Epidemiological Survey of Cardiometabolic Multimorbidity and Related Risk Factors in Chinese Population: A Cross-Sectional Study

Epidemiological Survey of Cardiometabolic Multimorbidity and Related Risk Factors in Chinese Population: A Cross-Sectional Study | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Epidemiological Survey of Cardiometabolic Multimorbidity and Related Risk Factors in Chinese Population: A Cross-Sectional Study Siying Xu, Wenbin Wang, Jiabin Wang, Anping Cai, Xiaofei Jiang, and 1 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-3896393/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Background The widespread prevalence of Cardiometabolic Multimorbidity (CMM) presents significant challenges to global public health. While previous studies have primarily examined individual cardiometabolic diseases, there has been limited research on CMM. As such, we intend to assess the prevalence of CMM and identify predictive risk factors within the Chinese population which will hold considerable implications for the future management of CMM. Methods We employed data from The China Patient-Centered Evaluative Assessment of Cardiac Events Million Persons Project (China-PEACE MPP), enrolling a total of 102,358 participants aged 35–75 years. CMM was defined as the simultaneous presence of two or more of the following diseases: diabetes, hypertension, stroke, and coronary heart disease. Univariate and multivariate logistic regression analyses were performed on demographic variables and modifiable factors associated with CMM to identify its risk predictive factors. Results The participants, with an average age of 54.27 years, comprised 60.5% of women. The overall prevalence of CMM was 11.6%, with hypertension and diabetes coexisting as the most common comorbid combination at 8.5%. Multifactor logistic regression analysis revealed that increasing age (45–54 years (OR = 2.62, 95%CI: 2.39–2.88), 55–64 years (OR = 5.27, 95%CI: 4.83–5.78), and 65–75 years (OR = 8.36, 95%CI: 7.62–9.18) compared to 35–44 years), current alcohol consumption (OR = 1.23, 95%CI: 1.12–1.34), TG ≥ 2.3mmol/L (OR = 1.69, 95%CI: 1.61–1.78), recent use of lipid-lowering medications (OR = 3.47, 95%CI: 3.21–3.74), and recent use of antiplatelet aggregators (OR = 3.67, 95%CI: 3.33–4.04) were associated with an increased risk of CMM. Conversely, a reduced occurrence of CMM was associated with being female (OR = 0.74, 95%CI: 0.70–0.78), other marital statuses (OR = 0.91, 95%CI: 0.85–0.97), education level of high school or above (OR = 0.90, 95%CI: 0.85–0.94), annual household income not less than 50,000 yuan (OR = 0.93, 95% CI: 0.89–0.98, p = 0.004), and HDL-C ≥ 1.0mmol/L (OR = 0.84, 95%CI: 0.79–0.90). Conclusions In the general population of China, over one-tenth of individuals are affected by CMM, indicating a high current prevalence of the condition. This highlights the imperative for China to develop targeted intervention measures focusing on the risk factors of CMM to prevent its occurrence and progression, effectively manage the condition, and reduce associated adverse outcomes and healthcare resource consumption. Cardiometabolic Multimorbidity China Prevalence Epidemiology Risk factors Figures Figure 1 Figure 2 Introduction The increasing burden of morbidity presents a significant challenge for healthcare systems globally, particularly with the rapid rise in CMM, making it a focal point for research. However, many healthcare systems across countries primarily concentrate on managing individual diseases rather than addressing the complexities of morbidities. CMM denotes the concurrent presence of two or more such diseases and is linked to heightened risks of mortality( 1 – 3 ), disability( 4 ), cognitive decline( 5 – 7 ), diminished quality of life( 8 ), and adv erse drug events( 9 ),This places a substantial burden on both China’s healthcare system and society at large( 10 , 11 ). The high prevalence and intricate nature of morbidities present significant challenges in managing healthcare resources effectively. Several countries worldwide have conducted extensive research on the prevalence and impact of CMM( 1 , 8 , 12 – 18 ). However, in China, only a limited number of studies have been undertaken on this subject. One notable study, based on the Chinese Electronic Health Records Research in Yinzhou (CHERRY)study, revealed that the prevalence of CMM in the general population in China had more than doubled in just five years. The rapid growth of CMM is particularly noteworthy, with cardiovascular diseases emerging as the primary cause of death across all co-morbid combinations( 3 , 19 ). Furthermore, a recent prospective cohort study from the China Kadoorie Biobank highlighted the duration-dependent effects of cardiometabolic diseases and multi-morbidity on all-cause and cause-specific mortality. This study demonstrated that the risk of mortality increases with the number of CMM and varies according to the duration of the diseases. It was observed that the mortality rate of diabetes rises with prolonged disease duration, while stroke mortality remains consistently high. Interestingly, the study found a decreasing trend in mortality from ischemic heart disease( 20 ). However, there is a dearth of comprehensive research globally on risk factors specifically related to CMM. Most existing studies tend to focus on individual diseases rather than taking into account the complexities associated with CMM. It is common for many diseases to coexist and there may be interactions or shared pathogenic mechanisms among them. By solely studying diseases from a single-disease perspective, the potential interrelationships between different diseases are often overlooked. This can result in healthcare institutions and physicians focusing solely on the treatment and management of specific diseases, while disregarding the possibility that patients may have multiple diseases concurrently. Consequently, this approach can lead to incomplete treatment plans and an increased risk to patients’ overall health, and may also result in resource wastage. Current research indicates that factors such as body mass index (BMI), dyslipidemia, and smoking are linked to the development of cardiovascular diseases, diabetes, and stroke( 21 – 27 ). Given the intricate nature of CMM, it is crucial to establish a multidisciplinary research framework. Large-scale prospective cohort studies need to be conducted to gain a more profound understanding of the risk factors, disease progression, and outcomes related to CMM. This approach will serve as a foundation for governments and healthcare institutions to develop precise and effective interventions and treatment measures for these diseases. Therefore, we conducted an analysis of baseline data from the China Patient-Centered Evaluative Assessment of Cardiac Events Million Persons Project (China PEACE MPP) in the Guangdong region of China. The study included 102,358 residents aged 35–75. Our primary objective was to examine the prevalence of various combinations of CMM and explore the clinical risk factors associated with CMM. These risk factors encompassed sociodemographic characteristics, lifestyle factors, and physical and laboratory examination indicators. By doing so, we aimed to uncover new insights that could contribute to future disease management strategies. Undoubtedly, we are presently facing a new era that requires the establishment of effective prevention and control strategies to address the increasing prevalence of CMM. Methods Study population This study utilized baseline data from the China Peace MPP, the design and methods of which have been previously documented in the literature( 28 , 29 ). In brief, the China Peace MPP is a nationwide, government-funded, large-scale screening project aimed at identifying high-risk populations for cardiovascular disease (CVD) across China. In this study, participant data from eight screening sites in Guangdong Province, collected between January 1, 2016, and December 31, 2020, were selected. The project employed a cluster sampling approach and obtained detailed information about the population at the screening sites, including demographics and population mobility, through various departments. Project promotion was carried out through television, newspapers, bulletin boards, and other communication channels. Participants were selected based on the age structure of the local population, with exclusion criteria including severe audio-visual impairments, inability to comprehend the purpose and content of the study, collective screening of occupational groups, and incomplete information. An analysis was conducted on a total of 102,358 participants who had resided at the project sites for at least 6 months in the 12 months prior to screening and were aged between 35 and 75 years. The study was approved by the Ethics Committee of the National Cardiovascular Disease Center and the Ethics Committee of Guangdong Provincial People’s Hospital (GDREC2016438H(R2)), and informed consent was obtained from all participants. Data definition The primary outcome of this study was CMM, which was defined as having two or more of the following conditions: hypertension, diabetes, stroke, and coronary heart disease. Hypertension was determined by a systolic blood pressure (SBP) of ≥ 140mmHg and/or a diastolic blood pressure (DBP) of ≥ 90mmHg, self-reported use of antihypertensive medication, or self-reported history of hypertension. Blood pressure measurements were taken using an electronic blood pressure monitor (Omron HEM-7430, Omron Corporation, Kyoto, Japan) and a standardized protocol. Participants were seated with their right arm in a relaxed position, and blood pressure was measured twice after a 5-minute rest period. The average of these two measurements, taken 1 minute apart, was used for analysis. If the difference between the first two measurements exceeded 10mmHg, an additional measurement was taken, and the average of the three measurements was used as the final data. Information on diabetes, coronary heart disease, and stroke was collected via self-reported conditions during screening and medical records diagnosis prior to screening. A standardized questionnaire conducted during the initial screening asked participants, “Has your doctor ever told you that you have diabetes, coronary heart disease, or stroke?” to gather self-reported data on these conditions. Trained personnel also utilized the International Classification of Diseases, Tenth Edition (ICD) codes to identify participants’ hospital records related to diabetes, coronary heart disease, and stroke from the hospital registration database, including diabetes (E10-E14), coronary heart disease (I20-I25), and stroke (I60-I64,I69). Additionally, the definition of diabetes encompassed the use of glucose-lowering medication or a fasting blood glucose level of ≥ 7.0mmol/L. Assessment of Covariates The investigation comprised three components: a questionnaire survey, physical measurements, and laboratory testing. The questionnaire addressed sociodemographic information, lifestyle behaviors, and medication history over the past two weeks. Trained community healthcare workers conducted face-to-face interviews to collect this data. Sociodemographic information included age, gender, educational level, current occupation, household registration, marital status, family income, and medical insurance. Educational level was categorized as “junior high school or below” and “senior high school or above.” Family income was categorized as “annual income above 50,000 RMB” and “annual income below 50,000 RMB.” Lifestyle behaviors included smoking and drinking status. Participants’ current smoking and drinking status were determined by asking the question, “Do you currently smoke or drink alcohol?” Medication history in the past two weeks included antidiabetic, lipid-lowering, antiplatelet, and antihypertensive drugs. Body Mass Index (BMI) was calculated as weight (kg) divided by height (m) squared. According to the Chinese criteria for overweight and obesity, the BMI categories were defined as follows: normal weight (BMI ≤ 24 kg/m²), overweight (BMI 24–28 kg/m²), and obesity (BMI ≥ 28 kg/m²).For each participant, a rapid lipid analyzer (CardioChek PA Analyzer; Polymer Technology Systems) was used to collect lipid profiles, including triglycerides (TG), total cholesterol (TC), high-density lipoprotein cholesterol (HDL-C), and low-density lipoprotein cholesterol (LDL-C) in a fasting state. Fasting blood glucose (FBG) was measured using fingertip blood samples. Strict quality control measures were implemented before, during, and after the survey to ensure the accuracy and reliability of the data. Statistical analysis The analyzed sample included 102,358 participants who had complete data on four cardiovascular metabolic conditions. All analyses were conducted using R statistical software version 4.2.2(R Project for Statistical Computing), and all hypothesis testing was two-tailed with p < 0.05 set as statistically significant. During the data analysis process, we observed 6863 missing values for LDL-C. Considering the substantial number of missing values and the inability to determine the reasons for missing data, we applied the “mice” package(version 3.16.0) and utilized HDL-C, TG, and TC lipid indicators to perform data imputation based on multiple imputation methods (method=“rf”, seed = 1234). We selected the imputed data with the smallest differences compared to the original data for downstream analysis of LDL-C (mean ± SD for original vs imputed data: 2.718(0.999) vs 2.710(1.023), p = 0.062). Independent sample t-tests were employed for continuous variables, while chi-square tests were utilized for categorical variables to compare characteristics between individuals with and without CMM. The prevalence of the four cardiovascular metabolic conditions in various combinations was estimated and compared by gender using chi-square tests. Univariate and multivariable logistic regression analyses were performed to investigate the risk factors associated with CMM. Recognizing the potential impact of lipid-lowering medications on lipid levels, a sensitivity analysis was conducted by excluding the population taking lipid-lowering drugs. Odds ratios, along with their corresponding 95% confidence intervals, are reported. Results Baseline characteristics The baseline characteristics of the 102,358 participants are presented in Table 1 . The overall prevalence of CMM was 11.6% (n = 11,898), with females accounting for 60.5% of the cases. Nearly half of the participants resided in urban areas, while around 10% were farmers. The majority, approximately 90%, were married. The average age of the entire population was 54.27 years, with mean BMI and fasting blood glucose levels of 24.14 kg/m² and 5.89 mmol/L respectively. We detected significant differences between groups in all baseline characteristic variables, except for those related to medical insurance( P = 0.082), residence༈ P ༝ 0.928༉, and current smoking ( P = 0.10). In comparison to individuals without CMM, those with CMM were found to be older and more likely to work as farmers. They also exhibited a lower likelihood of being married, lower levels of education, lower income, and a higher probability of consuming alcohol. Additionally, individuals with CMM displayed higher values of fasting blood glucose and BMI, lower levels of LDL, higher levels of TG, lower levels of HDL, and a higher likelihood of using lipid-lowering drugs and antiplatelet medications. Table 1 Description of the sample Characteristic Overall non-CMM CMM P value Number 102358 90460 11898 Age(years), mean (SD) 54.27 (10.19) 53.47 (10.10) 60.31 (8.72) < 0.001 35–44 20514 (20.0) 19925 (22.0) 589 (5.0) < 0.001 45–54 32388 (31.6) 29858 (33.0) 2530 (21.3) 55–64 29949 (29.3) 25485 (28.2) 4464 (37.5) ≥ 65 19507 (19.1) 15192 (16.8) 4315 (36.3) Gender Male, n (%) 40440 (39.5) 35036 (38.7) 5404 (45.4) < 0.001 Famale, n (%) 61918 (60.5) 55424 (61.3) 6494 (54.6) Farmer (%) 12054 (11.8) 10415 (11.5) 1639 (13.8) < 0.001 Residence (urban), n (%) 49509 (48.4) 43749 (48.4) 5760 (48.4) 0.928 Marriage (married), n (%) 92599 (90.5) 82037 (90.7) 10562 (88.8) < 0.001 Educational status (high school or above), n (%) 30295 (29.6) 27606 (30.5) 2689 (22.6) < 0.001 Annual household income (50 000 RMB or above), n (%) 46420 (45.4) 41442 (45.8) 4978 (41.8) < 0.001 Medical insurance, n (%) 95465 (93.3) 84323 (93.2) 11142 (93.6) 0.082 Current smoking, n (%) 17600 (17.2) 15490 (17.1) 2110 (17.7) 0.100 Current drinking, n (%) 5426 (5.3) 4631 (5.1) 795 (6.7) < 0.001 GLU(mmol/L),mean (SD) 5.89 (1.68) 5.63 (1.30) 7.90 (2.60) < 0.001 FBG ≥ 7.0 mmol/L, n (%) 13812 (13.5) 5785 (6.4) 8027 (67.5) < 0.001 TC (mmol/L),mean (SD) 4.91 (1.23) 4.92 (1.21) 4.89 (1.35) 0.027 TC ≥ 6.2 