HOMA-IR correlations in MAFLD patients (β = 0.17, p < 0.001 at RC < 30 mg/dL) [ 23 , 26 ] . This interplay may explain why high-RC patients exhibited poorer glycemic control despite comparable antidiabetic therapy utilization. Clinically, RC monitoring could identify candidates for intensified therapy, such as fibrates or omega-3 fatty acids, which reduce RC by 25–40% through enhanced lipoprotein lipase activity—a strategy supported by trial data showing RC-lowering interventions reduce recurrent ischemia in metabolic syndrome populations [ 22 , 24 ] . The discordance between LDL-C and RC risk profiles (high-RC/low-LDL-C group: OR 2.04 for hypertension) underscores the need to expand lipid management beyond LDL-centric paradigms [ 24 ] . Our findings align with UK Biobank evidence where RC ≥ 1.0 mmol/L increased IHD-T2D multimorbidity risk by 214% (HR 3.14), suggesting RC quantification could refine secondary prevention strategies in young ACS survivors [ 22 ] . Future trials should evaluate whether targeting RC thresholds (e.g., < 0.82 mmol/L as proposed in breast cancer cohorts) improves outcomes in this population [ 26 , 27 ] . Several limitations of our study need to be noted. First, the single-center retrospective design introduces potential selection bias, though mitigated by consecutive enrollment and standardized protocols. Second, RC calculation using the Fried Ewald formula may underestimate true levels compared with direct measurement. Third, dietary patterns and physical activity metrics were unavailable for metabolic syndrome characterization. Fourth, the observational nature precludes causal inference between RC and outcomes. Finally, all patients in the present study were Chinese, so the results should be interpreted and generalized to other ethnic groups with caution since dissimilar metabolic levels exist among different races. 5 Conclusions In hospitalized PACS patients, the cumulative incidence of MACCE in high RC patients was significantly higher than that in low RC patients during a median follow-up of nearly 3 years. The incremental risk in high RC might be explained by significantly higher rates of hospitalization for unstable angina and composite for cardiac events. Therefore, we should pay close attention to the indicator of RC and pursue further exploration. Declarations Author Contributors: Study concept and design: MZ, BW, WX. Acquisition, analysis, or interpretation of data: MZ, JC, YS, ZY. Drafting of the manuscript: MZ, JC. Critical revision of the manuscript for important intellectual content: All authors. Obtained funding: BW. Administrative, technical, or material support: BW, WX. BW, and YS had full access to all the data in the study and take responsibility for the integrity of the data and the accuracy of the data analysis. All authors read and approved the final manuscript. Funding: This study was funded by grants from Qingdao South District Science and Technology Plan Project (2023-2-07-YY), Qingdao Medical and Health Research Guidance Project (2023-WJZD174). Institutional Review Board Statement: This study conformed to the STROBE (Strengthening the Reporting of Observational studies in Epidemiology) guidelines and was conducted in accordance with the Declaration of Helsinki. The Ethics Committee of Qingdao Municipal Hospital, University of Health and Rehabilitation Sciences approved this study (2023-KY-074). Informed Consent Statement: Informed consent was obtained from all subjects involved in the study. Conflicts of Interest: The authors declare no conflict of interest References Byrne RA, Rossello X, Coughlan JJ, Barbato E, Berry C, Chieffo A et al (2023) 2023 ESC Guidelines for the management of acute coronary syndromes. Eur Heart J 44(38):3720–3826 Bergmark BA, Mathenge N, Merlini PA, Lawrence-Wright MB, Giugliano RP (2022) Acute coronary syndromes. 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JAMA 310(20):2191–2194 Ibanez B, James S, Agewall S, Antunes MJ, Bucciarelli-Ducci C, Bueno H et al (2018) 2017 ESC Guidelines for the management of acute myocardial infarction in patients presenting with ST-segment elevation: The Task Force for the management of acute myocardial infarction in patients presenting with ST-segment elevation of the European Society of Cardiology (ESC). Eur Heart J 39(2):119–177 Du X, Ding J, Ma X, Yang R, Wang L, Sha D (2023) Remnant cholesterol has an important impact on increased carotid intima-media thickness in non-diabetic individuals. Int J Cardiovasc Imaging 39(12):2487–2496 Zhao Y, Zhuang Z, Li Y, Xiao W, Song Z, Huang N et al (2024) Elevated blood remnant cholesterol and triglycerides are causally related to the risks of cardiometabolic multimorbidity. Nat Commun 15(1):2451 Wang S, Zhang Q, Qin B (2024) Association between remnant cholesterol and insulin resistance levels in patients with metabolic-associated fatty liver disease. Sci Rep 14(1):4596 Shi L, Zhang D, Ju J, Wang A, Du T, Chen X et al (2023) Remnant cholesterol associates with hypertension beyond low-density lipoprotein cholesterol among the general US adult population. Front Endocrinol (Lausanne) 14:1260764 Sun Y, Miao X, Hu M, Xie X, Liu S, Song Z et al (2025) Remnant cholesterol and its variability independent of low density lipoprotein cholesterol predict metabolic dysfunction associated steatotic liver disease. Sci Rep 15(1):4455 Hu Y, Wang X, Lin L, Huan J, Li Y, Zhang L et al (2023) Association of remnant cholesterol with frailty: findings from observational and Mendelian randomization analyses. Lipids Health Dis 22(1):115 Shi J, Liu T, Liu C, Zhang H, Ruan G, Xie H et al (2024) Remnant cholesterol is an effective biomarker for predicting survival in patients with breast cancer. Nutr J 23(1):45 Additional Declarations No competing interests reported. Cite Share Download PDF Status: Published Journal Publication published 17 Jul, 2025 Read the published version in Journal of Thrombosis and Thrombolysis → Version 1 posted 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. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. 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-6510079","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":450752943,"identity":"a857ce95-7e25-428e-ac79-ac0b9516e48f","order_by":0,"name":"Ming Zhang","email":"","orcid":"","institution":"Shandong Second Medical University","correspondingAuthor":false,"prefix":"","firstName":"Ming","middleName":"","lastName":"Zhang","suffix":""},{"id":450752944,"identity":"c757f097-e7fb-4fdb-99e8-cbdcd93ede10","order_by":1,"name":"Jibin Chen","email":"","orcid":"","institution":"University of Health and Rehabilitation Sciences","correspondingAuthor":false,"prefix":"","firstName":"Jibin","middleName":"","lastName":"Chen","suffix":""},{"id":450752946,"identity":"512bd635-19a9-4135-8afb-dac6c85e49a2","order_by":2,"name":"Wei Xia","email":"","orcid":"","institution":"University of Health and Rehabilitation Sciences","correspondingAuthor":false,"prefix":"","firstName":"Wei","middleName":"","lastName":"Xia","suffix":""},{"id":450752951,"identity":"e6bfa743-0a4d-461a-94da-947aacf13280","order_by":3,"name":"Zhen Yu","email":"","orcid":"","institution":"University of Health and Rehabilitation Sciences","correspondingAuthor":false,"prefix":"","firstName":"Zhen","middleName":"","lastName":"Yu","suffix":""},{"id":450752953,"identity":"943ad6aa-214f-455a-a09d-6c6f3d939225","order_by":4,"name":"Chunquan Zhang","email":"","orcid":"","institution":"University of Health and Rehabilitation Sciences","correspondingAuthor":false,"prefix":"","firstName":"Chunquan","middleName":"","lastName":"Zhang","suffix":""},{"id":450752957,"identity":"912beea4-af0f-4822-bf9a-c8fa0d8ecbe4","order_by":5,"name":"Wenying Wang","email":"","orcid":"","institution":"University of Health and Rehabilitation Sciences","correspondingAuthor":false,"prefix":"","firstName":"Wenying","middleName":"","lastName":"Wang","suffix":""},{"id":450752960,"identity":"3c5f9264-2ea7-4c01-8870-8b565229e4be","order_by":6,"name":"Yibing Shao","email":"","orcid":"","institution":"University of Health and Rehabilitation