Association of Dietary Patterns with All-Cause and Cause-Specific Mortality in a High Cardiovascular Risk Population in China

Association of Dietary Patterns with All-Cause and Cause-Specific Mortality in a High Cardiovascular Risk Population in China | 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 Association of Dietary Patterns with All-Cause and Cause-Specific Mortality in a High Cardiovascular Risk Population in China Yilin Lv, Xian Xie, Yuan Liu, Lei Yin, Li Yin, Xingli Li This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-8943220/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 11 You are reading this latest preprint version Abstract Purpose This study investigated dietary patterns among populations at high risk for cardiovascular disease (CVD) in Hunan Province and examined their associations with mortality. Methods Data were obtained from the China Health Evaluation and Risk Reduction through Nationwide Teamwork (ChinaHEART) project conducted between 2014 and 2023. A total of 46,239 individuals aged 35–75 years who were identified as being at high risk for CVD were included in the analysis. Major dietary patterns were derived using exploratory factor analysis. Baseline characteristics, survival analyses, and Cox proportional hazards regression models were performed using SPSS version 25.0 and R version 4.4.3 to estimate hazard ratios (HRs) and corresponding 95% confidence intervals (CIs). Results Compared with the "Rice Pattern" greater adherence to the "Meat, Eggs, and Fresh Vegetables Pattern" was associated with significantly lower risks of all-cause mortality (HR = 0.64, 95% CI: 0.50–0.82; P < 0.001) and chronic cardiovascular disease mortality (HR = 0.44, 95% CI: 0.24–0.77; P < 0.01). These associations were consistently observed across population subgroups in stratified analyses and remained robust in sensitivity analyses. Conclusion These findings provide evidence-based support for dietary guidance in high-risk populations and have important implications for the prevention and management of cardiovascular disease. Cardiovascular diseases High-risk population Dietary patterns All-cause mortality Specific cause mortality Figures Figure 1 Figure 2 Figure 3 1. Introduction Cardiovascular disease (CVD) remains one of the most important causes of mortality and disease burden worldwide [ 1 ] . By 2023, an estimated 18 million deaths annually were attributable to cardiovascular diseases, accounting for approximately 32% of all global deaths [ 2 ] . The China Cardiovascular Health and Disease Report 2024 indicates that the number of individuals living with cardiovascular disease in China has exceeded 350 million [ 3 ] . Driven by population aging and the increasing prevalence of unhealthy lifestyles, both the prevalence of cardiovascular disease and the associated mortality risk in China remain high [ 4 ] . Moreover, the burden of cardiovascular disease in central and western regions has been reported to exceed that in eastern coastal areas [ 5 ] . Epidemiological evidence further indicates that the burden of noncommunicable diseases, including cardiovascular disease and diabetes, attributable to elevated body mass index (BMI ≥ 25.0 kg/m²) has steadily increased in Hunan Province over the past three decades [ 6 ] . Therefore, strengthening early prevention strategies and health management for populations at high risk of cardiovascular disease is of critical importance for the effective prevention and control of chronic cardiovascular diseases in China [ 7 ] . High-risk cardiovascular individuals are defined as those with a substantially elevated risk of future major adverse cardiovascular events (MACE) or cardiovascular mortality [ 8 ] . This population is typically characterized by heightened chronic inflammation, metabolic dysfunction, and vascular impairment and constitutes a major source of cardiovascular mortality and recurrent events [ 9 ] . Updated U.S. dietary guidelines emphasize diet as a key