Cardiovascular disease and all-cause mortality associated with individual and combined cardiometabolic risk factors

Study population

China Hypertension Survey (CHS) is a nationally representative population-based study, which recruiting ~ 0.5 million participants from 31 provinces between October 2012 to December 2015 in mainland China. Design details were published previously [13,14,15]. This sub-study was based on CHS, and 16 cities and 17 counties were selected using a simple random sampling method from eastern, central, and western regions according to their geographical location and economic level [16]. Next, at least three communities or villages were randomly selected from each region. Then a given number of participants aged ≥ 35 years were selected from communities or villages. Finally, a total of 30,036 participants with incomplete physical examinations at baseline between 2012 to 2015 were followed up in 2018–2019. This study was approved by the Ethics Committee of Fuwai Hospital (Beijing, China) and performed under the guidelines of the Helsinki Declaration. Informed consent was obtained from each participant.

We dropped out participants who lost to follow-up (n = 4711), then subjects with medical history of CVD (coronary heart disease (CHD), stroke and other heart diseases that are not clearly defined, n = 2135) and 594 (2.0%) participants with missing data on covariates at baseline were also excluded. Ultimately, we included 22,596 participants who were free of CVD and had complete information on diabetes, hypertension, and lipids at baseline into the main analysis (Figure S1 in supplementary materials).

Definition and classification of cardiometabolic factors

Prevalent hypertension was defined as systolic blood pressure (SBP) ≥ 140 mmHg, and /or diastolic blood pressure (DBP) ≥ 90 mmHg, and/or self-reported physician-diagnosed hypertension, and/or medication use for antihypertensive at baseline. According to Chinese Diabetes Society 2017 criteria, diabetes was defined as fasting plasma glucose (FPG) level of 7.0 mmol/L (126 mg/dL) or more, or self-reported previous diagnosis for type 2 diabetes mellites by health care professionals, or taking anti-diabetic drugs [17]. Low-density lipoprotein cholesterol (LDL-C) was selected as the primary lipid factor of interest, and LDL-C ≥ 4.12 mmol/L (160mg/dL) was considered as high LDL-C [18].

Covariates

At baseline, information on demographic characteristics, lifestyle risk factors, and medical history were collected by well-trained interviewers via a structured questionnaire. Age of participants was categorized into five groups: 35–44, 45–54, 55–64, 65–74, and ≥ 75 years old. Educational attainment was classed into two groups: middle-high school or lower and high school or above. Information on employment status was also collected through self-reporting and the classification included employed, retired, students and unemployed. Participants who had at least one parent or siblings with a medical history of CVD was considered as having family history of CVD. Health-related factors included alcohol consumption, smoking (never, former and current) and medical history of CVD (CHD, stroke and other heart diseases that are not clearly defined). In the past month, participants who had a history of drinking at least once per week were defined as alcohol consumption. Height and body weight were measured with participants wearing thin clothing using a standardized right-angle device and an OMRON body fat and weight measurement device (V-body HBF-371, Omron, Japan). Body mass index (BMI), computed by dividing weight (kg) by height squared (m²), was divided into < 18.50 kg/m², 18.50–23.9 kg/m², 24.00–27.9 kg/m², and ≥ 28 kg/m² [19]. Resting Blood pressure was measured 3 times on the right arm in the sitting position after resting at least for 5 min using the Omron HBP-1300 professional portable blood pressure monitor (Omron, Kyoto, Japan), with 30 s between each measurement. The average of the three readings was used for the final analysis. Laboratory tests including TC, TG, HDL-C, LDL-C and FPG were detected by a central core laboratory (Beijing Adicon Clinical Laboratories, Inc., Beijing, China) by collecting venous blood for at least 8 h fasting.

