Skip to main content
Download PDF

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

BMC Public Health volume 23, Article number: 1725 (2023) Cite this article

Abstract

Background

Previous studies have investigated the association between cardiometabolic risk factors and cardiovascular disease (CVD), but evidence of the attributable burden of individual and combined cardiometabolic risk factors for CVD and mortality is limited. We aimed to investigate and quantify the associations and population attributable fraction (PAF) of cardiometabolic risk factors on CVD and all-cause mortality, and calculate the loss of CVD-free years and years of life lost in relation to the presence of cardiometabolic risk factors.

Methods

Twenty-two thousand five hundred ninety-six participants aged ≥ 35 without CVD at baseline were included between October 2012 and December 2015. The outcomes were the composite of fatal and nonfatal CVD events and all-cause mortality, which were followed up in 2018 and 2019 and ascertained by hospital records and death certificates. Cox regression was applied to evaluate the association of individual and combined cardiometabolic risk factors (including hypertension, diabetes and high low-density lipoprotein cholesterol (LDL-C)) with CVD risk and all-cause mortality. We also described the PAF for CVD and reductions in CVD-free years and life expectancy associated with different combination of cardiometabolic conditions.

Results

During the 4.92 years of follow-up, we detected 991 CVD events and 1126 deaths. Hazard ratio were 1.59 (95% confidential interval (CI) 1.37–1.85), 1.82 (95%CI 1.49–2.24) and 2.97 (95%CI 1.85–4.75) for CVD and 1.38 (95%CI 1.20–1.58), 1.66 (95%CI 1.37–2.02) and 2.97 (95%CI 1.88–4.69) for all-cause mortality, respectively, in participants with one, two or three cardiometabolic risk factors compared with participants without diabetes, hypertension, and high LDL-C. 21.48% of CVD and 15.38% of all-cause mortality were attributable to the combined effect of diabetes and hypertension. Participants aged between 40 and 60 years old, with three cardiometabolic disorders, had approximately 4.3-year reductions life expectancy compared with participants without any abnormalities of cardiometabolic disorders.

Conclusions

Cardiometabolic risk factors were associated with a multiplicative risk of CVD incidence and all-cause mortality, highlighting the importance of comprehensive management for hypertension, diabetes and dyslipidemia in the prevention of CVD.

Peer Review reports

Introduction

Cardiovascular disease (CVD) remains the leading cause of death in China, and nearly 4.58 million deaths were from CVD in 2020 [1]. Over the past two decades, China has experienced rapid economic and demographic transitions, CVD burden are likely to increase due to the aging population and unfavorable life-style behaviors that can lead to an increase in hypertension, dyslipidemia, diabetes as well as other non-communicable disorders. It has been estimated that the prevalence of hypertension, diabetes and dyslipidemia in China were 23.2%, 11.2% and 33.8% [2,3,4], respectively, which may contribute a larger CVD burden to society and government, because hypertension, diabetes, and hyperlipidemia are the major metabolically attributable risk factors for CVD disease burden [5, 6]. Moreover, hypertension, high cholesterol, and hyperglycemia often coexist, which have common metabolic abnormalities, and real-world evidence found that approximately 66% of those diagnosed with diabetes had dyslipidemia and hypertension [7].

Previous studies have investigated the association of blood pressure, blood glucose, and lipids with CVD and all-cause mortality risk and their attributable disease burden separately [8, 9]. However, few have systematically clarified the association between combined cardiometabolic disorders and CVD and all-cause mortality, especially for Chinese people. To our best knowledge, only one study has investigated the associations of individual and combined hypertension, diabetes and dyslipidemia with CVD risk among Chinese, and found that both individual and combined cardiometabolic risk factors are related to CVD risk, and these three risk factors showed a multiplicative risk of CVD incidence. However, this study had a relatively short follow-up duration, which might limit the statistical power [10]. Besides, the evidence regarding the attributable burden of comorbidities for CVD and mortality was insufficient, and the relatively few data from China were involved. Beyond that, few studies have quantitatively assessed such relationships using absolute metrics such as disease-free years, which was easy to understand for both the general public and healthcare professionals [11, 12].