Sciences","correspondingAuthor":false,"prefix":"","firstName":"Yibing","middleName":"","lastName":"Shao","suffix":""},{"id":450752963,"identity":"0ac86cee-2292-4afd-a4a5-2a0be75ad40b","order_by":7,"name":"Bin Wang","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAAxUlEQVRIiWNgGAWjYDACCQYDMM3PfADMlSFSSwIDg2RbApjLQ7wWg2NgLQyEtRjcbt744OcPm3zjY8zHpG7UWPAwsB8+ugGvljvHig17EtIstx1jSzbOOQZ0GE9a2g18Wsxu5JhJMyQcNjC732P4OIcNqEWCx4w4LcZtPAaHc/6RosWAjcfwcW4bEVrswX5JSzOQAPklt0+Ch42QXyRnA0Psh42NAX8b8zHpnG91cvzsh4/h1YIJ2EhTPgpGwSgYBaMAGwAAu7tCV5xhVZUAAAAASUVORK5CYII=","orcid":"","institution":"University of Health and Rehabilitation Sciences","correspondingAuthor":true,"prefix":"","firstName":"Bin","middleName":"","lastName":"Wang","suffix":""}],"badges":[],"createdAt":"2025-04-23 07:38:48","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-6510079/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-6510079/v1","draftVersion":[],"editorialEvents":[{"content":"https://doi.org/10.1007/s11239-025-03147-6","type":"published","date":"2025-07-17T15:57:20+00:00"}],"editorialNote":"","failedWorkflow":false,"files":[{"id":82150879,"identity":"33df9aa3-0d30-4ca5-b5a4-0dc40ea30bc5","added_by":"auto","created_at":"2025-05-07 07:18:40","extension":"jpeg","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":300852,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eStudy Flowchart\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eACS: acute coronary syndrome, MACCE: major adverse cardiovascular and cerebrovascular event.\u003c/p\u003e","description":"","filename":"floatimage1.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-6510079/v1/1f905bdd54266cfc9454bdc9.jpeg"},{"id":82150874,"identity":"97830247-2cc2-45cb-9cca-2d0fe084baeb","added_by":"auto","created_at":"2025-05-07 07:18:40","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":95893,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eCumulative Incidence of MACCE, Composite for cardiovascular death, MI, or stroke, and Composite for cardiac events by 2 groups.\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eKaplan-Meier estimates and fully-adjusted HR for cumulative incidence of MACCE(A), Composite for cardiovascular death, MI, or stroke(B), and Composite for cardiac events (includes cardiovascular death, MI, ischemia-driven revascularization or hospitalization for unstable angina or heart failure, C) high RC and Low RC. HR: hazard ratio; MACCE: major adverse cardiovascular and cerebrovascular event; MI: myocardial infarction.\u003c/p\u003e","description":"","filename":"floatimage2.png","url":"https://assets-eu.researchsquare.com/files/rs-6510079/v1/e949e4af5810c38a3ebf4ea1.png"},{"id":88506172,"identity":"eec84a18-d3d3-4d46-a400-a35466eb487a","added_by":"auto","created_at":"2025-08-07 07:32:10","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1491215,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-6510079/v1/f30c6088-ff2e-4df6-973f-c70e48e0131e.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Elevated Remnant Cholesterol Predicts Poor Outcome in Patients with Premature Acute Coronary Syndrome：A Retrospective, Single-Center Study","fulltext":[{"header":"1 Introduction","content":"\u003cp\u003eAcute coronary syndrome (ACS), representing a spectrum of clinical manifestations ranging from unstable angina (UA) to myocardial infarction (MI), constitutes a major global health challenge\u003csup\u003e[\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e, \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e]\u003c/sup\u003e. The emerging phenomenon of ACS onset in younger populations, clinically defined as premature ACS (PACS) with age cutoffs varying between \u0026lt;\u0026thinsp;45\u0026ndash;55 years depending on study populations, is particularly concerning due to its strong association with elevated risks of recurrent cardiovascular events and accelerated mortality\u003csup\u003e[\u003cspan additionalcitationids=\"CR4 CR5\" citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e]\u003c/sup\u003e. The identification of reliable biomarkers capable of precisely stratifying prognostic outcomes in this vulnerable population is crucial for implementing targeted preventive measures and optimized therapeutic interventions.\u003c/p\u003e \u003cp\u003eWhile low-density lipoprotein cholesterol (LDL-c) has long been considered the cornerstone of lipid management in ACS patients\u003csup\u003e[\u003cspan additionalcitationids=\"CR8\" citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e]\u003c/sup\u003e, contemporary research reveals significant limitations in this conventional paradigm. Emerging data indicate that non-LDL lipid fractions - not adequately reflected in traditional lipid profiling-exert critical pathophysiological effects on the initiation and advancement of atherosclerotic lesions\u003csup\u003e[\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e]\u003c/sup\u003e. Notably, remnant cholesterol (RC) has garnered increasing attention as a clinically significant risk determinant for adverse cardiovascular outcomes\u003csup\u003e[\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e, \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e]\u003c/sup\u003e. This metabolically active fraction encompasses atherogenic lipoproteins including intermediate-density lipoproteins (IDL) and very-low-density lipoprotein (VLDL) remnants, which retain cholesterol-rich cores following triglyceride hydrolysis\u003csup\u003e[\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e]\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eAccumulating evidence demonstrates remnant cholesterol (RC) exerts multifaceted atherogenic effects, being mechanistically implicated in endothelial dysfunction, oxidative stress-mediated inflammation, and accelerated progression of vulnerable atherosclerotic plaques\u003csup\u003e[\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e, \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e]\u003c/sup\u003e. Notably, elevated RC concentrations independently predict incident coronary heart disease, persisting as a residual cardiovascular risk factor even among normolipidemic individuals with optimal LDL-c control (LDL-c\u0026thinsp;\u0026lt;\u0026thinsp;70 mg/dL)\u003csup\u003e[\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e, \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e]\u003c/sup\u003e. Importantly, the pathological lipid triad - characterized by concomitant dysregulation of triglyceride-rich lipoproteins, low HDL-c, and small dense LDL particles - is disproportionately prevalent in PACS populations, underscoring the imperative to delineate RC's specific contributions within this unique metabolic milieu. Therefore, based on our cohort of patients with PACS, our study aims to elucidate the clinical characteristics and subsequent cardiovascular events in patients with PACS who have high versus low levels of RC.\u003c/p\u003e"},{"header":"2 Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003e2.1 Study Design and Participants\u003c/h2\u003e \u003cp\u003eThe project was a large-scale, retrospective cohort study to assess clinical characteristics and subsequent cardiovascular outcomes of hospitalized PACS patients in Qingdao Municipal Hospital, University of Health and Rehabilitation Sciences (Qingdao, China) between January 2019 and January 2020, with follow-up until December 2023.\u003c/p\u003e \u003cp\u003ePatients with one of two forms of ACS, either ST-elevation myocardial infarction (STEMI) or non-ST-elevation ACS (NSTE-ACS), were included in this study. STEMI was diagnosed as symptoms characteristic of cardiac ischemia with persistent ST-segment elevation on electrocardiography. Patients with NSTE-ACS included those with non-ST-elevation myocardial infarction (NSTEMI) or unstable angina (UA). NSTEMI was diagnosed based on the presence of persistent ischemic symptoms with elevated cardiac troponin levels but no ST-segment elevation on electrocardiography. UA produced symptoms suggestive of cardiac ischemia without elevated cardiac troponin levels. According to the National Cholesterol Education Program Adult Treatment Panel III guidelines, PACS is defined as an initial disease onset occurring at \u0026le;\u0026thinsp;55 years of age in men or \u0026le;\u0026thinsp;65 years of age in women\u003csup\u003e[\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e]\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eThis study conformed to the Strengthening the Reporting of Observational Studies in Epidemiology guidelines\u003csup\u003e[\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e]\u003c/sup\u003e, and was conducted in accordance with the amended Declaration of Helsinki\u003csup\u003e[\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e]\u003c/sup\u003e. The protocol was approved by the ethics committee of Qingdao Municipal Hospital, University of Health and Rehabilitation Sciences (XS202311001). All patients provided written informed consent.