Ascertainment of incident CVD events and mortality

The outcomes were the composite of fatal and nonfatal CVD events and mortality, and CVD events were defined as combined CHD, stroke, chronic heart failure, and death due to CVD. CHD was defined as non-fatal CHD (including myocardial infarction, coronary artery bypass graft surgery, or percutaneous coronary intervention) and fatal CHD (such as fatal myocardial infarction and other coronary deaths). Stroke included non-fatal stroke and fatal stroke (subarachnoid hemorrhage, intracerebral hemorrhage, ischemic stroke, unspecified stroke). Mortality outcomes included all-cause mortality and cause-specific mortality. CVD events and deaths were initially identified by trained health care staff, and then ascertained by the central adjudication committee of Fuwai Hospital (Beijing, China) through verification of hospital records and death certificates. Specifically, for those who have been hospitalized due to CVD, CVD events were evaluated based on the medical records or diagnosis and treatment records, such as medical history, main symptoms and signs and medical examinations, etc.; or for emergency patients, we verified these events via medical history, the disease process, the local doctor’s diagnosis and treatment provided by their relatives and local doctors. All events were coded according to the International Classification of Diseases, 10th Revision (ICD-10) by trained healthcare staffs blinded to baseline information. Moreover, during the follow-up, we conducted strict quality control by formulating a unified work plan and rigorous training, and the incident Diagnosis Committee has successively evaluated the completeness and accuracy of case identification. Additionally, we randomly selected 10% of the participants to check the false negative rate.

Statistical analysis

In this present study, the status of cardiometabolic disorder were categorized into the one of diabetes, hypertension, and high LDL-C and those with two or three combined cardiometabolic disorders at the same time according to the numbers of cardiometabolic disease at baseline. Besides, we also categorized subjects in to the following 8 mutually exclusive groups according to baseline the status of cardiometabolic disease: (1) none of these (reference group), (2) diabetes, (3) high LDL-C, (4) hypertension, (5) diabetes and high LDL-C, (6) diabetes and hypertension, (7) high LDL-C and hypertension, and (8) diabetes, high LDL-C, and hypertension.

Data were described as means (standard deviation) for continuous variables and frequency and percentages for categorical variables according to the numbers of cardiometabolic disorders at baseline. The Kolmogorov–Smirnov test was used to compare the empirical cumulative distribution function of sample data with the normal distribution. The analysis of Kruskal–Wallis Test, and χ2 test, as appropriate, were used to compare participants between the different combinations of cardiometabolic risk factors, respectively. Hazard ratios (HRs) and 95% confidential internal (CI) were calculated by using Cox proportional hazards models and the proportional hazard assumption were tested by weighted Schoenfeld residuals and P-values were greater than 0.05 for all outcomes. For all analyses, we adjusted for age, sex, BMI, alcohol consumption, smoking, educational level, employment status, urbanization (urban and rural), geographical region (east, central and western) and family history of CVD at baseline to assess accurately the relationship between individual and combined cardiometabolic disorders and the risk for CVD. Additionally, regarding that the potential impact of non-cardiovascular death as competing risk events rather than the censored, competing-risks regression based on Fine and Gray’s proportional sub-hazards model was used to evaluate this association [20, 21]. Then we redefined hypertension as the SBP ≥ 130 and/or DBP ≥ 80 according to the American Heart Association/ American College of Cardiology (AHA/ACC), and then assessed the association of the individual and combined cardiometabolic disorders with the risk for CVD and all-cause mortality. Besides, we used Poisson regression model to obtain CVD incidence rate and mortality rate for different combination of cardiometabolic risk factors adjusting for sex, age (liner and quadratic terms) and interactions of age at baseline with cardiometabolic risk factors.

The PAF for CVD and mortality attributable to hypertension, diabetes and high LDL-C separately was calculated using the formula [22]:

where ({P}_{i}) is the actual prevalence of hypertension, diabetes and high LDL-C, and ({HR}_{i}) is the adjusted hazard ratio of CVD and mortality associated with hypertension, diabetes or high LDL-C. We then computed the PAF for combined effects of cardiometabolic risk factors, which were considered as independent risk factors in this current study, by the following formula [23]:

where ({PAF}_{i}) is the PAF of individual risk factors. We reported the 2·5th and 97·5th values as 95% CIs, which were computed via 1000 bootstrap resampling. Lastly, we evaluated the loss of CVD-free years and reductions in life expectancy related to different status of cardiometabolic disorders according to trapezium rule. Briefly, reductions in CVD-free years and life expectancy were estimated as the different of areas under any two survival curves compared based on multivariate adjusted Cox proportional models, with age as the timescale. The formula was [11]:

where ({S}_{ref}left(mu right)) is the survival probability for participants without any of the three cardiometabolic disorders at age μ, and ({S}_{i}left(mu right)) is the survival probability at age μ for other status of cardiometabolic disorders (i).

All analyses were performed by using SAS version 9.4 (SAS Institute) and R version 4.0.3 (the R Foundation), and a two-sided P value < 0.05 was statistically significant.