Therefore, it is of paramount importance to clarify and quantify both the individual and combined associations and PAF of cardiometabolic risk factors, including hypertension, diabetes and high LDL-C, with the risk of CVD and mortality, and calculate the loss of CVD-free years and years of life lost in relation to the presence of cardiometabolic risk factors.

Materials and methods

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]:

$${PAF}_i=P_i\times({HR}_i-1)/\lbrack P_i\times({HR}_i-1)+1\rbrack,$$

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]:

$$PAF=1-\prod\nolimits_{i=1}^n(1-{PAF}_i),$$

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]:

$$\mathrm{Loss}\;\mathrm{of}\;\mathrm{CVD}\;-\;\mathrm{free}\;\mathrm{years}\;=\int\nolimits_{\mathrm{baseline}\;\mathrm{age}}^{95}\;\left\{{\mathrm S}_{\mathrm{ref}}\left(\mathrm\mu\right)-{\mathrm S}_{\mathrm i}\left(\mathrm\mu\right)\right\}\mathrm{du},$$

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.

Results

Characteristics of participants

At baseline, the mean age was 56.2 ± 13.1 years. Baseline characteristics based upon the numbers of cardiometabolic risk factors were demonstrated in Table 1. Of these 22,596 participants, most of them had a history of only one of hypertension, diabetes and high LDL-C (37.33%), 8.28% had two components for cardiometabolic factors, 6.06% had a history of diabetes, hypertension and high LDL-C. Compare with participants without cardiometabolic disorders, those with two or three cardiometabolic risk factors were older, were more likely to live in eastern urban areas and had a family history of CVD. Overall, BMI, FPG, and lipid levels increased markedly as the cardiometabolic health deteriorated, and mean SBP was as high as 152.7 mmHg in patients with all three cardiometabolic risk factors. Baseline characteristics of the sample population and the participants included in the final analysis are showed in Table S1.

Table 1 Characteristics of study participants by the number of cardiometabolic risk factors
Full size table

Associations of cardiometabolic disorders with CVD and all-cause mortality risk

During the follow-up period, we detected 991 individuals with incident fatal or nonfatal CVD events (stroke, 596; CHD, 303; and other cardiovascular events, 92), the cumulative incidence of CVD and all-cause mortality were 9.48 and 10.83 per 1000 person-years. Table 2 showed the association between different combinations of cardiometabolic risk factors and the risk of CVD and all-cause mortality. Multivariable-adjusted model showed a significantly high risk of new-onset CVD among those with one, two or three cardiometabolic risk factors compared with participants without diabetes, hypertension, and high LDL-C, with the HR were 1.59 (95%CI 1.37–1.85), 1.82 (95%CI 1.49–2.24) and 2.97 (95%CI 1.85–4.75), respectively. Subjects with the combination of diabetes, hypertension, and high LDL-C had greater than twofold increased risk for CVD, CHD, stroke and all-cause mortality. For participants with only one cardiometabolic risk component, those with only hypertension showed significantly higher risk for stroke. However, we failed to detect an increased risk for CVD and its subcategories in participant with diabetes or high LDL-C only. Additionally, participants with diabetes only at baseline had a higher risk for all-cause mortality (HR = 1.79, 95%CI 1.36–2.37). Moreover, the HRs were 2.06 (95%CI 1.64–2.58) and 1.45 (95%CI 1.03–2.05) for total CVD in participants with diabetes plus hypertension and high LDL-C plus hypertension. Specifically, the risks of CHD incidence for participants with diabetes plus hypertension substantially increased through roughly 2.41 times compared with those without cardiometabolic risk factor, which was also higher than those with high LDL-C plus hypertension (HR = 2.06 (95%CI 1.17–3.63)). Moreover, for CVD risk, the conclusions were consistent after non-cardiovascular death was considered as competing risk events rather than the censored based on the Fine-Gray model (Table S2). After we redefined the hypertension according to the AHA/ACC, the risk of stroke increased further for participants with one or two cardiometabolic risk factors. In the reference group, the sex- and age- adjusted CVD incidence rate and mortality rate were 7.31 and 9.74 per 1000 person-years. By contrast, the age- and sex- adjusted CVD incidence and mortality rate were 31.48 (95%CI 22.00–45.06) and 22.93 (95%CI 19.55–26.90) per 1000 person-years among participants with diabetes, high LDL-C and hypertension (Fig. 1).