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003e2.2 Procedures and management\u003c/h2\u003e \u003cp\u003eAll patients received standard care during index ACS hospitalization included, the administration of aspirin, clopidogrel/ticagrelor, low molecular weight heparin, statins, isosorbide mononitrate tablets, angiotensin converting enzyme inhibitors/angiotensin II receptor blocker or metoprolol sustained-release tablets, depending on the heart rate and blood pressure of the individual patient, according to current guidelines\u003csup\u003e[\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e]\u003c/sup\u003e. Experienced senior interventional cardiologists performed the PCI procedures and visual estimation of lesion characteristics. Medical therapies for secondary prevention included dual Antiplatelet therapy (DAPT) at discharge, β-blockers at discharge, statins at discharge, angiotensin converting enzyme inhibitors/angiotensin receptor blockers at discharge, smoking cessation counseling, and cardiac rehabilitation counseling.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec5\" class=\"Section2\"\u003e \u003ch2\u003e2.3 Follow-Up and End-points\u003c/h2\u003e \u003cp\u003eAll patients were followed-up and Clinical events were collected via clinic visit, medical records or telephone calls by research staff who were blinded to the patients\u0026rsquo; clinical characteristics.\u003c/p\u003e \u003cp\u003eThe primary end-point was major adverse cardiovascular and cerebrovascular events (MACCE), defined as a composite of cardiovascular death, MI, stroke, ischemia-driven revascularization or hospitalization for UA or heart failure. Secondary end-points included the individual components of the primary end-point, a composite of cardiovascular death, MI or ischemic stroke, a composite of cardiac event (cardiovascular death, MI, ischemia-driven revascularization or hospitalization for UA or heart failure), all-cause death and repeat revascularization.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec6\" class=\"Section2\"\u003e \u003ch2\u003e2.4 Statistical analyses\u003c/h2\u003e \u003cp\u003eWe used counts and percentages (%) to describe qualitative variables and mean\u0026thinsp;\u0026plusmn;\u0026thinsp;SD or medians with interquartile ranges (IQRs) for continuous variables. Categorical variables were analyzed by χ2 or Fisher\u0026rsquo;s exact tests, as appropriate. Continuous variables, where appropriate, were compared by an unpaired t-test or Mann-Whitney U test. Kaplan-Meier survival analysis was performed by log-rank test. Hazard ratios (HR) and 95% confidence interval (95% CI) were calculated with the fully adjusted Cox proportional hazard regression models, which including age, body mass index, smoking, hypertension, diabetes mellitus, hyperlipidemia, prior MI, prior stroke, left ventricular ejection fraction, multi-vessel disease and clinical presentation (acute MI vs unstable angina). For patient who experienced more than one adverse outcome, only the first adverse event was considered for this analysis. All statistical analyses were calculated by SPSS V.26.0 (IBM SPSS, Armonk, New York, USA). A two-sided P\u0026thinsp;\u0026lt;\u0026thinsp;0.05 was considered statistically significant.\u003c/p\u003e \u003c/div\u003e"},{"header":"3 Results","content":"\u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003e3.1 Baseline clinical and procedural characteristics\u003c/h2\u003e \u003cp\u003eAmong 820 patients enrolled, 277 (33.8%) were high RC and 543 (66.2%) were low RC. High RC had higher prevalence of traditional risk factors including diabetes (33.6% vs 27.3%, p\u0026thinsp;=\u0026thinsp;0.04), hypertension (68.2% vs 61.3%, p\u0026thinsp;=\u0026thinsp;0.04), and hyperlipidemia (43.3% vs 31.3%, p\u0026thinsp;=\u0026thinsp;0.001). The levels of glucose (p\u0026thinsp;\u0026lt;\u0026thinsp;0.001), hemoglobin A1C (p\u0026thinsp;=\u0026thinsp;0.005), triglyceride (p\u0026thinsp;\u0026lt;\u0026thinsp;0.001), total cholesterol (p\u0026thinsp;\u0026lt;\u0026thinsp;0.001) and LDL-C (p\u0026thinsp;=\u0026thinsp;0.017) in high RC group were significantly higher than those in low RC group, while the levels of HDL-C (p\u0026thinsp;=\u0026thinsp;0.001) were lower. There were no differences in clinical characteristics and discharge medications between the two groups (Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eBaseline clinical characteristics by 2 groups\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 \u003cp\u003eSubjects\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eAll\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eHigh RC\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eLow RC\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003ep-value\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e(n\u0026thinsp;=\u0026thinsp;820)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e(n\u0026thinsp;=\u0026thinsp;277)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e(n\u0026thinsp;=\u0026thinsp;543)\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\u003e\u003cb\u003eDemographics\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 \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAge, years\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e49.5\u0026thinsp;\u0026plusmn;\u0026thinsp;7.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e49.2\u0026thinsp;\u0026plusmn;\u0026thinsp;8.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e49.6\u0026thinsp;\u0026plusmn;\u0026thinsp;7.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.42\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBMI, kg\u0026middot;m\u003csup\u003e\u0026minus;\u0026thinsp;2\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e27.6\u0026thinsp;\u0026plusmn;\u0026thinsp;3.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e28.0\u0026thinsp;\u0026plusmn;\u0026thinsp;3.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e27.4\u0026thinsp;\u0026plusmn;\u0026thinsp;3.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.005\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eWaist, cm\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e100 (94\u0026ndash;106)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e101 (96\u0026ndash;107)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e98 (92\u0026ndash;105)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eWaist-to-hip ratio\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.98 (0.94\u0026ndash;1.01)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.98 (0.95\u0026ndash;1.02)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.97 (0.94-1.00)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSystolic BP, mm Hg\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e125 (116\u0026ndash;137)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e127 (118\u0026ndash;137)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e125 (116\u0026ndash;137)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.42\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDiastolic BP, mm Hg\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e78 (70\u0026ndash;86)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e79 (70\u0026ndash;88)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e77 (70\u0026ndash;85)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.10\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eMedical history\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 \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDiabetes mellitus\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e241 (29.