mmol/L, n (%) 14288 (14.0) 12401 (13.7) 1887 (15.9) < 0.001 LDL-C (mmol/L),mean (SD) 2.71 (1.02) 2.72 (1.01) 2.66 (1.13) < 0.001 LDL-C ≥ 4.1 mmol/L, n (%) 9929 (9.7) 8627 (9.5) 1302 (10.9) < 0.001 TG (mmol/L),mean (SD) 1.63 (0.97) 1.58 (0.93) 1.96 (1.14) < 0.001 TG ≥ 2.3 mmol/L, n (%) 17185 (16.8) 13969 (15.4) 3216 (27.0) < 0.001 HDL-C (mmol/L),mean (SD) 1.48 (0.44) 1.49 (0.44) 1.37 (0.40) < 0.001 HDL-C<1.0 mmol/L, n (%) 12214 (11.9) 10354 (11.4) 1860 (15.6) < 0.001 BMI(kg/m2), mean (SD) 24.14 (3.30) 23.95 (3.24) 25.56 (3.44) < 0.001 Normal weight(<24), n (%) 52360 (51.2) 48330 (53.4) 4030 (33.9) < 0.001 Overweight(≥ 24,<28), n (%) 37724 (36.9) 32491 (35.9) 5233 (44.0) Obesity(≥ 28), n (%) 12274 (12.0) 9639 (10.7) 2635 (22.1) Current use of lipid-lowering drugs, n (%) 3767 (3.7) 2183 (2.4) 1584 (13.3) < 0.001 Current use of antiplatelet drugs, n (%) 2258 (2.2) 1176 (1.3) 1082 (9.1) < 0.001 Data was presented as median (inter quartile range) for non-normally distributed variables, and number (percentage) for categorical variables. Abbreviations: CMM,cardiometabolic multimorbidity; BMI, body mass index; GLU, glucose;FBG, fasting blood glucose; TC, total cholesterol; LDL-C, low density lipoprotein-cholesterol; HDL-C, high density lipoprotein-cholesterol; TG, triglyceride. Prevalence of cardiometabolic combinations The prevalence of hypertension was found to be 39.9% (95% CI: 39.6–40.2), diabetes was 16.1% (95% CI: 15.8–16.3), coronary heart disease was observed in 2.0% (95% CI: 1.9–2.1) of the population, while stroke affected 2.4% (95% CI: 2.3–2.5). Table 2 provides an overview of the prevalence of different combinations of metabolic cardiovascular diseases. The most prevalent combination was hypertension and diabetes, affecting 8.5% (95% CI: 8.3–8.6) of the population. The second and third most common dual comorbidities were hypertension with stroke (1.0%, 1.0-1.1) and hypertension with coronary heart disease (0.7%, 0.6–0.7), respectively. Among the combinations of three diseases, the most common were hypertension, diabetes, and stroke, affecting 0.5% (0.5–0.5) of the population. When examining gender differences, it was observed that the prevalence of both hypertension and diabetes was significantly higher in males compared to females. The co-occurrence of hypertension and diabetes was also more prevalent in males. Overall, the prevalence of metabolic cardiovascular diseases was higher in males than females. Furthermore, the prevalence of both single and multiple metabolic cardiovascular diseases increased with age(Fig. 1 ). Table 2 Prevalence of cardiometabolic combinations Cardiometabolic condition Total Male Female P value % 95%CI n % 95%CI % 95%CI One condition 35.8 [35.6–36.1] 36691 39.1 [38.6–39.6] 33.7 [33.4–34.1] < 0.001 HTN 28.6 [28.3–28.9] 29287 31.0 [30.6–31.5] 27.1 [26.7–27.4] < 0.001 DM 6.3 [6.2–6.5] 6479 7.0 [6.7–7.2] 5.9 [5.7–6.1] < 0.001 STROKE 0.5 [0.4–0.5] 480 0.5 [0.4–0.6] 0.4 [0.4–0.5] 0.229 CHD 0.4 [0.4–0.5] 445 0.6 [0.6–0.7] 0.3 [0.3–0.4] < 0.001 Two conditions 10.5 [10.3–10.6] 10703 11.8 [11.5–12.1] 9.6 [9.3–9.8] < 0.001 HTN,DM 8.5 [8.3–8.6] 8666 9.1 [8.8–9.4] 8.0 [7.8–8.3] < 0.001 HTN,CHD 0.7 [0.6–0.7] 695 1.0 [0.9–1.1] 0.5 [0.4–0.5] < 0.001 HTN,STROKE 1.0 [1.0-1.1] 1042 1.3 [1.2–1.4] 0.8 [0.8–0.9] < 0.001 DM,CHD 0.1 [0.1–0.2] 147 0.2 [0.2–0.3] 0.1 [0.1–0.1] < 0.001 DM,STROKE 0.1 [0.1–0.1] 115 0.1 [0.1–0.2] 0.1 [0.1–0.1] 0.007 CHD,STROKE 0.0 [0.0-0.1] 38 0.0 [0.0-0.1] 0.0 [0.0-0.1] 0.622 Three conditions 1.1 [1.0-1.1] 1097 1.4 [1.3–1.6] 0.8 [0.8–0.9] < 0.001 HTN,DM,CHD 0.4 [0.4–0.4] 403 0.6 [0.5–0.7] 0.3 [0.2–0.3] < 0.001 HTN,DM,STROKE 0.5 [0.5–0.5] 507 0.6 [0.5–0.7] 0.4 [0.4–0.5] < 0.001 HTN,CHD,STROKE 0.2 [0.1–0.2] 168 0.2 [0.2–0.3] 0.1 [0.1–0.2] 0.001 DM,CHD,STROKE 0.0 [0.0–0.0] 19 0.0 [0.0–0.0] 0.0 [0.0–0.0] 0.160 All conditions 0.1 [0.1–0.1] 98 0.1 [0.1–0.2] 0.1 [0.1–0.1] 0.069 Overall HTN 39.9 [39.6–40.2] 40866 43.9 [43.4–44.4] 37.3 [36.9–37.7] < 0.001 DM 16.1 [15.8–16.3] 16434 17.8 [17.4–18.1] 14.9 [14.7–15.2] < 0.001 CHD 2.0 [1.9–2.1] 2013 2.8 [2.7-3.0] 1.4 [1.3–1.5] < 0.001 STROKE 2.4 [2.3–2.5] 2467 3.0 [2.8–3.1] 2.0 [1.9–2.2] < 0.001 Abbreviations:HTN, hypertension;DM, diabetes mellitus;CHD,coronary heart disease; CI, conicity index. p < 0.05 indicating a significant difference between males and females. Univariate and multivariate analysis for CMM The results of the univariate analysis (Table 3 ) showed that being male, not being a farmer, being married, having an education level lower than high school, having an annual income lower than 50,000, being a current alcohol drinker, having TC ≥ 6.2 mmol/L, LDL-C ≥ 4.1 mmol/L, TG ≥ 2.3 mmol/L, HDL-C < 1.0 mmol/L, recent use of lipid-lowering drugs or antiplatelet aggregation drugs were significantly associated with a higher risk of CMM. The risk of CMM also significantly increased with age. Additionally, an increase in BMI (p < 0.001) was significantly associated with an increased risk of CMM. In the multivariate regression analysis model, being 45–54 years old (OR = 2.62, 95% CI: 2.39–2.88, p < 0.001), 55–64 years old (OR = 5.27, 95% CI: 4.83–5.78, p < 0.001) and 65–75 years old (OR = 8.36, 95% CI: 7.62–9.18, p < 0.001) (compared with 35–44 years old), being a current alcohol drinker (compared with non-drinkers, OR = 1.23, 95% CI: 1.12–1.34, p < 0.001), TG ≥ 2.3 mmol/L (compared with TG < 2.3 mmol/L, OR = 1.69, 95% CI: 1.61–1.78, p < 0.001), recent use of lipid-lowering drugs (compared with non-users, OR = 3.47, 95% CI: 3.21–3.74, p < 0.001), and recent use of antiplatelet aggregation drugs (compared with non-users, OR = 3.67, 95% CI: 3.33–4.04, p < 0.001) were associated with an increased risk of CMM. Conversely, being female (compared with males, OR = 0.74, 95% CI: 0.70–0.78, p < 0.001), other marital status (compared with married, OR = 0.91, 95% CI: 0.85–0.97, p = 0.005), having an education level of high school or above (compared with below high school, OR = 0.90, 95% CI: 0.85–0.94, p < 0.001), Annual household income not less than 50,000 yuan (compared with less than 50,000 yuan ,OR = 0.93, 95% CI: 0.89–0.98, p = 0.004),being a current smoker (compared with non-smokers, OR = 0.80, 95% CI: 0.76–0.86, p < 0.001), and HDL-C ≥ 1.0 mmol/L (compared with HDL-C < 1.0 mmol/L, OR = 0.84, 95% CI: 0.79–0.90, p < 0.001) were associated with a reduced risk of CMM. The results are shown in Fig. 2 . Table 3 Cardiometabolic multimorbidity by related factors. Characteristic Prevalence Univariable logistic regression Multivariable logistic regression % 95%CI OR(95%CI) P value OR(95%CI) P value Age(years) 35–44 2.9 [2.6-3] Ref Ref 45–54 7.8 [7.5-8] 2.87[2.62–3.14] <0.001 2.62[2.39–2.88] <0.001 55–64 14.9 [14.5–15] 5.93[5.43–6.48] <0.001 5.27[4.82–5.78] <0.001 65–75 22.1 [21.5–23] 9.61[8.80-10.51] <0.001 8.36[7.62–9.18] <0.001 Gender Men 13.4 [13.0–14] Ref Ref Women 10.5 [10.2–11] 0.76[0.73–0.79] <0.001 0.74[0.70–0.78] <0.001 Occupation Farmer 11.4 [11.2–12] Ref Ref Non-farmer 13.6 [13.0–14] 1.23[1.16–1.30] <0.001 1.045[0.99–1.11] 0.127 Residence Rural 11.6 [11.3–12] Ref Ref Urban 11.6 [11.4–12] 1.00[0.96–1.04] 0.920 0.97[0.93–1.01] 0.145 Marital status Married 13.7 [13.0–14] Ref Ref Others 11.4 [11.2–12] 0.81[0.76–0.86] <0.001 0.91[0.85–0.97] 0.005 Educational status junior high school and below 12.8 [12.5–13] Ref Ref high school and above 8.9 [8.6-9] 0.67[0.64–0.70] <0.001 0.90[0.85–0.94] <0.001 Annual household income(RMB) <50,000 12.4 [12.1–13] Ref Ref ≥ 50,000 10.7 [10.4–11] 0.85[0.82–0.88] <0.001 0.93[0.89–0.97] 0.001 Medical insurance No 11 [10.2–12] Ref Ref Yes 11.7 [11.5–12] 1.07[0.99–1.16] 0.078 1.11[1.02–1.21] 0.015 Current smoking No 11.5 [11.3–12] Ref Ref Yes 12 [11.5–12] 1.04[0.99–1.10] 0.097 0.80[0.76–0.86] <0.001 Current drinking No 11.5 [11.3–12] Ref Ref Yes 14.7 [13.7–16] 1.33[1.23–1.43] <0.001 1.23[1.12–1.34] <0.001 TC (mmol/L) <6.2 11.4 [11.2–12] Ref Ref ≥ 6.2 13.2 [12.7–14] 1.19[1.13–1.25] <0.001 0.99[0.91–1.06] 0.679 LDL-C (mmol/L) <4.1 11.5 [11.3–12] Ref Ref ≥ 4.1 13.1 [12.5–14] 1.17[1.10–1.24] <0.001 1[0.92–1.09] 0.994 TG (mmol/L) <2.3 10.2 [10.0–10] Ref Ref ≥ 2.3 18.7 [18.1–19] 2.03[1.94–2.12] <0.001 1.69[1.61–1.78] <0.001 HDL-C (mmol/L) <1.0 15.2 [14.6–16] Ref Ref ≥ 1.0 11.1 [10.9–11] 0.70[0.66–0.74] <0.001 0.84[0.79–0.90] <0.001 BMI(kg/m2) Normal weight(<24) 7.7 [7.5-8] Ref Ref Overweight(≥ 24,<28) 13.9 [13.5–14] 1.93[1.85–2.02] <0.001 1.74[1.66–1.82] <0.001 Obesity (≥ 28) 21.5 [20.7–22] 3.28[3.11–3.46] <0.001 2.97[2.8–3.14] <0.001 current use of lipid-lowering drugs No 10.5 [10.3–11] Ref Ref Yes 42 [40.5–44] 6.21[5.80–6.65] <0.001 3.47[3.21–3.74] <0.001 Current use of antiplatelet drugs No 10.8 [10.6–11] Ref Ref Yes 47.9 [45.8–50] 7.60[6.98–8.27] <0.001 3.67[3.33–4.04] <0.001 Abbreviations:Odds Ratio (OR) Considering that LDL-C and TC did not show statistical significance in the multivariate analysis of metabolic cardiovascular comorbidity groups, we conducted additional analyses by excluding individuals using lipid-lowering drugs. However, the results remained non-significant. We hypothesize that this lack of significance may stem from the heterogeneity of metabolic cardiovascular comorbidities, which refers to the variability and complexity of the disease status among individuals. Given the interplay between different metabolic cardiovascular diseases, patients with multiple diseases may require consideration of the interactions and effects of various medications between these diseases. In order to better understand the associated risk factors, a more comprehensive analysis is warranted. Discussion 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. Conclusions In conclusion, our study findings suggest that the prevalence of comorbid metabolic cardiovascular diseases in Guangdong, China, is significant, affecting more than 10% of the population, with a higher incidence observed among older individuals. This emphasizes the critical importance of prioritizing the implementation of effective management strategies for these comorbidities to mitigate potential healthcare expenditures and adverse health outcomes in the future. Interventions emphasizing the management of factors including obesity and lifestyle modifications are essential for the prevention and control of comorbid metabolic cardiovascular diseases. Furthermore, further comprehensive research is warranted to provide guidance for the implementation of more precise prevention and control measures. Declarations Ethics approval and consent to participate This study was approved by both the Central Ethics Committee at the China National Center for Cardiovascular Disease and the Ethics Committee of Guangdong Provincial People’s Hospital (No. GDREC2016438H (R2)). Written informed consents were obtained from all participants. All methods were carried out in accordance with relevant guidelines and regulations. Consent for publication Not Applicable. Availability of data and materials All data generated or analyzed during this study are included in this published article and its supplementary information files. Competing interests The authors declare that they have no competing interests. Funding This work was supported by the Guangdong Provincial Key Laboratory of Coronary Heart Disease Prevention (2017B030314041), the Climbing Plan of Guangdong Provincial People's Hospital (DFJH2020022), Guangdong Provincial Clinical Research Center for Cardiovascular disease (2020B1111170011), the Ministry of Finance of China and National Health Commission of China, the Clinical Research Promotion Project of Zhuhai People's Hospital (2023LCTS-34), and Guangdong Provincial Medical Science and Technology Research Fund Project (20211124162510276). Authors' contributions XSY participated in the design of the study and drafted the manuscript. WWB conceived of the study and performed the statistical analysis. WJB contributed to project execution and data quality control. CAP helped to draft the manuscript. FYQ conceived of the study and participated in its design. All authors read and approved the final manuscript. Acknowledgements We acknowledge the contribution the all staff who participated in this study as well as the study participants who shared their time with us. Author information XSY, Department of Cardiology, Zhuhai hospital affiliated with Jinan University (Zhuhai People's Hospital), Zhuhai, China; Department of Cardiology, Hypertension Research Laboratory, Guangdong Cardiovascular Institute, Guangdong Provincial People's Hospital, Guangdong Academy of Medical Sciences, Guangzhou, China, Email: [email protected] ; WWB, Department of Cardiology, Hypertension Research Laboratory, Guangdong Cardiovascular Institute, Guangdong Provincial People's Hospital, Guangdong Academy of Medical Sciences，Guangzhou, China, Email: [email protected] ; WJB, Global Health Research Center, Guangdong Cardiovascular Institute, Guangdong Provincial People's Hospital, Guangdong Academy of Medical Sciences, Guangzhou, China, Email: [email protected] ; CAP, Department of Cardiology, Hypertension Research Laboratory, Guangdong Cardiovascular Institute, Guangdong Provincial People's Hospital, Guangdong Academy of Medical Science，Guangzhou, China, Email: [email protected] ;JXF, Department of Cardiology, Zhuhai People's Hospital (Zhuhai Clinical Medical College of Jinan University), Zhuhai, China, Email: [email protected] References 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). Di Angelantonio E, Kaptoge S, Wormser D, Willeit P, Butterworth AS, Bansal N et al. Association of Cardiometabolic Multimorbidity With Mortality. JAMA. 2015;314(1). 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. Ma T, He L, Luo Y, Fu D, Huang J, Zhang G, et al. Frailty, an Independent Risk Factor in Progression Trajectory of Cardiometabolic Multimorbidity: A Prospective Study of UK Biobank. J Gerontol A Biol Sci Med Sci. 2023;78(11):2127–35. Dove A, Marseglia A, Shang Y, Grande G, Vetrano DL, Laukka EJ et al. Cardiometabolic multimorbidity accelerates cognitive decline and dementia progression. Alzheimers Dement. 2022. Dove A, Guo J, Marseglia A, Fastbom J, Vetrano DL, Fratiglioni L, et al. Cardiometabolic multimorbidity and incident dementia: the Swedish twin registry. Eur Heart J. 2023;44(7):573–82. Jin Y, Liang J, Hong C, Liang R, Luo Y. Cardiometabolic multimorbidity, lifestyle behaviours, and cognitive function: a multicohort study. The Lancet Healthy Longevity. 2023;4(6):e265–e73. Fishbook BN, Brinton CD, Siever J, Klassen TD, Sakakibara BM. Cardiometabolic multimorbidity and activity limitation: a cross-sectional study of adults using the Canadian Longitudinal Study on Aging data. Fam Pract. 2022;39(3):455–63. Cheung JTK, Yu R, Woo J. Is polypharmacy beneficial or detrimental for older adults with cardiometabolic multimorbidity? Pooled analysis of studies from Hong Kong and Europe. Fam Pract. 2020;37(6):793–800. Wang HH, Wang JJ, Wong SY, Wong MC, Li FJ, Wang PX, et al. Epidemiology of multimorbidity in China and implications for the healthcare system: cross-sectional survey among 162,464 community household residents in southern China. BMC Med. 2014;12:188. Zhang Z, Yuan M, Shi K, Xu C, Lin J, Shi Z, et al. Association between multimorbidity trajectories, healthcare utilization, and health expenditures among middle-aged and older adults: China Health and Retirement Longitudinal Study. J Affect Disord. 2023;330:24–32. Staimez LR, Wei MY, Kim M, Narayan KMV, Saydah SH. Multimorbidity of Four Cardiometabolic and Chronic Pulmonary Disease Groups: Prevalence and Attributable Fraction in US Adults, 2007–2012. J Comorbidity. 2017;7(1):22–32. Jani BD, Hanlon P, Nicholl BI, McQueenie R, Gallacher KI, Lee D et al. Relationship between multimorbidity, demographic factors and mortality: findings from the UK Biobank cohort. BMC Med. 2019;17(1). Sakakibara BM, Obembe AO, Eng JJ. The prevalence of cardiometabolic multimorbidity and its association with physical activity, diet, and stress in Canada: evidence from a population-based cross-sectional study. BMC Public Health. 2019;19(1). Sewpaul R, Mbewu AD, Fagbamigbe AF, Kandala N-B, Reddy SP. Prevalence of multimorbidity of cardiometabolic conditions and associated risk factors in a population-based sample of South Africans: A cross-sectional study. Public Health in Practice. 2021;2. Yang L, Sun J, Zhao M, Magnussen CG, Xi B. Trends in Cardiometabolic and Cancer Multimorbidity Prevalence and Its Risk With All-Cause and Cause-Specific Mortality in U.S. Adults: Prospective Cohort Study. Front Cardiovasc Med. 2021;8. Cheng X, Ma T, Ouyang F, Zhang G, Bai Y. Trends in the Prevalence of Cardiometabolic Multimorbidity in the United States, 1999–2018. Int J Environ Res Public Health. 