Non-Traditional Lipid Profiles and 1-Year Vascular Outcomes in Ischemic Stroke Patients with Prior Statin Therapy and LDL-C <100 mg/dL

Non-Traditional Lipid Profiles and 1-Year Vascular Outcomes in Ischemic Stroke Patients with Prior Statin Therapy and LDL-C <100 mg/dL | 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 Article Non-Traditional Lipid Profiles and 1-Year Vascular Outcomes in Ischemic Stroke Patients with Prior Statin Therapy and LDL-C <100 mg/dL Hyunsoo Kim, Joon-Tae Kim, Ji Sung Lee, Beom Joon Kim, Jihoon Kang, and 31 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-4567821/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 01 Oct, 2024 Read the published version in Scientific Reports → Version 1 posted 10 You are reading this latest preprint version Abstract This study aimed to investigate the association between non-traditional lipid profiles and the risk of 1-year vascular events in patients who were already using statins before stroke and had admission LDL-C < 100 mg/dL. This study was an analysis of a prospective, multicenter, nationwide registry of consecutive patients with acute ischemic stroke patients who treated with statin before index stroke and LDL-C < 100mg/dL on admission. Non-traditional lipid profiles including non-HDL, TC/HDL ratio, LDL/HDL ratio, and TG/HDL ratio were analyzed as a continuous or categorical variable. The primary vascular outcome within one year was a composite of recurrent stroke (either hemorrhagic or ischemic), myocardial infarction (MI) and all-cause mortality. Hazard ratios (95% Cis) for 1-year vascular outcomes were analyzed using the Cox PH model for each non-traditional lipid profiles groups. A total of 7,028 patients (age 70.3 ± 10.8years, male 59.8%) were finally analyzed for the study. In unadjusted analysis, no significant associations were observed in the quartiles of LDL/HDL ratio and 1-year primary outcome. However, after adjustment of relevant variables, compared with Q1 of the LDL/HDL ratio, Q4 was significantly associated with increasing the risk of 1-year primary outcome (HR 1.48 [1.19–1.83]). For the LDL/HDL ratio, a linear relationship was observed (P for linearity < 0.001). Higher quartiles of the LDL/HDL ratio were significantly and linearly associated with increasing the risk of 1-year primary vascular outcomes. These findings suggest that even during statin therapy with LDL-C < 100mg/dl on admission, there should be consideration for residual risk based on the LDL/HDL ratio, following stroke. Health sciences/Endocrinology Health sciences/Neurology non-traditional lipid profiles lipid ratio LDL/HDL ratio LDL-cholesterol acute ischemic stroke vascular outcome Figures Figure 1 Figure 2 Figure 3 INTRODUCTION Stroke constitutes a significant cause of morbidity and mortality, especially among the elderly population. Lowering low-density lipoprotein cholesterol (LDL-C) through statin treatment is crucial for preventing atherosclerotic cardiovascular diseases, including stroke. 1 For secondary prevention, the Stroke Prevention by Aggressive Reduction in Cholesterol Levels (SPARCL) trial emphasized the benefit of high-dose statin therapy for stroke patients with admission LDL-C > 100mg/dL. 2 Additionally, the Treat Stroke to Target (TST) trial demonstrated that maintaining LDL-C < 70 mg/dL significantly reduces the risk of long-term vascular events compared to keeping LDL-C in the range of 90-110mg/dL in atherosclerotic ischemic stroke. 3 In addition to LDL-C, non-traditional lipid profiles, such as non-high density lipoprotein (non-HDL) cholesterol and lipid ratios, have been recognized as contributors to residual vascular risk. 4 Previous studies have considered non-HDL cholesterol, triglyceride (TG)/HDL ratio, and LDL/HDL ratio as residual risk factors for cardiovascular diseases in patients with coronary artery diseases. 5, 6 Elevated lipid ratios in patients with general risk factors have been also associated with an increased risk of stroke. 7 Among ischemic stroke patients, with the escalating use of cholesterol-lowering therapy for primary and secondary prevention, there is an increasing proportion of patients who were already on statin treatment before the index event and had well-controlled LDL-C levels (< 100 mg/dL) upon admission. In a previous study, there was a relationship between admission LDL-C levels and early vascular outcomes for patients not taking a statin at the time of the index event but not for patients already on statins. 8 Therefore, the best post-stroke target lipid level for patients who were already on statins and had well-controlled LDL-C levels at the time of their index stroke remains unclear. In such cases, the impact of non-traditional lipid profiles on outcome might need consideration. Research on the prognostic implications and targets for treatment of non-traditional lipid profiles for patients with statin pretreatment and baseline LDL-C < 100 mg/dL remains limited. While statin therapy may be associated with the reduced risk of vascular outcome for these patients with low LDL-C on admission, 9 understanding the clinical significance of non-traditional lipid profiles beyond LDL-C could hold importance. Therefore, this study aimed to investigate the association between non-traditional lipid profiles and the risk of 1-year vascular events in patients who were already using statins before index stroke and had admission LDL-C < 100 mg/dL. METHODS Subjects This study was an analysis of a prospective, multicenter, nationwide registry of consecutive patients with acute stroke or transient ischemic attack (TIA) admitted to 18 academic hospitals in South Korea, the Clinical Research Center for Stroke-Korea (CRCS-K) registry. Detailed methodologic information about the CRCS-K registry has been reported previously. 10, 11 We identified patients with acute cerebrovascular events admitted between January 2011 and July 2020 (N = 75690). Among the patients with acute cerebrovascular events, we included ischemic stroke or TIA with lesion-positive on diffusion weighted imaging (DWI) within 7 days of onset (N = 68468), non-cardioembolic ischemic stroke (N = 52878), statin pretreatment before index stroke (N = 10189) and admission LDL-C < 100mg/dl(N = 7063). Patients without information on fasting lipid profiles at admission were excluded. A detailed patient selection flowchart is shown in Supplemental Fig. 1. Ethics statement Clinical information was collected from the CRCS-K registry with approval from the local institutional review boards of all the participating centers. A waiver for informed consent was provided because of study subject anonymity and minimal risk to the participants. The data used in this study are available upon reasonable request following the submission of a legitimate academic research proposal to be assessed by the CRCS-K steering committee. Ethics approval: The current study was approved by local institutional review boards at all participating centers, including Chonnam National University Hospital (CNUH-2024-032). Data collection Demographic, clinical, imaging, and laboratory data were prospectively collected. Lipid profiles, including LDL-C, total cholesterol (TC), HDL-C, and TG levels, were obtained during the first fasting period after admission. Four non-traditional lipid profile parameters, non-HDL, TC/HDL ratio, LDL/HDL ratio, and TG/HDL ratio, were analyzed as continuous and categorical variables. The patients were classified into quartiles for each non-traditional lipid profile for comparison. Ischemic stroke subtypes were classified according to the Trial of Org 10172 in Acute Stroke Treatment (TOAST) criteria, which were refined to incorporate additional information based on modern imaging studies. 12, 13 Outcomes The primary vascular outcome within one year was a composite of recurrent stroke (either hemorrhagic or ischemic), myocardial infarction (MI) and all-cause mortality. The secondary vascular outcomes were the individual outcomes of a) all-cause mortality, b) stroke (either ischemic or hemorrhagic), and c) MI. Detailed definitions of the vascular outcome events and methods of outcome capture used in the current study are described in the Supplemental Methods and previous reports. 10, 11 Statistical analysis Baseline characteristics and outcomes were compared among each non-traditional lipid profiles quartiles by using the chi-square test, ANOVA, or Kruskal‒Wallis test according to the type of variable. The event probability of 1-year vascular outcomes according to the non-traditional lipid profiles quartiles in all patients and in patients by stroke subtype was calculated by using the Kaplan–Meier method, and the log-rank test was performed to analyze differences among the groups. Hazard ratios (HRs) and 95% confidence intervals (95% Cis) for 1-year vascular outcomes were analyzed using the Cox proportional hazards model for each non-traditional lipid profiles groups. Adjustments were made for the following 18 predetermined variables with clinically relevant associations with the outcome variables; age, male sex, BMI, NIHSS score, history of stroke, history of coronary artery diseases, HTN, DM, dyslipidemia, smoking, prior antiplatelet, in-hospital anti-diabetic treatment, in-hospital antihypertensive treatment, statin, glucose, creatinine, LDL-C and SBP. In the analysis of the LDL/HDL ratio, although LDL-C has been restricted to less than 100mg/dl, it remains a crucial risk marker with significant variability. To investigate whether the LDL/HDL ratio has independent clinical significance, LDL-C was included as an adjusted variable. The modifying effect of stroke subtype on the relationships between each non-traditional lipid profiles groups and clinical outcomes was explored by separately introducing an interaction term of ischemic stroke subtype and LDL/HDL ratio quartile groups (and TC/HDL ratio) into the models. Two-sided p values < 0.05 were considered indicative of significance. Given the known insensitivity of interaction testing, evidence of heterogeneity was considered present with p values ≤ 0.10. In addition, the goodness of fit of the four models were compared using the Akaike (AIC) and Bayesian information criterion (BIC). Lower AIC and BIC indicate a better fit. Statistical analyses were performed with R software using the “rms” package (version 3.6.0, R Foundation for Statistical Computing, Vienna, Austria) and SAS version 9.4 (SAS Institute Inc., Cary, NC, USA). RESULTS General characteristics A total of 7,028 patients (mean age 70.3 ± 10.8 years, male 59.8%) met study entry criteria. The median NIHSS score was 3 (IQR 1–6) and the mean LDL-C level on admission was 69.7 ± 17.3mg/dL. For lipid profiles at admission, the mean levels of HDL-C, TG, TC, and non-HDL were 43.2 ± 12.5mg/dL, 116.4 ± 72.1mg/dL, 130.4 ± 24.8mg/dL, and 87.2 ± 22.3mg/dL, respectively. The mean LDL-C/HDL-C ratio, TG/HDL ratio, and TC/HDL ratio was 1.72 ± 0.61, 3.06 ± 2.63, and 3.20 ± 1.08, respectively. Non-statin lipid-lowering agents, in addition to statins, were used in 6.5% of patients before index stroke. At discharge, 92.2% of the study subjects received statin treatment. The demographic and clinical characteristics of subjects according to the quartiles of non-traditional lipid profiles (LDL/HDL ratio, TC/HDL ratio, TG/HDL ratio, and non-HDL) are presented in Table 1 and Supplemental Table 1–3. As the quartile of the LDL/HDL ratio increased, there were significant trends of decreasing age and increasing body weight and BMI. Additionally, the frequency of DM, HTN, and history of CAD significantly increased as the quartile of LDL/HDL ratio increased (Table 1 ). Table 1 General characteristics of subjects according to the quartiles of LDL/HDL ratio. All Q1 Q2 Q3 Q4 P † P trend ‡ N 7028 1767 1738 1751 1772 LDL/HDL ratio Age, mean ± SD 70.3 ± 10.8 70.9 ± 11.0 70.4 ± 10.6 70.5 ± 10.6 69.5 ± 11.1 0.001 0.001 Male, n (%) 4206 (59.8) 1000 (56.6) 962 (55.4) 1055 (60.3) 1189 (67.1) < .0001 < .0001 Onset-to-arrival within 24h, n (%) 4432 (63.1) 1151 (65.1) 1125 (64.7) 1108 (63.3) 1048 (59.1) 0.001 0.0002 Height, cm, mean ± SD 161.8 ± 8.8 161.3 ± 8.9 160.9 ± 8.7 161.9 ± 8.8 163.0 ± 8.9 < .0001 < .0001 Weight, kg, mean ± SD 63.3 ± 11.3 61.4 ± 11.0 62.3 ± 10.7 63.9 ± 11.2 65.3 ± 11.8 < .0001 < .0001 BMI, % 24.1 ± 3.4 23.5 ± 3.4 24.0 ± 3.3 24.3 ± 3.3 24.5 ± 3.4 < .0001 < .0001 NIHSS, med (IQR) 3 ( 1 – 6 ) 3 ( 1 – 6 ) 3 ( 1 – 6 ) 3 ( 1 – 6 ) 3 ( 1 – 6 ) 0.82 0.06 Premorbid mRS 0–1 5629 (80.1) 1376 (77.9) 1417 (81.5) 1419 (81.0) 1417 (80.0) 0.033 0.17 History of stroke 3222 (45.8) 900 (50.9) 826 (47.5) 742 (42.4) 754 (42.6) < .0001 < .0001 History of TIA 285 (4.1) 71 (4.0) 69 (4.0) 71 (4.1) 74 (4.2) 0.99 0.79 History of PAD 98 (1.4) 18 (1.0) 25 (1.4) 29 (1.7) 26 (1.5) 0.43 0.21 History of CAD 1476 (21.0) 333 (18.8) 352 (20.3) 391 (22.3) 400 (22.6) 0.02 0.002 HTN 5851 (83.3) 1450 (82.1) 1449 (83.4) 1475 (84.2) 1477 (83.4) 0.38 0.23 DM 3637 (51.8) 820 (46.4) 858 (49.4) 921 (52.6) 1038 (58.6) < .0001 < .0001 Dyslipidemia 5108 (72.7) 1262 (71.4) 1270 (73.1) 1310 (74.8) 1266 (71.4) 0.07 0.71 Smoking < .0001 0.003 Never 4559 (64.9) 1199 (67.9) 1175 (67.6) 1133 (64.7) 1052 (59.4) Current 1228 (17.5) 244 (13.8) 273 (15.7) 316 (18.0) 395 (22.3) Ex (beyond 5 year) 821 (11.7) 202 (11.4) 209 (12.0) 198 (11.3) 212 (12.0) Recent (within 5 year) 420 (6.0) 122 (6.9) 81 (4.7) 104 (5.9) 113 (6.4) Medication history Antihypertensive 5402 (76.9) 1306 (73.9) 1324 (76.2) 1394 (79.6) 1378 (77.8) 0.001 0.001 Andi-diabetics 3226 (45.9) 748 (42.3) 771 (44.4) 806 (46.0) 901 (50.8) < .0001 < .0001 Lipid lowering agents 455 (6.5) 128 (7.2) 120 (6.9) 88 (5.0) 119 (6.7) 0.04 0.19 Antiplatelet 4909 (69.8) 1263 (71.5) 1218 (70.1) 1190 (68.0) 1238 (69.9) 0.16 0.16 aspirin 3332 (47.4) 825 (46.7) 827 (47.6) 803 (45.9) 877 (49.5) 0.16 0.21 clopidogrel 2426 (34.5) 628 (35.5) 599 (34.5) 576 (32.9) 623 (35.2) 0.36 0.59 Reperfusion therapy 0.07 0.01 No 6215 (88.4) 1537 (87.0) 1531 (88.1) 1553 (88.7) 1594 (90.0) IVT 441 (6.3) 120 (6.8) 107 (6.2) 111 (6.3) 103 (5.8) EVT 216 (3.1) 69 (3.9) 61 (3.5) 41 (2.3) 45 (2.5) IV + EVT 156 (2.2) 41 (2.3) 39 (2.2) 46 (2.6) 30 (1.7) Laboratory findings WBC counts 8.1 ± 3.1 8.0 ± 3.3 8.0 ± 3.2 8.0 ± 3.0 8.3 ± 3.1 0.02 0.0062 Platelet counts 227.4 ± 77.6 220.0 ± 81.5 227.4 ± 78.5 227.5 ± 72.5 234.6 ± 76.9 < .0001 < .0001 Hemoglobin 13.0 ± 2.0 12.9 ± 1.9 13.0 ± 2.0 13.1 ± 2.0 13.2 ± 2.2 0.002 0.0001 Glucose 152.2 ± 68.2 147.5 ± 68.0 147.8 ± 61.5 153.0 ± 67.3 160.2 ± 74.6 < .0001 < .0001 BUN 18.6 ± 10.0 18.6 ± 10.2 18.2 ± 9.0 18.6 ± 9.6 19.2 ± 10.9 0.03 0.0439 Creatinine 1.2 ± 1.3 1.1 ± 1.0 1.1 ± 0.9 1.1 ± 1.1 1.3 ± 1.9 < .0001 < .0001 LDL-cholesterol 69.7 ± 17.3 54.4 ± 15.2 67.9 ± 14.5 74.6 ± 13.4 81.7 ± 12.8 < .0001 < .0001 TC 130.4 ± 24.8 122.5 ± 26.8 129.3 ± 25.2 133.0 ± 22.9 136.9 ± 21.9 < .0001 < .0001 TG 116.4 ± 72.1 91.5 ± 57.4 106.0 ± 59.9 121.6 ± 71.5 146.2 ± 84.4 < .0001 < .0001 HDL 43.2 ± 12.5 53.9 ± 14.0 45.8 ± 9.8 40.3 ± 7.5 32.9 ± 6.3 < .0001 < .0001 non-HDL 87.2 ± 22.3 68.6 ± 18.1 83.4 ± 17.6 92.7 ± 17.7 103.9 ± 19.1 < .0001 < .0001 SBP 146.5 ± 25.9 146.0 ± 25.8 146.8 ± 26.4 147.5 ± 25.7 145.6 ± 25.8 0.14 0.83 In-hospital treatment Aspirin 5934 (84.4) 1444 (81.7) 1496 (86.1) 1481 (84.6) 1513 (85.4) 0.002 0.01 Clopidogrel 4183 (59.5) 1028 (58.2) 1054 (60.6) 1040 (59.4) 1061 (59.9) 0.51 0.46 Other antiplatelet 743 (10.6) 226 (12.8) 171 (9.8) 174 (9.9) 172 (9.7) 0.01 0.01 Anti-diabetics 2710 (38.6) 616 (34.9) 649 (37.3) 693 (39.6) 752 (42.4) < .0001 < .0001 Anti-hypertensives 3645 (51.9) 890 (50.4) 882 (50.7) 943 (53.9) 930 (52.5) 0.14 0.08 Lipid lowering agents other than statin 165 (2.3) 47 (2.7) 34 (2.0) 35 (2.0) 49 (2.8) 0.24 0.82 Statin 6483 (92.2) 1606 (90.9) 1613 (92.8) 1628 (93.0) 1636 (92.3) 0.08 0.12 † P-value by Chi-square test, ANOVA and Kruskal-Wallis Test. ‡ P-value by Cochran-Armitage trend test, Cochran-Mantel-Haenszel test and linear contrasts test in ANOVA One-year vascular outcomes The mean follow-up duration was 330 ± 88.9 days, and 91.5% of the study subjects completed 1-year of follow-up. The 1-year cumulative incidences of the composite of stroke, MI and all-cause mortality was 14.5%; all-cause mortality, 8.9%; stroke (either ischemic or hemorrhagic), 7.2%; and MI, 0.5%. In crude analysis, the 1-year cumulative incidences of the composite of stroke, MI, and all-cause mortality did not significantly differ among the quartiles of the LDL/HDL ratio: 14.9% in the lowest quartile (Q1) of the LDL/HDL ratio, 13.4% in the second quartile (Q2), 14.1% in the third quartile (Q3), and 15.7% in the highest quartile (Q4)(Table 2 , p for trend = 0.59). The 1-year cumulative incidences of stroke, all-cause mortality, and MI as individual outcomes also did not show significant differences according to the quartiles of LDL/HDL ratio. Similar observations with no significant differences were made for 1-year cumulative incidences of vascular events according to the quartiles of TG/HDL ratio and TC/HDL ratio (Supplemental Table 4). However, non-HDL showed a significant association of increasing quartile levels with decreased event rates of 1-year composite vascular events and secondary outcome variables (Supplemental Table 4). Table 2 One-year vascular outcomes according to the quartiles of the LDL/HDL ratio. All Q1 Q2 Q3 Q4 P trend b N 7028 1767 1738 1751 1772 Primary outcome No. of events 963 249 221 231 262 1-yar event rate (%, 95% CI) a 14.52 (13.67–15.38) 14.88 (13.17–16.59) 13.42 (11.77–15.08) 14.05 (12.36–15.74) 15.70 (13.94–17.45) 0.59 Stroke No. of events 463 111 113 123 116 1-yar event rate (%, 95% CI) a 7.24 (6.60–7.88) 6.88 (5.64–8.12) 6.95 (5.70–8.19) 7.80 (6.47–9.14) 7.30 (6.01–8.60) 0.68 All-cause mortality No. of events 572 155 122 131 164 1-yar event rate (%, 95% CI) a 8.91 (8.21–9.61) 9.55 (8.11–10.99) 7.80 (6.46–9.13) 8.18 (6.83–9.52) 10.09 (8.62–11.56) 0.60 MI No. of events 33 6 8 9 10 1-yar event rate (%, 95% CI) a 0.54 (0.35–0.72) 0.38 (0.08–0.69) 0.52 (0.16–0.89) 0.57 (0.20–0.94) 0.68 (0.26–1.10) 0.33 a Based on the Kaplan-Meier estimates b P-value by log-rank test for trend The unadjusted and adjusted associations of LDL/HDL ratio with 1-year vascular outcomes are shown in Table 3 . In unadjusted analysis, no significant associations were observed in the quartiles of LDL/HDL ratio and 1-year primary outcome and secondary outcomes. However, after adjustment for the 18 prespecified clinically relevant variables, compared with Q1 of the LDL/HDL ratio, Q4 of the LDL/HDL ratio was significantly associated with increasing the risk of 1-year composite of stroke, MI, and all-cause mortality (HR 1.48 [1.19–1.83]). Similarly, compared with Q1 of the TC/HDL ratio, Q2, Q3, and Q4 of the TC/HDL ratio were significantly associated with increasing the risk of 1-year primary outcome (aHR 1.21 [1.00-1.45], 1.26 [1.04–1.53], and 1.40 [1.15–1.70], respectively)(Supplemental Table 5). For TG/HDL ratio, Q4 of the TG/HDL ratio, compared with Q1, was more likely to occur to 1-year primary outcome (aHR 1.30 [1.08–1.57])(Supplemental Table 6). However, there were no significant associations between the quartiles of non-HDL and 1-year primary outcome (Supplemental Table 7). Kaplan-Meier survival plots for these are presented in Fig. 1 and Supplemental Fig. 2 (A-C). Table 3 Associations of LDL/HDL ratio with one-year vascular outcomes. Unadjusted HR (95% CI) P Adjusted HR (95% CI) P Primary outcomes Q1 1(Ref) 1(Ref) Q2 0.89 (0.74–1.06) 0.19 1.09 (0.90–1.32) 0.39 Q3 0.92 (0.77–1.10) 0.37 1.22 (1.00-1.49) 0.05 Q4 1.04 (0.87–1.23) 0.68 1.48 (1.19–1.83) 0.0004 Stroke Q1 1(Ref) 1(Ref) Q2 1.02 (0.78–1.32) 0.89 1.14 (0.86–1.51) 0.36 Q3 1.10 (0.85–1.42) 0.46 1.34 (1.00-1.80) 0.05 Q4 1.03 (0.80–1.34) 0.81 1.33 (0.95–1.84) 0.09 All-cause mortality Q1 1(Ref) 1(Ref) Q2 0.79 (0.62-1.00) 0.05 1.03 (0.80–1.32) 0.81 Q3 0.84 (0.66–1.06) 0.14 1.16 (0.89–1.50) 0.27 Q4 1.04 (0.84–1.30) 0.72 1.50 (1.15–1.97) 0.003 MI Q1 1(Ref) 1(Ref) Q2 1.33 (0.46–3.83) 0.60 1.16 (0.38–3.51) 0.80 Q3 1.48 (0.53–4.17) 0.45 1.06 (0.34–3.37) 0.92 Q4 1.64 (0.60–4.51) 0.34 1.06 (0.30–3.69) 0.93 Adjusted variable: age, male, NIHSS, BMI, LDL-C, history of stroke, history of CAD, HTN, DM, dyslipidemia, smoking status, prior antiplatelet, creatinine, glucose, SBP, in-hospital anti-hypertensive, in-hospital antidiabetics, in-hospital lipid lowering agents. Adjusted HR plots for each non-traditional lipid profiles as continuous variables are presented in Fig. 2 and Supplemental Fig. 3 (A-C). For the LDL/HDL ratio, a linear relationship was observed (P for linearity < 0.001), and the cut-off value associated with significant increase in the composite outcome within 1-year was 2.09 of LDL/HDL ratio. For other lipid profiles of TC/HDL ratio and TG/HDL ratio, non-linear associations were observed. Among the four lipid profiles models, the model for the LDL/HDL ratio had the lowest Akaike Information Criteria and Bayes Information Criterion (Supplemental Table 8). TOAST subgroup analysis There were significant interactions between LDL/HDL ratio and TOAST subgroups with 1-year primary outcome (P interaction = 0.01). Among the LAA and SVO subtypes, compared with Q1 of LDL/HDL ratio, higher quartiles were more likely to be associated with increasing the risk of 1-year primary vascular outcomes, while no associations were observed among UD subtype (Fig. 3 ). For other lipid ratios and non-HDL cholesterol, there were no potential interactions of stroke subtypes with 1-year primary outcome (Supplemental Fig. 4). DISCUSSION Our study, which focused on over 7,000 patients with acute ischemic stroke who were treated with statins before the index stroke and had LDL-cholesterol levels < 100mg/dl upon admission, demonstrated an association between non-traditional lipid profiles and an increased risk of 1-year vascular outcomes. Higher quartiles of the LDL/HDL ratio, TC/HDL ratio, and TG/HDL ratio, though not non-HDL levels, were significantly associated with increasing the risk of 1-year composite vascular events. These findings suggest that even during statin therapy with LDL-C < 100mg/dl on admission, there should be consideration for residual risk based on non-traditional lipid profiles. Among the parameters examined in this study, the best performance model was the LDL/HDL ratio, which is the most widely used in clinical practice. Our study is noteworthy of finding a linear relationship between the LDL/HDL ratio and the increasing risk of 1-year vascular events in ischemic stroke patients under statin treatment and admission LDL-C < 100mg/dl. Compared with the lowest quartile of the LDL/HDL ratio, the highest quartile of was more likely to be associated with increasing the risk of 1-year composite of vascular events and all-cause mortality by relatively 48% and 50%, respectively. In a prior study, when statins had already been taken before index stroke and LDL-C levels were well controlled at admission, LDL-C levels had little association with early vascular outcomes in ischemic stroke. 8 Therefore, our study provides important insights that even in patients undergoing appropriate LDL-C lowering treatment before index stroke, there could still be residual risk, which may be predicted through lipid ratio like the LDL/HDL ratio. This finding is consistent with findings in other cardiovascular diseases. Several studies have found associations between the LDL/HDL ratio, and cardiovascular event risk in patients with coronary artery diseases. 14, 15 Additionally, a previous study found that an elevated LDL/HDL ratio could be a positive predictor of aortogenic cerebral embolism. 16 In contrast, some studies found an opposing finding that a high LDL/HDL ratio protected against death, recurrence, and moderate disability within 3 months following stroke onset. 17 However, the population in these studies differed from the current investigation as were not confined to patients who were already taking statins and had their LDL-C levels appropriately controlled. In a previous study of general population without DM or cardiovascular diseases, when LDL-C was controlled below 100mg/dL by statin therapy, the LDL/HDL ratio had an HR for cardiovascular diseases event and death of 1.43 and 1.34, respectively. 18 In our study, beyond LDL-C, the risk of 1-year composite of stroke, MI, and all-cause mortality significantly and linearly increased when the LDL/HDL ratio surpassed 2.09. Currently, guidelines for dyslipidemia in stroke patients specify a target goal for LDL-C levels, but do not clearly define targets for other lipid profiles. 19 We included patients with the LDL-C level at < 100mg/dl on admission, not 100mg/dl as the criterion for high-intensity statin therapy. 19 The SPARCL study included patients with acute ischemic stroke and LDL-C > 100mg/dl. 3 Recent guidelines mention LDL targets of < 70mg/dl or even < 55mg/dl, 19, 20 but these targets primarily guide atherosclerotic stroke management. The applicability to other stroke mechanism or etiologies such as SVO or UD remains uncertain and requires further research. Initiating lipid-lowering treatment in non-CE stroke may be considered when LDL-C is > 100mg/dl. In a previous meta-analysis, the TC/HDL ratio demonstrated a linear correlation with stroke outcomes. 21 For each 1-unit increase in the TC/HDL ratio, the risk of stroke increased by 16%. In contrast, we found a non-linear relationship of the TC/HDL ratio and 1-year composite of stroke, MI, and all-cause mortality, with lower risk with a lower TC/HDL ratio. In addition, compared with Q1 of the TC/HDL ratio, higher quartiles were significantly associated with increasing risk of 1-year composite of stroke, MI, and all-cause mortality (adjusted HR 1.21, 1.26, and 1.40 in Q2, Q3, and Q4, respectively) after adjustments of relevant variables. When the TC/HDL ratio was 3.65 or lower, there was a reduced HR for a one-year composite of stroke, MI, and all-cause mortality. Our study also revealed a non-linear relationship for the TG/HDL ratio, with the highest risk observed at 3.6. The highest quartile of the TG/HDL ratio, compared with lowest quartile, was associated with relative 30% increased risk of composite vascular events within 1 year. In a meta-analysis investigating the TG/HDL ratio and stroke risk, it was found that the highest category had a 1.24 times greater risk of stroke compared to the lowest category. 21 However, it's worth noting that other studies have reported inconsistent findings, with some suggesting that higher TG/HDL ratio is linked to favorable outcomes. 22 In our study, we found that non-HDL does not have a strong association with predicting residual risk, similar to LDL-C. While dyslipidemia guidelines recommend maintaining non-HDL below 100mg/dl for ASCVD patients, when stroke patients are already on statins and have LDL levels below 100 mg/dl, it appears that lipid ratios may be more helpful in predicting the risk of vascular events than non-HDL. There are several limitations of this study. First, information regarding aspects of statin pretreatment were lacking. Details such as intensity, duration, type of statin, or dose of statin pretreatment were not available. Second, as a registry-based retrospective study, there are inherent limitations of observational data to consider. Despite adjustment for various variables, the potential impact of unmeasured or residual confounding variables may not be entirely eliminated. Third, this study was conducted in only South Korea, which could introduce an additional element of confounding when considering differences in lipid profiles among different ethnic groups. Fourth, there is a possibility of index event bias in the analysis of stroke patients who received statin treatment with LDL-C levels of 100 or lower. 