A Non-Linear Association of Low-density lipoprotein cholesterol with All-Cause and Cardiovascular Mortality Among Patients with Hypertension

A Non-Linear Association of Low-density lipoprotein cholesterol with All-Cause and Cardiovascular Mortality Among Patients with Hypertension | 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 A Non-Linear Association of Low-density lipoprotein cholesterol with All-Cause and Cardiovascular Mortality Among Patients with Hypertension Guoliang Liang, Wenhao Zhang, Xinxin Gu, Qiong Zhang, Ankang Liu, and 2 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-4644141/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Background Although a few studies have examined the correlation between low-density lipoprotein cholesterol (LDL-C) and mortality, no study has explored these associations in hypertensive populations. This study aims to investigate the relationship between low-density lipoprotein cholesterol and cardiovascular and all-cause mortality in adults with hypertension. Methods Hypertensive participants aged ≥ 18 years old from the National Health and Nutrition Examination Survey (NHANES) 1999–2018 with blood lipid testing data and complete follow-up data until December 31, 2019 were enrolled in analysis. Univariate and multivariate Cox regression were conducted for the calculation of hazard ratios (HR) and 95% confidence intervals (CIs). Restricted cubic spline (RCS) curve was performed to visually represent the relationship between LDL-C and mortality. Survival analysis of Kaplan-Meier and stratification analysis were also carried out. Results We finally analyzed a cohort of 9,635 participants (49.6% male, mean age of 59.4 years). Following a median of 98 months of follow-up, there were 2,283(23.7%) instances of all-cause fatalities, with 758(7.9%) cases attributed to cardiovascular disease. Multivariate Cox regression analysis showed lower levels of LDL-C groups had a higher risk of all-cause and cardiovascular mortality; the LDL-C group's lowest level (< 2.198 mmol/L) still showed a 19.6% increased risk of all-cause mortality (p = 0.0068) in the model that has been completely adjusted. Both all-cause mortality and cardiovascular mortality showed a non-linear association with LDL-C concentration in restricted cubic spline regression analysis. Conclusions In individuals with hypertension, LDL-C was linked to cardiovascular and all-cause mortality, and we further demonstrated that this relationship was non-linear. Health sciences/Cardiology Health sciences/Cardiology/Cardiovascular biology Health sciences/Cardiology/Cardiovascular biology/Cardiovascular diseases Health sciences/Cardiology/Cardiovascular biology/Cardiovascular diseases/Dyslipidaemias Low-density lipoprotein cholesterol All-Cause Mortality Cardiovascular Mortality Hypertension NHANES Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Figure 7 BACKGROUND Over the past decades, as an indicator of atherosclerosis and cardiovascular disease, low-density lipoprotein cholesterol (LDL-C) has been recognized as a risk factor 1–3 . There has been evidence from several studies in various populations throughout the world that higher LDL-C levels are associated with mortality due to all causes and cause-specific (cardiovascular disease, stroke, etc.) mortality 4–8 . A large number of randomized controlled trials also demonstrated that lipid-lowering drugs were associated with a lower risk of cardiovascular events and mortality associated with atherosclerosis 9–12 . However, with further research, conflicting results revealed regarding LDL-C levels and mortality risk. Some studies found deaths from all causes were negatively correlated with LDL-C levels 13,14 and some showed no association 15,16 . A recent extensive prospective cohort research in Denmark revealed a U-shaped correlation between different LDL-C levels and death 17 . Relationship between concentrations of LDL-C and mortality remains unclear. Moreover, we noticed that studies are usually conducted in the general population, older population, or diabetes population; no study focused on patients with hypertension. Globally, the hypertension population has reached a staggering amount of 874 million and approximately one in four adults has hypertension 18 . Previous studies have shown that hypertension is associated with dyslipidemia and the LDL-C may be a modifiable risk factor for hypertension on its own 19,20 . A study by Bonaa et al. showed a positive correlation between blood pressure and lipid levels 20 . Hence, with such a large population of hypertension, providing some guidelines for risk hierarchy management of hypertensive people and investigating the associations between LDL-C levels and all-cause and cardiovascular mortality may be quite helpful. METHODS Data Source and Study Population In this cohort study, all data were obtained from the National Health and Nutrition Examination Survey (NHANES, https://www.cdc.gov/nchs/nhanes/index.htm ). NHANES is a major program of the National Center for Health Statistics (NCHS), which is part of the Centers for Disease Control and Prevention (CDC). The National Health and Nutrition Examination Survey (NHANES) is one of the main programs of the National Center for Health Statistics (NCHS) and this program aims to assess the health and nutritional status of adults and children in the United States 21 . Since 1999, health data gathered by interviews, physical exams, and laboratory testing from representative American population samples would be published in their official website every two years 22 , and also ongoing follow-up mortality data would be posted in the National Death Index death certificate records ( www.cdc.gov/nchs/data-linkage/mortality-public.htm ). In our analysis, we collected 11 cycles of datasets (NHANES 1999–2018, every two year a cycle), and then extracted demographic data, blood pressure and body mass index data from examinations, and lipid testing data from laboratories, questionnaire data about smoking status, antihypertensive and lipid-lowering drug use. Participants under 18 years old, those with missing blood lipid and follow-up data, missing body mass index and smoking data, or those without hypertension at baseline were excluded from the study. Following the application of the above criteria for exclusion, 9,635 individuals were ultimately included for analysis (Fig. 1 ). Participants' survival status and death details were tracked through December 31, 2019. The Centers for Disease Control and Prevention's Institutional Review Board gave its approval to the NHANES research methodology. Every participant provided their consent before participating, and all of the methods for the survey were conducted out in accordance with the relevant rules and regulations( https://www.cdc.gov/nchs/nhanes/about_nhanes.htm ). Data Collection Demographic information (age, gender, race, marital status, and education level) was recorded at the beginning of every cycle of survey as questionnaire data. We extracted it from the mentioned 11 cycles of datasets above and converted race, marital status, education level to binary categorical variable. Race was categorized as White (Mexican American, Other Hispanic, Non-Hispanic White)/Non-white (Non-Hispanic Black, Other Race), marital status was categorized as Married/Other (Widowed, Divorced, Separated, Refused and so on), and education level was categorized as Less than high school (Less Than 9th Grade, 9-11th Grade, Refused, Don't Know)/ High school or above (High School Grad/GED or Equivalent, Some College or AA degree, College Graduate or above). Blood pressure and body measures data were collected and stored in examination data module, and we extracted systolic blood pressure, diastolic blood pressure and body mass index (BMI) data. Referring to the American Heart Association Blood Pressure Guidelines 2018, we defined hypertension as systolic blood pressure ≥ 140mmHg and/or diastolic blood pressure ≥ 90mmHg or self-reported hypertension history and use of anti-hypertensive medication 1,23 . According to NHANES component description, body mass index (BMI) was calculated using weight (kg) divided by the square of height (m 2 ). Cholesterol measurements data were stored in the module of laboratory data, we extracted total cholesterol (TC), triglyceride (TG), low-density lipoprotein cholesterol (LDL-C), high density lipoprotein cholesterol (HDL-C) concentration (mmol/L) data for our study. According to NHANES instructions, all measurements were taken in the morning on an empty stomach (at least 8 hours). Smoking status questionnaire data were also extracted and we defined the answer of SMQ020(Have you/Has SP smoked at least 100 cigarettes in your/his/her entire life?), Yes means smoking, No/Refused/Don’t know/Missing means no. As to medication history, questionnaire data of BPQ050A (Are you/Is SP now taking prescribed medicine for hypertension?) and BPQ100D (Are you/Is SP now following taking prescribed medicine to lower (your/his/her) blood cholesterol?) were selected to define, answer Yes means yes, No/Refused/Don’t know/Missing means no. Outcomes and follow-up All-cause mortality and cardiovascular mortality were selected as our study outcomes. All-cause mortality is defined as death from any cause, including Diseases of heart, Malignant neoplasms, Chronic lower respiratory diseases, Accidents, Cerebrovascular diseases, Alzheimer's disease, Diabetes mellitus, Influenza and pneumonia, Nephritis, nephrotic and all other causes. Cardiovascular mortality was estimated using The International Classification of Disease Tenth Revision (ICD-10), codes (I00-I09, I11, I13, I20-I51 and I60–I69) were used to define cardiovascular deaths. Mortality data of NHANES 1999–2018 were linked to mortality data from the National Death Index death certificate records until December 31, 2019. All participants enrolled in this study had complete follow-up data and when death occurred, causes of death were recorded. Statistical Analysis In accordance with the blood LDL-C concentration, LDL-C levels was divided into five groups based on quintiles (Q1: < 20th percentile, Q2: ≤20 to 40th percentile, Q3: ≤40 to 60th percentile, Q4: ≤60-80th percentile, Q5: ≤ 80th percentile). LDL-C level of Q3 (2.689–3.155 mmol/L) was selected as a reference to study the relationship between LDL and all-cause and cardiovascular mortality. In this study, continuous variables were described by means ± standard deviations (SD) and compared using an analysis of variance (ANOVA). We compared categorical variables using the Chi-square test, expressing them as number (n) and percentage (%). We used univariate cox regression to identify potential risk factors that may affect all-cause mortality and cardiovascular mortality, the results are represented as hazard ratios (HRs) with 95% confidence intervals (CIs). An analysis of multivariate cox regression models was carried out to determine if LDL-C levels are associated with mortality due to all causes and cardiovascular disease. Three models were constructed, Model I is a crude model and adjusts for none. Model II adjusts for age, gender and race. Model III is a comprehensive model that includes adjustments for smoking, systolic blood pressure, diastolic blood pressure, and medication use (antihypertensive medicines, lipid-lowering medicines) beyond what was included in Model II. A restricted cubic spline (RCS) curve was used to analyze and visualize the relationship between LDL-C concentration and mortality on a continuous scale, which is based on multivariate adjusted cox regression. The survival analysis of Kaplan-Meier curve was carried out to show how survival varies between different level groups of LDL-C. Finally, we also conducted stratification analysis to identify the subgroup that shows a significant connection between LDL-C level and all-cause and cardiovascular death, including age, gender, race, marital status, education level, smoking, body mass index and medicine use. The statistical significance level was determined by p values of less than 0.05(p < 0.05) on two sides. All statistical analysis were performed using R version 4.3.1(R Foundation for Statistical Computing, Vienna, Austria, https://www.r-project.org/ ). RESULTS Characteristics of the study population at different levels of LDL Table 1 shows the distribution of baseline characteristics of research participants based on various levels of LDL-C concentrations. A total of 9,635 participants were included in our analysis, of which 49.6% were male and 50.4% were female, with mean age of 59.4 ± 15.6 years. Most of them were White (68.2%) and received high school or above education (85.7%). Following a median of 98 months of follow up, there were 2,283 instances of all-cause fatalities, with 758 cases attributed to cardiovascular disease. Compared with the higher groups (Q4 and Q5), the lower groups (Q1 and Q2) were more likely to be older, male, smoker and were more likely to take antihypertensive drugs and lipid-lowering drugs. Among various LDL-C classification levels, with the exception of marital status, education level and body mass index, all other