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e93 (33.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e148 (27.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.04\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHypertension\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e522 (63.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e189 (68.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e333 (61.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.04\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHyperlipidemia\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e290 (35.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e120 (43.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e170 (31.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFamily history of PCAD\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e49 (6.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e23 (8.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e26 (4.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.04\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePrior myocardial infarction\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e121 (14.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e41 (14.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e80 (14.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.97\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePrior stroke\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e58 (7.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e21 (7.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e37 (6.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.69\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePrevious PCI\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e161 (19.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e52 (18.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e109 (20.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.66\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePrevious CABG\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e8 (1.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e3 (1.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e5 (0.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.82\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSmoking\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"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=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.57\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e405 (49.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e133 (48.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e272 (50.1)\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\u003eNo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e415 (50.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e144 (52.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e271 (49.9)\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\u003eDrinking\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=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.29\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e270 (32.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e98 (35.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e172 (31.7)\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\u003eNo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e550 (67.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e179 (64.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e371 (68.3)\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\u003e\u003cb\u003eBaseline tests\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 \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGlucose, mmol/L\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e5.87 (5.28\u0026ndash;7.55)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e6.13 (5.35\u0026ndash;8.48)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e5.76 (5.23\u0026ndash;7.05)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHemoglobin A1C, %\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e6.0 (5.5\u0026ndash;6.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e6.1 (5.6\u0026ndash;7.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e5.9 (5.5\u0026ndash;6.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.005\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTriglyceride, mmol/L\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.57 (1.14\u0026ndash;2.19)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2.45 (1.87\u0026ndash;3.27)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.33 (1.01\u0026ndash;1.67)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTotal Cholesterol, mmol/L\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e4.13 (3.45\u0026ndash;4.91)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e4.60 (3.55\u0026ndash;5.42)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e3.91 (3.26\u0026ndash;4.57)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHDL-C, mmol/L\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.98 (0.86\u0026ndash;1.15)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.94 (0.83\u0026ndash;1.10)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.01 (0.87\u0026ndash;1.17)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLDL-C, mmol/L\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2.45 (1.91\u0026ndash;3.17)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2.57 (2.00-3.33)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2.40 (1.83\u0026ndash;3.07)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.017\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHs-CRP, mmol/L\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2.13 (0.8\u0026ndash;6.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2.46 (1.05\u0026ndash;6.28)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2.00 (0.69\u0026ndash;6.60)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.13\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLVEF, %\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e62 (58\u0026ndash;66)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e62 (58\u0026ndash;66)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e62 (56\u0026ndash;65)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.13\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"5\"\u003eData are presented as n, mean\u0026thinsp;\u0026plusmn;\u0026thinsp;SD, median (interquartile range) or n (%), unless otherwise stated. BMI: body mass index; BP: blood pressure; CABG: coronary artery bypass grafting; HDL-C: high density lipoprotein cholesterol; hs-CRP: high-sensitivity C-reactive protein; LDL-C: low-density lipoprotein cholesterol; LVEF: left ventricular ejection fraction; PCAD: premature coronary artery disease; PCI: percutaneous coronary intervention.\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=\"Tab2\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eClinical presentations and management by 2 groups\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 \u003cp\u003eSubjects\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eAll\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eHigh RC\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eLow RC\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003ep-value\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e(n\u0026thinsp;=\u0026thinsp;820)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e(n\u0026thinsp;=\u0026thinsp;277)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e(n\u0026thinsp;=\u0026thinsp;543)\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\u003e\u003cb\u003eDiagnosis\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=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.05\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSTEMI\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e166 (20.