23 This might affect the relationship between baseline risk factors and the outcome of interest. However, the findings of this study would still be applicable to patients matching those analyzed. In conclusion, our study found that, in ischemic stroke patients whose LDL-C levels were already controlled with statin, higher LDL/HDL ratio, TC/HDL ratio, and TG/HDL ratio, though not non-HDL levels, were associated with residual risk of 1-year composite of stroke, MI, and all-cause mortality. The risk with LDL/HDL particularly increased when the ratio value reached 2.09 or higher and showed a linear association with the 1-year primary vascular outcome. Our results suggest that non-traditional lipid profiles, particularly the LDL/HDL ratio, may be helpful in predicting the risk of subsequent vascular events for patients with ischemic stroke occurring despite well-controlled LDL with statin pretreatment. Declarations Acknowledgements: None Declaration of Competing interests: The authors declare that they have no competing interests. Disclosure: None Funding: This research was supported by funding (2023-ER1006-00) from Research of Korea Centers for Disease Control and Prevention. This study was supported by a grant (BCRI24042) of Chonnam National University Hospital Biomedical Research Institute. Data availability: Data used in this study are available upon reasonable request following submission of a legitimate academic research proposal to be assessed by the CRCS-K steering committee. Acknowledgements: None Authors’ contributions: - Study concept and design: JT Kim, H Kim, HJ Bae - Acquisition of data: JT Kim, MS Park, BJ Kim, J Kang, KJ Lee, JM Park, K Kang, SJ Lee, JG Kim, JK Cha, DH Kim, TH Park, K Lee, J Lee, KS Hong, YJ Cho, HK Park, BC Lee, KY Yu, MS Oh, DE Kim, WS Ryu, JC Choi, JH Kwon, WJ Kim, DI Shin, KS Yum, SI Sohn, JH Hong, J Lee, KY Park, HJ Bae - Analysis and interpretation of data: JT Kim, HJ Bae, JS Lee - Drafting of the manuscript: JT Kim, H Kim, JLS - All authors read and approved the final manuscript. References Cholesterol Treatment Trialists C, Mihaylova B, Emberson J, et al. The effects of lowering LDL cholesterol with statin therapy in people at low risk of vascular disease: meta-analysis of individual data from 27 randomised trials. Lancet 2012; 380: 581-590. 20120517. DOI: 10.1016/S0140-6736(12)60367-5. Amarenco P, Bogousslavsky J, Callahan A, 3rd, et al. High-dose atorvastatin after stroke or transient ischemic attack. N Engl J Med 2006; 355: 549-559. DOI: 10.1056/NEJMoa061894. Amarenco P, Kim JS, Labreuche J, et al. A Comparison of Two LDL Cholesterol Targets after Ischemic Stroke. N Engl J Med 2020; 382: 9. 20191118. DOI: 10.1056/NEJMoa1910355. Nesto RW. Beyond low-density lipoprotein: addressing the atherogenic lipid triad in type 2 diabetes mellitus and the metabolic syndrome. Am J Cardiovasc Drugs 2005; 5: 379-387. DOI: 10.2165/00129784-200505060-00005. Di Giorgi N, Michelucci E, Smit JM, et al. A specific plasma lipid signature associated with high triglycerides and low HDL cholesterol identifies residual CAD risk in patients with chronic coronary syndrome. Atherosclerosis 2021; 339: 1-11. 20211111. DOI: 10.1016/j.atherosclerosis.2021.11.013. Johannesen CDL, Mortensen MB, Langsted A, et al. Apolipoprotein B and Non-HDL Cholesterol Better Reflect Residual Risk Than LDL Cholesterol in Statin-Treated Patients. J Am Coll Cardiol 2021; 77: 1439-1450. DOI: 10.1016/j.jacc.2021.01.027. Liu X, Yan L and Xue F. The associations of lipids and lipid ratios with stroke: A prospective cohort study. J Clin Hypertens (Greenwich) 2019; 21: 127-135. 20181121. DOI: 10.1111/jch.13441. Kim JT, Lee JS, Kim BJ, et al. Admission LDL-cholesterol, statin pretreatment and early outcomes in acute ischemic stroke. J Clin Lipidol 2023 20230808. DOI: 10.1016/j.jacl.2023.08.002. Kim JT, Lee JS, Kim BJ, et al. Statin Treatment in Patients With Stroke With Low-Density Lipoprotein Cholesterol Levels Below 70 mg/dL. J Am Heart Assoc 2023; 12: e030738. 20230908. DOI: 10.1161/JAHA.123.030738. Kim BJ, Han MK, Park TH, et al. Current status of acute stroke management in Korea: a report on a multicenter, comprehensive acute stroke registry. Int J Stroke 2014; 9: 514-518. 2013/11/22. DOI: 10.1111/ijs.12199. Kim BJ, Park JM, Kang K, et al. Case characteristics, hyperacute treatment, and outcome information from the clinical research center for stroke-fifth division registry in South Korea. J Stroke 2015; 17: 38-53. 2015/02/19. DOI: 10.5853/jos.2015.17.1.38. Adams HP, Jr., Bendixen BH, Kappelle LJ, et al. Classification of subtype of acute ischemic stroke. Definitions for use in a multicenter clinical trial. TOAST. Trial of Org 10172 in Acute Stroke Treatment. Stroke 1993; 24: 35-41. 1993/01/01. DOI: 10.1161/01.str.24.1.35. Ko Y, Lee S, Chung JW, et al. MRI-based Algorithm for Acute Ischemic Stroke Subtype Classification. J Stroke 2014; 16: 161-172. 2014/10/21. DOI: 10.5853/jos.2014.16.3.161. Yang T, Liu Y, Li L, et al. Correlation between the triglyceride-to-high-density lipoprotein cholesterol ratio and other unconventional lipid parameters with the risk of prediabetes and Type 2 diabetes in patients with coronary heart disease: a RCSCD-TCM study in China. Cardiovasc Diabetol 2022; 21: 93. 20220603. DOI: 10.1186/s12933-022-01531-7. Sun T, Chen M, Shen H, et al. Predictive value of LDL/HDL ratio in coronary atherosclerotic heart disease. BMC Cardiovasc Disord 2022; 22: 273. 20220617. DOI: 10.1186/s12872-022-02706-6. Okuzumi A, Ueno Y, Shimada Y, et al. Impact of low-density lipoprotein to high-density lipoprotein ratio on aortic arch atherosclerosis in unexplained stroke. J Neurol Sci 2013; 326: 83-88. 20130212. DOI: 10.1016/j.jns.2013.01.019. Liu L, Yin P, Lu C, et al. Association of LDL-C/HDL-C Ratio With Stroke Outcomes Within 1 Year After Onset: A Hospital-Based Follow-Up Study. Front Neurol 2020; 11: 408. 20200515. DOI: 10.3389/fneur.2020.00408. Mora S, Glynn RJ, Boekholdt SM, et al. On-treatment non-high-density lipoprotein cholesterol, apolipoprotein B, triglycerides, and lipid ratios in relation to residual vascular risk after treatment with potent statin therapy: JUPITER (justification for the use of statins in prevention: an intervention trial evaluating rosuvastatin). J Am Coll Cardiol 2012; 59: 1521-1528. DOI: 10.1016/j.jacc.2011.12.035. Kleindorfer DO, Towfighi A, Chaturvedi S, et al. 2021 Guideline for the Prevention of Stroke in Patients With Stroke and Transient Ischemic Attack: A Guideline From the American Heart Association/American Stroke Association. Stroke 2021; 52: e364-e467. 20210524. DOI: 10.1161/STR.0000000000000375. Authors/Task Force M, Guidelines ESCCfP and Societies ESCNC. 2019 ESC/EAS guidelines for the management of dyslipidaemias: Lipid modification to reduce cardiovascular risk. Atherosclerosis 2019; 290: 140-205. 20190831. DOI: 10.1016/j.atherosclerosis.2019.08.014. Liu Y, Jin X, Fu K, et al. Non-traditional lipid profiles and the risk of stroke: A systematic review and meta-analysis. Nutr Metab Cardiovasc Dis 2023; 33: 698-714. 20230111. DOI: 10.1016/j.numecd.2023.01.003. Deng QW, Li S, Wang H, et al. The Short-term Prognostic Value of the Triglyceride-to-high-density Lipoprotein Cholesterol Ratio in Acute Ischemic Stroke. Aging Dis 2018; 9: 498-506. 20180601. DOI: 10.14336/AD.2017.0629. Dahabreh IJ and Kent DM. Index event bias as an explanation for the paradoxes of recurrence risk research. JAMA 2011; 305: 822-823. DOI: 10.1001/jama.2011.163. Additional Declarations No competing interests reported. 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Saver","email":"","orcid":"","institution":"University of California","correspondingAuthor":false,"prefix":"","firstName":"Jeffrey","middleName":"L.","lastName":"Saver","suffix":""},{"id":319844249,"identity":"df264edf-ef68-45aa-bf8e-2bfa004fb0ea","order_by":35,"name":"Hee-Joon Bae","email":"","orcid":"","institution":"Seoul National University Bundang Hospital","correspondingAuthor":false,"prefix":"","firstName":"Hee-Joon","middleName":"","lastName":"Bae","suffix":""}],"badges":[],"createdAt":"2024-06-12 06:06:06","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-4567821/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-4567821/v1","draftVersion":[],"editorialEvents":[{"content":"https://doi.org/10.1038/s41598-024-73851-5","type":"published","date":"2024-10-01T15:56:53+00:00"}],"editorialNote":"","failedWorkflow":false,"files":[{"id":60188148,"identity":"0576f752-e0f5-4cb4-9540-b9577446c276","added_by":"auto","created_at":"2024-07-12 19:14:56","extension":"jpg","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":386408,"visible":true,"origin":"","legend":"\u003cp\u003eKaplan-Meier survival plots for 1-year primary outcome according to the LDL/HDL ratio, unadjusted (A) and adjusted plots (B).\u003c/p\u003e","description":"","filename":"figure1.jpg","url":"https://assets-eu.researchsquare.com/files/rs-4567821/v1/8097c06dc6c9da79ee3ef912.jpg"},{"id":60189011,"identity":"448dfea2-9e51-414c-a7ea-e111b680f46e","added_by":"auto","created_at":"2024-07-12 19:22:56","extension":"jpg","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":204687,"visible":true,"origin":"","legend":"\u003cp\u003eAdjusted HR of continuous LDL/HDL ratio for primary outcome within 1 year.\u003c/p\u003e","description":"","filename":"figure2.jpg","url":"https://assets-eu.researchsquare.com/files/rs-4567821/v1/84aa1fcad3572acf945f2070.jpg"},{"id":60188145,"identity":"04c7dfd5-a79d-4a6f-999d-dec10cb7dd42","added_by":"auto","created_at":"2024-07-12 19:14:56","extension":"jpg","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":342720,"visible":true,"origin":"","legend":"\u003cp\u003eAssociations of LDL/HDL ratio with one-year vascular outcomes according to the ischemic stroke subtypes.\u003c/p\u003e","description":"","filename":"figure3.jpg","url":"https://assets-eu.researchsquare.com/files/rs-4567821/v1/0dfe501b0aa0165c160ef0ad.jpg"},{"id":66097076,"identity":"4cd0260d-d315-4f64-97ac-5bef5dd958ab","added_by":"auto","created_at":"2024-10-07 16:13:23","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1796877,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-4567821/v1/9d125611-9622-43c5-8acb-4a466c396989.pdf"},{"id":60188149,"identity":"76d5d688-7065-4f3b-b6aa-09a8fb25aef8","added_by":"auto","created_at":"2024-07-12 19:14:57","extension":"docx","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":1103385,"visible":true,"origin":"","legend":"","description":"","filename":"ONLINESUPPLEMENTSsubmit.docx","url":"https://assets-eu.researchsquare.com/files/rs-4567821/v1/c38f7f27c951cc71dc80d56e.docx"}],"financialInterests":"No competing interests reported.","formattedTitle":"Non-Traditional Lipid Profiles and 1-Year Vascular Outcomes in Ischemic Stroke Patients with Prior Statin Therapy and LDL-C \u003c100 mg/dL","fulltext":[{"header":"INTRODUCTION","content":"\u003cp\u003eStroke constitutes a significant cause of morbidity and mortality, especially among the elderly population. Lowering low-density lipoprotein cholesterol (LDL-C) through statin treatment is crucial for preventing atherosclerotic cardiovascular diseases, including stroke. \u003csup\u003e1\u003c/sup\u003e For secondary prevention, the Stroke Prevention by Aggressive Reduction in Cholesterol Levels (SPARCL) trial emphasized the benefit of high-dose statin therapy for stroke patients with admission LDL-C\u0026thinsp;\u0026gt;\u0026thinsp;100mg/dL. \u003csup\u003e2\u003c/sup\u003e Additionally, the Treat Stroke to Target (TST) trial demonstrated that maintaining LDL-C\u0026thinsp;\u0026lt;\u0026thinsp;70 mg/dL significantly reduces the risk of long-term vascular events compared to keeping LDL-C in the range of 90-110mg/dL in atherosclerotic ischemic stroke. \u003csup\u003e3\u003c/sup\u003e\u003c/p\u003e \u003cp\u003eIn addition to LDL-C, non-traditional lipid profiles, such as non-high density lipoprotein (non-HDL) cholesterol and lipid ratios, have been recognized as contributors to residual vascular risk. \u003csup\u003e4\u003c/sup\u003e Previous studies have considered non-HDL cholesterol, triglyceride (TG)/HDL ratio, and LDL/HDL ratio as residual risk factors for cardiovascular diseases in patients with coronary artery diseases. \u003csup\u003e5, 6\u003c/sup\u003e Elevated lipid ratios in patients with general risk factors have been also associated with an increased risk of stroke. \u003csup\u003e7\u003c/sup\u003e\u003c/p\u003e \u003cp\u003eAmong ischemic stroke patients, with the escalating use of cholesterol-lowering therapy for primary and secondary prevention, there is an increasing proportion of patients who were already on statin treatment before the index event and had well-controlled LDL-C levels (\u0026lt;\u0026thinsp;100 mg/dL) upon admission. In a previous study, there was a relationship between admission LDL-C levels and early vascular outcomes for patients not taking a statin at the time of the index event but not for patients already on statins. \u003csup\u003e8\u003c/sup\u003e Therefore, the best post-stroke target lipid level for patients who were already on statins and had well-controlled LDL-C levels at the time of their index stroke remains unclear. In such cases, the impact of non-traditional lipid profiles on outcome might need consideration. Research on the prognostic implications and targets for treatment of non-traditional lipid profiles for patients with statin pretreatment and baseline LDL-C\u0026thinsp;\u0026lt;\u0026thinsp;100 mg/dL remains limited. While statin therapy may be associated with the reduced risk of vascular outcome for these patients with low LDL-C on admission, \u003csup\u003e9\u003c/sup\u003e understanding the clinical significance of non-traditional lipid profiles beyond LDL-C could hold importance.