Plasma Lipid Profile of the Iranian Adult Population: Findings of the Nationally Representative STEPs Survey 2021

Plasma Lipid Profile of the Iranian Adult Population: Findings of the Nationally Representative STEPs Survey 2021 | 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 Plasma Lipid Profile of the Iranian Adult Population: Findings of the Nationally Representative STEPs Survey 2021 Javad Khanali, Erfan Ghasemi, Mohammad-Mahdi Rashidi, Naser Ahmadi, and 22 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-2677772/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 19 Sep, 2023 Read the published version in Scientific Reports → Version 1 posted 12 You are reading this latest preprint version Abstract The study aimed to estimate the prevalence of lipid abnormalities in Iranian adults by demographic characterization, geographical distribution, and associated risk factors using national and sub-national representative samples of the STEPs 2021 survey in Iran. In this population-based household survey, a total of 18,119 individuals aged higher than 25 years provided blood samples for biochemical analysis. Dyslipidemia was defined by the presence of at least one of the lipid abnormalities of hypertriglyceridemia (≥ 150 mg/dL), hypercholesterolemia (≥ 200 mg/dL), high LDL-C (≥ 130 mg/dL), and low HDL-C (< 50 mg/dL in women, < 40 mg/dL in men), or self-reported use of lipid-lowering medications. Mixed dyslipidemia was characterized as the coexistence of high LDL-C with at least one of the hypertriglyceridemia and low HDL-C. The prevalence of each lipid abnormality was determined by each population strata, and the determinants of abnormal lipid levels were identified using a multiple logistic regression model. The prevalence was 39.7% for hypertriglyceridemia, 21.2% for hypercholesterolemia, 16.4% for high LDL-C, 68.4% for low HDL-C, and 81.0% for dyslipidemia. Hypercholesterolemia and low HDL-C were more prevalent in women, and hypertriglyceridemia was more prevalent in men. The prevalence of dyslipidemia was higher in women (OR = 1.8), obese (OR = 2.8) and overweight (OR = 2.3) persons, those residents in urban areas (OR = 1.1), those with inappropriate physical activity (OR = 1.2), patients with diabetes (OR = 2.7) and hypertension (OR = 1.9), and participants with a history (OR = 1.6) or familial history of CVDs (OR = 1.2). Mixed dyslipidemia prevalence was 13.6% in women and 11.4% in men (P < 0.05). The prevalence of lipid abnormalities was highly heterogeneous among provinces, and East Azarbaijan with 85.3% (81.5–89.1) and Golestan with 68.5% (64.8–72.2) had the highest and lowest prevalence of dyslipidemia, respectively. Although the prevalence of high cholesterol and LDL-C had a descending trend in the 2016–2021 period, the prevalence of dyslipidemia remained unchanged. There are modifiable risk factors associated with dyslipidemia that can be targeted by the primary healthcare system. To modify these risk factors and promote metabolic health in the country, action plans should come to action through a multi-sectoral and collaborative approach. Health sciences/Endocrinology Health sciences/Risk factors Figures Figure 1 Figure 2 Introduction Metabolic risk factors are considerable public health concerns, and despite measures taken, their global burden has not only increased but also accelerated its growing trend since 1990 1 . Among these risk factors, dyslipidemia has a proven role in atherosclerosis and cardiovascular diseases (CVDs). 4.4 million deaths and 98.6 million disability-adjusted life years (DALYs) in 2019 could be attributed to high LDL cholesterol (LDL-C) 2 . Knowing whether dyslipidemia is declining, stagnating, or even increasing provides governments and international organizations with information on the new health priority and also gives insight into where current efforts are effective or inadequate 2 . Therefore, repeated cross-sectional and population-based surveys are needed to regularly assess the current circumstance of the risk factor. Considering the poor metabolic health in the middle-east, tracking dyslipidemia in the region countries, including Iran, would be of especial importance 3 . It is shown that high LDL-C contributed to 16.1% of deaths and 7.8% of DALYs caused by non-communicable diseases (NCDs) in the Iranian population in 2019 4 . The high consumption of dietary fats, obesity, physical inactivity, non-adherence to treatment guidelines by patients and physicians, as well as the increasing consumption of carbohydrate-rich foods could predispose the Iranian population to dyslipidemia 5 – 9 . Meanwhile, The widespread prescribing of statins by general practitioners, restrictions on Trans fats in edible oils, and increased public awareness on the risk factor are policies and trends that may decrease dyslipidemia prevalence in Iran 3 , 10 . After recognizing the global need for data on risk factors that drive NCDs, WHO initiated the STEP-wise approach to NCD risk factor surveillance (STEPs) as national household surveys in 2002 11 . Iran is among the few countries in the region that have done regular STEPs surveys on metabolic risk factors, including dyslipidemia 3 . Eight rounds of STEPs surveys have been conducted in Iran; however, only the latest published report, STEPs 2016, presented a complete lipid profile of Iranian adults comprising total cholesterol (TC), triglyceride (TG), LDL-C, and HDL cholesterol (HDL-C) 10 . Therefore, the precise figure of the prevalence of lipid abnormalities in Iran has remained to become clear. The present study aimed to estimate the prevalence of lipid abnormalities in Iranian adults by demographic characterization, associated risk factors, and geographical distribution. Here, national and sub-national representative samples of the STEPs 2021 survey were analyzed and presented. Methods Based on the STEPwise approach to NCD risk factor surveillance developed by WHO 11 , the STEPs 2021 survey was designed and conducted in Iran with representative samples from urban and rural areas of the country. The details of the procedures and methods of STEPs 2021 are published 12 , and just a few crucial requirements were discussed here. According to the STEPs established framework, risk factors were assessed in three steps: filling out a questionnaire, obtaining objective information by physical assessment, and collecting participants' blood and urine samples for biochemical analysis (in those aged above 25). All laboratory measurements were performed in the coordinating center in Tehran. Sampling and study population Sampling was done in proportion to the adult population of urban and rural areas of 31 provinces of Iran. Accordingly, a systematic random sampling frame was designed, and 28,821 individuals in 3176 clusters were selected (each including 9 participants). The variables considered in the representative samples were age, gender, area of residence (rural/urban), and province. The number of participants for the third step of the survey was 18,119, including those aged higher than 25 and accepted to participate in lab measurements. The ethics committee of the National Institute for Health Research approved the study protocol (ID: IR.TUMS.NIHR.REC.1398.006), and the study was performed in accordance with the Declaration of Helsinki. The study objectives and methods were clearly explained to all participants along with the fact that participation in the study is voluntary and that refusing to participate will not affect their access to health care. Definition and measurement of variables Using household asset data, the participants' wealth index was calculated by principal component analysis, and the first component was assigned to the wealth index and categorized into five quintiles, from the poorest (first quintile) to the richest (fifth quintile). Smoking status was defined as positive for those who are current daily smokers of any tobacco product, including cigarettes, hookah, pipes, smokeless tobacco, and electronic cigarettes. The second version of the global physical activity questionnaire was utilized to evaluate physical activity. Accordingly, appropriate physical activity was defined as having either high or moderate physical activity, while inappropriate physical activity was defined as having low physical activity. People who ate fast food during the previous week were categorized as those who consume fast food. Those who were consuming at least 3 servings of fruits and 4 servings of vegetables per day were categorized as those with appropriate fruits and vegetable consumption. To check for the history of coronary heart disease, patients were asked "have you ever been told by a physician or health staff that you had a heart attack, chest pain (angina), or have you ever undergone angioplasty (balloon or stent) or coronary artery bypass?". Accordingly, the history of stroke was determined by checking whether the patient was informed by a physician or medical staff to have a stroke. The family history of CVDs was asked using the following question: "Have your father, brother, or son under age 65, or your mother, sister, or daughter under age 55 had a heart attack or stroke, or sudden death?" Hypertension was defined as SBP ≥ 140 mmHg or DBP ≥ 90 or the use of antihypertensive medications. BMI was categorized accordingly: lower than 18.5 as underweight, above or equal to 18.5 and below 25 as normal, equal to or above 25 but less than 30 as overweight, and equal to or greater than 30 as obese. Fasting plasma glucose, serum TC, HDL-C, and triglyceride (TG) were assessed by the autoanalyzer (Cobas C311 Hitachi High–Technologies Corporation, Japan). Non–HDL-C was calculated by subtracting HDL-C values from TC. LDL-C was estimated using the Friedewald formula. According to the American Diabetes Association definitions, diabetes is defined by fasting plasma glucose of ≥ 126 mg/dL (7 mmol/L) or the use of antihyperglycemic medications 13 . National Cholesterol Education Program, Expert Panel on Detection, Evaluation, and Treatment of High Blood Cholesterol in Adults Treatment Panel III criteria were used to define lipid abnormalities 14 , 15 . Plasma lipid abnormalities were defined as follows: Hypertriglyceridemia was defined as serum TG ≥ 150 mg/dL (≥ 1.7 mmol/L). Hypercholesterolemia was defined as TC concentrations of ≥ 200 mg/dL (TC ≥ 5.2 mmol/L). High LDL-C and very high LDL-C were defined as LDL-C concentrations of ≥ 130 mg/dL (LDL-C ≥ 3.4 mmol/L) and ≥ 190 mg/dL (LDL-C ≥ 4.9 mmol/L), respectively. High non– HDL-C was defined as non–HDL-C ≥ 160 mg/dL (non–HDL-C ≥ 4.1 mmol/L). Low HDL-C was defined as serum HDL-C lower than 40 mg/dL (HDL-C < 1.03 mmol/L) in men, and 50 mg/dL (HDL-C < 1.29 mmol/L) in women. Dyslipidemia was characterized by the presence of at least one lipid abnormality of hypertriglyceridemia, hypercholesterolemia, high LDL-C, and low HDL-C or self-reported use of lipid-lowering medications. Mixed dyslipidemia was defined as the coexistence of high LDL-C with at least one of the hypertriglyceridemia and low HDL-C. Statistical analysis The prevalence of any lipid abnormality was calculated after applying weights to the samples. The study population was shown to be representative at the national and provincial levels using probability sample tests. Data was summarized by mean and 95% confidence intervals in parentheses [mean (95% confidence interval)]. The chi-square test and One-way ANOVA tests were used to compare categorical and continuous variables between different groups. The determinants of abnormal lipid levels were identified using multiple logistic regression models. For each model, odds ratios (OR) and 95% confidence intervals (CI) were calculated using multiple logistic regression and adjusted for sex, age, and wealth index. All statistical analyses were performed with the STATA software version 12 (StataCorp, Texas, USA). Figures were depicted by R. Software version 3.2.1 (Vienna, Austria). Two-tailed P values of 0.05 were considered statistically significant. Results Population Characteristics Of the total 18,119 participants, 7826 (43.1%) participants were female, and 10,293 (56.8%) participants were male. 24.7% of participants were residents in rural areas (Table 1 ). Our results showed 66.6% of the study population were overweight/obese, 57.5% of participants did not have appropriate physical activity, and only 6.9% of them consumed an appropriate amount of fruit and vegetable. 19.8% of the study population were smoking tobacco products daily, including 7.4% of women and 35.5% of men. Table 1 Population characteristics Variable Category Women Men Both Age category, number percent (95% CI) 25–39 3454 33.5 (32.2,34.81) 2382 32.29 (30.77,33.8) 5836 32.96 (31.98,33.95) 40–54 3675 34.77 (33.49,36.06) 2597 32.06 (30.63,33.5) 6272 33.57 (32.61,34.53) 55–64 1811 17.89 (16.84,18.94) 1501 18.63 (17.42,19.83) 3312 18.22 (17.42,19.01) 65+ 1353 13.83 (12.81,14.85) 1346 17.02 (15.8,18.24) 2699 15.25 (14.46,16.03) Area of residence, number percent (95% CI) Rural 3344 24.49 (23.53,25.46) 2520 24.88 (23.76,26) 5864 24.67 (23.95,25.39) Wealth index, number percent (95% CI) Poor 2190 20.85 (19.68,22.01) 1488 17.71 (16.55,18.86) 3678 18.37 (17.59,19.16) 2nd quintile 1947 21.76 (20.54,22.99) 1381 18.14 (16.94,19.34) 3328 19.03 (18.21,19.85) Middle 1944 18.12 (17.13,19.11) 1744 19.93 (18.79,21.08) 3688 17.95 (17.24,18.66) 4th quintile 1813 19.43 (18.33,20.53) 1658 21.23 (20.01,22.45) 3471 19.18 (18.4,19.96) Rich 1563 19.84 (18.6,21.08) 1419 22.99 (21.42,24.56) 2982 20.16 (19.22,21.1) BMI category, number percent (95% CI) Underweight 217 1.84 (1.55,2.13) 248 2.69 (2.26,3.12) 465 2.2 (1.96,2.45) Normal weight 2636 25.32 (24.13,26.52) 2994 37.67 (36.15,39.19) 5630 30.63 (29.68,31.57) Overweight 3925 38.96 (37.6,40.32) 3131 41.02 (39.45,42.58) 7056 39.64 (38.62,40.67) Obese 3468 33.88 (32.58,35.18) 1419 18.63 (17.35,19.9) 4887 26.95 (26.03,27.87) Appropriate Fruit and vegetable consumption, number percent (95% CI) Yes 755 6.94 (6.3,7.58) 434 5.41 (4.72,6.1) 1189 6.25 (5.79,6.72) Inappropriate Physical activity, number percent (95% CI) Yes 5530 57.51 (56.14,58.88) 2590 41.8 (40.05,43.54) 8120 46.66 (45.63,47.7) Smoking (current daily smoking), number percent (95% CI) Yes 762 7.37 (6.7,8.03) 2756 35.55 (34.02,37.09) 3518 19.83 (18.99,20.67) History of Diabetes, number percent (95% CI) Yes 1456 14.71 (13.76,15.65) 987 13.45 (12.32,14.57) 2443 14.13 (13.41,14.86) History of Hypertension, number percent (95% CI) Yes 3847 36.84 (35.5,38.17) 2807 34.71 (33.2,36.22) 6654 35.75 (34.75,36.75) History of coronary heart disease, number percent (95% CI) Yes 637 6.42 (5.72,7.11) 718 9.42 (8.5,10.33) 1355 7.73 (7.17,8.29) History of stroke, number percent (95% CI) Yes 128 1.16 (0.9,1.42) 149 1.86 (1.45,2.27) 277 1.47 (1.23,1.7) Familial History of cardiovascular disease, number percent (95% CI) Yes 1451 15.14 (14.14,16.13) 850 11.54 (10.49,12.58) 2301 13.2 (12.49,13.9) Regarding past medical and family history, 14.1% of participants were diabetic and 35.7% had hypertension. 7.7% and 1.5% of participants mentioned they had a history of coronary heart disease and stroke, respectively. 13.2% of participants mentioned the family history of CVD. Prevalence of Lipid Abnormalities in Different Population Strata The prevalence of dyslipidemia was 81.0% (80.2–81.9) among the Iranian adult population, affecting 84.4% (83.4–85.4) of women and 75.7% (74.4–77.1) of men (Table 2 ). Of the total female adult population, 35.5% (34.2–36.9) had hypertriglyceridemia, 23.0% (21.8–24.2) had hypercholesterolemia, 17.1% (16.0-18.3) had high LDL-C, and 72.7% (71.5–73.9) had low HDL-C. Of the total male adult population, 43.4% (41.9–45.0) had hypertriglyceridemia, 18.9% (17.7–20.2) had hypercholesterolemia, 15.4% (14.2–16.5) had high LDL-C, and 62.0% (60.5–63.5) had low HDL-C. Accordingly, hypercholesterolemia and low HDL-C were more prevalent in women, and hypertriglyceridemia was more prevalent in men. 19.4% (18.5–20.3) of the population had high non-HDL-C, and the abnormality had almost similar prevalence among men and women. The age group of 55–64 had the highest prevalence of dyslipidemia, which was significantly higher than the 25–39 age groups (83.3% vs. 77.0%; P < 0.05) (Table 2 ). Compared to the reference age group (25–39), other age groups had higher odds of having hypertriglyceridemia, hypercholesterolemia, and high LDL-C; however, those older than 55 had lower odds of having low HDL-C (Table 3 ). Supplementary Table S1 shows the value of serum lipids in different centiles for different age strata. Table 2 Prevalence of lipid abnormalities according to study population strata Variable Category Hypertriglyceridemia Percent. (95% CI) Hypercholesterolemia Percent. (95% CI) High LDL-C Percent. (95% CI) Very High LDL-C Percent. (95% CI) High non–HDL-C Percent. (95% CI) Low HDL-C Percent. (95% CI) Dyslipidemia Percent. (95% CI) All participants … 39.7 (38.6,40.79) 21.24 (20.32,22.17) 16.42 (15.56,17.29) 0.74 (0.52,0.95) 19.43 (18.54,20.33) 68.42 (67.41,69.42) 81.02 (80.17,81.87) Sex Women 35.54 (34.21,36.87) 23.04 (21.84,24.23) 17.15 (16.04,18.27) 0.76 (0.58,0.94) 19.03 (17.9,20.15) 72.68 (71.48,73.89) 84.41 (83.43,85.4) Men 43.45 (41.87,45.04) 18.94 (17.69,20.18) 15.36 (14.19,16.53) 0.74 (0.35,1.12) 19.68 (18.42,20.94) 62 (60.48,63.51) 75.73 (74.4,77.06) Age 25–39 32.52 (30.71,34.33) 12.14 (10.95,13.33) 9.43 (8.28,10.58) 0.33 (0.18,0.49) 11.76 (10.61,12.92) 70.63 (69.02,72.24) 77.05 (75.56,78.55) 40–54 42.79 (41.1,44.48) 24.65 (23.17,26.13) 18.44 (17.11,19.76) 0.89 (0.39,1.39) 23.02 (21.56,24.48) 69.37 (67.79,70.96) 82.01 (80.67,83.34) 55–64 44.61 (42.23,46.98) 29 (26.84,31.15) 22.36 (20.35,24.38) 0.93 (0.58,1.29) 25.01 (23,27.03) 65.09 (62.8,67.37) 83.29 (81.42,85.15) 65+ 38.34 (35.44,41.25) 24.02 (21.3,26.74) 19.61 (16.94,22.27) 1.14 (0.71,1.57) 20.7 (18.03,23.37) 62.36 (59.67,65.05) 81.7 (79.68,83.73) Area Rural 35.67 (34.2,37.13) 21.35 (20.08,22.62) 17.19 (16.02,18.36) 0.83 (0.57,1.09) 19.46 (18.22,20.69) 66.68 (65.25,68.11) 78.8 (77.56,80.04) Urban 40.16 (38.88,41.43) 21.17 (20.1,22.25) 16.09 (15.08,17.09) 0.73 (0.48,0.97) 19.27 (18.23,20.3) 68.35 (67.18,69.53) 81.14 (80.15,82.13) Wealth index Poor 35.95 (33.62,38.28) 22.04 (19.82,24.27) 17.08 (14.93,19.23) 0.95 (0.6,1.31) 19.09 (17,21.17) 67.75 (65.65,69.85) 79.69 (77.84,81.54) 2nd quintile 39.85 (37.45,42.25) 19.21 (17.5,20.93) 14.61 (13.1,16.12) 0.76 (0.46,1.07) 17.68 (16.03,19.33) 68.71 (66.51,70.92) 80.96 (79.07,82.84) Middle 39.56 (37.44,41.68) 20.03 (18.28,21.78) 15.76 (14.12,17.4) 0.72 (0.41,1.04) 19.3 (17.55,21.04) 68.15 (66.24,70.06) 79.98 (78.36,81.59) 4th quintile 40.26 (38.1,42.43) 22.05 (20.24,23.87) 16 (14.45,17.54) 0.5 (0.26,0.74) 20.05 (18.3,21.81) 67.01 (64.89,69.13) 79.95 (78.03,81.87) Rich 40.48 (37.74,43.21) 22.08 (19.77,24.39) 17.94 (15.68,20.2) 0.96 (0.16,1.76) 20.51 (18.22,22.79) 66.91 (64.36,69.45) 81.1 (79.09,83.12) BMI Category Underweight 6.15 (3.93,8.37) 8.76 (6.01,11.51) 8.81 (5.87,11.74) 0.4 (-0.09,0.89) 5.21 (3.23,7.18) 39.05 (33.57,44.53) 48.52 (42.86,54.17) Normal weight 27.38 (25.67,29.08) 17.36 (15.88,18.84) 14.45 (13.08,15.83) 0.38 (0.22,0.55) 15.05 (13.63,16.47) 57.67 (55.85,59.48) 70.48 (68.84,72.11) Overweight 41.82 (40.16,43.48) 22.84 (21.44,24.24) 17.32 (16.04,18.61) 1.04 (0.6,1.49) 20.93 (19.62,22.25) 71.7 (70.21,73.18) 84.81 (83.58,86.05) Obese 50.67 (48.67,52.68) 23.91 (22.16,25.66) 17.5 (15.83,19.17) 0.78 (0.53,1.04) 22.7 (20.93,24.47) 76.38 (74.71,78.04) 88.17 (86.84,89.49) Fruit and vegetable consumption Inappropriate 39.01 (37.95,40.08) 21.24 (20.34,22.14) 16.34 (15.5,17.18) 0.76 (0.55,0.97) 19.29 (18.42,20.16) 67.77 (66.78,68.76) 80.35 (79.51,81.19) Appropriate 40.01 (36.27,43.75) 21.03 (17.83,24.23) 16.74 (13.71,19.78) 0.64 (0.18,1.09) 19.83 (16.82,22.83) 70.15 (66.58,73.71) 83.36 (80.65,86.06) Physical activity Inappropriate 40.24 (38.71,41.78) 21.99 (20.64,23.34) 16.9 (15.63,18.18) 0.74 (0.37,1.1) 19.78 (18.47,21.09) 70.58 (69.22,71.94) 83.31 (82.22,84.4) appropriate 38.55 (37.01,40.09) 20.95 (19.7,22.2) 16.07 (14.91,17.22) 0.76 (0.56,0.96) 19.08 (17.88,20.28) 66.91 (65.45,68.38) 79.44 (78.16,80.71) Smoking No 37.9 (36.77,39.03) 21.73 (20.75,22.71) 16.65 (15.73,17.57) 0.7 (0.56,0.85) 19.24 (18.3,20.18) 68.3 (67.23,69.36) 80.97 (80.07,81.87) Yes 43.56 (41.15,45.97) 19.06 (17.24,20.89) 15.07 (13.4,16.74) 0.95 (0.14,1.75) 19.52 (17.68,21.36) 66.57 (64.42,68.72) 78.91 (77.08,80.74) Diabetes No 35.9 (34.8,37) 21.07 (20.13,22.02) 16.67 (15.78,17.56) 0.73 (0.5,0.95) 18.95 (18.05,19.86) 66.52 (65.48,67.56) 78.76 (77.86,79.66) Yes 58.18 (55.43,60.93) 22.05 (19.83,24.27) 14.43 (12.58,16.27) 0.9 (0.52,1.28) 21.47 (19.28,23.67) 76.63 (74.31,78.94) 91.38 (89.71,93.04) Hypertension No 34.16 (32.92,35.39) 18.89 (17.86,19.93) 14.82 (13.86,15.78) 0.58 (0.31,0.86) 16.99 (16,17.97) 66.07 (64.86,67.29) 77.12 (76.05,78.19) Yes 47.61 (45.85,49.37) 25.21 (23.66,26.75) 19.03 (17.58,20.49) 1.06 (0.79,1.32) 23.36 (21.83,24.88) 71.3 (69.78,72.82) 86.58 (85.41,87.76) History of coronary heart disease No 38.71 (37.64,39.78) 21.55 (20.64,22.46) 16.66 (15.8,17.51) 0.76 (0.55,0.97) 19.66 (18.78,20.54) 67.58 (66.59,68.58) 80.04 (79.19,80.89) Yes 42.76 (39.01,46.51) 16.99 (14.29,19.68) 12.36 (10.04,14.68) 0.64 (0.19,1.08) 14.93 (12.39,17.47) 72.2 (68.89,75.52) 86.42 (83.8,89.03) History of stroke No 39.02 (37.98,40.05) 21.18 (20.3,22.05) 16.31 (15.5,17.13) 0.76 (0.56,0.96) 19.28 (18.43,20.12) 67.95 (66.99,68.91) 80.46 (79.64,81.27) Yes 39.7 (31.8,47.59) 22.33 (15.16,29.5) 17.01 (10.86,23.16) 0.22 (-0.09,0.53) 20.35 (13.79,26.92) 67.72 (60.29,75.15) 85.92 (80.11,91.74) Familial History of CVD No 38.22 (37.11,39.33) 20.91 (19.97,21.84) 16.12 (15.24,17) 0.61 (0.48,0.74) 18.74 (17.84,19.64) 67.26 (66.22,68.31) 79.95 (79.06,80.85) Yes 43.86 (40.96,46.76) 22.63 (20.2,25.07) 17.41 (15.13,19.69) 1.54 (0.32,2.77) 22.02 (19.57,24.48) 71.6 (69.12,74.08) 83.76 (81.69,85.83) Table 3 Lipid abnormalities in association with the study population characteristics Variable (reference group) Category Hypertriglyceridemia Hypercholesterolemia High LDL-C Low HDL-C Dyslipidemia Crude OR (95% CI) Adjusted OR (95% CI) Crude OR (95% CI) Adjusted OR (95% CI) Crude OR (95% CI) Adjusted OR (95% CI) Crude OR (95% CI) Adjusted OR (95% CI) Crude OR (95% CI) Adjusted OR (95% CI) Sex (female) Male 1.39 (1.28,1.52) 1.38 (1.26,1.51) 0.78 (0.70,0.87) 0.77 (0.69,0.86) 0.88 (0.78,0.99) 0.87 (0.77,0.98) 0.61 (0.56,0.67) 0.62 (0.56,0.68) 0.588 (0.52,0.64) 0.56 (0.50,0.63) Age (25–39 y) 40–54 1.55 (1.39,1.73) 1.56 (1.40,1.74) 2.37 (2.06,2.71) 2.34 (2.03,2.70) 2.17 (1.85,2.55) 2.13 (1.80,2.51) 0.94 (0.85,1.05) 0.95 (0.85,1.06) 1.36 (1.20,1.54) 1.37 (1.21,1.56) 55–64 1.67 (1.47,1.90) 1.64 (1.44,1.87) 2.95 (2.54,3.44) 2.94 (2.51,3.45) 2.77 (2.32,3.30) 2.75 (2.29,3.30) 0.77 (0.68,0.88) 0.78 (0.69,0.89) 1.48 (1.27,1.74) 1.51 (1.28,1.78) > 65 1.29 (1.11,1.50) 1.28 (1.10,1.49) 2.29 (1.90,2.75) 2.31 (1.91,2.81) 2.34 (1.89,2.91) 2.34 (1.87,2.93) 0.69 (0.60,0.79) 0.71 (0.62,0.82) 1.30 (1.13,1.56) 1.38 (1.17,1.63) Area (rural) Urban 1.21 (1.11,1.32) 1.21 (1.10,1.32) 0.99 (0.90,1.09) 0.97 (0.87,1.085) 0.92 (0.83,1.03) 0.89 (0.78,1.01) 1.08 (0.99,1.174) 1.13 (1.03,1.24) 1.16 (1.05,1.28) 1.14 (1.02,1.27) Wealth index (poor) 2nd quintile 1.18 (1.02,1.36) 1.17 (1.02,1.35) 0.841 (0.71,1.00) 0.84 (0.71,0.99) 0.83 (0.68,1.01) 0.83 (0.69,1.01) 1.05 (0.91,1.20) 1.03 (0.89,1.19) 1.08 (0.92,1.28) 1.08 (0.91,1.28) Middle 1.17 (1.02,1.33) 1.13 (0.99,1.29) 0.89 (0.75,1.05) 0.91 (0.77,1.08) 0.91 (0.75,1.10) 0.94 (0.77,1.14) 1.02 (0.89,1.16) 1.02 (0.89,1.16) 1.02 (0.87,1.18) 1.06 (0.91,1.24) 4th quintile 1.20 (1.05,1.37) 1.17 (1.02,1.33) 1.00 (0.85,1.18) 1.04 (0.88,1.23) 0.92 (0.76,1.12) 0.96 (0.79,1.16) 0.97 (0.84,1.11) 0.96 (0.84,1.11) 1.02 (0.86,1.20) 1.06 (0.9,1.26) Rich 1.21 (1.04,1.41) 1.16 (0.99,1.35) 1.00 (0.83,1.21) 1.03 (0.86,1.24) 1.06 (0.86,1.32) 1.10 (0.89,1.36) 0.96 (0.83,1.12) 0.96 (0.83,1.12) 1.09 (0.92,1.30) 1.15 (0.96,1.37) BMI category (underweight) Normal weight 5.75 (3.88,8.53) 5.44 (3.63,8.15) 2.19 (1.53,3.13) 2.06 (1.43,2.96) 1.75 (1.19,2.56) 1.69 (1.14,2.51) 2.13 (1.67,2.71) 2.28 (1.75,2.96) 2.53 (1.99,3.22) 2.54 (1.98,3.26) Overweight 10.97 (7.42,16.21) 10.64 (7.11,15.93) 3.08 (2.17,4.39) 2.63 (1.83,3.76) 2.17 (1.49,3.16) 1.94 (1.32,2.87) 3.95 (3.11,5.03) 4.30 (3.29,5.61) 5.93 (4.64,7.58) 5.73 (4.42,7.42) Obese 15.68 (10.58,23.22) 16.37 (10.88,24.63) 3.27 (2.29,4.68) 2.51 (1.73,3.63) 2.20 (1.50,3.22) 1.81 (1.20,2.72) 5.05 (3.94,6.46) 5.32 (4.05,7.001) 7.91 (6.10,10.25) 7.02 (5.34,9.22) Fruit and vegetable consumption (Inappropriate) appropriate 1.04 (0.89,1.23) 1.02 (0.86,1.22) 0.99 (0.81,1.21) 0.92 (0.75,1.14) 1.03 (0.82,1.29) 0.97 (0.77,1.22) 1.12 (0.94,1.33) 1.08 (0.89,1.30) 1.22 (1.00,1.50) 1.18 (0.95,1.47) Physical activity (appropriate) Inappropriate 1.07 (0.98,1.18) 1.15 (1.04,1.26) 1.06 (0.95,1.19) 0.99 (0.88,1.11) 1.06 (0.94,1.20) 1.00 (0.88,1.14) 1.19 (1.08,1.30) 1.13 (1.03,1.25) 1.29 (1.16,1.44) 1.19 (1.06,1.33) Smoking (No) Yes 1.26 (1.13,1.41) 1.14 (1.01,1.28) 0.85 (0.74,0.97) 0.96 (0.83,1.10) 0.89 (0.77,1.03) 0.94 (0.80,1.10) 0.92 (0.83,1.03) 1.13 (1.00,1.27) 0.88 (0.78,1.00) 1.13 (0.98,1.29) Diabetes (No) Yes 2.48 (2.20,2.81) 2.39 (2.09,2.73) 1.06 (0.92,1.22) 0.78 (0.67,0.92) 0.84 (0.72,0.99) 0.61 (0.51,0.73) 1.65 (1.44,1.89) 1.90 (1.63,2.20) 2.86 (2.30,3.55) 2.69 (2.14,3.38) Hypertension (No) Yes 1.75 (1.60,1.92) 1.84 (1.65,2.04) 1.45 (1.30,1.61) 1.08 (0.95,1.23) 1.35 (1.20,1.53) 1.00 (0.86,1.16) 1.28 (1.16,1.40) 1.57 (1.40,1.74) 1.91 (1.70,2.15) 1.91 (1.67,2.18) History of coronary heart disease (No) Yes 1.18 (1.01,1.39) 1.05 (0.88,1.25) 0.74 (0.61,0.91) 0.56 (0.45,0.70) 0.71 (0.56,0.88) 0.52 (0.41,0.66) 1.25 (1.05,1.48) 1.58 (1.31,1.91) 1.59 (1.26,1.99) 1.61 (1.26,2.06) History of stroke (No) Yes 1.03 (0.74,1.43) 0.91 (0.64,1.28) 1.07 (0.71,1.62) 0.89 (0.58,1.39) 1.05 (0.68,1.63) 0.86 (0.54,1.36) 1.00 (0.70,1.39) 1.19 (0.84,1.70) 1.48 (0.91,2.40) 1.43 (0.87,2.34) Familial history of CVD (No) Yes 1.26 (1.11,1.43) 1.26 (1.11,1.44) 1.11 (0.95,1.29) 1.06 (0.90,1.24) 1.10 (0.92,1.30) 1.06 (0.88,1.26) 1.23 (1.08,1.40) 1.18 (1.03,1.36) 1.29 (1.10,1.52) 1.20 (1.01,1.42) Adjusted for sex, age, and wealth index Dyslipidemia was more prevalent in obese people (88.1%; 95% CI: 86.8–89.5), in residents of urban areas (81.1%, 95% CI: 80.1–82.1), in those with inappropriate physical activity (83.3%; 95% CI: 82.2–84.4), and patients with diabetes (91.4%, 95% CI: 89.7–93.0) and hypertension (86.6%, 95% CI: 85.4–87.8) (Table 2 ). Compared to underweight people, those who were normal weight, overweight, and obese had 2.5 (2.0-3.3), 5.7 (4.4–7.4), and 7.0 (5.3–9.2) times higher odds of having dyslipidemia, respectively (Table 3 ). Residence in urban areas, Inappropriate physical activity, diabetes, and hypertension also increased the odds of having dyslipidemia by 1.1 (1-1.3), 1.2 (1.1–1.3), 2.7 (2.1–3.4), and 1.9 (1.7–2.2) times. Patients with a history of coronary heart disease and stroke also showed higher prevalence and odds for dyslipidemia. The prevalence in patients with a history of coronary heart disease was 86.4% (83.8–89.0) and the odds ratio was 1.6 (1.3–2.1) (Table 3 ). Dyslipidemia prevalence in patients with a history of stroke was also higher (85.9% in participants with positive history vs. 80.5 in participants with negative history); however, the difference was not significant due to the small number of cases (n = 277). Those with familial history of CVDs also showed a slightly higher prevalence of dyslipidemia with an odds ratio of 1.2 (1.0-1.4). People with a different wealth index, fruit and vegetable consumption, and smoking status did not show any difference in the prevalence of dyslipidemia, albeit it was not the case for all kinds of lipid abnormalities (Table 2 , Table 3 ). The mean value of serum lipids in each population strata is demonstrated in Supplementary Table S2 . Mixed Lipid Abnormalities Mixed dyslipidemia which was defined as the coexistence of high LDL-C with at least one of the hypertriglyceridemia and low HDL-C, was prevalent in 13.6% of women and 11.4% of men (Table 4 ). In comparison, the isolated high LDL-C had a low prevalence, 3.6% (3.1–4.1) in women and 3.9% (3.4–4.5) in men. Among different mixed dyslipidemias, having high LDL-C and TG and Low HDL-C combined was the most prevalent in both sexes, with a prevalence of 7.4% (6.5–8.2) in women and 6.5% (5.7–7.4) in men. Those with higher age had higher odds of having all types of mixed dyslipidemias (Table 4 ). Moreover, higher BMI scores were associated with having high LDL-C and TG and Low HDL-C combined (OR = 10.2 for normal weight, 15.9 for overweight, and 20.1 for obese participants). The same pattern was shown in those with low physical activity (OR = 1.3; 95% CI: 1.0-1.5) and hypertension (OR = 1.4; 95% CI: 1.1–1.8). On the other side, a history of coronary heart disease was negatively associated with all types of mixed dyslipidemias. Having diabetes was also negatively associated with having high LDL-C and Low HDL-C combined (OR = 0.3; 95% CI: 0.2–0.5). Prevalence of Lipid Abnormalities by Province East Azarbaijan, Ardabil, and Kohgiluyeh and Boyer-Ahmad had the highest prevalence of dyslipidemia among provinces, with 85.3% (81.5–89.1), 84.6% (80.7–88.6), and 84.5% (80.9–88.0), respectively. On the other hand, Golestan with 68.5% (64.8–72.2), Kerman with 74.1% (69.6–78.6), and Razavi Khorasan with 74.3% (71.2–77.4) had the lowest prevalence. However, the ranking of provinces by dyslipidemia prevalence was different in specified sex groups or areas of residence (Fig. 1 ). The prevalence of lipid abnormalities was highly heterogeneous among provinces. Hypertriglyceridemia prevalence ranged from 50.0–26.7%, hypercholesterolemia prevalence ranged from 24.4–12.1%, high LDL-C prevalence ranged from 19.3–10.4%, high non-HDL-C prevalence ranged from 24.1 to 12.0, and high HDL-C prevalence ranged from 74.4–56.0%. Ardabil and East Azarbaijan were among the top ten provinces in the prevalence of all mentioned lipid abnormalities (high TC, TG, LDL-C, non-HDL-C, and low HDL-C). Provinces ranking in this regard was different in specified lipid abnormality and sex groups (Fig. 2 ). Supplementary Table S3 demonstrates the prevalence of lipid abnormalities in different provinces of the country. Discussion The study showed that 81 percent of the Iranian adult population had at least one serum lipid abnormality or use lipid-lowering medications. Whether young or old, men or women, resident in rural or urban areas, more than 60 percent of the Iranian adult population had low HDL-C cholesterol. The prevalence of dyslipidemia in the study was one of the highest across the globe; however, it was in accordance with the previous report of the STEPs 2016 10 , and the MASHAD prospective cohort study 15 . Among studies with similar definition criteria for dyslipidemia, the prevalence of dyslipidemia in healthy populations was reported at 79% in India 16 , 78.7% in Turkey 17 , 75.7% in Jordan 18 , and 62.1% in northeastern China 19 . The prevalence of low HDL-C in the studies was 72.3% in India, 41.5% in Turkey, 40.7% in Jordan, and 8.8% in northeastern China. The high prevalence of dyslipidemia in Iran can be justified by the high consumption of dietary fats, obesity, and physical inactivity 3 . Inappropriate physical activity was reported in 54.7% of the Iranian adult population 20 . Obesity also has considerably increased in recent decades, and STEPs 2021 reported overweight/obesity in 63% of the adult population 21 . This study showed obese persons are 2.8 times more at risk for dyslipidemia than normal-weight adults. This strong association and growing trend of obesity could warn the health system about the upcoming higher burden of dyslipidemia in the country. The Low HDL levels shown in the study may also be due to the high consumption of carbohydrate-rich foods, such as refined grains, which are generally inexpensive and readily available 3 . It is shown that fat intake is not only associated with higher concentrations of TC and LDL-C but also with higher HDL-C and apolipoprotein A1 and lower triglycerides 7 . In case reduced intake of trans fat has been substituted with carbohydrates rather than proteins, it would be plausible to affect HDL-C and TG negatively 10 . On the plus side, the prevalence of hypercholesterolemia and high LDL-C was low and was seen in 21.2% and 16.4% of participants, respectively. The prevalence of hypercholesterolemia in this study was much lower than 48.8% reported in Jordan, 43% reported in Turkey, and 33.5% reported in northeastern China 17 – 19 . The prevalence of high LDL-C was reported at 40.7% in Jordan and 36.7% in Turkey 17 , 18 . A study from Spain, which used more sensitive criteria for high LDL-C than our study, reported a 23.3% prevalence for high LDL-C 22 . Efforts taken by the government may have contributed to controlling and lowering the total cholesterol and LDL-C of the population. As of 2000, the ministry of health restricted the amount of saturated fatty acids to a maximum of 25% and trans fatty acids to a maximum of 10% in all oil products. Moreover, Iran took action to increase public awareness about the hazards of saturated and trans fatty acids. Widespread statin prescription by general practitioners was also among the country's strategies to lower serum cholesterol and prevent CVDs 10 . Iranian national action plan targets on NCDs have included the two goals of receiving drug therapy and counseling to prevent heart attacks and strokes in at least 70% of eligible persons, and zero trans fatty acids in food & oily products to be achieved by 2025 23 . Despite all the efforts, comparing the results of STEPs 2016 and 2021 showed that the prevalence of dyslipidemia has been unchanged from 80.1% (95% CI: 79.4–80.8) in 2016 to 81% (95% CI: 80.2–81.9) in 2021. In the same period, the prevalence of hypertriglyceridemia increased from 26.7% (95% CI: 25.9–27.5) to 39.7% (95% CI: 38.6–40.8). This shortcoming may be attributed to the mentioned obesity epidemic and changes in dietary habits. Moreover, the awareness of patients on having dyslipidemia is shown to be as low as 20% in Iran, highlighting a gap between primary and secondary care 8 . Other reasons may be poor control of dyslipidemia despite increased medical therapy due to a lack of patients' or physicians' adherence to prescription and treatment guidelines, not optimally-dosed statin prescriptions, and not using combination therapies when necessary 24 . The COVID-19 epidemic may also impact the serum cholesterol control of the Iranian population 25 – 27 . Due to the curtailment in routine outpatient laboratory testing, lipid-lowering medical therapy has been delayed, and drug shortages and misinformation may compromise adherence to these medications. Furthermore, the mobilization of the health workforce to combat COVID-19 could limit access to health care. From a societal perspective, the unprecedented contraction of social and economic activities has led to social isolation and decreased physical activity. The study showed almost 75% of persons with high LDL-C had a mixed pattern of dyslipidemia. Participants with higher BMI and low physical activity showed higher odds of having high LDL-C and TG and Low HDL-C combined. It is shown that mixed dyslipidemia can be resolved by combination drug therapy along with weight loss through diet and exercise, and consuming low saturated fats 27 . Unexpectedly, the participants with a history of coronary heart disease showed a lower prevalence of high LDL-C and mixed dyslipidemia. Similarly, diabetes did not show a positive association with dyslipidemia. These results could be justified considering that these groups may benefit from a higher rate of consuming lipid-lowering medications and more regular serum screening. Dyslipidemia was more prevalent in northwestern parts of the country (Fig. 1 ). Iran is a country with great heterogeneity in cultural heritage and ethnicity. From a cultural view, it is shown that undergoing urbanization and westernization, and having a food culture providing a higher uptake of calories are associated with dyslipidemia. Ethnic diversity could also translate into substantial variation in the prevalence of dyslipidemia and plasma lipid levels between and within countries. Ethnicity also can affect response to statin therapy, which is related to genetic differences in the metabolism of statins 28 . However, research about such geographical differences is scarce in Iran, and the object needs further studies. The STEPs 2021 is the second population-based STEPs survey in Iran that represented a complete lipid profile of the Iranian adult population and is the first one that covers all provinces of the country in this regard. As a result of the COVID-19 pandemic specifications, the design and implementation of the survey with special considerations for COVID-19 protection and safety is the most significant achievement of this STEPs survey in Iran. The findings of our study, however, should be interpreted with an understanding of the limitation that dyslipidemia and other lipid abnormalities are defined differently in different studies; and it is important to carefully compare our results with those of others. Poor metabolic health is a major concern in Iran, which is now a country with a population exceeding 80 million, mostly living in urban regions, in which the burden of non-communicable diseases is increasing. The substantial increase in the prevalence of overweight/obesity and especially among adolescents might soon lead to larger increases in diabetes and dyslipidemia 29 . Therefore, controlling dyslipidemia would be a milestone to maintain the declining rate of premature deaths due to cardiovascular diseases in the country and achieve a one-third reduction in mortality from NCDs by 2030 30 . This target would not be accomplished without continuous data gathering and