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e44 (15.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e122 (22.5)\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\u003eNSTEMI\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e149 (18.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e59 (21.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e90 (16.6)\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\u003eUnstable angina\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e505 (61.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e114 (62.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e331 (61.0)\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\u003e\u003cb\u003eProcedures\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 \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCoronary angiography\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e797 (97.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e268 (96.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e529 (97.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.58\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLM\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e50 (6.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e9 (5.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e41 (6.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.74\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLAD\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e607 (74.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e118 (73.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e489 (74.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.81\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLCX\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e451 (55.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e79 (49.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e372 (56.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.09\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRCA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e431 (52.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e74 (46.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e357 (54.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.06\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNumber of vascular lesions\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=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.37\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e89 (10.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e25 (9.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e64 (11.8)\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\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e224 (27.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e73 (26.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e151 (27.8)\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\u003e\u0026ge;\u0026thinsp;2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e507 (61.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e179 (64.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e328 (60.4)\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\u003ePCI\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e516 (62.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e182 (65.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e334 (61.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.24\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCABG\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e38 (4.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e11 (4.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e27 (5.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.52\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eMedications on discharge\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 \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAspirin\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e802 (97.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e270 (97.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e532 (98.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.64\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eP\u003csub\u003e2\u003c/sub\u003eY\u003csub\u003e12\u003c/sub\u003e inhibitors\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e746 (91.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e249 (89.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e497 (91.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.44\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eβ-blockers\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e656 (80.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e224 (80.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e432 (79.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.66\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eACEIs/ARBs\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e500 (61.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e176 (63.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e324 (59.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.28\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eStatins\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e806 (98.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e268 (96.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e538 (99.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.52\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"5\"\u003eData are presented as n, median (interquartile range), n (%) or n/N (%), unless otherwise stated.\u003c/td\u003e\u003c/tr\u003e \u003ctr\u003e\u003ctd colspan=\"5\"\u003eACEI: angiotensin-converting enzymes inhibitor; ARB: angiotensin receptor blocker; CABG: coronary artery bypass grafting; LAD: left anterior descending artery; LCX: left circumflex artery;\u003c/td\u003e\u003c/tr\u003e \u003ctr\u003e\u003ctd colspan=\"5\"\u003eLM: left main artery; NSTEMI: non-ST-segment-elevation myocardial infarction; PCI: percutaneous coronary intervention; RCA: right coronary artery; STEMI: ST-segment-elevation myocardial infarction.\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\u003eClinical events up to 3 years by 2 groups\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 \u003cp\u003eVariables\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eAll\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eHigh RC\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eLow RC\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003ep-value\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e(n\u0026thinsp;=\u0026thinsp;820)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e(n\u0026thinsp;=\u0026thinsp;277)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e(n\u0026thinsp;=\u0026thinsp;543)\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\u003e\u003cb\u003eMACCE\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e104 (12.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e45 (16.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e59 (10.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.029\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCardiovascular death\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e5 (0.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e3 (1.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2 (0.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.21\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMyocardial infarction\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e13 (1.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e5 (1.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e8 (1.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.46\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eStroke\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e12 (1.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e4 (1.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e8 (1.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.62\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eIschemia-driven revascularization\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e48 (5.