2022;19(8). Joseph JJ, Rajwani A, Roper D, Zhao S, Kline D, Odei J et al. Associations of Cardiometabolic Multimorbidity With All-Cause and Coronary Heart Disease Mortality Among Black Adults in the Jackson Heart Study. JAMA Netw Open. 2022;5(10). Roth GA, Mensah GA, Johnson CO, Addolorato G, Ammirati E, Baddour LM, et al. Global Burden of Cardiovascular Diseases and Risk Factors, 1990–2019: Update From the GBD 2019 Study. J Am Coll Cardiol. 2020;76(25):2982–3021. Han Y, Hu Y, Yu C, Sun D, Pang Y, Pei P et al. Duration-dependent impact of cardiometabolic diseases and multimorbidity on all-cause and cause-specific mortality: a prospective cohort study of 0.5 million participants. Cardiovasc Diabetol. 2023;22(1). Dawber TR, Meadors GF, Moore FE. Jr. Epidemiological approaches to heart disease: the Framingham Study. Am J Public Health Nations Health. 1951;41(3):279–81. Yusuf S, Hawken S, Ounpuu S, Dans T, Avezum A, Lanas F, et al. Effect of potentially modifiable risk factors associated with myocardial infarction in 52 countries (the INTERHEART study): case-control study. Lancet. 2004;364(9438):937–52. Wong ND. Epidemiological studies of CHD and the evolution of preventive cardiology. Nat Rev Cardiol. 2014;11(5):276–89. Wang A, Li H, Yuan J, Zuo Y, Zhang Y, Chen S, et al. Visit-to-Visit Variability of Lipids Measurements and the Risk of Stroke and Stroke Types: A Prospective Cohort Study. J Stroke. 2020;22(1):119–29. Liu S, Gao Z, Dai Y, Guo R, Wang Y, Sun Z, et al. Association of general and abdominal obesity and their changes with stroke in Chinese adults: Results from an 11.8-year follow-up study. Nutr Metab Cardiovasc Dis. 2020;30(11):2001–7. Zhou Y, Zhang X, Zhang L, Li Z, Wu Q, Jin Z, et al. Increased Stroke Risk in Metabolically Abnormal Normal Weight: a 10-Year Follow-up of 102,037 Participants in China. Transl Stroke Res. 2021;12(5):725–34. Epstein KA, Viscoli CM, Spence JD, Young LH, Inzucchi SE, Gorman M, et al. Smoking cessation and outcome after ischemic stroke or TIA. Neurology. 2017;89(16):1723–9. Li X, Wu C, Lu J, Chen B, Li Y, Yang Y, et al. Cardiovascular risk factors in China: a nationwide population-based cohort study. Lancet Public Health. 2020;5(12):e672–e81. Lu J, Lu Y, Wang X, Li X, Linderman GC, Wu C, et al. Prevalence, awareness, treatment, and control of hypertension in China: data from 1·7 million adults in a population-based screening study (China PEACE Million Persons Project). Lancet. 2017;390(10112):2549–58. Salive ME. Multimorbidity in Older Adults. Epidemiol Rev. 2013;35(1):75–83. Rahimi K, Singh-Manoux A, Fayosse A, Sabia S, Tabak A, Shipley M et al. Clinical, socioeconomic, and behavioural factors at age 50 years and risk of cardiometabolic multimorbidity and mortality: A cohort study. PLoS Med. 2018;15(5). 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). Violan C, Foguet-Boreu Q, Flores-Mateo G, Salisbury C, Blom J, Freitag M, et al. Prevalence, determinants and patterns of multimorbidity in primary care: a systematic review of observational studies. PLoS ONE. 2014;9(7):e102149. Regitz-Zagrosek VLE, Mahmoodzadeh S. . Gend Med. 2007. Ventura-Clapier R, Dworatzek E, Seeland U, Kararigas G, Arnal J-F, Brunelleschi S, et al. Sex in basic research: concepts in the cardiovascular field. Cardiovascular Res. 2017;113(7):711–24. Zheng Y, Zhou Z, Wu T, Zhong K, Hu H, Zhang H et al. Association between composite lifestyle factors and cardiometabolic multimorbidity in Chongqing, China: A cross-sectional exploratory study in people over 45 years and older. Front Public Health. 2023;11. Zhang X, Lu J, Wu C, Cui J, Wu Y, Hu A et al. Healthy lifestyle behaviours and all-cause and cardiovascular mortality among 0.9 million Chinese adults. Int J Behav Nutr Phys Activity. 2021;18(1). Xie H, Li J, Zhu X, Li J, Yin J, Ma T et al. Association between healthy lifestyle and the occurrence of cardiometabolic multimorbidity in hypertensive patients: a prospective cohort study of UK Biobank. Cardiovasc Diabetol. 2022;21(1). Han Y, Hu Y, Yu C, Guo Y, Pei P, Yang L, et al. Lifestyle, cardiometabolic disease, and multimorbidity in a prospective Chinese study. Eur Heart J. 2021;42(34):3374–84. Chen Y, Yin Z, Xie Q. Suggestions to ameliorate the inequity in urban/rural allocation of healthcare resources in China. Int J Equity Health. 2014;13:34. Sibley LM, Weiner JP. An evaluation of access to health care services along the rural-urban continuum in Canada. BMC Health Serv Res. 2011;11:20. Xu Y, Ma J, Wu N, Fan X, Zhang T, Zhou Z, et al. Catastrophic health expenditure in households with chronic disease patients: A pre-post comparison of the New Health Care Reform in Shaanxi Province, China. PLoS ONE. 2018;13(3):e0194539. Wang L, Zhou B, Zhao Z, Yang L, Zhang M, Jiang Y, et al. Body-mass index and obesity in urban and rural China: findings from consecutive nationally representative surveys during 2004-18. Lancet. 2021;398(10294):53–63. Pan XF, Wang L, Pan A. Epidemiology and determinants of obesity in China. Lancet Diabetes Endocrinol. 2021;9(6):373–92. Ma S, Xi B, Yang L, Sun J, Zhao M, Bovet P. Trends in the prevalence of overweight, obesity, and abdominal obesity among Chinese adults between 1993 and 2015. Int J Obes (Lond). 2021;45(2):427–37. Mu L, Liu J, Zhou G, Wu C, Chen B, Lu Y, et al. Obesity Prevalence and Risks Among Chinese Adults: Findings From the China PEACE Million Persons Project, 2014–2018. Circ Cardiovasc Qual Outcomes. 2021;14(6):e007292. Shen C, Zhou Z, Lai S, Tao X, Zhao D, Dong W, et al. Urban-rural-specific trend in prevalence of general and central obesity, and association with hypertension in Chinese adults, aged 18–65 years. BMC Public Health. 2019;19(1):661. Li Y, Teng D, Shi X, Teng X, Teng W, Shan Z, et al. Changes in the prevalence of obesity and hypertension and demographic risk factor profiles in China over 10 years: two national cross-sectional surveys. Lancet Reg Health West Pac. 2021;15:100227. Kivimäki M, Kuosma E, Ferrie JE, Luukkonen R, Nyberg ST, Alfredsson L, et al. Overweight, obesity, and risk of cardiometabolic multimorbidity: pooled analysis of individual-level data for 120 813 adults from 16 cohort studies from the USA and Europe. The Lancet Public Health. 2017;2(6):e277–e85. Feng L, Jehan I, de Silva HA, Naheed A, Farazdaq H, Hirani S et al. Prevalence and correlates of cardiometabolic multimorbidity among hypertensive individuals: a cross-sectional study in rural South Asia—Bangladesh, Pakistan and Sri Lanka. BMJ Open. 2019;9(9). Lu Y, Liu S, Qiao Y, Li G, Wu Y, Ke C. Waist-to-height ratio, waist circumference, body mass index, waist divided by height0.5 and the risk of cardiometabolic multimorbidity: A national longitudinal cohort study. Nutr Metabolism Cardiovasc Dis. 2021;31(9):2644–51. Singh-Manoux A, Fayosse A, Sabia S, Tabak A, Shipley M, Dugravot A, et al. Clinical, socioeconomic, and behavioural factors at age 50 years and risk of cardiometabolic multimorbidity and mortality: A cohort study. PLoS Med. 2018;15(5):e1002571. Xu X, Mishra GD, Jones M. Evidence on multimorbidity from definition to intervention: An overview of systematic reviews. Ageing Res Rev. 2017;37:53–68. Sommer I, Griebler U, Mahlknecht P, Thaler K, Bouskill K, Gartlehner G, et al. Socioeconomic inequalities in non-communicable diseases and their risk factors: an overview of systematic reviews. BMC Public Health. 2015;15:914. Pati S, Swain S, Hussain MA, Kadam S, Salisbury C. Prevalence, correlates, and outcomes of multimorbidity among patients attending primary care in Odisha, India. Ann Fam Med. 2015;13(5):446–50. Ogunsina K, Dibaba DT, Akinyemiju T. Association between life-course socio-economic status and prevalence of cardio-metabolic risk ractors in five middle-income countries. J Glob Health. 2018;8(2):020405. Additional Declarations No competing interests reported. Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-3896393","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":269203506,"identity":"5df73c67-d440-4edb-8e57-bcaf95035aa7","order_by":0,"name":"Siying Xu","email":"","orcid":"","institution":"Zhuhai hospital affiliated with Jinan University (Zhuhai People's Hospital)","correspondingAuthor":false,"prefix":"","firstName":"Siying","middleName":"","lastName":"Xu","suffix":""},{"id":269203507,"identity":"6be0a308-db90-4805-8ef7-235144117aab","order_by":1,"name":"Wenbin Wang","email":"","orcid":"","institution":"Guangdong Cardiovascular Institute, Guangdong Academy of Medical Sciences","correspondingAuthor":false,"prefix":"","firstName":"Wenbin","middleName":"","lastName":"Wang","suffix":""},{"id":269203508,"identity":"913fa8fa-9035-4d5a-a2e0-c7621ee66813","order_by":2,"name":"Jiabin Wang","email":"","orcid":"","institution":"Guangdong Academy of Medical Sciences","correspondingAuthor":false,"prefix":"","firstName":"Jiabin","middleName":"","lastName":"Wang","suffix":""},{"id":269203509,"identity":"eeef9407-c935-496c-8213-ed3730f4db47","order_by":3,"name":"Anping Cai","email":"","orcid":"","institution":"Guangdong Cardiovascular Institute, Guangdong Academy of Medical Sciences","correspondingAuthor":false,"prefix":"","firstName":"Anping","middleName":"","lastName":"Cai","suffix":""},{"id":269203510,"identity":"f6a1879d-7f1d-46dc-9b8e-b1aead0b92fa","order_by":4,"name":"Xiaofei Jiang","email":"","orcid":"","institution":"Zhuhai People's Hospital (Zhuhai Clinical Medical College of Jinan University)","correspondingAuthor":false,"prefix":"","firstName":"Xiaofei","middleName":"","lastName":"Jiang","suffix":""},{"id":269203511,"identity":"2c03f6a7-3bd2-4320-a89a-f186a440932f","order_by":5,"name":"Yingqing Feng","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAABCElEQVRIie3RMUsDMRTA8RcCd8uz8ymCXyEQOCkn16/SI9Au7SHc4hgQOtnOin4IP0IgYBfRL+BWcGmHKzgEucFX7NAl540F84eELD8evACEQkdYrHe3oMPp1HSLvwiaA8Luu5N9HDuReG7XeP2RL5Yndn31nZWXwF+2X5CXXoJvowzFp3qwvVE2XYyrvo7U4zmoykcGySSVKKwSFlM5vbPFs0HJEzCF9k252ByQfieSoFwRyYnIFbhfwuo2gpOUPQk7PKUpbK7HlbCRor0pP4lfZb1p7KD3Tg/XZKVY3lrmbnIvoaIzBCi0gShhMxju/pRj++/wraPNgQFeQ0OEYq5VhEKh0D/rB3prWbO0e/1sAAAAAElFTkSuQmCC","orcid":"","institution":"Zhuhai hospital affiliated with Jinan University (Zhuhai People's Hospital)","correspondingAuthor":true,"prefix":"","firstName":"Yingqing","middleName":"","lastName":"Feng","suffix":""}],"badges":[],"createdAt":"2024-01-25 07:44:24","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-3896393/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-3896393/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":50387294,"identity":"53d86e9f-2cfd-4214-a378-d4b1ea780897","added_by":"auto","created_at":"2024-01-30 17:55:21","extension":"jpeg","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":297553,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003ePrevalence of number of cardiometabolic conditions by age group\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"floatimage1.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-3896393/v1/e941596065a9facbe2322894.jpeg"},{"id":50387295,"identity":"0d35f1df-3953-40ee-bcee-687c47987586","added_by":"auto","created_at":"2024-01-30 17:55:21","extension":"jpeg","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":519183,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eMultivariate predictors of cardiometabolic multimorbidity among individuals in China\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"floatimage3.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-3896393/v1/0e6e2ba4f23a5a362362b88e.jpeg"},{"id":51460571,"identity":"d56eb922-d3e4-4232-be61-35fbd96d1cc4","added_by":"auto","created_at":"2024-02-22 04:08:01","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":781359,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-3896393/v1/acae0735-65d7-4b98-81a3-1c53386f4ca2.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Epidemiological Survey of Cardiometabolic Multimorbidity and Related Risk Factors in Chinese Population: A Cross-Sectional Study","fulltext":[{"header":"Introduction","content":"\u003cp\u003eThe increasing burden of morbidity presents a significant challenge for healthcare systems globally, particularly with the rapid rise in CMM, making it a focal point for research. However, many healthcare systems across countries primarily concentrate on managing individual diseases rather than addressing the complexities of morbidities. CMM denotes the concurrent presence of two or more such diseases and is linked to heightened risks of mortality(\u003cspan additionalcitationids=\"CR2\" citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e), disability(\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e), cognitive decline(\u003cspan additionalcitationids=\"CR6\" citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e), diminished quality of life(\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e), and adv erse drug events(\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e),This places a substantial burden on both China\u0026rsquo;s healthcare system and society at large(\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e, \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e). The high prevalence and intricate nature of morbidities present significant challenges in managing healthcare resources effectively.\u003c/p\u003e \u003cp\u003eSeveral countries worldwide have conducted extensive research on the prevalence and impact of CMM(\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e, \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e, \u003cspan additionalcitationids=\"CR13 CR14 CR15 CR16 CR17\" citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e). However, in China, only a limited number of studies have been undertaken on this subject. One notable study, based on the Chinese Electronic Health Records Research in Yinzhou (CHERRY)study, revealed that the prevalence of CMM in the general population in China had more than doubled in just five years. The rapid growth of CMM is particularly noteworthy, with cardiovascular diseases emerging as the primary cause of death across all co-morbid combinations(\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e, \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e). Furthermore, a recent prospective cohort study from the China Kadoorie Biobank highlighted the duration-dependent effects of cardiometabolic diseases and multi-morbidity on all-cause and cause-specific mortality. This study demonstrated that the risk of mortality increases with the number of CMM and varies according to the duration of the diseases. It was observed that the mortality rate of diabetes rises with prolonged disease duration, while stroke mortality remains consistently high. Interestingly, the study found a decreasing trend in mortality from ischemic heart disease(\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e). However, there is a dearth of comprehensive research globally on risk factors specifically related to CMM. Most existing studies tend to focus on individual diseases rather than taking into account the complexities associated with CMM. It is common for many diseases to coexist and there may be interactions or shared pathogenic mechanisms among them. By solely studying diseases from a single-disease perspective, the potential interrelationships between different diseases are often overlooked. This can result in healthcare institutions and physicians focusing solely on the treatment and management of specific diseases, while disregarding the possibility that patients may have multiple diseases concurrently. Consequently, this approach can lead to incomplete treatment plans and an increased risk to patients\u0026rsquo; overall health, and may also result in resource wastage. Current research indicates that factors such as body mass index (BMI), dyslipidemia, and smoking are linked to the development of cardiovascular diseases, diabetes, and stroke(\u003cspan additionalcitationids=\"CR22 CR23 CR24 CR25 CR26\" citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e). Given the intricate nature of CMM, it is crucial to establish a multidisciplinary research framework. Large-scale prospective cohort studies need to be conducted to gain a more profound understanding of the risk factors, disease progression, and outcomes related to CMM. This approach will serve as a foundation for governments and healthcare institutions to develop precise and effective interventions and treatment measures for these diseases.\u003c/p\u003e \u003cp\u003eTherefore, we conducted an analysis of baseline data from the China Patient-Centered Evaluative Assessment of Cardiac Events Million Persons Project (China PEACE MPP) in the Guangdong region of China. The study included 102,358 residents aged 35\u0026ndash;75. Our primary objective was to examine the prevalence of various combinations of CMM and explore the clinical risk factors associated with CMM. These risk factors encompassed sociodemographic characteristics, lifestyle factors, and physical and laboratory examination indicators. By doing so, we aimed to uncover new insights that could contribute to future disease management strategies. Undoubtedly, we are presently facing a new era that requires the establishment of effective prevention and control strategies to address the increasing prevalence of CMM.