social demographic and health-related disease factors show statistical significance at baseline (p < 0.05). Table 1 LDL-C level quintile-based baseline characteristics of study cohort. Characteristics Total Quintiles of the LDL-C(mmol/L) p Q1(< 2.198) Q2(2.198–2.689) Q3(2.689–3.155) Q4(3.155–3.75) Q5(≤ 3.75) Number 9635 1921 1901 1913 1947 1953 Age, years 59.4 ± 15.6 62.2 ± 16.0 59.5 ± 16.2 58.7 ± 15.3 58.5 ± 15.4 58.3 ± 14.6 < 0.001 Gender, n (%) < 0.001 Male 4780 (49.6) 1037 (54.0) 955 (50.2) 935 (48.9) 949 (48.7) 904 (46.3) Female 4855 (50.4) 884 (46.0) 946 (49.8) 978 (51.1) 998 (51.3) 1049 (53.7) Race, n (%) 0.005 White 6570 (68.2) 1265 (65.9) 1277 (67.2) 1332 (69.6) 1380 (70.9) 1316 (67.4) Non-white 3065 (31.8) 656 (34.1) 624 (32.8) 581 (30.4) 567 (29.1) 637 (32.6) Marital status, n (%) 0.112 Married 5211 (54.1) 1036 (53.9) 1058 (55.7) 991 (51.8) 1079 (55.4) 1047 (53.6) Other 4424 (45.9) 885 (46.1) 843 (44.3) 922 (48.2) 868 (44.6) 906 (46.4) Education level, n (%) 0.800 Less than high school 1381 (14.3) 280 (14.6) 271 (14.3) 269 (14.1) 267 (13.7) 294 (15.1) High school or above 8254 (85.7) 1641 (85.4) 1630 (85.7) 1644 (85.9) 1680 (86.3) 1659 (84.9) Smoking, n (%) < 0.001 No 4865 (50.5) 903 (47.0) 912 (48.0) 979 (51.2) 1036 (53.2) 1035 (53.0) Yes 4770 (49.5) 1018 (53.0) 989 (52.0) 934 (48.8) 911 (46.8) 918 (47.0) Body mass index, kg/m 2 30.4 ± 7.15 30.5 ± 7.53 30.4 ± 7.17 30.6 ± 7.57 30.5 ± 6.87 30.2 ± 6.59 0.523 Systolic blood pressure, mmHg 137 ± 20.2 134 ± 20.1 136 ± 20.1 136 ± 20.6 137 ± 19.1 140 ± 20.9 < 0.001 Diastolic blood pressure,mmHg 73.0 ± 13.8 69.1 ± 13.6 72.3 ± 13.5 73.0 ± 13.7 74.3 ± 13.4 76.4 ± 13.5 < 0.001 Total cholesterol, mmol/L 5.06 ± 1.09 3.75 ± 0.569 4.49 ± 0.453 4.98 ± 0.441 5.52 ± 0.448 6.53 ± 0.751 < 0.001 HDL cholesterol, mmol/L 1.39 ± 0.425 1.35 ± 0.464 1.40 ± 0.431 1.40 ± 0.434 1.38 ± 0.399 1.40 ± 0.392 < 0.001 Triglycerides, mmol/L 1.50 ± 0.789 1.40 ± 0.863 1.44 ± 0.798 1.46 ± 0.754 1.56 ± 0.756 1.64 ± 0.748 < 0.001 LDL cholesterol, mmol/L 2.99 ± 0.950 1.75 ± 0.328 2.44 ± 0.140 2.91 ± 0.135 3.43 ± 0.170 4.38 ± 0.599 < 0.001 Antihypertensive drugs, n (%) < 0.001 No 4051 (42.0) 531 (27.6) 712 (37.5) 837 (43.8) 932 (47.9) 1039 (53.2) Yes 5584 (58.0) 1390 (72.4) 1189 (62.5) 1076 (56.2) 1015 (52.1) 914 (46.8) Lipid-lowering drugs, n (%) < 0.001 No 6776 (70.3) 995 (51.8) 1180 (62.1) 1419 (74.2) 1568 (80.5) 1614 (82.6) Yes 2859 (29.7) 926 (48.2) 721 (37.9) 494 (25.8) 379 (19.5) 339 (17.4) Outcomes, n (%) All-cause mortality < 0.001 No 7352 (76.3) 1409 (73.3) 1421 (74.8) 1465 (76.6) 1528 (78.5) 1529 (78.3) Yes 2283 (23.7) 512 (26.7) 480 (25.2) 448 (23.4) 419 (21.5) 424 (21.7) Cardiovascular mortality < 0.001 No 8877 (92.1) 1739 (90.5) 1746 (91.8) 1768 (92.4) 1827 (93.8) 1797 (92.0) Yes 758 (7.9) 182 (9.5) 155 (8.2) 145 (7.6) 120 (6.2) 156 (8.0) Abbreviations: Q, quintiles; n, number; HDL, high density lipoprotein; LDL, low-density lipoprotein. Values are mean ± standard deviation for continuous variables or n (%) for categorical variables. Table 2 Result of Univariate analysis. Characteristics All-cause mortality Cardiovascular mortality Events/numbers HR (95% CI) p Events/numbers HR (95% CI) p LDL-C < 2.198 512/1921 1.459 (1.285, 1.657) < 0.001 182/1921 1.609 (1.293, 2.002) < 0.001 2.198–2.689 480/1901 1.218 (1.070, 1.385) 0.0027 155/1901 1.217 (0.970, 1.527) 0.089 2.689–3.155 448/1913 1(Ref) 145/1913 1(Ref) 3.155–3.75 419/1947 0.874 (0.765, 0.998) 0.0466 120/1947 0.772 (0.606, 0.984) 0.036 ≤ 3.75 424/1953 0.847 (0.742, 0.967) 0.0143 156/1953 0.962 (0.767, 1.206) 0.734 Gender Male 1236/4780 1(Ref) 420/4780 1(Ref) Female 1047/4855 0.784 (0.722, 0.851) < 0.001 338/4855 0.744 (0.645, 0.859) < 0.001 Race White 1775/6570 0.728 (0.659, 0.803) < 0.001 575/6570 0.811 (0.686, 0.958) 0.014 Non-white 508/3065 1(Ref) 183/3065 1(Ref) Marital status Married 1105/5211 1(Ref) 351/5211 1(Ref) Other 1178/4424 1.400 (1.290, 1.520) < 0.001 407/4424 1.520 (1.320, 1.760) < 0.001 Education level Less than high school 485/1381 1(Ref) < 0.001 157/1381 1(Ref) High school or above 1798/8254 0.663 (0.600, 0.733) 601/8254 0.686 (0.576, 0.818) < 0.001 Smoking No 937/4865 0.651 (0.599, 0.707) < 0.001 342/4865 0.769 (0.667, 0.887) < 0.001 Yes 1346/4770 1(Ref) 416/4770 1(Ref) Antihypertensive drugs No 1489/4051 0.597 (0.547, 0.651) < 0.001 238/4051 0.510 (0.437, 0.595) < 0.001 Yes 794/5584 1(Ref) 520/5584 1(Ref) Lipid-lowering drugs No 1571/6776 0.744 (0.681, 0.814) < 0.001 500/6776 0.650 (0.559, 0.756) < 0.001 Yes 712/2859 1(Ref) 258/2859 1(Ref) Abbreviations: LDL-C, low-density lipoprotein cholesterol; HR, hazard ratio; CI, confidence interval; Ref, reference. Table 3 Result of Multivariate Cox regression analysis. Model I Model II Model III HR (95%CI), p value HR (95%CI), p value HR (95%CI), p value All-cause mortality LDL-C Levels < 2.198 1.459 (1.285, 1.657), < 0.001 1.170 (1.029, 1.329), 0.0160 1.196 (1.051, 1.361), 0.0068 2.198–2.689 1.218 (1.070, 1.385), 0.0027 1.166 (1.025, 1.327), 0.0190 1.184 (1.040, 1.348), 0.0108 2.689–3.155 1(Ref) 1(Ref) 1(Ref) 3.155–3.75 0.874 (0.765, 0.998), 0.0466 0.861 (0.753, 0.983), 0.0270 0.852 (0.745, 0.973), 0.0186 ≤ 3.75 0.847 (0.742, 0.967), 0.0143 0.898 (0.786, 1.026), 0.1130 0.879 (0.769, 1.006), 0.0611 Cardiovascular mortality LDL-C Levels < 2.198 1.609 (1.293, 2.002), < 0.001 1.242 (0.997, 1.547), 0.0537 1.234 (0.987, 1.542), 0.0647 2.198–2.689 1.217 (0.970, 1.527), 0.0890 1.164 (0.927, 1.460), 0.2265 1.153 (0.917, 1.448), 0.2230 2.689–3.155 1(Ref) 1(Ref) 1(Ref) 3.155–3.75 0.772 (0.606, 0.984), 0.0360 0.761 (0.597, 0.969), 0.0269 0.761 (0.597, 0.970), 0.0272 ≤ 3.75 0.962 (0.767, 1.206), 0.7340 1.026 (0.818, 1.288), 0.8219 1.012 (0.804, 1.272), 0.9214 Abbreviations: LDL-C, low-density lipoprotein cholesterol; HR, hazard ratio; CI, confidence interval; Ref, reference. Model I adjust for none. Model II adjust for age, gender, and race. Model III adjust for age, gender, race, marital status, education level, smoking, body mass index, systolic blood pressure, diastolic blood pressure, and medicine use (antihypertensive drugs, lipid-lowering drugs). Association between LDL‑C concentration and all‑cause and cardiovascular mortality To further explore the association between LDL-C concentration (as a continuous variable) and all-cause and cardiovascular mortality, we performed restricted cubic spline regression analysis on our included data and used an RCS curve to visually show the result. The analysis models were based on multivariate adjusted cox regression and fully adjusted for confounders. As showed in results, both all-cause mortality ( Figure 2 ) and cardiovascular mortality ( Figure 3 ) had a non-linear association with LDL-C concentration. When LDL-C concentrations were below 2.89 mmol/L, both the risk of all-cause death and cardiovascular death became higher as the LDL-C concentration decreased. Risks of all-cause and cardiovascular death tended to further decrease and then increase while blood LDL-C concentration above 2.89mmol/L. Differently, risk of cardiovascular death seemed to get increased at a lower LDL-C concentration than all-cause death. Red shaded area represents the 95% CI of the curve. Figure 2 . Restricted cubic spline curve of LDL-C concentration (mmol/L) and All-cause mortality. Figure 3 . Restricted cubic spline curve of LDL-C concentration(mmol/L) and Cardiovascular mortality. Survival Analysis and Stratification Analysis Figure 4 and Figure 5 showed the results of survival analysis of Kaplan-Meier curve, both all-cause and cardiovascular mortality were significantly different from the other groups when LDL-C at a lowest level. Figure 6 and Figure 7 presented results of stratification analysis stratified by all confounders included in this study. Consistent with results of univariate analysis, the lower level of LDL-C groups had a higher risk of all-cause and cardiovascular death in all subgroup. Specially, we turned continuous variables age (<60 years old and ≥60 years old) and body mass index (<25 kg/m 2 and ≥25 kg/m 2 ) into categorical variables for further study. As results showed, patients with hypertension aged <60 years old may have a higher risk of all-cause mortality when at a lower level of LDL-C. However, to population aged ≥60 years old, a higher risk of cardiovascular mortality was statistically significant. As to body mass index, both higher risk of all-cause and cardiovascular mortality were observed while body mass index ≥25 kg/m 2 . Figure 4 . Kaplan-Meier curve of all-cause mortality by different levels of LDL-C. Figure 5 . Kaplan-Meier curve of cardiovascular mortality by different levels of LDL-C. Figure 6 . Stratified analysis of the relationship between LDL-C level and all-cause and cardiovascular mortality. Figure 7 . Stratified analysis of the relationship between LDL-C level and all-cause and cardiovascular mortality. Discussion In this retrospective cohort study of 9,635 hypertensive patients, our key finding was the non-linear association between LDL-C and all-cause and cardiovascular mortality. After adjusting confounders of age, gender, race, marital status, education level, smoking, body mass index, systolic and diastolic blood pressure, and medication use, the RCS curve based on multivariate adjusted cox regression well revealed the correlation between them on a continuous scale. Distinguishing from the traditional impression that lower LDL-C level were better for health, we found both low and high LDL-C levels contributed to increased risk of death in hypertension population. As to the lowest risk of all-cause and cardiovascular mortality, we had preliminary view that the LDL-C concentration of slightly higher than 2.89mmol/L may be optimal in hypertensive patients according to RCS curve. These new findings may provide some reference for lipid control in hypertensive populations. As the major culprit in development of atherosclerosis, the elevated levels of LDL-C are strongly associated with cardiovascular disease with no doubt. According to statistics from the World Health Organization report in 2021, cardiovascular disease causes 17.9 million deaths in 2019, making up 32% of total global deaths 24 , so it can be easily understood that higher levels of LDL-C accompanied by higher risk of death. But when come to the lower LDL-C levels, the higher death risk seems incomprehensible. For this counterintuitive result, there are several probable explanations. First, it is hypothesized that debilitation and disease can lead to lower cholesterol levels 17 , 25 , 26 and, in this study, patients with lower levels of LDL-C had an older age (Q1: mean age of 62.2 ± 16.0, Q2: mean age of 59.5 ± 16.2) than higher level groups (Q4: mean age of 58.5 ± 15.4, Q5: mean age of 58.3 ± 14.6) indeed. Individual comorbidity profiles were not included in our study, but can be inferred from individual medication histories, the low-level groups had higher percentages of medication use. Second, although most studies had spared no effort to emphasize the benefits of lipid lowering, the long-term safety and efficacy of LDL-C lowering therapies remains a question to be further explored 27 . Moreover, a number of studies have reported neurocognitive deficits, hemorrhagic stroke, and new-onset diabetes in the presence of reduced LDL-C 27 – 29 , which may invariably increase the risk of all-cause mortality. Third, Kaysen GA et al found higher LDL-C was significantly associated with lower infection-related mortality an international retrospective cohort study 13 , in other words, the risk of infectious death may increase when LDL-C at a low level, and so of all-cause mortality. Finally, as the world's second most common cause of death, cancer was related to low LDL-C levels, which is repeatedly mentioned in multiple studies 30 – 33 . In total, reduced LDL-C levels might elevate the likelihood of mortality from the possible reason above, which and then results in increased all-cause mortality. Consistent with our study result, some previous studies conducted in other populations have demonstrated a correlation between LDL-C levels and the risk of all-cause and cardiovascular mortality. Zhen Zhou et al. and Vale ́rie Tikhonoff et al. had explored relationships in older people. Zhen Z et al. reported there was a U-shaped relationship between untreated LDL-C level and all-cause mortality 34 and Tikhonoff V et al. found LDL-C concentration is a multifaceted risk factor in older adults 35 . Chang C-H et al. demonstrated both lower and higher levels of mean LDL-C were associated with increased all-cause and cardiovascular mortality in type 2 diabetes patients through 36 . Also, multiple studies in general population had got the similar result 17 , 37 – 39 . Additionally, through a prospective cohort study of 108 243 individuals in Denmark, Johannesen CDL et al. found the lowest risk of all-cause mortality were at concentrations of LDL-C of 3.6-3.7 mmol/L 17 . However, from our current study, lipid controlling in hypertensive populations should be even more strict. Study Strengths and Limitations Thanks to the ongoing NHANES project and continued data collection, we were able to build such a large sample size cohort of hypertensive people