The Association of Healthy Behaviors and Metabolic Factors With Dyslipidemia Among Miao Adults: The China Multi-Ethnic Cohort (CMEC) Study

The Association of Healthy Behaviors and Metabolic Factors With Dyslipidemia Among Miao Adults: The China Multi-Ethnic Cohort (CMEC) Study | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article The Association of Healthy Behaviors and Metabolic Factors With Dyslipidemia Among Miao Adults: The China Multi-Ethnic Cohort (CMEC) Study Fang Nie, Ziyun Wang, Qibing Zeng, Han Guan, Jingyuan Yang, Peng Luo, and 3 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-110397/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 8 You are reading this latest preprint version Abstract Background: Behavioral and metabolic risk factors will increase the risk of dyslipidemia, the association of behaviors and metabolic factors with dyslipidemia among Miao adults are still unclear. Objective: To evaluate the association between behaviors, metabolic factors and dyslipidemia. Methods: Based on the CMEC study, a representative samples of 5,559 Miao participants aged 30 to 79 years old who were included in the baseline survey from 2018 to 2019 were analyzed. A binary logistic regression model was utilized to evaluate the odds ratios (OR) and 95% confidence intervals (CI) of the associations of healthy behaviors and metabolic factors with dyslipidemia. Results: In both sexes, only a small percentage of females with ideal levels of waist-to-hip ratio (WHR) (27.2%). However, participants were more likely to have ideal levels of fasting blood glucose (FBG). In addition, males with dyslipidemia had poor levels of body mass index (BMI) (60.3%), WHR (59.2%) and FBG (65.8%). While females with dyslipidemia had poor levels of FBG (60.0%). Notably, our study found that WHR, BMI, FBG, and blood pressure were major risk factors for almost all dyslipidemia components. Conclusions: The rate of dyslipidemia cannot be ignored, particularly high TG levels. In addition, healthy behaviors and metabolic factors, especially WHR, BMI, FBG levels, and blood pressure were significantly associated with dyslipidemia, which may have become a major challenge to public health problems in the ethnic minority areas of Guizhou. Health Economics & Outcomes Research Health Policy Dyslipidemia Cardiovascular diseases Healthy behavior Metabolic factors Ethnic minorities. Figures Figure 1 Figure 2 Figure 3 Background Due to its high incidence, disability rates and mortality rates, CVD has become the leading cause of death in the global, and it has become a public health problem that urgently needs to be overcome. Studies have found that dyslipidemia is an independent risk factor for CVD [1] . Dyslipidemia characterized by elevated total cholesterol (TC) or LDL-C levels is generally considered to be a major risk factor for atherosclerosis [2] . According to the latest data from the WHO, more than 50% of the global incidence of coronary heart disease is related to the increased TC levels. The American Heart Association (AHA) has long recognized that elevated levels of certain lipid components are important markers of CVD risk. However, there are relatively few studies on lipid components among ethnic minorities in southwest China. Given the rapid prevalence trend of high TC levels, high TG levels and other lipid phenotypes in Chinese individuals, it is of great importance to explore the typical risk factors of lipid components in people with different characteristics for the control of CVD. Recently, due to the prevalence of risk factors such as physical activity deficiency, smoking, obesity and hypertension, the incidence of dyslipidemia is increasing at an alarming speed and showing a younger trend [3] . Research reported that approximately 64.4% of persons in china have at least one type of dyslipidemia [4] . The increasing westernization of chinese dietary patterns (i.e., high cholesterol dietary intake) contributed to this phenomenon to a large extent. In china, the form of prevention and control of dyslipidemia is severe. The release of the Healthy China Action (2019-2030) will further strengthen the management of blood lipid levels in residents aged 35 and above. Therefore, it is a priority to put forward targeted prevention and control measures based on different characteristics of the population. The blood lipid level is linearly related to the progression of atherosclerosis. The risk of cardiovascular events is reduced by 21% for every 1mmol/L decrease in LDL-C levels [5] , which can effectively control dyslipidemia and identify potential modifiable risk factors of dyslipidemia that are essential to reducing the risk of CVD. The blood lipid level is closely related to behavior and metabolic factors, and the superposition and complex interaction of life behavior and metabolic factors are important reasons for the disorder of blood lipid metabolism [6, 7] . Several studies have pointed out that poor behaviors and metabolic factors can increase the levels of TC, TG, and LDL-C and reduce the concentration of high-density lipoprotein cholesterol (HDL-C) [8] . Healthy behaviors and metabolic factors are essential primary preventive measures for disease. Smoking cessation, weight loss and blood sugar reduction have a profound effects on dyslipidemia. Studies have shown that changing poor behaviors and metabolic factors can reduce dyslipidemia as effectively as clinical drug therapy [9] . However, although healthy behaviors and metabolic factors were negatively correlated with the disease risk [10, 11] , but the proportions of healthy behaviors and metabolic factors that meet the ideal levels were extremely low [12] . Guizhou is the main settlement of the Miao nationality in southwest China. In the Sixth Population Census, the Miao populations in Guizhou accounted for 42.1% of the national Miao populations [13] . Due to the influence of many factors such as the natural geographical environment, ethnic beliefs, and traditional customs, the Miao residents still maintain simple and primitive customs and traditional cultural concepts, and have a unique way of lifestyles (such as drinking customs and dietary structure), which are obviously different from the Han and other ethnic minorities [14] . Previous studies on lifestyle and dyslipidemia have been reported, but there have still been few large-scale studies of Miao adults in southwest China, and almost no studies had fully explored the abnormalities in lipid components. In addition, there had been few comprehensive studies on healthy behaviors and metabolic factors in recent years, and the evidence of their association with dyslipidemia is limited. Thus, the associations between healthy behaviors, metabolic factors and dyslipidemia in this special ethnic group remains to be clarified. Therefore, the purpose of this study is to systematically explore the relationships between healthy behaviors, metabolic factors and dyslipidemia by using baseline data from CMEC project sites in Guizhou, in order to provide more constructive information for the prevention and management of dyslipidemia and even CVD of ethnic minority areas in Southwest China. Materials And Methods Study participants The CMEC study is a large-scale prospective cohort study based on community population in five provinces of southwest China, Guizhou, Yunnan, Sichuan, Chongqing and Tibet. From May 2018 to September 2019, 99,556 members aged 30 to 79 years old (Tibetan populations include those aged 18 to 30 years) were recruited from ethnic minority communities for a baseline survey. Further details are available elsewhere [15] . In our study, participants has to meet following inclusion criteria: (i) aged 30~79 years on the day of the investigation; (ii) three generations of direct relatives who were permanent residents of the Miao nationality (duration of residence≧6months); (iii) capability of completing baseline surveys and the follow-up study; (iv) no mental disorders and other related diseases. The exclusion criteria were as follows: the participants were excluded if they were pregnant or had missing data (smoking history, drinking history, blood pressure, etc), fasting time < 8 hours and those who taking any antihyperlipidemic drugs. A multi-stage stratified cluster sampling method was used. According to the characteristics of ethnic minorities in Guizhou, the Miao and Dong Autonomous Prefecture of Qiandongnan and the Bouyei and Miao Autonomous Prefecture of Qiannan were selected from 3 minority autonomous prefectures as investigation areas. From the Qiandongnan and Qiannan Prefectures, Kaili City, Liping County, and Libo County were selected as secondary sampling units. Ultimately, 5,559 subjects were recruited in the present analyses. Electronic questionnaire An application (CMEC App) developed by the CMEC project team was used to collect questionnaire information through tablets. The questionnaire assessed personal identification information, social demographic characteristics, behaviour patterns (e.g., smoking, drinking and physical activity) and health status. The questionnaire information was collected by trained local medical college students using tablets through face-to-face interviews. Participants were required to bring a second- generation ID card or household register to the designated site to participate in the questionnaire survey. Medical examinations Measures included height (cm), weight (kg), waist circumference (cm), hip circumference (cm), and blood pressure (mmHg). Participants were required to fast before medical examinations. Blood pressure was measured by an ohmic electronic sphygmomanometer with an interval of 5 minutes between each measurement, and a total of 3 measurements. The analysis was based on the average values of the three blood pressure readings. For the height measurement, subjects were told to wear light clothes, with their hat off, standing barefoot and to keep their bodies upright when measured using an ultrasonic height measuring instrument. For weight measurement, the subjects removed heavy clothes and stood barefoot in the centre of the weighing scales. Waist circumference was measured approximately 1 cm above the navel with a soft tape, and the hip circumference was measured at the maximum extension of the hip, circling the soft tape around the hip for a week and closing to the skin for reading. Clinical laboratory tests Fasting venous blood was collected by professional nurses from participants who had fasted for 8 hours. Next, the blood samples were centrifuged and sub-packaged, refrigerated at 4℃ and sent to the JinYu Medical Laboratory Center, Guizhou Province. Finally, biochemical indexes, i.e., FBG, TC, TG, HDL-C and LDL-C, were assessed by an automatic biochemical instrument (Model: P800, Roche, Switzerland). Definition of dyslipidemia According to the guidelines for the prevention and treatment of dyslipidemia in Chinese adults [16] , dyslipidemia was defined as abnormality in any of the four indicators of blood lipids: TC≧6.22mmol/L, TG≧2.26mmol/L, LDL-C≧4.14 mmol/L，and HDL-C< 1.04mmol/L. Assessment of behavioral and metabolic factors Based on the questionnaire assessment of smoking history, participants were divided into non-smoker, previous smoker (smoking cessation ≧1 year), and currently smoker (so far more than 100 cigarettes) groups. Based on the self-report of the frequency of alcohol consumption in participants over the past year, participants were divided into non-drinker or almost non-drinker, occasional drinker and regular drinker. Individual physical activity was assessed through the sum of the metabolic equivalents task (MET) of occupational and non-occupational physical activity. Sleep duration was assessed based on average daily sleep duration (excluding lunch break). WHR was calculated as a person’s waist circumference divided by the hip circumference. BMI was defined as a person’s weight in kilograms divided by the square of the height in meters (kg/m 2 ). Classification of healthy behaviors and metabolic factors With reference to the healthy lifestyle behavior proposed by the AHA and combined with the characteristics of Chinese behavior, the definitions of healthy behavior and metabolic factors are shown in Table 1. A single index was respectively assigned the values 2, 1 and 0 for ideal, intermediate and poor, and a score of 0~16 was assessed for overall healthy behaviors and metabolic factors. Then, the scores were divided into three levels, including ideal (11~16), intermediate (9~10), poor (0~8). Statistical analysis Calculations of the distribution of participants' social demographic characteristics used descriptive statistical methods for the stratification of dyslipidemia. In addition, considering the characteristics of male and female behaviour patterns, we also described the dyslipidemia of participants with different healthy behaviors and metabolic factors based on gender stratification. The age-standardized rate of dyslipidemia was calculated according to the data of the sixth census of Guizhou in 2010. Categorical variables were expressed as n(%). The chi-square( X 2 ) test was performed to assess the differences between the groups with each categorical variable. Based on gender stratification, a binary logistic regression model was used to analyze the OR and 95% CI of different healthy behaviors and metabolic factors indicators associated with dyslipidemia. The dependent variables of binary logistic regression models respectively were high TC, high TG, high LDL-C or low HDL-C levels. The independent variables included smoking history, drinking of alcohol, physical activity, sleep duration, WHR, BMI, FBG levels, and blood pressure. Covariates for model adjustment included age, residence, educational, and occupation. SPSS 22.0 and R 4.0.2 software were used for statistical analysis. A P value of less than 0.05 was considered to be significant. Results Baseline characteristics of the Miao participants Among the 5,559 Miao participants recruited, 5,032 (90.5%) had a complete lifestyle assessment and completed a physical examination. Table 2 shows the social demographic characteristics of the participants. The average age of males and females was (53.3±12.0) years old, (50.9±11.0) years old. Except for marital status, the differences in dyslipidemia among different social demographic variables were statistically significant (all P value < 0.05). The dyslipidemia rate of the participants was 32.8% (age-standardized rate of 23.3%). In addition, individuals with dyslipidemia were more likely to be males, manager or professional technology fields, higher level of education and urban residents (all P value< 0.05). Dyslipidemia in Participants with different healthy behaviors and metabolic factors by gender Table 3 show, males were more likely to have poor levels of blood pressure, BMI, and become current smoker, and alcohol drinker. And males with dyslipidemia were had poor levels of blood pressure, WHR, FBG, BMI, especially with poor BMI (60.3%), poor FBG (65.8%) levels of dyslipidemia were more outstanding. Conversely, females tended to have poor levels of WHR and BMI. Females with dyslipidemia were had poor levels of FBG (60.0%). Moreover, the trend chi-square test showed that the rate of dyslipidemia increased with the decrease of the level in healthy behaviors and metabolic factors classification ( P value ≦0.05), except for sleep duration, smoking history, drinking of alcohol, physical activity. Figure 1 shows that the abnormal rate of blood lipid components according to the grading of overall healthy behaviors and metabolic factors scores. After stratification, the abnormal rate of blood lipid components increased with the decrease in overall healthy behaviors and metabolic factors score. And abnormal rate of blood lipid components in men fluctuated greatly with the score of overall healthy behaviors and metabolic factors. Abnormal lipid components in participants with different healthy behaviors and metabolic factors As shown in Table 4, the rate of abnormal TG levels in individuals with adverse healthy behaviors and metabolic factors was higher, followed by the rate of abnormal TC levels, among all the lipid components. Relationship between healthy behaviors, metabolic factors and dyslipidemia components Upon gender stratification, the associations of healthy behaviors and metabolic factors with dyslipidemia components are shown in Figure 2 and Figure 3. WHR was the major risk factor of dyslipidemia in men, with the strongest correlation between WHR and high LDL-C levels (adjusted OR=3.11, 95% CI=1.89-5.11). BMI was the main risk factor for abnormal TG and HDL-C levels, and the risk of high TG and low HDL-C levels increased as BMI increased. Participants with poor blood pressure levels were at higher risk of high TC (adjusted OR=1.82, 95% CI=1.11-3.00) and high TG levels (adjusted OR=1.78, 95% CI=1.27-2.50) than those with normal blood pressure levels. This study did not observe an association between FBG and LDL-C levels ( P value >0.05). Smoking history was independently associated with HDL-C, and men with poor smoking behaviors had a higher risk of low HDL-C (adjusted OR=1.48, 95% CI=1.08-2.03). Men who drank occasionally (adjusted OR=1.63, 95% CI=1.20-2.22) and who drank often (adjusted OR=1.73, 95% CI=1.29-2.32) had a higher risk of high TG levels. Notably, there was a negative correlation between alcohol consumption and LDL-C levels. A similar phenomenon has been observed in women, in that BMI was independently associated with high TG and low HDL-C levels, and the risk of high TG and low HDL-C levels increased with the increased of BMI. However, FBG levels may be a typical risk factor for dyslipidemia in Miao women. With the exception of HDL-C levels, the increased risk of high TG, high TC and high LDL-C levels increased with the increase of FBG levels. With the increase of blood pressure, the risk of high TC and high TG gradually increased, while the risk of high LDL-C levels was found only in the adverse blood pressure level was found. WHR had the strongest correlation with HDL-C levels (adjusted OR=3.04, 95% CI=1.72-5.40). Furthermore, compared with ideal physical activity, those who with moderate physical activity had a higher risk of low HDL-C (adjusted OR=1.59, 95% CI=1.04-2.42). Discussion This study provided a systematic analysis of the association of healthy behaviors and metabolic factors with the risk of dyslipidemia. The results showed that Miao adults had a large proportions of poor levels of WHR and blood pressure, especially the WHR, which may be related to the tendency of body fat to accumulate in the abdomen. Furthermore, individuals with dyslipidemia had poor levels of WHR, BMI, FBG levels and blood pressure, which may be the reasons for the increased burden of CVD in minority populations in recent years, which deserves increased attention. Strikingly, BMI, WHR, FBG, and blood pressure showed strong associations with dyslipidemia. In contrast, certain behaviors (smoking, alcohol consumption and physical activity) were weakly associated with dyslipidemia. These results revealed that the management of obesity, hypertension and hyperglycemia should be important entry points for the intervention of dyslipidemia components. Compared with the studies of Guangxi, Jiangsu, Beijing and other locations [17-20] , dyslipidemia and its abnormal lipid components among Miao adults were relatively low, the reasons for which may be due to differences in genetic background, socioeconomic level and lifestyle of the subjects. Guizhou belongs to a mountainous karst landform plateau, and the ethnic minority residents live in a special environment. Their diet culture has distinct regional characteristics (sour soup), a unique primitive lifestyle (manual batik and ethnic embroidery), and the level of economic development was limited, which may be one of the reasons for the low rate of dyslipidemia in this region. The abnormal rates of lipid components of Miao residents were significantly