strategy for reducing the burden of chronic diseases [ 10 ] . As a safe, cost-effective, and sustainable strategy, adherence to healthy dietary patterns has been shown to improve blood pressure and lipid profiles [ 11 ] , thereby reducing the risks of cardiovascular and cerebrovascular events [ 12 ] and alleviating overall disease burden [ 13 ] . Moreover, dietary pattern–based interventions have also been recognized as having important implications for the primary prevention of cardiovascular disease among populations without established high cardiovascular risk [ 14 ] . Hunan Province, located in central-southern China, is characterized by a humid and hot climate. The local diet is predominantly rice-based, with cooking methods involving substantial use of oil and salt, often accompanied by pickled and high-fat foods. Previous studies have indicated that unhealthy dietary patterns—marked by excessive intake of oil, salt, fat, and sugar, alongside low consumption of vegetables and fruits—not only contribute to overweight and obesity [ 15 ] , but also promote chronic inflammation, impair vascular endothelial function, and increase the risk of cardiovascular mortality [ 16 ] . Dietary patterns such as the Mediterranean diet [ 17 ] , Dietary Approaches to Stop Hypertension (DASH) diet [ 18 ] and plant-based diets [ 19 ] have been shown to reduce the incidence and mortality of cardiovascular disease by improving cardiometabolic indicators and exerting anti-inflammatory and antioxidant effects. However, the majority of research on these dietary patterns has been conducted in Western populations. The effects of the Hunan dietary pattern—characterized by rice as the staple food and cuisine high in oil, salt, and spices—on cardiovascular disease remain largely unexplored. Moreover, existing studies have primarily focused on the general population, with limited evidence available regarding dietary patterns in high-risk cardiovascular groups. In this context, a large-scale population cohort was utilized to characterize dietary patterns among high-risk cardiovascular populations in Hunan Province, China. Associations between different dietary patterns and risks of all-cause and cause-specific mortality were examined, providing evidence to inform personalized dietary intervention strategies for high-risk cardiovascular populations. 2. Materials and methods 2.1 Study population This study was conducted based on the China Health Evaluation And risk Reduction through nationwide Teamwork (ChinaHEART) project, a nationwide initiative aimed at early screening and comprehensive intervention among populations at high risk of cardiovascular disease in China [ 20 ] . The project was implemented in 14 prefecture-level cities and autonomous prefectures across Hunan Province between 2014 and 2023. This study population consisted of permanent residents aged 35–75 years who had lived in their communities for at least six months during the 12 months preceding enrollment. Baseline data, including questionnaires, physical examinations, and laboratory tests, were collected from all enrolled participants. Cardiovascular high-risk individuals were identified based on predefined criteria. After excluding those with missing dietary records ( n = 354) or missing survival records ( n = 328) within the high-risk group, a total of 46239 eligible participants were included in the final analysis (Fig. 1 ). 2.2 Definition of high-risk cardiovascular individuals According to the technical specifications of the national project, individuals meeting any of the following criteria were classified as being at high risk for cardiovascular disease: (1) History of major cardiovascular events; (2) Abnormal blood pressure or lipid levels; (3) A 10-year predicted risk of cardiovascular disease ≥ 20% as estimated using the World Health Organization (WHO) 2008 cardiovascular disease risk prediction chart; or (4) Family history of cardiovascular disease or hereditary cardiovascular conditions. Detailed criteria for the identification of high-risk cardiovascular individuals are provided in Table S1 . 