guided policymaking. In conclusion, the current nationwide study showed that although the prevalence of high cholesterol and LDL-C have a favorable trend, the prevalence remained high in the case of low HDL-C and aggravated in the case of high TG. Altogether, the prevalence of dyslipidemia remained unchanged from 2016 to 2021. Several modifiable risk factors such as obesity and inappropriate physical activity shown to be associated with dyslipidemia. Moreover, some intermediate- and high-risk groups such as patients having diabetes and hypertension would benefit from more public education and screening plans. Great achievements of the country's health system in controlling infectious diseases and decreasing child and adult mortality have been obtained through scattered and ad-hoc efforts of different governments and policymakers during the past decades 29 . To modify NCD risk factors and promote metabolic health in the country, action plans should come to action through a multi-sectoral and collaborative approach. Declarations Ethics approval and consent to participate All participants were informed about the methods and goals of the survey and the fact that participation was voluntary. All participants provided written informed consent. The final dataset was de-identified for analysis. The survey database was accessible only to the primary investigator and the database manager. This study was ethically approved by the National Institute for Health Research's ethical committee (ID: IR.TUMS.NIHR.REC.1398.006), and was performed in accordance with the Declaration of Helsinki. As part of the survey, strict COVID-19 prevention guidelines were implemented during the pandemic for all participants and those involved in the survey/data-gathering step. Availability of data and materials The datasets used and/or analysed during the current study available from the corresponding author on reasonable request. Competing interests The authors declare no competing interests. Funding This study was supported by Ministry of Health and Medical Education and the National Institute for Health Research, Tehran University of Medical Sciences, Iran (grant No.241/M/9839). Authors affirm not having entered into an agreement with the funder that may have limited their ability to complete the research as planned and indicate that they have had full control of all primary data. Authors' contributions Conceptualization, S.D., F.F.; Data Collection, A.D-M, Y.F., A.K., M.Y., A.H., E.A.,; Data analysis, E.G., N.A.; Data visualization, J.K., E.G., N.A.; Writing – Original Draft, J.K., S.M.H.,; Writing–Review & Editing, M-M.R., S-H.G., M.A-Kh., M-R.M., M.A-Ka, Mo.N., S.R., S.A., N.F., Ma.N.; Resources, M-M.R., Ne.R, R.H., Na.R, F.F.; Supervision, Na.R., S.D., B.L., F.F. All authors have read and approved the manuscript prior to submission. Acknowledgments The authors would like to thank the Ministry of Health and Medical Education and the National Institute for Health Research for the financial support of the study. The authors would like to appreciate the partnership of the Deputy for Research and Technology and the Deputy of Health of the Ministry of Health and Medical Education, the National Institute for Health Research, the World Health Organization, and many scholars and experts in related fields. 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Degli Esposti, L., Buda, S., Nappi, C., Paoli, D. & Perrone, V. Implications of COVID-19 Infection on Medication Adherence with Chronic Therapies in Italy: A Proposed Observational Investigation by the Fail-to-Refill Project. Risk Manag. Healthc. Policy 13 , 3179–3185 (2020). Hassan, T. A. et al. New Strategies to Improve Patient Adherence to Medications for Noncommunicable Diseases During and After the COVID-19 Era Identified via a Literature Review. J. Multidiscip. Healthc. 14 , 2453–2465 (2021). Pirillo, A., Casula, M., Olmastroni, E., Norata, G. D. & Catapano, A. L. Global epidemiology of dyslipidaemias. Nat. Rev. Cardiol. 18 , 689–700 (2021). Danaei, G. et al. Iran in transition. Lancet (London, England) 393 , 1984–2005 (2019). NCD Countdown 2030 collaborators. NCD Countdown 2030: worldwide trends in non-communicable disease mortality and progress towards Sustainable Development Goal target 3.4. Lancet (London, England) 392 , 1072–1088 (2018). Table Table 4 is available in the Supplementary Files section. Additional Declarations No competing interests reported. Supplementary Files SupplementaryTable1.pdf SupplementaryTable2.pdf SupplementaryTable3.pdf Table4.docx Cite Share Download PDF Status: Published Journal Publication published 19 Sep, 2023 Read the published version in Scientific Reports → Version 1 posted Editorial decision: Major revision 28 Jun, 2023 Reviews received at journal 27 Jun, 2023 Reviewers agreed at journal 25 Jun, 2023 Reviews received at journal 19 Apr, 2023 Reviews received at journal 03 Apr, 2023 Reviewers agreed at journal 01 Apr, 2023 Reviewers agreed at journal 23 Mar, 2023 Reviewers invited by journal 20 Mar, 2023 Editor assigned by journal 20 Mar, 2023 Editor invited by journal 20 Mar, 2023 Submission checks completed at journal 20 Mar, 2023 First submitted to journal 10 Mar, 2023 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. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-2677772","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":184896825,"identity":"a5b0ffed-c8e4-4418-abbc-945de20ac20a","order_by":0,"name":"Javad Khanali","email":"","orcid":"","institution":"Tehran University of Medical Sciences","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Javad","middleName":"","lastName":"Khanali","suffix":""},{"id":184896826,"identity":"5b729dab-a93b-45c5-96b8-1bbbb0439ba4","order_by":1,"name":"Erfan Ghasemi","email":"","orcid":"","institution":"Tehran University of Medical 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Farzadfar","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA50lEQVRIiWNgGAWjYBAC9gbGByCasb2BAcICgwQ8WngOMBuAtfQcYGA2OECqFjaJA3hUIrQwMDN+rvhjJ9vDfvZZ9ce2bfYM7IcfMDzcg1cLs+QZnmTjHp50sxsH224nNvCkGTAkPMOtxV7+/QHJBgnmxP0MaWwgLUBf5AD9gseJIFt+NhjUJ/bwP2MrAGqxZ+B/Q1ALm2RDwuHEHok0NgagFsYGCcK2sFk2HDhu3CPxjFnizLnbiW0SzwwOEHLYzYY/1bI9/GmMHyrKbtvz8yc/fPgDjxZMwAbEJGkYBaNgFIyCUYAJAH6bTm3t6R7EAAAAAElFTkSuQmCC","orcid":"","institution":"Tehran University of Medical Sciences","correspondingAuthor":true,"submittingAuthor":false,"prefix":"","firstName":"Farshad","middleName":"","lastName":"Farzadfar","suffix":""}],"badges":[],"createdAt":"2023-03-10 11:59:19","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-2677772/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-2677772/v1","draftVersion":[],"editorialEvents":[{"content":"https://doi.org/10.1038/s41598-023-42341-5","type":"published","date":"2023-09-19T15:01:46+00:00"}],"editorialNote":"","failedWorkflow":false,"files":[{"id":34723007,"identity":"a8d55211-cd67-4d2a-8f60-e2e8d06f6947","added_by":"auto","created_at":"2023-03-23 17:21:48","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":1344897,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eMap of Age-standardized prevalence of dyslipidemia in 2021 by residual area and sex \u003c/strong\u003eThe figure shows the age-standardized prevalence of dyslipidemia in (A) females in both rural and urban areas combined (B) males in both rural and urban areas combined (C) both sexes in both areas combined (D) females in urban areas (E) males in urban areas (F) both sexes in urban areas (G) females in rural areas (H) males in rural areas (I) both sexes in rural areas\u003c/p\u003e\n\u003cp\u003eWA indicates West Azarbayjan, WE: East Azarbayjan, AR: Ardabil, KD: Kurdistan, ZA: Zanjan, GI: Gilan, KS: Kermanshah, HD: Hamadan, QZ: Qazvin, AL: Alborz, MN: Mazandaran, GO: Golestan, IL: Ilam, LO: Lorestan, MK: Markazi, QM: Qom, TE: Tehran, SM: Semnan, NK: North Khorasan, RK: Khorasan Razavi, KZ: Khuzestan, CM: Chaharmahal and Bakhtiari, KB: Kohkiluye and Bouyerahmad, ES:Isfahan, YA: Yazd, SK: South Khorasan, BS: Boushehr, FA: Fars, KE: Kerman, SB: Sistan and Balouchestan, HG: Hormozgan\u003c/p\u003e","description":"","filename":"1.png","url":"https://assets-eu.researchsquare.com/files/rs-2677772/v1/da1eec583803e34d02bb65ee.png"},{"id":34723004,"identity":"182d88f0-d6b2-4a43-863b-07b96fccbd65","added_by":"auto","created_at":"2023-03-23 17:21:48","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":244915,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eMean Cholesterol and LDL-C in Iran’s provinces \u003c/strong\u003eThe figure shows the mean (dots) and 95% confidence interval (lines) of (A) Female total cholesterol, (B) Male total cholesterol, (C) Female LDL-C, (D) Male LDL-C, in different provinces of the country\u003c/p\u003e","description":"","filename":"2.png","url":"https://assets-eu.researchsquare.com/files/rs-2677772/v1/c3b2a3ebcc7a6304bff4b114.png"},{"id":43640593,"identity":"e98209b3-49c1-4ccb-b04e-6315f04bfd5d","added_by":"auto","created_at":"2023-09-25 15:07:53","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1391931,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-2677772/v1/5bada60e-cf03-473a-8872-69c078a8bb13.pdf"},{"id":34723008,"identity":"1d36ba31-77c1-4b39-a65f-f26feb4d90f2","added_by":"auto","created_at":"2023-03-23 17:21:48","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":266477,"visible":true,"origin":"","legend":"","description":"","filename":"SupplementaryTable1.pdf","url":"https://assets-eu.researchsquare.com/files/rs-2677772/v1/ceba518a1f6ed41dc378dbbb.pdf"},{"id":34723359,"identity":"8f223859-fac3-48ea-86fb-f2350da5dab9","added_by":"auto","created_at":"2023-03-23 17:29:48","extension":"pdf","order_by":2,"title":"","display":"","copyAsset":false,"role":"supplement","size":323833,"visible":true,"origin":"","legend":"","description":"","filename":"SupplementaryTable2.pdf","url":"https://assets-eu.researchsquare.com/files/rs-2677772/v1/38eb4a8b4b0ff4a5c9e3c737.pdf"},{"id":34723009,"identity":"6710e56e-2d46-4bf5-aaf5-296e0b108dd8","added_by":"auto","created_at":"2023-03-23 17:21:48","extension":"pdf","order_by":3,"title":"","display":"","copyAsset":false,"role":"supplement","size":333053,"visible":true,"origin":"","legend":"","description":"","filename":"SupplementaryTable3.pdf","url":"https://assets-eu.researchsquare.com/files/rs-2677772/v1/f3d99eba8af0d4f435746ed5.pdf"},{"id":34723005,"identity":"545a3e70-667d-47cc-8f86-afb6287c3106","added_by":"auto","created_at":"2023-03-23 17:21:48","extension":"docx","order_by":4,"title":"","display":"","copyAsset":false,"role":"supplement","size":28195,"visible":true,"origin":"","legend":"","description":"","filename":"Table4.docx","url":"https://assets-eu.researchsquare.com/files/rs-2677772/v1/a46fd262fe9c7d0aa92640fe.docx"}],"financialInterests":"No competing interests reported.","formattedTitle":"Plasma Lipid Profile of the Iranian Adult Population: Findings of the Nationally Representative STEPs Survey 2021","fulltext":[{"header":"Introduction","content":"\u003cp\u003eMetabolic risk factors are considerable public health concerns, and despite measures taken, their global burden has not only increased but also accelerated its growing trend since 1990 \u003csup\u003e1\u003c/sup\u003e. Among these risk factors, dyslipidemia has a proven role in atherosclerosis and cardiovascular diseases (CVDs). 4.4\u0026nbsp;million deaths and 98.6\u0026nbsp;million disability-adjusted life years (DALYs) in 2019 could be attributed to high LDL cholesterol (LDL-C) \u003csup\u003e\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e\u003c/sup\u003e. Knowing whether dyslipidemia is declining, stagnating, or even increasing provides governments and international organizations with information on the new health priority and also gives insight into where current efforts are effective or inadequate \u003csup\u003e\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e\u003c/sup\u003e. Therefore, repeated cross-sectional and population-based surveys are needed to regularly assess the current circumstance of the risk factor.\u003c/p\u003e \u003cp\u003eConsidering the poor metabolic health in the middle-east, tracking dyslipidemia in the region countries, including Iran, would be of especial importance \u003csup\u003e\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e\u003c/sup\u003e. It is shown that high LDL-C contributed to 16.1% of deaths and 7.8% of DALYs caused by non-communicable diseases (NCDs) in the Iranian population in 2019 \u003csup\u003e4\u003c/sup\u003e. The high consumption of dietary fats, obesity, physical inactivity, non-adherence to treatment guidelines by patients and physicians, as well as the increasing consumption of carbohydrate-rich foods could predispose the Iranian population to dyslipidemia \u003csup\u003e\u003cspan additionalcitationids=\"CR6 CR7 CR8\" citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e\u003c/sup\u003e. Meanwhile, The widespread prescribing of statins by general practitioners, restrictions on Trans fats in edible oils, and increased public awareness on the risk factor are policies and trends that may decrease dyslipidemia prevalence in Iran \u003csup\u003e\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e,\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eAfter recognizing the global need for data on risk factors that drive NCDs, WHO initiated the STEP-wise approach to NCD risk factor surveillance (STEPs) as national household surveys in 2002 \u003csup\u003e11\u003c/sup\u003e. Iran is among the few countries in the region that have done regular STEPs surveys on metabolic risk factors, including dyslipidemia \u003csup\u003e\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e\u003c/sup\u003e. Eight rounds of STEPs surveys have been conducted in Iran; however, only the latest published report, STEPs 2016, presented a complete lipid profile of Iranian adults comprising total cholesterol (TC), triglyceride (TG), LDL-C, and HDL cholesterol (HDL-C) \u003csup\u003e\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e\u003c/sup\u003e. Therefore, the precise figure of the prevalence of lipid abnormalities in Iran has remained to become clear. The present study aimed to estimate the prevalence of lipid abnormalities in Iranian adults by demographic characterization, associated risk factors, and geographical distribution. Here, national and sub-national representative samples of the STEPs 2021 survey were analyzed and presented.\u003c/p\u003e"},{"header":"Methods","content":"\u003cp\u003eBased on the STEPwise approach to NCD risk factor surveillance developed by WHO \u003csup\u003e\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e\u003c/sup\u003e,\u003c/p\u003e \u003cp\u003ethe STEPs 2021 survey was designed and conducted in Iran with representative samples from urban and rural areas of the country. The details of the procedures and methods of STEPs 2021 are published \u003csup\u003e\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e\u003c/sup\u003e, and just a few crucial requirements were discussed here. According to the STEPs established framework, risk factors were assessed in three steps: filling out a questionnaire, obtaining objective information by physical assessment, and collecting participants' blood and urine samples for biochemical analysis (in those aged above 25). All laboratory measurements were performed in the coordinating center in Tehran.\u003c/p\u003e \u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003eSampling and study population\u003c/h2\u003e \u003cp\u003eSampling was done in proportion to the adult population of urban and rural areas of 31 provinces of Iran. Accordingly, a systematic random sampling frame was designed, and 28,821 individuals in 3176 clusters were selected (each including 9 participants). The variables considered in the representative samples were age, gender, area of residence (rural/urban), and province. The number of participants for the third step of the survey was 18,119, including those aged higher than 25 and accepted to participate in lab measurements. The ethics committee of the National Institute for Health Research approved the study protocol (ID: IR.TUMS.NIHR.REC.1398.006), and the study was performed in accordance with the Declaration of Helsinki. The study objectives and methods were clearly explained to all participants along with the fact that participation in the study is voluntary and that refusing to participate will not affect their access to health care.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003eDefinition and measurement of variables\u003c/h2\u003e \u003cp\u003eUsing household asset data, the participants' wealth index was calculated by principal component analysis, and the first component was assigned to the wealth index and categorized into five quintiles, from the poorest (first quintile) to the richest (fifth quintile). Smoking status was defined as positive for those who are current daily smokers of any tobacco product, including cigarettes, hookah, pipes, smokeless tobacco, and electronic cigarettes. The second version of the global physical activity questionnaire was utilized to evaluate physical activity. Accordingly, appropriate physical activity was defined as having either high or moderate physical activity, while inappropriate physical activity was defined as having low physical activity. People who ate fast food during the previous week were categorized as those who consume fast food. Those who were consuming at least 3 servings of fruits and 4 servings of vegetables per day were categorized as those with appropriate fruits and vegetable consumption. To check for the history of coronary heart disease, patients were asked \"have you ever been told by a physician or health staff that you had a heart attack, chest pain (angina), or have you ever undergone angioplasty (balloon or stent) or coronary artery bypass?\". Accordingly, the history of stroke was determined by checking whether the patient was informed by a physician or medical staff to have a stroke. The family history of CVDs was asked using the following question: \"Have your father, brother, or son under age 65, or your mother, sister, or daughter under age 55 had a heart attack or stroke, or sudden death?