different: the main types were high TG (21.8%), which was consistent with other studies in China [21, 22] . Epidemiological studies have suggested that elevated TG levels are associated with increased risk of CVD [23] . Therefore, the monitoring of TG levels should be strengthened to reduce the burden of CVD in Miao adults. We explored the relationship between healthy behaviors, metabolic factors and dyslipidemia components according to gender. In both sexes, the results showed that the influencing factors of dyslipidemia were not identical. Overall, after adjusting for potential confounding factors, BMI, WHR, FBG levels and blood pressure were the most typical risk factors for dyslipidemia components, as confirmed in many previous studies [24, 25] . Obesity, diabetes and hypertension are prominent metabolic risk factors for lipid metabolic disorders. Dyslipidemia caused by these indicators has been demonstrated to be closely related to the progression of atherosclerotic diseases [26] . At the same time, these metabolic factors have been identified as important biomarkers for the screening of dyslipidemia. Therefore, to further strengthen the management of dyslipidemia, individuals with adverse metabolic factors should be paid more attention, suggesting that prevention and control strategies for dyslipidemia should be formulated. This study found that males with current smoking were at higher risk of low HDL-C levels, consistent with other findings [27, 28] . Previous studies have reported that smoking increased TC and TG levels and decreased HDL-C level compared with non-smokers [29] , while our study only observed the association between smoking and HDL-C levels. Our study also found that males with regular drinking were at greater risk of high TG levels. However, similar result was not observed in women. The association may be underestimated because this study assessed that alcohol consumption was classified only according to the frequency of drinking and lacked a specific level of alcohol intake analysis. Furthermore, we found that alcohol consumption was negatively correlated with LDL-C in men, which may be because wine with rice and glutinous rice as raw materials was self-brewed by Miao residents. The low alcohol content of self-brewed wine had no significant effect on blood lipid levels, and it may also be related to the genetic susceptibility of ethnic minorities. In short, the association between alcohol consumption and blood lipids of Miao adults should be further explored. Previous study reported that as physical activity decreased, the risk of dyslipidemia increased, and active physical activity was associated with improved blood lipid levels [30] . Except for HDL-C, we did not observe that physical activity was significantly associated with any lipid components, which may be related the evaluations of individual physical activity through the metabolic equivalent of total physical activity in our study, and did not distinguish the correlation between physical activity in specific fields and blood lipids. Therefore, subsequent research can further explore the association between physical activity in specific fields and lipid components. Shigeki Kinuhata observed a positive correlation between sleep duration and high TG levels [31] . Zhan found that sleep duration was significantly associated with risk of dyslipidemia in women, but not in men [32] . However, in both sexes, there was no association of sleep duration with any lipid components was observed in our study (all P value >0.05). It may be that as the level of economic development in this region was limited, minority participants still followed the traditional habits of rest; thus, the vast majority of participants with normal sleep duration may have no obvious effect on lipid components. However, the explanation of this phenomenon remains to be further studied. With the transformation of social and economic development, dyslipidemia is increasing at an alarming speed, and it has become a huge challenge for the health promotion. This study revealed that individuals with poor behaviors and metabolic factors have a relatively high risk for dyslipidemia. Thus, the prevention of dyslipidemia may benefit from interventions for behaviors and metabolic risk factors. Additionally, it is suggested that populations with unhealthy lifestyles should be continuously monitored and managed to control dyslipidemia as effectively as possible to improve the health level of ethnic minority groups. Despite the following limitations, our study was unique. First，to our knowledge, this was the first large-scale cohort study focusing on ethnic minority groups in China. Second, medical examinations were conducted by professional medical staff according to strict criteria rather than self-reporting by participants. However, several limitations needed to be considered. First, considering the influence of potential confounding factors, this study adjusted more covariates as much as possible to control confounding factor interference. Second, studies on the genetic susceptibility to dyslipidemia in ethnic minorities were scarce, and further research support was needed. Furthermore, we excluded participants with aged beyond 30 to 79 who might have missed information on early life exposures. Thus, subsequent research may consider expanding the age range of participants. Finally, our study was based on cross-sectional data analysis to confirm that the causal capacity was limited, which needs to be further supplemented by follow-up data. Conclusions The dyslipidemia rate of Miao adults in Guizhou was lower than the national average (40.40%), but the overall situation was still not optimistic, especially regarding the high TG abnormal. Therefore, continuous monitoring of blood lipid levels among ethnic minorities are essential. Furthermore, this study has identified the typical risk factors for dyslipidemia components in Miao adults. These findings are extremely critical for identifying target groups with high risk factors to implement screening dyslipidemia. Given the worldwide prevalence of dyslipidemia, present findings have important public health significance for the prevention and control of dyslipidemia in ethnic minorities. Abbreviations CMEC:The China Multi-Ethnic Cohort Study; CVD: Cardiovascular diseases; OR: Odds ratios; CI: Confidence intervals; WHR: Waist-to-hip ratio; FBG: Fasting blood glucose; BMI: Body mass index; TG: Triglycerides; LDL-C: Low-density lipoprotein cholesterol; TC: Total cholesterol; AHA: American Heart Association; HDL-C: High-density lipoprotein cholesterol. Declarations Acknowledgements Particularly thanks to the Guiyang city Center for Disease Control and Prevention, the Affiliated Hospital of Guizhou Medical University and local governments to assist and support the field investigation work. Appreciation is expressed to the CMEC team members for conceiving and designing this research and their great contributions to the initiation of the project. Authors’ contributions Each author has been involved in and contributed to this paper. F.N. carried out the statistical analysis, collected the data and wrote the manuscript. F.H., P.L. contributed to the study design of this paper. W.D., Y.W., H.G., Y.Y. and B.Z. participated in the data collection, study management and coordination. H.W. contributed to guide the paper and correcting the English. All author read and approved the final manuscript. Funding This work was supported by the National key Research and Development Program "Precision Medicine Research" in 2017 (N0.2017YFC0907301). The funder played no role in the design of the study and collection, analysis, and interpretation of data and in writing the manuscript. Availability of data and materials Currently, the database used to support this study are not freely available in view of participants’ privacy protection but are available from the corresponding author on reasonable data request. Researchers interested in our study could contact the corresponding author Dr.Feng Hong ( [email protected] ) who will review the data request. Ethics approval and consent to participate This study protocol was conducted in accordance with the principles of the Declaration of Helsinki and was approved by the Medical Ethical Committee of Sichuan University (K2016038) and the Ethics Committee of the Affiliated Hospital of Guizhou Medical University (2018[094]). And written informed consent was obtained from each subject. Consent for publication All authors read and approved to publication. Competing interests The authors declare that they have no competing interests. Author affiliations 1 School of Public Health, The Key Laboratory Of Environmental Pollution Monitoring and Disease Control, Ministry of Education, Guizhou Medical University, Guiyang 550025, China. 2 School of Public Health, Guizhou Medical University, Guiyang, 550025, China. References Yusuf S, Bosch J, Dagenais G. Cholesterol Lowering in Intermediate-Risk Persons Without Cardiovascular Disease . J. Vasc. 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Epidemiol. 1999; 15: 341-8.http://www.ncbi.nlm.nih.gov/entrez/query.fcgicmd=Retrieve&db=pubmed&dopt=Abstract&list_uids=10414374&query_hl=1. Moradinazar M, Pasdar Y, Najafi F, Shahsavari S, Shakiba E, Hamzeh B, et al. Association between dyslipidemia and blood lipids concentration with smoking habits in the Kurdish population of Iran . BMC Public Health . 2020; 20. https://doi.org/10.1186/s12889-020-08809-z. Zhou J, Zhou Q, Wang DP, Zhang T, Wang HJ, Song Y, et al. Associations of sedentary behavior and physical activity with dyslipidemia . Beijing Da Xue Xue Bao Yi Xue Ban . 2017; 49: 418-423. http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?cmd=Retrieve&db=pubmed&dopt=Abstract&list_uids=28628141&query_hl=1. Kinuhata S, Hayashi T, Sato KK, Uehara S, Oue K, Endo G, et al. Sleep duration and the risk of future lipid profile abnormalities in middle-aged men: the Kansai Healthcare Study . Sleep Med. 2014; 15: 1379-1385. https://doi.org/10.1016/j.sleep.2014.06.011 . Zhan Y, Chen R, Yu J. Sleep duration and abnormal serum lipids: the China Health and Nutrition Survey . Sleep Med. 2014; 15: 833-839. https://doi.org/10.1016/j.sleep.2014.02.006. Tables Table 1 Definition of healthy behaviors and metabolic factors Variables Ideal Intermediate Poor Smoking history Non-smoker Previous smoker (smoking Current smoker cessation ≧1 year) Drinking of alcohol Never/nearly non-drinker Occasional drinker Regular drinker Sleep duration, hr 7～8 5～6 or 9～10 10 BMI, kg/m2 < 24 24～27.9 ≥28 WHR < 0.90 (Male) 0.90～0.94 (Male) ≥0.95 (Male) or < 0.85 (Female) or 0.85～0.89 (Female) or ≥0.90 (Female) Blood pressure, mmHg Non-drug therapy < 120/80 120～139/80～89 or ≥140/90 taking anti-hypertensive drugs< 140/90 FBG, mmol/L Non-drug therapy < 5.6 5.6～6.9 or ≥7.0 taking anti-diabetic drugs < 5.6 Physical activity, (MET-hr/day) ≥ P50 P25～P50 < P25 BMI body mass index, FBG fasting blood glucose, WHR waist-to-hip ratio, MET metabolic equivalent task. Table 2 Social demographic characteristics of participants Variables Total (n=5032) Normal (n=3379) Dyslipidemia (n=1653) χ 2 P value Gender (%) 114.7 < 0.001 Male 1845 (36.7) 1067 (57.8) （） 778 (42.2) Female 3187 (63.3) 2312 (72.5) 875 (27.5) Age (years, %) 6.2 0.013 30 -39 862 (17.2) 647 (75.1) 215 (24.9) 40 -49 ac 1478 (29.4) 994 (67.3) 484 (32.7) 50 -59 abde 1396 (27.7) 849 (60.8) 547 (39.2) 60 -69 ac 893 (17.7) 597 (66.9) 296 (33.1) 70 -79 c 403 (8.0) 292 (72.5) 111 (27.5) Residence (%) 18.8 < 0.001 Rural 3584(71.2) 2472 (69.0) 1112 (31.0) Urban 1448（28.8) 907 (62.6) 541 (37.4) Educational level (%) 14.7 < 0.001 No formal school 2190 (43.5) 1504 (68.7) 686 (31.3) Primary school acde 687 (13.7) 475 (69.1) 212 (30.9) Middle school bde 1004 (20.0) 696 (69.3) 308 (30.7) High School or college abc 884 (17.5) 544 (61.5) 340 (38.5) University or above abc 267 (5.3) 160 (59.9) 107 (40.1) Occupation (%) 8.3 0.004 Farmers 1767 (35.1) 1255 (71.0) 512 (29.0) Workers ade 400 (7.9) 258 (64.5) 142 (35.5) Administration or manager ae 165 (3.3) 97 (58.8) 68 (41.2) Specialist ae 441 (8.8) 257 (58.3) 184 (41.7) Other occupations abcd 2259 (44.9) 1512 (66.9) 747 (33.1) Marital status (%) Married or cohabiting 4367 (86.8) 2917 (66.8) 1450 (33.2) 1.03 0.310 Separated or divorced 183 (3.6) 128 (69.9) 55 (30.1) Widowed 427 (8.5) 301 (70.5) 126 (29.5) Never married 55 (1.1) 33 (60.0) 22 (40.0) Family income (Yuan/year, %) 5.1 0.024 < 20,000 2101 (41.7) 1446 (68.8) 655 (31.2) 20,000 -59,999 1650 (32.8) 1098 (66.5) 552 (33.5) 60,000 -99,999 763 (15.2) 500 (65.5) 263 (34.5) ≧100,000 518 (10.3) 335 (64.7) 183 (35.3) abcde , respectively, represent pairwise comparisons: compared with the first group, P < 0.05; compared with the second group, P < 0.05; compared with the third group, P < 0.05; compared with the fourth group, P < 0.05; compared with the fifth group, P < 0.05. Table 3 Different healthy behaviors and metabolic factors of dyslipidemia by gender Healthy behaviors and Male Female metabolic factors Total Normal Dyslipidemia P value Total Normal Dyslipidemia P value Smoking history (%) 0.604 0.212 Ideal 780 (42.2) 454 (58.2) 326 (41.8) 3153 (98.9) 2284 (72.4) 869 (27.6) Intermediate 184 (10.0) 111 (60.3) 73 (39.7) 6 (0.2) 5 (83.3) 1 (16.7) Poor 881 (47.8) 502 (57.0) 379 (43.0) 28 (0.9) 23 (82.1) 5 (17.9) Drinking of alcohol (%) 0.008 0.130 Ideal 541 (29.3) 351 (64.9) 190 (35.1) 1710 (53.7) 1212 (70.9) 498 (29.1) Intermediate 709 (38.4) 378 (53.3) 331 (46.7) 1172 (36.7) 882 (75.3) 290 (24.7) Poor 595 (32.3) 338 (56.8) 257 (43.2) 305 (9.6) 218 (71.5) 87 (28.5) Physical activity (%) 0.962 < 0.001 Ideal 948 (51.4) 555 (58.5) 393 (41.5) 1568 (49.2) 1188 (75.8) 380 (24.2) Intermediate 445 (24.1) 243 (54.6) 202 (45.4) 813 (25.5) 579 (71.2) 234 (28.8) Poor 452 (24.5) 269 (59.5) 183 (40.5) 806 (25.3) 545 (67.6) 261 (32.4) WHR (%) < 0.001 < 0.001 Ideal 788 (42.7) 570 (72.3) 218 (27.7) 866 (27.2) 734 (84.8) 132 (15.2) Intermediate 462 (25.0) 254 (55.0) 208 (45.0) 722 (22.7) 550 (76.2) 172 (23.8) Poor 595 (32.3) 243 (40.8) 352 (59.2) 1599 (50.1) 1028 (64.3) 571 (35.7) BMI (%) < 0.001 < 0.001 Ideal 782 (42.4) 577 (73.8) 205 (26.2) 1239 (38.9) 1013 (81.8) 226 (18.2) Intermediate 776 (42.0) 376 (48.5) 400 (51.5) 1317 (41.3) 921 (69.9) 396 (30.1) Poor 287 (15.6) 114 (39.7) 173 (60.3) 631 (19.8) 378 (59.9) 253 (40.1) FBG (%) < 0.001 < 0.001 Ideal 1226 (66.5) 777 (63.4) 449 (36.6) 2475 (77.7) 1899 (76.7) 576 (23.3) Intermediate 458 (24.8) 235 (51.3) 223 (48.7) 577 (18.1) 359 (62.2) 218 (37.8) Poor 161 (8.7) 55 (34.2) 106 (65.8) 135 (4.2) 54 (40.0) 81 (60.0) Blood pressure (%) < 0.001 < 0.001 Ideal 391 (21.2) 269 (68.8) 122 (31.2) 1292 (40.5) 1054 (81.6) 238 (18.4) Intermediate 741 (40.2) 433 (58.4) 308 (41.6) 1121 (35.2) 783 (69.8) 338 (30.2) Poor 713 (38.6) 365 (51.2) 348 (48.8) 774 (24.3) 475 (61.4) 299 (38.6) Sleep duration (%) 0.642 0.047 Ideal 904 (49.0) 525 (58.1) 379 (41.9) 1675 (52.6) 1231 (73.5) 444 (26.5) Intermediate 776 (42.1) 438 (56.4) 338 (43.6) 1304 (40.9) 945 (72.5) 359 (27.5) Poor 165 (8.9) 104 (63.0) 61 (37.0) 208 (6.5) 136 (65.4) 72 (34.6) P value was expressed as trend chi-square test. BMI body mass index, FBG fasting blood glucose, WHR waist-to-hip ratio. Table 4 The abnormal lipid components in different healthy behaviors and metabolic factors Healthy behaviors and TC TG LDL-C HDL-C metabolic factors ≧ 6.22 P value ≧ 2.26 P value ≧ 4.14 P value < 1.04 P value Total 534（10.6） 1098（21.8） 385（7.7） 371（7.4） Smoking history (%) 0.902 < 0.001 0.804 < 0.001 Ideal 417 (10.6) 771 (19.6) 304 (7.7) 227 (5.8) Intermediate 23 (12.1) 52 (27.4) 12 (6.3) 20 (10.5) Poor 94 (10.3) 275 (30.3) 69 (7.6) 124 (13.6) Drinking of alcohol (%) 0.554 < 0.001 0.079 0.003 Ideal 248 (11.0) 412 (18.3) 191 (8.5) 143 (6.4) Intermediate 191 (10.2) 430 (22.9) 131 (7.0) 143 (7.6) Poor 95 (10.6) 256 (28.4) 63 (7.0) 85 (9.4) Physical activity (%) 0.006 0.067 0.014 0.017 Ideal 241 (9.6) 513 (20.4) 172 (6.8) 156 (6.2) Intermediate 136 (10.8) 302 (24.0) 99 (7.9) 115 (9.1) Poor 157 (12.5) 283 (22.5) 114 (9.1) 100 (7.9) WHR (%) < 0.001 < 0.001 < 0.001 < 0.001 Ideal 110 (6.7) 196 (11.9) 58 (3.5) 68 (4.1) Intermediate 107 (9.0) 270 (22.8) 76 (6.4) 74 (6.3) Poor 317 (14.4) 632 (28.8) 251 (11.4) 229 (10.4) BMI (%) < 0.001 < 0.001 < 0.001 < 0.001 Ideal 160 (7.9) 230 (11.4) 115 (5.7) 82 (4.1) Intermediate 242 (11.6) 550 (26.3) 174 (8.3) 185 (8.8) Poor 132 (14.4) 318 (34.6) 96 (10.5) 104 (11.3) FBG (%) < 0.001 < 0.001 < 0.001 < 0.001 Ideal 321 (8.7) 645 (17.4) 234 (6.3) 224 (6.1) Intermediate 144 (13.9) 308 (29.8) 112 (10.8) 99 (9.6) Poor 69 (23.3) 145 (49.0) 39 (13.2) 48 (16.2) Blood pressure (%) < 0.001 < 0.001 < 0.001 0.001 Ideal 96 (5.7) 210 (12.5) 84 (5.0) 92 (5.5) Intermediate 205 (11.0) 424 (22.8) 150 (8.1) 153 (8.2) Poor 233 (15.7) 464 (31.2) 151 (10.2) 126 (8.5) Sleep duration (%) 0.304 0.137 0.054 0.361 Ideal 267 (10.4) 544 (21.1) 189 (7.3) 179 (6.9) Intermediate 220 (10.6) 464 (22.3) 153 (7.4) 165 (7.9) Poor 47 (12.6) 90 (24.1) 43 (11.5) 27 (7.2) P value was expressed as trend chi-square test. BMI body mass index, FBG fasting blood glucose, WHR waist-to-hip ratio. TC total cholesterol, TG triglyceride, LDL-C low -density lipoprotein cholesterol, HDL-C high-density lipoprotein cholesterol. Cite Share Download PDF Status: Under Review Version 1 posted Editorial decision: Major revision 04 Jan, 2021 Reviews received at journal 23 Dec, 2020 Reviewers agreed at journal 02 Dec, 2020 Reviewers invited by journal 28 Nov, 2020 Editor assigned by journal 27 Nov, 2020 Editor invited by journal 24 Nov, 2020 Submission checks completed at journal 24 Nov, 2020 First submitted to journal 17 Nov, 2020 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. 