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e16 (5.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e32 (5.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.94\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHospitalization for unstable angina\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e77 (9.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e34 (12.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e43 (7.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.031\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHospitalization for heart failure\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2 (0.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1 (0.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1 (0.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.63\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eComposite for cardiovascular death, MI, or ischemic stroke\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e30 (3.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e12 (4.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e18 (3.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.46\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eComposite for cardiac events\u003c/b\u003e\u003csup\u003e\u003cb\u003e#\u003c/b\u003e\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e94 (11.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e41 (14.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e53 (9.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.032\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eAllcause death\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e9 (1.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e4 (1.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e5 (0.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.50\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eAll repeat revascularization\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e81 (9.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e31 (11.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e50 (9.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.37\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"5\"\u003eMACCE: major adverse cardiovascular and cerebrovascular event; MI: myocardial infarction.\u003c/td\u003e\u003c/tr\u003e \u003ctr\u003e\u003ctd colspan=\"5\"\u003e#: includes cardiovascular death, myocardial infarction, ischemia-driven revascularization or hospitalization for unstable angina or heart failure.\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=\"Tab4\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 4\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eCox regression analyses evaluating the risk of cardiovascular events in the 2 groups\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"5\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eVariables\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003eUnadjusted\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e \u003cp\u003eFully adjusted\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\u003eHR (95% CI)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003ep-value\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eHR (95% CI)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003ep-value\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eMACCE\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.69 (1.14\u0026ndash;2.49)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.009\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.68 (1.10\u0026ndash;2.59)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.02\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCardiovascular death\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e3.30 (0.55\u0026ndash;19.79)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.19\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e5.77 (0.78\u0026ndash;42.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.09\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMyocardial infarction\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.24 (0.40\u0026ndash;3.81)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.71\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.55 (0.43\u0026ndash;5.63)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.51\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eStroke\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.06 (0.32\u0026ndash;3.53)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.92\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.51 (0.10\u0026ndash;2.72)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.43\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eIschemia-driven revascularization\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.03 (0.56\u0026ndash;1.87)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.93\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.10 (0.57\u0026ndash;2.10)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.78\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHospitalization for unstable angina\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.72 (1.10\u0026ndash;2.70)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.02\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.69 (1.03\u0026ndash;2.75)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.03\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHospitalization for heart failure\u003csup\u003e\u003cb\u003e\u0026dagger;\u003c/b\u003e\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e2.35 (0.15\u0026ndash;37.57)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.55\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eComposite for cardiovascular death, MI, or stroke\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.39 (0.67\u0026ndash;2.89)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.38\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.42 (0.60\u0026ndash;3.37)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.42\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eComposite for cardiac events\u003c/b\u003e\u003csup\u003e\u003cb\u003e#\u003c/b\u003e\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.71 (1.13\u0026ndash;2.57)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.01\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.75 (1.12\u0026ndash;2.73)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.01\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eAllcause death\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.74 (0.47\u0026ndash;6.52)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.41\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e4.32 (0.86\u0026ndash;21.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.08\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eAll repeat revascularization\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.27 (0.81\u0026ndash;1.99)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.30\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.23 (0.75\u0026ndash;2.02)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.42\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"5\"\u003eData are presented as median (IQR). \u003csup\u003e#\u003c/sup\u003e: includes cardiovascular death, myocardial infarction, ischemia-driven revascularization or hospitalization for unstable angina or heart failure. Model adjusted for age, body mass index, smoking, hypertension, diabetes mellitus, hyperlipidemia, prior myocardial infarction, prior stroke, left ventricular ejection fraction, multi-vessel disease and clinical presentation (acute myocardial infarction vs unstable angina). \u003csup\u003e\u003cb\u003e\u0026dagger;\u003c/b\u003e\u003c/sup\u003e: Univariate and/or multivariate Cox regression was not done due to no or few number of events. CI: confidence interval; HR: hazard ratio; MACCE: major adverse cardiovascular and cerebrovascular event\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec9\" class=\"Section2\"\u003e \u003ch2\u003e3.2 Outcomes by RC\u003c/h2\u003e \u003cp\u003eDuring 3 years of follow-up, high RC, compared with low RC, have a significantly higher cumulative incidence of MACCE (16.2% vs 10.9%; adjusted HR 1.68, 95% CI 1.10\u0026ndash;2.59; p\u0026thinsp;=\u0026thinsp;0.02). The increased risk of MACCE in high RC was primarily attributable to significantly higher rates of hospitalization for UA (12.3% vs 7.9%; adjusted HR 1.69, 95% CI 1.03\u0026ndash;2.75; p\u0026thinsp;=\u0026thinsp;0.03) and composite for cardiac events including cardiovascular death, MI, ischemia-driven revascularization or hospitalization for UA or heart failure (14.8% vs 9.8%; adjusted HR 1.75, 95% CI 1.12\u0026ndash;2.73; p\u0026thinsp;=\u0026thinsp;0.01).