\"\u003c/p\u003e \u003cp\u003eHypertension was defined as SBP\u0026thinsp;\u0026ge;\u0026thinsp;140 mmHg or DBP\u0026thinsp;\u0026ge;\u0026thinsp;90 or the use of antihypertensive medications. BMI was categorized accordingly: lower than 18.5 as underweight, above or equal to 18.5 and below 25 as normal, equal to or above 25 but less than 30 as overweight, and equal to or greater than 30 as obese. Fasting plasma glucose, serum TC, HDL-C, and triglyceride (TG) were assessed by the autoanalyzer (Cobas C311 Hitachi High\u0026ndash;Technologies Corporation, Japan). Non\u0026ndash;HDL-C was calculated by subtracting HDL-C values from TC. LDL-C was estimated using the Friedewald formula. According to the American Diabetes Association definitions, diabetes is defined by fasting plasma glucose of \u0026ge;\u0026thinsp;126 mg/dL (7 mmol/L) or the use of antihyperglycemic medications \u003csup\u003e\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e\u003c/sup\u003e. National Cholesterol Education Program, Expert Panel on Detection, Evaluation, and Treatment of High Blood Cholesterol in Adults Treatment Panel III criteria were used to define lipid abnormalities \u003csup\u003e\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e,\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e\u003c/sup\u003e. Plasma lipid abnormalities were defined as follows: Hypertriglyceridemia was defined as serum TG\u0026thinsp;\u0026ge;\u0026thinsp;150 mg/dL (\u0026ge;\u0026thinsp;1.7 mmol/L). Hypercholesterolemia was defined as TC concentrations of \u0026ge;\u0026thinsp;200 mg/dL (TC\u0026thinsp;\u0026ge;\u0026thinsp;5.2 mmol/L). High LDL-C and very high LDL-C were defined as LDL-C concentrations of \u0026ge;\u0026thinsp;130 mg/dL (LDL-C\u0026thinsp;\u0026ge;\u0026thinsp;3.4 mmol/L) and \u0026ge;\u0026thinsp;190 mg/dL (LDL-C\u0026thinsp;\u0026ge;\u0026thinsp;4.9 mmol/L), respectively. High non\u0026ndash; HDL-C was defined as non\u0026ndash;HDL-C\u0026thinsp;\u0026ge;\u0026thinsp;160 mg/dL (non\u0026ndash;HDL-C\u0026thinsp;\u0026ge;\u0026thinsp;4.1 mmol/L). Low HDL-C was defined as serum HDL-C lower than 40 mg/dL (HDL-C\u0026thinsp;\u0026lt;\u0026thinsp;1.03 mmol/L) in men, and 50 mg/dL (HDL-C\u0026thinsp;\u0026lt;\u0026thinsp;1.29 mmol/L) in women. Dyslipidemia was characterized by the presence of at least one lipid abnormality of hypertriglyceridemia, hypercholesterolemia, high LDL-C, and low HDL-C or self-reported use of lipid-lowering medications. Mixed dyslipidemia was defined as the coexistence of high LDL-C with at least one of the hypertriglyceridemia and low HDL-C.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec5\" class=\"Section2\"\u003e \u003ch2\u003eStatistical analysis\u003c/h2\u003e \u003cp\u003eThe prevalence of any lipid abnormality was calculated after applying weights to the samples. The study population was shown to be representative at the national and provincial levels using probability sample tests. Data was summarized by mean and 95% confidence intervals in parentheses [mean (95% confidence interval)]. The chi-square test and One-way ANOVA tests were used to compare categorical and continuous variables between different groups. The determinants of abnormal lipid levels were identified using multiple logistic regression models. For each model, odds ratios (OR) and 95% confidence intervals (CI) were calculated using multiple logistic regression and adjusted for sex, age, and wealth index. All statistical analyses were performed with the STATA software version 12 (StataCorp, Texas, USA). Figures were depicted by R. Software version 3.2.1 (Vienna, Austria). Two-tailed P values of 0.05 were considered statistically significant.\u003c/p\u003e \u003c/div\u003e"},{"header":"Results","content":"\u003cdiv class=\"Section2\" id=\"Sec7\"\u003e\n \u003ch2\u003ePopulation Characteristics\u003c/h2\u003e\n \u003cp\u003eOf the total 18,119 participants, 7826 (43.1%) participants were female, and 10,293 (56.8%) participants were male. 24.7% of participants were residents in rural areas (Table \u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003e). Our results showed 66.6% of the study population were overweight/obese, 57.5% of participants did not have appropriate physical activity, and only 6.9% of them consumed an appropriate amount of fruit and vegetable. 19.8% of the study population were smoking tobacco products daily, including 7.4% of women and 35.5% of men.\u0026nbsp;\u003c/p\u003e\n \u003ctable border=\"1\" id=\"Tab1\"\u003e\n \u003ccaption language=\"En\"\u003e\n \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e\n \u003cdiv class=\"CaptionContent\"\u003e\n \u003cp\u003ePopulation characteristics\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"\"\u003e\n \u003cp\u003eVariable\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"\"\u003e\n \u003cp\u003eCategory\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"\"\u003e\n \u003cp\u003eWomen\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"\"\u003e\n \u003cp\u003eMen\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"\"\u003e\n \u003cp\u003eBoth\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"\" rowspan=\"4\"\u003e\n \u003cp\u003eAge category, number\u003c/p\u003e\n \u003cp\u003epercent (95% CI)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"\"\u003e\n \u003cp\u003e25\u0026ndash;39\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e3454\u003c/p\u003e\n \u003cp\u003e33.5 (32.2,34.81)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e2382\u003c/p\u003e\n \u003cp\u003e32.29 (30.77,33.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e5836\u003c/p\u003e\n \u003cp\u003e32.96 (31.98,33.95)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"\"\u003e\n \u003cp\u003e40\u0026ndash;54\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e3675\u003c/p\u003e\n \u003cp\u003e34.77 (33.49,36.06)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e2597\u003c/p\u003e\n \u003cp\u003e32.06 (30.63,33.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e6272\u003c/p\u003e\n \u003cp\u003e33.57 (32.61,34.53)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"\"\u003e\n \u003cp\u003e55\u0026ndash;64\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1811\u003c/p\u003e\n \u003cp\u003e17.89 (16.84,18.94)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1501\u003c/p\u003e\n \u003cp\u003e18.63 (17.42,19.83)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e3312\u003c/p\u003e\n \u003cp\u003e18.22 (17.42,19.01)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"\"\u003e\n \u003cp\u003e65+\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1353\u003c/p\u003e\n \u003cp\u003e13.83 (12.81,14.85)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1346\u003c/p\u003e\n \u003cp\u003e17.02 (15.8,18.24)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e2699\u003c/p\u003e\n \u003cp\u003e15.25 (14.46,16.03)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"\"\u003e\n \u003cp\u003eArea of residence, number\u003c/p\u003e\n \u003cp\u003epercent (95% CI)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"\"\u003e\n \u003cp\u003eRural\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e3344\u003c/p\u003e\n \u003cp\u003e24.49 (23.53,25.46)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e2520\u003c/p\u003e\n \u003cp\u003e24.88 (23.76,26)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e5864\u003c/p\u003e\n \u003cp\u003e24.67 (23.95,25.39)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"\" rowspan=\"5\"\u003e\n \u003cp\u003eWealth index, number\u003c/p\u003e\n \u003cp\u003epercent (95% CI)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"\"\u003e\n \u003cp\u003ePoor\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e2190\u003c/p\u003e\n \u003cp\u003e20.85 (19.68,22.01)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1488\u003c/p\u003e\n \u003cp\u003e17.71 (16.55,18.86)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e3678\u003c/p\u003e\n \u003cp\u003e18.37 (17.59,19.16)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"\"\u003e\n \u003cp\u003e2nd quintile\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1947\u003c/p\u003e\n \u003cp\u003e21.76 (20.54,22.99)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1381\u003c/p\u003e\n \u003cp\u003e18.14 (16.94,19.34)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e3328\u003c/p\u003e\n \u003cp\u003e19.03 (18.21,19.85)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"\"\u003e\n \u003cp\u003eMiddle\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1944\u003c/p\u003e\n \u003cp\u003e18.12 (17.13,19.11)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1744\u003c/p\u003e\n \u003cp\u003e19.93 (18.79,21.08)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e3688\u003c/p\u003e\n \u003cp\u003e17.95 (17.24,18.66)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"\"\u003e\n \u003cp\u003e4th quintile\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1813\u003c/p\u003e\n \u003cp\u003e19.43 (18.33,20.53)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1658\u003c/p\u003e\n \u003cp\u003e21.23 (20.01,22.45)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e3471\u003c/p\u003e\n \u003cp\u003e19.18 (18.4,19.96)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"\"\u003e\n \u003cp\u003eRich\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1563\u003c/p\u003e\n \u003cp\u003e19.84 (18.6,21.08)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1419\u003c/p\u003e\n \u003cp\u003e22.99 (21.42,24.56)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e2982\u003c/p\u003e\n \u003cp\u003e20.16 (19.22,21.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"\" rowspan=\"4\"\u003e\n \u003cp\u003eBMI category, number\u003c/p\u003e\n \u003cp\u003epercent (95% CI)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"\"\u003e\n \u003cp\u003eUnderweight\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e217\u003c/p\u003e\n \u003cp\u003e1.84 (1.55,2.13)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e248\u003c/p\u003e\n \u003cp\u003e2.69 (2.26,3.12)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e465\u003c/p\u003e\n \u003cp\u003e2.2 (1.96,2.45)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"\"\u003e\n \u003cp\u003eNormal weight\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e2636\u003c/p\u003e\n \u003cp\u003e25.32 (24.13,26.52)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e2994\u003c/p\u003e\n \u003cp\u003e37.67 (36.15,39.19)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e5630\u003c/p\u003e\n \u003cp\u003e30.63 (29.68,31.57)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"\"\u003e\n \u003cp\u003eOverweight\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e3925\u003c/p\u003e\n \u003cp\u003e38.96 (37.6,40.32)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e3131\u003c/p\u003e\n \u003cp\u003e41.02 (39.45,42.58)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e7056\u003c/p\u003e\n \u003cp\u003e39.64 (38.62,40.67)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"\"\u003e\n \u003cp\u003eObese\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e3468\u003c/p\u003e\n \u003cp\u003e33.88 (32.58,35.18)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1419\u003c/p\u003e\n \u003cp\u003e18.63 (17.35,19.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e4887\u003c/p\u003e\n \u003cp\u003e26.95 (26.03,27.87)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"\"\u003e\n \u003cp\u003eAppropriate Fruit and vegetable consumption, number\u003c/p\u003e\n \u003cp\u003epercent (95% CI)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e755\u003c/p\u003e\n \u003cp\u003e6.94 (6.3,7.58)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e434\u003c/p\u003e\n \u003cp\u003e5.41 (4.72,6.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1189\u003c/p\u003e\n \u003cp\u003e6.25 (5.79,6.72)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"\"\u003e\n \u003cp\u003eInappropriate Physical activity, number\u003c/p\u003e\n \u003cp\u003epercent (95% CI)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e5530\u003c/p\u003e\n \u003cp\u003e57.51 (56.14,58.88)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e2590\u003c/p\u003e\n \u003cp\u003e41.8 (40.05,43.54)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e8120\u003c/p\u003e\n \u003cp\u003e46.66 (45.63,47.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"\"\u003e\n \u003cp\u003eSmoking (current daily smoking), number\u003c/p\u003e\n \u003cp\u003epercent (95% CI)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e762\u003c/p\u003e\n \u003cp\u003e7.37 (6.7,8.03)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e2756\u003c/p\u003e\n \u003cp\u003e35.55 (34.02,37.09)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e3518\u003c/p\u003e\n \u003cp\u003e19.83 (18.99,20.67)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"\"\u003e\n \u003cp\u003eHistory of Diabetes, number\u003c/p\u003e\n \u003cp\u003epercent (95% CI)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1456\u003c/p\u003e\n \u003cp\u003e14.71 (13.76,15.65)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e987\u003c/p\u003e\n \u003cp\u003e13.45 (12.32,14.57)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e2443\u003c/p\u003e\n \u003cp\u003e14.13 (13.41,14.86)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"\"\u003e\n \u003cp\u003eHistory of Hypertension, number\u003c/p\u003e\n \u003cp\u003epercent (95% CI)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e3847\u003c/p\u003e\n \u003cp\u003e36.84 (35.5,38.17)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e2807\u003c/p\u003e\n \u003cp\u003e34.71 (33.2,36.22)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e6654\u003c/p\u003e\n \u003cp\u003e35.75 (34.75,36.75)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"\"\u003e\n \u003cp\u003eHistory of coronary heart disease, number\u003c/p\u003e\n \u003cp\u003epercent (95% CI)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e637\u003c/p\u003e\n \u003cp\u003e6.42 (5.72,7.11)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e718\u003c/p\u003e\n \u003cp\u003e9.42 (8.5,10.33)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1355\u003c/p\u003e\n \u003cp\u003e7.73 (7.17,8.29)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"\"\u003e\n \u003cp\u003eHistory of stroke, number\u003c/p\u003e\n \u003cp\u003epercent (95% CI)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e128\u003c/p\u003e\n \u003cp\u003e1.16 (0.9,1.42)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e149\u003c/p\u003e\n \u003cp\u003e1.86 (1.45,2.27)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e277\u003c/p\u003e\n \u003cp\u003e1.47 (1.23,1.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"\"\u003e\n \u003cp\u003eFamilial History of cardiovascular disease, number\u003c/p\u003e\n \u003cp\u003epercent (95% CI)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1451\u003c/p\u003e\n \u003cp\u003e15.14 (14.14,16.13)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e850\u003c/p\u003e\n \u003cp\u003e11.54 (10.49,12.58)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e2301\u003c/p\u003e\n \u003cp\u003e13.2 (12.49,13.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n \u003cp\u003e\u003cbr\u003e\u003c/p\u003e\n \u003cp\u003eRegarding past medical and family history, 14.1% of participants were diabetic and 35.7% had hypertension. 7.7% and 1.5% of participants mentioned they had a history of coronary heart disease and stroke, respectively. 13.2% of participants mentioned the family history of CVD. \u003cstrong\u003ePrevalence of Lipid Abnormalities in Different Population Strata\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003eThe prevalence of dyslipidemia was 81.0% (80.2\u0026ndash;81.9) among the Iranian adult population, affecting 84.4% (83.4\u0026ndash;85.4) of women and 75.7% (74.4\u0026ndash;77.1) of men (Table \u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003e). Of the total female adult population, 35.5% (34.2\u0026ndash;36.9) had hypertriglyceridemia, 23.0% (21.8\u0026ndash;24.2) had hypercholesterolemia, 17.1% (16.0-18.3) had high LDL-C, and 72.7% (71.5\u0026ndash;73.9) had low HDL-C. Of the total male adult population, 43.4% (41.9\u0026ndash;45.0) had hypertriglyceridemia, 18.9% (17.7\u0026ndash;20.2) had hypercholesterolemia, 15.4% (14.2\u0026ndash;16.5) had high LDL-C, and 62.0% (60.5\u0026ndash;63.5) had low HDL-C. Accordingly, hypercholesterolemia and low HDL-C were more prevalent in women, and hypertriglyceridemia was more prevalent in men. 19.4% (18.5\u0026ndash;20.3) of the population had high non-HDL-C, and the abnormality had almost similar prevalence among men and women. The age group of 55\u0026ndash;64 had the highest prevalence of dyslipidemia, which was significantly higher than the 25\u0026ndash;39 age groups (83.3% vs. 77.0%; P\u0026thinsp;\u0026lt;\u0026thinsp;0.05) (Table \u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003e). Compared to the reference age group (25\u0026ndash;39), other age groups had higher odds of having hypertriglyceridemia, hypercholesterolemia, and high LDL-C; however, those older than 55 had lower odds of having low HDL-C (Table \u003cspan class=\"InternalRef\"\u003e3\u003c/span\u003e). Supplementary Table \u003cspan class=\"InternalRef\"\u003eS1\u003c/span\u003e shows the value of serum lipids in different centiles for different age strata.\u003c/p\u003e\n \u003ctable border=\"1\" id=\"Tab2\"\u003e\n \u003ccaption language=\"En\"\u003e\n \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e\n \u003cdiv class=\"CaptionContent\"\u003e\n \u003cp\u003ePrevalence of lipid abnormalities according to study population strata\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"\"\u003e\n \u003cp\u003eVariable\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"\"\u003e\n \u003cp\u003eCategory\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"\"\u003e\n \u003cp\u003eHypertriglyceridemia\u003c/p\u003e\n \u003cp\u003ePercent. (95% CI)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"\"\u003e\n \u003cp\u003eHypercholesterolemia\u003c/p\u003e\n \u003cp\u003ePercent. (95% CI)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"\"\u003e\n \u003cp\u003eHigh LDL-C\u003c/p\u003e\n \u003cp\u003ePercent. (95% CI)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"\"\u003e\n \u003cp\u003eVery High LDL-C\u003c/p\u003e\n \u003cp\u003ePercent. (95% CI)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"\"\u003e\n \u003cp\u003eHigh non\u0026ndash;HDL-C\u003c/p\u003e\n \u003cp\u003ePercent. (95% CI)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"\"\u003e\n \u003cp\u003eLow HDL-C\u003c/p\u003e\n \u003cp\u003ePercent. (95% CI)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"\"\u003e\n \u003cp\u003eDyslipidemia\u003c/p\u003e\n \u003cp\u003ePercent. (95% CI)\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"\"\u003e\n \u003cp\u003eAll participants\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"\"\u003e\n \u003cp\u003e\u0026hellip;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e39.7 (38.6,40.79)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e21.24 (20.32,22.17)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e16.42 (15.56,17.29)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.74 (0.52,0.95)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e19.43 (18.54,20.33)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e68.42 (67.41,69.42)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e81.02 (80.17,81.87)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"\" rowspan=\"2\"\u003e\n \u003cp\u003eSex\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"\"\u003e\n \u003cp\u003eWomen\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e35.54 (34.21,36.87)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e23.04 (21.84,24.23)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e17.15 (16.04,18.27)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.76 (0.58,0.94)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e19.03 (17.9,20.15)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e72.68 (71.48,73.89)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e84.41 (83.43,85.