\u003c/p\u003e"},{"header":"Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003eStudy population\u003c/h2\u003e \u003cp\u003eThis study utilized baseline data from the China Peace MPP, the design and methods of which have been previously documented in the literature(\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e, \u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e). In brief, the China Peace MPP is a nationwide, government-funded, large-scale screening project aimed at identifying high-risk populations for cardiovascular disease (CVD) across China. In this study, participant data from eight screening sites in Guangdong Province, collected between January 1, 2016, and December 31, 2020, were selected. The project employed a cluster sampling approach and obtained detailed information about the population at the screening sites, including demographics and population mobility, through various departments. Project promotion was carried out through television, newspapers, bulletin boards, and other communication channels. Participants were selected based on the age structure of the local population, with exclusion criteria including severe audio-visual impairments, inability to comprehend the purpose and content of the study, collective screening of occupational groups, and incomplete information. An analysis was conducted on a total of 102,358 participants who had resided at the project sites for at least 6 months in the 12 months prior to screening and were aged between 35 and 75 years. The study was approved by the Ethics Committee of the National Cardiovascular Disease Center and the Ethics Committee of Guangdong Provincial People\u0026rsquo;s Hospital (GDREC2016438H(R2)), and informed consent was obtained from all participants.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003eData definition\u003c/h2\u003e \u003cp\u003eThe primary outcome of this study was CMM, which was defined as having two or more of the following conditions: hypertension, diabetes, stroke, and coronary heart disease. Hypertension was determined by a systolic blood pressure (SBP) of \u0026ge;\u0026thinsp;140mmHg and/or a diastolic blood pressure (DBP) of \u0026ge;\u0026thinsp;90mmHg, self-reported use of antihypertensive medication, or self-reported history of hypertension. Blood pressure measurements were taken using an electronic blood pressure monitor (Omron HEM-7430, Omron Corporation, Kyoto, Japan) and a standardized protocol. Participants were seated with their right arm in a relaxed position, and blood pressure was measured twice after a 5-minute rest period. The average of these two measurements, taken 1 minute apart, was used for analysis. If the difference between the first two measurements exceeded 10mmHg, an additional measurement was taken, and the average of the three measurements was used as the final data. Information on diabetes, coronary heart disease, and stroke was collected via self-reported conditions during screening and medical records diagnosis prior to screening. A standardized questionnaire conducted during the initial screening asked participants, \u0026ldquo;Has your doctor ever told you that you have diabetes, coronary heart disease, or stroke?\u0026rdquo; to gather self-reported data on these conditions. Trained personnel also utilized the International Classification of Diseases, Tenth Edition (ICD) codes to identify participants\u0026rsquo; hospital records related to diabetes, coronary heart disease, and stroke from the hospital registration database, including diabetes (E10-E14), coronary heart disease (I20-I25), and stroke (I60-I64,I69). Additionally, the definition of diabetes encompassed the use of glucose-lowering medication or a fasting blood glucose level of \u0026ge;\u0026thinsp;7.0mmol/L.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec5\" class=\"Section2\"\u003e \u003ch2\u003eAssessment of Covariates\u003c/h2\u003e \u003cp\u003eThe investigation comprised three components: a questionnaire survey, physical measurements, and laboratory testing. The questionnaire addressed sociodemographic information, lifestyle behaviors, and medication history over the past two weeks. Trained community healthcare workers conducted face-to-face interviews to collect this data. Sociodemographic information included age, gender, educational level, current occupation, household registration, marital status, family income, and medical insurance. Educational level was categorized as \u0026ldquo;junior high school or below\u0026rdquo; and \u0026ldquo;senior high school or above.\u0026rdquo; Family income was categorized as \u0026ldquo;annual income above 50,000 RMB\u0026rdquo; and \u0026ldquo;annual income below 50,000 RMB.\u0026rdquo; Lifestyle behaviors included smoking and drinking status. Participants\u0026rsquo; current smoking and drinking status were determined by asking the question, \u0026ldquo;Do you currently smoke or drink alcohol?\u0026rdquo; Medication history in the past two weeks included antidiabetic, lipid-lowering, antiplatelet, and antihypertensive drugs. Body Mass Index (BMI) was calculated as weight (kg) divided by height (m) squared. According to the Chinese criteria for overweight and obesity, the BMI categories were defined as follows: normal weight (BMI\u0026thinsp;\u0026le;\u0026thinsp;24 kg/m\u0026sup2;), overweight (BMI 24\u0026ndash;28 kg/m\u0026sup2;), and obesity (BMI\u0026thinsp;\u0026ge;\u0026thinsp;28 kg/m\u0026sup2;).For each participant, a rapid lipid analyzer (CardioChek PA Analyzer; Polymer Technology Systems) was used to collect lipid profiles, including triglycerides (TG), total cholesterol (TC), high-density lipoprotein cholesterol (HDL-C), and low-density lipoprotein cholesterol (LDL-C) in a fasting state. Fasting blood glucose (FBG) was measured using fingertip blood samples. Strict quality control measures were implemented before, during, and after the survey to ensure the accuracy and reliability of the data.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec6\" class=\"Section2\"\u003e \u003ch2\u003eStatistical analysis\u003c/h2\u003e \u003cp\u003eThe analyzed sample included 102,358 participants who had complete data on four cardiovascular metabolic conditions. All analyses were conducted using R statistical software version 4.2.2(R Project for Statistical Computing), and all hypothesis testing was two-tailed with p\u0026thinsp;\u0026lt;\u0026thinsp;0.05 set as statistically significant. During the data analysis process, we observed 6863 missing values for LDL-C. Considering the substantial number of missing values and the inability to determine the reasons for missing data, we applied the \u0026ldquo;mice\u0026rdquo; package(version 3.16.0) and utilized HDL-C, TG, and TC lipid indicators to perform data imputation based on multiple imputation methods (method=\u0026ldquo;rf\u0026rdquo;, seed\u0026thinsp;=\u0026thinsp;1234). We selected the imputed data with the smallest differences compared to the original data for downstream analysis of LDL-C (mean\u0026thinsp;\u0026plusmn;\u0026thinsp;SD for original vs imputed data: 2.718(0.999) vs 2.710(1.023), p\u0026thinsp;=\u0026thinsp;0.062). Independent sample t-tests were employed for continuous variables, while chi-square tests were utilized for categorical variables to compare characteristics between individuals with and without CMM. The prevalence of the four cardiovascular metabolic conditions in various combinations was estimated and compared by gender using chi-square tests. Univariate and multivariable logistic regression analyses were performed to investigate the risk factors associated with CMM. Recognizing the potential impact of lipid-lowering medications on lipid levels, a sensitivity analysis was conducted by excluding the population taking lipid-lowering drugs. Odds ratios, along with their corresponding 95% confidence intervals, are reported.\u003c/p\u003e \u003c/div\u003e"},{"header":"Results","content":"\u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003eBaseline characteristics\u003c/h2\u003e \u003cp\u003eThe baseline characteristics of the 102,358 participants are presented in Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e. The overall prevalence of CMM was 11.6% (n\u0026thinsp;=\u0026thinsp;11,898), with females accounting for 60.5% of the cases. Nearly half of the participants resided in urban areas, while around 10% were farmers. The majority, approximately 90%, were married. The average age of the entire population was 54.27 years, with mean BMI and fasting blood glucose levels of 24.14 kg/m\u0026sup2; and 5.89 mmol/L respectively. We detected significant differences between groups in all baseline characteristic variables, except for those related to medical insurance(\u003cem\u003eP\u003c/em\u003e = 0.082), residence༈\u003cem\u003eP\u003c/em\u003e ༝ 0.928༉, and current smoking (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.10). In comparison to individuals without CMM, those with CMM were found to be older and more likely to work as farmers. They also exhibited a lower likelihood of being married, lower levels of education, lower income, and a higher probability of consuming alcohol. Additionally, individuals with CMM displayed higher values of fasting blood glucose and BMI, lower levels of LDL, higher levels of TG, lower levels of HDL, and a higher likelihood of using lipid-lowering drugs and antiplatelet medications.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003e\u003cb\u003eDescription of the sample\u003c/b\u003e\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"5\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCharacteristic\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eOverall\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003enon-CMM\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eCMM\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eP value\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNumber\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e102358\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e90460\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e11898\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAge(years), mean (SD)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e54.27 (10.19)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e53.47 (10.10)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e60.31 (8.72)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e35\u0026ndash;44\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e20514 (20.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e19925 (22.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e589 (5.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e45\u0026ndash;54\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e32388 (31.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e29858 (33.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2530 (21.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e55\u0026ndash;64\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e29949 (29.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e25485 (28.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e4464 (37.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026ge;\u0026thinsp;65\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e19507 (19.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e15192 (16.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e4315 (36.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGender\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMale, n (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e40440 (39.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e35036 (38.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e5404 (45.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFamale, n (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e61918 (60.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e55424 (61.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e6494 (54.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFarmer (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e12054 (11.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e10415 (11.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1639 (13.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eResidence (urban), n (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e49509 (48.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e43749 (48.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e5760 (48.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.928\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMarriage (married), n (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e92599 (90.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e82037 (90.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e10562 (88.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eEducational status (high school or above), n (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e30295 (29.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e27606 (30.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2689 (22.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAnnual household income (50 000 RMB or above), n (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e46420 (45.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e41442 (45.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e4978 (41.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMedical insurance, n (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e95465 (93.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e84323 (93.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e11142 (93.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.082\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCurrent smoking, n (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e17600 (17.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e15490 (17.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2110 (17.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.100\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCurrent drinking, n (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e5426 (5.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e4631 (5.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e795 (6.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGLU(mmol/L),mean (SD)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e5.89 (1.68)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e5.63 (1.30)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e7.90 (2.60)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFBG\u0026thinsp;\u0026ge;\u0026thinsp;7.0 mmol/L, n (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e13812 (13.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e5785 (6.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e8027 (67.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTC (mmol/L),mean (SD)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e4.91 (1.23)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e4.92 (1.21)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e4.89 (1.35)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.027\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTC\u0026thinsp;\u0026ge;\u0026thinsp;6.2 mmol/L, n (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e14288 (14.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e12401 (13.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1887 (15.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLDL-C (mmol/L),mean (SD)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2.71 (1.02)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2.72 (1.01)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2.66 (1.13)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLDL-C\u0026thinsp;\u0026ge;\u0026thinsp;4.1 mmol/L, n (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e9929 (9.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e8627 (9.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1302 (10.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTG (mmol/L),mean (SD)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.63 (0.97)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.58 (0.93)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.96 (1.14)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTG\u0026thinsp;\u0026ge;\u0026thinsp;2.3 mmol/L, n (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e17185 (16.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e13969 (15.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e3216 (27.