2.3 Dietary assessment Dietary information was collected using a semi-quantitative food frequency questionnaire [ 21 ] . Common foods were categorized into 12 major groups: rice, wheat, grain, meat, poultry, seafood, eggs, fresh vegetables, pickle, fresh fruits, bean, and dairy. Consumption frequency for each food group was recorded on a five-level scale: "daily = 1", "4–6 days per week = 2", "1–3 days per week = 3", "1–3 days per month = 4" and "never or rarely consumed = 5". These scores were subsequently used for dietary pattern analysis. 2.4 Mortality outcomes Causes of death were classified according to the International Classification of Diseases, 10th Revision (ICD-10) [ 22 ] . Cause-specific mortality was classified into three categories according to ICD-10 codes: acute cardiovascular events (I21–I22, I46, I49.0, I60–I63), chronic cardiovascular disease (I20, I50, I30–I52, I70–I79), and non-cardiovascular causes (non–I00–I99). The detailed classification of disease categories used in this study is provided in Table S2 . 2.5 Statistical analysis To account for differences in measurement scales, dietary scores were standardized prior to analysis. Exploratory factor analysis was performed using principal components methods in SPSS 25.0, with orthogonal (maximum variance) rotation applied to improve factor interpretability and independence. The number of factors (dietary patterns) was determined based on eigenvalues greater than 1, examination of scree plots, and cumulative variance explained exceeding 50%. Four dietary patterns were extracted and subsequently labeled according to the food groups with factor loadings greater than 0.5 on each dimension. Baseline characteristics of participants across the four dietary patterns were analyzed using RStudio 4.4.3. Categorical variables were summarized as frequencies and percentages, whereas continuous variables were expressed as means ± standard deviations or medians with interquartile ranges, as appropriate. Kaplan-Meier survival curves were constructed to compare survival probabilities among high-risk individuals across dietary patterns, with the "Rice Pattern" selected as the reference due to the predominance of rice-based dietary habits in Hunan Province. Associations between dietary patterns and all-cause or cause-specific mortality were evaluated using Cox proportional hazards regression models. An unadjusted model and three sequentially adjusted models were fitted, with adjustments for demographic characteristics, lifestyle factors, and physiological indicators, respectively, to account for potential confounding. Results are reported as hazard ratios (HRs) with corresponding 95% confidence intervals (CIs). Further stratified analyses were performed according to age (> 60 years vs. ≤60 years), sex (male vs. female), smoking status (yes vs. no), drinking status (yes vs. no), and BMI (≥ 28 vs. <28), and interaction terms were tested to evaluate the consistency of associations across subgroups. 3. Results 3.1 Types and Characteristics of Dietary Patterns The results of the exploratory factor analysis indicated that the Kaiser-Meyer-Olkin (KMO) value was 0.809 (> 0.6), and Bartlett's sphericity test was significant ( X² = 106204.556, P < 0.001), demonstrating that the data were suitable for factor analysis. Four common factors with eigenvalues greater than 1 were extracted (3.393, 1.482, 1.046, and 1.038), cumulatively accounting for 57.99% of the total variance. Based on factor loadings derived from the 12 food groups, four distinct dietary patterns were identified (Table 1 ). The "Balanced Pattern" was characterized by higher intakes of wheat, grain, poultry, fresh fruits, bean, and dairy, reflecting a relatively balanced nutritional profile. The "Meat, Eggs, and Fresh Vegetables Pattern" was characterized by higher consumption of meat, seafood, eggs, and fresh