Table 2 HR for CVD and all-cause mortality associated with different combination of cardiometabolic risk factors
Full size table
Fig. 1
figure 1

Sex- and age- adjusted CVD incidence and all-cause mortality rates by different combinations of cardiometabolic risk factors. CVD, cardiovascular disease; CI, confidential interval. a, CVD incidence and mortality rate is per 1000 person-years

Full size image

Population attributable fraction and reductions in CVD-free years and life expectancy

The proportion of the total CVD attributable to diabetes and hypertension were 2.65% (95%CI 2.50–2.81) and 19.35% (95%CI 18.97–19.72), respectively (Table 3). We failed to detect a significant association between high LDL-C and the risk of CVD, therefore, we did not further calculate the PAF. The PAFs for all-cause mortality attributable to diabetes and hypertension were 5.08% (95%CI 4.80–5.37) and 10.85% (95%CI 10.61–11.08), respectively. Besides, we found that 21.48% of the CVD and 15.38% of all-cause mortality were attributable to the combined effect of diabetes and hypertension (Table 3).

Table 3 Population attributable fractions for CVD and all-cause mortality associated with cardiometabolic risk factors
Full size table

We estimated that participants aged between 40 and 60 years old, with three cardiometabolic disorder together, would develop CVD on average 3.1 years earlier compared with those without any abnormalities of cardiometabolic factors at baseline (Fig. 2A). Subjects with one and two combinations of cardiometabolic risk factors had lost 1.3 and 1.7 CVD disease-free years, respectively. Corresponding reductions for CHD-free were 4.7 years among those had all three cardiometabolic risk factors, however, the reduction in stroke-free years was 2.2, which was slightly lower than the loss for CHD-free years (Figs. 2B and C). The loss of CVD disease-free years decreased with age for people who had cardiometabolic disorders after the age of 60. Besides, individuals over 75 years of age with identified cardiometabolic risk factors were substantially no CVD-free year loss. Estimated reductions in life expectancy in participants with all three cardiometabolic risk factors were 4.3 years at the index of 40 (Fig. 2D).

Fig. 2
figure 2

CVD-free years lost and years of life lost from 40 years of age onward in different numbers of cardiometabolic risk factors. CVD-free years lost and years of life lost were calculated in participants with one, two or three cardiometabolic disorders compared with participants without diabetes, hypertension and high HDL-C

Full size image

Discussion

The current prospective study found that compared with participants without diabetes, hypertension or high LDL-C, those who had cardiometabolic multi-morbidities had a higher risk of incident CVD and all-cause mortality, and the cardiometabolic risk factors showed a multiplicative increased risk for CVD and all-cause mortality. Besides, The PAFs for CVD, CHD, stroke and all-cause mortality attributable to combined effect of diabetes and hypertension were 21.48%, 22.46%, 20.60% and 15.38%, respectively. Our results also suggested that both individual and combined cardiometabolic risk factors among participants aged between 40 and 60 years old are associated with significant reductions in CVD-free years and life expectancy compared participants without any of diabetes, hypertension or high LDL-C.