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHDL-C (mmol/L),mean (SD)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.48 (0.44)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.49 (0.44)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.37 (0.40)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHDL-C\u0026lt;1.0 mmol/L, n (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e12214 (11.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e10354 (11.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1860 (15.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBMI(kg/m2), mean (SD)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e24.14 (3.30)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e23.95 (3.24)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e25.56 (3.44)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNormal weight(\u0026lt;24), n (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e52360 (51.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e48330 (53.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e4030 (33.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eOverweight(\u0026ge;\u0026thinsp;24,\u0026lt;28), n (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e37724 (36.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e32491 (35.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e5233 (44.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eObesity(\u0026ge;\u0026thinsp;28), n (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e12274 (12.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e9639 (10.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2635 (22.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCurrent use of lipid-lowering drugs, n (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e3767 (3.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2183 (2.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1584 (13.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCurrent use of antiplatelet drugs, n (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2258 (2.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1176 (1.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1082 (9.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"5\"\u003eData was presented as median (inter quartile range) for non-normally distributed variables, and number (percentage) for categorical variables.\u003c/td\u003e\u003c/tr\u003e \u003ctr\u003e\u003ctd colspan=\"5\"\u003eAbbreviations: CMM,cardiometabolic multimorbidity; BMI, body mass index; GLU, glucose;FBG, fasting blood glucose; TC, total cholesterol; LDL-C, low density lipoprotein-cholesterol; HDL-C, high density lipoprotein-cholesterol; TG, triglyceride.\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec9\" class=\"Section2\"\u003e \u003ch2\u003ePrevalence of cardiometabolic combinations\u003c/h2\u003e \u003cp\u003eThe prevalence of hypertension was found to be 39.9% (95% CI: 39.6\u0026ndash;40.2), diabetes was 16.1% (95% CI: 15.8\u0026ndash;16.3), coronary heart disease was observed in 2.0% (95% CI: 1.9\u0026ndash;2.1) of the population, while stroke affected 2.4% (95% CI: 2.3\u0026ndash;2.5). Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e provides an overview of the prevalence of different combinations of metabolic cardiovascular diseases. The most prevalent combination was hypertension and diabetes, affecting 8.5% (95% CI: 8.3\u0026ndash;8.6) of the population. The second and third most common dual comorbidities were hypertension with stroke (1.0%, 1.0-1.1) and hypertension with coronary heart disease (0.7%, 0.6\u0026ndash;0.7), respectively. Among the combinations of three diseases, the most common were hypertension, diabetes, and stroke, affecting 0.5% (0.5\u0026ndash;0.5) of the population. When examining gender differences, it was observed that the prevalence of both hypertension and diabetes was significantly higher in males compared to females. The co-occurrence of hypertension and diabetes was also more prevalent in males. Overall, the prevalence of metabolic cardiovascular diseases was higher in males than females. Furthermore, the prevalence of both single and multiple metabolic cardiovascular diseases increased with age(Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003e\u003cb\u003ePrevalence of cardiometabolic combinations\u003c/b\u003e\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"12\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c9\" colnum=\"9\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c10\" colnum=\"10\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c11\" colnum=\"11\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c12\" colnum=\"12\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eCardiometabolic condition\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eTotal\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c6\" namest=\"c5\"\u003e \u003cp\u003eMale\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c8\" morerows=\"1\" rowspan=\"2\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c9\"\u003e \u003cp\u003eFemale\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c11\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c12\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eP value\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003e%\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003e95%CI\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003en\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003e%\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003e95%CI\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c9\"\u003e \u003cp\u003e%\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c10\"\u003e \u003cp\u003e95%CI\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c11\"\u003e\u0026nbsp;\u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eOne condition\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e35.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e[35.6\u0026ndash;36.1]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e36691\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e39.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e[38.6\u0026ndash;39.6]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e33.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e[33.4\u0026ndash;34.1]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c12\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHTN\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e28.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e[28.3\u0026ndash;28.9]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e29287\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e31.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e[30.6\u0026ndash;31.5]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e27.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e[26.7\u0026ndash;27.4]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c12\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDM\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e6.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e[6.2\u0026ndash;6.5]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e6479\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e7.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e[6.7\u0026ndash;7.2]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e5.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e[5.7\u0026ndash;6.1]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c12\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSTROKE\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e[0.4\u0026ndash;0.5]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e480\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e[0.4\u0026ndash;0.6]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e0.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e[0.4\u0026ndash;0.5]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c12\"\u003e \u003cp\u003e0.229\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCHD\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e[0.4\u0026ndash;0.5]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e445\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e[0.6\u0026ndash;0.7]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e0.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e[0.3\u0026ndash;0.4]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c12\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTwo conditions\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e10.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e[10.3\u0026ndash;10.6]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e10703\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e11.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e[11.5\u0026ndash;12.1]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e9.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e[9.3\u0026ndash;9.8]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c12\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHTN,DM\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e8.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e[8.3\u0026ndash;8.6]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e8666\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e9.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e[8.8\u0026ndash;9.4]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e8.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e[7.8\u0026ndash;8.3]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c12\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHTN,CHD\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e[0.6\u0026ndash;0.7]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e695\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e1.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e[0.9\u0026ndash;1.1]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e0.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e[0.4\u0026ndash;0.5]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c12\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHTN,STROKE\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e[1.0-1.1]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1042\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e1.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e[1.2\u0026ndash;1.4]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e0.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e[0.8\u0026ndash;0.9]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c12\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDM,CHD\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e[0.1\u0026ndash;0.2]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e147\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e[0.2\u0026ndash;0.3]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e0.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e[0.1\u0026ndash;0.1]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c12\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDM,STROKE\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e[0.1\u0026ndash;0.1]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e115\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e[0.1\u0026ndash;0.2]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e0.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e[0.1\u0026ndash;0.1]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c12\"\u003e \u003cp\u003e0.007\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCHD,STROKE\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e[0.0-0.1]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e38\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e[0.0-0.1]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e0.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e[0.0-0.1]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c12\"\u003e \u003cp\u003e0.622\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eThree conditions\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e[1.0-1.1]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1097\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e1.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e[1.3\u0026ndash;1.6]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e0.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e[0.8\u0026ndash;0.9]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c12\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHTN,DM,CHD\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e[0.4\u0026ndash;0.4]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e403\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e[0.5\u0026ndash;0.7]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e0.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e[0.2\u0026ndash;0.3]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c12\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHTN,DM,STROKE\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e[0.5\u0026ndash;0.5]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e507\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e[0.5\u0026ndash;0.7]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e0.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e[0.4\u0026ndash;0.5]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c12\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHTN,CHD,STROKE\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e[0.1\u0026ndash;0.2]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e168\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e[0.2\u0026ndash;0.3]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e0.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e[0.1\u0026ndash;0.2]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c12\"\u003e \u003cp\u003e0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDM,CHD,STROKE\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e[0.0\u0026ndash;0.0]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e19\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e[0.0\u0026ndash;0.0]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e0.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e[0.0\u0026ndash;0.0]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c12\"\u003e \u003cp\u003e0.160\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAll conditions\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e[0.1\u0026ndash;0.1]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e98\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\" morerows=\"1\" rowspan=\"2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e[0.1\u0026ndash;0.2]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\" morerows=\"1\" rowspan=\"2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e0.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e[0.1\u0026ndash;0.1]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c12\"\u003e \u003cp\u003e0.069\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eOverall\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHTN\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e39.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e[39.6\u0026ndash;40.2]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e40866\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e43.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e[43.4\u0026ndash;44.4]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e37.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e[36.9\u0026ndash;37.7]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c12\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDM\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e16.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e[15.8\u0026ndash;16.3]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e16434\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e17.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e[17.4\u0026ndash;18.1]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e14.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e[14.7\u0026ndash;15.2]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c12\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCHD\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e2.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e[1.9\u0026ndash;2.1]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e2013\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e2.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e[2.7-3.0]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e1.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e[1.3\u0026ndash;1.5]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c12\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSTROKE\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e2.