for our analysis. No individuals lost to follow-up and the cause of death of every participant was recorded on the National Death Index death certificate records. As far as our knowledge extends, relationship between low-density and all-cause and cardiovascular mortality remains controversial, a few studies explored it in general or other populations, but specifically in hypertensive populations, our study may be the first attempt to do so. Another strength of our study is that we adjusted for several confounders which may influence the accuracy of analysis results. However, limitations also should be considered. First, the population we included were only living in United States, other countries or ethnicities may be not applicable. Second, several variables we included such as smoking status and medicine use were may cause recall bias, because they were subjective from participants. Third, we did not consider changes in LDL-C concentration over time or changes influenced by the initiation or cessation of lipid-lowering treatment throughout the observation period, this may make the findings unreliable. Finally, given the observational nature of the study, causality cannot be definitively established. Therefore, it is imperative to interpret the findings with caution, considering both potential causal and reverse relationships. Subsequent research is warranted to elucidate the possible causal link between LDL-C levels and mortality. Conclusion The study revealed a non-linear association between LDL-C levels and both all-cause mortality and cardiovascular mortality in individuals with high blood pressure. Maintaining LDL-C within a specific range may confer benefits for cardiovascular health and long-term survival when compared to lower or higher concentrations. Nevertheless, additional research is necessary to determine the optimal LDL-C concentration range. Abbreviations LDL-C: low-density lipoprotein cholesterol; BMI: body mass index; NHANES: National Health and Nutrition Examination Survey; HR: hazard ratio; CI: confidence interval; RCS: restricted cubic spline. Declarations Acknowledgements We express our sincere gratitude to the National Health and Nutrition Examination Surveys for providing the data. Authors’ contributions Guoliang Liang: conceptualization, formal analysis, software, and writing-original draft. Wenhao Zhang: conceptualization and software. Xinxin Gu: formal analysis and writing-original draft. Qiong Zhang: investigation and writing-review draft. Ankang Liu: formal analysis and project administration. Xinran Qing: conceptualization writing-review draft, and project administration. Jiangwei Ma: validation and supervision. All authors have read and agreed to the published version of the manuscript. Funding This work was supported by the Shanghai Fengxian District Science and Technology Development Fund Program (No.20221201) Data Availability All data we used for our analysis are publicly available on the website (www.cdc.gov/nchs/nhanes/). Competing interests The authors declare no competing interests. Ethics approval and consent to participate The studies involving human participants were reviewed and approved by NCHS Ethics Review Board. The participants provided their written informed consent to participate in this study. Consent for publication Not applicable. References Grundy, S. M. et al. 2018 AHA/ACC/AACVPR/AAPA/ABC/ACPM/ADA/AGS/APhA/ASPC/NLA/PCNA Guideline on the Management of Blood Cholesterol: A Report of the American College of Cardiology/American Heart Association Task Force on Clinical Practice Guidelines. Circulation 139 , e1082–e1143 (2019). Ference, B. A. et al. Low-density lipoproteins cause atherosclerotic cardiovascular disease. 1. Evidence from genetic, epidemiologic, and clinical studies. A consensus statement from the European Atherosclerosis Society Consensus Panel. European Heart Journal 38 , 2459–2472 (2017). Libby, P. et al. Atherosclerosis. Nat Rev Dis Primers 5 , 56 (2019). Abdullah, S. M. et al. Long-Term Association of Low-Density Lipoprotein Cholesterol With Cardiovascular Mortality in Individuals at Low 10-Year Risk of Atherosclerotic Cardiovascular Disease. Circulation 138 , 2315–2325 (2018). Navarese, E. P. et al. Association Between Baseline LDL-C Level and Total and Cardiovascular Mortality After LDL-C Lowering. JAMA 319 , 1566 (2018). Jung, E., Kong, S. Y., Ro, Y. S., Ryu, H. H. & Shin, S. D. Serum Cholesterol Levels and Risk of Cardiovascular Death: A Systematic Review and a Dose-Response Meta-Analysis of Prospective Cohort Studies. IJERPH 19 , 8272 (2022). Kim, S. H. & Son, K. Y. Association between lipoprotein cholesterol and future cardiovascular disease and mortality in older adults: a Korean nationwide longitudinal study. Lipids Health Dis 20 , 3 (2021). Ni, W. et al. Associations of low-density lipoprotein cholesterol with all-cause and cause-specific mortality in older adults in China. J Clin Endocrinol Metab dgae116 (2024) doi:2024031419400971400. Baigent, C. et al. Efficacy and safety of cholesterol-lowering treatment: prospective meta-analysis of data from 90,056 participants in 14 randomised trials of statins. Lancet 366 , 1267–1278 (2005). Cholesterol Treatment Trialists’ (CTT) Collaboration et al. Efficacy and safety of more intensive lowering of LDL cholesterol: a meta-analysis of data from 170,000 participants in 26 randomised trials. Lancet 376 , 1670–1681 (2010). Silverman, M. G. et al. Association Between Lowering LDL-C and Cardiovascular Risk Reduction Among Different Therapeutic Interventions. JAMA 316 , 1289 (2016). Law, M. R. Quantifying effect of statins on low density lipoprotein cholesterol, ischaemic heart disease, and stroke: systematic review and meta-analysis. BMJ 326 , 1423–0 (2003). Kaysen, G. A. et al. Lipid levels are inversely associated with infectious and all-cause mortality: international MONDO study results. J Lipid Res 59 , 1519–1528 (2018). Chang, J. J. et al. Higher low-density lipoprotein cholesterol levels are associated with decreased mortality in patients with intracerebral hemorrhage. Atherosclerosis 269 , 14–20 (2018). Psaty, B. M. et al. The Association Between Lipid Levels and the Risks of Incident Myocardial Infarction, Stroke, and Total Mortality: The Cardiovascular Health Study. J American Geriatrics Society 52 , 1639–1647 (2004). Fried, L. P. et al. Risk factors for 5-year mortality in older adults: the Cardiovascular Health Study. JAMA 279 , 585–592 (1998). Johannesen, C. D. L., Langsted, A., Mortensen, M. B. & Nordestgaard, B. G. Association between low density lipoprotein and all cause and cause specific mortality in Denmark: prospective cohort study. BMJ 371 , m4266 (2020). Oparil, S. et al. Hypertension. Nat Rev Dis Primers 4 , 18014 (2018). Cheng, Q. et al. The U-Shaped Association of Non-High-Density Lipoprotein Cholesterol Levels With All-Cause and Cardiovascular Mortality Among Patients With Hypertension. Front. Cardiovasc. Med. 8 , 707701 (2021). Bønaa, K. H. & Thelle, D. S. Association between blood pressure and serum lipids in a population. The Tromsø Study. Circulation 83 , 1305–1314 (1991). duiyimuhan, G. & Maimaiti, N. The association between atherogenic index of plasma and all-cause mortality and cardiovascular disease-specific mortality in hypertension patients: a retrospective cohort study of NHANES. BMC Cardiovasc Disord 23 , 452 (2023). Yin, B. et al. Non-linear association of atherogenic index of plasma with insulin resistance and type 2 diabetes: a cross-sectional study. Cardiovasc Diabetol 22 , 157 (2023). Yu, Y. et al. A U-shaped association between the LDL-cholesterol to HDL-cholesterol ratio and all-cause mortality in elderly hypertensive patients: a prospective cohort study. Lipids Health Dis 19 , 238 (2020). Qiao, Y.-N., Zou, Y.-L. & Guo, S.-D. Low-density lipoprotein particles in atherosclerosis. Front. Physiol. 13 , 931931 (2022). Jacobs, D. et al. Report of the Conference on Low Blood Cholesterol: Mortality Associations. Circulation 86 , 1046–1060 (1992). Ranieri Renzo Rozzini Simone Franzo, P. Serum Cholesterol Levels as a Measure of Frailty in Elderly Patients. Experimental Aging Research 24 , 169–179 (1998). Gagel, A., Zghyer, F., Samuel, C. & Martin, S. S. What is the Optimal Low-Density Lipoprotein Cholesterol? Medical Clinics of North America 106 , 285–298 (2022). Yu, Q., Chen, Y. & Xu, C.-B. Statins and New-Onset Diabetes Mellitus: LDL Receptor May Provide a Key Link. Front. Pharmacol. 8 , 372 (2017). Parhofer, K. G. Interaction between Glucose and Lipid Metabolism: More than Diabetic Dyslipidemia. Diabetes Metab J 39 , 353 (2015). Tanne, J. H. Meta-analysis says low LDL cholesterol may be associated with greater risk of cancer. BMJ 335 , 177.2–177 (2007). Li, L., Yu, Z., Ren, J. & Niu, T. Low cholesterol levels are associated with increasing risk of plasma cell neoplasm: A UK biobank cohort study. Cancer Medicine 12 , 20964–20975 (2023). Kritz, H., Zielinski, C. & Sinzinger, H. Low cholesterol and cancer. JCO 14 , 3043–3048 (1996). Kritchevsky, S. B. & Kritchevsky, D. Serum Cholesterol and Cancer Risk: An Epidemiologic Perspective. Annu. Rev. Nutr. 12 , 391–416 (1992). Zhou, Z. et al. Low-Density-Lipoprotein Cholesterol and Mortality Outcomes Among Healthy Older Adults: A Post Hoc Analysis of ASPREE Trial. J Gerontol A Biol Sci Med Sci 79 , glad268 (2024). Tikhonoff, V. et al. Low-Density Lipoprotein Cholesterol and Mortality in Older People. J American Geriatrics Society 53 , 2159–2164 (2005). Chang, C.-H., Yeh, S.-T., Ooi, S.-W., Li, C.-Y. & Chen, H.-F. The relationship of low-density lipoprotein cholesterol and all-cause or cardiovascular mortality in patients with type 2 diabetes: a retrospective study. PeerJ 11 , e14609 (2023). Kawamoto, R., Kikuchi, A., Akase, T., Ninomiya, D. & Kumagi, T. Low density lipoprotein cholesterol and all-cause mortality rate: findings from a study on Japanese community-dwelling persons. Lipids Health Dis 20 , 105 (2021). Kim, B. J., Lee, M. Y., Choi, H.-I., Kwon, M.-J. & Kang, J.-G. Lipoprotein(a)-related cardiovascular and all-cause mortalities in Korean adults. Eur J Prev Cardiol 30 , 308–317 (2023). Liu, Y. et al. Association between low density lipoprotein cholesterol and all-cause mortality: results from the NHANES 1999–2014. Sci Rep 11 , 22111 (2021). Additional Declarations No competing interests reported. Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-4644141","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":328272199,"identity":"a298bc14-4a83-4d54-bfc2-ab1e4ce57df9","order_by":0,"name":"Guoliang Liang","email":"","orcid":"","institution":"Anhui University of Science and Technology Affiliated Fengxian Hospital","correspondingAuthor":false,"prefix":"","firstName":"Guoliang","middleName":"","lastName":"Liang","suffix":""},{"id":328272200,"identity":"f4a4d409-e7fd-4c87-ab00-fc8be3d25bc4","order_by":1,"name":"Wenhao Zhang","email":"","orcid":"","institution":"Anhui University of Science and Technology Affiliated Fengxian Hospital","correspondingAuthor":false,"prefix":"","firstName":"Wenhao","middleName":"","lastName":"Zhang","suffix":""},{"id":328272205,"identity":"036e9e41-a169-45f4-8ed9-3132b5488ea5","order_by":2,"name":"Xinxin Gu","email":"","orcid":"","institution":"Anhui University of Science and Technology Affiliated Fengxian Hospital","correspondingAuthor":false,"prefix":"","firstName":"Xinxin","middleName":"","lastName":"Gu","suffix":""},{"id":328272208,"identity":"d4a37d1a-179f-422a-8072-21eb8f59caa1","order_by":3,"name":"Qiong Zhang","email":"","orcid":"","institution":"Anhui University of Science and Technology Affiliated Fengxian Hospital","correspondingAuthor":false,"prefix":"","firstName":"Qiong","middleName":"","lastName":"Zhang","suffix":""},{"id":328272210,"identity":"4092b40e-4386-45ab-b740-207d5db77a08","order_by":4,"name":"Ankang Liu","email":"","orcid":"","institution":"Anhui University of Science and Technology Affiliated Fengxian Hospital","correspondingAuthor":false,"prefix":"","firstName":"Ankang","middleName":"","lastName":"Liu","suffix":""},{"id":328272211,"identity":"a48bf4e1-4aa5-427d-9fe2-d5f4997bc669","order_by":5,"name":"Xinran Qing","email":"","orcid":"","institution":"Anhui University of Science and Technology Affiliated Fengxian Hospital","correspondingAuthor":false,"prefix":"","firstName":"Xinran","middleName":"","lastName":"Qing","suffix":""},{"id":328272212,"identity":"43e70519-f307-4d0d-813a-40a8c9e61f23","order_by":6,"name":"Jiangwei 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16:44:08","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-4644141/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-4644141/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":60850831,"identity":"0cca938c-8cb9-40ec-a5fb-e26d2cb31a7c","added_by":"auto","created_at":"2024-07-22 20:56:10","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":495696,"visible":true,"origin":"","legend":"\u003cp\u003eFlow chart of participants selected from NHANES 1999-2018.