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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-110397","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":5258149,"identity":"d8f62867-e280-41f0-9d67-d2d47f5f4b6a","order_by":0,"name":"Fang Nie","email":"","orcid":"","institution":"Guizhou Medical University","correspondingAuthor":false,"prefix":"","firstName":"Fang","middleName":"","lastName":"Nie","suffix":""},{"id":5258150,"identity":"edc5e664-30fe-46a5-9f32-874e5fdd1821","order_by":1,"name":"Ziyun Wang","email":"","orcid":"","institution":"Guizhou Medical University","correspondingAuthor":false,"prefix":"","firstName":"Ziyun","middleName":"","lastName":"Wang","suffix":""},{"id":5258151,"identity":"368d750e-8f72-4726-b76f-93e2fa0b1d92","order_by":2,"name":"Qibing Zeng","email":"","orcid":"","institution":"Guizhou Medical University","correspondingAuthor":false,"prefix":"","firstName":"Qibing","middleName":"","lastName":"Zeng","suffix":""},{"id":5258152,"identity":"956f32e2-56b7-4759-8bfb-2f0f1ba53dde","order_by":3,"name":"Han Guan","email":"","orcid":"","institution":"Guizhou Medical University","correspondingAuthor":false,"prefix":"","firstName":"Han","middleName":"","lastName":"Guan","suffix":""},{"id":5258153,"identity":"b0a2e5b0-f4a5-484b-839a-f67987b85c66","order_by":4,"name":"Jingyuan Yang","email":"","orcid":"","institution":"Guizhou Medical University","correspondingAuthor":false,"prefix":"","firstName":"Jingyuan","middleName":"","lastName":"Yang","suffix":""},{"id":5258154,"identity":"57f26046-18ef-45cc-a407-2265074eb63a","order_by":5,"name":"Peng Luo","email":"","orcid":"","institution":"Guizhou Medical University","correspondingAuthor":false,"prefix":"","firstName":"Peng","middleName":"","lastName":"Luo","suffix":""},{"id":5258155,"identity":"da481138-63ef-475d-aac9-2a6f61c285be","order_by":6,"name":"Lunwei Du","email":"","orcid":"","institution":"Guizhou Medical University","correspondingAuthor":false,"prefix":"","firstName":"Lunwei","middleName":"","lastName":"Du","suffix":""},{"id":5258156,"identity":"4689370c-fd66-4473-9632-36af24a308af","order_by":7,"name":"Junhua Wang","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAAxUlEQVRIiWNgGAWjYBAC+wMHGw584LGRs29vIFbPwcMHH86QSTM24DlArJbDx5KNOWwOJxpIJBCpg7HtjJk0Qw5zgrnk4403GGpsoglqYeYBaik4w5ZnOTut2ILhWFpuAyEtbBJALTN7eIoZbueYSTA2HCashUf+jZk07z+JxIabZ4jUIsEA9D4Pj0Hihhs8RGoxYAAFMk+CsWQP0C8JxPjFgAEclf/l+NkPb7zxocaGsBYU7URHDZIWUnWMglEwCkbByAAAB/1C6OKc1zsAAAAASUVORK5CYII=","orcid":"","institution":"Guizhou Medical University","correspondingAuthor":true,"prefix":"","firstName":"Junhua","middleName":"","lastName":"Wang","suffix":""},{"id":5258157,"identity":"a252e563-21d0-44e3-9c4a-a6373830d364","order_by":8,"name":"Feng Hong","email":"","orcid":"","institution":"Guizhou Medical University","correspondingAuthor":false,"prefix":"","firstName":"Feng","middleName":"","lastName":"Hong","suffix":""}],"badges":[],"createdAt":"2020-11-17 17:46:19","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-110397/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-110397/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":3898764,"identity":"69cc2971-19c4-479a-8b3a-438c32f43347","added_by":"auto","created_at":"2020-11-30 21:10:16","extension":"jpg","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":202903,"visible":true,"origin":"","legend":"Prevalence of dyslipidemia components by classification of overall healthy behaviors and metabolic factors score. Compared with ideal group, **meant P \u003c 0.01, ***meant P \u003c 0.001. TC total cholesterol, TG triglyceride, LDL-C low-density lipoprotein cholesterol, HDL-C high-density lipoprotein cholesterol.","description":"","filename":"Figure1.jpg","url":"https://assets-eu.researchsquare.com/files/rs-110397/v1/5c8261ea7f37f333b5194e09.jpg"},{"id":3898765,"identity":"fe6b0faa-d3a6-4fb8-b8b8-f54eda8d79af","added_by":"auto","created_at":"2020-11-30 21:10:16","extension":"jpg","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":139473,"visible":true,"origin":"","legend":"Associations between risks of dyslipidemia components and healthy behaviors, metabolic factors in men. Ideal was considered as reference in the model; OR were adjusted for age, residence, education level, and occupation. BMI body mass index, FBG fasting blood glucose, WHR waist-to-hip ratio.","description":"","filename":"Figure2.jpg","url":"https://assets-eu.researchsquare.com/files/rs-110397/v1/0db94ebe74a068589bb66c47.jpg"},{"id":3898766,"identity":"dc79530d-6fa4-40a6-a674-cf3f44d15b07","added_by":"auto","created_at":"2020-11-30 21:10:16","extension":"jpg","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":137998,"visible":true,"origin":"","legend":"Associations between risk of dyslipidemia components and healthy behaviors, metabolic factors in women. Ideal was considered as reference in the model; OR was adjusted for age, residence, education level, and occupation. BMI body mass index, FBG fasting blood glucose, WHR waist-to-hip ratio.","description":"","filename":"Figure3.jpg","url":"https://assets-eu.researchsquare.com/files/rs-110397/v1/b103bee6db36f41396f8c783.jpg"},{"id":13620548,"identity":"2382c377-9ab7-4f3f-842d-17d9a4de01f6","added_by":"auto","created_at":"2021-09-17 07:06:15","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":728177,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-110397/v1/27287ee3-116f-4ea6-b82f-e85a4835b6e4.pdf"}],"financialInterests":"","formattedTitle":"\u003cp\u003eThe Association of Healthy Behaviors and Metabolic Factors With Dyslipidemia Among Miao Adults:\u0026nbsp;The\u0026nbsp;China\u0026nbsp;Multi-Ethnic\u0026nbsp;Cohort\u0026nbsp;(CMEC)\u0026nbsp;Study\u003c/p\u003e","fulltext":[{"header":"Background","content":"\u003cp\u003eDue to its high incidence, disability rates and mortality rates, CVD has become the leading cause of death in the global, and it has become a public health problem that urgently needs to be overcome. Studies have found that dyslipidemia is an independent risk factor for CVD \u003csup\u003e[1]\u003c/sup\u003e. Dyslipidemia characterized by elevated total cholesterol (TC) or LDL-C levels is generally considered to be a major risk factor for atherosclerosis \u003csup\u003e[2]\u003c/sup\u003e. According to the latest data from the WHO, more than 50% of the global incidence of coronary heart disease is related to the increased TC levels. The American Heart Association (AHA) has long recognized that elevated levels of certain lipid components are important markers of CVD risk. However, there are relatively few studies on lipid components among ethnic minorities in southwest China. Given the rapid prevalence trend of high TC levels, high TG levels and other lipid phenotypes in Chinese individuals, it is of great importance to explore the typical risk factors of lipid components in people with different characteristics for the control of CVD.\u003c/p\u003e\n\u003cp\u003eRecently, due to the prevalence of risk factors such as physical activity deficiency, smoking, obesity and hypertension, the incidence of dyslipidemia is increasing at an alarming speed and showing a younger trend \u003csup\u003e[3]\u003c/sup\u003e. Research reported that approximately 64.4% of persons in china have at least one type of dyslipidemia \u003csup\u003e[4]\u003c/sup\u003e. The increasing westernization of chinese dietary patterns (i.e., high cholesterol dietary intake) contributed to this phenomenon to a large extent. In china, the form of prevention and control of dyslipidemia is severe. The release of the Healthy China Action (2019-2030) will further strengthen the management of blood lipid levels in residents aged 35 and above. Therefore, it is a priority to put forward targeted prevention and control measures based on different characteristics of the population.\u003c/p\u003e\n\u003cp\u003eThe blood lipid level is linearly related to the progression of atherosclerosis. The risk of cardiovascular events is reduced by 21% for every 1mmol/L decrease in LDL-C levels \u003csup\u003e[5]\u003c/sup\u003e, which can effectively control dyslipidemia and identify potential modifiable risk factors of dyslipidemia that are essential to reducing the risk of CVD. The blood lipid level is closely related to behavior and metabolic factors, and the superposition and complex interaction of life behavior and metabolic factors are important reasons for the disorder of blood lipid metabolism \u003csup\u003e[6, 7]\u003c/sup\u003e. Several studies have pointed out that poor behaviors and metabolic factors can increase the levels of TC, TG, and LDL-C and reduce the concentration of high-density lipoprotein cholesterol (HDL-C) \u003csup\u003e[8]\u003c/sup\u003e. Healthy behaviors and metabolic factors are essential primary preventive measures for disease. Smoking cessation, weight loss and blood sugar reduction have a profound effects on dyslipidemia. Studies have shown that changing poor behaviors and metabolic factors can reduce dyslipidemia as effectively as clinical drug therapy \u003csup\u003e[9]\u003c/sup\u003e. However, although healthy behaviors and metabolic factors were negatively correlated with the disease risk \u003csup\u003e[10, 11]\u003c/sup\u003e, but the proportions of healthy behaviors and metabolic factors that meet the ideal levels were extremely low \u003csup\u003e[12]\u003c/sup\u003e.\u003c/p\u003e\n\u003cp\u003eGuizhou is the main settlement of the Miao nationality in southwest China. In the Sixth Population Census, the Miao populations in Guizhou accounted for 42.1% of the national Miao populations \u003csup\u003e[13]\u003c/sup\u003e. Due to the influence of many factors such as the natural geographical environment, ethnic beliefs, and traditional customs, the Miao residents still maintain simple and primitive customs and traditional cultural concepts, and have a unique way of lifestyles (such as drinking customs and dietary structure), which are obviously different from the Han and other ethnic minorities \u003csup\u003e[14]\u003c/sup\u003e. Previous studies on lifestyle and dyslipidemia have been reported, but there have still been few large-scale studies of Miao adults in southwest China, and almost no studies had fully explored the abnormalities in lipid components. In addition, there had been few comprehensive studies on healthy behaviors and metabolic factors in recent years, and the evidence of their association with dyslipidemia is limited. Thus, the associations between healthy behaviors, metabolic factors and dyslipidemia in this special ethnic group remains to be clarified. Therefore, the purpose of this study is to systematically explore the relationships between healthy behaviors, metabolic factors and dyslipidemia by using baseline data from CMEC project sites in Guizhou, in order to provide more constructive information for the prevention and management of dyslipidemia and even CVD of ethnic minority areas in Southwest China.\u003c/p\u003e"},{"header":"Materials And Methods","content":"\u003cp\u003e\u003cstrong\u003eStudy participants\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe CMEC study is a large-scale prospective cohort study based on community population in five provinces of southwest China, Guizhou, Yunnan, Sichuan, Chongqing and Tibet. From May 2018 to September 2019, 99,556 members aged 30 to 79 years old (Tibetan populations include those aged 18 to 30 years) were recruited from ethnic minority communities for a baseline survey. Further details are available elsewhere \u003csup\u003e[15]\u003c/sup\u003e.\u003c/p\u003e\n\u003cp\u003eIn our study, participants has to meet following inclusion criteria: (i) aged 30~79 years on the day of the investigation; (ii) three generations of direct relatives who were permanent residents of the Miao nationality (duration of residence≧6months); (iii) capability of completing baseline surveys and the follow-up study; (iv) no mental disorders and other related diseases. The exclusion criteria were as follows: the participants were excluded if they were pregnant or had missing data (smoking history, drinking history, blood pressure, etc), fasting time \u0026lt; 8 hours and those who taking any antihyperlipidemic drugs.\u003c/p\u003e\n\u003cp\u003eA multi-stage stratified cluster sampling method was used. According to the characteristics of ethnic minorities in Guizhou, the Miao and Dong Autonomous Prefecture of Qiandongnan and the Bouyei and Miao Autonomous Prefecture of Qiannan were selected from 3 minority autonomous prefectures as investigation areas. From the Qiandongnan and Qiannan Prefectures, Kaili City, Liping County, and Libo County were selected as secondary sampling units. Ultimately, 5,559 subjects were recruited in the present analyses.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eElectronic questionnaire\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAn application (CMEC App) developed by the CMEC project team was used to collect questionnaire information through tablets. The questionnaire assessed personal identification information, social demographic characteristics, behaviour patterns (e.g., smoking, drinking and physical activity) and health status. The questionnaire information was collected by trained local medical college students using tablets through face-to-face interviews. Participants were required to bring a second- generation ID card or household register to the designated site to participate in the questionnaire survey.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eMedical examinations\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eMeasures included height (cm), weight (kg), waist circumference (cm), hip circumference (cm), and blood pressure (mmHg). Participants were required to fast before medical examinations. Blood pressure was measured by an ohmic electronic sphygmomanometer with an interval of 5 minutes between each measurement, and a total of 3 measurements. The analysis was based on the average values of the three blood pressure readings. For the height measurement, subjects were told to wear light clothes, with their hat off, standing barefoot and to keep their bodies upright when measured using an ultrasonic height measuring instrument. For weight measurement, the subjects removed heavy\u0026nbsp;clothes and stood barefoot in the centre of the weighing scales. Waist circumference was measured approximately 1 cm above the navel with a soft tape, and the hip circumference was measured at the maximum extension of the hip, circling the soft tape around the hip for a week and closing to the skin for reading.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eClinical laboratory tests\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eFasting venous blood was collected by professional nurses from participants who had fasted for 8 hours. Next, the blood samples were centrifuged and sub-packaged, refrigerated at 4℃ and sent to the JinYu Medical Laboratory Center, Guizhou Province. Finally, biochemical indexes, i.e., FBG, TC, TG, HDL-C and LDL-C, were assessed by an automatic biochemical instrument (Model: P800, Roche, Switzerland).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eDefinition of dyslipidemia\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAccording to the guidelines for the prevention and treatment of dyslipidemia in Chinese adults \u003csup\u003e[16]\u003c/sup\u003e, dyslipidemia was defined as abnormality in any of the four indicators of blood lipids: TC≧6.22mmol/L, TG≧2.26mmol/L, LDL-C≧4.14 mmol/L，and HDL-C\u0026lt; 1.04mmol/L.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAssessment of behavioral and metabolic factors\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eBased on the questionnaire assessment of smoking history, participants were divided into non-smoker, previous smoker (smoking cessation ≧1 year), and currently smoker (so far more than 100 cigarettes) groups. Based on the self-report of the frequency of alcohol consumption in participants over the past year, participants were divided into non-drinker or almost non-drinker, occasional drinker and regular drinker. Individual physical activity was assessed through the sum of the metabolic equivalents task (MET) of occupational and non-occupational physical activity. Sleep duration was assessed based on average daily sleep duration (excluding lunch break). WHR was calculated as a person\u0026rsquo;s waist circumference divided by the hip circumference. BMI was defined as a person\u0026rsquo;s weight in kilograms divided by the square of the height in meters (kg/m\u003csup\u003e2\u003c/sup\u003e).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eClassification of healthy behaviors and metabolic factors\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWith reference to the healthy lifestyle behavior proposed by the AHA and combined with the characteristics of Chinese behavior, the definitions of healthy behavior and metabolic factors are shown in Table 1. A single index was respectively assigned the values 2, 1 and 0 for ideal, intermediate and poor, and a score of 0~16 was assessed for overall healthy behaviors and metabolic factors. Then, the scores were divided into three levels, including ideal (11~16), intermediate (9~10), poor (0~8).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eStatistical analysis\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eCalculations of the distribution of participants' social demographic characteristics used descriptive statistical methods for the stratification of dyslipidemia. In addition, considering the characteristics of male and female behaviour patterns, we also described the dyslipidemia of participants with different healthy behaviors and metabolic factors based on gender stratification. The age-standardized rate of dyslipidemia was calculated according to the data of the sixth census of Guizhou in 2010. Categorical variables were expressed as n(%). The chi-square(\u003cem\u003eX\u003csup\u003e2\u003c/sup\u003e\u003c/em\u003e) test was performed to assess the differences between the groups with each categorical variable.\u003c/p\u003e\n\u003cp\u003eBased on gender stratification, a binary logistic regression model was used to analyze the OR and 95% CI of different healthy behaviors and metabolic factors indicators associated with dyslipidemia. The dependent variables of binary logistic regression models respectively were high TC, high TG, high LDL-C or low HDL-C levels. The independent variables included smoking history, drinking of alcohol, physical activity, sleep duration, WHR, BMI, FBG levels, and blood pressure. Covariates for model adjustment included age, residence, educational, and occupation.\u003c/p\u003e\n\u003cp\u003eSPSS 22.0 and R 4.0.2 software were used for statistical analysis. A \u003cem\u003eP\u003c/em\u003e value of less than 0.05 was considered to be significant.\u003c/p\u003e"},{"header":"Results","content":"\u003cp\u003e\u003cstrong\u003eBaseline characteristics of the Miao participants\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAmong the 5,559 Miao participants recruited, 5,032 (90.5%) had a complete lifestyle assessment and completed a physical examination. Table 2 shows the social demographic characteristics of the participants. The average age of males and females was (53.3\u0026plusmn;12.0) years old, (50.9\u0026plusmn;11.0) years old. Except for marital status, the differences in dyslipidemia among different social demographic variables were statistically significant (all \u003cem\u003eP \u003c/em\u003evalue \u0026lt; 0.05). The dyslipidemia rate of the participants was 32.8% (age-standardized rate of 23.3%). In addition, individuals with dyslipidemia were more likely to be males, manager or professional technology fields, higher level of education and urban residents (all \u003cem\u003eP \u003c/em\u003evalue\u0026lt; 0.05).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eDyslipidemia in Participants with different healthy behaviors and metabolic factors by gender\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eTable 3 show, males were more likely to have poor levels of blood pressure, BMI, and become current smoker, and alcohol drinker. And males with dyslipidemia were had poor levels of blood pressure, WHR, FBG, BMI, especially with poor BMI (60.3%), poor FBG (65.8%) levels of dyslipidemia were more outstanding. Conversely, females tended to have poor levels of WHR and BMI. Females with dyslipidemia were had poor levels of FBG (60.0%). Moreover, the trend chi-square test showed that the rate of dyslipidemia increased with the decrease of the level in healthy behaviors and metabolic factors classification (\u003cem\u003eP \u003c/em\u003evalue ≦0.05), except for sleep duration, smoking history, drinking of alcohol, physical activity.\u003c/p\u003e\n\u003cp\u003eFigure 1 shows that the abnormal rate of blood lipid components according to the grading of overall healthy behaviors and metabolic factors scores. After stratification, the abnormal rate of blood lipid components increased with the decrease in overall healthy behaviors and metabolic factors score. And abnormal rate of blood lipid components in men fluctuated greatly with the score of overall healthy behaviors and metabolic factors.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAbnormal lipid components in participants with different healthy behaviors and metabolic factors\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAs shown in Table 4, the rate of abnormal TG levels in individuals with adverse healthy behaviors and metabolic factors was higher, followed by the rate of abnormal TC levels, among all the lipid components.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eRelationship between healthy behaviors, metabolic factors and dyslipidemia components\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eUpon gender stratification, the associations of healthy behaviors and metabolic factors with dyslipidemia components are shown in Figure 2 and Figure 3. WHR was the major risk factor of dyslipidemia in men, with the strongest correlation between WHR and high LDL-C levels (adjusted OR=3.11, 95% CI=1.89-5.11). BMI was the main risk factor for abnormal TG and HDL-C levels, and the risk of high TG and low HDL-C levels increased as BMI increased. Participants with poor blood pressure levels were at higher risk of high TC (adjusted OR=1.82, 95% CI=1.11-3.00) and high TG levels (adjusted OR=1.78, 95% CI=1.27-2.50) than those with normal blood pressure levels. This study did not observe an association between FBG and LDL-C levels (\u003cem\u003eP \u003c/em\u003evalue \u0026gt;0.05). Smoking history was independently associated with HDL-C, and men with poor smoking behaviors had a higher risk of low HDL-C (adjusted OR=1.48, 95% CI=1.08-2.03). Men who drank occasionally (adjusted OR=1.63, 95% CI=1.20-2.22) and who drank often (adjusted OR=1.73, 95% CI=1.29-2.32) had a higher risk of high TG levels. Notably, there was a negative correlation between alcohol consumption and LDL-C levels.