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e[2.3\u0026ndash;2.5]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e2467\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e3.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e[2.8\u0026ndash;3.1]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e2.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e[1.9\u0026ndash;2.2]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c12\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"12\"\u003eAbbreviations:HTN, hypertension;DM, diabetes mellitus;CHD,coronary heart disease; CI, conicity index.\u003c/td\u003e\u003c/tr\u003e \u003ctr\u003e\u003ctd colspan=\"12\"\u003ep\u0026thinsp;\u0026lt;\u0026thinsp;0.05 indicating a significant difference between males and females.\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec10\" class=\"Section2\"\u003e \u003ch2\u003eUnivariate and multivariate analysis for CMM\u003c/h2\u003e \u003cp\u003eThe results of the univariate analysis (Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e) showed that being male, not being a farmer, being married, having an education level lower than high school, having an annual income lower than 50,000, being a current alcohol drinker, having TC\u0026thinsp;\u0026ge;\u0026thinsp;6.2 mmol/L, LDL-C\u0026thinsp;\u0026ge;\u0026thinsp;4.1 mmol/L, TG\u0026thinsp;\u0026ge;\u0026thinsp;2.3 mmol/L, HDL-C\u0026thinsp;\u0026lt;\u0026thinsp;1.0 mmol/L, recent use of lipid-lowering drugs or antiplatelet aggregation drugs were significantly associated with a higher risk of CMM. The risk of CMM also significantly increased with age. Additionally, an increase in BMI (p\u0026thinsp;\u0026lt;\u0026thinsp;0.001) was significantly associated with an increased risk of CMM. In the multivariate regression analysis model, being 45\u0026ndash;54 years old (OR\u0026thinsp;=\u0026thinsp;2.62, 95% CI: 2.39\u0026ndash;2.88, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001), 55\u0026ndash;64 years old (OR\u0026thinsp;=\u0026thinsp;5.27, 95% CI: 4.83\u0026ndash;5.78, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001) and 65\u0026ndash;75 years old (OR\u0026thinsp;=\u0026thinsp;8.36, 95% CI: 7.62\u0026ndash;9.18, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001) (compared with 35\u0026ndash;44 years old), being a current alcohol drinker (compared with non-drinkers, OR\u0026thinsp;=\u0026thinsp;1.23, 95% CI: 1.12\u0026ndash;1.34, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001), TG\u0026thinsp;\u0026ge;\u0026thinsp;2.3 mmol/L (compared with TG\u0026thinsp;\u0026lt;\u0026thinsp;2.3 mmol/L, OR\u0026thinsp;=\u0026thinsp;1.69, 95% CI: 1.61\u0026ndash;1.78, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001), recent use of lipid-lowering drugs (compared with non-users, OR\u0026thinsp;=\u0026thinsp;3.47, 95% CI: 3.21\u0026ndash;3.74, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001), and recent use of antiplatelet aggregation drugs (compared with non-users, OR\u0026thinsp;=\u0026thinsp;3.67, 95% CI: 3.33\u0026ndash;4.04, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001) were associated with an increased risk of CMM. Conversely, being female (compared with males, OR\u0026thinsp;=\u0026thinsp;0.74, 95% CI: 0.70\u0026ndash;0.78, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001), other marital status (compared with married, OR\u0026thinsp;=\u0026thinsp;0.91, 95% CI: 0.85\u0026ndash;0.97, p\u0026thinsp;=\u0026thinsp;0.005), having an education level of high school or above (compared with below high school, OR\u0026thinsp;=\u0026thinsp;0.90, 95% CI: 0.85\u0026ndash;0.94, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001), Annual household income not less than 50,000 yuan (compared with less than 50,000 yuan ,OR\u0026thinsp;=\u0026thinsp;0.93, 95% CI: 0.89\u0026ndash;0.98, p\u0026thinsp;=\u0026thinsp;0.004),being a current smoker (compared with non-smokers, OR\u0026thinsp;=\u0026thinsp;0.80, 95% CI: 0.76\u0026ndash;0.86, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001), and HDL-C\u0026thinsp;\u0026ge;\u0026thinsp;1.0 mmol/L (compared with HDL-C\u0026thinsp;\u0026lt;\u0026thinsp;1.0 mmol/L, OR\u0026thinsp;=\u0026thinsp;0.84, 95% CI: 0.79\u0026ndash;0.90, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001) were associated with a reduced risk of CMM. The results are shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab3\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003e\u003cb\u003eCardiometabolic multimorbidity by related factors.\u003c/b\u003e\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"9\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c9\" colnum=\"9\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eCharacteristic\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003ePrevalence\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c6\" namest=\"c5\"\u003e \u003cp\u003eUnivariable logistic regression\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c9\" namest=\"c8\"\u003e \u003cp\u003eMultivariable logistic regression\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003e%\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003e95%CI\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eOR(95%CI)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eP value\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c8\"\u003e \u003cp\u003eOR(95%CI)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c9\"\u003e \u003cp\u003eP value\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAge(years)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e35\u0026ndash;44\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e[2.6-3]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eRef\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003eRef\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e45\u0026ndash;54\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e7.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e[7.5-8]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e2.87[2.62\u0026ndash;3.14]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e\u0026lt;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e2.62[2.39\u0026ndash;2.88]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e\u0026lt;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e55\u0026ndash;64\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e14.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e[14.5\u0026ndash;15]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e5.93[5.43\u0026ndash;6.48]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e\u0026lt;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e5.27[4.82\u0026ndash;5.78]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e\u0026lt;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e65\u0026ndash;75\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e22.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e[21.5\u0026ndash;23]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e9.61[8.80-10.51]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e\u0026lt;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e8.36[7.62\u0026ndash;9.18]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e\u0026lt;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eGender\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMen\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e13.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e[13.0\u0026ndash;14]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eRef\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003eRef\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eWomen\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e10.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e[10.2\u0026ndash;11]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.76[0.73\u0026ndash;0.79]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e\u0026lt;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.74[0.70\u0026ndash;0.78]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e\u0026lt;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eOccupation\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFarmer\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e11.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e[11.2\u0026ndash;12]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eRef\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003eRef\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNon-farmer\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e13.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e[13.0\u0026ndash;14]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.23[1.16\u0026ndash;1.30]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e\u0026lt;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e1.045[0.99\u0026ndash;1.11]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e0.127\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eResidence\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRural\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e11.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e[11.3\u0026ndash;12]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eRef\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003eRef\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eUrban\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e11.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e[11.4\u0026ndash;12]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.00[0.96\u0026ndash;1.04]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.920\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.97[0.93\u0026ndash;1.01]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e0.145\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eMarital status\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMarried\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e13.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e[13.0\u0026ndash;14]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eRef\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003eRef\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eOthers\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e11.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e[11.2\u0026ndash;12]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.81[0.76\u0026ndash;0.86]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e\u0026lt;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.91[0.85\u0026ndash;0.97]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e0.005\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eEducational status\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ejunior high school and below\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e12.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e[12.5\u0026ndash;13]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eRef\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003eRef\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ehigh school and above\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e8.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e[8.6-9]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.67[0.64\u0026ndash;0.70]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e\u0026lt;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.90[0.85\u0026ndash;0.94]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e\u0026lt;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eAnnual household income(RMB)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026lt;50,000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e12.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e[12.1\u0026ndash;13]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eRef\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003eRef\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026ge;\u0026thinsp;50,000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e10.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e[10.4\u0026ndash;11]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.85[0.82\u0026ndash;0.88]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e\u0026lt;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.93[0.89\u0026ndash;0.97]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eMedical insurance\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e11\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e[10.2\u0026ndash;12]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eRef\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003eRef\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e11.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e[11.5\u0026ndash;12]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.07[0.99\u0026ndash;1.16]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.078\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e1.11[1.02\u0026ndash;1.21]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e0.015\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eCurrent smoking\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e11.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e[11.3\u0026ndash;12]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eRef\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003eRef\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e12\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e[11.5\u0026ndash;12]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.04[0.99\u0026ndash;1.10]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.097\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.80[0.76\u0026ndash;0.86]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e\u0026lt;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eCurrent drinking\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e11.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e[11.3\u0026ndash;12]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eRef\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003eRef\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e14.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e[13.7\u0026ndash;16]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.33[1.23\u0026ndash;1.43]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e\u0026lt;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e1.23[1.12\u0026ndash;1.34]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e\u0026lt;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eTC (mmol/L)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026lt;6.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e11.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e[11.2\u0026ndash;12]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eRef\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003eRef\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026ge;\u0026thinsp;6.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e13.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e[12.7\u0026ndash;14]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.19[1.13\u0026ndash;1.25]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e\u0026lt;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.99[0.91\u0026ndash;1.06]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e0.679\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eLDL-C (mmol/L)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026lt;4.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e11.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e[11.3\u0026ndash;12]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eRef\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003eRef\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026ge;\u0026thinsp;4.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e13.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e[12.5\u0026ndash;14]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.17[1.10\u0026ndash;1.24]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e\u0026lt;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e1[0.92\u0026ndash;1.09]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e0.994\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eTG (mmol/L)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026lt;2.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e10.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e[10.0\u0026ndash;10]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eRef\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003eRef\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026ge;\u0026thinsp;2.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e18.