\u003c/p\u003e","description":"","filename":"Figure1FlowchartofparticipantsselectedfromNHANES1999to2018.png","url":"https://assets-eu.researchsquare.com/files/rs-4644141/v1/f9412916f8a642bdc0c8711e.png"},{"id":60850830,"identity":"a1b32669-0d5c-467e-892e-265fc18aefef","added_by":"auto","created_at":"2024-07-22 20:56:10","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":566218,"visible":true,"origin":"","legend":"\u003cp\u003eRestricted cubic spline curve of LDL-C concentration (mmol/L) and All-cause mortality.\u003c/p\u003e","description":"","filename":"Figure2RestrictedcubicsplinecurveofLDLCconcentrationandAllcausemortality.png","url":"https://assets-eu.researchsquare.com/files/rs-4644141/v1/c7d28ceefb235d343f1245ec.png"},{"id":60850832,"identity":"df9ac425-3fc1-468c-87b5-a26b3bb1af1b","added_by":"auto","created_at":"2024-07-22 20:56:10","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":565141,"visible":true,"origin":"","legend":"\u003cp\u003eRestricted cubic splinecurve of LDL-C concentration(mmol/L) and Cardiovascular mortality.\u003c/p\u003e","description":"","filename":"Figure3RestrictedcubicsplinecurveofLDLCconcentrationandCardiovascularmortality.png","url":"https://assets-eu.researchsquare.com/files/rs-4644141/v1/b526566bcb4da1b34d559406.png"},{"id":60851706,"identity":"5e09d08f-8c72-4f1e-ba35-82aa00756ac6","added_by":"auto","created_at":"2024-07-22 21:04:11","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":549986,"visible":true,"origin":"","legend":"\u003cp\u003eKaplan-Meier curve of all-cause mortality by different levels of LDL-C.\u003c/p\u003e","description":"","filename":"Figure4KaplanMeiercurveofallcausemortalitybydifferentlevelsofLDLC.png","url":"https://assets-eu.researchsquare.com/files/rs-4644141/v1/8172e89a8750d4021ce1e87d.png"},{"id":60850837,"identity":"a4a395c9-6d67-4fdc-8c0b-36b99c8c6671","added_by":"auto","created_at":"2024-07-22 20:56:11","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":615784,"visible":true,"origin":"","legend":"\u003cp\u003eKaplan-Meier curve of cardiovascular mortality by different levels of LDL-C.\u003c/p\u003e","description":"","filename":"Figure5KaplanMeiercurveofcardiovascularmortalitybydifferentlevelsofLDLC.png","url":"https://assets-eu.researchsquare.com/files/rs-4644141/v1/05ea12a7d0a1505f08a66962.png"},{"id":60851705,"identity":"3a0b8605-12c9-4e5b-a773-eec3ced6ee91","added_by":"auto","created_at":"2024-07-22 21:04:10","extension":"png","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":972568,"visible":true,"origin":"","legend":"\u003cp\u003eStratified analysis of the relationship between LDL-C level and all-cause and cardiovascular mortality.\u003c/p\u003e","description":"","filename":"Figure6StratifiedanalysisoftherelationshipbetweenLDLClevelandallcauseandcardiovascularmortality.png","url":"https://assets-eu.researchsquare.com/files/rs-4644141/v1/6efa942124e670d91e331e48.png"},{"id":60850835,"identity":"2027c806-e8e5-4c09-96b1-9c830d634418","added_by":"auto","created_at":"2024-07-22 20:56:10","extension":"png","order_by":7,"title":"Figure 7","display":"","copyAsset":false,"role":"figure","size":799415,"visible":true,"origin":"","legend":"\u003cp\u003eStratified analysis of the relationship between LDL-C level and all-cause and cardiovascular mortality.\u003c/p\u003e","description":"","filename":"Figure7StratifiedanalysisoftherelationshipbetweenLDLClevelandallcauseandcardiovascularmortality.png","url":"https://assets-eu.researchsquare.com/files/rs-4644141/v1/7fa9f2b788fa1770fa3ad968.png"}],"financialInterests":"No competing interests reported.","formattedTitle":"A Non-Linear Association of Low-density lipoprotein cholesterol with All-Cause and Cardiovascular Mortality Among Patients with Hypertension","fulltext":[{"header":"BACKGROUND","content":"\u003cp\u003eOver the past decades, as an indicator of atherosclerosis and cardiovascular disease, low-density lipoprotein cholesterol (LDL-C) has been recognized as a risk factor\u003csup\u003e1\u0026ndash;3\u003c/sup\u003e. There has been evidence from several studies in various populations throughout the world that higher LDL-C levels are associated with mortality due to all causes and cause-specific (cardiovascular disease, stroke, etc.) mortality \u003csup\u003e4\u0026ndash;8\u003c/sup\u003e. A large number of randomized controlled trials also demonstrated that lipid-lowering drugs were associated with a lower risk of cardiovascular events and mortality associated with atherosclerosis \u003csup\u003e9\u0026ndash;12\u003c/sup\u003e. However, with further research, conflicting results revealed regarding LDL-C levels and mortality risk. Some studies found deaths from all causes were negatively correlated with LDL-C levels \u003csup\u003e13,14\u003c/sup\u003e and some showed no association \u003csup\u003e15,16\u003c/sup\u003e. A recent extensive prospective cohort research in Denmark revealed a U-shaped correlation between different LDL-C levels and death \u003csup\u003e17\u003c/sup\u003e. Relationship between concentrations of LDL-C and mortality remains unclear.\u003c/p\u003e \u003cp\u003eMoreover, we noticed that studies are usually conducted in the general population, older population, or diabetes population; no study focused on patients with hypertension. Globally, the hypertension population has reached a staggering amount of 874\u0026nbsp;million and approximately one in four adults has hypertension \u003csup\u003e18\u003c/sup\u003e. Previous studies have shown that hypertension is associated with dyslipidemia and the LDL-C may be a modifiable risk factor for hypertension on its own \u003csup\u003e19,20\u003c/sup\u003e. A study by Bonaa et al. showed a positive correlation between blood pressure and lipid levels \u003csup\u003e20\u003c/sup\u003e. Hence, with such a large population of hypertension, providing some guidelines for risk hierarchy management of hypertensive people and investigating the associations between LDL-C levels and all-cause and cardiovascular mortality may be quite helpful.\u003c/p\u003e"},{"header":"METHODS","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003eData Source and Study Population\u003c/h2\u003e \u003cp\u003eIn this cohort study, all data were obtained from the National Health and Nutrition Examination Survey (NHANES, \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.cdc.gov/nchs/nhanes/index.htm\u003c/span\u003e\u003cspan address=\"https://www.cdc.gov/nchs/nhanes/index.htm\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e). NHANES is a major program of the National Center for Health Statistics (NCHS), which is part of the Centers for Disease Control and Prevention (CDC). The National Health and Nutrition Examination Survey (NHANES) is one of the main programs of the National Center for Health Statistics (NCHS) and this program aims to assess the health and nutritional status of adults and children in the United States \u003csup\u003e21\u003c/sup\u003e. Since 1999, health data gathered by interviews, physical exams, and laboratory testing from representative American population samples would be published in their official website every two years \u003csup\u003e22\u003c/sup\u003e, and also ongoing follow-up mortality data would be posted in the National Death Index death certificate records (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e\u003ca href=\"https://www.cdc.gov/nchs/nhanes/index.htm\" target=\"_blank\"\u003ewww.cdc.gov/nchs/data-linkage/mortality-public.htm\u003c/a\u003e\u003c/span\u003e\u003cspan address=\"http://www.cdc.gov/nchs/data-linkage/mortality-public.htm\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eIn our analysis, we collected 11 cycles of datasets (NHANES 1999\u0026ndash;2018, every two year a cycle), and then extracted demographic data, blood pressure and body mass index data from examinations, and lipid testing data from laboratories, questionnaire data about smoking status, antihypertensive and lipid-lowering drug use. Participants under 18 years old, those with missing blood lipid and follow-up data, missing body mass index and smoking data, or those without hypertension at baseline were excluded from the study. Following the application of the above criteria for exclusion, 9,635 individuals were ultimately included for analysis (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). Participants' survival status and death details were tracked through December 31, 2019. The Centers for Disease Control and Prevention's Institutional Review Board gave its approval to the NHANES research methodology. Every participant provided their consent before participating, and all of the methods for the survey were conducted out in accordance with the relevant rules and regulations( \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.cdc.gov/nchs/nhanes/about_nhanes.htm\u003c/span\u003e\u003cspan address=\"https://www.cdc.gov/nchs/nhanes/about_nhanes.htm\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e ).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003eData Collection\u003c/h2\u003e \u003cp\u003eDemographic information (age, gender, race, marital status, and education level) was recorded at the beginning of every cycle of survey as questionnaire data. We extracted it from the mentioned 11 cycles of datasets above and converted race, marital status, education level to binary categorical variable. Race was categorized as White (Mexican American, Other Hispanic, Non-Hispanic White)/Non-white (Non-Hispanic Black, Other Race), marital status was categorized as Married/Other (Widowed, Divorced, Separated, Refused and so on), and education level was categorized as Less than high school (Less Than 9th Grade, 9-11th Grade, Refused, Don't Know)/ High school or above (High School Grad/GED or Equivalent, Some College or AA degree, College Graduate or above).\u003c/p\u003e \u003cp\u003eBlood pressure and body measures data were collected and stored in examination data module, and we extracted systolic blood pressure, diastolic blood pressure and body mass index (BMI) data. Referring to the American Heart Association Blood Pressure Guidelines 2018, we defined hypertension as systolic blood pressure\u0026thinsp;\u0026ge;\u0026thinsp;140mmHg and/or diastolic blood pressure\u0026thinsp;\u0026ge;\u0026thinsp;90mmHg or self-reported hypertension history and use of anti-hypertensive medication\u003csup\u003e1,23\u003c/sup\u003e. According to NHANES component description, body mass index (BMI) was calculated using weight (kg) divided by the square of height (m\u003csup\u003e2\u003c/sup\u003e).\u003c/p\u003e \u003cp\u003eCholesterol measurements data were stored in the module of laboratory data, we extracted total cholesterol (TC), triglyceride (TG), low-density lipoprotein cholesterol (LDL-C), high density lipoprotein cholesterol (HDL-C) concentration (mmol/L) data for our study. According to NHANES instructions, all measurements were taken in the morning on an empty stomach (at least 8 hours).\u003c/p\u003e \u003cp\u003eSmoking status questionnaire data were also extracted and we defined the answer of SMQ020(Have you/Has SP smoked at least 100 cigarettes in your/his/her entire life?), Yes means smoking, No/Refused/Don\u0026rsquo;t know/Missing means no. As to medication history, questionnaire data of BPQ050A (Are you/Is SP now taking prescribed medicine for hypertension?) and BPQ100D (Are you/Is SP now following taking prescribed medicine to lower (your/his/her) blood cholesterol?) were selected to define, answer Yes means yes, No/Refused/Don\u0026rsquo;t know/Missing means no.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec5\" class=\"Section2\"\u003e \u003ch2\u003eOutcomes and follow-up\u003c/h2\u003e \u003cp\u003eAll-cause mortality and cardiovascular mortality were selected as our study outcomes. All-cause mortality is defined as death from any cause, including Diseases of heart, Malignant neoplasms, Chronic lower respiratory diseases, Accidents, Cerebrovascular diseases, Alzheimer's disease, Diabetes mellitus, Influenza and pneumonia, Nephritis, nephrotic and all other causes.\u003c/p\u003e \u003cp\u003eCardiovascular mortality was estimated using The International Classification of Disease Tenth Revision (ICD-10), codes (I00-I09, I11, I13, I20-I51 and I60\u0026ndash;I69) were used to define cardiovascular deaths. Mortality data of NHANES 1999\u0026ndash;2018 were linked to mortality data from the National Death Index death certificate records until December 31, 2019.\u003c/p\u003e \u003cp\u003eAll participants enrolled in this study had complete follow-up data and when death occurred, causes of death were recorded.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec6\" class=\"Section2\"\u003e \u003ch2\u003eStatistical Analysis\u003c/h2\u003e \u003cp\u003eIn accordance with the blood LDL-C concentration, LDL-C levels was divided into five groups based on quintiles (Q1: \u0026lt; 20th percentile, Q2: \u0026le;20 to 40th percentile, Q3: \u0026le;40 to 60th percentile, Q4: \u0026le;60-80th percentile, Q5: \u0026le; 80th percentile). LDL-C level of Q3 (2.689\u0026ndash;3.155 mmol/L) was selected as a reference to study the relationship between LDL and all-cause and cardiovascular mortality.