Research on the prevalence of CMM in China is limited. This cross-sectional survey utilized a large-scale population sample from Guangdong, China to estimate the prevalence of various CMM and to explore the relationship between CMM and various modifiable and non-modifiable risk factors. In our study, the overall prevalence of CMM was 11.6%, indicating its significant presence. Moreover, we observe a substantial rise in CMM cases with increasing age. Furthermore, aside from age, factors such as male gender, low education level, low annual income, unmarried status, and recent utilization of antiplatelet or lipid-lowering medications exhibit independent associations with CMM. The Chinese population, similar to populations in other countries, is confronting a severe crisis of metabolic cardiovascular diseases. Numerous studies have consistently demonstrated that there is a strong association between comorbid metabolic cardiovascular diseases and higher mortality rates(1–3, 6, 16, 18), particularly among older adults and individuals who are obese(30). This may be attributed to accelerated changes in dietary patterns and lifestyle behaviors resulting from population and socio-economic transitions over the past few decades, long-term psychological stress, increasing environmental pollution, as well as declining mortality rates, and population aging(31). Many developed and some developing countries have conducted extensive studies exploring the epidemiological status of multimorbidity, including CMM. Currently, there is no unified standard for defining CMM, The common metabolic cardiovascular diseases included in research studies are hypertension, ischemic heart disease, stroke, diabetes, chronic kidney disease, and dyslipidemia. The lack of consistency in defining CMM has resulted in variations in the estimated rates of multimorbidity in different studies. The overall prevalence of CMM in our study is slightly higher than the findings of Sewpaul, R, Canoy, D and others(1, 15). In addition to the aforementioned variations in defining CMM, our study includes not only patient self-reported case data but also data from cases diagnosed during hospitalization. Besides, we hypothesize that the disparity may also stem from factors such as the age distribution of the study population, the sampling methodology employed, regional and demographic disparities, temporal considerations, data quality and reliability, as well as issues of data incompleteness and selection biases. Additionally, we have also discovered that among the four metabolic cardiovascular diseases included in this study, hypertension had the highest prevalence, followed by diabetes, stroke, and coronary heart disease. One-quarter of hypertension individuals had one or more other metabolic cardiovascular diseases. Among patients with diabetes, approximately one-third had coexisting combinations of two metabolic cardiovascular diseases; the combination of hypertension and diabetes was the most common, and the combination of hypertension, diabetes, and stroke was the most common among the three combinations of metabolic cardiovascular diseases. Our discoveries hold significant implications for the prevention and management of CMM.
The lower prevalence of multimorbidity in women in the current study differs with certain prior research findings(10, 32, 33), and the underlying causes for this inconsistency remain unclear. Based on our speculation, several factors may contribute to this phenomenon, such as higher levels of testosterone in males(34), genetic predisposition within families(35), and male inclinations towards unhealthy habits in terms of diet, exercise, smoking, and alcohol consumption, among others. In the future, there may be a need for further exploration of disease management approaches tailored to different gender groups.
The substantial impact of advancing age on the prevalence of multimorbidity comes as no surprise, given the extensive body of research conducted worldwide that has consistently demonstrated this association(12). According to the “Opinions of the Central Committee of the Communist Party of China and the State Council on Promoting the Development of Elderly Services” issued by the General Office of the State Council of China, by the end of 2022, the population of individuals aged 60 and above in China had reached 370 million, accounting for approximately 26.8% of the total population. Furthermore, the growth rate of the elderly population is continuously accelerating. According to data from the National Bureau of Statistics of China, it is projected that by 2035, the population of individuals aged 60 and above in China will reach 400 million, accounting for over 30% of the total population, entering an “ultra-aging society”. Therefore, an increasing number of people may experience CMM, which will pose significant challenges to future socio-economic development, healthcare, and elderly care.