\u003c/p\u003e \u003cp\u003eTherefore, this study aimed to investigate the association between non-traditional lipid profiles and the risk of 1-year vascular events in patients who were already using statins before index stroke and had admission LDL-C\u0026thinsp;\u0026lt;\u0026thinsp;100 mg/dL.\u003c/p\u003e"},{"header":"METHODS","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003eSubjects\u003c/h2\u003e \u003cp\u003eThis study was an analysis of a prospective, multicenter, nationwide registry of consecutive patients with acute stroke or transient ischemic attack (TIA) admitted to 18 academic hospitals in South Korea, the Clinical Research Center for Stroke-Korea (CRCS-K) registry. Detailed methodologic information about the CRCS-K registry has been reported previously. \u003csup\u003e10, 11\u003c/sup\u003e We identified patients with acute cerebrovascular events admitted between January 2011 and July 2020 (N\u0026thinsp;=\u0026thinsp;75690). Among the patients with acute cerebrovascular events, we included ischemic stroke or TIA with lesion-positive on diffusion weighted imaging (DWI) within 7 days of onset (N\u0026thinsp;=\u0026thinsp;68468), non-cardioembolic ischemic stroke (N\u0026thinsp;=\u0026thinsp;52878), statin pretreatment before index stroke (N\u0026thinsp;=\u0026thinsp;10189) and admission LDL-C\u0026thinsp;\u0026lt;\u0026thinsp;100mg/dl(N\u0026thinsp;=\u0026thinsp;7063). Patients without information on fasting lipid profiles at admission were excluded. A detailed patient selection flowchart is shown in Supplemental Fig.\u0026nbsp;1.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003eEthics statement\u003c/h2\u003e \u003cp\u003e Clinical information was collected from the CRCS-K registry with approval from the local institutional review boards of all the participating centers. A waiver for informed consent was provided because of study subject anonymity and minimal risk to the participants. The data used in this study are available upon reasonable request following the submission of a legitimate academic research proposal to be assessed by the CRCS-K steering committee. Ethics approval: The current study was approved by local institutional review boards at all participating centers, including Chonnam National University Hospital (CNUH-2024-032).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec5\" class=\"Section2\"\u003e \u003ch2\u003eData collection\u003c/h2\u003e \u003cp\u003eDemographic, clinical, imaging, and laboratory data were prospectively collected. Lipid profiles, including LDL-C, total cholesterol (TC), HDL-C, and TG levels, were obtained during the first fasting period after admission. Four non-traditional lipid profile parameters, non-HDL, TC/HDL ratio, LDL/HDL ratio, and TG/HDL ratio, were analyzed as continuous and categorical variables. The patients were classified into quartiles for each non-traditional lipid profile for comparison. Ischemic stroke subtypes were classified according to the Trial of Org 10172 in Acute Stroke Treatment (TOAST) criteria, which were refined to incorporate additional information based on modern imaging studies. \u003csup\u003e12, 13\u003c/sup\u003e\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec6\" class=\"Section2\"\u003e \u003ch2\u003eOutcomes\u003c/h2\u003e \u003cp\u003eThe primary vascular outcome within one year was a composite of recurrent stroke (either hemorrhagic or ischemic), myocardial infarction (MI) and all-cause mortality. The secondary vascular outcomes were the individual outcomes of a) all-cause mortality, b) stroke (either ischemic or hemorrhagic), and c) MI. Detailed definitions of the vascular outcome events and methods of outcome capture used in the current study are described in the Supplemental Methods and previous reports. \u003csup\u003e10, 11\u003c/sup\u003e\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec7\" class=\"Section2\"\u003e \u003ch2\u003eStatistical analysis\u003c/h2\u003e \u003cp\u003eBaseline characteristics and outcomes were compared among each non-traditional lipid profiles quartiles by using the chi-square test, ANOVA, or Kruskal‒Wallis test according to the type of variable. The event probability of 1-year vascular outcomes according to the non-traditional lipid profiles quartiles in all patients and in patients by stroke subtype was calculated by using the Kaplan\u0026ndash;Meier method, and the log-rank test was performed to analyze differences among the groups. Hazard ratios (HRs) and 95% confidence intervals (95% Cis) for 1-year vascular outcomes were analyzed using the Cox proportional hazards model for each non-traditional lipid profiles groups. Adjustments were made for the following 18 predetermined variables with clinically relevant associations with the outcome variables; age, male sex, BMI, NIHSS score, history of stroke, history of coronary artery diseases, HTN, DM, dyslipidemia, smoking, prior antiplatelet, in-hospital anti-diabetic treatment, in-hospital antihypertensive treatment, statin, glucose, creatinine, LDL-C and SBP. In the analysis of the LDL/HDL ratio, although LDL-C has been restricted to less than 100mg/dl, it remains a crucial risk marker with significant variability. To investigate whether the LDL/HDL ratio has independent clinical significance, LDL-C was included as an adjusted variable. The modifying effect of stroke subtype on the relationships between each non-traditional lipid profiles groups and clinical outcomes was explored by separately introducing an interaction term of ischemic stroke subtype and LDL/HDL ratio quartile groups (and TC/HDL ratio) into the models.\u003c/p\u003e \u003cp\u003eTwo-sided p values\u0026thinsp;\u0026lt;\u0026thinsp;0.05 were considered indicative of significance. Given the known insensitivity of interaction testing, evidence of heterogeneity was considered present with p values\u0026thinsp;\u0026le;\u0026thinsp;0.10. In addition, the goodness of fit of the four models were compared using the Akaike (AIC) and Bayesian information criterion (BIC). Lower AIC and BIC indicate a better fit. Statistical analyses were performed with R software using the \u0026ldquo;rms\u0026rdquo; package (version 3.6.0, R Foundation for Statistical Computing, Vienna, Austria) and SAS version 9.4 (SAS Institute Inc., Cary, NC, USA).\u003c/p\u003e \u003c/div\u003e"},{"header":"RESULTS","content":"\u003cdiv id=\"Sec9\" class=\"Section2\"\u003e \u003ch2\u003eGeneral characteristics\u003c/h2\u003e \u003cp\u003eA total of 7,028 patients (mean age 70.3\u0026thinsp;\u0026plusmn;\u0026thinsp;10.8 years, male 59.8%) met study entry criteria. The median NIHSS score was 3 (IQR 1\u0026ndash;6) and the mean LDL-C level on admission was 69.7\u0026thinsp;\u0026plusmn;\u0026thinsp;17.3mg/dL. For lipid profiles at admission, the mean levels of HDL-C, TG, TC, and non-HDL were 43.2\u0026thinsp;\u0026plusmn;\u0026thinsp;12.5mg/dL, 116.4\u0026thinsp;\u0026plusmn;\u0026thinsp;72.1mg/dL, 130.4\u0026thinsp;\u0026plusmn;\u0026thinsp;24.8mg/dL, and 87.2\u0026thinsp;\u0026plusmn;\u0026thinsp;22.3mg/dL, respectively. The mean LDL-C/HDL-C ratio, TG/HDL ratio, and TC/HDL ratio was 1.72\u0026thinsp;\u0026plusmn;\u0026thinsp;0.61, 3.06\u0026thinsp;\u0026plusmn;\u0026thinsp;2.63, and 3.20\u0026thinsp;\u0026plusmn;\u0026thinsp;1.08, respectively. Non-statin lipid-lowering agents, in addition to statins, were used in 6.5% of patients before index stroke. At discharge, 92.2% of the study subjects received statin treatment.\u003c/p\u003e \u003cp\u003eThe demographic and clinical characteristics of subjects according to the quartiles of non-traditional lipid profiles (LDL/HDL ratio, TC/HDL ratio, TG/HDL ratio, and non-HDL) are presented in Table \u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e and Supplemental Table\u0026nbsp;1\u0026ndash;3. As the quartile of the LDL/HDL ratio increased, there were significant trends of decreasing age and increasing body weight and BMI. Additionally, the frequency of DM, HTN, and history of CAD significantly increased as the quartile of LDL/HDL ratio increased (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eGeneral characteristics of subjects according to the quartiles of LDL/HDL ratio.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"8\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eAll\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eQ1\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eQ2\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eQ3\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eQ4\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003eP\u003csup\u003e\u0026dagger;\u003c/sup\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c8\"\u003e \u003cp\u003eP\u003csub\u003etrend\u003c/sub\u003e\u003csup\u003e\u0026Dagger;\u003c/sup\u003e\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eN\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e7028\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1767\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1738\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1751\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1772\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLDL/HDL ratio\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAge, mean\u0026thinsp;\u0026plusmn;\u0026thinsp;SD\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e70.3\u0026thinsp;\u0026plusmn;\u0026thinsp;10.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e70.9\u0026thinsp;\u0026plusmn;\u0026thinsp;11.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e70.4\u0026thinsp;\u0026plusmn;\u0026thinsp;10.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e70.5\u0026thinsp;\u0026plusmn;\u0026thinsp;10.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e69.5\u0026thinsp;\u0026plusmn;\u0026thinsp;11.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMale, n (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e4206 (59.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1000 (56.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e962 (55.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1055 (60.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1189 (67.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;.0001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;.0001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eOnset-to-arrival\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ewithin 24h, n (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e4432 (63.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1151 (65.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1125 (64.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1108 (63.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1048 (59.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.0002\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHeight, cm, mean\u0026thinsp;\u0026plusmn;\u0026thinsp;SD\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e161.8\u0026thinsp;\u0026plusmn;\u0026thinsp;8.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e161.3\u0026thinsp;\u0026plusmn;\u0026thinsp;8.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e160.9\u0026thinsp;\u0026plusmn;\u0026thinsp;8.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e161.9\u0026thinsp;\u0026plusmn;\u0026thinsp;8.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e163.0\u0026thinsp;\u0026plusmn;\u0026thinsp;8.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;.0001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;.0001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eWeight, kg, mean\u0026thinsp;\u0026plusmn;\u0026thinsp;SD\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e63.3\u0026thinsp;\u0026plusmn;\u0026thinsp;11.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e61.4\u0026thinsp;\u0026plusmn;\u0026thinsp;11.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e62.3\u0026thinsp;\u0026plusmn;\u0026thinsp;10.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e63.9\u0026thinsp;\u0026plusmn;\u0026thinsp;11.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e65.3\u0026thinsp;\u0026plusmn;\u0026thinsp;11.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;.0001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;.0001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBMI, %\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e24.1\u0026thinsp;\u0026plusmn;\u0026thinsp;3.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e23.5\u0026thinsp;\u0026plusmn;\u0026thinsp;3.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e24.0\u0026thinsp;\u0026plusmn;\u0026thinsp;3.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e24.3\u0026thinsp;\u0026plusmn;\u0026thinsp;3.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e24.5\u0026thinsp;\u0026plusmn;\u0026thinsp;3.