Cardiovascular disease are the leading cause of disability and death, and most of them are attributable to cardiometabolic risk factors, such as hypertension, diabetes, and high LDL-C [9, 10, 24, 25]. Recent results from the China Cardiometabolic Disease and Cancer Cohort (4C) study showed that approximately 37.42% of major cardiovascular events are attributable to the clustering of metabolic risk factors [10]. However, comprehensive control rate of hypertension, diabetes and dyslipidemia was low, a study based on Chinese participants, including 25,817 patients with diabetes, demonstrated that 72% of patients with type 2 diabetes have hypertension and/or dyslipidemia, and the integrated control rate is only 5.6% [26]. Chinese government proposed integration for hypertension, diabetes and dyslipidemia, which is in line with the principles of health economics and integrated management and control for cardiovascular disease. This study found that associations of cardiometabolic multi-morbidities with the risk of CVD incidence and all-cause mortality are multiplicative, which is of paramount importance to provide evidence for the primary prevention of CVD and comprehensive management for hypertension, diabetes and dyslipidemia, in China, and other countries.

While several studies have focus on the association of hypertension, diabetes and dyslipidemia with CVD risk separately [8, 9, 27], few have systematically clarified the relative association between combined cardiometabolic disorders with CVD, especially for Chinese people. 4C study, recruiting 193,846 adults aged over the age of 40, investigated the associations of individual and combined hypertension, diabetes and dyslipidemia with CVD risk among Chinese, and found that people with only hypertension or diabetes have a significantly increased risk of CVD compared with adults without hypertension, diabetes or dyslipidemia [10]. However, it failed to find an association of dyslipidemia only with the risk of CVD incidence, which was consistent with our study. One of the reasons is that the definition of dyslipidemia was based on abnormal TC, LDL-C, and HDL-C, and previous study showed inconsistent association between HDL-C and CVD risk [28,29,30], which may affect the estimation of the effect and overall test efficacy. Our findings also indicated hypertension is the largest cardiometabolic risk factor for total CVD and all-cause mortality, and accounting for approximately 19.35% and 10.85% of the PAF, respectively. A longitudinal cohort study enrolling 1,038,704 adults in China revealed that 3.3%, 1.73% and 0.3% all-cause mortality are due to one, two and three cardiometabolic multimorbidity [31]. Moreover, the latest research revealed that there are about 245 million adults in China have hypertension [4], indicating that CVD burden caused by hypertension remain serious, thus preventive interventions should be implemented to lower the prevalence of high SBP among Chinese overall in order to reduce the SBP-related CVD adverse health effect.

Our results noted that most combinations of cardiometabolic risk factors are associated with significant reductions in CVD-free years and life expectancy compared participants without diabetes, hypertension or high LDL-C among participants aged between 40 and 60 years old. Although several previous studies have investigated the association of cardiometabolic multi-morbidities with all-cause mortality and the associated reduction in life expectancy, these studies have basically focused on secondary prevention for CVD rather than primary prevention [31,32,33], and limited large-scale studies were conducted to explore the reduction in CVD-free years associated with individual and combined cardiometabolic disorders. Recent research noted that people with cardiometabolic multi-morbidity have significantly lower life expectancy and any combination of multi-morbidity was associated with the increased risk of all-cause mortality [33]. However, most of these studies focus on secondary prevention, and the definition of cardiometabolic multi-morbidity mainly includes stroke, myocardial infarction, diabetes. This present study, balancing the primary and secondary prevention of CVD, clarified that the relationship between different combination of cardiometabolic risk factor and CVD risk and the losses of CVD-free years caused by cardiometabolic risk factors. Recent researches have presented that healthy lifestyle can effectively reduce cardiometabolic diseases [34, 35], thus, popularizing health education and advocating a healthy lifestyle is a cost-effective approach to the primary prevention of CVD and the reduction of CVD burden caused by cardiometabolic risk factors, especially for people under 65 years of age.