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"\"\u003e\n \u003cp\u003eMen\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e43.45 (41.87,45.04)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e18.94 (17.69,20.18)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e15.36 (14.19,16.53)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.74 (0.35,1.12)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e19.68 (18.42,20.94)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e62 (60.48,63.51)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e75.73 (74.4,77.06)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"\" rowspan=\"4\"\u003e\n \u003cp\u003eAge\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"\"\u003e\n \u003cp\u003e25\u0026ndash;39\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e32.52 (30.71,34.33)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e12.14 (10.95,13.33)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e9.43\u003c/p\u003e\n \u003cp\u003e(8.28,10.58)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.33 (0.18,0.49)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e11.76 (10.61,12.92)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e70.63 (69.02,72.24)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e77.05 (75.56,78.55)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"\"\u003e\n \u003cp\u003e40\u0026ndash;54\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e42.79 (41.1,44.48)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e24.65 (23.17,26.13)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e18.44 (17.11,19.76)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.89 (0.39,1.39)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e23.02 (21.56,24.48)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e69.37 (67.79,70.96)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e82.01 (80.67,83.34)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"\"\u003e\n \u003cp\u003e55\u0026ndash;64\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e44.61 (42.23,46.98)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e29 (26.84,31.15)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e22.36 (20.35,24.38)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.93 (0.58,1.29)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e25.01\u003c/p\u003e\n \u003cp\u003e(23,27.03)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e65.09 (62.8,67.37)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e83.29 (81.42,85.15)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"\"\u003e\n \u003cp\u003e65+\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e38.34 (35.44,41.25)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e24.02 (21.3,26.74)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e19.61 (16.94,22.27)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1.14 (0.71,1.57)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e20.7 (18.03,23.37)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e62.36 (59.67,65.05)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e81.7 (79.68,83.73)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"\" rowspan=\"2\"\u003e\n \u003cp\u003eArea\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"\"\u003e\n \u003cp\u003eRural\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e35.67 (34.2,37.13)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e21.35 (20.08,22.62)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e17.19 (16.02,18.36)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.83 (0.57,1.09)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e19.46 (18.22,20.69)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e66.68 (65.25,68.11)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e78.8 (77.56,80.04)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"\"\u003e\n \u003cp\u003eUrban\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e40.16 (38.88,41.43)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e21.17 (20.1,22.25)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e16.09 (15.08,17.09)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.73 (0.48,0.97)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e19.27 (18.23,20.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e68.35 (67.18,69.53)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e81.14 (80.15,82.13)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"\" rowspan=\"5\"\u003e\n \u003cp\u003eWealth index\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"\"\u003e\n \u003cp\u003ePoor\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e35.95 (33.62,38.28)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e22.04 (19.82,24.27)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e17.08 (14.93,19.23)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.95 (0.6,1.31)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e19.09 (17,21.17)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e67.75 (65.65,69.85)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e79.69 (77.84,81.54)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"\"\u003e\n \u003cp\u003e2nd quintile\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e39.85 (37.45,42.25)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e19.21 (17.5,20.93)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e14.61 (13.1,16.12)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.76 (0.46,1.07)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e17.68 (16.03,19.33)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e68.71 (66.51,70.92)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e80.96 (79.07,82.84)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"\"\u003e\n \u003cp\u003eMiddle\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e39.56 (37.44,41.68)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e20.03 (18.28,21.78)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e15.76 (14.12,17.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.72 (0.41,1.04)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e19.3 (17.55,21.04)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e68.15 (66.24,70.06)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e79.98 (78.36,81.59)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"\"\u003e\n \u003cp\u003e4th quintile\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e40.26 (38.1,42.43)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e22.05 (20.24,23.87)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e16 (14.45,17.54)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.5 (0.26,0.74)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e20.05 (18.3,21.81)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e67.01 (64.89,69.13)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e79.95 (78.03,81.87)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"\"\u003e\n \u003cp\u003eRich\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e40.48 (37.74,43.21)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e22.08 (19.77,24.39)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e17.94 (15.68,20.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.96 (0.16,1.76)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e20.51 (18.22,22.79)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e66.91 (64.36,69.45)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e81.1 (79.09,83.12)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"\" rowspan=\"4\"\u003e\n \u003cp\u003eBMI Category\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"\"\u003e\n \u003cp\u003eUnderweight\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e6.15 (3.93,8.37)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e8.76 (6.01,11.51)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e8.81 (5.87,11.74)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.4 (-0.09,0.89)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e5.21 (3.23,7.18)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e39.05 (33.57,44.53)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e48.52 (42.86,54.17)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"\"\u003e\n \u003cp\u003eNormal weight\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e27.38 (25.67,29.08)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e17.36 (15.88,18.84)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e14.45 (13.08,15.83)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.38 (0.22,0.55)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e15.05 (13.63,16.47)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e57.67 (55.85,59.48)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e70.48 (68.84,72.11)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"\"\u003e\n \u003cp\u003eOverweight\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e41.82 (40.16,43.48)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e22.84 (21.44,24.24)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e17.32 (16.04,18.61)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1.04 (0.6,1.49)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e20.93 (19.62,22.25)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e71.7 (70.21,73.18)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e84.81 (83.58,86.05)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"\"\u003e\n \u003cp\u003eObese\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e50.67 (48.67,52.68)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e23.91 (22.16,25.66)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e17.5 (15.83,19.17)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.78 (0.53,1.04)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e22.7 (20.93,24.47)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e76.38 (74.71,78.04)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e88.17 (86.84,89.49)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"\" rowspan=\"2\"\u003e\n \u003cp\u003eFruit and vegetable consumption\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"\"\u003e\n \u003cp\u003eInappropriate\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e39.01 (37.95,40.08)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e21.24 (20.34,22.14)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e16.34 (15.5,17.18)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.76 (0.55,0.97)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e19.29 (18.42,20.16)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e67.77 (66.78,68.76)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e80.35 (79.51,81.19)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"\"\u003e\n \u003cp\u003eAppropriate\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e40.01 (36.27,43.75)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e21.03 (17.83,24.23)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e16.74 (13.71,19.78)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.64 (0.18,1.09)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e19.83 (16.82,22.83)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e70.15 (66.58,73.71)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e83.36 (80.65,86.06)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"\" rowspan=\"2\"\u003e\n \u003cp\u003ePhysical activity\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"\"\u003e\n \u003cp\u003eInappropriate\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e40.24 (38.71,41.78)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e21.99 (20.64,23.34)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e16.9 (15.63,18.18)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.74 (0.37,1.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e19.78 (18.47,21.09)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e70.58 (69.22,71.94)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e83.31 (82.22,84.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"\"\u003e\n \u003cp\u003eappropriate\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e38.55 (37.01,40.09)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e20.95 (19.7,22.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e16.07 (14.91,17.22)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.76 (0.56,0.96)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e19.08 (17.88,20.28)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e66.91 (65.45,68.38)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e79.44 (78.16,80.71)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"\" rowspan=\"2\"\u003e\n \u003cp\u003eSmoking\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"\"\u003e\n \u003cp\u003eNo\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e37.9 (36.77,39.03)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e21.73 (20.75,22.71)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e16.65 (15.73,17.57)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.7 (0.56,0.85)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e19.24 (18.3,20.18)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e68.3 (67.23,69.36)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e80.97 (80.07,81.87)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e43.56 (41.15,45.97)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e19.06 (17.24,20.89)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e15.07 (13.4,16.74)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.95 (0.14,1.75)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e19.52 (17.68,21.36)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e66.57 (64.42,68.72)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e78.91 (77.08,80.74)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"\" rowspan=\"2\"\u003e\n \u003cp\u003eDiabetes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"\"\u003e\n \u003cp\u003eNo\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e35.9 (34.8,37)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e21.07 (20.13,22.02)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e16.67 (15.78,17.56)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.73 (0.5,0.95)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e18.95 (18.05,19.86)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e66.52 (65.48,67.56)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e78.76 (77.86,79.66)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e58.18 (55.43,60.93)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e22.05 (19.83,24.27)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e14.43 (12.58,16.27)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.9 (0.52,1.28)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e21.47 (19.28,23.67)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e76.63 (74.31,78.94)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e91.38 (89.71,93.04)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"\" rowspan=\"2\"\u003e\n \u003cp\u003eHypertension\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"\"\u003e\n \u003cp\u003eNo\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e34.16 (32.92,35.39)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e18.89 (17.86,19.93)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e14.82 (13.86,15.78)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.58 (0.31,0.86)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e16.99 (16,17.97)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e66.07 (64.86,67.29)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e77.12 (76.05,78.19)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e47.61 (45.85,49.37)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e25.21 (23.66,26.75)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e19.03 (17.58,20.49)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1.06 (0.79,1.32)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e23.36 (21.83,24.88)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e71.3 (69.78,72.82)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e86.58 (85.41,87.76)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"\" rowspan=\"2\"\u003e\n \u003cp\u003eHistory of coronary heart disease\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"\"\u003e\n \u003cp\u003eNo\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e38.71 (37.64,39.78)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e21.55 (20.64,22.46)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e16.66 (15.8,17.51)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.76 (0.55,0.97)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e19.66 (18.78,20.54)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e67.58 (66.59,68.58)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e80.04 (79.19,80.89)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e42.76 (39.01,46.51)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e16.99 (14.29,19.68)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e12.36 (10.04,14.68)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.64 (0.19,1.08)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e14.93 (12.39,17.47)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e72.2 (68.89,75.52)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e86.42 (83.8,89.03)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"\" rowspan=\"2\"\u003e\n \u003cp\u003eHistory of stroke\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"\"\u003e\n \u003cp\u003eNo\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e39.02 (37.98,40.05)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e21.18 (20.3,22.05)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e16.31 (15.5,17.13)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.76 (0.56,0.96)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e19.28 (18.43,20.12)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e67.95 (66.99,68.91)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e80.46 (79.64,81.27)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e39.7\u003c/p\u003e\n \u003cp\u003e(31.8,47.59)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e22.33 (15.16,29.