vegetables, corresponding to increased protein and dietary fiber intake. The "Pickle Pattern" was predominantly defined by pickled food consumption, whereas the “Rice Pattern” was primarily characterized by high rice intake. Table 1 Factor-loading matric for the major dietary patterns identified by factor analysis Balanced Pattern Meat, Eggs, and Fresh Vegetables Pattern Pickle Pattern Rice Pattern rice 0.089 0.039 -0.012 0.931 wheat 0.567 0.210 -0.267 -0.092 grain 0.713 -0.193 0.279 0.061 meat 0.063 0.681 0.261 0.274 poultry 0.569 0.297 0.367 0.054 seafood 0.241 0.531 0.316 -0.279 eggs 0.465 0.570 -0.171 -0.066 fresh vegetables -0.190 0.628 -0.093 -0.006 pickle 0.123 0.053 0.827 -0.020 fresh fruits 0.671 0.114 -0.028 0.098 bean 0.729 0.028 0.229 -0.006 dairy 0.737 -0.072 0.100 0.012 3.2 Baseline characteristics A total of 46239 participants were included in the analysis, of whom 41% were male, and the mean age of the cohort was 61 years. Baseline characteristics of the participants are summarized in Table 2 . Stratified by dietary pattern, the "Balanced Pattern" accounted for the largest proportion of participants (approximately 30%) and was characterized by a higher representation of rural residents, individuals with lower educational attainment and income levels, and those who reported smoking or alcohol consumption. Participants in the "Meat, Eggs, and Fresh Vegetables Pattern" were predominantly urban residents, had higher educational attainment, and were more likely to be non-smokers and non-drinkers. The "Pickle Pattern" was primarily composed of Han Chinese participants and exhibited a relatively higher proportion of individuals with high income compared with other groups. The "Rice Pattern" accounted for the smallest proportion of participants (18%) and was characterized by relatively higher educational levels and a greater prevalence of habitual salt addition during meals. Table 2 Basic characteristics of the study population for the four dietary patterns Overall ( n = 46239) Balanced Pattern ( n = 13,796) Meat, Eggs, and Fresh Vegetables Pattern ( n = 10,900) Pickle Pattern ( n = 13,217) Rice Pattern ( n = 8,326) Age, median (IQR), year 61.00(54.00–68.00) 62.00 (54.00–67.00) 62.00(54.00–68.00) 61.00(54.00–67.00) 60.00(53.00–67.00) Sex, n (%) Male 18815(41%) 5,613 (41%) 4,312 (40%) 5,440 (41%) 3,450 (41%) Female 27424(59%) 8,183 (59%) 6,588 (60%) 7,777 (59%) 4,876 (59%) Urbanity, n (%) Rural 34517(75%) 12,298 (89%) 6,747 (62%) 9,517 (72%) 5,955 (72%) Urban 11722(25%) 1,498 (11%) 4,153 (38%) 3,700 (28%) 2,371 (28%) Ethnicity, n (%) Han 42952(93%) 12,745 (93%) 9,334 (86%) 12,848 (98%) 8,025 (97%) Ethnic minority 3112(6.8%) 998 (7.3%) 1,540 (14%) 314 (2.4%) 260 (3.1%) Annual income (CNY), n (%) >50000 10280(22%) 2,154 (16%) 2,685 (25%) 3,662 (28%) 1,779 (21%) ≦50000 35959(78%) 11,642 (84%) 8,215 (75%) 9,555 (72%) 6,547 (79%) Education level, n (%) Below high school 39468(85%) 12,564 (91%) 9,143 (84%) 10,982 (83%) 6,779 (81%) High school or above 6771(15%) 1,232 (8.9%) 1,757 (16%) 2,235 (17%) 1,547 (19%) Smoking status, n (%) No 35530(77%) 10,381 (75%) 8,510 (78%) 10,319 (78%) 6,320 (76%) Yes 10709(23%) 3,415 (25%) 2,390 (22%) 2,898 (22%) 2,006 (24%) Alcohol consumption, n (%) No 41592(90%) 12,195 (88%) 10,007 (92%) 11,912 (90%) 7,478 (90%) Yes 4647(10%) 1,601 (12%) 893 (8.2%) 1,305 (9.9%) 848 (10%) Additional salt added, n (%) No 38691(84%) 12,135 (88%) 9,002 (83%) 11,077 (84%) 6,477 (78%) Yes 7434(16%) 1,651 (12%) 1,876 (17%) 2,116 (16%) 1,791 (22%) BMI, n (%) <28 38630(84%) 11,331 (82%) 9,198 (84%) 11,121 (84%) 6,980 (84%) ≥28 7609(16%) 2,465 (18%) 1,702 (16%) 2,096 (16%) 1,346 (16%) BP, median (IQR), mmHg SBP 159.00(136.00-169.00) 161.00 (139.00-171.00) 158.50 (135.00-169.00) 159.00 (135.00-169.00) 154.00 (134.00-167.50) DBP 86.50(77.50–96.00) 87.00 (78.50–96.50) 86.00 (77.00-95.50) 86.50 (77.00–96.00) 86.50 (78.00-96.50) TC, median (IQR), mmol/L 5.12(4.28–6.19) 5.10 (4 .26-6.11) 5.09 (4.22–6.20) 5.08 (4.27–6.14) 5.26 (4.38–6.38) Glu, median (IQR), mmol/L 5.80(5.30–6.70) 5.80 (5.30–6.70) 5.90 (5.30–6.70) 5.80 (5.30–6.60) 5.80 (5.30–6.70) WC, median (IQR), cm 84.00(78.00–90.00) 84.00 (78.00–90.00) 84.00 (79.00–90.00) 84.00 (78.00–90.00) 84.00 (78.00–90.00) Height, median (IQR), cm 156.50(151.50-162.50) 155.50 (155.00-161.50) 157.00(151.00-162.80) 157.00(152.00-162.50) 158.00(152.50-163.10) Weight, median (IQR), kg 60.20(54.00–68.00) 60.00 (53.40, 67.40) 60.00 (53.90, 67.80) 60.20 (54.00, 68.00) 61.10 (54.50, 68.90) 3.3 Survival analysis of all-cause mortality During follow-up, 792 all-cause deaths were observed among 46239 participants over a median duration of 45 months (range: 3–81 months). Kaplan-Meier survival analysis demonstrated statistically significant differences in survival curves among the four dietary patterns (log-rank test, P < 0.0001) (Fig. 2 ). During the median follow-up period of 45 months, restricted mean survival time (RMST) analysis indicated that, compared with the "Rice Pattern", the RMST was prolonged by 0.05 months for the "Balanced Pattern", by 0.03 months for the "Meat, Eggs, and Fresh Vegetables Pattern", and by 0.10 months for the "Pickle Pattern" (Table S3 ). The "Meat, Eggs, and Fresh Vegetables Pattern" exhibited the highest cumulative survival rates among the four dietary patterns at both the 3-year and 5-year follow-up time points, reaching 99.25% (95% CI: 99.07%–99.53%) and 98.17% (95% CI: 97.48%–98.97%), respectively. The survival rate for the "Pickle Pattern" was higher than that of the reference group ("Rice Pattern") during years 1–4, but lower during years 5–6 (Table S4 ). 3.4 Association between dietary patterns and mortality Multivariate Cox proportional hazards models were applied to estimate hazard ratios and corresponding 95% confidence intervals for the associations between the four dietary patterns and cause-specific mortality. The "Meat, Eggs, and Fresh Vegetables Pattern" was associated with significantly lower risks of all-cause mortality (HR = 0.64, 95% CI: 0.50–0.82, P < 0.001), Chronic CVD mortality (HR = 0.44, 95% CI: 0.24–0.77, P < 0.01), and non-CVD mortality (HR = 0.71, 95% CI: 0.51–0.98, P < 0.05) (Table 3 ). After full adjustment, the "Balanced Pattern" was also associated with a reduced risk of non-CVD mortality (HR = 0.70, 95% CI: 0.53–0.93, P < 0.05). Table 3 Hazard ratio (95% CI) for all-cause mortality and cause-specific mortality across different dietary patterns. Balanced Pattern Meat, Eggs, and Fresh Vegetables Pattern Pickle Pattern Rice Pattern All-cause mortality No. of deaths 304 124 229 135 Model 1 a 1.08(0.88–1.33) 0.66(0.51–0.84) *** 0.99(0.80–1.22) 1(Ref.) Model 2 b 0.82(0.67–1.01) 0.64(0.50–0.83) *** 0.90(0.73–1.12) 1(Ref.) Model 3 c 0.82(0.67–1.01) 0.64(0.50–0.83) *** 0.90(0.73–1.12) 1(Ref.) Model 4 d 0.83(0.68–1.02) 0.64(0.50–0.82) *** 0.91(0.74–1.13) 1(Ref.) Acute CVD mortality No. of deaths 89 27 59 28 Model 1 a 1.54(1.01–2.35) * 0.68(0.40–1.15) 1.22 (0.78–1.92) 1(Ref.) Model 2 b 1.19(0.77–1.84) 0.67(0.39–1.15) 1.14(0.72–1.78) 1(Ref.) Model 3 c 1.20(0.78–1.84) 0.67(0.39–1.15) 1.13(0.72–1.78) 1(Ref.) Model 4 d 1.20(0.78–1.85) 0.69(0.40–1.17) 1.14(0.73–1.80) 1(Ref.) Chronic CVD mortality No. of deaths 71 23 45 28 Model 1 a 1.24(0.80–1.92) 0.56(0.32–0.98) * 0.93(0.58–1.49) 1(Ref.) Model 2 b 0.80(0.51–1.26) 0.45(0.26–0.80) ** 0.80(0.50–1.29) 1(Ref.) Model 3 c 0.82(0.52–1.28) 0.50(0.25–0.79) ** 0.80(0.50–1.28) 1(Ref.) Model 4 d 0.81(0.52–1.28) 0.44(0.24–0.77) ** 0.81(0.50–1.30) 1(Ref.) Non-CVD mortality No. of deaths 144 74 125 79 Model 1 a 0.87(0.66–1.14) 0.68(0.50–0.94) * 0.92(0.70–1.22) 1(Ref.) Model 2 b 0.69(0.52–0.91) ** 0.72(0.52–0.99) * 0.86(0.65–1.14) 1(Ref.) Model 3 c 0.69(0.52–0.91) ** 0.72(0.52–0.99) * 0.86 (0.65–1.14) 1(Ref.) Model 4 d 0.70(0.53–0.93) * 0.71(0.51–0.98) * 0.86(0.65–1.15) 1(Ref.) *: p < 0.05, **: p < 0.01, *** p < 0.001. a Model 1: No adjustments made. b Model 2: Adjusted for age, sex, urbanity, ethnicity, annual household income, and education level. c Model 3: Model 2 further adjusted for smoking status, and alcohol consumption. d Model 4: Model 3 further adjusted for BMI, hypertension, dyslipidemia, and diabetes. 