Strengths and limitations

There are several strengths in this study. Firstly, to our best knowledge, our study is the first to assess the PAF for CVD and its subcategories attributable to different combination of hypertension, diabetes and high LDL-C among Chinese people. Moreover, findings from our study emphasized the importance of an integrated management of cardiometabolic morbidities in order to improve the efficiency of prevention and control of CVD. However, we acknowledge some specific limitations. First, the relatively short follow-up duration limited the number of CVD events, so this study did not provide the loss of CVD-free years for all possible combinations of cardiometabolic disorders. In addition, we do not distinguish between ischemic and hemorrhagic strokes although the effects of cardiometabolic risk factors may vary among different types of strokes. Third, we were unable to further assess the association of individual and combined cardiometabolic risk factor with the risk of CVD incidence by different treatment status due to the limited sample size. Forth, it’s better to apply a more complex stratified multistage sampling method to have a more representative sample, namely, in the first stage, probability proportional to size should be used to obtain cities/counties stratified by urban and rural areas and economic level. Besides, information on the cardiometabolic risk factors may change over time, which could increase the possibility of misclassification bias during the follow-up and influence the accuracy of the estimates. Finally, although extensive covariates were adjusted in the analyses, residual and unmeasured confounders and intricate interaction remain exit, which may influence the precise effects of cardiometabolic risk factors on the risk of CVD incidence.

Conclusions

In summary, our finding shows that cardiometabolic risk factors, including hypertension, diabetes and high LDL-C, were associated with multiplicative risk for CVD incidence and all-cause mortality, and a large proportion of CVD and mortality were attributable to cardiometabolic risk factors. The reduction in CVD-free years and life expectancy were substantially higher among people with combination of two and three cardiometabolic risk factors. This result highlights the importance of cardiometabolic multi-morbidities in the primary prevention of CVD and comprehensive management for hypertension, diabetes and abnormal lipids.

Availability of data and materials

The datasets used and/or analysed during the current study are available from the corresponding author on reasonable request.

Abbreviations

CVD:

Cardiovascular disease

PAF:

Population attributable fraction

CI:

Confidential interval

CHS:

China Hypertension Survey

CHD:

Coronary heart disease

SBP:

Systolic blood pressure

FPG:

Fasting plasma glucose

TC:

Total cholesterol

TG:

Triglycerides

LDL-C:

Low-density lipoprotein cholesterol

HDL-C:

High-density lipoprotein cholesterol

BMI:

Body mass index

ICD-10:

The International Classification of Diseases, 10th Revision

HR:

Hazard ratio

4C:

The China Cardiometabolic Disease and Cancer Cohort

References

  1. Wang W, Liu Y, Liu J, Yin P, Wang L, Qi J, et al. Mortality and years of life lost of cardiovascular diseases in China, 2005–2020: empirical evidence from national mortality surveillance system. Int J Cardiol. 2021;340:105–12.

    Article  PubMed  Google Scholar 

  2. Li Y, Teng D, Shi X, Qin G, Qin Y, Quan H, et al. Prevalence of diabetes recorded in mainland China using 2018 diagnostic criteria from the American Diabetes Association: national cross sectional study. BMJ. 2020;369: m997.

    Article  PubMed  PubMed Central  Google Scholar 

  3. Lu Y, Zhang H, Lu J, Ding Q, Li X, Wang X, et al. Prevalence of dyslipidemia and availability of lipid-lowering medications among primary health care settings in China. JAMA Netw Open. 2021;4(9): e2127573.

    Article  PubMed  PubMed Central  Google Scholar 

  4. Wang Z, Chen Z, Zhang L, Wang X, Hao G, Zhang Z, et al. Status of hypertension in china: results from the China hypertension survey, 2012–2015. Circulation. 2018;137(22):2344–56.

    Article  PubMed  Google Scholar 

  5. Collaborators GBDRF. Global burden of 87 risk factors in 204 countries and territories, 1990–2019: a systematic analysis for the Global Burden of Disease Study 2019. Lancet. 2020;396(10258):1223–49.

  6. Zhou M, Wang H, Zeng X, Yin P, Zhu J, Chen W, et al. Mortality, morbidity, and risk factors in China and its provinces, 1990–2017: a systematic analysis for the Global Burden of Disease Study 2017. Lancet. 2019;394(10204):1145–58.

