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 .
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.
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.
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 , 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/m2).
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).
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(X2) 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.