\u003c/p\u003e \u003cp\u003eIn this study, continuous variables were described by means\u0026thinsp;\u0026plusmn;\u0026thinsp;standard deviations (SD) and compared using an analysis of variance (ANOVA). We compared categorical variables using the Chi-square test, expressing them as number (n) and percentage (%). We used univariate cox regression to identify potential risk factors that may affect all-cause mortality and cardiovascular mortality, the results are represented as hazard ratios (HRs) with 95% confidence intervals (CIs). An analysis of multivariate cox regression models was carried out to determine if LDL-C levels are associated with mortality due to all causes and cardiovascular disease. Three models were constructed, Model I is a crude model and adjusts for none. Model II adjusts for age, gender and race. Model III is a comprehensive model that includes adjustments for smoking, systolic blood pressure, diastolic blood pressure, and medication use (antihypertensive medicines, lipid-lowering medicines) beyond what was included in Model II. A restricted cubic spline (RCS) curve was used to analyze and visualize the relationship between LDL-C concentration and mortality on a continuous scale, which is based on multivariate adjusted cox regression. The survival analysis of Kaplan-Meier curve was carried out to show how survival varies between different level groups of LDL-C. Finally, we also conducted stratification analysis to identify the subgroup that shows a significant connection between LDL-C level and all-cause and cardiovascular death, including age, gender, race, marital status, education level, smoking, body mass index and medicine use. The statistical significance level was determined by p values of less than 0.05(p\u0026thinsp;\u0026lt;\u0026thinsp;0.05) on two sides. All statistical analysis were performed using R version 4.3.1(R Foundation for Statistical Computing, Vienna, Austria, \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.r-project.org/\u003c/span\u003e\u003cspan address=\"https://www.r-project.org/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e ).\u003c/p\u003e \u003c/div\u003e"},{"header":"RESULTS","content":"\u003cdiv id=\"Sec8\" class=\"Section2\"\u003e\n \u003ch2\u003eCharacteristics of the study population at different levels of LDL\u003c/h2\u003e\n \u003cp\u003eTable\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003e shows the distribution of baseline characteristics of research participants based on various levels of LDL-C concentrations. A total of 9,635 participants were included in our analysis, of which 49.6% were male and 50.4% were female, with mean age of 59.4\u0026thinsp;\u0026plusmn;\u0026thinsp;15.6 years. Most of them were White (68.2%) and received high school or above education (85.7%). Following a median of 98 months of follow up, there were 2,283 instances of all-cause fatalities, with 758 cases attributed to cardiovascular disease. Compared with the higher groups (Q4 and Q5), the lower groups (Q1 and Q2) were more likely to be older, male, smoker and were more likely to take antihypertensive drugs and lipid-lowering drugs. Among various LDL-C classification levels, with the exception of marital status, education level and body mass index, all other social demographic and health-related disease factors show statistical significance at baseline (p\u0026thinsp;\u0026lt;\u0026thinsp;0.05).\u003c/p\u003e\n \u003cdiv class=\"gridtable\"\u003e\u0026nbsp;\u003ctable id=\"Tab1\" border=\"1\"\u003e\n \u003ccaption language=\"En\"\u003e\n \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e\n \u003cdiv class=\"CaptionContent\"\u003e\n \u003cp\u003eLDL-C level quintile-based baseline characteristics of study cohort.\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003ccolgroup cols=\"8\"\u003e\u003c/colgroup\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eCharacteristics\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eTotal\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" colspan=\"5\"\u003e\n \u003cp\u003eQuintiles of the LDL-C(mmol/L)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003e\u003cem\u003ep\u003c/em\u003e\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\u0026nbsp;\u003c/th\u003e\n \u003cth align=\"left\"\u003e\u0026nbsp;\u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eQ1(\u0026lt;\u0026thinsp;2.198)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eQ2(2.198\u0026ndash;2.689)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eQ3(2.689\u0026ndash;3.155)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eQ4(3.155\u0026ndash;3.75)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eQ5(\u0026le;\u0026thinsp;3.75)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\u0026nbsp;\u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNumber\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e9635\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1921\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1901\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1913\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1947\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1953\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eAge, years\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e59.4\u0026thinsp;\u0026plusmn;\u0026thinsp;15.6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e62.2\u0026thinsp;\u0026plusmn;\u0026thinsp;16.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e59.5\u0026thinsp;\u0026plusmn;\u0026thinsp;16.2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e58.7\u0026thinsp;\u0026plusmn;\u0026thinsp;15.3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e58.5\u0026thinsp;\u0026plusmn;\u0026thinsp;15.4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e58.3\u0026thinsp;\u0026plusmn;\u0026thinsp;14.6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eGender, n (%)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eMale\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e4780 (49.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1037 (54.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e955 (50.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e935 (48.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e949 (48.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e904 (46.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eFemale\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e4855 (50.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e884 (46.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e946 (49.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e978 (51.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e998 (51.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1049 (53.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eRace, n (%)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.005\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eWhite\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e6570 (68.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1265 (65.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1277 (67.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1332 (69.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1380 (70.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1316 (67.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNon-white\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3065 (31.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e656 (34.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e624 (32.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e581 (30.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e567 (29.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e637 (32.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eMarital status, n (%)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.112\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eMarried\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e5211 (54.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1036 (53.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1058 (55.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e991 (51.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1079 (55.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1047 (53.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eOther\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e4424 (45.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e885 (46.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e843 (44.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e922 (48.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e868 (44.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e906 (46.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eEducation level, n (%)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.800\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eLess than high school\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1381 (14.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e280 (14.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e271 (14.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e269 (14.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e267 (13.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e294 (15.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eHigh school or above\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e8254 (85.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1641 (85.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1630 (85.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1644 (85.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1680 (86.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1659 (84.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eSmoking, n (%)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNo\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e4865 (50.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e903 (47.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e912 (48.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e979 (51.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1036 (53.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1035 (53.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e4770 (49.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1018 (53.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e989 (52.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e934 (48.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e911 (46.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e918 (47.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eBody mass index, kg/m\u003csup\u003e2\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e30.4\u0026thinsp;\u0026plusmn;\u0026thinsp;7.15\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e30.5\u0026thinsp;\u0026plusmn;\u0026thinsp;7.53\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e30.4\u0026thinsp;\u0026plusmn;\u0026thinsp;7.17\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e30.6\u0026thinsp;\u0026plusmn;\u0026thinsp;7.57\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e30.5\u0026thinsp;\u0026plusmn;\u0026thinsp;6.87\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e30.2\u0026thinsp;\u0026plusmn;\u0026thinsp;6.59\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.523\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eSystolic blood pressure, mmHg\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e137\u0026thinsp;\u0026plusmn;\u0026thinsp;20.2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e134\u0026thinsp;\u0026plusmn;\u0026thinsp;20.1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e136\u0026thinsp;\u0026plusmn;\u0026thinsp;20.1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e136\u0026thinsp;\u0026plusmn;\u0026thinsp;20.6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e137\u0026thinsp;\u0026plusmn;\u0026thinsp;19.1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e140\u0026thinsp;\u0026plusmn;\u0026thinsp;20.9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eDiastolic blood pressure,mmHg\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e73.0\u0026thinsp;\u0026plusmn;\u0026thinsp;13.8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e69.1\u0026thinsp;\u0026plusmn;\u0026thinsp;13.6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e72.3\u0026thinsp;\u0026plusmn;\u0026thinsp;13.5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e73.0\u0026thinsp;\u0026plusmn;\u0026thinsp;13.7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e74.3\u0026thinsp;\u0026plusmn;\u0026thinsp;13.4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e76.4\u0026thinsp;\u0026plusmn;\u0026thinsp;13.5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eTotal cholesterol, mmol/L\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e5.06\u0026thinsp;\u0026plusmn;\u0026thinsp;1.09\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3.75\u0026thinsp;\u0026plusmn;\u0026thinsp;0.569\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e4.49\u0026thinsp;\u0026plusmn;\u0026thinsp;0.453\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e4.98\u0026thinsp;\u0026plusmn;\u0026thinsp;0.441\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e5.52\u0026thinsp;\u0026plusmn;\u0026thinsp;0.448\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e6.53\u0026thinsp;\u0026plusmn;\u0026thinsp;0.751\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eHDL