3.5 Subgroup and interaction analyses Subgroup analyses were performed using the fully adjusted Model 4 in which all covariates were included except for the stratification variable corresponding to the subgroup of interest. The direction of the associations between the four dietary patterns and the risks of all-cause and cause-specific mortality was generally consistent across subgroups and was concordant with the findings observed in the overall population (Fig. 3 ). Across subgroups defined by basic demographic characteristics, selected lifestyle factors, and metabolism-related variables, the "Meat, Eggs, and Fresh Vegetables Pattern" was consistently associated with lower risks of all-cause mortality and chronic cardiovascular disease mortality, demonstrating robust and stable protective effects across different population strata. Further interaction analyses showed no statistically significant interactions between dietary patterns and age, sex, urbanity, annual income, education level, smoking status, alcohol consumption, hypertension, dyslipidemia, diabetes, physical activity, waist circumference, or BMI for all-cause, acute, chronic, and non-cardiovascular mortality ( P interaction > 0.05) (Table S5 –S8). These results indicate that the observed associations were generally consistent across different population subgroups and causes of death. 3.6 Sensitivity analyses After excluding participants with follow-up durations of less than one year, no substantial changes were observed in the estimated risk of all-cause mortality, acute cardiovascular mortality, chronic cardiovascular mortality, or non-cardiovascular mortality across the four dietary patterns (Table S9 –S12). 4. Discussion Four major dietary patterns were identified based on 12 dietary components among high-risk cardiovascular populations in Hunan Province. "Meat, Eggs, and Fresh Vegetables Pattern" was associated with a longer median survival time as well as higher 3-year and 5-year survival rates. Compared with the reference group ("Rice Pattern"), adherence to this dietary pattern was associated with significantly lower risks of all-cause mortality, chronic cardiovascular disease progression, and non-CVD mortality, findings that are consistent with previous studies [ 23 , 24 ] . These associations were consistently observed across multiple subgroups and remained robust after excluding participants with a follow-up duration of less than one year. Greater cardiovascular health benefits have been reported for the "Balanced Pattern" compared with single dietary components or isolated dietary patterns [ 25 ] 。Among individuals at high cardiovascular risk, dietary approaches characterized by increased intake of plant-based foods and high-quality protein, while overall dietary balance is maintained, have been considered particularly important [ 26 ] . The dietary pattern extracted in the present study—characterized by high consumption of fresh vegetables [ 27 ] 、eggs [ 28 ] 、meat and moderate grains [ 29 ] —was found to share core features with established cardioprotective dietary models, including the Mediterranean diet [ 30 ] 、DASH diet [ 31 ] and Oriental Healthy Dietary Pattern (OHDP) [ 32 ] . Furthermore, consistency with the principles outlined in the updated U.S. Dietary Guidelines, which emphasize higher-quality protein intake and the consumption of minimally processed foods, was observed [ 10 ] . The "Meat, Eggs, and Fresh Vegetables Pattern" was characterized by high-quality protein and fresh vegetables, and potential cardiovascular benefits were suggested to be mediated through multiple biological pathways. Improved glycemic control, favorable lipid profiles [ 33 ] , and maintenance of skeletal muscle mass [ 34 ] , were observed in association with diets combining high-quality protein and low