\u003c/p\u003e\n\u003cp\u003eA similar phenomenon has been observed in women, in that BMI was independently associated with high TG and low HDL-C levels, and the risk of high TG and low HDL-C levels increased with the increased of BMI. However, FBG levels may be a typical risk factor for dyslipidemia in Miao women. With the exception of HDL-C levels, the increased risk of high TG, high TC and high LDL-C levels increased with the increase of FBG levels. With the increase of blood pressure, the risk of high TC and high TG gradually increased, while the risk of high LDL-C levels was found only in the adverse blood pressure level was found. WHR had the strongest correlation with HDL-C levels (adjusted OR=3.04, 95% CI=1.72-5.40). Furthermore, compared with ideal physical activity, those who with moderate physical activity had a higher risk of low HDL-C (adjusted OR=1.59, 95% CI=1.04-2.42).\u003c/p\u003e"},{"header":"Discussion","content":"\u003cp\u003eThis study provided a systematic analysis of the association of healthy behaviors and metabolic factors with the risk of dyslipidemia. The results showed that Miao adults had a large proportions of poor levels of WHR and blood pressure, especially the WHR, which may be related to the tendency of body fat to accumulate in the abdomen. Furthermore, individuals with dyslipidemia had poor levels of WHR, BMI, FBG levels and blood pressure, which may be the reasons for the increased burden of CVD in minority populations in recent years, which deserves increased attention. Strikingly, BMI, WHR, FBG, and blood pressure showed strong associations with dyslipidemia. In contrast, certain behaviors (smoking, alcohol consumption and physical activity) were weakly associated with dyslipidemia. These results revealed that the management of obesity, hypertension and hyperglycemia should be important entry points for the intervention of dyslipidemia components.\u003c/p\u003e\n\u003cp\u003eCompared with the studies of Guangxi, Jiangsu, Beijing and other locations \u003csup\u003e[17-20]\u003c/sup\u003e, dyslipidemia and its abnormal lipid components among Miao adults were relatively low, the reasons for which may be due to differences in genetic background, socioeconomic level and lifestyle of the subjects. Guizhou belongs to a mountainous karst landform plateau, and the ethnic minority residents live in a special environment. Their diet culture has distinct regional characteristics (sour soup), a unique primitive lifestyle (manual batik and ethnic embroidery), and the level of economic development was limited, which may be one of the reasons for the low rate of dyslipidemia in this region. The abnormal rates of lipid components of Miao residents were significantly different: the main types were high TG (21.8%), which was consistent with other studies in China \u003csup\u003e[21, 22]\u003c/sup\u003e. Epidemiological studies have suggested that elevated TG levels are associated with increased risk of CVD \u003csup\u003e[23]\u003c/sup\u003e. Therefore, the monitoring of TG levels should be strengthened to reduce the burden of CVD in Miao adults.\u003c/p\u003e\n\u003cp\u003eWe explored the relationship between healthy behaviors, metabolic factors and dyslipidemia components according to gender. In both sexes, the results showed that the influencing factors of dyslipidemia were not identical. Overall, after adjusting for potential confounding factors, BMI, WHR, FBG levels and blood pressure were the most typical risk factors for dyslipidemia components, as confirmed in many previous studies \u003csup\u003e[24, 25]\u003c/sup\u003e. Obesity, diabetes and hypertension are prominent metabolic risk factors for lipid metabolic disorders. Dyslipidemia caused by these indicators has been demonstrated to be closely related to the progression of atherosclerotic diseases \u003csup\u003e[26]\u003c/sup\u003e. At the same time, these metabolic factors have been identified as important biomarkers for the screening of dyslipidemia. Therefore, to further strengthen the management of dyslipidemia, individuals with adverse metabolic factors should be paid more attention, suggesting that prevention and control strategies for dyslipidemia should be formulated.\u003c/p\u003e\n\u003cp\u003eThis study found that males with current smoking were at higher risk of low HDL-C levels, consistent with other findings \u003csup\u003e[27, 28]\u003c/sup\u003e. Previous studies have reported that smoking increased TC and TG levels and decreased HDL-C level compared with non-smokers \u003csup\u003e[29]\u003c/sup\u003e, while our study only observed the association between smoking and HDL-C levels. Our study also found that males with regular drinking were at greater risk of high TG levels. However, similar result was not observed in women. The association may be underestimated because this study assessed that alcohol consumption was classified only according to the frequency of drinking and lacked a specific level of alcohol intake analysis. Furthermore, we found that alcohol consumption was negatively correlated with LDL-C in men, which may be because wine with rice and glutinous rice as raw materials was self-brewed by Miao residents. The low alcohol content of self-brewed wine had no significant effect on blood lipid levels, and it may also be related to the genetic susceptibility of ethnic minorities. In short, the association between alcohol consumption and blood lipids of Miao adults should be further explored.\u003c/p\u003e\n\u003cp\u003ePrevious study reported that as physical activity decreased, the risk of dyslipidemia increased, and active physical activity was associated with improved blood lipid levels \u003csup\u003e[30]\u003c/sup\u003e. Except for HDL-C, we did not observe that physical activity was significantly associated with any lipid components, which may be related the evaluations of individual physical activity through the metabolic equivalent of total physical activity in our study, and did not distinguish the correlation between physical activity in specific fields and blood lipids. Therefore, subsequent research can further explore the association between physical activity in specific fields and lipid components. Shigeki Kinuhata observed a positive correlation between sleep duration and high TG levels \u003csup\u003e[31]\u003c/sup\u003e. Zhan found that sleep duration was significantly associated with risk of dyslipidemia in women, but not in men \u003csup\u003e[32]\u003c/sup\u003e. However, in both sexes, there was no association of sleep duration with any lipid components was observed in our study (all \u003cem\u003eP\u003c/em\u003e value \u0026gt;0.05). It may be that as the level of economic development in this region was limited, minority participants still followed the traditional habits of rest; thus, the vast majority of participants with normal sleep duration may have no obvious effect on lipid components. However, the explanation of this phenomenon remains to be further studied.\u003c/p\u003e\n\u003cp\u003eWith the transformation of social and economic development, dyslipidemia is increasing at an alarming speed, and it has become a huge challenge for the health promotion. This study revealed that individuals with poor behaviors and metabolic factors have a relatively high risk for dyslipidemia. Thus, the prevention of dyslipidemia may benefit from interventions for behaviors and metabolic risk factors. Additionally, it is suggested that populations with unhealthy lifestyles should be continuously monitored and managed to control dyslipidemia as effectively as possible to improve the health level of ethnic minority groups.\u003c/p\u003e\n\u003cp\u003eDespite the following limitations, our study was unique. First，to our knowledge, this was the first large-scale cohort study focusing on ethnic minority groups in China. Second, medical examinations were conducted by professional medical staff according to strict criteria rather than self-reporting by participants. However, several limitations needed to be considered. First, considering the influence of potential confounding factors, this study adjusted more covariates as much as possible to control confounding factor interference. Second, studies on the genetic susceptibility to dyslipidemia in ethnic minorities were scarce, and further research support was needed. Furthermore, we excluded participants with aged beyond 30 to 79 who might have missed information on early life exposures. Thus, subsequent research may consider expanding the age range of participants. Finally, our study was based on cross-sectional data analysis to confirm that the causal capacity was limited, which needs to be further supplemented by follow-up data.\u003c/p\u003e"},{"header":"Conclusions","content":"\u003cp\u003eThe dyslipidemia rate of Miao adults in Guizhou was lower than the national average (40.40%), but the overall situation was still not optimistic, especially regarding the high TG abnormal. Therefore, continuous monitoring of blood lipid levels among ethnic minorities are essential. Furthermore, this study has identified the typical risk factors for dyslipidemia components in Miao adults. These findings are extremely critical for identifying target groups with high risk factors to implement screening dyslipidemia. Given the worldwide prevalence of dyslipidemia, present findings have important public health significance for the prevention and control of dyslipidemia in ethnic minorities.\u003c/p\u003e"},{"header":"Abbreviations","content":"\u003cp\u003eCMEC:The China\u0026nbsp;Multi-Ethnic\u0026nbsp;Cohort Study; CVD: Cardiovascular diseases; OR: Odds ratios; CI: Confidence intervals; WHR: Waist-to-hip ratio; FBG: Fasting blood glucose; BMI: Body mass index; TG: Triglycerides; LDL-C: Low-density lipoprotein cholesterol; TC: Total cholesterol; AHA: American Heart Association; HDL-C: High-density lipoprotein cholesterol.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eAcknowledgements\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eParticularly thanks to the Guiyang city Center for Disease Control and Prevention, the Affiliated Hospital of Guizhou Medical University and local governments to assist and support the field investigation work. Appreciation is expressed to the CMEC team members for conceiving and designing this research and their great contributions to the initiation of the project.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthors\u0026rsquo; contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eEach author has been involved in and contributed to this paper. F.N. carried out the statistical analysis, collected the data and wrote the manuscript. F.H., P.L. contributed to the study design of this paper. W.D., Y.W., H.G., Y.Y. and B.Z. participated in the data collection, study management and coordination. H.W. contributed to guide the paper and correcting the English. All author read and approved the final manuscript.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis work was supported by the National key Research and Development Program \"Precision Medicine Research\" in 2017 (N0.2017YFC0907301). The funder played no role in the design of the study and collection, analysis, and interpretation of data and in writing the manuscript.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAvailability of data and materials\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eCurrently, the database used to support this study are not freely available in view of participants\u0026rsquo; privacy protection but are available from the corresponding author on reasonable data request. Researchers interested in our study could contact the corresponding author Dr.Feng Hong (\u003ca href=\"mailto:[email protected]\"\[email protected]\u003c/a\u003e) who will review the data request.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEthics approval and consent to participate\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis study protocol was conducted in accordance with the principles of the Declaration of Helsinki and was approved by the Medical Ethical Committee of Sichuan University (K2016038) and the Ethics Committee of the Affiliated Hospital of Guizhou Medical University (2018[094]). And written informed consent was obtained from each subject.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent for publication\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAll authors read and approved to publication.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting interests\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare that they have no competing interests.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthor affiliations\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003csup\u003e1\u003c/sup\u003eSchool of Public Health, The Key Laboratory Of Environmental Pollution Monitoring and Disease Control, Ministry of Education, Guizhou Medical University, Guiyang 550025, China. \u003csup\u003e2\u003c/sup\u003eSchool of Public Health, Guizhou Medical University, Guiyang, 550025, China.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eYusuf S, Bosch J, Dagenais G. 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Analysison prevalence of overweight and obesity and theirrelation with diabetes，hypertension，dyslipidemia among adults in Pinghu City\u003cem\u003e.\u003c/em\u003e\u003cem\u003eShang hai Journal of Preventive Medicine\u003c/em\u003e. 2016; 28: 361-365. http:// kns.cnki.net/KCMS/detail/ detail. aspx?File Name=SHYI201606003\u0026amp;DbName=CJFQ2016.\u003c/li\u003e\n\u003cli\u003eZhao Y, Liu X, Mao Z, Hou J, Huo W, Wang C, et al. Relationship between multiple healthy lifestyles and serum lipids among adults in rural China: A population-based cross-sectional study\u003cem\u003e.\u003c/em\u003e\u003cem\u003ePrev. Med.\u003c/em\u003e 2020; 138: 106158. http://doi.org/10.1016/j.ypmed.2020.106158.\u003c/li\u003e\n\u003cli\u003eDudum R, Juraschek SP, Appel LJ. Dose-dependent effects of lifestyle interventions on blood lipid levels: Results from the PREMIER trial\u003cem\u003e.\u003c/em\u003e\u003cem\u003ePatient Educ. Couns.\u003c/em\u003e 2019; 102: 1882-1891. http://doi.org/10.1016/j.pec.2019.05.005.\u003c/li\u003e\n\u003cli\u003ePerrot N, Verbeek R, Sandhu M, Boekholdt SM, Hovingh GK, Wareham NJ, et al. Ideal cardiovascular health influences cardiovascular disease risk associated with high lipoprotein(a) levels and genotype: The EPIC-Norfolk prospective population study\u003cem\u003e.\u003c/em\u003e\u003cem\u003eAtherosclerosis\u003c/em\u003e. 2017; 256: 47-52. http://doi.org/10.1016/j.atherosclerosis.2016.11.010.\u003c/li\u003e\n\u003cli\u003eIsiozor NM, Kunutsor SK, Voutilainen A, Kurl S, Kauhanen J, Laukkanen JA. Ideal cardiovascular health and risk of acute myocardial infarction among Finnish men\u003cem\u003e.\u003c/em\u003e\u003cem\u003eAtherosclerosis\u003c/em\u003e. 2019; 289: 126-131. http://doi.org/10.1016/j.atherosclerosis.2019.08.024.\u003c/li\u003e\n\u003cli\u003eLv J, Yu C, Guo Y, Bian Z, Yang L, Chen Y, et al. Adherence to Healthy Lifestyle and Cardiovascular Diseases in the Chinese\u0026nbsp;Population\u003cem\u003e.\u003c/em\u003e\u003cem\u003eJ. Am. Coll. Cardiol.\u003c/em\u003e 2017; 69: 1116-1125. https://doi.org/10.1016/j.jacc.2016.11.076.\u003c/li\u003e\n\u003cli\u003eBarbaresko J, Rienks J, N\u0026ouml;thlings U. Lifestyle Indices and Cardiovascular Disease Risk: A Meta-analysis\u003cem\u003e.\u003c/em\u003e\u003cem\u003eAm. J. Prev. Med.\u003c/em\u003e 2018; 55: 555-564. http://doi.org/10.1016/j.amepre.2018. 04.046.\u003c/li\u003e\n\u003cli\u003eZhang X. The Impact of the Migration of the Chinese Miao from Their Rural Villages to Cities\u003cem\u003e.\u003c/em\u003e\u003cem\u003eGuizhou Ethnic Studies\u003c/em\u003e. 2013; 34: 41-44. http://kns.cnki.net/KCMS/detail/detail.aspx?File Name =GZNY201306013\u0026amp;DbName=CJFQ2013.\u003c/li\u003e\n\u003cli\u003eLiu G, Mao L, Li N. Health Values and Health-related Behaviors in Ethnic Minority Groups in Guizhou Province\u003cem\u003e.\u003c/em\u003e\u003cem\u003eJournal of Sichuan University (Medical Edition)\u003c/em\u003e. 2007: 475-479. http://kns.cnki.net/KCMS/detail/detail.aspx?FileName=HXYK200703028\u0026amp;DbName=CJFQ2007.\u003c/li\u003e\n\u003cli\u003eZhao X, Hong F, Yin J, Tang W, Zhang G, Liang X, et al. Cohort Profile: The China Multi-Ethnic Cohort (CMEC) Study\u003cem\u003e.\u003c/em\u003e 2020. https://doi.org/10.1101/2020.02.14.20022970.\u003c/li\u003e\n\u003cli\u003eCommittee CJ. Guidelines for the Prevention and Control of dyslipidemia in Chinese Adults\u003cem\u003e.\u003c/em\u003e\u003cem\u003eChinese Journal of Cardiovascular Diseases\u003c/em\u003e. 2007; 35: 7-8.\u003c/li\u003e\n\u003cli\u003eXi Y, Niu L, Cao N, Bao H, Xu X, Zhu H, et al. Prevalence of dyslipidemia and associated risk factors among adults aged \u0026ge;35\u0026thinsp;years in northern China: a cross-sectional study\u003cem\u003e.\u003c/em\u003e\u003cem\u003eBMC Public Health\u003c/em\u003e. 2020; 20. https://doi.org/10.1186/s12889-020-09172-9.\u003c/li\u003e\n\u003cli\u003eLuo S, Yang H, Meng X, Huang T, Xu J, Xu Y. Prevalence rate and risk factors of dyslipidemia among adults in Guangxi Province\u003cem\u003e.\u003c/em\u003e\u003cem\u003eApplied Preventive Medicine\u003c/em\u003e. 2014; 20: 129-133. http://kns.cnki.net/KCMS/detail/detail.aspx?FileName=GXYX201403002\u0026amp;DbName=CJFQ2014.\u003c/li\u003e\n\u003cli\u003eWang S, Xu L, Jonas JB, You QS, Wang YX, Yang H. Prevalence and associated factors of dyslipidemia in the adult Chinese population\u003cem\u003e.\u003c/em\u003e\u003cem\u003ePLoS One\u003c/em\u003e. 2011; 6: e17326. https://doi.org /10.1371/journal.pone.0017326.\u003c/li\u003e\n\u003cli\u003eWang Y, Dai Y, Wang S, Zhang J, Zhu Q, Wei X. Prevalence and related factors of dyslipidemia among the adult residents in Jiangsu Province in 2014\u003cem\u003e.\u003c/em\u003e\u003cem\u003eJOURNAL OF HYGIENE RESEARCH\u003c/em\u003e. 2019; 48: 945-952. http://kns.cnki.net/KCMS/detail/detail.aspx?File Name=WSYJ201906015 \u0026amp;DbName=DKFX2019\u003c/li\u003e\n\u003cli\u003eDeng Q, Zhou X, Liang M, Yu H, Deng Q, Lu J, et al. Investigation on risk factors and differences of dyslipidemia between Mulam and Miao adult women ;in Guangxi, China\u003cem\u003e.\u003c/em\u003e\u003cem\u003eChinese Journal of Anatomy and Clinics\u003c/em\u003e. 2015: 224-229. http://www.wanfangdata.com.cn/details/detail.do?_type =perio\u0026amp;id=jiepxzz201503010 .\u003c/li\u003e\n\u003cli\u003eLi Z, Yang R, Xu G, Xia T. Serum Lipid Concentrations and Prevalence of Dyslipidemia in a Large Professional Population in Beijing\u003cem\u003e.\u003c/em\u003e\u003cem\u003eClin. Chem.\u003c/em\u003e 2005; 51: 144-150. https://doi.org /10.1373/clinchem.2004.038646.\u003c/li\u003e\n\u003cli\u003eToth PP, Fazio S, Wong ND, Hull M, Nichols GA. Risk of cardiovascular events in patients with hypertriglyceridaemia: A review of real‐world evidence\u003cem\u003e.\u003c/em\u003e\u003cem\u003eDiabetes, Obesity and Metabolism\u003c/em\u003e. 2020; 22: 279-289. https://doi.org /10.1111/dom.13921.\u003c/li\u003e\n\u003cli\u003eBayram F, Kocer D, Gundogan K, Kaya A, Demir O, Coskun R, et al. Prevalence of dyslipidemia and associated risk factors in Turkish adults\u003cem\u003e.\u003c/em\u003e\u003cem\u003eJ. Clin. Lipidol.\u003c/em\u003e 2014; 8: 206-216. https://doi.org /10.1016/j.jacl.2013.12.011.