    Article  PubMed  PubMed Central  Google Scholar 

  7. Ling JZJ, Montvida O, Khunti K, Zhang AL, Xue CC, Paul SK. Therapeutic inertia in the management of dyslipidaemia and hypertension in incident type 2 diabetes and the resulting risk factor burden: real-world evidence from primary care. Diabetes Obes Metab. 2021;23(7):1518–31.

    Article  CAS  PubMed  Google Scholar 

  8. Cohorts Consortium of Latin A, the C. Impact of common cardio-metabolic risk factors on fatal and non-fatal cardiovascular disease in Latin America and the Caribbean: an individual-level pooled analysis of 31 cohort studies. Lancet Reg Health Am. 2021;4:None.

  9. Yusuf S, Joseph P, Rangarajan S, Islam S, Mente A, Hystad P, et al. Modifiable risk factors, cardiovascular disease, and mortality in 155 722 individuals from 21 high-income, middle-income, and low-income countries (PURE): a prospective cohort study. Lancet. 2020;395(10226):795–808.

    Article  PubMed  Google Scholar 

  10. Wang J, Wang Z, Guo F, Zhang Y, Ji H, Chen G, et al. Individual and combined cardiometabolic morbidities and the subsequent risk of cardiovascular events in Chinese adults. J Clin Endocrinol Metab. 2022;107(1):e84–94.

    Article  PubMed  Google Scholar 

  11. Liu FC, Li JX, Chen JC, Hu DS, Li Y, Huang JF, et al. Predicting lifetime risk for developing atherosclerotic cardiovascular disease in Chinese population: the China-PAR project. Sci Bull. 2018;63(12):779–87.

    Article  Google Scholar 

  12. Chudasama YV, Khunti K, Gillies CL, Dhalwani NN, Davies MJ, Yates T, et al. Healthy lifestyle and life expectancy in people with multimorbidity in the UK Biobank: a longitudinal cohort study. PLoS Med. 2020;17(9): e1003332.

    Article  PubMed  PubMed Central  Google Scholar 

  13. Wang Z, Chen Z, Zhang L, Wang X, Hao G, Zhang Z, et al. Status of hypertension in China. Circulation. 2018;137(22):2344–56.

    Article  PubMed  Google Scholar 

  14. Hao G, Wang X, Chen Z, Zhang L, Zhang Y, Wei B, et al. Prevalence of heart failure and left ventricular dysfunction in China: the China Hypertension Survey, 2012–2015. Eur J Heart Fail. 2019;21(11):1329–37.

    Article  PubMed  Google Scholar 

  15. Cao X, Tang H, Zheng C, Kang Y, Zhang L, Wang X, et al. Association of heating fuel types with mortality and cardiovascular events among non-smokers in China. Environ Pollut. 2021;291: 118207.

    Article  CAS  PubMed  Google Scholar 

  16. Wang Z, Chen Z, Wang X, Zhang L, Li S, Tian Y, et al. The disease burden of atrial fibrillation in china from a national cross-sectional survey. Am J Cardiol. 2018;122(5):793–8.

    Article  PubMed  Google Scholar 

  17. Chinese Diabetes Society. Guidelines for the prevention and control of type 2 diabetes in China(2017 Edition) [in Chinese] 2018. 64 p.

  18. Zhu J, Gao R, Zhao S, Lu G, Zhao D, Li J. Chinese guidelines on prevention and treatment of dyslipidemia in adults. Chinese Circulation J. 2016;31(10):937–53.

  19. Chen C, Lu FC, Department of Disease Control Ministry of Health PRC. The guidelines for prevention and control of overweight and obesity in Chinese adults. Biomed Environ Sci. 2004;17(Suppl):1–36.

  20. Austin PC, Lee DS, Fine JP. Introduction to the analysis of survival data in the presence of competing risks. Circulation. 2016;133(6):601–9.