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;.0001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;.0001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNIHSS, med (IQR)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e3 (\u003cspan additionalcitationids=\"CR2 CR3 CR4 CR5\" citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e3 (\u003cspan additionalcitationids=\"CR2 CR3 CR4 CR5\" citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e3 (\u003cspan additionalcitationids=\"CR2 CR3 CR4 CR5\" citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e3 (\u003cspan additionalcitationids=\"CR2 CR3 CR4 CR5\" citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e3 (\u003cspan additionalcitationids=\"CR2 CR3 CR4 CR5\" citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.82\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.06\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePremorbid mRS 0\u0026ndash;1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e5629 (80.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1376 (77.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1417 (81.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1419 (81.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1417 (80.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.033\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.17\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHistory of stroke\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e3222 (45.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e900 (50.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e826 (47.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e742 (42.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e754 (42.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;.0001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;.0001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHistory of TIA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e285 (4.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e71 (4.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e69 (4.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e71 (4.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e74 (4.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.99\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.79\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHistory of PAD\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e98 (1.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e18 (1.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e25 (1.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e29 (1.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e26 (1.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.43\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.21\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHistory of CAD\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1476 (21.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e333 (18.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e352 (20.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e391 (22.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e400 (22.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.02\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.002\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHTN\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e5851 (83.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1450 (82.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1449 (83.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1475 (84.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1477 (83.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.38\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.23\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDM\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e3637 (51.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e820 (46.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e858 (49.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e921 (52.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1038 (58.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;.0001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;.0001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDyslipidemia\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e5108 (72.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1262 (71.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1270 (73.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1310 (74.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1266 (71.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.07\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.71\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=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;.0001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.003\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNever\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e4559 (64.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1199 (67.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1175 (67.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1133 (64.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1052 (59.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCurrent\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1228 (17.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e244 (13.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e273 (15.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e316 (18.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e395 (22.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eEx (beyond 5\u0026nbsp;year)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e821 (11.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e202 (11.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e209 (12.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e198 (11.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e212 (12.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRecent (within 5\u0026nbsp;year)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e420 (6.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e122 (6.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e81 (4.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e104 (5.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e113 (6.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMedication history\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAntihypertensive\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e5402 (76.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1306 (73.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1324 (76.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1394 (79.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1378 (77.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAndi-diabetics\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e3226 (45.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e748 (42.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e771 (44.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e806 (46.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e901 (50.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;.0001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;.0001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLipid lowering agents\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e455 (6.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e128 (7.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e120 (6.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e88 (5.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e119 (6.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.04\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.19\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAntiplatelet\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e4909 (69.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1263 (71.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1218 (70.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1190 (68.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1238 (69.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.16\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.16\u003c/p\u003e \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\u003e3332 (47.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e825 (46.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e827 (47.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e803 (45.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e877 (49.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.16\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.21\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eclopidogrel\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2426 (34.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e628 (35.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e599 (34.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e576 (32.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e623 (35.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.36\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.59\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eReperfusion therapy\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.07\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.01\u003c/p\u003e \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\u003e6215 (88.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1537 (87.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1531 (88.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1553 (88.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1594 (90.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eIVT\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e441 (6.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e120 (6.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e107 (6.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e111 (6.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e103 (5.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eEVT\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e216 (3.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e69 (3.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e61 (3.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e41 (2.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e45 (2.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eIV\u0026thinsp;+\u0026thinsp;EVT\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e156 (2.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e41 (2.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e39 (2.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e46 (2.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e30 (1.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLaboratory findings\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eWBC counts\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e8.1\u0026thinsp;\u0026plusmn;\u0026thinsp;3.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e8.0\u0026thinsp;\u0026plusmn;\u0026thinsp;3.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e8.0\u0026thinsp;\u0026plusmn;\u0026thinsp;3.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e8.0\u0026thinsp;\u0026plusmn;\u0026thinsp;3.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e8.3\u0026thinsp;\u0026plusmn;\u0026thinsp;3.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.02\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.0062\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePlatelet counts\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e227.4\u0026thinsp;\u0026plusmn;\u0026thinsp;77.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e220.0\u0026thinsp;\u0026plusmn;\u0026thinsp;81.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e227.4\u0026thinsp;\u0026plusmn;\u0026thinsp;78.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e227.5\u0026thinsp;\u0026plusmn;\u0026thinsp;72.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e234.6\u0026thinsp;\u0026plusmn;\u0026thinsp;76.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;.0001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;.0001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHemoglobin\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e13.0\u0026thinsp;\u0026plusmn;\u0026thinsp;2.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e12.9\u0026thinsp;\u0026plusmn;\u0026thinsp;1.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e13.0\u0026thinsp;\u0026plusmn;\u0026thinsp;2.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e13.1\u0026thinsp;\u0026plusmn;\u0026thinsp;2.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e13.2\u0026thinsp;\u0026plusmn;\u0026thinsp;2.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.002\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.0001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGlucose\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e152.2\u0026thinsp;\u0026plusmn;\u0026thinsp;68.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e147.5\u0026thinsp;\u0026plusmn;\u0026thinsp;68.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e147.8\u0026thinsp;\u0026plusmn;\u0026thinsp;61.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e153.0\u0026thinsp;\u0026plusmn;\u0026thinsp;67.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e160.2\u0026thinsp;\u0026plusmn;\u0026thinsp;74.