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e[18.1\u0026ndash;19]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e2.03[1.94\u0026ndash;2.12]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e\u0026lt;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e1.69[1.61\u0026ndash;1.78]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e\u0026lt;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eHDL-C (mmol/L)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026lt;1.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e15.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e[14.6\u0026ndash;16]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eRef\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003eRef\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026ge;\u0026thinsp;1.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e11.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e[10.9\u0026ndash;11]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.70[0.66\u0026ndash;0.74]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e\u0026lt;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.84[0.79\u0026ndash;0.90]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e\u0026lt;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eBMI(kg/m2)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNormal weight(\u0026lt;24)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e7.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e[7.5-8]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eRef\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003eRef\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eOverweight(\u0026ge;\u0026thinsp;24,\u0026lt;28)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e13.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e[13.5\u0026ndash;14]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.93[1.85\u0026ndash;2.02]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e\u0026lt;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e1.74[1.66\u0026ndash;1.82]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e\u0026lt;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eObesity (\u0026ge;\u0026thinsp;28)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e21.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e[20.7\u0026ndash;22]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e3.28[3.11\u0026ndash;3.46]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e\u0026lt;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e2.97[2.8\u0026ndash;3.14]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e\u0026lt;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003ecurrent use of lipid-lowering drugs\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e10.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e[10.3\u0026ndash;11]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eRef\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003eRef\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e42\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e[40.5\u0026ndash;44]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e6.21[5.80\u0026ndash;6.65]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e\u0026lt;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e3.47[3.21\u0026ndash;3.74]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e\u0026lt;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eCurrent use of antiplatelet drugs\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e10.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e[10.6\u0026ndash;11]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eRef\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003eRef\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e47.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e[45.8\u0026ndash;50]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e7.60[6.98\u0026ndash;8.27]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e\u0026lt;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e3.67[3.33\u0026ndash;4.04]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e\u0026lt;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"9\"\u003eAbbreviations:Odds Ratio (OR)\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eConsidering that LDL-C and TC did not show statistical significance in the multivariate analysis of metabolic cardiovascular comorbidity groups, we conducted additional analyses by excluding individuals using lipid-lowering drugs. However, the results remained non-significant. We hypothesize that this lack of significance may stem from the heterogeneity of metabolic cardiovascular comorbidities, which refers to the variability and complexity of the disease status among individuals. Given the interplay between different metabolic cardiovascular diseases, patients with multiple diseases may require consideration of the interactions and effects of various medications between these diseases. In order to better understand the associated risk factors, a more comprehensive analysis is warranted.\u003c/p\u003e \u003c/div\u003e"},{"header":"Discussion","content":"\u003cp\u003eResearch 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(\u003cspan additionalcitationids=\"CR2\" citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e, \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e, \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e, \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e), particularly among older adults and individuals who are obese(\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e). 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(\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e). 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(\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e, \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e). 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.\u003c/p\u003e \u003cp\u003eThe lower prevalence of multimorbidity in women in the current study differs with certain prior research findings(\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e, \u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e, \u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e), 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(\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e), genetic predisposition within families(\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e), 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.\u003c/p\u003e \u003cp\u003eThe 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(\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e). According to the \u0026ldquo;Opinions of the Central Committee of the Communist Party of China and the State Council on Promoting the Development of Elderly Services\u0026rdquo; 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\u0026nbsp;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\u0026nbsp;million, accounting for over 30% of the total population, entering an \u0026ldquo;ultra-aging society\u0026rdquo;. Therefore, an increasing number of people may experience CMM, which will pose significant challenges to future socio-economic development, healthcare, and elderly care.\u003c/p\u003e \u003cp\u003eOur 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\u0026rsquo; diet tends to be characterized by high-calorie, high-fat, and high-salt foods(\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e). Additionally, individuals in rural areas engage in relatively more physical activity but lack regular aerobic exercise(\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e, \u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e). These lifestyle habits contribute to an increased risk of developing CMM(\u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e). 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(\u003cspan additionalcitationids=\"CR41\" citationid=\"CR40\" class=\"CitationRef\"\u003e40\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e42\u003c/span\u003e). Henceforth, an imperative arises to bolster the propagation of salubrious lifestyle conduct, enhance healthcare inclusivity, and propel the holistic advancement of rural domains.\u003c/p\u003e \u003cp\u003eSeveral 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(\u003cspan additionalcitationids=\"CR44 CR45\" citationid=\"CR43\" class=\"CitationRef\"\u003e43\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e46\u003c/span\u003e).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(\u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e47\u003c/span\u003e, \u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e48\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eIn our study, the average BMI of the total population was determined to be 24.14 kg/m\u0026sup2;, 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(\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e), 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(\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e, \u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e49\u003c/span\u003e), 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(\u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e50\u003c/span\u003e). 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(\u003cspan citationid=\"CR51\" class=\"CitationRef\"\u003e51\u003c/span\u003e). 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.\u003c/p\u003e \u003cp\u003eOur 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(\u003cspan additionalcitationids=\"CR53\" citationid=\"CR52\" class=\"CitationRef\"\u003e52\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR54\" class=\"CitationRef\"\u003e54\u003c/span\u003e). 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(\u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e50\u003c/span\u003e, \u003cspan citationid=\"CR55\" class=\"CitationRef\"\u003e55\u003c/span\u003e, \u003cspan citationid=\"CR56\" class=\"CitationRef\"\u003e56\u003c/span\u003e). Our findings appear to align more closely with those of HICs. China has now become the world\u0026rsquo;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\u0026rsquo;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.\u003c/p\u003e \u003cp\u003eRecent 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\u0026rsquo; 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.\u003c/p\u003e \u003cp\u003eOur 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.\u003c/p\u003e"},{"header":"Conclusions","content":"\u003cp\u003eIn conclusion, our study findings suggest that the prevalence of comorbid metabolic cardiovascular diseases in Guangdong, China, is significant, affecting more than 10% of the population, with a higher incidence observed among older individuals. This emphasizes the critical importance of prioritizing the implementation of effective management strategies for these comorbidities to mitigate potential healthcare expenditures and adverse health outcomes in the future. Interventions emphasizing the management of factors including obesity and lifestyle modifications are essential for the prevention and control of comorbid metabolic cardiovascular diseases. Furthermore, further comprehensive research is warranted to provide guidance for the implementation of more precise prevention and control measures.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eEthics approval and consent to participate\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis study was approved by both the Central Ethics Committee at the China National Center for Cardiovascular Disease and the Ethics Committee of Guangdong Provincial People\u0026rsquo;s Hospital (No. GDREC2016438H (R2)). Written informed consents were obtained from all participants. All methods were carried out in accordance with relevant guidelines and regulations.\u003c/p\u003e\n\u003ch4\u003eConsent for publication\u003c/h4\u003e\n\u003cp\u003eNot Applicable.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAvailability of data and materials\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAll data generated or analyzed during this study are included in this published article and its supplementary information files.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting interests\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare that they have no competing interests.\u003c/p\u003e\n\u003ch4\u003eFunding\u003c/h4\u003e\n\u003cp\u003eThis work was supported by the Guangdong Provincial Key Laboratory of Coronary Heart Disease Prevention (2017B030314041), the Climbing Plan of Guangdong Provincial People\u0026apos;s Hospital (DFJH2020022), Guangdong Provincial Clinical Research Center for Cardiovascular disease (2020B1111170011), the Ministry of Finance of China and National Health Commission of China, the Clinical Research Promotion Project of Zhuhai People\u0026apos;s Hospital (2023LCTS-34), and Guangdong Provincial Medical Science and Technology Research Fund Project (20211124162510276).\u003c/p\u003e\n\u003ch4\u003eAuthors\u0026apos; contributions\u003c/h4\u003e\n\u003cp\u003eXSY participated in the design of the study and drafted the manuscript. WWB conceived of the study and performed the statistical analysis. WJB contributed to project execution and data quality control. CAP helped to draft the manuscript. FYQ conceived of the study and participated in its design. All authors read and approved the final manuscript.\u003c/p\u003e\n\u003ch4\u003eAcknowledgements\u003c/h4\u003e\n\u003cp\u003eWe acknowledge the contribution the all staff who participated in this study as well as the study participants who shared their time with us.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthor information\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eXSY, Department of Cardiology, Zhuhai hospital affiliated with Jinan University (Zhuhai People\u0026apos;s Hospital), Zhuhai, China; Department of Cardiology, Hypertension Research Laboratory, Guangdong Cardiovascular Institute, Guangdong Provincial People\u0026apos;s Hospital, Guangdong Academy of Medical Sciences, Guangzhou, China, Email: [email protected]; WWB, Department of Cardiology, Hypertension Research Laboratory, Guangdong Cardiovascular Institute, Guangdong Provincial People\u0026apos;s Hospital, Guangdong Academy of Medical Sciences，Guangzhou, China, Email: [email protected]; WJB, Global Health Research Center, Guangdong Cardiovascular Institute, Guangdong Provincial People\u0026apos;s Hospital, Guangdong Academy of Medical Sciences, Guangzhou, China, Email: [email protected]; CAP, Department of Cardiology, Hypertension Research Laboratory, Guangdong Cardiovascular Institute, Guangdong Provincial People\u0026apos;s Hospital, Guangdong Academy of Medical Science，Guangzhou, China, Email: [email protected];JXF, Department of Cardiology, Zhuhai People\u0026apos;s Hospital (Zhuhai Clinical Medical College of Jinan University), Zhuhai, China, Email: [email protected]\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eCanoy 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).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eDi Angelantonio E, Kaptoge S, Wormser D, Willeit P, Butterworth AS, Bansal N et al. Association of Cardiometabolic Multimorbidity With Mortality. JAMA. 2015;314(1).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eZhang 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.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMa T, He L, Luo Y, Fu D, Huang J, Zhang G, et al. Frailty, an Independent Risk Factor in Progression Trajectory of Cardiometabolic Multimorbidity: A Prospective Study of UK Biobank. J Gerontol A Biol Sci Med Sci. 2023;78(11):2127\u0026ndash;35.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eDove A, Marseglia A, Shang Y, Grande G, Vetrano DL, Laukka EJ et al. Cardiometabolic multimorbidity accelerates cognitive decline and dementia progression. Alzheimers Dement. 2022.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eDove A, Guo J, Marseglia A, Fastbom J, Vetrano DL, Fratiglioni L, et al. Cardiometabolic multimorbidity and incident dementia: the Swedish twin registry. Eur Heart J. 2023;44(7):573\u0026ndash;82.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eJin Y, Liang J, Hong C, Liang R, Luo Y. Cardiometabolic multimorbidity, lifestyle behaviours, and cognitive function: a multicohort study. The Lancet Healthy Longevity. 2023;4(6):e265\u0026ndash;e73.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eFishbook BN, Brinton CD, Siever J, Klassen TD, Sakakibara BM. Cardiometabolic multimorbidity and activity limitation: a cross-sectional study of adults using the Canadian Longitudinal Study on Aging data. Fam Pract. 2022;39(3):455\u0026ndash;63.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eCheung JTK, Yu R, Woo J. Is polypharmacy beneficial or detrimental for older adults with cardiometabolic multimorbidity? Pooled analysis of studies from Hong Kong and Europe. Fam Pract. 2020;37(6):793\u0026ndash;800.