intake of refined carbohydrates, which may contribute to reduced metabolic burden. Systemic inflammation and oxidative stress were reported to be attenuated by polyphenols and antioxidants abundant in fresh vegetables, both of which are recognized contributors to cardiovascular disease progression [ 35 ] . In contrast, pickled vegetables, a traditional dietary component in Hunan Province, were reported to be associated with elevated blood pressure, which may be induced by increased sodium intake, plasma volume expansion, and activation of the renin–angiotensin system [ 36 ] . Based on region-specific evidence from high-risk cardiovascular populations in Hunan Province, adherence to the "Meat, Eggs, and Fresh Vegetables Pattern" was associated with more favorable survival outcomes. In addition, the implementation of healthy lifestyle interventions, including tobacco control, alcohol restriction, and appropriate physical activity, in combination with a healthy diet, was suggested to confer potential benefits for cardiovascular health [ 37 ] . However, several limitations were identified. First, dietary intake was assessed only at baseline, which may have failed to capture dietary changes during follow-up and may have introduced exposure misclassification. Second, causes of death were determined using registration systems, which may have introduced outcome misclassification. Further studies integrating repeated dietary assessments, dietary quality indices, objective biomarkers, and interventional designs are warranted to validate and clarify potential causal associations [ 38 ] . 5. Conclusion In summary, four dietary patterns were identified, and their dietary characteristics were delineated among high-risk cardiovascular populations in Hunan Province. The "Meat, Eggs, and Fresh Vegetables" dietary pattern was significantly associated with reduced mortality risk and aligned with the principles of the Oriental Healthy Dietary Pattern. These findings provide evidence-based support for dietary interventions in high-risk cardiovascular populations, underscoring the feasibility and potential benefits of adopting dietary practices to reduce the overall burden of cardiovascular disease. Declarations Ethics approval and consent to participate The ChinaHEART project has been approved by the Central Ethics Committee of Fuwai Hospital (2014-574) and registered on ClinicalTrials.gov (NCT02536456). All participants provided written informed consent through registration. Consent for publication All authors have given consent for publishing this study. CRediT authorship contribution statement Yilin Lv: Writing – original draft, Visualization, Formal analysis, Data curation. Xian Xie: Validation, Data curation, Visualization. Yuan Liu: Project administration, Conceptualization, Software. Lei Yin: Resources, Supervision. Li Yin: Writing - review and editing, Data curation. Xingli Li : Conceptualization, Funding acquisition, Supervision, Writing – review and editing. Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper. Acknowledgements The authors thank all participants and staff involved in the China Health Evaluation And risk Reduction through nationwide Teamwork (ChinaHEART) project for their contributions to this study. Data availability Data will be made available on request. References Angela P, Danilo NG. The burden of hypercholesterolemia and ischemic heart disease in an ageing world. Pharmacol Res. 2023;193:106814–106814. A A W. WTC, A Z I., A C A.M. A, Pankaj AC, L., B S C M, B A Z., B A K., B A E. CM, Yvonne EMSV, Chete EKREN, Setri F, Giuliano G, Swapnil HDGH, Rizwan HJEK, Darae KDSK, Junxiu LDAL, Jun M, M E D. MJW, P N I. MMENSD, Remy P, Mary RH, R G A., S, N S., S, O M Pierre TEL, Yuh VSSVJHWN, Kristine Y. M S S., Heart Disease and Stroke Statistics—2023 Update: A Report From the American Heart Association, Circulation (2023). Liu MB, He XY, Yang XH, Wang ZW. Interpretation of Annual Report on Cardiovascular Health and Diseases in China 2024, Biomedical and environmental sciences: BES 38 (2025) 893–917. 