cholesterol, mmol/L\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.39\u0026thinsp;\u0026plusmn;\u0026thinsp;0.425\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.35\u0026thinsp;\u0026plusmn;\u0026thinsp;0.464\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.40\u0026thinsp;\u0026plusmn;\u0026thinsp;0.431\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.40\u0026thinsp;\u0026plusmn;\u0026thinsp;0.434\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.38\u0026thinsp;\u0026plusmn;\u0026thinsp;0.399\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.40\u0026thinsp;\u0026plusmn;\u0026thinsp;0.392\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eTriglycerides, mmol/L\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.50\u0026thinsp;\u0026plusmn;\u0026thinsp;0.789\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.40\u0026thinsp;\u0026plusmn;\u0026thinsp;0.863\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.44\u0026thinsp;\u0026plusmn;\u0026thinsp;0.798\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.46\u0026thinsp;\u0026plusmn;\u0026thinsp;0.754\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.56\u0026thinsp;\u0026plusmn;\u0026thinsp;0.756\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.64\u0026thinsp;\u0026plusmn;\u0026thinsp;0.748\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eLDL cholesterol, mmol/L\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2.99\u0026thinsp;\u0026plusmn;\u0026thinsp;0.950\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.75\u0026thinsp;\u0026plusmn;\u0026thinsp;0.328\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2.44\u0026thinsp;\u0026plusmn;\u0026thinsp;0.140\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2.91\u0026thinsp;\u0026plusmn;\u0026thinsp;0.135\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3.43\u0026thinsp;\u0026plusmn;\u0026thinsp;0.170\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e4.38\u0026thinsp;\u0026plusmn;\u0026thinsp;0.599\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eAntihypertensive drugs, n (%)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNo\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e4051 (42.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e531 (27.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e712 (37.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e837 (43.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e932 (47.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1039 (53.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e5584 (58.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1390 (72.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1189 (62.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1076 (56.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1015 (52.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e914 (46.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eLipid-lowering drugs, n (%)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNo\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e6776 (70.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e995 (51.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1180 (62.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1419 (74.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1568 (80.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1614 (82.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2859 (29.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e926 (48.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e721 (37.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e494 (25.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e379 (19.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e339 (17.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eOutcomes, n (%)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eAll-cause mortality\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNo\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e7352 (76.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1409 (73.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1421 (74.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1465 (76.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1528 (78.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1529 (78.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2283 (23.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e512 (26.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e480 (25.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e448 (23.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e419 (21.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e424 (21.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eCardiovascular mortality\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNo\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e8877 (92.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1739 (90.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1746 (91.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1768 (92.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1827 (93.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1797 (92.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e758 (7.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e182 (9.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e155 (8.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e145 (7.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e120 (6.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e156 (8.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003ctfoot\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"8\"\u003eAbbreviations: Q, quintiles; n, number; HDL, high density lipoprotein; LDL, low-density lipoprotein. Values are mean\u0026thinsp;\u0026plusmn;\u0026thinsp;standard deviation for continuous variables or n (%) for categorical variables.\u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tfoot\u003e\n \u003c/table\u003e\n \u003c/div\u003e\n \u003cdiv class=\"gridtable\"\u003e\n \u003cdiv align=\"char\" class=\"colspec\"\u003e\u003cbr\u003e\u003c/div\u003e\u0026nbsp;\u003ctable id=\"Tab2\" border=\"1\"\u003e\n \u003ccaption language=\"En\"\u003e\n \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e\n \u003cdiv class=\"CaptionContent\"\u003e\n \u003cp\u003eResult of Univariate analysis.\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003ccolgroup cols=\"7\"\u003e\u003c/colgroup\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eCharacteristics\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003eAll-cause mortality\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\u0026nbsp;\u003c/th\u003e\n \u003cth align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003eCardiovascular mortality\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\u0026nbsp;\u003c/th\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\u0026nbsp;\u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eEvents/numbers\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eHR (95% CI)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003e\u003cem\u003ep\u003c/em\u003e\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eEvents/numbers\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eHR (95% CI)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003e\u003cem\u003ep\u003c/em\u003e\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eLDL-C\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\u0026nbsp;\u003c/th\u003e\n \u003cth align=\"left\"\u003e\u0026nbsp;\u003c/th\u003e\n \u003cth align=\"left\"\u003e\u0026nbsp;\u003c/th\u003e\n \u003cth align=\"left\"\u003e\u0026nbsp;\u003c/th\u003e\n \u003cth align=\"left\"\u003e\u0026nbsp;\u003c/th\u003e\n \u003cth align=\"left\"\u003e\u0026nbsp;\u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;2.198\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e512/1921\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.459 (1.285, 1.657)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e182/1921\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.609 (1.293, 2.002)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2.198\u0026ndash;2.689\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e480/1901\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.218 (1.070, 1.385)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.0027\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e155/1901\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.217 (0.970, 1.527)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.089\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2.689\u0026ndash;3.155\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e448/1913\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1(Ref)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e145/1913\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1(Ref)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3.155\u0026ndash;3.75\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e419/1947\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.874 (0.765, 0.998)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.0466\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e120/1947\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.772 (0.606, 0.984)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.036\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026le;\u0026thinsp;3.75\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e424/1953\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.847 (0.742, 0.967)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.0143\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e156/1953\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.962 (0.767, 1.206)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.734\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eGender\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eMale\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1236/4780\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1(Ref)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e420/4780\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1(Ref)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eFemale\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1047/4855\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.784 (0.722, 0.851)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e338/4855\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.744 (0.645, 0.859)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eRace\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eWhite\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1775/6570\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.728 (0.659, 0.803)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e575/6570\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.811 (0.686, 0.958)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.014\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNon-white\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e508/3065\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1(Ref)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e183/3065\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1(Ref)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eMarital status\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eMarried\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1105/5211\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1(Ref)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e351/5211\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1(Ref)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eOther\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1178/4424\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.400 (1.290, 1.520)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e407/4424\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.520 (1.320, 1.760)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eEducation level\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eLess than high school\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e485/1381\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1(Ref)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e157/1381\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1(Ref)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eHigh school or above\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1798/8254\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.663 (0.600, 0.733)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e601/8254\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.686 (0.576, 0.818)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eSmoking\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNo\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e937/4865\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.651 (0.599, 0.707)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e342/4865\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.769 (0.667, 0.887)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1346/4770\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1(Ref)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e416/4770\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1(Ref)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eAntihypertensive drugs\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNo\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1489/4051\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.597 (0.547, 0.651)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e238/4051\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.510 (0.437, 0.595)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e794/5584\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1(Ref)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e520/5584\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1(Ref)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eLipid-lowering drugs\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNo\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1571/6776\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.744 (0.681, 0.814)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e500/6776\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.650 (0.559, 0.756)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e712/2859\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1(Ref)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e258/2859\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1(Ref)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003ctfoot\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"7\"\u003eAbbreviations: LDL-C, low-density lipoprotein cholesterol; HR, hazard ratio; CI, confidence interval; Ref, reference.