\u003c/li\u003e\n\u003cli\u003eShen Z, Munker S, Wang C, Xu L, Ye H, Chen H, et al. Association between alcohol intake, overweight, and serum lipid levels and the risk analysis associated with the development of dyslipidemia\u003cem\u003e.\u003c/em\u003e\u003cem\u003eJ. Clin. Lipidol.\u003c/em\u003e 2014; 8: 273-278. https://doi.org /10.1016/j.jacl.2014.02.003.\u003c/li\u003e\n\u003cli\u003eZaid M, Miura K, Okayama A, Nakagawa H, Sakata K, Saitoh S, et al. Associations of High-Density Lipoprotein Particle and High-Density Lipoprotein Cholesterol With Alcohol Intake, Smoking, and Body Mass Index-The INTERLIPID Study\u003cem\u003e.\u003c/em\u003e\u003cem\u003eCirc. J.\u003c/em\u003e 2018; 82: 2557-2565. https://doi.org /10.1253/circj.CJ-18-0341.\u003c/li\u003e\n\u003cli\u003eWannamethee G, Shaper AG. Blood lipids: the relationship with alcohol intake, smoking, and body weight\u003cem\u003e.\u003c/em\u003e\u003cem\u003eJ Epidemiol Community Health\u003c/em\u003e. 1992; 46: 197-202.https://doi.org/ 10.1136/jech.46.3.197.\u003c/li\u003e\n\u003cli\u003eNakanishi N, Nakamura K, Ichikawa S, Suzuki K, Tatara K. Relationship between lifestyle and serum lipid and lipoprotein levels in middle-aged Japanese men\u003cem\u003e.\u003c/em\u003e\u003cem\u003eEur. J. Epidemiol.\u003c/em\u003e 1999; 15: 341-8.http://www.ncbi.nlm.nih.gov/entrez/query.fcgicmd=Retrieve\u0026amp;db=pubmed\u0026amp;dopt=Abstract\u0026amp;list_uids=10414374\u0026amp;query_hl=1.\u003c/li\u003e\n\u003cli\u003eMoradinazar M, Pasdar Y, Najafi F, Shahsavari S, Shakiba E, Hamzeh B, et al. Association between dyslipidemia and blood lipids concentration with smoking habits in the Kurdish population of Iran\u003cem\u003e.\u003c/em\u003e\u003cem\u003eBMC Public Health\u003c/em\u003e. 2020; 20. https://doi.org/10.1186/s12889-020-08809-z.\u003c/li\u003e\n\u003cli\u003eZhou J, Zhou Q, Wang DP, Zhang T, Wang HJ, Song Y, et al. Associations of sedentary behavior and physical activity with dyslipidemia\u003cem\u003e.\u003c/em\u003e\u003cem\u003eBeijing Da Xue Xue Bao Yi Xue Ban\u003c/em\u003e. 2017; 49: 418-423. http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?cmd=Retrieve\u0026amp;db=pubmed\u0026amp;dopt=Abstract\u0026amp;list_uids=28628141\u0026amp;query_hl=1.\u003c/li\u003e\n\u003cli\u003eKinuhata S, Hayashi T, Sato KK, Uehara S, Oue K, Endo G, et al. Sleep duration and the risk of future lipid profile abnormalities in middle-aged men: the Kansai Healthcare Study\u003cem\u003e.\u003c/em\u003e\u003cem\u003eSleep Med.\u003c/em\u003e 2014; 15: 1379-1385. \u003ca href=\"https://doi.org/10.1016/j.sleep.2014.06.011\"\u003ehttps://doi.org/10.1016/j.sleep.2014.06.011\u003c/a\u003e.\u003c/li\u003e\n\u003cli\u003eZhan Y, Chen R, Yu J. Sleep duration and abnormal serum lipids: the China Health and Nutrition Survey\u003cem\u003e.\u003c/em\u003e\u003cem\u003eSleep Med.\u003c/em\u003e 2014; 15: 833-839. https://doi.org/10.1016/j.sleep.2014.02.006.\u003c/li\u003e\n\u003c/ol\u003e"},{"header":"Tables","content":"\u003cp\u003e\u003cstrong\u003eTable 1\u003c/strong\u003e\u0026nbsp;Definition of healthy behaviors and metabolic factors\u003c/p\u003e\n\u003ctable border=\"1\"\u003e\n\u003ctbody\u003e\n\u003ctr\u003e\n\u003ctd width=\"207\"\u003e\n\u003cp\u003eVariables\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"170\"\u003e\n\u003cp\u003eIdeal\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"236\"\u003e\n\u003cp\u003eIntermediate\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"113\"\u003e\n\u003cp\u003ePoor\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"207\"\u003e\n\u003cp\u003eSmoking history\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"170\"\u003e\n\u003cp\u003eNon-smoker\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"236\"\u003e\n\u003cp\u003ePrevious smoker (smoking\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"113\"\u003e\n\u003cp\u003eCurrent smoker\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"207\"\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"170\"\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"236\"\u003e\n\u003cp\u003ecessation ≧1 year)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"113\"\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"207\"\u003e\n\u003cp\u003eDrinking of alcohol\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"170\"\u003e\n\u003cp\u003eNever/nearly non-drinker\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"236\"\u003e\n\u003cp\u003eOccasional drinker\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"113\"\u003e\n\u003cp\u003eRegular drinker\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"207\"\u003e\n\u003cp\u003eSleep duration, hr\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"170\"\u003e\n\u003cp\u003e7～8\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"236\"\u003e\n\u003cp\u003e5～6 or 9～10\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"113\"\u003e\n\u003cp\u003e\u0026lt; 5 or \u0026gt;10\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"207\"\u003e\n\u003cp\u003eBMI, kg/m2\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"170\"\u003e\n\u003cp\u003e\u0026lt; 24\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"236\"\u003e\n\u003cp\u003e24～27.9\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"113\"\u003e\n\u003cp\u003e\u0026ge;28\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"207\"\u003e\n\u003cp\u003eWHR\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"170\"\u003e\n\u003cp\u003e\u0026lt; 0.90 (Male)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"236\"\u003e\n\u003cp\u003e0.90～0.94 (Male)\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"113\"\u003e\n\u003cp\u003e\u0026ge;0.95 (Male)\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"207\"\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"170\"\u003e\n\u003cp\u003eor \u0026lt; 0.85 (Female)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"236\"\u003e\n\u003cp\u003eor 0.85～0.89 (Female)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"113\"\u003e\n\u003cp\u003eor \u0026ge;0.90 (Female)\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"207\"\u003e\n\u003cp\u003eBlood pressure, mmHg\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"170\"\u003e\n\u003cp\u003eNon-drug therapy \u0026lt; 120/80\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"236\"\u003e\n\u003cp\u003e120～139/80～89 or\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"113\"\u003e\n\u003cp\u003e\u0026ge;140/90\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"207\"\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"170\"\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"236\"\u003e\n\u003cp\u003etaking anti-hypertensive drugs\u0026lt; 140/90\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"113\"\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"207\"\u003e\n\u003cp\u003eFBG, mmol/L\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"170\"\u003e\n\u003cp\u003eNon-drug therapy \u0026lt; 5.6\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"236\"\u003e\n\u003cp\u003e5.6～6.9 or\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"113\"\u003e\n\u003cp\u003e\u0026ge;7.0\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"207\"\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"170\"\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"236\"\u003e\n\u003cp\u003etaking anti-diabetic drugs \u0026lt; 5.6\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"113\"\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"207\"\u003e\n\u003cp\u003ePhysical activity, (MET-hr/day)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"170\"\u003e\n\u003cp\u003e\u0026ge; P50\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"236\"\u003e\n\u003cp\u003eP25～P50\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"113\"\u003e\n\u003cp\u003e\u0026lt; P25\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd colspan=\"4\" width=\"726\"\u003e\n\u003cp\u003eBMI body mass index, FBG fasting blood glucose, WHR waist-to-hip ratio, MET metabolic equivalent task.\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cbr /\u003eTable 2\u003c/strong\u003e\u0026nbsp;Social demographic characteristics of participants\u003c/p\u003e\n\u003ctable border=\"1\"\u003e\n\u003ctbody\u003e\n\u003ctr\u003e\n\u003ctd width=\"205\"\u003e\n\u003cp\u003eVariables\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"136\"\u003e\n\u003cp\u003eTotal (n=5032)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"117\"\u003e\n\u003cp\u003eNormal (n=3379)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"143\"\u003e\n\u003cp\u003eDyslipidemia (n=1653)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"59\"\u003e\n\u003cp\u003e\u0026chi;\u003csup\u003e2\u003c/sup\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"65\"\u003e\n\u003cp\u003e\u003cem\u003eP \u003c/em\u003evalue\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"205\"\u003e\n\u003cp\u003eGender (%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"136\"\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"117\"\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"143\"\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"59\"\u003e\n\u003cp\u003e114.7\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"65\"\u003e\n\u003cp\u003e\u0026lt; 0.001\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"205\"\u003e\n\u003cp\u003eMale\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"136\"\u003e\n\u003cp\u003e1845 (36.7)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"117\"\u003e\n\u003cp\u003e1067 (57.8)\u003c/p\u003e\n\u003cp\u003e（）\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"143\"\u003e\n\u003cp\u003e778 (42.2)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"59\"\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"65\"\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"205\"\u003e\n\u003cp\u003eFemale\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"136\"\u003e\n\u003cp\u003e3187 (63.3)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"117\"\u003e\n\u003cp\u003e2312 (72.5)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"143\"\u003e\n\u003cp\u003e875 (27.5)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"59\"\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"65\"\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"205\"\u003e\n\u003cp\u003eAge (years, %)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"136\"\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"117\"\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"143\"\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"59\"\u003e\n\u003cp\u003e6.2\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"65\"\u003e\n\u003cp\u003e0.013\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"205\"\u003e\n\u003cp\u003e30 -39\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"136\"\u003e\n\u003cp\u003e862 (17.2)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"117\"\u003e\n\u003cp\u003e647 (75.1)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"143\"\u003e\n\u003cp\u003e215 (24.9)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"59\"\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"65\"\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"205\"\u003e\n\u003cp\u003e40 -49\u003csup\u003eac\u003c/sup\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"136\"\u003e\n\u003cp\u003e1478 (29.4)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"117\"\u003e\n\u003cp\u003e994 (67.3)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"143\"\u003e\n\u003cp\u003e484 (32.7)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"59\"\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"65\"\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"205\"\u003e\n\u003cp\u003e50 -59\u003csup\u003eabde\u003c/sup\u003e\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"136\"\u003e\n\u003cp\u003e1396 (27.7)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"117\"\u003e\n\u003cp\u003e849 (60.8)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"143\"\u003e\n\u003cp\u003e547 (39.2)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"59\"\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"65\"\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"205\"\u003e\n\u003cp\u003e60 -69\u003csup\u003eac\u003c/sup\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"136\"\u003e\n\u003cp\u003e893 (17.7)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"117\"\u003e\n\u003cp\u003e597 (66.9)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"143\"\u003e\n\u003cp\u003e296 (33.1)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"59\"\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"65\"\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"205\"\u003e\n\u003cp\u003e70 -79\u003csup\u003ec\u003c/sup\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"136\"\u003e\n\u003cp\u003e403 (8.0)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"117\"\u003e\n\u003cp\u003e292 (72.5)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"143\"\u003e\n\u003cp\u003e111 (27.5)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"59\"\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"65\"\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"205\"\u003e\n\u003cp\u003eResidence (%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"136\"\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"117\"\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"143\"\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"59\"\u003e\n\u003cp\u003e18.8\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"65\"\u003e\n\u003cp\u003e\u0026lt; 0.001\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"205\"\u003e\n\u003cp\u003eRural\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"136\"\u003e\n\u003cp\u003e3584(71.2)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"117\"\u003e\n\u003cp\u003e2472 (69.0)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"143\"\u003e\n\u003cp\u003e1112 (31.0)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"59\"\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"65\"\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"205\"\u003e\n\u003cp\u003eUrban\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"136\"\u003e\n\u003cp\u003e1448（28.8)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"117\"\u003e\n\u003cp\u003e907 (62.6)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"143\"\u003e\n\u003cp\u003e541 (37.4)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"59\"\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"65\"\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"205\"\u003e\n\u003cp\u003eEducational level (%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"136\"\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"117\"\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"143\"\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"59\"\u003e\n\u003cp\u003e14.7\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"65\"\u003e\n\u003cp\u003e\u0026lt; 0.001\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"205\"\u003e\n\u003cp\u003eNo formal school\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"136\"\u003e\n\u003cp\u003e2190 (43.5)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"117\"\u003e\n\u003cp\u003e1504 (68.7)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"143\"\u003e\n\u003cp\u003e686 (31.3)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"59\"\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"65\"\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"205\"\u003e\n\u003cp\u003ePrimary school\u003csup\u003eacde\u003c/sup\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"136\"\u003e\n\u003cp\u003e687 (13.7)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"117\"\u003e\n\u003cp\u003e475 (69.1)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"143\"\u003e\n\u003cp\u003e212 (30.9)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"59\"\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"65\"\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"205\"\u003e\n\u003cp\u003eMiddle school\u003csup\u003ebde\u003c/sup\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"136\"\u003e\n\u003cp\u003e1004 (20.0)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"117\"\u003e\n\u003cp\u003e696 (69.3)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"143\"\u003e\n\u003cp\u003e308 (30.7)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"59\"\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"65\"\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"205\"\u003e\n\u003cp\u003eHigh School or college\u003csup\u003eabc\u003c/sup\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"136\"\u003e\n\u003cp\u003e884 (17.5)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"117\"\u003e\n\u003cp\u003e544 (61.5)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"143\"\u003e\n\u003cp\u003e340 (38.5)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"59\"\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"65\"\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"205\"\u003e\n\u003cp\u003eUniversity or above\u003csup\u003eabc\u003c/sup\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"136\"\u003e\n\u003cp\u003e267 (5.3)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"117\"\u003e\n\u003cp\u003e160 (59.9)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"143\"\u003e\n\u003cp\u003e107 (40.1)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"59\"\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"65\"\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"205\"\u003e\n\u003cp\u003eOccupation (%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"136\"\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"117\"\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"143\"\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"59\"\u003e\n\u003cp\u003e8.3\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"65\"\u003e\n\u003cp\u003e0.004\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"205\"\u003e\n\u003cp\u003eFarmers\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"136\"\u003e\n\u003cp\u003e1767 (35.1)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"117\"\u003e\n\u003cp\u003e1255 (71.0)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"143\"\u003e\n\u003cp\u003e512 (29.0)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"59\"\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"65\"\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"205\"\u003e\n\u003cp\u003eWorkers\u003csup\u003eade\u003c/sup\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"136\"\u003e\n\u003cp\u003e400 (7.9)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"117\"\u003e\n\u003cp\u003e258 (64.5)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"143\"\u003e\n\u003cp\u003e142 (35.5)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"59\"\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"65\"\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"205\"\u003e\n\u003cp\u003eAdministration or manager\u003csup\u003eae\u003c/sup\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"136\"\u003e\n\u003cp\u003e165 (3.3)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"117\"\u003e\n\u003cp\u003e97 (58.8)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"143\"\u003e\n\u003cp\u003e68 (41.2)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"59\"\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"65\"\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"205\"\u003e\n\u003cp\u003eSpecialist\u003csup\u003eae\u003c/sup\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"136\"\u003e\n\u003cp\u003e441 (8.8)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"117\"\u003e\n\u003cp\u003e257 (58.3)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"143\"\u003e\n\u003cp\u003e184 (41.7)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"59\"\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"65\"\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"205\"\u003e\n\u003cp\u003eOther