Northeast China Rural Cardiovascular Health Study (NCRCHS) is a cohort study in a general population. The methods of the study, including design, personnel recruitment, and laboratory techniques, have been described in previous publications [16, 17]. Between January 2013 and August 2013, 11,956 subjects aged ≥ 35 years were recruited from rural areas of Liaoning province. Subsequently, subjects were invited to attend follow-up visits in 2015 and 2018, and 6017 hypertensive participants were consented and eligible for the follow-up study. A total of 5249 participants of hypertension completed at least one follow-up visit. In the present study, we excluded baseline history of coronary heart disease (n = 355) and stroke (n = 590), and missing data (n = 60). Eventually, data from 4244 participants were available for analysis. The Ethics Committee of the First Hospital of China Medical University (Shenyang, China) approved the study. All participants wrote the informed consent.
Data was collected during a single clinic visit by cardiologists and trained nurses using a standard questionnaire by face-to-face interview. During data collection, our inspectors had further instructions and support.
All participants were asked about the current status of smoking, drinking and the history of diseases. All participants were performed according to the BMI levels of China (BMI < 18.5 kg/m2, 18.5 kg/m2 ≤ BMI < 24 kg/m2, 24 kg/m2 ≤ BMI < 30 kg/m2, BMI ≥ 30 kg/m2). WC divided by height is the waist-to-height ratio. We categorized WHtR according to the Ashwell’s reports. The reference group was the participants with WHtR between 0.40 and 0.50 [18,19,20].
According to American Heart Association protocol, blood pressure was measured three times at 2-min intervals after at least 5 min of rest using a standardized automatic electronic sphygmomanometer (HEM-907; Omron). The mean of three blood pressure measures was calculated and used in all analyses. Hypertension was defined as a mean DBP ≥ 90 mmHg, and/or a mean SBP ≥ 140 mmHg, and/or use of the antihypertensive medication in the previous 2 weeks [21, 22]. Diabetes mellitus was defined as FBG ≥ 7.0 mmol/l and/or self-reported physician-confirmed diagnosis . Fasting blood samples were collected after at least 10 h of fasting. Blood samples were taken from an antecubital vein into BD Vacutainer tubes containing ethylenediaminetetraacetic acid. Serum was subsequently isolated from the whole blood, and all serum samples were frozen at − 80 °C for testing at a central, certified laboratory. We used the Olympus AU640 auto-analyzer (Olympus, Kobe, Japan) for analyzing blood biochemical indexes. All blinded duplicate samples were used for these analyses.
Judgment and definition of clinical outcomes
We collected all available clinical information about possible diagnoses or mortality, including data from medical records and death certificates. CVD was defined as stroke or Coronary heart disease (CHD). Stroke were diagnosed by neurologists following the examination of computed tomography and magnetic resonance imaging data in accordance with World Health Organization (WHO) criteria . CHD was defined as a diagnosis of angina requiring hospitalization, myocardial infarction (MI), revascularization procedure and CHD-related mortality .
Continuous variables were presented as means and SDs and categorical variables were reported as frequencies and percentages in each group. Differences between categories were evaluated using the t test, or the Chi-Square test. Kaplan–Meier method was used to calculate the cumulative incidence for adverse events, and log-rank test was used to compare differences. We used Cox proportional hazards models to estimate the Hazard ratios (HR) and 95% confidence intervals (95% CI) for the association between anthropometric obesity indicators and CVD event. To evaluate the improvement in risk prediction for adverse outcomes by adding WHtR to the conventional model (including age, sex, current smoking, current drinking, SBP, DBP, TC, HDL-C, LDL-C, triglyceride, and diabetes), net reclassification improvement (NRI) and integrated discrimination improvement (IDI) was calculated for CVD prediction models respectively (conventional model vs. conventional model + WHtR). The calculation method is IDI = (Pnew, events-Pold, events)-(Pnew, non-events-Pold, non-events). With the larger value of IDI, the new model has the better prediction ability.
SPSS software version 22.0 was used for statistical analysis and statistical software packages R (http://www.R-project.org, The R Foundation). P < 0.05 was considered to be statistically significant.
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