\u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tfoot\u003e\n \u003c/table\u003e\n \u003c/div\u003e\n \u003cdiv class=\"gridtable\"\u003e\n \u003cdiv align=\"left\" class=\"colspec\"\u003e\u003cbr\u003e\u003c/div\u003e\u0026nbsp;\u003ctable id=\"Tab3\" border=\"1\"\u003e\n \u003ccaption language=\"En\"\u003e\n \u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e\n \u003cdiv class=\"CaptionContent\"\u003e\n \u003cp\u003eResult of Multivariate Cox regression analysis.\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003ccolgroup cols=\"4\"\u003e\u003c/colgroup\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\u0026nbsp;\u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eModel I\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eModel II\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eModel III\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\u0026nbsp;\u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eHR (95%CI), \u003cem\u003ep\u003c/em\u003e value\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eHR (95%CI), \u003cem\u003ep\u003c/em\u003e value\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eHR (95%CI), \u003cem\u003ep\u003c/em\u003e value\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eAll-cause mortality\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\u0026nbsp;\u003c/th\u003e\n \u003cth align=\"left\"\u003e\u0026nbsp;\u003c/th\u003e\n \u003cth align=\"left\"\u003e\u0026nbsp;\u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eLDL-C Levels\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;2.198\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.459 (1.285, 1.657), \u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.170 (1.029, 1.329), 0.0160\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.196 (1.051, 1.361), 0.0068\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2.198\u0026ndash;2.689\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.218 (1.070, 1.385), 0.0027\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.166 (1.025, 1.327), 0.0190\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.184 (1.040, 1.348), 0.0108\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2.689\u0026ndash;3.155\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1(Ref)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1(Ref)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1(Ref)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3.155\u0026ndash;3.75\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.874 (0.765, 0.998), 0.0466\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.861 (0.753, 0.983), 0.0270\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.852 (0.745, 0.973), 0.0186\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026le;\u0026thinsp;3.75\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.847 (0.742, 0.967), 0.0143\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.898 (0.786, 1.026), 0.1130\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.879 (0.769, 1.006), 0.0611\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eCardiovascular mortality\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eLDL-C Levels\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;2.198\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.609 (1.293, 2.002), \u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.242 (0.997, 1.547), 0.0537\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.234 (0.987, 1.542), 0.0647\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2.198\u0026ndash;2.689\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.217 (0.970, 1.527), 0.0890\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.164 (0.927, 1.460), 0.2265\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.153 (0.917, 1.448), 0.2230\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2.689\u0026ndash;3.155\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1(Ref)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1(Ref)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1(Ref)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3.155\u0026ndash;3.75\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.772 (0.606, 0.984), 0.0360\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.761 (0.597, 0.969), 0.0269\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.761 (0.597, 0.970), 0.0272\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026le;\u0026thinsp;3.75\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.962 (0.767, 1.206), 0.7340\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.026 (0.818, 1.288), 0.8219\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.012 (0.804, 1.272), 0.9214\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003ctfoot\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"4\"\u003eAbbreviations: LDL-C, low-density lipoprotein cholesterol; HR, hazard ratio; CI, confidence interval; Ref, reference.\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"4\"\u003eModel I adjust for none.\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"4\"\u003eModel II adjust for age, gender, and race.\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"4\"\u003eModel III adjust for age, gender, race, marital status, education level, smoking, body mass index, systolic blood pressure, diastolic blood pressure, and medicine use (antihypertensive drugs, lipid-lowering drugs).\u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tfoot\u003e\n \u003c/table\u003e\n \u003c/div\u003e\n \u003cp\u003e\u003cstrong\u003eAssociation between LDL‑C concentration and\u0026nbsp;\u003c/strong\u003e\u003cstrong\u003eall‑cause\u003c/strong\u003e\u003cstrong\u003e\u0026nbsp;and cardiovascular mortality\u003c/strong\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003eTo further explore the association between LDL-C concentration (as a continuous variable) and all-cause and cardiovascular mortality, we performed\u0026nbsp;restricted cubic spline regression analysis on our included data and used an RCS curve to visually show the result. The analysis models were based on multivariate adjusted cox regression\u0026nbsp;and fully adjusted for confounders. As showed in results, both all-cause mortality (\u003cstrong\u003eFigure 2\u003c/strong\u003e) and cardiovascular mortality (\u003cstrong\u003eFigure 3\u003c/strong\u003e) had a non-linear association with LDL-C concentration. When LDL-C concentrations were below 2.89 mmol/L, both the risk of all-cause death and cardiovascular death became higher as the LDL-C concentration decreased. Risks of all-cause and cardiovascular death tended to further decrease and then increase while blood LDL-C concentration above 2.89mmol/L. Differently, risk of cardiovascular death seemed to get increased at a lower LDL-C concentration than all-cause death. Red shaded area represents the 95% CI of the curve.\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003eFigure\u0026nbsp;\u003c/strong\u003e\u003cstrong\u003e2\u003c/strong\u003e\u003cstrong\u003e.\u0026nbsp;\u003c/strong\u003eRestricted cubic spline curve of LDL-C concentration (mmol/L) and All-cause mortality. \u0026nbsp;\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003eFigure\u0026nbsp;\u003c/strong\u003e\u003cstrong\u003e3\u003c/strong\u003e\u003cstrong\u003e.\u0026nbsp;\u003c/strong\u003eRestricted cubic spline curve of LDL-C concentration(mmol/L) and Cardiovascular mortality.\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003eSurvival Analysis and Stratification\u003c/strong\u003e\u003cstrong\u003e\u0026nbsp;Analysis\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003eFigure 4\u003c/strong\u003e and \u003cstrong\u003eFigure 5\u003c/strong\u003e showed the results of survival analysis of Kaplan-Meier curve, both all-cause and cardiovascular mortality were significantly different from the other groups when LDL-C at a lowest level. \u003cstrong\u003eFigure 6\u003c/strong\u003e and \u003cstrong\u003eFigure 7\u003c/strong\u003e presented results of stratification analysis stratified by all confounders included in this study. Consistent with results of univariate analysis, the lower level of LDL-C groups had a higher risk of all-cause and cardiovascular death in all subgroup. Specially, we turned continuous variables age (\u0026lt;60 years old and \u0026ge;60 years old) and body mass index (\u0026lt;25 kg/m\u003csup\u003e2\u003c/sup\u003e and \u0026ge;25 kg/m\u003csup\u003e2\u003c/sup\u003e) into\u0026nbsp;categorical variables for further study. As results showed, patients with hypertension aged \u0026lt;60 years old may have a higher risk of all-cause mortality when at a lower level of LDL-C.\u0026nbsp;However, to population aged \u0026ge;60 years old,\u0026nbsp;a higher risk of cardiovascular mortality was statistically significant. As to body mass index, both higher risk of all-cause and cardiovascular mortality were observed while body mass index \u0026ge;25 kg/m\u003csup\u003e2\u003c/sup\u003e.\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003eFigure\u0026nbsp;\u003c/strong\u003e\u003cstrong\u003e4\u003c/strong\u003e\u003cstrong\u003e.\u0026nbsp;\u003c/strong\u003eKaplan-Meier curve of all-cause mortality by different levels of LDL-C.\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003eFigure\u0026nbsp;\u003c/strong\u003e\u003cstrong\u003e5\u003c/strong\u003e\u003cstrong\u003e.\u0026nbsp;\u003c/strong\u003eKaplan-Meier curve of cardiovascular mortality by different levels of LDL-C.\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003eFigure\u0026nbsp;\u003c/strong\u003e\u003cstrong\u003e6\u003c/strong\u003e\u003cstrong\u003e.\u0026nbsp;\u003c/strong\u003eStratified analysis of the relationship between LDL-C level and all-cause and cardiovascular mortality.\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003eFigure\u0026nbsp;\u003c/strong\u003e\u003cstrong\u003e7\u003c/strong\u003e\u003cstrong\u003e.\u0026nbsp;\u003c/strong\u003eStratified analysis of the relationship between LDL-C level and all-cause and cardiovascular mortality.\u003c/p\u003e\n\u003c/div\u003e"},{"header":"Discussion","content":"\u003cp\u003eIn this retrospective cohort study of 9,635 hypertensive patients, our key finding was the non-linear association between LDL-C and all-cause and cardiovascular mortality. After adjusting confounders of age, gender, race, marital status, education level, smoking, body mass index, systolic and diastolic blood pressure, and medication use, the RCS curve based on multivariate adjusted cox regression well revealed the correlation between them on a continuous scale. Distinguishing from the traditional impression that lower LDL-C level were better for health, we found both low and high LDL-C levels contributed to increased risk of death in hypertension population. As to the lowest risk of all-cause and cardiovascular mortality, we had preliminary view that the LDL-C concentration of slightly higher than 2.89mmol/L may be optimal in hypertensive patients according to RCS curve. These new findings may provide some reference for lipid control in hypertensive populations.\u003c/p\u003e\n\u003cp\u003eAs the major culprit in development of atherosclerosis, the elevated levels of LDL-C are strongly associated with cardiovascular disease with no doubt.