Study population
The China Health and Retirement Longitudinal Study (CHARLS) is a nationally representative longitudinal cohort of persons aged 45 years or above. The participants were recruited from 150 counties of 28 provinces in China using a four-stage stratified cluster sampling method19. The baseline investigation was conducted in 2011-2012 and follow-up surveys were conducted every two years. In this study, we used data from Wave1 (2011–2012) to Wave4 (2018) with Wave1 as the baseline.
A total of 15,292 participants aged ≥45 years participated in biomarker measurement at least once in Wave1-3. To obtain the maximum sample size, the independent variables were values in the earliest available measurement. Subjects who met the following criteria were excluded: (1) missing data on e-CRF components (age, sex, BMI, WC, rHR, physical activity level, smoking), (2) had heart disease and stroke at baseline, (3) loss to follow-up. Ultimately, a total of 10,507 participants were include into this study. Ethics approval for the data collection was obtained from the Biomedical Ethics Review Committee of Peking University.
Background characteristics and anthropometric measurements
Participants were interviewed face-to-face using a structured questionnaire, which asked for demographic information (e.g., date of birth, sex, education attainment,), lifestyle data (e.g., smoking, alcohol use, physical activity), self-reported diseases (heart disease, stroke, hypertension, dyslipidemia, diabetes, and cancer) and use of medications for corresponding diseases. In this study, smoking/ drinking were defined as current or previous smoking/drinking. Each subject reported their amount of physical activity in a usual week including vigorous physical activity (VPA), moderate physical activity (MPA) and light physical activity (LPA). Subjects who had conducted VPA/MPA/LPA for at least 10 minutes continuously in a usual week were further asked about the frequency and duration of VPA/MPA/LPA. Conducting a total of VPA or MPA greater than 4 days per week was considered as active physical activity.
Anthropometric indexes including weight, height, WC, rHR, systolic blood pressure (SBP) and diastolic blood pressure (DBP) were measured by trained staff following a standard protocol. BMI was calculated as weight (kg)/height (m)2. Hypertension was diagnosed by the following criteria: 1) SBP≥140 mmHg and/or DBP≥90 mmHg, 2) any treatment for hypertension, and 3) self-reported history of hypertension diagnosed by clinical physicians.
The whole blood and serum samples were collected from each subject after a 12-h overnight fasting. Biochemical variables including TG, total cholesterol (TC), HDL-C and low-density lipoprotein cholesterol (LDL-C) and fasting plasma glucose (FPG) were tested in metabolic examinations.
Assessment of e-CRF
e-CRF in METs was estimated using the sex-specific BMI models constructed by Jackson et al14. These models have been validated in adults and the generated e-CRF have shown association with long-term health risk6,15-17,20,21. The models are as follows:
Male: e-CRF (METs) = 21.2870 + (age × 0.1654) – (age2 × 0.0023) – (BMI × 0.2318) – (WC × 0.0337) – (rHR × 0.0390) + (active physical activity × 0.6351) – (smoker × 0.4263);
Female: e-CRF (METs) = 14.7873 + (age × 0.1159) – (age2 × 0.0017) – (BMI × 0.1534) – (WC × 0.0088) – (rHR × 0.0364) + (active physical activity × 0.5987) – (smoker × 0.2994).
Active physical activity =1 if the participant is classified as active physical activity or 0 if not. Smoker = 1 if the participant is current or ever smoker or 0 if not.
Ascertainment of outcome
In this study, CVD includes heart disease and stroke. Heart disease was defined as heart attack, coronary heart disease (CHD), angina, congestive heart failure or other heart problems. Heart disease and stroke were assessed by asking the participant whether they had been diagnosed with any heart disease/stroke by a doctor and whether they had been under treatment because of heart disease/stroke?” Participants who reported either of the above were classified as incident heart disease/stroke cases. The diagnosis date of incident heart disease or stroke cases was determined by the date between the time of the survey that first recorded heart disease/stroke patients and the time of the previous round of survey.
Statistical analyses
After applying the algorithms of e-CRF, subjects were divided into four groups according to sex-specific quartile. Continuous variables were described as mean (standard deviation, [SD]) and were analyzed using the one-way ANOVA. Categorical variables were described as number (%) and were compared by chi-square test.
Cox regression was used to estimate hazard ratios (HRs) and 95% confidence intervals (CIs) of incidence of CVD, heart disease and stroke associated with e-CRF as categorical variable by quartile, with the first quartile (Q1) as reference group, and as continuous variable. Models were separately constructed for males, females and all participants. Age, resident area, smoking status, drinking status, hypertension, TG, HDL-C, FPG and drugs used to treat for hypertension, dyslipidemia and diabetes were adjusted. The follow-up time for each participant was defined as the duration from baseline date to the diagnosis date of heart disease and stroke or the end of following up (Wave4, 2018), whichever came first. The incidence rate of CVD, heart disease and stroke was calculated by the number of incident events divided by the person-years. Proportional hazards assumptions were tested using standard Schoenfeld residuals and no violation was observed. The linear trend was tested by modelling e-CRF as an ordinal variable. Sensitivity analyses were carried out by excluding subjects whose outcomes occurred in the first year of follow-up to avoid reverse causation and using the equations of e-CRF developed by Nes et al9. Subgroup analysis was performed by age group (≤ 60 and > 60 years).
A two-tailed P <0.05 was defined as statistical significance. All statistical analyses were conducted using R software (4.2.0).