Cross-sectional study design conducted from April to June 2017.
The data of this study abstracted from the project of Ningxia Hui Autonomous Region 13th Five-Year Technology Major Project, which was aimed to exploring the clinical function and application of intestinal flora in chronic diseases. A multi-stage sampling protocol was used to select the subjects in the project. In summary, first, four counties/ districts (Xingqing District, Litong District, Pengyang County, and Xiji County) were selected from a total of 22 counties/districts using the stratified sampling design. The target counties/districts were classified into four stratus depending on the proportion of the minority population and the economic status (less minority population with lower economic status, less minority population with higher economic status, more minority population with lower economic status, more minority population with higher economic status). Second, ten communities were selected from each district/county using random sampling stratified by urban and rural areas, results a total of forty communities consist of twenty rural communities and twenty urban communities forward to the next step. Third, 115 households were selected in each community by the systematic sampling method. Finally, the KISH Table was used to select one eligible family member from each household, there were 615 households do not get touched after three times attempts which result in a total of 3985 eligible participants were selected and be invited to receive a face-to-face survey and physical examination. Of them, 2418 participants accepted the invitation and finished both the full questionnaire and physical examination with cardiometabolic biomarkers test include in the final data analysis. The response rate in the rural area slightly higher than in the urban community (65.5% vs. 60.8%).
The inclusion criteria for this study were as follows: a) living at the present address for least 6 months, and; b) age ranging from 18 to 80 y. The exclusion criteria were as follows: a) unconsciousness caused by any forms condition; b) the acute phase of a cerebrovascular accident; c) a severe illness that prevents communication; d) any obvious cognitive disabilities or deafness, aphasia or other language barriers; and e) with sleep disorders and taking hypnotics, as well as some particular work need to going to bed late.
All the participants underwent a careful physical examination and provide the blood sample for cardiometabolic biomarkers test. All those laboratory tests were finished in the hospital laboratory according to standard procedure.
A face-to-face interview was performed by trained medical students using a structured questionnaire. One item question “What time do you usually go to sleep at night?”, another item question “What time do you usually rise in the morning?” asked to identify SOT and sleep duration. The SOT was divided into six groups as before 8:00 pm, 8:00 pm-9:00 pm, 9:00 pm-10:00 pm, 10:00 pm-11:00 pm, 11:00 pm-12:00 midnight, and after 12:00 midnight.
Health-related behaviors included smoking, alcohol use, tea-drinking, exercise. Those health-related behavior variables employed in this paper were operationally defined. Smoking was defined as at least one cigarette per day and last for 6 months or more. Alcohol use was defined as at least one glass of alcohol use in the past 12 months. Tea drinking frequency was assessed by asking the question “How often do you drink tea (days per week)?” with the possible response: once a day or more, 5–6 times/week, 3–4 times/week, 1–2 times/week, less than once a week and never. Physical exercise was assessed by asking the question “Do you perform at least 30 minutes of physical activity at work and/or leisure time more than 4 days a week?”, with a yes/ no response.
Physical health characteristics include diabetes (yes vs. no) and hypertension (yes vs. no). The diabetes mellitus (DM) was diagnosed according to the WHO criteria through performing oral glucose tolerance test (OGTT) when a two hours post glucose load over 11.1 mmol/L was defined as DM. Hypertension was defined as systolic blood pressure ≥ 140 mmHg or diastolic blood pressure ≥ 90 mmHg. Weight and height were measured by trained nurses according to standard instruction, body mass index (BMI) was calculated with the formula: BMI = weight (kg)/height (m)2.
Demographic information collected included age, gender, education (was measured with question: how many years of school education do you have"), marital status (married vs. unmarried), residence (rural vs. urban), ethnicity (Han vs. minority), occupation, family income (measured by the self-reported family average individual income per month (in local currency RMB) and was divided into five groups: < 1000, 1000-1999, 2000-2999, 3000–4999 and 5000 or more).
All the analysis was performed using the Software for Statistics and Data Science (STATA) 14.0. The quantitative variables were described as means (median) and standard deviations (quartiles). Categorical variables were described as counts and proportions. Differences in demographic, health-related behaviors and psychical health between urban participants and rural participants were examined using the Student’s t-test for quantitative variables and the chi-square test for categorical variables. The cardiometabolic biomarkers were examined using the Wilcoxon rank-sum test. The multiple mixed-effect linear regression was employed to examine the association between SOT and the cardiometabolic biomarkers, three separate models were launched to control the covariates step by step, in model 1 adjusted for demographic variables (age, gender, ethnicity, education, marital status, occupation, economic condition); The model 2 based on model 1 plus health-related behaviors (smoking, alcohol use, tea-drinking, physical exercise); then the model 3 based on model 2 plus physical health (diabetes mellitus, hypertension, BMI). Of the independent variables, the rural/urban was fitted as a random intercept model. Due to the possible interaction of SOT and sleep duration, we performed the regression process stratified by sleep duration for models 1 to 3. The results of the regression models are summarized via beta coefficients (slopes for continuous measures, the beta coefficients reported for SOT refer to every one hour delay in SOT, and differences between groups for categorical measures) and their 95% confidence intervals.