    Article  PubMed  PubMed Central  Google Scholar 

  21. Wolbers M, Koller MT, Stel VS, Schaer B, Jager KJ, Leffondre K, et al. Competing risks analyses: objectives and approaches. Eur Heart J. 2014;35(42):2936–41.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  22. Xia C, Zheng R, Zeng H, Zhou M, Wang L, Zhang S, et al. Provincial-level cancer burden attributable to active and second-hand smoking in China. Tob Control. 2019;28(6):669–75.

    Article  PubMed  Google Scholar 

  23. Ezzati M, Hoorn SV, Rodgers A, Lopez AD, Mathers CD, Murray CJ, et al. Estimates of global and regional potential health gains from reducing multiple major risk factors. Lancet. 2003;362(9380):271–80.

    Article  PubMed  Google Scholar 

  24. N. C. D. Risk Factor Collaboration. Worldwide trends in hypertension prevalence and progress in treatment and control from 1990 to 2019: a pooled analysis of 1201 population-representative studies with 104 million participants. Lancet. 2021;398(10304):957–80.

  25. Ma Q, Li R, Wang L, Yin P, Wang Y, Yan C, et al. Temporal trend and attributable risk factors of stroke burden in China, 1990–2019: an analysis for the Global Burden of Disease Study 2019. Lancet Public Health. 2021;6(12):e897–906.

    Article  PubMed  PubMed Central  Google Scholar 

  26. Ji L, Hu D, Pan C, Weng J, Huo Y, Ma C, et al. Primacy of the 3B approach to control risk factors for cardiovascular disease in type 2 diabetes patients. Am J Med. 2013;126(10):925 e11–22.

  27. Gami AS, Witt BJ, Howard DE, Erwin PJ, Gami LA, Somers VK, et al. Metabolic syndrome and risk of incident cardiovascular events and death: a systematic review and meta-analysis of longitudinal studies. J Am Coll Cardiol. 2007;49(4):403–14.

    Article  CAS  PubMed  Google Scholar 

  28. Holmes MV, Asselbergs FW, Palmer TM, Drenos F, Lanktree MB, Nelson CP, et al. Mendelian randomization of blood lipids for coronary heart disease. Eur Heart J. 2015;36(9):539–50.

    Article  CAS  PubMed  Google Scholar 

  29. Yang X, Li J, Hu D, Chen J, Li Y, Huang J, et al. Predicting the 10-year risks of atherosclerotic cardiovascular disease in Chinese population: the China-PAR Project (Prediction for ASCVD Risk in China). Circulation. 2016;134(19):1430–40.

    Article  Google Scholar 

  30. Yang ZM, Wu MY, Lu JM, Zhu Y, Li D, Yu ZB, et al. HDL-C, longitudinal change and risk of mortality in a Chinese cohort study. Nutr Metab Cardiovasc Dis. 2021;31(9):2669–77.

    Article  CAS  PubMed  Google Scholar 

  31. Zhang D, Tang X, Shen P, Si Y, Liu X, Xu Z, et al. Multimorbidity of cardiometabolic diseases: prevalence and risk for mortality from one million Chinese adults in a longitudinal cohort study. BMJ Open. 2019;9(3): e024476.

    Article  PubMed  PubMed Central  Google Scholar 

  32. Canoy D, Tran J, Zottoli M, Ramakrishnan R, Hassaine A, Rao S, et al. Association between cardiometabolic disease multimorbidity and all-cause mortality in 2 million women and men registered in UK general practices. BMC Med. 2021;19(1):258.

    Article  PubMed  PubMed Central  Google Scholar 

  33. Emerging Risk Factors C, Di Angelantonio E, Kaptoge S, Wormser D, Willeit P, Butterworth AS, et al. Association of Cardiometabolic Multimorbidity With Mortality. JAMA. 2015;314(1):52–60.

  34. Lv J, Yu C, Guo Y, Bian Z, Yang L, Chen Y, et al. Adherence to a healthy lifestyle and the risk of type 2 diabetes in Chinese adults. Int J Epidemiol. 2017;46(5):1410–20.