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;.0001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;.0001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBUN\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e18.6\u0026thinsp;\u0026plusmn;\u0026thinsp;10.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e18.6\u0026thinsp;\u0026plusmn;\u0026thinsp;10.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e18.2\u0026thinsp;\u0026plusmn;\u0026thinsp;9.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e18.6\u0026thinsp;\u0026plusmn;\u0026thinsp;9.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e19.2\u0026thinsp;\u0026plusmn;\u0026thinsp;10.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.03\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.0439\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCreatinine\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.2\u0026thinsp;\u0026plusmn;\u0026thinsp;1.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.1\u0026thinsp;\u0026plusmn;\u0026thinsp;1.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.1\u0026thinsp;\u0026plusmn;\u0026thinsp;0.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.1\u0026thinsp;\u0026plusmn;\u0026thinsp;1.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1.3\u0026thinsp;\u0026plusmn;\u0026thinsp;1.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;.0001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;.0001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLDL-cholesterol\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e69.7\u0026thinsp;\u0026plusmn;\u0026thinsp;17.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e54.4\u0026thinsp;\u0026plusmn;\u0026thinsp;15.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e67.9\u0026thinsp;\u0026plusmn;\u0026thinsp;14.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e74.6\u0026thinsp;\u0026plusmn;\u0026thinsp;13.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e81.7\u0026thinsp;\u0026plusmn;\u0026thinsp;12.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;.0001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;.0001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTC\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e130.4\u0026thinsp;\u0026plusmn;\u0026thinsp;24.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e122.5\u0026thinsp;\u0026plusmn;\u0026thinsp;26.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e129.3\u0026thinsp;\u0026plusmn;\u0026thinsp;25.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e133.0\u0026thinsp;\u0026plusmn;\u0026thinsp;22.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e136.9\u0026thinsp;\u0026plusmn;\u0026thinsp;21.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;.0001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;.0001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTG\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e116.4\u0026thinsp;\u0026plusmn;\u0026thinsp;72.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e91.5\u0026thinsp;\u0026plusmn;\u0026thinsp;57.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e106.0\u0026thinsp;\u0026plusmn;\u0026thinsp;59.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e121.6\u0026thinsp;\u0026plusmn;\u0026thinsp;71.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e146.2\u0026thinsp;\u0026plusmn;\u0026thinsp;84.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;.0001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;.0001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHDL\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e43.2\u0026thinsp;\u0026plusmn;\u0026thinsp;12.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e53.9\u0026thinsp;\u0026plusmn;\u0026thinsp;14.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e45.8\u0026thinsp;\u0026plusmn;\u0026thinsp;9.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e40.3\u0026thinsp;\u0026plusmn;\u0026thinsp;7.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e32.9\u0026thinsp;\u0026plusmn;\u0026thinsp;6.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;.0001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;.0001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003enon-HDL\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e87.2\u0026thinsp;\u0026plusmn;\u0026thinsp;22.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e68.6\u0026thinsp;\u0026plusmn;\u0026thinsp;18.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e83.4\u0026thinsp;\u0026plusmn;\u0026thinsp;17.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e92.7\u0026thinsp;\u0026plusmn;\u0026thinsp;17.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e103.9\u0026thinsp;\u0026plusmn;\u0026thinsp;19.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;.0001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;.0001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSBP\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e146.5\u0026thinsp;\u0026plusmn;\u0026thinsp;25.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e146.0\u0026thinsp;\u0026plusmn;\u0026thinsp;25.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e146.8\u0026thinsp;\u0026plusmn;\u0026thinsp;26.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e147.5\u0026thinsp;\u0026plusmn;\u0026thinsp;25.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e145.6\u0026thinsp;\u0026plusmn;\u0026thinsp;25.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.14\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.83\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eIn-hospital treatment\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\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\u003e5934 (84.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1444 (81.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1496 (86.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1481 (84.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1513 (85.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.002\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.01\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eClopidogrel\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e4183 (59.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1028 (58.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1054 (60.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1040 (59.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1061 (59.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.51\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.46\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eOther antiplatelet\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e743 (10.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e226 (12.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e171 (9.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e174 (9.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e172 (9.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.01\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.01\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAnti-diabetics\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2710 (38.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e616 (34.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e649 (37.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e693 (39.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e752 (42.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;.0001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;.0001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAnti-hypertensives\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e3645 (51.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e890 (50.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e882 (50.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e943 (53.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e930 (52.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.14\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.08\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLipid lowering agents other than statin\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e165 (2.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e47 (2.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e34 (2.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e35 (2.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e49 (2.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.24\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.82\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eStatin\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e6483 (92.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1606 (90.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1613 (92.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1628 (93.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1636 (92.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.08\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.12\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"8\"\u003e\u0026dagger; P-value by Chi-square test, ANOVA and Kruskal-Wallis Test.\u003c/td\u003e\u003c/tr\u003e \u003ctr\u003e\u003ctd colspan=\"8\"\u003e\u0026Dagger; P-value by Cochran-Armitage trend test, Cochran-Mantel-Haenszel test and linear contrasts test in ANOVA\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec10\" class=\"Section2\"\u003e \u003ch2\u003eOne-year vascular outcomes\u003c/h2\u003e \u003cp\u003eThe mean follow-up duration was 330\u0026thinsp;\u0026plusmn;\u0026thinsp;88.9 days, and 91.5% of the study subjects completed 1-year of follow-up. The 1-year cumulative incidences of the composite of stroke, MI and all-cause mortality was 14.5%; all-cause mortality, 8.9%; stroke (either ischemic or hemorrhagic), 7.2%; and MI, 0.5%. In crude analysis, the 1-year cumulative incidences of the composite of stroke, MI, and all-cause mortality did not significantly differ among the quartiles of the LDL/HDL ratio: 14.9% in the lowest quartile (Q1) of the LDL/HDL ratio, 13.4% in the second quartile (Q2), 14.1% in the third quartile (Q3), and 15.7% in the highest quartile (Q4)(Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e, p for trend\u0026thinsp;=\u0026thinsp;0.59). The 1-year cumulative incidences of stroke, all-cause mortality, and MI as individual outcomes also did not show significant differences according to the quartiles of LDL/HDL ratio. Similar observations with no significant differences were made for 1-year cumulative incidences of vascular events according to the quartiles of TG/HDL ratio and TC/HDL ratio (Supplemental Table\u0026nbsp;4). However, non-HDL showed a significant association of increasing quartile levels with decreased event rates of 1-year composite vascular events and secondary outcome variables (Supplemental Table\u0026nbsp;4).\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\u003eOne-year vascular outcomes according to the quartiles of the LDL/HDL ratio.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"7\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eAll\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eQ1\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eQ2\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eQ3\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eQ4\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003eP \u003csub\u003etrend\u003c/sub\u003e\u003csup\u003eb\u003c/sup\u003e\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eN\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e7028\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1767\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1738\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1751\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1772\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePrimary outcome\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNo. of events\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e963\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e249\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e221\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e231\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e262\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e1-yar event rate (%, 95% CI)\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e14.52 (13.67\u0026ndash;15.38)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e14.88 (13.17\u0026ndash;16.59)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e13.42 (11.77\u0026ndash;15.08)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e14.05 (12.36\u0026ndash;15.74)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e15.70 (13.94\u0026ndash;17.45)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.59\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\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNo. of events\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e463\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e111\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e113\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e123\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e116\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e1-yar event rate (%, 95% CI)\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e7.24 (6.60\u0026ndash;7.88)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e6.88 (5.64\u0026ndash;8.12)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e6.95 (5.70\u0026ndash;8.19)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e7.80 (6.47\u0026ndash;9.14)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e7.30 (6.01\u0026ndash;8.60)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.68\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAll-cause mortality\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNo. of events\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e572\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e155\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e122\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e131\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e164\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e1-yar event rate (%, 95% CI)\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e8.91 (8.21\u0026ndash;9.61)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e9.55 (8.11\u0026ndash;10.99)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e7.80 (6.46\u0026ndash;9.13)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e8.18 (6.83\u0026ndash;9.52)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e10.09 (8.62\u0026ndash;11.56)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.60\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMI\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNo. of events\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e33\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e1-yar event rate (%, 95% CI)\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.54 (0.35\u0026ndash;0.72)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.38 (0.08\u0026ndash;0.69)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.52 (0.16\u0026ndash;0.89)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.57 (0.20\u0026ndash;0.94)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.68 (0.26\u0026ndash;1.10)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.33\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"7\"\u003ea Based on the Kaplan-Meier estimates\u003c/td\u003e\u003c/tr\u003e \u003ctr\u003e\u003ctd colspan=\"7\"\u003eb P-value by log-rank test for trend\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eThe unadjusted and adjusted associations of LDL/HDL ratio with 1-year vascular outcomes are shown in Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e. In unadjusted analysis, no significant associations were observed in the quartiles of LDL/HDL ratio and 1-year primary outcome and secondary outcomes. However, after adjustment for the 18 prespecified clinically relevant variables, compared with Q1 of the LDL/HDL ratio, Q4 of the LDL/HDL ratio was significantly associated with increasing the risk of 1-year composite of stroke, MI, and all-cause mortality (HR 1.48 [1.19\u0026ndash;1.83]). Similarly, compared with Q1 of the TC/HDL ratio, Q2, Q3, and Q4 of the TC/HDL ratio were significantly associated with increasing the risk of 1-year primary outcome (aHR 1.21 [1.00-1.45], 1.26 [1.04\u0026ndash;1.53], and 1.40 [1.15\u0026ndash;1.70], respectively)(Supplemental Table\u0026nbsp;5). For TG/HDL ratio, Q4 of the TG/HDL ratio, compared with Q1, was more likely to occur to 1-year primary outcome (aHR 1.30 [1.08\u0026ndash;1.57])(Supplemental Table\u0026nbsp;6). However, there were no significant associations between the quartiles of non-HDL and 1-year primary outcome (Supplemental Table\u0026nbsp;7). Kaplan-Meier survival plots for these are presented in Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e and Supplemental Fig.\u0026nbsp;2 (A-C).\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\u003eAssociations of LDL/HDL ratio with one-year vascular outcomes.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"6\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eUnadjusted HR\u003c/p\u003e \u003cp\u003e(95% CI)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eP\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eAdjusted HR\u003c/p\u003e \u003cp\u003e(95% CI)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eP\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePrimary outcomes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eQ1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1(Ref)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1(Ref)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eQ2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.89 (0.74\u0026ndash;1.06)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.19\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.09 (0.90\u0026ndash;1.32)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.39\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eQ3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.92 (0.77\u0026ndash;1.10)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.37\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.22 (1.00-1.49)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.05\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eQ4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.04 (0.87\u0026ndash;1.23)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.68\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.48 (1.19\u0026ndash;1.83)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.0004\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\u003eQ1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1(Ref)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1(Ref)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eQ2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.02 (0.78\u0026ndash;1.32)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.89\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.14 (0.86\u0026ndash;1.51)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.36\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eQ3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.10 (0.85\u0026ndash;1.42)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.46\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.34 (1.00-1.80)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.05\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eQ4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.03 (0.80\u0026ndash;1.34)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.81\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.33 (0.95\u0026ndash;1.84)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.09\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAll-cause mortality\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eQ1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1(Ref)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1(Ref)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eQ2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.79 (0.62-1.00)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.05\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.03 (0.80\u0026ndash;1.32)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.81\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eQ3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.84 (0.66\u0026ndash;1.06)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.14\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.16 (0.89\u0026ndash;1.50)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.27\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eQ4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.04 (0.84\u0026ndash;1.30)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.72\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.50 (1.15\u0026ndash;1.97)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.003\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMI\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eQ1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1(Ref)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1(Ref)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eQ2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.33 (0.46\u0026ndash;3.83)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.60\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.16 (0.38\u0026ndash;3.51)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.80\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eQ3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.48 (0.53\u0026ndash;4.17)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.45\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.06 (0.34\u0026ndash;3.37)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.92\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eQ4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.64 (0.60\u0026ndash;4.51)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.34\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.06 (0.30\u0026ndash;3.69)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.93\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"6\"\u003eAdjusted variable: age, male, NIHSS, BMI, LDL-C, history of stroke, history of CAD, HTN, DM, dyslipidemia, smoking status, prior antiplatelet, creatinine, glucose, SBP, in-hospital anti-hypertensive, in-hospital antidiabetics, in-hospital lipid lowering agents.\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eAdjusted HR plots for each non-traditional lipid profiles as continuous variables are presented in Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e and Supplemental Fig.\u0026nbsp;3 (A-C). For the LDL/HDL ratio, a linear relationship was observed (P for linearity\u0026thinsp;\u0026lt;\u0026thinsp;0.001), and the cut-off value associated with significant increase in the composite outcome within 1-year was 2.09 of LDL/HDL ratio. For other lipid profiles of TC/HDL ratio and TG/HDL ratio, non-linear associations were observed. Among the four lipid profiles models, the model for the LDL/HDL ratio had the lowest Akaike Information Criteria and Bayes Information Criterion (Supplemental Table\u0026nbsp;8).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003eTOAST subgroup analysis\u003c/h2\u003e \u003cp\u003eThere were significant interactions between LDL/HDL ratio and TOAST subgroups with 1-year primary outcome (P interaction\u0026thinsp;=\u0026thinsp;0.01). Among the LAA and SVO subtypes, compared with Q1 of LDL/HDL ratio, higher quartiles were more likely to be associated with increasing the risk of 1-year primary vascular outcomes, while no associations were observed among UD subtype (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e). For other lipid ratios and non-HDL cholesterol, there were no potential interactions of stroke subtypes with 1-year primary outcome (Supplemental Fig.\u0026nbsp;4).\u003c/p\u003e \u003c/div\u003e"},{"header":"DISCUSSION","content":"\u003cp\u003eOur study, which focused on over 7,000 patients with acute ischemic stroke who were treated with statins before the index stroke and had LDL-cholesterol levels\u0026thinsp;\u0026lt;\u0026thinsp;100mg/dl upon admission, demonstrated an association between non-traditional lipid profiles and an increased risk of 1-year vascular outcomes. Higher quartiles of the LDL/HDL ratio, TC/HDL ratio, and TG/HDL ratio, though not non-HDL levels, were significantly associated with increasing the risk of 1-year composite vascular events. These findings suggest that even during statin therapy with LDL-C\u0026thinsp;\u0026lt;\u0026thinsp;100mg/dl on admission, there should be consideration for residual risk based on non-traditional lipid profiles.\u003c/p\u003e \u003cp\u003eAmong the parameters examined in this study, the best performance model was the LDL/HDL ratio, which is the most widely used in clinical practice. Our study is noteworthy of finding a linear relationship between the LDL/HDL ratio and the increasing risk of 1-year vascular events in ischemic stroke patients under statin treatment and admission LDL-C\u0026thinsp;\u0026lt;\u0026thinsp;100mg/dl. Compared with the lowest quartile of the LDL/HDL ratio, the highest quartile of was more likely to be associated with increasing the risk of 1-year composite of vascular events and all-cause mortality by relatively 48% and 50%, respectively. In a prior study, when statins had already been taken before index stroke and LDL-C levels were well controlled at admission, LDL-C levels had little association with early vascular outcomes in ischemic stroke. \u003csup\u003e8\u003c/sup\u003e Therefore, our study provides important insights that even in patients undergoing appropriate LDL-C lowering treatment before index stroke, there could still be residual risk, which may be predicted through lipid ratio like the LDL/HDL ratio.