\u003c/p\u003e \u003c/div\u003e"},{"header":"4 Discussion","content":"\u003cp\u003eOur study provides novel insights into the prognostic implications of remnant cholesterol (RC) in patients with premature acute coronary syndrome (PACS), revealing three principal findings: First, elevated RC levels (\u0026ge;\u0026thinsp;66.6th percentile) were independently associated with a 68% increased risk of MACCE during medium-term follow-up. Second, this excess risk predominantly manifested as recurrent unstable angina requiring hospitalization and composite cardiac events. Third, the high-RC phenotype exhibited distinct metabolic derangements characterized by atherogenic dyslipidemia (elevated triglycerides, total cholesterol, and LDL-c with concomitant HDL-c depression), impaired glucose homeostasis, and clustering of traditional cardiovascular risk factors.\u003c/p\u003e \u003cp\u003eThe heightened MACCE risk aligns with RC's pathophysiological role in atherosclerosis progression. RC-rich lipoprotein remnants infiltrate arterial walls, undergoing oxidative modification to trigger endothelial dysfunction and foam cell formation through matrix metalloproteinase activation and fibrous cap destabilization\u003csup\u003e[\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e, \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e]\u003c/sup\u003e. This mechanism is particularly relevant in PACS populations where non-obstructive coronary disease and microvascular dysfunction prevail, as RC's small particle size (\u0026lt;\u0026thinsp;70 nm) facilitates endothelial penetration and promotes ischemia through both macrovascular and microvascular pathways\u003csup\u003e[\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e, \u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e]\u003c/sup\u003e. The pro-inflammatory milieu in high-RC states, evidenced by elevated HbA1c and fasting glucose levels, may exacerbate endothelial progenitor cell dysfunction\u0026mdash;a finding corroborated by studies linking RC to impaired vascular repair mechanisms in metabolic syndrome populations\u003csup\u003e[\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e, \u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e]\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eNotably, RC demonstrated prognostic discrimination beyond conventional lipid parameters, with 31.3% of low-RC patients meeting hyperlipidemia criteria yet exhibiting better outcomes. This residual risk phenomenon mirrors findings from large cohort studies where RC variability independently predicted metabolic dysfunction-associated steatoses liver disease (MASLD) and carotid intima-media thickening, even among normolipidemic individuals\u003csup\u003e[\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e, \u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e]\u003c/sup\u003e. Our results extend recent Mendelian randomization evidence showing triglyceride-rich lipoproteins mediate cardiovascular risk independent of LDL-c, suggesting RC may serve as a superior therapeutic target in statin-treated PACS patients with residual dyslipidemia\u003csup\u003e[\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e]\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eThe strong RC-diabetes association (33.6% vs 27.3%, p\u0026thinsp;=\u0026thinsp;0.04) unveils a bidirectional metabolic cycle: insulin resistance enhances hepatic VLDL secretion while impairing lipoprotein lipase activity, perpetuating RC accumulation - mechanism supported by NHANES data showing RC-HOMA-IR correlations in MAFLD patients (β\u0026thinsp;=\u0026thinsp;0.17, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001 at RC\u0026thinsp;\u0026lt;\u0026thinsp;30 mg/dL)\u003csup\u003e[\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e, \u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e]\u003c/sup\u003e. This interplay may explain why high-RC patients exhibited poorer glycemic control despite comparable antidiabetic therapy utilization. Clinically, RC monitoring could identify candidates for intensified therapy, such as fibrates or omega-3 fatty acids, which reduce RC by 25\u0026ndash;40% through enhanced lipoprotein lipase activity\u0026mdash;a strategy supported by trial data showing RC-lowering interventions reduce recurrent ischemia in metabolic syndrome populations\u003csup\u003e[\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e, \u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e]\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eThe discordance between LDL-C and RC risk profiles (high-RC/low-LDL-C group: OR 2.04 for hypertension) underscores the need to expand lipid management beyond LDL-centric paradigms\u003csup\u003e[\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e]\u003c/sup\u003e. Our findings align with UK Biobank evidence where RC\u0026thinsp;\u0026ge;\u0026thinsp;1.0 mmol/L increased IHD-T2D multimorbidity risk by 214% (HR 3.14), suggesting RC quantification could refine secondary prevention strategies in young ACS survivors\u003csup\u003e[\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e]\u003c/sup\u003e. Future trials should evaluate whether targeting RC thresholds (e.g., \u0026lt;\u0026thinsp;0.82 mmol/L as proposed in breast cancer cohorts) improves outcomes in this population\u003csup\u003e[\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e, \u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e]\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eSeveral limitations of our study need to be noted. First, the single-center retrospective design introduces potential selection bias, though mitigated by consecutive enrollment and standardized protocols. Second, RC calculation using the Fried Ewald formula may underestimate true levels compared with direct measurement. Third, dietary patterns and physical activity metrics were unavailable for metabolic syndrome characterization. Fourth, the observational nature precludes causal inference between RC and outcomes. Finally, all patients in the present study were Chinese, so the results should be interpreted and generalized to other ethnic groups with caution since dissimilar metabolic levels exist among different races.\u003c/p\u003e"},{"header":"5 Conclusions","content":"\u003cp\u003eIn hospitalized PACS patients, the cumulative incidence of MACCE in high RC patients was significantly higher than that in low RC patients during a median follow-up of nearly 3 years. The incremental risk in high RC might be explained by significantly higher rates of hospitalization for unstable angina and composite for cardiac events. Therefore, we should pay close attention to the indicator of RC and pursue further exploration.\u003c/p\u003e "},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eAuthor Contributors:\u0026nbsp;\u003c/strong\u003eStudy concept and design: MZ, BW, WX. Acquisition, analysis, or interpretation of data: MZ, JC, YS, ZY. Drafting of the manuscript: MZ, JC. Critical revision of the manuscript for important intellectual content: All authors. Obtained funding: BW. Administrative, technical, or material support: BW, WX. BW, and YS had full access to all the data in the study and take responsibility for the integrity of the data and the accuracy of the data analysis. All authors read and approved the final manuscript.