Study Populations
The design and methods of the Kailuan study have been described previously[20]. This is a prospective, community-based study of 101,510 Chinese adults (81,110 men and 20,400 women) aged 18 years or older who were residents of the Kailuan community in Tangshan, Hebei, China in 2006. These participants were recruited from 11 hospitals affiliated with the Kailuan community and were followed biennially.
In 2006 and 2007, participants completed a baseline survey including a detailed health, medical and lifestyle questionnaire and a structured physical examination, and provided a blood sample for laboratory analyses. Participants were reexamined biennially in 2008, 2010, 2012 and 2014. There were no entry criteria regarding lipid profiles concentrations.
Individuals were excluded from this analysis for the presence of atherosclerotic CVD or cancer (n=4,114), a lack of information on HDL-C concentrations or DM status (n=4,144) at baseline, and use of lipid-lowering drugs (n=1,898). The final sample comprised of 91,354 participants.
Ascertainment of lipid profiles and DM status
Fasting (8-12 hours) venous blood samples were collected by venipuncture into vacuum tubes containing EDTA at baseline and the subsequent biannual follow-up visits. Blood samples were stored at -80℃ and were analyzed in a blinded manner at the Central Laboratory of Kailuan General Hospital.
Fasting serum concentrations of lipid profiles, creatinine and glucose were measured on an auto-analyzer (Hitachi 747; Hitachi, Tokyo, Japan). Fasting blood glucose concentrations were determined with hexokinase/glucose-6-phosphate dehydrogenase method (BioSino Bio-technology and Science Inc., Beijing, China). The coefficient of variation using blind quality control specimens was <2.0% for fasting blood glucose. Fasting HDL-C and LDL-C concentrations were measured with direct test method (Mind Bioengineering Co. Ltd, Shanghai, China) and with upper limits of detection of 12.90 and 3.88 mmol/L, respectively[21]. Triglyceride with enzymatic colorimetric method (Mind Bioengineering Co. Ltd, Shanghai, China), and creatinine with sarcosine oxidase assay method (BioSino Bio-technology and Science Inc., Beijing, China). The intra- and inter-assay variable coefficients for each measurement were <10%.
Information on physician diagnosed DM and use of glucose lowering medication was collected via a questionnaire in 2006 and was updated every two years during the follow-up. Individuals who were considered DM in the current analysis is they had a fasting blood glucose concentration of ≥7mmol/L, physician-diagnosed DM or self-reported use of glucose lowering medication[22]. We also conducted a secondary analysis using updated DM information during the follow-up.
Ascertainment of incident death and CVD events
The primary endpoint of interest was a composite of myocardial infarction, ischemic stroke or hemorrhage stroke. All death and CVD events and vital status were identified by directly contacting participants’ family, or reviewing medical records or death certificates at the Municipal Social Insurance Institution and all the 11 Kailuan Hospitals’ Discharge Register, which included all the Kailuan study participants. Information regarding past medical history of CVD, coded as International Classification of Diseases-10th Revision was collected via biennial questionnaire since 2006[23]. Specifically, myocardial infarction was diagnosed based on cardiac symptoms, positive cardiac biomarkers or electrocardiography[24]. Ischemic stroke and hemorrhagic stroke were defined as neurological deficit of cerebrovascular cause that lasted more than 24 hours or a significant lesion detected by computed tomography or magnetic resonance imaging[25]. A blinded panel of three experienced cardiologists reviewed the medical records, discharge summaries and death certificates from local hospitals or vital statistics offices.
Assessment of potential covariates
Age, sex, lifestyle (e.g., cigarette smoking, alcohol consumption and habitual physical activity), family history of CVD and socio-demographic data (e.g., education level, occupation and monthly salary) were collected in 2006 and updated every 2 years using questionnaires, as detailed previously[26]. Cigarette smoking was divided into 4 categories: “never”, “former”, “occasional (current smoking <1cigarette/d),’’ or ‘‘daily (current smoking≥1 cigarette/d)”. Alcohol intake was assessed by a questionnaire in 2006 and participants were categorized as never, former, occasional (current drinking <1time/day) or daily (current drinking ≥1time/day). Physical activity was captured by questionnaire according to activities associated with occupation and leisure time and was classified as “inactive”, “moderately active” and “vigorously active”[26].