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eWang HH, Wang JJ, Wong SY, Wong MC, Li FJ, Wang PX, et al. Epidemiology of multimorbidity in China and implications for the healthcare system: cross-sectional survey among 162,464 community household residents in southern China. BMC Med. 2014;12:188.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eZhang Z, Yuan M, Shi K, Xu C, Lin J, Shi Z, et al. Association between multimorbidity trajectories, healthcare utilization, and health expenditures among middle-aged and older adults: China Health and Retirement Longitudinal Study. J Affect Disord. 2023;330:24\u0026ndash;32.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eStaimez LR, Wei MY, Kim M, Narayan KMV, Saydah SH. Multimorbidity of Four Cardiometabolic and Chronic Pulmonary Disease Groups: Prevalence and Attributable Fraction in US Adults, 2007\u0026ndash;2012. J Comorbidity. 2017;7(1):22\u0026ndash;32.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eJani BD, Hanlon P, Nicholl BI, McQueenie R, Gallacher KI, Lee D et al. Relationship between multimorbidity, demographic factors and mortality: findings from the UK Biobank cohort. BMC Med. 2019;17(1).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSakakibara BM, Obembe AO, Eng JJ. The prevalence of cardiometabolic multimorbidity and its association with physical activity, diet, and stress in Canada: evidence from a population-based cross-sectional study. BMC Public Health. 2019;19(1).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSewpaul R, Mbewu AD, Fagbamigbe AF, Kandala N-B, Reddy SP. Prevalence of multimorbidity of cardiometabolic conditions and associated risk factors in a population-based sample of South Africans: A cross-sectional study. Public Health in Practice. 2021;2.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eYang L, Sun J, Zhao M, Magnussen CG, Xi B. Trends in Cardiometabolic and Cancer Multimorbidity Prevalence and Its Risk With All-Cause and Cause-Specific Mortality in U.S. Adults: Prospective Cohort Study. Front Cardiovasc Med. 2021;8.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eCheng X, Ma T, Ouyang F, Zhang G, Bai Y. Trends in the Prevalence of Cardiometabolic Multimorbidity in the United States, 1999\u0026ndash;2018. Int J Environ Res Public Health. 2022;19(8).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eJoseph JJ, Rajwani A, Roper D, Zhao S, Kline D, Odei J et al. Associations of Cardiometabolic Multimorbidity With All-Cause and Coronary Heart Disease Mortality Among Black Adults in the Jackson Heart Study. JAMA Netw Open. 2022;5(10).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eRoth GA, Mensah GA, Johnson CO, Addolorato G, Ammirati E, Baddour LM, et al. Global Burden of Cardiovascular Diseases and Risk Factors, 1990\u0026ndash;2019: Update From the GBD 2019 Study. J Am Coll Cardiol. 2020;76(25):2982\u0026ndash;3021.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eHan Y, Hu Y, Yu C, Sun D, Pang Y, Pei P et al. Duration-dependent impact of cardiometabolic diseases and multimorbidity on all-cause and cause-specific mortality: a prospective cohort study of 0.5 million participants. Cardiovasc Diabetol. 2023;22(1).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eDawber TR, Meadors GF, Moore FE. Jr. Epidemiological approaches to heart disease: the Framingham Study. Am J Public Health Nations Health. 1951;41(3):279\u0026ndash;81.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eYusuf S, Hawken S, Ounpuu S, Dans T, Avezum A, Lanas F, et al. Effect of potentially modifiable risk factors associated with myocardial infarction in 52 countries (the INTERHEART study): case-control study. Lancet. 2004;364(9438):937\u0026ndash;52.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eWong ND. Epidemiological studies of CHD and the evolution of preventive cardiology. Nat Rev Cardiol. 2014;11(5):276\u0026ndash;89.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eWang A, Li H, Yuan J, Zuo Y, Zhang Y, Chen S, et al. Visit-to-Visit Variability of Lipids Measurements and the Risk of Stroke and Stroke Types: A Prospective Cohort Study. J Stroke. 2020;22(1):119\u0026ndash;29.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLiu S, Gao Z, Dai Y, Guo R, Wang Y, Sun Z, et al. Association of general and abdominal obesity and their changes with stroke in Chinese adults: Results from an 11.8-year follow-up study. Nutr Metab Cardiovasc Dis. 2020;30(11):2001\u0026ndash;7.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eZhou Y, Zhang X, Zhang L, Li Z, Wu Q, Jin Z, et al. Increased Stroke Risk in Metabolically Abnormal Normal Weight: a 10-Year Follow-up of 102,037 Participants in China. Transl Stroke Res. 2021;12(5):725\u0026ndash;34.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eEpstein KA, Viscoli CM, Spence JD, Young LH, Inzucchi SE, Gorman M, et al. Smoking cessation and outcome after ischemic stroke or TIA. Neurology. 2017;89(16):1723\u0026ndash;9.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLi X, Wu C, Lu J, Chen B, Li Y, Yang Y, et al. Cardiovascular risk factors in China: a nationwide population-based cohort study. Lancet Public Health. 2020;5(12):e672\u0026ndash;e81.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLu J, Lu Y, Wang X, Li X, Linderman GC, Wu C, et al. Prevalence, awareness, treatment, and control of hypertension in China: data from 1\u0026middot;7 million adults in a population-based screening study (China PEACE Million Persons Project). Lancet. 2017;390(10112):2549\u0026ndash;58.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSalive ME. Multimorbidity in Older Adults. Epidemiol Rev. 2013;35(1):75\u0026ndash;83.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eRahimi K, Singh-Manoux A, Fayosse A, Sabia S, Tabak A, Shipley M et al. Clinical, socioeconomic, and behavioural factors at age 50 years and risk of cardiometabolic multimorbidity and mortality: A cohort study. PLoS Med. 2018;15(5).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eZhang 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).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eViolan C, Foguet-Boreu Q, Flores-Mateo G, Salisbury C, Blom J, Freitag M, et al. Prevalence, determinants and patterns of multimorbidity in primary care: a systematic review of observational studies. PLoS ONE. 2014;9(7):e102149.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eRegitz-Zagrosek VLE, Mahmoodzadeh S. \u0026lt;gender a=\"as=\" aspects=\"cardiovascular=\" disease.pdf=\"factor=\" for=\"metabolic=\" of=\"risk=\" role=\"syndrome=\" the=\"\"\u0026gt;\u0026lt;/gender\u0026gt;. Gend Med. 2007.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eVentura-Clapier R, Dworatzek E, Seeland U, Kararigas G, Arnal J-F, Brunelleschi S, et al. Sex in basic research: concepts in the cardiovascular field. Cardiovascular Res. 2017;113(7):711\u0026ndash;24.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eZheng Y, Zhou Z, Wu T, Zhong K, Hu H, Zhang H et al. Association between composite lifestyle factors and cardiometabolic multimorbidity in Chongqing, China: A cross-sectional exploratory study in people over 45 years and older. Front Public Health. 2023;11.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eZhang X, Lu J, Wu C, Cui J, Wu Y, Hu A et al. Healthy lifestyle behaviours and all-cause and cardiovascular mortality among 0.9 million Chinese adults. Int J Behav Nutr Phys Activity. 2021;18(1).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eXie H, Li J, Zhu X, Li J, Yin J, Ma T et al. Association between healthy lifestyle and the occurrence of cardiometabolic multimorbidity in hypertensive patients: a prospective cohort study of UK Biobank. Cardiovasc Diabetol. 2022;21(1).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eHan Y, Hu Y, Yu C, Guo Y, Pei P, Yang L, et al. Lifestyle, cardiometabolic disease, and multimorbidity in a prospective Chinese study. Eur Heart J. 2021;42(34):3374\u0026ndash;84.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eChen Y, Yin Z, Xie Q. Suggestions to ameliorate the inequity in urban/rural allocation of healthcare resources in China. Int J Equity Health. 2014;13:34.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSibley LM, Weiner JP. An evaluation of access to health care services along the rural-urban continuum in Canada. BMC Health Serv Res. 2011;11:20.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eXu Y, Ma J, Wu N, Fan X, Zhang T, Zhou Z, et al. Catastrophic health expenditure in households with chronic disease patients: A pre-post comparison of the New Health Care Reform in Shaanxi Province, China. PLoS ONE. 2018;13(3):e0194539.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eWang L, Zhou B, Zhao Z, Yang L, Zhang M, Jiang Y, et al. Body-mass index and obesity in urban and rural China: findings from consecutive nationally representative surveys during 2004-18. Lancet. 2021;398(10294):53\u0026ndash;63.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003ePan XF, Wang L, Pan A. Epidemiology and determinants of obesity in China. Lancet Diabetes Endocrinol. 2021;9(6):373\u0026ndash;92.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMa S, Xi B, Yang L, Sun J, Zhao M, Bovet P. Trends in the prevalence of overweight, obesity, and abdominal obesity among Chinese adults between 1993 and 2015. Int J Obes (Lond). 2021;45(2):427\u0026ndash;37.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMu L, Liu J, Zhou G, Wu C, Chen B, Lu Y, et al. Obesity Prevalence and Risks Among Chinese Adults: Findings From the China PEACE Million Persons Project, 2014\u0026ndash;2018. Circ Cardiovasc Qual Outcomes. 2021;14(6):e007292.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eShen C, Zhou Z, Lai S, Tao X, Zhao D, Dong W, et al. Urban-rural-specific trend in prevalence of general and central obesity, and association with hypertension in Chinese adults, aged 18\u0026ndash;65 years. BMC Public Health. 2019;19(1):661.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLi Y, Teng D, Shi X, Teng X, Teng W, Shan Z, et al. Changes in the prevalence of obesity and hypertension and demographic risk factor profiles in China over 10 years: two national cross-sectional surveys. Lancet Reg Health West Pac. 2021;15:100227.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eKivim\u0026auml;ki M, Kuosma E, Ferrie JE, Luukkonen R, Nyberg ST, Alfredsson L, et al. Overweight, obesity, and risk of cardiometabolic multimorbidity: pooled analysis of individual-level data for 120 813 adults from 16 cohort studies from the USA and Europe. The Lancet Public Health. 2017;2(6):e277\u0026ndash;e85.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eFeng L, Jehan I, de Silva HA, Naheed A, Farazdaq H, Hirani S et al. Prevalence and correlates of cardiometabolic multimorbidity among hypertensive individuals: a cross-sectional study in rural South Asia\u0026mdash;Bangladesh, Pakistan and Sri Lanka. BMJ Open. 2019;9(9).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLu Y, Liu S, Qiao Y, Li G, Wu Y, Ke C. Waist-to-height ratio, waist circumference, body mass index, waist divided by height0.5 and the risk of cardiometabolic multimorbidity: A national longitudinal cohort study. Nutr Metabolism Cardiovasc Dis. 2021;31(9):2644\u0026ndash;51.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSingh-Manoux A, Fayosse A, Sabia S, Tabak A, Shipley M, Dugravot A, et al. Clinical, socioeconomic, and behavioural factors at age 50 years and risk of cardiometabolic multimorbidity and mortality: A cohort study. PLoS Med. 2018;15(5):e1002571.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eXu X, Mishra GD, Jones M. Evidence on multimorbidity from definition to intervention: An overview of systematic reviews. Ageing Res Rev. 2017;37:53\u0026ndash;68.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSommer I, Griebler U, Mahlknecht P, Thaler K, Bouskill K, Gartlehner G, et al. Socioeconomic inequalities in non-communicable diseases and their risk factors: an overview of systematic reviews. BMC Public Health. 2015;15:914.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003ePati S, Swain S, Hussain MA, Kadam S, Salisbury C. Prevalence, correlates, and outcomes of multimorbidity among patients attending primary care in Odisha, India. Ann Fam Med. 2015;13(5):446\u0026ndash;50.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eOgunsina K, Dibaba DT, Akinyemiju T. Association between life-course socio-economic status and prevalence of cardio-metabolic risk ractors in five middle-income countries. J Glob Health. 2018;8(2):020405.\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"Cardiometabolic Multimorbidity, China, Prevalence, Epidemiology, Risk factors","lastPublishedDoi":"10.21203/rs.3.rs-3896393/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-3896393/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eBackground\u003c/h2\u003e \u003cp\u003eThe widespread prevalence of Cardiometabolic Multimorbidity (CMM) presents significant challenges to global public health. While previous studies have primarily examined individual cardiometabolic diseases, there has been limited research on CMM. As such, we intend to assess the prevalence of CMM and identify predictive risk factors within the Chinese population which will hold considerable implications for the future management of CMM.\u003c/p\u003e\u003ch2\u003eMethods\u003c/h2\u003e \u003cp\u003eWe employed data from The China Patient-Centered Evaluative Assessment of Cardiac Events Million Persons Project (China-PEACE MPP), enrolling a total of 102,358 participants aged 35\u0026ndash;75 years. CMM was defined as the simultaneous presence of two or more of the following diseases: diabetes, hypertension, stroke, and coronary heart disease. Univariate and multivariate logistic regression analyses were performed on demographic variables and modifiable factors associated with CMM to identify its risk predictive factors.\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e \u003cp\u003eThe participants, with an average age of 54.27 years, comprised 60.5% of women. The overall prevalence of CMM was 11.6%, with hypertension and diabetes coexisting as the most common comorbid combination at 8.5%. Multifactor logistic regression analysis revealed that increasing age (45\u0026ndash;54 years (OR\u0026thinsp;=\u0026thinsp;2.62, 95%CI: 2.39\u0026ndash;2.88), 55\u0026ndash;64 years (OR\u0026thinsp;=\u0026thinsp;5.27, 95%CI: 4.83\u0026ndash;5.78), and 65\u0026ndash;75 years (OR\u0026thinsp;=\u0026thinsp;8.36, 95%CI: 7.62\u0026ndash;9.18) compared to 35\u0026ndash;44 years), current alcohol consumption (OR\u0026thinsp;=\u0026thinsp;1.23, 95%CI: 1.12\u0026ndash;1.34), TG\u0026thinsp;\u0026ge;\u0026thinsp;2.3mmol/L (OR\u0026thinsp;=\u0026thinsp;1.69, 95%CI: 1.61\u0026ndash;1.78), recent use of lipid-lowering medications (OR\u0026thinsp;=\u0026thinsp;3.47, 95%CI: 3.21\u0026ndash;3.74), and recent use of antiplatelet aggregators (OR\u0026thinsp;=\u0026thinsp;3.67, 95%CI: 3.33\u0026ndash;4.04) were associated with an increased risk of CMM. Conversely, a reduced occurrence of CMM was associated with being female (OR\u0026thinsp;=\u0026thinsp;0.74, 95%CI: 0.70\u0026ndash;0.78), other marital statuses (OR\u0026thinsp;=\u0026thinsp;0.91, 95%CI: 0.85\u0026ndash;0.97), education level of high school or above (OR\u0026thinsp;=\u0026thinsp;0.90, 95%CI: 0.85\u0026ndash;0.94), annual household income not less than 50,000 yuan (OR\u0026thinsp;=\u0026thinsp;0.93, 95% CI: 0.89\u0026ndash;0.98, p\u0026thinsp;=\u0026thinsp;0.004), and HDL-C\u0026thinsp;\u0026ge;\u0026thinsp;1.0mmol/L (OR\u0026thinsp;=\u0026thinsp;0.84, 95%CI: 0.79\u0026ndash;0.90).\u003c/p\u003e\u003ch2\u003eConclusions\u003c/h2\u003e \u003cp\u003eIn the general population of China, over one-tenth of individuals are affected by CMM, indicating a high current prevalence of the condition. This highlights the imperative for China to develop targeted intervention measures focusing on the risk factors of CMM to prevent its occurrence and progression, effectively manage the condition, and reduce associated adverse outcomes and healthcare resource consumption.\u003c/p\u003e","manuscriptTitle":"Epidemiological Survey of Cardiometabolic Multimorbidity and Related Risk Factors in Chinese Population: A Cross-Sectional Study","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-01-30 17:55:17","doi":"10.21203/rs.3.rs-3896393/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"339b6d0e-c87d-4dce-9ce8-a1282ac327d5","owner":[],"postedDate":"January 30th, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2024-04-18T11:24:03+00:00","versionOfRecord":[],"versionCreatedAt":"2024-01-30 17:55:17","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-3896393","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-3896393","identity":"rs-3896393","version":["v1"]},"buildId":"qtupq5eGEP_6zYnWcrvyt","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}