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding:\u0026nbsp;\u003c/strong\u003eThis study was funded by grants from Qingdao South District Science and Technology Plan Project (2023-2-07-YY), Qingdao Medical and Health Research Guidance Project (2023-WJZD174).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eInstitutional Review Board Statement:\u003c/strong\u003e This study conformed to the STROBE (Strengthening the Reporting of Observational studies in Epidemiology) guidelines and was conducted in accordance with the Declaration of Helsinki. The Ethics Committee of Qingdao Municipal Hospital, University of Health and Rehabilitation Sciences approved this study (2023-KY-074).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eInformed Consent Statement:\u003c/strong\u003e Informed consent was obtained from all subjects involved in the study.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConflicts of Interest:\u003c/strong\u003e The authors declare no conflict of interest\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eByrne RA, Rossello X, Coughlan JJ, Barbato E, Berry C, Chieffo A et al (2023) 2023 ESC Guidelines for the management of acute coronary syndromes. Eur Heart J 44(38):3720\u0026ndash;3826\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBergmark BA, Mathenge N, Merlini PA, Lawrence-Wright MB, Giugliano RP (2022) Acute coronary syndromes. Lancet (London England) 399(10332):1347\u0026ndash;1358\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eChhabra ST, Kaur T, Masson S, Soni RK, Bansal N, Takkar B et al (2018) Early onset ACS: An age based clinico-epidemiologic and angiographic comparison. Atherosclerosis 279:45\u0026ndash;51\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eTsao CW, Aday AW, Almarzooq ZI, Anderson CAM, Arora P, Avery CL et al (2023) Heart Disease and Stroke Statistics-2023 Update: A Report From the American Heart Association. Circulation ; 147(8)\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eYusuf S, Hawken S, Ounpuu S, Dans T, Avezum A, Lanas F et al (2004) Effect of potentially modifiable risk factors associated with myocardial infarction in 52 countries (the INTERHEART study): case-control study. Lancet (London England) 364(9438):937\u0026ndash;952\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eTimmis A, Kazakiewicz D, Townsend N, Huculeci R, Aboyans V, Vardas P (2023) Global epidemiology of acute coronary syndromes. Nat Reviews Cardiol 20(11):778\u0026ndash;788\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eChen L, Chen S, Bai X, Su M, He L, Li G et al (2024) Low-Density Lipoprotein Cholesterol, Cardiovascular Disease Risk, and Mortality in China. JAMA Netw Open 7(7):e2422558\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eDomanski MJ, Tian X, Wu CO, Reis JP, Dey AK, Gu Y et al (2020) Time Course of LDL Cholesterol Exposure and Cardiovascular Disease Event Risk. J Am Coll Cardiol 76(13):1507\u0026ndash;1516\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSud M, Han L, Koh M, Abdel-Qadir H, Austin PC, Farkouh ME et al (2020) Low-Density Lipoprotein Cholesterol and Adverse Cardiovascular Events After Percutaneous Coronary Intervention. J Am Coll Cardiol 76(12):1440\u0026ndash;1450\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eWong ND, Zhao Y, Quek RGW, Blumenthal RS, Budoff MJ, Cushman M et al (2017) Residual atherosclerotic cardiovascular disease risk in statin-treated adults: The Multi-Ethnic Study of Atherosclerosis. J Clin Lipidol 11(5):1223\u0026ndash;1233\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eJoshi PH, Martin SS, Blumenthal RS (2015) The remnants of residual risk. 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Nutr J 23(1):45\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"premature acute coronary syndrome, elevated remnant cholesterol, outcomes","lastPublishedDoi":"10.21203/rs.3.rs-6510079/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-6510079/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eBackground\u003c/h2\u003e \u003cp\u003eThe association of remnant cholesterol (RC) with recurrent cardiovascular events following acute coronary syndrome (ACS) is well documented. However, no RC-stratified analysis focused on patients with premature ACS (PACS).\u003c/p\u003e\u003ch2\u003eObjectives\u003c/h2\u003e \u003cp\u003eThis study sought to elucidate the clinical characteristics and subsequent cardiovascular events in high RC and low RC with PACS.\u003c/p\u003e\u003ch2\u003eMethods\u003c/h2\u003e \u003cp\u003eIn this retrospective cohort study, we consecutively recruited 820 PACS patients between January 2019 and January 2020. RC was calculated as total cholesterol minus high-density lipoprotein cholesterol minus low-density lipoprotein cholesterol. RC\u0026thinsp;\u0026ge;\u0026thinsp;66.6 percentile of the cohort was defined as high RC. The primary endpoint was major adverse cardiovascular and cerebrovascular event (MACCE), including cardiovascular death, myocardial infarction (MI), stroke, ischemia-driven revascularization, or hospitalization for unstable angina or heart failure.\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e \u003cp\u003eAmong 820 patients enrolled, 277 (33.8%) were high RC and 543 (66.2%) were low RC. High RC had higher prevalence of traditional risk factors including diabetes (33.6% vs 27.3%, p\u0026thinsp;=\u0026thinsp;0.04), hypertension (68.2% vs 61.3%, p\u0026thinsp;=\u0026thinsp;0.04), and hyperlipidemia (43.3% vs 31.3%, p\u0026thinsp;=\u0026thinsp;0.001). The levels of glucose (p\u0026thinsp;\u0026lt;\u0026thinsp;0.001), hemoglobin A1C (p\u0026thinsp;=\u0026thinsp;0.005), triglyceride (p\u0026thinsp;\u0026lt;\u0026thinsp;0.001), total cholesterol (p\u0026thinsp;\u0026lt;\u0026thinsp;0.001) and LDL-C (p\u0026thinsp;=\u0026thinsp;0.017) in high RC group were significantly higher than those in low RC group, while the levels of HDL-C (p\u0026thinsp;=\u0026thinsp;0.001) were lower. During 3 years of follow-up, high RC, compared with low RC, have a significantly higher cumulative incidence of MACCE (16.2% vs 10.9%; adjusted HR 1.68, 95% CI 1.10\u0026ndash;2.59; p\u0026thinsp;=\u0026thinsp;0.02). The increased risk of MACCE in high RC was primarily attributable to significantly higher rates of hospitalization for unstable angina (12.3% vs 7.9%; adjusted HR 1.69, 95% CI 1.03\u0026ndash;2.75; p\u0026thinsp;=\u0026thinsp;0.03) and composite for cardiac events including cardiovascular death, MI, ischemia-driven revascularization or hospitalization for unstable angina or heart failure (14.8% vs 9.8%; adjusted HR 1.75, 95% CI 1.12\u0026ndash;2.73; p\u0026thinsp;=\u0026thinsp;0.01).\u003c/p\u003e\u003ch2\u003eConclusions\u003c/h2\u003e \u003cp\u003eIn hospitalized PACS patients, the cumulative incidence of MACCE in high RC patients was significantly higher than that in low RC patients during a median follow-up of nearly 3 years. The incremental risk in high RC might be explained by significantly higher rates of hospitalization for unstable angina and composite for cardiac events. Therefore, we should pay close attention to the indicator of RC and pursue further exploration.\u003c/p\u003e","manuscriptTitle":"Elevated Remnant Cholesterol Predicts Poor Outcome in Patients with Premature Acute Coronary Syndrome：A Retrospective, Single-Center Study","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-05-07 07:18:35","doi":"10.21203/rs.3.rs-6510079/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"3a57910f-6413-4f18-8214-288191a6f315","owner":[],"postedDate":"May 7th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2025-08-07T07:20:13+00:00","versionOfRecord":{"articleIdentity":"rs-6510079","link":"https://doi.org/10.1007/s11239-025-03147-6","journal":{"identity":"journal-of-thrombosis-and-thrombolysis","isVorOnly":false,"title":"Journal of Thrombosis and Thrombolysis"},"publishedOn":"2025-07-17 15:57:20","publishedOnDateReadable":"July 17th, 2025"},"versionCreatedAt":"2025-05-07 07:18:35","video":"","vorDoi":"10.1007/s11239-025-03147-6","vorDoiUrl":"https://doi.org/10.1007/s11239-025-03147-6","workflowStages":[]},"version":"v1","identity":"rs-6510079","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-6510079","identity":"rs-6510079","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}