Blood pressure, waist circumference and anthropometric measurements (weight and height) were measured according to standardized procedures by trained study staff[20]. Body mass index was calculated by dividing weight in kilograms by the square of the height in meters. Blood pressure was measured with a random zero sphygmomanometer after individuals had been seated quietly for at least 5 min. Hypertension was defined as systolic blood pressure of ≥140mmHg or diastolic blood pressure of ≥90 mmHg or a history of physician- diagnosed hypertension or if on anti-hypertensive agents; pre-hypertension was defined as systolic blood pressure of 120-139mmHg or diastolic blood pressure of 80-90mmHg; normotension was defined as systolic blood pressure of <120mmHg and diastolic blood pressure of <80mmHg[27].
High sensitivity C-reactive protein concentrations were measured using a high-sensitivity particle- enhanced immunonephelometry assay (Cias Latex CRP-H, Kanto Chemical Co. Inc., Japan)[20]. Estimated glomerular filtration rate was calculated according to the Chronic Kidney Disease Epidemiology Collaboration equation considering creatinine, sex, and age[28].
Statistical analysis
The person-time for each participant was accumulated from the finishing date of the baseline survey to whichever came first: CVD events, death or termination of follow-up (December 31, 2017). The cumulative average concentrations of HDL-C and other continuous variables were used as a primary exposure because they represent long-term patterns of individuals[29]. For example, we used the 2006 HDL-C concentrations to predict outcomes occurring from 2006 to 2008, the average of the 2006 and 2008 HDL-C concentrations to predict outcomes occurring from 2008 to 2010, the average of the 2006, 2008, and 2010 HDL-C concentrations to predict outcomes occurring from 2010 to 2012, the average of the 2006, 2008, 2010 and 2012 HDL-C concentrations to predict outcomes occurring from 2012 to 2014, and the average of the 2006, 2008, 2010, 2012 and 2014 HDL-C concentrations to predict outcomes occurring after 2014. The linearity of HDL-C for mortality and CVD risk stratified by DM status was assessed using restricted cubic spline Cox model. We chose 5 knots based on the 5th, 27.5th, 50th, 72.5th and 95th percentiles of HDL-C concentrations that can offer adequate fit of the model.
The association between HDL-C concentrations and CVD events was further examined using Cox regression analysis with corresponding 95% confidence intervals (CIs) based on predefined groups with clinically meaningful cut-offs of HDL-C concentrations and deciles. HDL-C concentrations were categorized as <1.04, 1.04 to 1.30, 1.30 to 1.42, 1.43 to 1.55, 1.55 to 1.81, 1.81 to 2.07 and >2.07 mmol/L[30, 31]. Reference groups were selected as the ones with the lowest hazard ratios (HRs) of CVD outcomes in the spline Cox regression model. All analyses were adjusted for age, sex, body mass index, waist circumference, cigarette smoking, alcohol consumption, monthly income, education, occupation, physical activity, baseline blood pressure status, systolic blood pressure and diastolic blood pressure during follow-up, family history of myocardial infarction, stroke, hypertension or DM, estimated glomerular filtration rate, circulating concentrations of LDL-C, triglyceride, glucose and high sensitivity C-reactive protein. Joint effects of HDL-C concentrations (5 groups) and DM status (yes/no) were further examined in a secondary analysis.
Interactions of HDL-C/DM, with age, sex, cigarette smoking, hypertension and triglyceride, in relation to CVD were assessed by likelihood ratio testing, adjusting for aforementioned covariates. Several sensitivity analyses were conducted to test the robustness of results. Because circulating HDL-C concentrations may fluctuate due to impending CVD events and yield reverse causal association, we excluded incident CVD events occurring during the first 2 years of follow-up. Given that potential pharmacologic effect might confound the results, in a separate sub-group analysis, we excluded the participants who used glucose lowering drugs (n=3,618). Statistical analyses were performed using STATA12.0 (STATA Institute) and a 2-sided p value <0.05 was considered significant.