occupations\u003csup\u003eabcd\u003c/sup\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"136\"\u003e\n\u003cp\u003e2259 (44.9)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"117\"\u003e\n\u003cp\u003e1512 (66.9)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"143\"\u003e\n\u003cp\u003e747 (33.1)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"59\"\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"65\"\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"205\"\u003e\n\u003cp\u003eMarital status (%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"136\"\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"117\"\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"143\"\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"59\"\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"65\"\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"205\"\u003e\n\u003cp\u003eMarried or cohabiting\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"136\"\u003e\n\u003cp\u003e4367 (86.8)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"117\"\u003e\n\u003cp\u003e2917 (66.8)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"143\"\u003e\n\u003cp\u003e1450 (33.2)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"59\"\u003e\n\u003cp\u003e1.03\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"65\"\u003e\n\u003cp\u003e0.310\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"205\"\u003e\n\u003cp\u003eSeparated or divorced\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"136\"\u003e\n\u003cp\u003e183 (3.6)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"117\"\u003e\n\u003cp\u003e128 (69.9)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"143\"\u003e\n\u003cp\u003e55 (30.1)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"59\"\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"65\"\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"205\"\u003e\n\u003cp\u003eWidowed\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"136\"\u003e\n\u003cp\u003e427 (8.5)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"117\"\u003e\n\u003cp\u003e301 (70.5)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"143\"\u003e\n\u003cp\u003e126 (29.5)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"59\"\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"65\"\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"205\"\u003e\n\u003cp\u003eNever married\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"136\"\u003e\n\u003cp\u003e55 (1.1)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"117\"\u003e\n\u003cp\u003e33 (60.0)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"143\"\u003e\n\u003cp\u003e22 (40.0)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"59\"\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"65\"\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"205\"\u003e\n\u003cp\u003eFamily income (Yuan/year, %)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"136\"\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"117\"\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"143\"\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"59\"\u003e\n\u003cp\u003e5.1\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"65\"\u003e\n\u003cp\u003e0.024\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"205\"\u003e\n\u003cp\u003e\u0026lt; 20,000\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"136\"\u003e\n\u003cp\u003e2101 (41.7)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"117\"\u003e\n\u003cp\u003e1446 (68.8)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"143\"\u003e\n\u003cp\u003e655 (31.2)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"59\"\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"65\"\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"205\"\u003e\n\u003cp\u003e20,000 -59,999\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"136\"\u003e\n\u003cp\u003e1650 (32.8)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"117\"\u003e\n\u003cp\u003e1098 (66.5)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"143\"\u003e\n\u003cp\u003e552 (33.5)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"59\"\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"65\"\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"205\"\u003e\n\u003cp\u003e60,000 -99,999\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"136\"\u003e\n\u003cp\u003e763 (15.2)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"117\"\u003e\n\u003cp\u003e500 (65.5)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"143\"\u003e\n\u003cp\u003e263 (34.5)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"59\"\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"65\"\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"205\"\u003e\n\u003cp\u003e≧100,000\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"136\"\u003e\n\u003cp\u003e518 (10.3)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"117\"\u003e\n\u003cp\u003e335 (64.7)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"143\"\u003e\n\u003cp\u003e183 (35.3)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"59\"\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"65\"\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd colspan=\"6\" width=\"726\"\u003e\n\u003cp\u003e\u003csup\u003eabcde\u003c/sup\u003e, respectively, represent pairwise comparisons: compared with the first group, \u003cem\u003eP\u003c/em\u003e\u0026lt; 0.05; compared with the second group,\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd colspan=\"6\" width=\"726\"\u003e\n\u003cp\u003e\u003cem\u003eP\u003c/em\u003e \u0026lt; 0.05; compared with the third group, \u003cem\u003eP\u003c/em\u003e\u0026lt; 0.05; compared with the fourth group, \u003cem\u003eP\u003c/em\u003e\u0026lt; 0.05; compared with the fifth group,\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd colspan=\"6\" width=\"726\"\u003e\n\u003cp\u003e\u003cem\u003eP\u003c/em\u003e \u0026lt; 0.05.\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cbr /\u003eTable 3\u003c/strong\u003e\u0026nbsp;Different healthy behaviors and metabolic factors of dyslipidemia by gender\u003c/p\u003e\n\u003ctable border=\"1\"\u003e\n\u003ctbody\u003e\n\u003ctr\u003e\n\u003ctd width=\"174\"\u003e\n\u003cp\u003eHealthy behaviors and\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd colspan=\"4\" width=\"366\"\u003e\n\u003cp\u003eMale\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd colspan=\"4\" width=\"351\"\u003e\n\u003cp\u003eFemale\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"174\"\u003e\n\u003cp\u003emetabolic factors\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"98\"\u003e\n\u003cp\u003eTotal\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"97\"\u003e\n\u003cp\u003eNormal\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"104\"\u003e\n\u003cp\u003eDyslipidemia\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"66\"\u003e\n\u003cp\u003e\u003cem\u003eP\u003c/em\u003e value\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"96\"\u003e\n\u003cp\u003eTotal\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"86\"\u003e\n\u003cp\u003eNormal\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"103\"\u003e\n\u003cp\u003eDyslipidemia\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"66\"\u003e\n\u003cp\u003e\u003cem\u003eP\u003c/em\u003e value\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"174\"\u003e\n\u003cp\u003eSmoking history (%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"98\"\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"97\"\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"104\"\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"66\"\u003e\n\u003cp\u003e0.604\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"96\"\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"86\"\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"103\"\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"66\"\u003e\n\u003cp\u003e0.212\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"174\"\u003e\n\u003cp\u003eIdeal\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"98\"\u003e\n\u003cp\u003e780 (42.2)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"97\"\u003e\n\u003cp\u003e454 (58.2)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"104\"\u003e\n\u003cp\u003e326 (41.8)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"66\"\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"96\"\u003e\n\u003cp\u003e3153 (98.9)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"86\"\u003e\n\u003cp\u003e2284 (72.4)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"103\"\u003e\n\u003cp\u003e869 (27.6)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"66\"\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"174\"\u003e\n\u003cp\u003eIntermediate\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"98\"\u003e\n\u003cp\u003e184 (10.0)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"97\"\u003e\n\u003cp\u003e111 (60.3)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"104\"\u003e\n\u003cp\u003e73 (39.7)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"66\"\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"96\"\u003e\n\u003cp\u003e6 (0.2)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"86\"\u003e\n\u003cp\u003e5 (83.3)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"103\"\u003e\n\u003cp\u003e1 (16.7)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"66\"\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"174\"\u003e\n\u003cp\u003ePoor\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"98\"\u003e\n\u003cp\u003e881 (47.8)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"97\"\u003e\n\u003cp\u003e502 (57.0)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"104\"\u003e\n\u003cp\u003e379 (43.0)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"66\"\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"96\"\u003e\n\u003cp\u003e28 (0.9)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"86\"\u003e\n\u003cp\u003e23 (82.1)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"103\"\u003e\n\u003cp\u003e5 (17.9)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"66\"\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"174\"\u003e\n\u003cp\u003eDrinking of alcohol (%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"98\"\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"97\"\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"104\"\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"66\"\u003e\n\u003cp\u003e0.008\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"96\"\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"86\"\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"103\"\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"66\"\u003e\n\u003cp\u003e0.130\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"174\"\u003e\n\u003cp\u003eIdeal\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"98\"\u003e\n\u003cp\u003e541 (29.3)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"97\"\u003e\n\u003cp\u003e351 (64.9)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"104\"\u003e\n\u003cp\u003e190 (35.1)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"66\"\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"96\"\u003e\n\u003cp\u003e1710 (53.7)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"86\"\u003e\n\u003cp\u003e1212 (70.9)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"103\"\u003e\n\u003cp\u003e498 (29.1)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"66\"\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"174\"\u003e\n\u003cp\u003eIntermediate\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"98\"\u003e\n\u003cp\u003e709 (38.4)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"97\"\u003e\n\u003cp\u003e378 (53.3)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"104\"\u003e\n\u003cp\u003e331 (46.7)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"66\"\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"96\"\u003e\n\u003cp\u003e1172 (36.7)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"86\"\u003e\n\u003cp\u003e882 (75.3)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"103\"\u003e\n\u003cp\u003e290 (24.7)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"66\"\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"174\"\u003e\n\u003cp\u003ePoor\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"98\"\u003e\n\u003cp\u003e595 (32.3)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"97\"\u003e\n\u003cp\u003e338 (56.8)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"104\"\u003e\n\u003cp\u003e257 (43.2)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"66\"\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"96\"\u003e\n\u003cp\u003e305 (9.6)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"86\"\u003e\n\u003cp\u003e218 (71.5)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"103\"\u003e\n\u003cp\u003e87 (28.5)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"66\"\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"174\"\u003e\n\u003cp\u003ePhysical activity (%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"98\"\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"97\"\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"104\"\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"66\"\u003e\n\u003cp\u003e0.962\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"96\"\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"86\"\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"103\"\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"66\"\u003e\n\u003cp\u003e\u0026lt; 0.001\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"174\"\u003e\n\u003cp\u003eIdeal\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"98\"\u003e\n\u003cp\u003e948 (51.4)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"97\"\u003e\n\u003cp\u003e555 (58.5)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"104\"\u003e\n\u003cp\u003e393 (41.5)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"66\"\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"96\"\u003e\n\u003cp\u003e1568 (49.2)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"86\"\u003e\n\u003cp\u003e1188 (75.8)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"103\"\u003e\n\u003cp\u003e380 (24.2)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"66\"\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"174\"\u003e\n\u003cp\u003eIntermediate\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"98\"\u003e\n\u003cp\u003e445 (24.1)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"97\"\u003e\n\u003cp\u003e243 (54.6)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"104\"\u003e\n\u003cp\u003e202 (45.4)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"66\"\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"96\"\u003e\n\u003cp\u003e813 (25.5)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"86\"\u003e\n\u003cp\u003e579 (71.2)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"103\"\u003e\n\u003cp\u003e234 (28.8)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"66\"\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"174\"\u003e\n\u003cp\u003ePoor\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"98\"\u003e\n\u003cp\u003e452 (24.5)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"97\"\u003e\n\u003cp\u003e269 (59.5)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"104\"\u003e\n\u003cp\u003e183 (40.5)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"66\"\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"96\"\u003e\n\u003cp\u003e806 (25.3)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"86\"\u003e\n\u003cp\u003e545 (67.6)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"103\"\u003e\n\u003cp\u003e261 (32.4)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"66\"\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"174\"\u003e\n\u003cp\u003eWHR (%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"98\"\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"97\"\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"104\"\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"66\"\u003e\n\u003cp\u003e\u0026lt; 0.001\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"96\"\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"86\"\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"103\"\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"66\"\u003e\n\u003cp\u003e\u0026lt; 0.001\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"174\"\u003e\n\u003cp\u003eIdeal\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"98\"\u003e\n\u003cp\u003e788 (42.7)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"97\"\u003e\n\u003cp\u003e570 (72.3)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"104\"\u003e\n\u003cp\u003e218 (27.7)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"66\"\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"96\"\u003e\n\u003cp\u003e866 (27.2)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"86\"\u003e\n\u003cp\u003e734 (84.8)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"103\"\u003e\n\u003cp\u003e132 (15.2)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"66\"\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"174\"\u003e\n\u003cp\u003eIntermediate\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"98\"\u003e\n\u003cp\u003e462 (25.0)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"97\"\u003e\n\u003cp\u003e254 (55.0)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"104\"\u003e\n\u003cp\u003e208 (45.0)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"66\"\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"96\"\u003e\n\u003cp\u003e722 (22.7)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"86\"\u003e\n\u003cp\u003e550 (76.2)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"103\"\u003e\n\u003cp\u003e172 (23.8)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"66\"\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"174\"\u003e\n\u003cp\u003ePoor\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"98\"\u003e\n\u003cp\u003e595 (32.3)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"97\"\u003e\n\u003cp\u003e243 (40.8)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"104\"\u003e\n\u003cp\u003e352 (59.2)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"66\"\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"96\"\u003e\n\u003cp\u003e1599 (50.1)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"86\"\u003e\n\u003cp\u003e1028 (64.3)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"103\"\u003e\n\u003cp\u003e571 (35.7)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"66\"\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"174\"\u003e\n\u003cp\u003eBMI (%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"98\"\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"97\"\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"104\"\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"66\"\u003e\n\u003cp\u003e\u0026lt; 0.001\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"96\"\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"86\"\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"103\"\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"66\"\u003e\n\u003cp\u003e\u0026lt; 0.001\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"174\"\u003e\n\u003cp\u003eIdeal\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"98\"\u003e\n\u003cp\u003e782 (42.4)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"97\"\u003e\n\u003cp\u003e577 (73.8)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"104\"\u003e\n\u003cp\u003e205 (26.2)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"66\"\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"96\"\u003e\n\u003cp\u003e1239 (38.9)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"86\"\u003e\n\u003cp\u003e1013 (81.8)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"103\"\u003e\n\u003cp\u003e226 (18.2)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"66\"\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"174\"\u003e\n\u003cp\u003eIntermediate\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"98\"\u003e\n\u003cp\u003e776 (42.0)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"97\"\u003e\n\u003cp\u003e376 (48.5)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"104\"\u003e\n\u003cp\u003e400 (51.5)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"66\"\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"96\"\u003e\n\u003cp\u003e1317 (41.3)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"86\"\u003e\n\u003cp\u003e921 (69.9)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"103\"\u003e\n\u003cp\u003e396 (30.1)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"66\"\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"174\"\u003e\n\u003cp\u003ePoor\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"98\"\u003e\n\u003cp\u003e287 (15.6)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"97\"\u003e\n\u003cp\u003e114 (39.7)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"104\"\u003e\n\u003cp\u003e173 (60.3)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"66\"\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"96\"\u003e\n\u003cp\u003e631 (19.8)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"86\"\u003e\n\u003cp\u003e378 (59.9)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"103\"\u003e\n\u003cp\u003e253 (40.1)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"66\"\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"174\"\u003e\n\u003cp\u003eFBG (%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"98\"\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"97\"\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"104\"\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"66\"\u003e\n\u003cp\u003e\u0026lt; 0.001\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"96\"\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"86\"\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"103\"\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"66\"\u003e\n\u003cp\u003e\u0026lt; 0.001\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"174\"\u003e\n\u003cp\u003eIdeal\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"98\"\u003e\n\u003cp\u003e1226 (66.5)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"97\"\u003e\n\u003cp\u003e777 (63.4)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"104\"\u003e\n\u003cp\u003e449 (36.6)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"66\"\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"96\"\u003e\n\u003cp\u003e2475 (77.7)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"86\"\u003e\n\u003cp\u003e1899 (76.7)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"103\"\u003e\n\u003cp\u003e576 (23.3)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"66\"\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"174\"\u003e\n\u003cp\u003eIntermediate\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"98\"\u003e\n\u003cp\u003e458 (24.8)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"97\"\u003e\n\u003cp\u003e235 (51.3)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"104\"\u003e\n\u003cp\u003e223 (48.7)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"66\"\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"96\"\u003e\n\u003cp\u003e577 (18.1)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"86\"\u003e\n\u003cp\u003e359 (62.2)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"103\"\u003e\n\u003cp\u003e218 (37.8)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"66\"\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"174\"\u003e\n\u003cp\u003ePoor\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"98\"\u003e\n\u003cp\u003e161 (8.7)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"97\"\u003e\n\u003cp\u003e55 (34.2)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"104\"\u003e\n\u003cp\u003e106 (65.8)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"66\"\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"96\"\u003e\n\u003cp\u003e135 (4.2)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"86\"\u003e\n\u003cp\u003e54 (40.0)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"103\"\u003e\n\u003cp\u003e81 (60.0)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"66\"\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"174\"\u003e\n\u003cp\u003eBlood pressure (%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"98\"\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"97\"\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"104\"\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"66\"\u003e\n\u003cp\u003e\u0026lt; 0.001\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"96\"\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"86\"\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"103\"\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"66\"\u003e\n\u003cp\u003e\u0026lt; 0.001\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"174\"\u003e\n\u003cp\u003eIdeal\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"98\"\u003e\n\u003cp\u003e391 (21.2)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"97\"\u003e\n\u003cp\u003e269 (68.8)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"104\"\u003e\n\u003cp\u003e122 (31.2)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"66\"\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"96\"\u003e\n\u003cp\u003e1292 (40.5)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"86\"\u003e\n\u003cp\u003e1054 (81.6)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"103\"\u003e\n\u003cp\u003e238 (18.4)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"66\"\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"174\"\u003e\n\u003cp\u003eIntermediate\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"98\"\u003e\n\u003cp\u003e741 (40.2)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"97\"\u003e\n\u003cp\u003e433 (58.4)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"104\"\u003e\n\u003cp\u003e308 (41.6)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"66\"\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"96\"\u003e\n\u003cp\u003e1121 (35.2)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"86\"\u003e\n\u003cp\u003e783 (69.8)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"103\"\u003e\n\u003cp\u003e338 (30.2)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"66\"\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"174\"\u003e\n\u003cp\u003ePoor\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"98\"\u003e\n\u003cp\u003e713 (38.6)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"97\"\u003e\n\u003cp\u003e365 (51.2)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"104\"\u003e\n\u003cp\u003e348 (48.8)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"66\"\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"96\"\u003e\n\u003cp\u003e774 (24.3)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"86\"\u003e\n\u003cp\u003e475 (61.4)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"103\"\u003e\n\u003cp\u003e299 (38.6)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"66\"\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"174\"\u003e\n\u003cp\u003eSleep duration (%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"98\"\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"97\"\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"104\"\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"66\"\u003e\n\u003cp\u003e0.642\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"96\"\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"86\"\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"103\"\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"66\"\u003e\n\u003cp\u003e0.047\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"174\"\u003e\n\u003cp\u003eIdeal\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"98\"\u003e\n\u003cp\u003e904 (49.0)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"97\"\u003e\n\u003cp\u003e525 (58.1)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"104\"\u003e\n\u003cp\u003e379 (41.9)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"66\"\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"96\"\u003e\n\u003cp\u003e1675 (52.6)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"86\"\u003e\n\u003cp\u003e1231 (73.5)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"103\"\u003e\n\u003cp\u003e444 (26.5)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"66\"\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"174\"\u003e\n\u003cp\u003eIntermediate\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"98\"\u003e\n\u003cp\u003e776 (42.1)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"97\"\u003e\n\u003cp\u003e438 (56.4)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"104\"\u003e\n\u003cp\u003e338 (43.6)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"66\"\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"96\"\u003e\n\u003cp\u003e1304 (40.9)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"86\"\u003e\n\u003cp\u003e945 (72.5)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"103\"\u003e\n\u003cp\u003e359 (27.5)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"66\"\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"174\"\u003e\n\u003cp\u003ePoor\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"98\"\u003e\n\u003cp\u003e165 (8.9)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"97\"\u003e\n\u003cp\u003e104 (63.0)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"104\"\u003e\n\u003cp\u003e61 (37.0)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"66\"\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"96\"\u003e\n\u003cp\u003e208 (6.5)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"86\"\u003e\n\u003cp\u003e136 (65.4)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"103\"\u003e\n\u003cp\u003e72 (34.6)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"66\"\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd colspan=\"9\" width=\"891\"\u003e\n\u003cp\u003e\u003cem\u003eP \u003c/em\u003evalue was expressed as trend chi-square test.\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd colspan=\"9\" width=\"891\"\u003e\n\u003cp\u003e\u003cem\u003eBMI\u003c/em\u003e body mass index, \u003cem\u003eFBG\u003c/em\u003e fasting blood glucose, \u003cem\u003eWHR\u003c/em\u003e waist-to-hip ratio.\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cbr /\u003eTable 4\u003c/strong\u003e\u0026nbsp;The abnormal lipid components in different healthy behaviors and metabolic factors\u003c/p\u003e\n\u003ctable border=\"1\"\u003e\n\u003ctbody\u003e\n\u003ctr\u003e\n\u003ctd width=\"153\"\u003e\n\u003cp\u003eHealthy behaviors and\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd colspan=\"2\" width=\"143\"\u003e\n\u003cp\u003eTC\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd colspan=\"2\" width=\"147\"\u003e\n\u003cp\u003eTG\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd colspan=\"2\" width=\"132\"\u003e\n\u003cp\u003eLDL-C\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd colspan=\"2\" width=\"151\"\u003e\n\u003cp\u003eHDL-C\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"153\"\u003e\n\u003cp\u003emetabolic factors\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"79\"\u003e\n\u003cp\u003e≧ 6.22\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"63\"\u003e\n\u003cp\u003e\u003cem\u003eP \u003c/em\u003evalue\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"84\"\u003e\n\u003cp\u003e≧ 2.26\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"63\"\u003e\n\u003cp\u003e\u003cem\u003eP\u003c/em\u003e value\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"74\"\u003e\n\u003cp\u003e≧ 4.14\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"58\"\u003e\n\u003cp\u003e\u003cem\u003eP\u003c/em\u003e value\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"87\"\u003e\n\u003cp\u003e\u0026lt; 1.04\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"64\"\u003e\n\u003cp\u003e\u003cem\u003eP \u003c/em\u003evalue\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"153\"\u003e\n\u003cp\u003eTotal\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"79\"\u003e\n\u003cp\u003e534（10.6）\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"63\"\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"84\"\u003e\n\u003cp\u003e1098（21.8）\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"63\"\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"74\"\u003e\n\u003cp\u003e385（7.7）\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"58\"\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"87\"\u003e\n\u003cp\u003e371（7.4）\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"64\"\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"153\"\u003e\n\u003cp\u003eSmoking history (%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"79\"\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"63\"\u003e\n\u003cp\u003e0.902\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"84\"\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"63\"\u003e\n\u003cp\u003e\u0026lt; 0.001\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"74\"\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"58\"\u003e\n\u003cp\u003e0.804\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"87\"\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"64\"\u003e\n\u003cp\u003e\u0026lt; 0.001\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"153\"\u003e\n\u003cp\u003eIdeal\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"79\"\u003e\n\u003cp\u003e417 (10.6)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"63\"\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"84\"\u003e\n\u003cp\u003e771 (19.6)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"63\"\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"74\"\u003e\n\u003cp\u003e304 (7.7)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"58\"\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"87\"\u003e\n\u003cp\u003e227 (5.8)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"64\"\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"153\"\u003e\n\u003cp\u003eIntermediate\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"79\"\u003e\n\u003cp\u003e23 (12.1)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"63\"\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"84\"\u003e\n\u003cp\u003e52 (27.4)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"63\"\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"74\"\u003e\n\u003cp\u003e12 (6.3)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"58\"\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"87\"\u003e\n\u003cp\u003e20 (10.5)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"64\"\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"153\"\u003e\n\u003cp\u003ePoor\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"79\"\u003e\n\u003cp\u003e94 (10.3)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"63\"\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"84\"\u003e\n\u003cp\u003e275 (30.3)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"63\"\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"74\"\u003e\n\u003cp\u003e69 (7.6)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"58\"\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"87\"\u003e\n\u003cp\u003e124 (13.6)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"64\"\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"153\"\u003e\n\u003cp\u003eDrinking of alcohol (%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"79\"\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"63\"\u003e\n\u003cp\u003e0.554\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"84\"\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"63\"\u003e\n\u003cp\u003e\u0026lt; 0.001\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"74\"\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"58\"\u003e\n\u003cp\u003e0.079\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"87\"\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"64\"\u003e\n\u003cp\u003e0.003\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"153\"\u003e\n\u003cp\u003eIdeal\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"79\"\u003e\n\u003cp\u003e248 (11.0)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"63\"\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"84\"\u003e\n\u003cp\u003e412 (18.3)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"63\"\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"74\"\u003e\n\u003cp\u003e191 (8.5)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"58\"\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"87\"\u003e\n\u003cp\u003e143 (6.4)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"64\"\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"153\"\u003e\n\u003cp\u003eIntermediate\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"79\"\u003e\n\u003cp\u003e191 (10.2)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"63\"\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"84\"\u003e\n\u003cp\u003e430 (22.9)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"63\"\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"74\"\u003e\n\u003cp\u003e131 (7.0)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"58\"\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"87\"\u003e\n\u003cp\u003e143 (7.6)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"64\"\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"153\"\u003e\n\u003cp\u003ePoor\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"79\"\u003e\n\u003cp\u003e95 (10.6)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"63\"\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"84\"\u003e\n\u003cp\u003e256 (28.4)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"63\"\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"74\"\u003e\n\u003cp\u003e63 (7.0)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"58\"\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"87\"\u003e\n\u003cp\u003e85 (9.4)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"64\"\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"153\"\u003e\n\u003cp\u003ePhysical activity (%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"79\"\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"63\"\u003e\n\u003cp\u003e0.006\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"84\"\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"63\"\u003e\n\u003cp\u003e0.067\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"74\"\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"58\"\u003e\n\u003cp\u003e0.014\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"87\"\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"64\"\u003e\n\u003cp\u003e0.017\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"153\"\u003e\n\u003cp\u003eIdeal\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"79\"\u003e\n\u003cp\u003e241 (9.6)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"63\"\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"84\"\u003e\n\u003cp\u003e513 (20.4)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"63\"\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"74\"\u003e\n\u003cp\u003e172 (6.8)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"58\"\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"87\"\u003e\n\u003cp\u003e156 (6.2)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"64\"\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"153\"\u003e\n\u003cp\u003eIntermediate\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"79\"\u003e\n\u003cp\u003e136 (10.8)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"63\"\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"84\"\u003e\n\u003cp\u003e302 (24.0)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"63\"\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"74\"\u003e\n\u003cp\u003e99 (7.9)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"58\"\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"87\"\u003e\n\u003cp\u003e115 (9.1)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"64\"\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"153\"\u003e\n\u003cp\u003ePoor\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"79\"\u003e\n\u003cp\u003e157 (12.5)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"63\"\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"84\"\u003e\n\u003cp\u003e283 (22.5)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"63\"\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"74\"\u003e\n\u003cp\u003e114 (9.1)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"58\"\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"87\"\u003e\n\u003cp\u003e100 (7.9)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"64\"\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"153\"\u003e\n\u003cp\u003eWHR (%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"79\"\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"63\"\u003e\n\u003cp\u003e\u0026lt; 0.001\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"84\"\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"63\"\u003e\n\u003cp\u003e\u0026lt; 0.001\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"74\"\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"58\"\u003e\n\u003cp\u003e\u0026lt; 0.001\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"87\"\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"64\"\u003e\n\u003cp\u003e\u0026lt; 0.001\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"153\"\u003e\n\u003cp\u003eIdeal\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"79\"\u003e\n\u003cp\u003e110 (6.7)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"63\"\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"84\"\u003e\n\u003cp\u003e196 (11.9)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"63\"\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"74\"\u003e\n\u003cp\u003e58 (3.5)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"58\"\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"87\"\u003e\n\u003cp\u003e68 (4.1)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"64\"\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"153\"\u003e\n\u003cp\u003eIntermediate\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"79\"\u003e\n\u003cp\u003e107 (9.0)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"63\"\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"84\"\u003e\n\u003cp\u003e270 (22.8)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"63\"\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"74\"\u003e\n\u003cp\u003e76 (6.4)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"58\"\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"87\"\u003e\n\u003cp\u003e74 (6.3)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"64\"\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"153\"\u003e\n\u003cp\u003ePoor\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"79\"\u003e\n\u003cp\u003e317 (14.4)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"63\"\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"84\"\u003e\n\u003cp\u003e632 (28.8)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"63\"\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"74\"\u003e\n\u003cp\u003e251 (11.4)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"58\"\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"87\"\u003e\n\u003cp\u003e229 (10.4)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"64\"\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"153\"\u003e\n\u003cp\u003eBMI (%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"79\"\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"63\"\u003e\n\u003cp\u003e\u0026lt; 0.001\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"84\"\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"63\"\u003e\n\u003cp\u003e\u0026lt; 0.001\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"74\"\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"58\"\u003e\n\u003cp\u003e\u0026lt; 0.001\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"87\"\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"64\"\u003e\n\u003cp\u003e\u0026lt; 0.001\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"153\"\u003e\n\u003cp\u003eIdeal\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"79\"\u003e\n\u003cp\u003e160 (7.9)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"63\"\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"84\"\u003e\n\u003cp\u003e230 (11.4)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"63\"\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"74\"\u003e\n\u003cp\u003e115 (5.7)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"58\"\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"87\"\u003e\n\u003cp\u003e82 (4.1)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"64\"\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"153\"\u003e\n\u003cp\u003eIntermediate\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"79\"\u003e\n\u003cp\u003e242 (11.6)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"63\"\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"84\"\u003e\n\u003cp\u003e550 (26.3)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"63\"\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"74\"\u003e\n\u003cp\u003e174 (8.3)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"58\"\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"87\"\u003e\n\u003cp\u003e185 (8.8)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"64\"\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"153\"\u003e\n\u003cp\u003ePoor\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"79\"\u003e\n\u003cp\u003e132 (14.4)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"63\"\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"84\"\u003e\n\u003cp\u003e318 (34.6)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"63\"\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"74\"\u003e\n\u003cp\u003e96 (10.5)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"58\"\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"87\"\u003e\n\u003cp\u003e104 (11.3)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"64\"\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"153\"\u003e\n\u003cp\u003eFBG (%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"79\"\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"63\"\u003e\n\u003cp\u003e\u0026lt; 0.001\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"84\"\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"63\"\u003e\n\u003cp\u003e\u0026lt; 0.001\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"74\"\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"58\"\u003e\n\u003cp\u003e\u0026lt; 0.001\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"87\"\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"64\"\u003e\n\u003cp\u003e\u0026lt; 0.001\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"153\"\u003e\n\u003cp\u003eIdeal\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"79\"\u003e\n\u003cp\u003e321 (8.7)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"63\"\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"84\"\u003e\n\u003cp\u003e645 (17.4)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"63\"\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"74\"\u003e\n\u003cp\u003e234 (6.3)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"58\"\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"87\"\u003e\n\u003cp\u003e224 (6.1)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"64\"\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"153\"\u003e\n\u003cp\u003eIntermediate\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"79\"\u003e\n\u003cp\u003e144 (13.9)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"63\"\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"84\"\u003e\n\u003cp\u003e308 (29.8)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"63\"\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"74\"\u003e\n\u003cp\u003e112 (10.8)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"58\"\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"87\"\u003e\n\u003cp\u003e99 (9.6)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"64\"\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"153\"\u003e\n\u003cp\u003ePoor\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"79\"\u003e\n\u003cp\u003e69 (23.3)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"63\"\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"84\"\u003e\n\u003cp\u003e145 (49.0)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"63\"\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"74\"\u003e\n\u003cp\u003e39 (13.2)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"58\"\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"87\"\u003e\n\u003cp\u003e48 (16.2)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"64\"\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"153\"\u003e\n\u003cp\u003eBlood pressure (%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"79\"\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"63\"\u003e\n\u003cp\u003e\u0026lt; 0.001\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"84\"\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"63\"\u003e\n\u003cp\u003e\u0026lt; 0.001\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"74\"\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"58\"\u003e\n\u003cp\u003e\u0026lt; 0.001\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"87\"\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"64\"\u003e\n\u003cp\u003e0.001\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"153\"\u003e\n\u003cp\u003eIdeal\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"79\"\u003e\n\u003cp\u003e96 (5.7)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"63\"\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"84\"\u003e\n\u003cp\u003e210 (12.5)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"63\"\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"74\"\u003e\n\u003cp\u003e84 (5.0)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"58\"\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"87\"\u003e\n\u003cp\u003e92 (5.5)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"64\"\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"153\"\u003e\n\u003cp\u003eIntermediate\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"79\"\u003e\n\u003cp\u003e205 (11.0)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"63\"\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"84\"\u003e\n\u003cp\u003e424 (22.8)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"63\"\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"74\"\u003e\n\u003cp\u003e150 (8.1)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"58\"\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"87\"\u003e\n\u003cp\u003e153 (8.2)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"64\"\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"153\"\u003e\n\u003cp\u003ePoor\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"79\"\u003e\n\u003cp\u003e233 (15.7)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"63\"\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"84\"\u003e\n\u003cp\u003e464 (31.2)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"63\"\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"74\"\u003e\n\u003cp\u003e151 (10.2)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"58\"\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"87\"\u003e\n\u003cp\u003e126 (8.5)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"64\"\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"153\"\u003e\n\u003cp\u003eSleep duration (%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"79\"\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"63\"\u003e\n\u003cp\u003e0.304\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"84\"\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"63\"\u003e\n\u003cp\u003e0.137\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"74\"\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"58\"\u003e\n\u003cp\u003e0.054\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"87\"\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"64\"\u003e\n\u003cp\u003e0.361\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"153\"\u003e\n\u003cp\u003eIdeal\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"79\"\u003e\n\u003cp\u003e267 (10.4)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"63\"\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"84\"\u003e\n\u003cp\u003e544 (21.1)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"63\"\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"74\"\u003e\n\u003cp\u003e189 (7.3)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"58\"\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"87\"\u003e\n\u003cp\u003e179 (6.9)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"64\"\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"153\"\u003e\n\u003cp\u003eIntermediate\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"79\"\u003e\n\u003cp\u003e220 (10.6)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"63\"\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"84\"\u003e\n\u003cp\u003e464 (22.3)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"63\"\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"74\"\u003e\n\u003cp\u003e153 (7.4)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"58\"\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"87\"\u003e\n\u003cp\u003e165 (7.9)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"64\"\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"153\"\u003e\n\u003cp\u003ePoor\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"79\"\u003e\n\u003cp\u003e47 (12.6)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"63\"\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"84\"\u003e\n\u003cp\u003e90 (24.1)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"63\"\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"74\"\u003e\n\u003cp\u003e43 (11.5)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"58\"\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"87\"\u003e\n\u003cp\u003e27 (7.2)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"64\"\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd colspan=\"9\" width=\"726\"\u003e\n\u003cp\u003e\u003cem\u003eP\u003c/em\u003e value was expressed as trend chi-square test.\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd colspan=\"9\" width=\"726\"\u003e\n\u003cp\u003e\u003cem\u003eBMI\u003c/em\u003e body mass index, \u003cem\u003eFBG\u003c/em\u003e fasting blood glucose, \u003cem\u003eWHR\u003c/em\u003e waist-to-hip ratio.\u0026nbsp;\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd colspan=\"9\" width=\"726\"\u003e\n\u003cp\u003e\u003cem\u003eTC\u003c/em\u003e total cholesterol, \u003cem\u003eTG\u003c/em\u003e triglyceride, \u003cem\u003eLDL-C\u003c/em\u003e low -density lipoprotein cholesterol, \u003cem\u003eHDL-C\u003c/em\u003e high-density lipoprotein cholesterol.\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003c/tbody\u003e\n\u003c/table\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":"bmc-public-health","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"pubh","sideBox":"Learn more about [BMC Public Health](http://bmcpublichealth.biomedcentral.com/)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/pubh/default.aspx","title":"BMC Public Health","twitterHandle":"@BMC_series","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"em","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"Dyslipidemia, Cardiovascular diseases, Healthy behavior, Metabolic factors, Ethnic minorities.","lastPublishedDoi":"10.21203/rs.3.rs-110397/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-110397/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003e\u003cstrong\u003eBackground: \u003c/strong\u003eBehavioral and metabolic risk factors will increase the risk of dyslipidemia, the association of behaviors and metabolic factors with dyslipidemia among Miao adults\u003cstrong\u003e \u003c/strong\u003eare still unclear.\u0026nbsp;\u003c/p\u003e\u003cp\u003e\u003cstrong\u003eObjective: \u003c/strong\u003eTo evaluate the association between behaviors, metabolic factors and dyslipidemia.\u003c/p\u003e\u003cp\u003e\u003cstrong\u003eMethods: \u003c/strong\u003eBased on the CMEC study, a representative samples of 5,559 Miao participants aged 30 to 79 years old who were included in the baseline survey from 2018 to 2019 were analyzed. A binary logistic regression model was utilized to evaluate the odds ratios (OR) and 95% confidence intervals (CI) of the associations of healthy behaviors and metabolic factors with dyslipidemia.\u003c/p\u003e\u003cp\u003e\u003cstrong\u003eResults: \u003c/strong\u003eIn both sexes, only a small percentage of females with ideal levels of waist-to-hip ratio (WHR) (27.2%). However, participants were more likely to have ideal levels of fasting blood glucose (FBG). In addition, males with dyslipidemia had poor levels of body mass index (BMI) (60.3%), WHR (59.2%) and FBG (65.8%). While females with dyslipidemia had poor levels of FBG (60.0%). Notably, our study found that WHR, BMI, FBG, and blood pressure were major risk factors for almost all dyslipidemia components.\u0026nbsp;\u003c/p\u003e\u003cp\u003e\u003cstrong\u003eConclusions: \u003c/strong\u003eThe rate of dyslipidemia cannot be ignored, particularly high TG levels. In addition, healthy behaviors and metabolic factors, especially WHR, BMI, FBG levels, and blood pressure were significantly associated with dyslipidemia, which may have become a major challenge to public health problems in the ethnic minority areas of Guizhou.\u003c/p\u003e","manuscriptTitle":"The Association of Healthy Behaviors and Metabolic Factors With Dyslipidemia Among Miao Adults:\u0026nbsp;The\u0026nbsp;China\u0026nbsp;Multi-Ethnic\u0026nbsp;Cohort\u0026nbsp;(CMEC)\u0026nbsp;Study","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2020-11-30 21:10:14","doi":"10.21203/rs.3.rs-110397/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Major revision","date":"2021-01-04T05:15:12+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2020-12-23T09:26:17+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"a6b09b03-bb93-4ff3-817f-b577fb4bc1f3","date":"2020-12-02T15:03:10+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2020-11-28T06:04:52+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2020-11-28T04:12:20+00:00","index":"","fulltext":""},{"type":"editorInvited","content":"","date":"2020-11-24T11:55:09+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2020-11-24T11:47:18+00:00","index":"","fulltext":""},{"type":"submitted","content":"BMC Public Health","date":"2020-11-17T17:21:40+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"bmc-public-health","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"pubh","sideBox":"Learn more about [BMC Public Health](http://bmcpublichealth.biomedcentral.com/)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/pubh/default.aspx","title":"BMC Public Health","twitterHandle":"@BMC_series","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"em","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"fdc6230d-1198-40f0-99bf-d61fd6eb851c","owner":[],"postedDate":"November 30th, 2020","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"under-review","subjectAreas":[{"id":1224420,"name":"Health Economics \u0026 Outcomes Research"},{"id":1224421,"name":"Health Policy"}],"tags":[],"updatedAt":"2021-04-19T03:59:08+00:00","versionOfRecord":[],"versionCreatedAt":"2020-11-30 21:10:14","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-110397","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-110397","identity":"rs-110397","version":["v1"]},"buildId":"_2-kVJe1T_tPrBINL-cwx","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}