Study design and participants
The China Health and Retirement Longitudinal Study (CHARLS) is a nationally population-based representative prospective study of Chinese adults aged ≥ 45 years. The CHARLS is designed to describe the dynamics of retirement and its impact on health, health insurance, and economic well-being. The baseline survey was conducted in 2011-2012 among 17,708 participants in 10,257 households were recruited from 150 counties of China’s 28 provinces, using the multistage stratified probability-proportional-to-size sampling technique. [24] All participants underwent an assessment using a standardized questionnaire to collect data on socioeconomic status, life style factors and health-related information. All participants were followed up every 2 years after the baseline survey.
The CHARLS data are available for the baseline survey in 2011-2012 (wave 1), the first follow-up survey in 2013-2014 (wave 2), the second follow-up survey in 2015-2016 (wave 3), and the third follow-up survey in 2018-2019 (wave 4). We excluded individuals with preexisting cardiovascular disease (defined as known previous heart disease or stroke), or statin use at baseline. Our final sample size was 9,631 participants. The CHARLS study was approved by the ethical review committee of Peking University and all participants provided written informed consent.
Outcome Ascertainment
The study outcome was incident CVD. Incident CVD was based on self-reported physician’s diagnosis following standardized questions: “Has a doctor ever told you that you had any heart disease [myocardial infarction, coronary heart disease, angina, congestive heart failure, or other heart problems] or “Has a doctor ever told you that you have been diagnosed with a stroke?” Participants who reported heart disease or stroke during the follow-up period were defined as having incident CVD. The date of CVD diagnosis was recorded as being between the date of the last interview and that of the interview reporting an incident CVD.
Determination of lipid profile
Serum samples were collected after an overnight fast and stored at -80℃ until analysis. Biochemical analyses were performed at the Youanmen Center for Clinical Laboratory of Capital Medical University. This laboratory has a regular external quality assessment organized by the Chinese Ministry of Health. [25] Serum total cholesterol (TC), triglycerides (TG), low-density lipoprotein cholesterol (LDL-C), high-density lipoprotein cholesterol (HDL-C) were measured by an enzymatic colorimetric test. Blood glucose was measured by the glucose oxidase method. Glycosylated hemoglobin (HbA1c) was measured by the affinity high performance liquid chromatography (HPLC) method.
Covariates
At baseline, trained interviewers collected information on social and demographic characteristics and health-related factors using a standard questionnaire, including age, sex (male, female), living residence (rural, urban), marital status (married and other marital status), education level (primary school and below, middle school and above). Self-reported health behaviors included smoking status (never, former, and current smoker), frequency of alcohol consumption in the past year (never, less than once a month, and more than once a month), self-reported physician-diagnosed chronic diseases (diabetes, hypertension, dyslipidemia, and chronic kidney disease), and use of prescribed medications for diabetes, hypertension. Height, weight, and blood pressure were measured by a trained nurse. Body mass index (BMI) was calculated as the weight in kilograms divided by the square of the height in meters.[26]
Definition and calculation
Hypertension was defined as systolic blood pressure (SBP) ≥140 mmHg and / or diastolic blood pressure (DBP) ≥90 mmHg or current use of the antihypertensive medication or self-reported history of hypertension. Diabetes was defined as fasting plasma glucose≥126 mg/dL or current use of antidiabetic medication or self-reported history of diabetes. Chronic kidney disease was defined as estimated glomerular filtration rate <60 mL/min/1.73 m2 or self-reported history of chronic kidney disease. The estimated glomerular filtration rate(eGFR) was calculated using the CKD- EPI equation.[27] Participants attributable risk percent (PARP)= (It - I0)/It× 100%, It: incidence of CVD in the participants over 70 years old stratified by LDL-C, I0: incidence of CVD in participants below the median group of LDL-C.
Statistics
Data were described as means and SDs for normally distributed continuous variables, and as medians and interquartile ranges for nonnormally distributed continuous variables. Frequency with percentage was used to describe categorical variables. Differences in baseline characteristics of the participants by age were evaluated by chi-square, analysis of variance, or Kruskal-Wallis tests, as appropriate.
Cox proportional hazards models were used to assess the association between elevated LDL-C and risk of CVD, in which the demographic characteristics (age, sex, rural residence, married, educational level), behavioral factors (smoking status, drinking status), history of disease and medication (history of comorbidities, history of medication use) and metabolism related status (SBP, DBP, BMI, FPG, HbA1c, TC, TG, HDL-C, eGFR and hs-CRP) were adjusted. These missing data of these covariates were inserted with multiple imputation. We tested the Cox proportional hazard assumption visually for covariates using Shoenfeld residuals and found no violations. In addition, we explored the potential nonlinear associations using 3-knotted restricted cubic spline regression.
Subgroup analyses were conducted in the participants with: sex, hypertension, diabetes, chronic kidney disease. In sensitivity analyses, a simpler E-value technique was used in this study to analysis the potential impact of unmeasured confounding in the association of LDL-C and CVD. The 2-sided significance level was set at p < 0.05. All statistical analyses were performed using the SPSS 25 software and Stata/SE 15.1.