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e17.01 (10.86,23.16)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.22 (-0.09,0.53)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e20.35 (13.79,26.92)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e67.72 (60.29,75.15)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e85.92 (80.11,91.74)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"\" rowspan=\"2\"\u003e\n \u003cp\u003eFamilial History of CVD\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"\"\u003e\n \u003cp\u003eNo\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e38.22 (37.11,39.33)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e20.91 (19.97,21.84)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e16.12 (15.24,17)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.61 (0.48,0.74)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e18.74 (17.84,19.64)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e67.26 (66.22,68.31)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e79.95 (79.06,80.85)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e43.86 (40.96,46.76)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e22.63 (20.2,25.07)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e17.41 (15.13,19.69)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1.54 (0.32,2.77)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e22.02 (19.57,24.48)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e71.6 (69.12,74.08)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e83.76 (81.69,85.83)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n \u003cbr\u003e\n \u003ctable border=\"1\" id=\"Tab3\"\u003e\n \u003ccaption language=\"En\"\u003e\n \u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e\n \u003cdiv class=\"CaptionContent\"\u003e\n \u003cp\u003eLipid abnormalities in association with the study population characteristics\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"\" rowspan=\"2\"\u003e\n \u003cp\u003eVariable (reference group)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"\" rowspan=\"2\"\u003e\n \u003cp\u003eCategory\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"\" colspan=\"2\"\u003e\n \u003cp\u003eHypertriglyceridemia\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"\" colspan=\"2\"\u003e\n \u003cp\u003eHypercholesterolemia\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"\" colspan=\"2\"\u003e\n \u003cp\u003eHigh LDL-C\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"\" colspan=\"2\"\u003e\n \u003cp\u003eLow HDL-C\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"\" colspan=\"2\"\u003e\n \u003cp\u003eDyslipidemia\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003cth align=\"\"\u003e\n \u003cp\u003eCrude OR (95% CI)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"\"\u003e\n \u003cp\u003eAdjusted OR (95% CI)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"\"\u003e\n \u003cp\u003eCrude OR (95% CI)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"\"\u003e\n \u003cp\u003eAdjusted OR (95% CI)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"\"\u003e\n \u003cp\u003eCrude OR (95% CI)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"\"\u003e\n \u003cp\u003eAdjusted OR (95% CI)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"\"\u003e\n \u003cp\u003eCrude OR (95% CI)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"\"\u003e\n \u003cp\u003eAdjusted OR (95% CI)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"\"\u003e\n \u003cp\u003eCrude OR (95% CI)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"\"\u003e\n \u003cp\u003eAdjusted OR (95% CI)\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"\"\u003e\n \u003cp\u003eSex (female)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"\"\u003e\n \u003cp\u003eMale\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"\"\u003e\n \u003cp\u003e1.39\u003c/p\u003e\n \u003cp\u003e(1.28,1.52)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"\"\u003e\n \u003cp\u003e1.38\u003c/p\u003e\n \u003cp\u003e(1.26,1.51)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"\"\u003e\n \u003cp\u003e0.78\u003c/p\u003e\n \u003cp\u003e(0.70,0.87)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"\"\u003e\n \u003cp\u003e0.77\u003c/p\u003e\n \u003cp\u003e(0.69,0.86)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"\"\u003e\n \u003cp\u003e0.88\u003c/p\u003e\n \u003cp\u003e(0.78,0.99)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"\"\u003e\n \u003cp\u003e0.87\u003c/p\u003e\n \u003cp\u003e(0.77,0.98)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"\"\u003e\n \u003cp\u003e0.61\u003c/p\u003e\n \u003cp\u003e(0.56,0.67)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"\"\u003e\n \u003cp\u003e0.62\u003c/p\u003e\n \u003cp\u003e(0.56,0.68)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"\"\u003e\n \u003cp\u003e0.588\u003c/p\u003e\n \u003cp\u003e(0.52,0.64)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"\"\u003e\n \u003cp\u003e0.56\u003c/p\u003e\n \u003cp\u003e(0.50,0.63)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"\" rowspan=\"3\"\u003e\n \u003cp\u003eAge (25\u0026ndash;39 y)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"\"\u003e\n \u003cp\u003e40\u0026ndash;54\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"\"\u003e\n \u003cp\u003e1.55\u003c/p\u003e\n \u003cp\u003e(1.39,1.73)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"\"\u003e\n \u003cp\u003e1.56\u003c/p\u003e\n \u003cp\u003e(1.40,1.74)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"\"\u003e\n \u003cp\u003e2.37\u003c/p\u003e\n \u003cp\u003e(2.06,2.71)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"\"\u003e\n \u003cp\u003e2.34\u003c/p\u003e\n \u003cp\u003e(2.03,2.70)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"\"\u003e\n \u003cp\u003e2.17\u003c/p\u003e\n \u003cp\u003e(1.85,2.55)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"\"\u003e\n \u003cp\u003e2.13\u003c/p\u003e\n \u003cp\u003e(1.80,2.51)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"\"\u003e\n \u003cp\u003e0.94\u003c/p\u003e\n \u003cp\u003e(0.85,1.05)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"\"\u003e\n \u003cp\u003e0.95\u003c/p\u003e\n \u003cp\u003e(0.85,1.06)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"\"\u003e\n \u003cp\u003e1.36\u003c/p\u003e\n \u003cp\u003e(1.20,1.54)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"\"\u003e\n \u003cp\u003e1.37\u003c/p\u003e\n \u003cp\u003e(1.21,1.56)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"\"\u003e\n \u003cp\u003e55\u0026ndash;64\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"\"\u003e\n \u003cp\u003e1.67\u003c/p\u003e\n \u003cp\u003e(1.47,1.90)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"\"\u003e\n \u003cp\u003e1.64\u003c/p\u003e\n \u003cp\u003e(1.44,1.87)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"\"\u003e\n \u003cp\u003e2.95\u003c/p\u003e\n \u003cp\u003e(2.54,3.44)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"\"\u003e\n \u003cp\u003e2.94\u003c/p\u003e\n \u003cp\u003e(2.51,3.45)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"\"\u003e\n \u003cp\u003e2.77\u003c/p\u003e\n \u003cp\u003e(2.32,3.30)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"\"\u003e\n \u003cp\u003e2.75\u003c/p\u003e\n \u003cp\u003e(2.29,3.30)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"\"\u003e\n \u003cp\u003e0.77\u003c/p\u003e\n \u003cp\u003e(0.68,0.88)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"\"\u003e\n \u003cp\u003e0.78\u003c/p\u003e\n \u003cp\u003e(0.69,0.89)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"\"\u003e\n \u003cp\u003e1.48\u003c/p\u003e\n \u003cp\u003e(1.27,1.74)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"\"\u003e\n \u003cp\u003e1.51\u003c/p\u003e\n \u003cp\u003e(1.28,1.78)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"\"\u003e\n \u003cp\u003e\u0026gt;\u0026thinsp;65\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"\"\u003e\n \u003cp\u003e1.29\u003c/p\u003e\n \u003cp\u003e(1.11,1.50)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"\"\u003e\n \u003cp\u003e1.28\u003c/p\u003e\n \u003cp\u003e(1.10,1.49)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"\"\u003e\n \u003cp\u003e2.29\u003c/p\u003e\n \u003cp\u003e(1.90,2.75)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"\"\u003e\n \u003cp\u003e2.31\u003c/p\u003e\n \u003cp\u003e(1.91,2.81)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"\"\u003e\n \u003cp\u003e2.34\u003c/p\u003e\n \u003cp\u003e(1.89,2.91)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"\"\u003e\n \u003cp\u003e2.34\u003c/p\u003e\n \u003cp\u003e(1.87,2.93)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"\"\u003e\n \u003cp\u003e0.69\u003c/p\u003e\n \u003cp\u003e(0.60,0.79)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"\"\u003e\n \u003cp\u003e0.71\u003c/p\u003e\n \u003cp\u003e(0.62,0.82)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"\"\u003e\n \u003cp\u003e1.30\u003c/p\u003e\n \u003cp\u003e(1.13,1.56)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"\"\u003e\n \u003cp\u003e1.38\u003c/p\u003e\n \u003cp\u003e(1.17,1.63)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"\"\u003e\n \u003cp\u003eArea (rural)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"\"\u003e\n \u003cp\u003eUrban\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"\"\u003e\n \u003cp\u003e1.21\u003c/p\u003e\n \u003cp\u003e(1.11,1.32)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"\"\u003e\n \u003cp\u003e1.21\u003c/p\u003e\n \u003cp\u003e(1.10,1.32)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"\"\u003e\n \u003cp\u003e0.99\u003c/p\u003e\n \u003cp\u003e(0.90,1.09)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"\"\u003e\n \u003cp\u003e0.97\u003c/p\u003e\n \u003cp\u003e(0.87,1.085)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"\"\u003e\n \u003cp\u003e0.92\u003c/p\u003e\n \u003cp\u003e(0.83,1.03)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"\"\u003e\n \u003cp\u003e0.89\u003c/p\u003e\n \u003cp\u003e(0.78,1.01)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"\"\u003e\n \u003cp\u003e1.08\u003c/p\u003e\n \u003cp\u003e(0.99,1.174)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"\"\u003e\n \u003cp\u003e1.13\u003c/p\u003e\n \u003cp\u003e(1.03,1.24)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"\"\u003e\n \u003cp\u003e1.16\u003c/p\u003e\n \u003cp\u003e(1.05,1.28)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"\"\u003e\n \u003cp\u003e1.14\u003c/p\u003e\n \u003cp\u003e(1.02,1.27)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"\" rowspan=\"4\"\u003e\n \u003cp\u003eWealth index (poor)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"\"\u003e\n \u003cp\u003e2nd quintile\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"\"\u003e\n \u003cp\u003e1.18\u003c/p\u003e\n \u003cp\u003e(1.02,1.36)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"\"\u003e\n \u003cp\u003e1.17\u003c/p\u003e\n \u003cp\u003e(1.02,1.35)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"\"\u003e\n \u003cp\u003e0.841\u003c/p\u003e\n \u003cp\u003e(0.71,1.00)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"\"\u003e\n \u003cp\u003e0.84\u003c/p\u003e\n \u003cp\u003e(0.71,0.99)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"\"\u003e\n \u003cp\u003e0.83\u003c/p\u003e\n \u003cp\u003e(0.68,1.01)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"\"\u003e\n \u003cp\u003e0.83\u003c/p\u003e\n \u003cp\u003e(0.69,1.01)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"\"\u003e\n \u003cp\u003e1.05\u003c/p\u003e\n \u003cp\u003e(0.91,1.20)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"\"\u003e\n \u003cp\u003e1.03\u003c/p\u003e\n \u003cp\u003e(0.89,1.19)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"\"\u003e\n \u003cp\u003e1.08\u003c/p\u003e\n \u003cp\u003e(0.92,1.28)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"\"\u003e\n \u003cp\u003e1.08\u003c/p\u003e\n \u003cp\u003e(0.91,1.28)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"\"\u003e\n \u003cp\u003eMiddle\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"\"\u003e\n \u003cp\u003e1.17\u003c/p\u003e\n \u003cp\u003e(1.02,1.33)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"\"\u003e\n \u003cp\u003e1.13\u003c/p\u003e\n \u003cp\u003e(0.99,1.29)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"\"\u003e\n \u003cp\u003e0.89\u003c/p\u003e\n \u003cp\u003e(0.75,1.05)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"\"\u003e\n \u003cp\u003e0.91\u003c/p\u003e\n \u003cp\u003e(0.77,1.08)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"\"\u003e\n \u003cp\u003e0.91\u003c/p\u003e\n \u003cp\u003e(0.75,1.10)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"\"\u003e\n \u003cp\u003e0.94\u003c/p\u003e\n \u003cp\u003e(0.77,1.14)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"\"\u003e\n \u003cp\u003e1.02\u003c/p\u003e\n \u003cp\u003e(0.89,1.16)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"\"\u003e\n \u003cp\u003e1.02\u003c/p\u003e\n \u003cp\u003e(0.89,1.16)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"\"\u003e\n \u003cp\u003e1.02\u003c/p\u003e\n \u003cp\u003e(0.87,1.18)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"\"\u003e\n \u003cp\u003e1.06\u003c/p\u003e\n \u003cp\u003e(0.91,1.24)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"\"\u003e\n \u003cp\u003e4th quintile\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"\"\u003e\n \u003cp\u003e1.20\u003c/p\u003e\n \u003cp\u003e(1.05,1.37)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"\"\u003e\n \u003cp\u003e1.17\u003c/p\u003e\n \u003cp\u003e(1.02,1.33)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"\"\u003e\n \u003cp\u003e1.00\u003c/p\u003e\n \u003cp\u003e(0.85,1.18)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"\"\u003e\n \u003cp\u003e1.04\u003c/p\u003e\n \u003cp\u003e(0.88,1.23)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"\"\u003e\n \u003cp\u003e0.92\u003c/p\u003e\n \u003cp\u003e(0.76,1.12)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"\"\u003e\n \u003cp\u003e0.96\u003c/p\u003e\n \u003cp\u003e(0.79,1.16)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"\"\u003e\n \u003cp\u003e0.97\u003c/p\u003e\n \u003cp\u003e(0.84,1.11)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"\"\u003e\n \u003cp\u003e0.96\u003c/p\u003e\n \u003cp\u003e(0.84,1.11)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"\"\u003e\n \u003cp\u003e1.02\u003c/p\u003e\n \u003cp\u003e(0.86,1.20)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"\"\u003e\n \u003cp\u003e1.06\u003c/p\u003e\n \u003cp\u003e(0.9,1.26)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"\"\u003e\n \u003cp\u003eRich\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"\"\u003e\n \u003cp\u003e1.21\u003c/p\u003e\n \u003cp\u003e(1.04,1.41)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"\"\u003e\n \u003cp\u003e1.16\u003c/p\u003e\n \u003cp\u003e(0.99,1.35)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"\"\u003e\n \u003cp\u003e1.00\u003c/p\u003e\n \u003cp\u003e(0.83,1.21)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"\"\u003e\n \u003cp\u003e1.03\u003c/p\u003e\n \u003cp\u003e(0.86,1.24)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"\"\u003e\n \u003cp\u003e1.06\u003c/p\u003e\n \u003cp\u003e(0.86,1.32)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"\"\u003e\n \u003cp\u003e1.10\u003c/p\u003e\n \u003cp\u003e(0.89,1.36)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"\"\u003e\n \u003cp\u003e0.96\u003c/p\u003e\n \u003cp\u003e(0.83,1.12)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"\"\u003e\n \u003cp\u003e0.96\u003c/p\u003e\n \u003cp\u003e(0.83,1.12)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"\"\u003e\n \u003cp\u003e1.09\u003c/p\u003e\n \u003cp\u003e(0.92,1.30)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"\"\u003e\n \u003cp\u003e1.15\u003c/p\u003e\n \u003cp\u003e(0.96,1.37)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"\" rowspan=\"3\"\u003e\n \u003cp\u003eBMI category (underweight)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"\"\u003e\n \u003cp\u003eNormal weight\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"\"\u003e\n \u003cp\u003e5.75\u003c/p\u003e\n \u003cp\u003e(3.88,8.53)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"\"\u003e\n \u003cp\u003e5.44\u003c/p\u003e\n \u003cp\u003e(3.63,8.15)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"\"\u003e\n \u003cp\u003e2.19\u003c/p\u003e\n \u003cp\u003e(1.53,3.13)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"\"\u003e\n \u003cp\u003e2.06\u003c/p\u003e\n \u003cp\u003e(1.43,2.96)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"\"\u003e\n \u003cp\u003e1.75\u003c/p\u003e\n \u003cp\u003e(1.19,2.56)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"\"\u003e\n \u003cp\u003e1.69\u003c/p\u003e\n \u003cp\u003e(1.14,2.51)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"\"\u003e\n \u003cp\u003e2.13\u003c/p\u003e\n \u003cp\u003e(1.67,2.71)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"\"\u003e\n \u003cp\u003e2.28\u003c/p\u003e\n \u003cp\u003e(1.75,2.96)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"\"\u003e\n \u003cp\u003e2.53\u003c/p\u003e\n \u003cp\u003e(1.99,3.22)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"\"\u003e\n \u003cp\u003e2.54\u003c/p\u003e\n \u003cp\u003e(1.98,3.26)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"\"\u003e\n \u003cp\u003eOverweight\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"\"\u003e\n \u003cp\u003e10.97\u003c/p\u003e\n \u003cp\u003e(7.42,16.21)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"\"\u003e\n \u003cp\u003e10.64\u003c/p\u003e\n \u003cp\u003e(7.11,15.93)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"\"\u003e\n \u003cp\u003e3.08\u003c/p\u003e\n \u003cp\u003e(2.17,4.39)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"\"\u003e\n \u003cp\u003e2.63\u003c/p\u003e\n \u003cp\u003e(1.83,3.76)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"\"\u003e\n \u003cp\u003e2.17\u003c/p\u003e\n \u003cp\u003e(1.49,3.16)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"\"\u003e\n \u003cp\u003e1.94\u003c/p\u003e\n \u003cp\u003e(1.32,2.87)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"\"\u003e\n \u003cp\u003e3.95\u003c/p\u003e\n \u003cp\u003e(3.11,5.03)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"\"\u003e\n \u003cp\u003e4.30\u003c/p\u003e\n \u003cp\u003e(3.29,5.61)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"\"\u003e\n \u003cp\u003e5.93\u003c/p\u003e\n \u003cp\u003e(4.64,7.58)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"\"\u003e\n \u003cp\u003e5.73\u003c/p\u003e\n \u003cp\u003e(4.42,7.42)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"\"\u003e\n \u003cp\u003eObese\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"\"\u003e\n \u003cp\u003e15.68\u003c/p\u003e\n \u003cp\u003e(10.58,23.22)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"\"\u003e\n \u003cp\u003e16.37\u003c/p\u003e\n \u003cp\u003e(10.88,24.63)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"\"\u003e\n \u003cp\u003e3.27\u003c/p\u003e\n \u003cp\u003e(2.29,4.68)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"\"\u003e\n \u003cp\u003e2.51\u003c/p\u003e\n \u003cp\u003e(1.73,3.63)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"\"\u003e\n \u003cp\u003e2.20\u003c/p\u003e\n \u003cp\u003e(1.50,3.22)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"\"\u003e\n \u003cp\u003e1.81\u003c/p\u003e\n \u003cp\u003e(1.20,2.72)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"\"\u003e\n \u003cp\u003e5.05\u003c/p\u003e\n \u003cp\u003e(3.94,6.46)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"\"\u003e\n \u003cp\u003e5.32\u003c/p\u003e\n \u003cp\u003e(4.05,7.001)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"\"\u003e\n \u003cp\u003e7.91\u003c/p\u003e\n \u003cp\u003e(6.10,10.25)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"\"\u003e\n \u003cp\u003e7.02\u003c/p\u003e\n \u003cp\u003e(5.34,9.22)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"\"\u003e\n \u003cp\u003eFruit and vegetable consumption (Inappropriate)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"\"\u003e\n \u003cp\u003eappropriate\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"\"\u003e\n \u003cp\u003e1.04\u003c/p\u003e\n \u003cp\u003e(0.89,1.23)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"\"\u003e\n \u003cp\u003e1.02\u003c/p\u003e\n \u003cp\u003e(0.86,1.22)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"\"\u003e\n \u003cp\u003e0.99\u003c/p\u003e\n \u003cp\u003e(0.81,1.21)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"\"\u003e\n \u003cp\u003e0.92\u003c/p\u003e\n \u003cp\u003e(0.75,1.14)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"\"\u003e\n \u003cp\u003e1.03\u003c/p\u003e\n \u003cp\u003e(0.82,1.29)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"\"\u003e\n \u003cp\u003e0.97\u003c/p\u003e\n \u003cp\u003e(0.77,1.22)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"\"\u003e\n \u003cp\u003e1.12\u003c/p\u003e\n \u003cp\u003e(0.94,1.33)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"\"\u003e\n \u003cp\u003e1.08\u003c/p\u003e\n \u003cp\u003e(0.89,1.30)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"\"\u003e\n \u003cp\u003e1.22\u003c/p\u003e\n \u003cp\u003e(1.00,1.50)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"\"\u003e\n \u003cp\u003e1.18\u003c/p\u003e\n \u003cp\u003e(0.95,1.47)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"\"\u003e\n \u003cp\u003ePhysical activity (appropriate)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"\"\u003e\n \u003cp\u003eInappropriate\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"\"\u003e\n \u003cp\u003e1.07\u003c/p\u003e\n \u003cp\u003e(0.98,1.18)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"\"\u003e\n \u003cp\u003e1.15\u003c/p\u003e\n \u003cp\u003e(1.04,1.26)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"\"\u003e\n \u003cp\u003e1.06\u003c/p\u003e\n \u003cp\u003e(0.95,1.19)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"\"\u003e\n \u003cp\u003e0.99\u003c/p\u003e\n \u003cp\u003e(0.88,1.11)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"\"\u003e\n \u003cp\u003e1.06\u003c/p\u003e\n \u003cp\u003e(0.94,1.20)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"\"\u003e\n \u003cp\u003e1.00\u003c/p\u003e\n \u003cp\u003e(0.88,1.14)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"\"\u003e\n \u003cp\u003e1.19\u003c/p\u003e\n \u003cp\u003e(1.08,1.30)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"\"\u003e\n \u003cp\u003e1.13\u003c/p\u003e\n \u003cp\u003e(1.03,1.25)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"\"\u003e\n \u003cp\u003e1.29\u003c/p\u003e\n \u003cp\u003e(1.16,1.44)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"\"\u003e\n \u003cp\u003e1.19\u003c/p\u003e\n \u003cp\u003e(1.06,1.33)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"\"\u003e\n \u003cp\u003eSmoking (No)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"\"\u003e\n \u003cp\u003e1.26\u003c/p\u003e\n \u003cp\u003e(1.13,1.41)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"\"\u003e\n \u003cp\u003e1.14\u003c/p\u003e\n \u003cp\u003e(1.01,1.28)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"\"\u003e\n \u003cp\u003e0.85\u003c/p\u003e\n \u003cp\u003e(0.74,0.97)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"\"\u003e\n \u003cp\u003e0.96\u003c/p\u003e\n \u003cp\u003e(0.83,1.10)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"\"\u003e\n \u003cp\u003e0.89\u003c/p\u003e\n \u003cp\u003e(0.77,1.03)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"\"\u003e\n \u003cp\u003e0.94\u003c/p\u003e\n \u003cp\u003e(0.80,1.10)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"\"\u003e\n \u003cp\u003e0.92\u003c/p\u003e\n \u003cp\u003e(0.83,1.03)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"\"\u003e\n \u003cp\u003e1.13\u003c/p\u003e\n \u003cp\u003e(1.00,1.27)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"\"\u003e\n \u003cp\u003e0.88\u003c/p\u003e\n \u003cp\u003e(0.78,1.00)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"\"\u003e\n \u003cp\u003e1.13\u003c/p\u003e\n \u003cp\u003e(0.98,1.29)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"\"\u003e\n \u003cp\u003eDiabetes (No)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"\"\u003e\n \u003cp\u003e2.48\u003c/p\u003e\n \u003cp\u003e(2.20,2.81)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"\"\u003e\n \u003cp\u003e2.39\u003c/p\u003e\n \u003cp\u003e(2.09,2.73)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"\"\u003e\n \u003cp\u003e1.06\u003c/p\u003e\n \u003cp\u003e(0.92,1.22)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"\"\u003e\n \u003cp\u003e0.78\u003c/p\u003e\n \u003cp\u003e(0.67,0.92)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"\"\u003e\n \u003cp\u003e0.84\u003c/p\u003e\n \u003cp\u003e(0.72,0.99)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"\"\u003e\n \u003cp\u003e0.61\u003c/p\u003e\n \u003cp\u003e(0.51,0.73)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"\"\u003e\n \u003cp\u003e1.65\u003c/p\u003e\n \u003cp\u003e(1.44,1.89)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"\"\u003e\n \u003cp\u003e1.90\u003c/p\u003e\n \u003cp\u003e(1.63,2.20)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"\"\u003e\n \u003cp\u003e2.86\u003c/p\u003e\n \u003cp\u003e(2.30,3.55)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"\"\u003e\n \u003cp\u003e2.69\u003c/p\u003e\n \u003cp\u003e(2.14,3.38)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"\"\u003e\n \u003cp\u003eHypertension (No)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"\"\u003e\n \u003cp\u003e1.75\u003c/p\u003e\n \u003cp\u003e(1.60,1.92)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"\"\u003e\n \u003cp\u003e1.84\u003c/p\u003e\n \u003cp\u003e(1.65,2.04)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"\"\u003e\n \u003cp\u003e1.45\u003c/p\u003e\n \u003cp\u003e(1.30,1.61)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"\"\u003e\n \u003cp\u003e1.08\u003c/p\u003e\n \u003cp\u003e(0.95,1.23)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"\"\u003e\n \u003cp\u003e1.35\u003c/p\u003e\n \u003cp\u003e(1.20,1.53)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"\"\u003e\n \u003cp\u003e1.00\u003c/p\u003e\n \u003cp\u003e(0.86,1.16)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"\"\u003e\n \u003cp\u003e1.28\u003c/p\u003e\n \u003cp\u003e(1.16,1.40)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"\"\u003e\n \u003cp\u003e1.57\u003c/p\u003e\n \u003cp\u003e(1.40,1.74)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"\"\u003e\n \u003cp\u003e1.91\u003c/p\u003e\n \u003cp\u003e(1.70,2.15)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"\"\u003e\n \u003cp\u003e1.91\u003c/p\u003e\n \u003cp\u003e(1.67,2.18)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"\"\u003e\n \u003cp\u003eHistory of coronary heart disease (No)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"\"\u003e\n \u003cp\u003e1.18\u003c/p\u003e\n \u003cp\u003e(1.01,1.39)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"\"\u003e\n \u003cp\u003e1.05\u003c/p\u003e\n \u003cp\u003e(0.88,1.25)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"\"\u003e\n \u003cp\u003e0.74\u003c/p\u003e\n \u003cp\u003e(0.61,0.91)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"\"\u003e\n \u003cp\u003e0.56\u003c/p\u003e\n \u003cp\u003e(0.45,0.70)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"\"\u003e\n \u003cp\u003e0.71\u003c/p\u003e\n \u003cp\u003e(0.56,0.88)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"\"\u003e\n \u003cp\u003e0.52\u003c/p\u003e\n \u003cp\u003e(0.41,0.66)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"\"\u003e\n \u003cp\u003e1.25\u003c/p\u003e\n \u003cp\u003e(1.05,1.48)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"\"\u003e\n \u003cp\u003e1.58\u003c/p\u003e\n \u003cp\u003e(1.31,1.91)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"\"\u003e\n \u003cp\u003e1.59\u003c/p\u003e\n \u003cp\u003e(1.26,1.99)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"\"\u003e\n \u003cp\u003e1.61\u003c/p\u003e\n \u003cp\u003e(1.26,2.06)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"\"\u003e\n \u003cp\u003eHistory of stroke (No)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"\"\u003e\n \u003cp\u003e1.03 (0.74,1.43)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"\"\u003e\n \u003cp\u003e0.91 (0.64,1.28)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"\"\u003e\n \u003cp\u003e1.07\u003c/p\u003e\n \u003cp\u003e(0.71,1.62)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"\"\u003e\n \u003cp\u003e0.89\u003c/p\u003e\n \u003cp\u003e(0.58,1.39)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"\"\u003e\n \u003cp\u003e1.05 (0.68,1.63)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"\"\u003e\n \u003cp\u003e0.86 (0.54,1.36)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"\"\u003e\n \u003cp\u003e1.00 (0.70,1.39)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"\"\u003e\n \u003cp\u003e1.19 (0.84,1.70)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"\"\u003e\n \u003cp\u003e1.48\u003c/p\u003e\n \u003cp\u003e(0.91,2.40)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"\"\u003e\n \u003cp\u003e1.43\u003c/p\u003e\n \u003cp\u003e(0.87,2.34)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"\"\u003e\n \u003cp\u003eFamilial history of CVD (No)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"\"\u003e\n \u003cp\u003e1.26\u003c/p\u003e\n \u003cp\u003e(1.11,1.43)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"\"\u003e\n \u003cp\u003e1.26\u003c/p\u003e\n \u003cp\u003e(1.11,1.44)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"\"\u003e\n \u003cp\u003e1.11\u003c/p\u003e\n \u003cp\u003e(0.95,1.29)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"\"\u003e\n \u003cp\u003e1.06\u003c/p\u003e\n \u003cp\u003e(0.90,1.24)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"\"\u003e\n \u003cp\u003e1.10\u003c/p\u003e\n \u003cp\u003e(0.92,1.30)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"\"\u003e\n \u003cp\u003e1.06\u003c/p\u003e\n \u003cp\u003e(0.88,1.26)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"\"\u003e\n \u003cp\u003e1.23\u003c/p\u003e\n \u003cp\u003e(1.08,1.40)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"\"\u003e\n \u003cp\u003e1.18\u003c/p\u003e\n \u003cp\u003e(1.03,1.36)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"\"\u003e\n \u003cp\u003e1.29\u003c/p\u003e\n \u003cp\u003e(1.10,1.52)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"\"\u003e\n \u003cp\u003e1.20\u003c/p\u003e\n \u003cp\u003e(1.01,1.42)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"\" colspan=\"12\"\u003e\n \u003cp\u003eAdjusted for sex, age, and wealth index\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n \u003cp\u003e\u003cbr\u003e\u003c/p\u003e\n \u003cp\u003eDyslipidemia was more prevalent in obese people (88.1%; 95% CI: 86.8\u0026ndash;89.5), in residents of urban areas (81.1%, 95% CI: 80.1\u0026ndash;82.1), in those with inappropriate physical activity (83.3%; 95% CI: 82.2\u0026ndash;84.4), and patients with diabetes (91.4%, 95% CI: 89.7\u0026ndash;93.0) and hypertension (86.6%, 95% CI: 85.4\u0026ndash;87.8) (Table \u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003e). Compared to underweight people, those who were normal weight, overweight, and obese had 2.5 (2.0-3.3), 5.7 (4.4\u0026ndash;7.4), and 7.0 (5.3\u0026ndash;9.2) times higher odds of having dyslipidemia, respectively (Table \u003cspan class=\"InternalRef\"\u003e3\u003c/span\u003e). Residence in urban areas, Inappropriate physical activity, diabetes, and hypertension also increased the odds of having dyslipidemia by 1.1 (1-1.3), 1.2 (1.1\u0026ndash;1.3), 2.7 (2.1\u0026ndash;3.4), and 1.9 (1.7\u0026ndash;2.2) times.\u003c/p\u003e\n \u003cp\u003ePatients with a history of coronary heart disease and stroke also showed higher prevalence and odds for dyslipidemia. The prevalence in patients with a history of coronary heart disease was 86.4% (83.8\u0026ndash;89.0) and the odds ratio was 1.6 (1.3\u0026ndash;2.1) (Table \u003cspan class=\"InternalRef\"\u003e3\u003c/span\u003e). Dyslipidemia prevalence in patients with a history of stroke was also higher (85.9% in participants with positive history vs. 80.5 in participants with negative history); however, the difference was not significant due to the small number of cases (n\u0026thinsp;=\u0026thinsp;277). Those with familial history of CVDs also showed a slightly higher prevalence of dyslipidemia with an odds ratio of 1.2 (1.0-1.4). People with a different wealth index, fruit and vegetable consumption, and smoking status did not show any difference in the prevalence of dyslipidemia, albeit it was not the case for all kinds of lipid abnormalities (Table \u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003e, Table \u003cspan class=\"InternalRef\"\u003e3\u003c/span\u003e). The mean value of serum lipids in each population strata is demonstrated in Supplementary Table \u003cspan class=\"InternalRef\"\u003eS2\u003c/span\u003e.\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv class=\"Section2\" id=\"Sec8\"\u003e\n \u003ch2\u003eMixed Lipid Abnormalities\u003c/h2\u003e\n \u003cp\u003eMixed dyslipidemia which was defined as the coexistence of high LDL-C with at least one of the hypertriglyceridemia and low HDL-C, was prevalent in 13.6% of women and 11.4% of men (Table \u003cspan class=\"InternalRef\"\u003e4\u003c/span\u003e). In comparison, the isolated high LDL-C had a low prevalence, 3.6% (3.1\u0026ndash;4.1) in women and 3.9% (3.4\u0026ndash;4.5) in men. Among different mixed dyslipidemias, having high LDL-C and TG and Low HDL-C combined was the most prevalent in both sexes, with a prevalence of 7.4% (6.5\u0026ndash;8.2) in women and 6.5% (5.7\u0026ndash;7.4) in men.\u0026nbsp;\u003c/p\u003e\n \u003cp\u003eThose with higher age had higher odds of having all types of mixed dyslipidemias (Table \u003cspan class=\"InternalRef\"\u003e4\u003c/span\u003e). Moreover, higher BMI scores were associated with having high LDL-C and TG and Low HDL-C combined (OR\u0026thinsp;=\u0026thinsp;10.2 for normal weight, 15.9 for overweight, and 20.1 for obese participants). The same pattern was shown in those with low physical activity (OR\u0026thinsp;=\u0026thinsp;1.3; 95% CI: 1.0-1.5) and hypertension (OR\u0026thinsp;=\u0026thinsp;1.4; 95% CI: 1.1\u0026ndash;1.8). On the other side, a history of coronary heart disease was negatively associated with all types of mixed dyslipidemias. Having diabetes was also negatively associated with having high LDL-C and Low HDL-C combined (OR\u0026thinsp;=\u0026thinsp;0.3; 95% CI: 0.2\u0026ndash;0.5).\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv class=\"Section2\" id=\"Sec9\"\u003e\n \u003ch2\u003ePrevalence of Lipid Abnormalities by Province\u003c/h2\u003e\n \u003cp\u003eEast Azarbaijan, Ardabil, and Kohgiluyeh and Boyer-Ahmad had the highest prevalence of dyslipidemia among provinces, with 85.3% (81.5\u0026ndash;89.1), 84.6% (80.7\u0026ndash;88.6), and 84.5% (80.9\u0026ndash;88.0), respectively. On the other hand, Golestan with 68.5% (64.8\u0026ndash;72.2), Kerman with 74.1% (69.6\u0026ndash;78.6), and Razavi Khorasan with 74.3% (71.2\u0026ndash;77.4) had the lowest prevalence. However, the ranking of provinces by dyslipidemia prevalence was different in specified sex groups or areas of residence (Fig. \u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003e). The prevalence of lipid abnormalities was highly heterogeneous among provinces. Hypertriglyceridemia prevalence ranged from 50.0\u0026ndash;26.7%, hypercholesterolemia prevalence ranged from 24.4\u0026ndash;12.1%, high LDL-C prevalence ranged from 19.3\u0026ndash;10.4%, high non-HDL-C prevalence ranged from 24.1 to 12.0, and high HDL-C prevalence ranged from 74.4\u0026ndash;56.0%. Ardabil and East Azarbaijan were among the top ten provinces in the prevalence of all mentioned lipid abnormalities (high TC, TG, LDL-C, non-HDL-C, and low HDL-C). Provinces ranking in this regard was different in specified lipid abnormality and sex groups (Fig. \u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003e). Supplementary Table \u003cspan class=\"InternalRef\"\u003eS3\u003c/span\u003e demonstrates the prevalence of lipid abnormalities in different provinces of the country.