\u003c/p\u003e \u003cp\u003eThis finding is consistent with findings in other cardiovascular diseases. Several studies have found associations between the LDL/HDL ratio, and cardiovascular event risk in patients with coronary artery diseases. \u003csup\u003e14, 15\u003c/sup\u003e Additionally, a previous study found that an elevated LDL/HDL ratio could be a positive predictor of aortogenic cerebral embolism. \u003csup\u003e16\u003c/sup\u003e In contrast, some studies found an opposing finding that a high LDL/HDL ratio protected against death, recurrence, and moderate disability within 3 months following stroke onset. \u003csup\u003e17\u003c/sup\u003e However, the population in these studies differed from the current investigation as were not confined to patients who were already taking statins and had their LDL-C levels appropriately controlled. In a previous study of general population without DM or cardiovascular diseases, when LDL-C was controlled below 100mg/dL by statin therapy, the LDL/HDL ratio had an HR for cardiovascular diseases event and death of 1.43 and 1.34, respectively. \u003csup\u003e18\u003c/sup\u003e In our study, beyond LDL-C, the risk of 1-year composite of stroke, MI, and all-cause mortality significantly and linearly increased when the LDL/HDL ratio surpassed 2.09. Currently, guidelines for dyslipidemia in stroke patients specify a target goal for LDL-C levels, but do not clearly define targets for other lipid profiles. \u003csup\u003e19\u003c/sup\u003e\u003c/p\u003e \u003cp\u003eWe included patients with the LDL-C level at \u0026lt;\u0026thinsp;100mg/dl on admission, not \u0026lt;\u0026thinsp;70mg/dl, based on current stroke guidelines that use LDL-C\u0026thinsp;\u0026gt;\u0026thinsp;100mg/dl as the criterion for high-intensity statin therapy. \u003csup\u003e19\u003c/sup\u003e The SPARCL study included patients with acute ischemic stroke and LDL-C\u0026thinsp;\u0026gt;\u0026thinsp;100mg/dl. \u003csup\u003e3\u003c/sup\u003e Recent guidelines mention LDL targets of \u0026lt;\u0026thinsp;70mg/dl or even \u0026lt;\u0026thinsp;55mg/dl, \u003csup\u003e19, 20\u003c/sup\u003e but these targets primarily guide atherosclerotic stroke management. The applicability to other stroke mechanism or etiologies such as SVO or UD remains uncertain and requires further research. Initiating lipid-lowering treatment in non-CE stroke may be considered when LDL-C is \u0026gt;\u0026thinsp;100mg/dl.\u003c/p\u003e \u003cp\u003eIn a previous meta-analysis, the TC/HDL ratio demonstrated a linear correlation with stroke outcomes. \u003csup\u003e21\u003c/sup\u003e For each 1-unit increase in the TC/HDL ratio, the risk of stroke increased by 16%. In contrast, we found a non-linear relationship of the TC/HDL ratio and 1-year composite of stroke, MI, and all-cause mortality, with lower risk with a lower TC/HDL ratio. In addition, compared with Q1 of the TC/HDL ratio, higher quartiles were significantly associated with increasing risk of 1-year composite of stroke, MI, and all-cause mortality (adjusted HR 1.21, 1.26, and 1.40 in Q2, Q3, and Q4, respectively) after adjustments of relevant variables. When the TC/HDL ratio was 3.65 or lower, there was a reduced HR for a one-year composite of stroke, MI, and all-cause mortality.\u003c/p\u003e \u003cp\u003eOur study also revealed a non-linear relationship for the TG/HDL ratio, with the highest risk observed at 3.6. The highest quartile of the TG/HDL ratio, compared with lowest quartile, was associated with relative 30% increased risk of composite vascular events within 1 year. In a meta-analysis investigating the TG/HDL ratio and stroke risk, it was found that the highest category had a 1.24 times greater risk of stroke compared to the lowest category. \u003csup\u003e21\u003c/sup\u003e However, it's worth noting that other studies have reported inconsistent findings, with some suggesting that higher TG/HDL ratio is linked to favorable outcomes. \u003csup\u003e22\u003c/sup\u003e\u003c/p\u003e \u003cp\u003eIn our study, we found that non-HDL does not have a strong association with predicting residual risk, similar to LDL-C. While dyslipidemia guidelines recommend maintaining non-HDL below 100mg/dl for ASCVD patients, when stroke patients are already on statins and have LDL levels below 100 mg/dl, it appears that lipid ratios may be more helpful in predicting the risk of vascular events than non-HDL.\u003c/p\u003e \u003cp\u003eThere are several limitations of this study. First, information regarding aspects of statin pretreatment were lacking. Details such as intensity, duration, type of statin, or dose of statin pretreatment were not available. Second, as a registry-based retrospective study, there are inherent limitations of observational data to consider. Despite adjustment for various variables, the potential impact of unmeasured or residual confounding variables may not be entirely eliminated. Third, this study was conducted in only South Korea, which could introduce an additional element of confounding when considering differences in lipid profiles among different ethnic groups. Fourth, there is a possibility of index event bias in the analysis of stroke patients who received statin treatment with LDL-C levels of 100 or lower. \u003csup\u003e23\u003c/sup\u003e This might affect the relationship between baseline risk factors and the outcome of interest. However, the findings of this study would still be applicable to patients matching those analyzed.\u003c/p\u003e \u003cp\u003eIn conclusion, our study found that, in ischemic stroke patients whose LDL-C levels were already controlled with statin, higher LDL/HDL ratio, TC/HDL ratio, and TG/HDL ratio, though not non-HDL levels, were associated with residual risk of 1-year composite of stroke, MI, and all-cause mortality. The risk with LDL/HDL particularly increased when the ratio value reached 2.09 or higher and showed a linear association with the 1-year primary vascular outcome. Our results suggest that non-traditional lipid profiles, particularly the LDL/HDL ratio, may be helpful in predicting the risk of subsequent vascular events for patients with ischemic stroke occurring despite well-controlled LDL with statin pretreatment.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003eAcknowledgements: None\u003c/p\u003e\n\u003cp\u003eDeclaration of Competing interests: The authors declare that they have no competing interests.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eDisclosure: None\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eFunding: This research was supported by funding (2023-ER1006-00) from Research of Korea Centers for Disease Control and Prevention. This study was supported by a grant (BCRI24042)\u0026nbsp;of Chonnam National University Hospital Biomedical Research Institute.\u003c/p\u003e\n\u003cp\u003eData availability: Data used in this study are available upon reasonable request following submission of a legitimate academic research proposal to be assessed by the CRCS-K steering committee.\u003c/p\u003e\n\u003cp\u003eAcknowledgements: None\u003c/p\u003e\n\u003cp\u003eAuthors\u0026rsquo; contributions:\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e- Study concept and design: JT Kim, H Kim, HJ Bae\u003c/p\u003e\n\u003cp\u003e- Acquisition of data: JT Kim, MS Park, BJ Kim, J Kang, KJ Lee, JM Park, K Kang, SJ Lee, JG Kim, JK Cha, DH Kim, TH Park, K Lee, J Lee, KS Hong, YJ Cho, HK Park, BC Lee, KY Yu, MS Oh, DE Kim, WS Ryu, JC Choi, JH Kwon, WJ Kim, DI Shin, KS Yum, SI Sohn, JH Hong, J Lee, KY Park, HJ Bae\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e- Analysis and interpretation of data: JT Kim, HJ Bae, JS Lee\u003c/p\u003e\n\u003cp\u003e- Drafting of the manuscript: JT Kim, H Kim, JLS\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e- All authors read and approved the final manuscript.\u0026nbsp;\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eCholesterol Treatment Trialists C, Mihaylova B, Emberson J, et al. 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DOI: 10.1016/j.jacc.2011.12.035.\u003c/li\u003e\n\u003cli\u003eKleindorfer DO, Towfighi A, Chaturvedi S, et al. 2021 Guideline for the Prevention of Stroke in Patients With Stroke and Transient Ischemic Attack: A Guideline From the American Heart Association/American Stroke Association. \u003cem\u003eStroke\u003c/em\u003e 2021; 52: e364-e467. 20210524. DOI: 10.1161/STR.0000000000000375.\u003c/li\u003e\n\u003cli\u003eAuthors/Task Force M, Guidelines ESCCfP and Societies ESCNC. 2019 ESC/EAS guidelines for the management of dyslipidaemias: Lipid modification to reduce cardiovascular risk. \u003cem\u003eAtherosclerosis\u003c/em\u003e 2019; 290: 140-205. 20190831. DOI: 10.1016/j.atherosclerosis.2019.08.014.\u003c/li\u003e\n\u003cli\u003eLiu Y, Jin X, Fu K, et al. Non-traditional lipid profiles and the risk of stroke: A systematic review and meta-analysis. \u003cem\u003eNutr Metab Cardiovasc Dis\u003c/em\u003e 2023; 33: 698-714. 20230111. DOI: 10.1016/j.numecd.2023.01.003.\u003c/li\u003e\n\u003cli\u003eDeng QW, Li S, Wang H, et al. The Short-term Prognostic Value of the Triglyceride-to-high-density Lipoprotein Cholesterol Ratio in Acute Ischemic Stroke. \u003cem\u003eAging Dis\u003c/em\u003e 2018; 9: 498-506. 20180601. DOI: 10.14336/AD.2017.0629.\u003c/li\u003e\n\u003cli\u003eDahabreh IJ and Kent DM. Index event bias as an explanation for the paradoxes of recurrence risk research. \u003cem\u003eJAMA\u003c/em\u003e 2011; 305: 822-823. DOI: 10.1001/jama.2011.163.\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"scientific-reports","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"scirep","sideBox":"Learn more about [Scientific Reports](http://www.nature.com/srep/)","snPcode":"","submissionUrl":"","title":"Scientific Reports","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Scientific Reports","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"non-traditional lipid profiles, lipid ratio, LDL/HDL ratio, LDL-cholesterol, acute ischemic stroke, vascular outcome","lastPublishedDoi":"10.21203/rs.3.rs-4567821/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-4567821/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eThis study aimed to investigate the association between non-traditional lipid profiles and the risk of 1-year vascular events in patients who were already using statins before stroke and had admission LDL-C\u0026thinsp;\u0026lt;\u0026thinsp;100 mg/dL. This study was an analysis of a prospective, multicenter, nationwide registry of consecutive patients with acute ischemic stroke patients who treated with statin before index stroke and LDL-C\u0026thinsp;\u0026lt;\u0026thinsp;100mg/dL on admission. Non-traditional lipid profiles including non-HDL, TC/HDL ratio, LDL/HDL ratio, and TG/HDL ratio were analyzed as a continuous or categorical variable. The primary vascular outcome within one year was a composite of recurrent stroke (either hemorrhagic or ischemic), myocardial infarction (MI) and all-cause mortality. Hazard ratios (95% Cis) for 1-year vascular outcomes were analyzed using the Cox PH model for each non-traditional lipid profiles groups. A total of 7,028 patients (age 70.3\u0026thinsp;\u0026plusmn;\u0026thinsp;10.8years, male 59.8%) were finally analyzed for the study. In unadjusted analysis, no significant associations were observed in the quartiles of LDL/HDL ratio and 1-year primary outcome. However, after adjustment of relevant variables, compared with Q1 of the LDL/HDL ratio, Q4 was significantly associated with increasing the risk of 1-year primary outcome (HR 1.48 [1.19\u0026ndash;1.83]). For the LDL/HDL ratio, a linear relationship was observed (P for linearity\u0026thinsp;\u0026lt;\u0026thinsp;0.001). Higher quartiles of the LDL/HDL ratio were significantly and linearly associated with increasing the risk of 1-year primary vascular outcomes. These findings suggest that even during statin therapy with LDL-C\u0026thinsp;\u0026lt;\u0026thinsp;100mg/dl on admission, there should be consideration for residual risk based on the LDL/HDL ratio, following stroke.\u003c/p\u003e","manuscriptTitle":"Non-Traditional Lipid Profiles and 1-Year Vascular Outcomes in Ischemic Stroke Patients with Prior Statin Therapy and LDL-C \u0026lt;100 mg/dL","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-07-12 19:14:52","doi":"10.21203/rs.3.rs-4567821/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2024-08-05T04:54:44+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2024-08-02T18:21:14+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"157863002444673793441801136195450666712","date":"2024-07-25T22:39:38+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2024-07-16T14:45:43+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"175864241095241394619607813608498764983","date":"2024-07-02T18:46:14+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2024-06-18T04:51:29+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2024-06-18T04:50:30+00:00","index":"","fulltext":""},{"type":"editorInvited","content":"","date":"2024-06-17T16:32:54+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2024-06-13T08:28:40+00:00","index":"","fulltext":""},{"type":"submitted","content":"Scientific Reports","date":"2024-06-12T06:03:48+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"scientific-reports","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"scirep","sideBox":"Learn more about [Scientific Reports](http://www.nature.com/srep/)","snPcode":"","submissionUrl":"","title":"Scientific Reports","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Scientific Reports","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"a99eefb6-68d4-451a-8c1f-a6e13a7fafa9","owner":[],"postedDate":"July 12th, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"published-in-journal","subjectAreas":[{"id":33819987,"name":"Health sciences/Endocrinology"},{"id":33819988,"name":"Health sciences/Neurology"}],"tags":[],"updatedAt":"2024-10-07T16:06:31+00:00","versionOfRecord":{"articleIdentity":"rs-4567821","link":"https://doi.org/10.1038/s41598-024-73851-5","journal":{"identity":"scientific-reports","isVorOnly":false,"title":"Scientific Reports"},"publishedOn":"2024-10-01 15:56:53","publishedOnDateReadable":"October 1st, 2024"},"versionCreatedAt":"2024-07-12 19:14:52","video":"","vorDoi":"10.1038/s41598-024-73851-5","vorDoiUrl":"https://doi.org/10.1038/s41598-024-73851-5","workflowStages":[]},"version":"v1","identity":"rs-4567821","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-4567821","identity":"rs-4567821","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}