\u0026nbsp;According to statistics from the\u0026nbsp;World Health Organization report in 2021, cardiovascular disease causes 17.9 million deaths in 2019, making up 32% of total global deaths\u0026nbsp;\u003csup\u003e24\u003c/sup\u003e,\u0026nbsp;so it can be easily understood that higher levels of LDL-C accompanied by higher risk of death. But when come to the lower LDL-C levels, the higher death risk seems incomprehensible. For this counterintuitive result, there are several probable explanations. First, it is hypothesized that debilitation and disease can lead to lower cholesterol levels\u0026nbsp;\u003csup\u003e17\u003c/sup\u003e\u003csup\u003e,\u003c/sup\u003e\u003csup\u003e25\u003c/sup\u003e\u003csup\u003e,\u003c/sup\u003e\u003csup\u003e26\u003c/sup\u003e and, in this study, patients with lower levels of LDL-C had an older age (Q1: mean age of 62.2 \u0026plusmn; 16.0, Q2: mean age of 59.5 \u0026plusmn; 16.2) than higher level groups (Q4: mean age of 58.5 \u0026plusmn; 15.4, Q5: mean age of 58.3 \u0026plusmn; 14.6) indeed. Individual comorbidity profiles were not included in our study, but can be inferred from individual medication histories, the low-level groups had higher percentages of medication use. Second, although most studies had spared no effort to emphasize the benefits of lipid lowering,\u0026nbsp;the long-term safety and efficacy of LDL-C lowering therapies\u0026nbsp;remains a question to be further explored \u003csup\u003e27\u003c/sup\u003e. Moreover, a number of studies have reported neurocognitive deficits,\u0026nbsp;hemorrhagic stroke, and new-onset diabetes in the presence of reduced LDL-C\u0026nbsp;\u003csup\u003e27\u003c/sup\u003e\u003csup\u003e\u0026ndash;\u003c/sup\u003e\u003csup\u003e29\u003c/sup\u003e, which may invariably increase the risk of all-cause mortality. Third, Kaysen GA et al found higher LDL-C was significantly associated with lower infection-related mortality\u0026nbsp;an international retrospective cohort study\u0026nbsp;\u003csup\u003e13\u003c/sup\u003e,\u0026nbsp;in other words, the risk of infectious death may increase when LDL-C at a low level, and so of all-cause mortality. Finally, as the world\u0026apos;s second most common cause of death,\u0026nbsp;cancer was related to low LDL-C\u0026nbsp;levels, which is repeatedly mentioned in multiple studies\u0026nbsp;\u003csup\u003e30\u003c/sup\u003e\u003csup\u003e\u0026ndash;\u003c/sup\u003e\u003csup\u003e33\u003c/sup\u003e.\u0026nbsp;In total, reduced LDL-C levels might elevate the likelihood of mortality from the possible reason above, which and then results in increased all-cause mortality.\u003c/p\u003e\n\u003cp\u003eConsistent with our study result, some previous studies conducted in other populations\u0026nbsp;have demonstrated a correlation between LDL-C levels and the risk of all-cause and cardiovascular mortality. Zhen Zhou et al. and Vale ́rie Tikhonoff et al. had explored relationships in older people. Zhen Z et al. reported there was a U-shaped relationship between untreated LDL-C level and all-cause mortality\u0026nbsp;\u003csup\u003e34\u003c/sup\u003e and Tikhonoff V et al. found LDL-C concentration is a multifaceted risk factor in older adults\u0026nbsp;\u003csup\u003e35\u003c/sup\u003e. Chang C-H et al. demonstrated both lower and higher levels of mean LDL-C were associated with increased all-cause and cardiovascular mortality in type 2 diabetes patients through\u0026nbsp;\u003csup\u003e36\u003c/sup\u003e. Also, multiple studies in general population had got the similar result\u0026nbsp;\u003csup\u003e17\u003c/sup\u003e\u003csup\u003e,\u003c/sup\u003e\u003csup\u003e37\u003c/sup\u003e\u003csup\u003e\u0026ndash;\u003c/sup\u003e\u003csup\u003e39\u003c/sup\u003e. Additionally, through a prospective cohort study of 108 243 individuals in Denmark, Johannesen CDL et al. found the lowest risk of all-cause mortality were at concentrations of LDL-C of 3.6-3.7 mmol/L\u0026nbsp;\u003csup\u003e17\u003c/sup\u003e\u003csup\u003e.\u003c/sup\u003e However, from our current study, lipid controlling in hypertensive populations should be even more strict.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eStudy Strengths and Limitations\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThanks to the ongoing NHANES project and continued data collection,\u0026nbsp;we were able to build such a large sample size cohort of hypertensive people for our analysis. No individuals lost to follow-up and the cause of death of every participant was recorded on the National Death Index death certificate records. As far as our knowledge extends,\u0026nbsp;relationship between low-density and all-cause and cardiovascular mortality remains controversial, a few studies explored it in general or other populations, but specifically in hypertensive populations, our study may be the first attempt to do so. Another strength of our study is that we adjusted for several confounders which may influence the accuracy of analysis results.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eHowever, limitations also should be considered. First, the population we included were only living in United States, other countries or ethnicities may be not applicable. Second, several variables we included such as smoking status and medicine use were may cause recall bias, because they were subjective from participants. Third, we did not consider changes in LDL-C concentration over time or changes influenced by the initiation or cessation of lipid-lowering treatment throughout the observation period, this may make the findings unreliable. Finally, given the observational nature of the study, causality cannot be definitively established. Therefore, it is imperative to interpret the findings with caution, considering both potential causal and reverse relationships. Subsequent research is warranted to elucidate the possible causal link between LDL-C levels and mortality.\u003c/p\u003e"},{"header":"Conclusion","content":"\u003cp\u003eThe study revealed a non-linear association between LDL-C levels and both all-cause mortality and cardiovascular mortality in individuals with high blood pressure. Maintaining LDL-C within a specific range may confer benefits for cardiovascular health and long-term survival when compared to lower or higher concentrations. Nevertheless, additional research is necessary to determine the optimal LDL-C concentration range.\u0026nbsp;\u003c/p\u003e"},{"header":"Abbreviations","content":"\u003cp\u003eLDL-C: low-density lipoprotein cholesterol; BMI: body mass index; NHANES: National Health and Nutrition Examination Survey; HR: hazard ratio; CI: confidence interval; RCS: restricted cubic spline.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eAcknowledgements\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWe express our sincere gratitude to the National Health and Nutrition Examination Surveys for providing the data.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthors\u0026rsquo; contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eGuoliang Liang: conceptualization, formal analysis, software, and writing-original draft. Wenhao Zhang:\u0026nbsp;conceptualization and software. Xinxin Gu: formal analysis and writing-original draft. Qiong Zhang: investigation and writing-review draft. Ankang Liu: formal analysis and project administration. Xinran Qing: conceptualization writing-review draft, and project administration. Jiangwei Ma: validation and supervision. All authors have read and agreed to the published version of the manuscript.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis work was supported by the Shanghai Fengxian District Science and Technology Development Fund Program (No.20221201)\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eData Availability\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAll data we used for our analysis are publicly available on the website (www.cdc.gov/nchs/nhanes/).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting interests\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare no competing interests.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEthics approval and consent to participate\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe studies involving human participants were reviewed and approved by NCHS Ethics Review Board. The participants provided their written informed consent to participate in this study.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent for publication\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eGrundy, S. M. \u003cem\u003eet al.\u003c/em\u003e 2018 AHA/ACC/AACVPR/AAPA/ABC/ACPM/ADA/AGS/APhA/ASPC/NLA/PCNA Guideline on the Management of Blood Cholesterol: A Report of the American College of Cardiology/American Heart Association Task Force on Clinical Practice Guidelines. \u003cem\u003eCirculation\u003c/em\u003e\u003cstrong\u003e139\u003c/strong\u003e, e1082\u0026ndash;e1143 (2019).\u003c/li\u003e\n\u003cli\u003eFerence, B. A. \u003cem\u003eet al.\u003c/em\u003e Low-density lipoproteins cause atherosclerotic cardiovascular disease. 1. Evidence from genetic, epidemiologic, and clinical studies. 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Lipoprotein(a)-related cardiovascular and all-cause mortalities in Korean adults. \u003cem\u003eEur J Prev Cardiol\u003c/em\u003e\u003cstrong\u003e30\u003c/strong\u003e, 308\u0026ndash;317 (2023).\u003c/li\u003e\n\u003cli\u003eLiu, Y. \u003cem\u003eet al.\u003c/em\u003e Association between low density lipoprotein cholesterol and all-cause mortality: results from the NHANES 1999\u0026ndash;2014. \u003cem\u003eSci Rep\u003c/em\u003e\u003cstrong\u003e11\u003c/strong\u003e, 22111 (2021).\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":false,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"Low-density lipoprotein cholesterol, All-Cause Mortality, Cardiovascular Mortality, Hypertension, NHANES","lastPublishedDoi":"10.21203/rs.3.rs-4644141/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-4644141/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eBackground\u003c/h2\u003e \u003cp\u003eAlthough a few studies have examined the correlation between low-density lipoprotein cholesterol (LDL-C) and mortality, no study has explored these associations in hypertensive populations. This study aims to investigate the relationship between low-density lipoprotein cholesterol and cardiovascular and all-cause mortality in adults with hypertension.\u003c/p\u003e\u003ch2\u003eMethods\u003c/h2\u003e \u003cp\u003eHypertensive participants aged\u0026thinsp;\u0026ge;\u0026thinsp;18 years old from the National Health and Nutrition Examination Survey (NHANES) 1999\u0026ndash;2018 with blood lipid testing data and complete follow-up data until December 31, 2019 were enrolled in analysis. Univariate and multivariate Cox regression were conducted for the calculation of hazard ratios (HR) and 95% confidence intervals (CIs). Restricted cubic spline (RCS) curve was performed to visually represent the relationship between LDL-C and mortality. Survival analysis of Kaplan-Meier and stratification analysis were also carried out.\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e \u003cp\u003eWe finally analyzed a cohort of 9,635 participants (49.6% male, mean age of 59.4 years). Following a median of 98 months of follow-up, there were 2,283(23.7%) instances of all-cause fatalities, with 758(7.9%) cases attributed to cardiovascular disease. Multivariate Cox regression analysis showed lower levels of LDL-C groups had a higher risk of all-cause and cardiovascular mortality; the LDL-C group's lowest level (\u0026lt;\u0026thinsp;2.198 mmol/L) still showed a 19.6% increased risk of all-cause mortality (p\u0026thinsp;=\u0026thinsp;0.0068) in the model that has been completely adjusted. Both all-cause mortality and cardiovascular mortality showed a non-linear association with LDL-C concentration in restricted cubic spline regression analysis.\u003c/p\u003e\u003ch2\u003eConclusions\u003c/h2\u003e \u003cp\u003eIn individuals with hypertension, LDL-C was linked to cardiovascular and all-cause mortality, and we further demonstrated that this relationship was non-linear.\u003c/p\u003e","manuscriptTitle":"A Non-Linear Association of Low-density lipoprotein cholesterol with All-Cause and Cardiovascular Mortality Among Patients with Hypertension","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-07-22 20:56:05","doi":"10.21203/rs.3.rs-4644141/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"61990757-0012-4da3-83f6-761e32dac772","owner":[],"postedDate":"July 22nd, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[{"id":34745223,"name":"Health sciences/Cardiology"},{"id":34745224,"name":"Health sciences/Cardiology/Cardiovascular biology"},{"id":34745225,"name":"Health sciences/Cardiology/Cardiovascular biology/Cardiovascular diseases"},{"id":34745226,"name":"Health sciences/Cardiology/Cardiovascular biology/Cardiovascular diseases/Dyslipidaemias"}],"tags":[],"updatedAt":"2025-10-31T09:54:29+00:00","versionOfRecord":[],"versionCreatedAt":"2024-07-22 20:56:05","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-4644141","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-4644141","identity":"rs-4644141","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}