Our study also indicates that farmers appear to be more susceptible to CMM, which may be attributed to the relatively unhealthy lifestyles and dietary habits prevalent in rural areas. Due to the lower economic and developmental levels in rural areas, individuals’ diet tends to be characterized by high-calorie, high-fat, and high-salt foods(36). Additionally, individuals in rural areas engage in relatively more physical activity but lack regular aerobic exercise(37, 38). These lifestyle habits contribute to an increased risk of developing CMM(39). Additionally, individuals in rural areas engage in relatively more physical activity but lack regular aerobic exercise, leading to an increased risk of CMM attributed to these lifestyle habits. Furthermore, the lack of education and health awareness is a significant issue in rural areas, as individuals often tend to overlook their own health issues. Conversely, medical resources and conditions in rural areas are relatively more limited compared to urban areas(40–42). Henceforth, an imperative arises to bolster the propagation of salubrious lifestyle conduct, enhance healthcare inclusivity, and propel the holistic advancement of rural domains.
Several studies have confirmed the association between overweight, obesity, and the comorbidity of metabolic cardiovascular diseases. The prevalence of overweight and obesity in China has markedly increased over the past few decades(43–46).According to the 2019 report on the prevalence of overweight in Chinese adults, the overweight rate was 30.9% and the obesity rate was 12.6%. Research surveys indicate variations in the prevalence of overweight and obesity across different regions of China, with generally higher rates observed in urban areas and relatively lower rates in rural areas. Unhealthy dietary habits, sedentary lifestyles, and increased life pressures have emerged as the primary contributing factors to this phenomenon(47, 48).
In our study, the average BMI of the total population was determined to be 24.14 kg/m², with 36.9% of individuals being overweight and 12% being obese. The rate of overweight individuals is significantly higher than the findings from Zhang, D. et al.'s study(32), where their research population was from Yinzhou, China, although the obesity rate is slightly lower than theirs. We speculate that this may be correlated with the age composition of our study population, as our population tends to be older. Additionally, it may also be related to regional dietary habits, as the Guangdong region of China tends to embrace a light cooking style, prioritizing fresh ingredients and employing low oil and salt cooking methods, which are relatively healthier. Finally, Yinzhou is a district under the jurisdiction of Ningbo in Zhejiang Province, China, and it serves as an important economic pillar for Ningbo with relatively strong economic strength. In contrast, our study includes populations from grassroots and rural areas in Guangdong, which may necessitate more rigorous and targeted research to validate the true underlying reasons for these results. Our results demonstrated that individuals who are overweight face a roughly twofold increased risk of CMM compared to those with normal weight, consistent with the findings of a cross-sectional study utilizing the South African National Health and Nutrition Examination Survey (SANHANES), as well as a pooled analysis of over 100,000 adults from 16 cohort studies in the United States and Europe(15, 49), on the contrary, there is a significant twofold increase in the risk of CMM among obese individuals. A study by Staimez et al. quantitatively assessed the contribution of various risk factors to the CMM using population attributable fractions (PAFs). Their results showed that the largest PAFs were associated with hypertension and obesity, highlighting the significant contribution of overweight and obesity to the burden of CMM. In addition to the conventional measure of obesity, body mass index (BMI), there is evidence that waist circumference may have a stronger correlation with the CMM than BMI(50). A study involving Asian populations evaluated the associations of waist-to-height ratio (WHtR), waist circumference (WC), waist divided by height^0.5 (WHT.5R), and BMI with the CMM. The results showed that WHtR, WC, WHT.5R, and BMI were independent predictors of CMM in the Chinese elderly population. WHtR, WC, and WHT.5R had better predictive abilities for CMM than BMI, with WHT.5R showing good predictive value for future CMM(51). In the future, there will likely be a heightened focus on the utilization of readily available and cost-effective screening indicators and interventions for identifying high-risk individuals with CMM. It is paramount to promote and establish healthy lifestyle habits, including the adoption of a nutritious diet, regular physical activity, and the reduction of sedentary behavior, as essential measures in combating overweight and obesity. Furthermore, collaborative efforts involving all segments of society, including the government, schools, families, and individuals, are indispensable in promoting healthy eating habits and fostering positive lifestyle choices to alleviate the health implications resulting from overweight and obesity.
