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


 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.

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 in uence 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 clari ed. 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.

Study participants
The CMEC study is a large-scale prospective cohort study based on community population in ve 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 strati ed 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 identi cation 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).

De nition of dyslipidemia
According to the guidelines for the prevention and treatment of dyslipidemia in Chinese adults [16] , dyslipidemia was de ned 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 de ned as a person's weight in kilograms divided by the square of the height in meters (kg/m 2 ).

Classi cation 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 de nitions 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 strati cation 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 strati cation. 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 strati cation, 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 signi cant.

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 signi cant (all P value < 0.05). The dyslipidemia rate of the participants was 32.8% (agestandardized rate of 23.3%). In addition, individuals with dyslipidemia were more likely to be males, manager or professional technology elds, 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 classi cation (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 strati cation, 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 uctuated 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 strati cation, 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][18][19][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 signi cantly 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 in uencing 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 con rmed 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 identi ed 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 ndings [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 classi ed only according to the frequency of drinking and lacked a speci c 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 signi cant 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 signi cantly 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 speci c elds and blood lipids. Therefore, subsequent research can further explore the association between physical activity in speci c elds and lipid components. Shigeki Kinuhata observed a positive correlation between sleep duration and high TG levels [31] . Zhan found that sleep duration was signi cantly 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 bene t 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 rst 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 in uence 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 con rm 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 identi ed the typical risk factors for dyslipidemia components in Miao adults. These ndings are extremely critical for identifying target groups with high risk factors to implement screening dyslipidemia.
Given the worldwide prevalence of dyslipidemia, present ndings have important public health signi cance for the prevention and    BMI body mass index, FBG fasting blood glucose, WHR waist-to-hip ratio.