    Article  PubMed  PubMed Central  Google Scholar 

  35. Forouzanfar MH, Liu P, Roth GA, Ng M, Biryukov S, Marczak L, et al. Global burden of hypertension and systolic blood pressure of at least 110 to 115 mm Hg, 1990–2015. JAMA. 2017;317(2):165–82.

    Article  PubMed  Google Scholar 

Download references

Acknowledgements

Not applicable.

Funding

This study was funded by National Health Commission of the People’s Republic of China (Approval NO. NHC2020-609), the China National Science & Technology Pillar Program (Grant No. 2011BAI11B01) and the CAMS Innovation Fund for Medical Sciences (Grant No. 2017-I2M-1–004). The funder had no role in the design of the study and collection, analysis, and interpretation of data and in writing the manuscript.

Author information

Authors and Affiliations

  1. Division of Prevention and Community Health, National Center for Cardiovascular Disease, National Clinical Research Center of Cardiovascular Disease, State Key Laboratory of Cardiovascular Disease, Fuwai Hospital, Peking Union Medical College & Chinese Academy of Medical Sciences, No. 15 (Lin), Fengcunxili, Mentougou District, Beijing, 102308, China

    Xue Cao, Linfeng Zhang, Xin Wang, Zuo Chen, Congyi Zheng, Lu Chen, Haoqi Zhou, Jiayin Cai, Zhen Hu, Yixin Tian, Runqing Gu, Yilin Huang & Zengwu Wang

Authors
  1. Xue Cao
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Linfeng Zhang
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Xin Wang
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Zuo Chen
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Congyi Zheng
    View author publications

    You can also search for this author in PubMed Google Scholar

  6. Lu Chen
    View author publications

    You can also search for this author in PubMed Google Scholar

  7. Haoqi Zhou
    View author publications

    You can also search for this author in PubMed Google Scholar

  8. Jiayin Cai
    View author publications

    You can also search for this author in PubMed Google Scholar

  9. Zhen Hu
    View author publications

    You can also search for this author in PubMed Google Scholar

  10. Yixin Tian
    View author publications

    You can also search for this author in PubMed Google Scholar

  11. Runqing Gu
    View author publications

    You can also search for this author in PubMed Google Scholar

  12. Yilin Huang
    View author publications

    You can also search for this author in PubMed Google Scholar

  13. Zengwu Wang
    View author publications

    You can also search for this author in PubMed Google Scholar

Contributions

ZWW contributed to the conceive and design of the study; XC drafted the manuscript of the article and conducted the data analysis; LFZ, XW, ZC, CYZ coordinated the participant recruitment and provided important comments on the manuscript. LC, HQZ, JYC, ZH, YXT, RQG and YLH contributed to data collection; All authors have read and approved the final manuscript.

Corresponding author

Correspondence to Zengwu Wang.

Ethics declarations

Ethics approval and consent to participate

This study was approved by the Ethics Committee of Fuwai Hospital (Beijing, China) and informed consent was obtained from each participant.

Consent for publication

Not applicable.

Competing interests

The authors declare no competing interests.

Additional information

Publisher's Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Supplementary Information

Additional file 1:

Table S1. Baseline characteristics of the sample population and the participants included in the analysis. Table S2. HR for CVD associated with different combination of cardiometabolic risk factors using competing risk model. Table S3. HR for CVD and all-cause mortality associated with different combination of cardiometabolic risk factors. Figure S1. Flow chart of study participants included and excluded in the study.

Rights and permissions

Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated in a credit line to the data.

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Cao, X., Zhang, L., Wang, X. et al. Cardiovascular disease and all-cause mortality associated with individual and combined cardiometabolic risk factors. BMC Public Health 23, 1725 (2023). https://doi.org/10.1186/s12889-023-16659-8

Download citation

  • Received:

  • Accepted:

  • Published:

  • DOI: https://doi.org/10.1186/s12889-023-16659-8

Keywords

BMC Public Health

ISSN: 1471-2458

Contact us