Our research has observed a correlation between lower levels of education, lower income, and increased risk of CMM. Previous related studies have found that in high-income countries (HICs) such as Europe and the United States, lower socioeconomic status is associated with an increased risk of CMM(52–54). However, studies in low and middle-income countries (LMICs) such as India and South Africa have found that higher socioeconomic status is often associated with an increased risk of non-communicable diseases and CMM. Further research indicates that an increase in economic status leads to higher levels of consumption, particularly a preference for high-calorie, high-fat, and high-sugar foods. Additionally, the higher-income population tends to have reduced physical activity, indicating a reversal of the social gradient in CMM(50, 55, 56). Our findings appear to align more closely with those of HICs. China has now become the world’s second-largest economy, renowned for its rapid economic growth. Our study is based in the economically developed Guangdong region, which may account for our findings. The lower level of education seems to impact various aspects, including the understanding of heart health knowledge and behaviors, chronic stress responses in social and psychological contexts, environmental exposure, and pollution, all contributing to a heightened risk of heart metabolism. Further research on this topic is warranted due to the paramount significance of the study. This is particularly pertinent as China’s economic development is progressing towards sustainability, innovation-driven initiatives, and high-quality advancement, demanding an urgent establishment of a medical security system adaptable to the current situation.
Recent studies consistently demonstrate that the presence of metabolic cardiovascular diseases, in any combination, significantly increases the risk of mortality and reduces life expectancy. These findings highlight the essential and pressing need to address both primary and secondary prevention of metabolic cardiovascular diseases. Building on the experiences of developed nations, it is evident that improving risk factors at the population level has the greatest impact on reducing mortality caused by these diseases. Therefore, our primary objective is to develop a comprehensive and effective primary prevention system for patients with complex metabolic cardiovascular diseases by investigating the associated risk factors. These findings can inform the development of effective prevention and management strategies for medical institutions and public health agencies by providing insight into the comorbidity of CMM. This understanding will facilitate planning for future disease prevention and health management efforts. Data investigation can help identify common comorbidity factors and high-risk populations, enabling the implementation of corresponding intervention measures to reduce the incidence and progression of diseases. Understanding comorbidity can aid in improving diagnostic and treatment outcomes by enabling a more comprehensive assessment of patients’ health status. Specifically, when it comes to comorbidity within CMM, treatment complexity and risk can be heightened. Therefore, having an understanding of comorbidity allows for the development of more effective and personalized diagnostic and treatment strategies to be implemented. In addition, having an understanding of potential drug interactions and side effects can provide safer and more effective treatment approaches. Guiding resource allocation and priority determination, investigating data can also assist decision-makers and health policy makers in better understanding the distribution and burden of comorbidity in CMM. Drawing upon the unique characteristics exhibited by diverse regions and populations, the strategic allocation of healthcare resources can be refined to accord primacy to the requirements of vulnerable populations and individuals grappling with multiple coexisting medical conditions, thereby fostering an enhanced optimization of public health resource management. Furthermore, these data actively promote scholarly dialogue and the dissemination of knowledge, thereby catalyzing progress in the medical field and elevating clinical methodologies.
Our study is characterized by several notable strengths, including a substantial sample size comprising a representative population from the community. Furthermore, the adoption of a uniform research design and standardized study procedures at all screening points enhances the robustness of our findings. Notably, stringent quality control measures were implemented during the final data entry process, further bolstering the reliability and accuracy of our results. Nevertheless, this study is subject to certain limitations. To begin with, the cross-sectional design of the study precludes causal inferences from the identified risk factors associated with comorbid metabolic cardiovascular diseases. Consequently, the findings of this research necessitate further validation through longitudinal investigations. Additionally, potential information biases may arise from the self-reported diagnoses of diabetes, coronary heart disease, and stroke. Although self-reported disease diagnosis is subject to bias, we have specialized experts who utilize ICD codes to accurately determine the presence of diseases in patients, thus mitigating this limitation. Moreover, the utilization of fingertip blood rather than serum samples to determine blood glucose values in this study may introduce the risk of misdiagnosing diabetes. To confirm our findings, additional research is required to utilize whole blood glucose for diagnosing diabetes. Lastly, it is worth noting that our study was conducted specifically on a population from Guangdong Province, China. As such, caution should be exercised when generalizing the results to CMM in other countries.