\u003c/p\u003e\n \u003cp\u003e\u003cbr\u003e\u003c/p\u003e\n \u003cp\u003e\u003cbr\u003e\u003c/p\u003e\n\u003c/div\u003e"},{"header":"Discussion","content":"\u003cp\u003eThe study showed that 81 percent of the Iranian adult population had at least one serum lipid abnormality or use lipid-lowering medications. Whether young or old, men or women, resident in rural or urban areas, more than 60 percent of the Iranian adult population had low HDL-C cholesterol. The prevalence of dyslipidemia in the study was one of the highest across the globe; however, it was in accordance with the previous report of the STEPs 2016 \u003csup\u003e10\u003c/sup\u003e, and the MASHAD prospective cohort study \u003csup\u003e\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e\u003c/sup\u003e. Among studies with similar definition criteria for dyslipidemia, the prevalence of dyslipidemia in healthy populations was reported at 79% in India \u003csup\u003e\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e\u003c/sup\u003e, 78.7% in Turkey \u003csup\u003e\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e\u003c/sup\u003e, 75.7% in Jordan \u003csup\u003e\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e\u003c/sup\u003e, and 62.1% in northeastern China \u003csup\u003e\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e\u003c/sup\u003e. The prevalence of low HDL-C in the studies was 72.3% in India, 41.5% in Turkey, 40.7% in Jordan, and 8.8% in northeastern China. The high prevalence of dyslipidemia in Iran can be justified by the high consumption of dietary fats, obesity, and physical inactivity \u003csup\u003e\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e\u003c/sup\u003e. Inappropriate physical activity was reported in 54.7% of the Iranian adult population \u003csup\u003e\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e\u003c/sup\u003e. Obesity also has considerably increased in recent decades, and STEPs 2021 reported overweight/obesity in 63% of the adult population \u003csup\u003e\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e\u003c/sup\u003e. This study showed obese persons are 2.8 times more at risk for dyslipidemia than normal-weight adults. This strong association and growing trend of obesity could warn the health system about the upcoming higher burden of dyslipidemia in the country. The Low HDL levels shown in the study may also be due to the high consumption of carbohydrate-rich foods, such as refined grains, which are generally inexpensive and readily available \u003csup\u003e\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e\u003c/sup\u003e. It is shown that fat intake is not only associated with higher concentrations of TC and LDL-C but also with higher HDL-C and apolipoprotein A1 and lower triglycerides \u003csup\u003e\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e\u003c/sup\u003e. In case reduced intake of trans fat has been substituted with carbohydrates rather than proteins, it would be plausible to affect HDL-C and TG negatively \u003csup\u003e\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eOn the plus side, the prevalence of hypercholesterolemia and high LDL-C was low and was seen in 21.2% and 16.4% of participants, respectively. The prevalence of hypercholesterolemia in this study was much lower than 48.8% reported in Jordan, 43% reported in Turkey, and 33.5% reported in northeastern China \u003csup\u003e\u003cspan additionalcitationids=\"CR18\" citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e\u003c/sup\u003e. The prevalence of high LDL-C was reported at 40.7% in Jordan and 36.7% in Turkey \u003csup\u003e\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e,\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e\u003c/sup\u003e. A study from Spain, which used more sensitive criteria for high LDL-C than our study, reported a 23.3% prevalence for high LDL-C \u003csup\u003e\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e\u003c/sup\u003e. Efforts taken by the government may have contributed to controlling and lowering the total cholesterol and LDL-C of the population. As of 2000, the ministry of health restricted the amount of saturated fatty acids to a maximum of 25% and trans fatty acids to a maximum of 10% in all oil products. Moreover, Iran took action to increase public awareness about the hazards of saturated and trans fatty acids. Widespread statin prescription by general practitioners was also among the country's strategies to lower serum cholesterol and prevent CVDs \u003csup\u003e\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e\u003c/sup\u003e. Iranian national action plan targets on NCDs have included the two goals of receiving drug therapy and counseling to prevent heart attacks and strokes in at least 70% of eligible persons, and zero trans fatty acids in food \u0026amp; oily products to be achieved by 2025 \u003csup\u003e23\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eDespite all the efforts, comparing the results of STEPs 2016 and 2021 showed that the prevalence of dyslipidemia has been unchanged from 80.1% (95% CI: 79.4\u0026ndash;80.8) in 2016 to 81% (95% CI: 80.2\u0026ndash;81.9) in 2021. In the same period, the prevalence of hypertriglyceridemia increased from 26.7% (95% CI: 25.9\u0026ndash;27.5) to 39.7% (95% CI: 38.6\u0026ndash;40.8). This shortcoming may be attributed to the mentioned obesity epidemic and changes in dietary habits. Moreover, the awareness of patients on having dyslipidemia is shown to be as low as 20% in Iran, highlighting a gap between primary and secondary care \u003csup\u003e\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e\u003c/sup\u003e. Other reasons may be poor control of dyslipidemia despite increased medical therapy due to a lack of patients' or physicians' adherence to prescription and treatment guidelines, not optimally-dosed statin prescriptions, and not using combination therapies when necessary \u003csup\u003e\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e\u003c/sup\u003e. The COVID-19 epidemic may also impact the serum cholesterol control of the Iranian population \u003csup\u003e\u003cspan additionalcitationids=\"CR26\" citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e\u003c/sup\u003e. Due to the curtailment in routine outpatient laboratory testing, lipid-lowering medical therapy has been delayed, and drug shortages and misinformation may compromise adherence to these medications. Furthermore, the mobilization of the health workforce to combat COVID-19 could limit access to health care. From a societal perspective, the unprecedented contraction of social and economic activities has led to social isolation and decreased physical activity.\u003c/p\u003e \u003cp\u003eThe study showed almost 75% of persons with high LDL-C had a mixed pattern of dyslipidemia. Participants with higher BMI and low physical activity showed higher odds of having high LDL-C and TG and Low HDL-C combined. It is shown that mixed dyslipidemia can be resolved by combination drug therapy along with weight loss through diet and exercise, and consuming low saturated fats \u003csup\u003e\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e\u003c/sup\u003e. Unexpectedly, the participants with a history of coronary heart disease showed a lower prevalence of high LDL-C and mixed dyslipidemia. Similarly, diabetes did not show a positive association with dyslipidemia. These results could be justified considering that these groups may benefit from a higher rate of consuming lipid-lowering medications and more regular serum screening.\u003c/p\u003e \u003cp\u003eDyslipidemia was more prevalent in northwestern parts of the country (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). Iran is a country with great heterogeneity in cultural heritage and ethnicity. From a cultural view, it is shown that undergoing urbanization and westernization, and having a food culture providing a higher uptake of calories are associated with dyslipidemia. Ethnic diversity could also translate into substantial variation in the prevalence of dyslipidemia and plasma lipid levels between and within countries. Ethnicity also can affect response to statin therapy, which is related to genetic differences in the metabolism of statins \u003csup\u003e\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e\u003c/sup\u003e. However, research about such geographical differences is scarce in Iran, and the object needs further studies.\u003c/p\u003e \u003cp\u003eThe STEPs 2021 is the second population-based STEPs survey in Iran that represented a complete lipid profile of the Iranian adult population and is the first one that covers all provinces of the country in this regard. As a result of the COVID-19 pandemic specifications, the design and implementation of the survey with special considerations for COVID-19 protection and safety is the most significant achievement of this STEPs survey in Iran. The findings of our study, however, should be interpreted with an understanding of the limitation that dyslipidemia and other lipid abnormalities are defined differently in different studies; and it is important to carefully compare our results with those of others.\u003c/p\u003e \u003cp\u003ePoor metabolic health is a major concern in Iran, which is now a country with a population exceeding 80\u0026nbsp;million, mostly living in urban regions, in which the burden of non-communicable diseases is increasing. The substantial increase in the prevalence of overweight/obesity and especially among adolescents might soon lead to larger increases in diabetes and dyslipidemia \u003csup\u003e\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e\u003c/sup\u003e. Therefore, controlling dyslipidemia would be a milestone to maintain the declining rate of premature deaths due to cardiovascular diseases in the country and achieve a one-third reduction in mortality from NCDs by 2030 \u003csup\u003e30\u003c/sup\u003e. This target would not be accomplished without continuous data gathering and guided policymaking. In conclusion, the current nationwide study showed that although the prevalence of high cholesterol and LDL-C have a favorable trend, the prevalence remained high in the case of low HDL-C and aggravated in the case of high TG. Altogether, the prevalence of dyslipidemia remained unchanged from 2016 to 2021. Several modifiable risk factors such as obesity and inappropriate physical activity shown to be associated with dyslipidemia. Moreover, some intermediate- and high-risk groups such as patients having diabetes and hypertension would benefit from more public education and screening plans. Great achievements of the country's health system in controlling infectious diseases and decreasing child and adult mortality have been obtained through scattered and ad-hoc efforts of different governments and policymakers during the past decades \u003csup\u003e\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e\u003c/sup\u003e. To modify NCD risk factors and promote metabolic health in the country, action plans should come to action through a multi-sectoral and collaborative approach.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eEthics approval and consent to participate\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAll participants were informed about the methods and goals of the survey and the fact that participation was voluntary. All participants provided written informed consent. The final dataset was de-identified for analysis. The survey database was accessible only to the primary investigator and the database manager. This study was ethically approved by the National Institute for Health Research\u0026apos;s ethical committee (ID: IR.TUMS.NIHR.REC.1398.006), and was performed in accordance with the Declaration of Helsinki. As part of the survey, strict COVID-19 prevention guidelines were implemented during the pandemic for all participants and those involved in the survey/data-gathering step.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAvailability of data and materials\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe datasets used and/or analysed during the current study available from the corresponding author on reasonable request.\u0026nbsp;\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\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis study was supported by Ministry of Health and Medical Education and the National Institute for Health Research, Tehran University of Medical Sciences, Iran (grant No.241/M/9839). Authors affirm not having entered into an agreement with the funder that may have limited their ability to complete the research as planned and indicate that they have had full control of all primary data.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthors\u0026apos; contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eConceptualization, S.D., F.F.; Data Collection, A.D-M,\u003cspan dir=\"RTL\"\u003e\u0026nbsp;\u003c/span\u003e Y.F., A.K., M.Y., A.H., E.A.,; Data analysis, E.G., N.A.; Data visualization, J.K., E.G., N.A.; Writing \u0026ndash; Original Draft, J.K., S.M.H.,; Writing\u0026ndash;Review \u0026amp; Editing, M-M.R., S-H.G., M.A-Kh., M-R.M., M.A-Ka, Mo.N., S.R., S.A., N.F., Ma.N.; Resources, M-M.R., Ne.R, R.H., Na.R, F.F.; Supervision, Na.R., S.D., B.L., F.F. All authors have read and approved the manuscript prior to submission.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAcknowledgments\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors would like to thank the Ministry of Health and Medical Education and the National Institute for Health Research for the financial support of the study. The authors would like to appreciate the partnership of the Deputy for Research and Technology and the Deputy of Health of the Ministry of Health and Medical Education, the National Institute for Health Research, the World Health Organization, and many scholars and experts in related fields. Our gratitude goes out to participants from across the country for making this survey possible.\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eRoth, G. 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Global epidemiology of dyslipidaemias. \u003cem\u003eNat. Rev. Cardiol.\u003c/em\u003e \u003cstrong\u003e18\u003c/strong\u003e, 689\u0026ndash;700 (2021).\u003c/li\u003e\n\u003cli\u003eDanaei, G. \u003cem\u003eet al.\u003c/em\u003e Iran in transition. \u003cem\u003eLancet (London, England)\u003c/em\u003e \u003cstrong\u003e393\u003c/strong\u003e, 1984\u0026ndash;2005 (2019).\u003c/li\u003e\n\u003cli\u003eNCD Countdown 2030 collaborators. NCD Countdown 2030: worldwide trends in non-communicable disease mortality and progress towards Sustainable Development Goal target 3.4. \u003cem\u003eLancet (London, England)\u003c/em\u003e \u003cstrong\u003e392\u003c/strong\u003e, 1072\u0026ndash;1088 (2018).\u003c/li\u003e\n\u003c/ol\u003e"},{"header":"Table","content":"\u003cp\u003eTable 4 is available in the Supplementary Files section.\u003c/p\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"scientific-reports","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"scirep","sideBox":"Learn more about [Scientific Reports](http://www.nature.com/srep/)","snPcode":"","submissionUrl":"","title":"Scientific Reports","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Scientific Reports","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"","lastPublishedDoi":"10.21203/rs.3.rs-2677772/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-2677772/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eThe study aimed to estimate the prevalence of lipid abnormalities in Iranian adults by demographic characterization, geographical distribution, and associated risk factors using national and sub-national representative samples of the STEPs 2021 survey in Iran. In this population-based household survey, a total of 18,119 individuals aged higher than 25 years provided blood samples for biochemical analysis. Dyslipidemia was defined by the presence of at least one of the lipid abnormalities of hypertriglyceridemia (\u0026ge;\u0026thinsp;150 mg/dL), hypercholesterolemia (\u0026ge;\u0026thinsp;200 mg/dL), high LDL-C (\u0026ge;\u0026thinsp;130 mg/dL), and low HDL-C (\u0026lt;\u0026thinsp;50 mg/dL in women, \u0026lt;\u0026thinsp;40 mg/dL in men), or self-reported use of lipid-lowering medications. Mixed dyslipidemia was characterized as the coexistence of high LDL-C with at least one of the hypertriglyceridemia and low HDL-C. The prevalence of each lipid abnormality was determined by each population strata, and the determinants of abnormal lipid levels were identified using a multiple logistic regression model. The prevalence was 39.7% for hypertriglyceridemia, 21.2% for hypercholesterolemia, 16.4% for high LDL-C, 68.4% for low HDL-C, and 81.0% for dyslipidemia. Hypercholesterolemia and low HDL-C were more prevalent in women, and hypertriglyceridemia was more prevalent in men. The prevalence of dyslipidemia was higher in women (OR\u0026thinsp;=\u0026thinsp;1.8), obese (OR\u0026thinsp;=\u0026thinsp;2.8) and overweight (OR\u0026thinsp;=\u0026thinsp;2.3) persons, those residents in urban areas (OR\u0026thinsp;=\u0026thinsp;1.1), those with inappropriate physical activity (OR\u0026thinsp;=\u0026thinsp;1.2), patients with diabetes (OR\u0026thinsp;=\u0026thinsp;2.7) and hypertension (OR\u0026thinsp;=\u0026thinsp;1.9), and participants with a history (OR\u0026thinsp;=\u0026thinsp;1.6) or familial history of CVDs (OR\u0026thinsp;=\u0026thinsp;1.2). Mixed dyslipidemia prevalence was 13.6% in women and 11.4% in men (P\u0026thinsp;\u0026lt;\u0026thinsp;0.05). The prevalence of lipid abnormalities was highly heterogeneous among provinces, and East Azarbaijan with 85.3% (81.5\u0026ndash;89.1) and Golestan with 68.5% (64.8\u0026ndash;72.2) had the highest and lowest prevalence of dyslipidemia, respectively. Although the prevalence of high cholesterol and LDL-C had a descending trend in the 2016\u0026ndash;2021 period, the prevalence of dyslipidemia remained unchanged. There are modifiable risk factors associated with dyslipidemia that can be targeted by the primary healthcare system. To modify these risk factors and promote metabolic health in the country, action plans should come to action through a multi-sectoral and collaborative approach.\u003c/p\u003e","manuscriptTitle":"Plasma Lipid Profile of the Iranian Adult Population: Findings of the Nationally Representative STEPs Survey 2021","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2023-03-23 17:21:43","doi":"10.21203/rs.3.rs-2677772/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Major revision","date":"2023-06-28T16:59:47+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2023-06-27T10:21:43+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"0dfc508b-827f-4acf-aa7b-f2d30455bc15","date":"2023-06-25T12:38:02+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2023-04-19T21:42:22+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2023-04-03T07:36:28+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"2fbb6828-d84e-4597-b6a1-041e09756080","date":"2023-04-01T19:34:52+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"0b7b5eba-b7ad-4d32-8d26-bbea3385e3c4","date":"2023-03-23T12:08:02+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2023-03-20T11:21:49+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2023-03-20T10:43:32+00:00","index":"","fulltext":""},{"type":"editorInvited","content":"","date":"2023-03-20T10:17:06+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2023-03-20T10:14:12+00:00","index":"","fulltext":""},{"type":"submitted","content":"Scientific Reports","date":"2023-03-10T11:47:13+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"scientific-reports","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"scirep","sideBox":"Learn more about [Scientific Reports](http://www.nature.com/srep/)","snPcode":"","submissionUrl":"","title":"Scientific Reports","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Scientific Reports","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"dff9d683-2373-4b26-ae7f-350ec0d39ee8","owner":[],"postedDate":"March 23rd, 2023","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"published-in-journal","subjectAreas":[{"id":20048092,"name":"Health sciences/Endocrinology"},{"id":20048093,"name":"Health sciences/Risk factors"}],"tags":[],"updatedAt":"2023-09-25T15:05:03+00:00","versionOfRecord":{"articleIdentity":"rs-2677772","link":"https://doi.org/10.1038/s41598-023-42341-5","journal":{"identity":"scientific-reports","isVorOnly":false,"title":"Scientific Reports"},"publishedOn":"2023-09-19 15:01:46","publishedOnDateReadable":"September 19th, 2023"},"versionCreatedAt":"2023-03-23 17:21:43","video":"","vorDoi":"10.1038/s41598-023-42341-5","vorDoiUrl":"https://doi.org/10.1038/s41598-023-42341-5","workflowStages":[]},"version":"v1","identity":"rs-2677772","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-2677772","identity":"rs-2677772","version":["v1"]},"buildId":"WrCJVZZCHTDjtuVLN7oU0","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}