10.3967/bes2025.099 Center For Cardiovascular Diseases The Writing Committee Of The Report On Cardiovascular H. China, Report on Cardiovascular Health and Diseases in China 2023: An Updated Summary. Biomed Environ Sci. 2024;37:949–92. 10.3967/bes2024.162 . Zheng C, Wang X, Gu R, Wang W, Tian Y, Pei X, Jia Q, Song Q, Cao X, Tian Y, Tuerdi N, Sun M, Liu S, Zhang Y, Wang Z. The Burden of Cardiovascular Disease in China: Incidence Estimates From National Surveillance (2023). J Am Coll Cardiol. 2025. 10.1016/j.jacc.2025.08.060 . He X, Zhang X, Mao F, Yao Y, Zhang R, Li J. Disease burden of non-communicable diseases attributable to high body mass index in China and its provinces, 1990–2021: A systematic analysis for the Global Burden of Disease Study 2021. Chin Med J (Engl). 2025. 10.1097/cm9.0000000000003900 . Li Y, Zhang C, Chen F, Zhang J. Prevalence and influencing factors of subjects at high risk for cardiovascular disease in different regions of Gansu province, China: a cross-sectional study of 100,725 residents from 2017 to 2022, Frontiers in Cardiovascular Medicine 11 (2024) 1373123–1373123. 10.3389/fcvm.2024.1373123 Mach F, Baigent C, Catapano AL, Koskinas KC, Casula M, Badimon L, Chapman MJ, De Backer GG, Delgado V, Ference BA, Graham IM, Halliday A, Landmesser U, Mihaylova B, Pedersen TR, Riccardi G, Richter DJ, Sabatine MS, Taskinen MR, Tokgozoglu L. Wiklund, 2019 ESC/EAS Guidelines for the management of dyslipidaemias: lipid modification to reduce cardiovascular risk. Eur Heart J. 2020;41:111–88. 10.1093/eurheartj/ehz455 . Nurul Izzati A, Fatin Syazwani AM, Kahar F, Amrina MA, Izzudin MP, Sazlina SG, Sabariah MN. Integrating CCL2 and TNF-α into the Framingham Risk Score for cardiovascular risk prediction: a cross-sectional study in a Malaysian cohort. Med J Malaysia. 2025;80:133–40. Services US, D o. A a U S D o H a H, Dietary Guidelines for Americans, 2025–2030, Washington, DC, 2026. Tian W, K C M C, Sophie M, Sayan RRV, Shane J, Andrius M, Luigi SAMF. Vegetarian Dietary Patterns and Cardiometabolic Risk in People With or at High Risk of Cardiovascular Disease: A Systematic Review and Meta-analysis. JAMA Netw open. 2023;6:e2325658–2325658. 10.1001/jamanetworkopen.2023.25658 . Cherian L, Agarwal P, Agrawal S, James BD, Yang D, Wagner M, Leurgans SE, Bennett DA, Aggarwal NT, Schneider JA. Dietary Patterns Associated With Risk of Intracranial Atherosclerosis in Older Adults With Hypertension or Myocardial Infarction. Neurology. 2025;105:e214147. 10.1212/wnl.0000000000214147 . Shreyaa R, Twisha P, k, sai V, Vaidheesh SS, AHMED P, Ridham K, Gunjan P, Anusha DH. Dineshbhai, s deepika, Abstract 4371831: Global Burden and Trends in Cardiovascular Disease Attributable to Dietary Risk Factors Among Women from 1990–2021: A Global Benchmarking Systematic Analysis, Circulation 152 (2025) A4371831-A4371831. 10.1161/circ.152.Suppl_3.4371831 Daneshzad E, Goodarzi S, Ghoreshi R, Shahrestanaki E, Hatami SE, Assar O, Abdar ZE, Sadeghi T, Zarif Yeganeh M, Mahmoodi Z, Qorbani M. The effect of a lifestyle intervention package on cardiovascular risk factors in the first grade relatives of hospitalized patients with cardiovascular diseases: a protocol of parallel-randomized trial. J Diabetes Metab Disord. 2025;24:188. 10.1007/s40200-025-01697-3 . Hu Y, Zhang Y, Zhong J, Wang Y, Zhou E, Hong F. Association between obesity phenotypes and dietary patterns: A two-step cluster analysis based on the China multi-ethnic cohort study. Prev Med. 2024;187:108100. 10.1016/j.ypmed.2024.108100 . Wilcox NS, Amit U, Reibel JB, Berlin E, Howell K, Ky B. Cardiovascular disease and cancer: shared risk factors and mechanisms, Nature reviews. Cardiology. 2024;21:617–31. Cardiovascular Program-ICCC R I-H S C i S P, Barcelona, Spain. MCIBERCV, Instituto de Salud Carlos III, Spain., R I-H S, C i. S P Cardiovascular Program-ICCC, Barcelona, Spain., D o Food, U F d S M Nutrition, Bolsista do CNPq, Brazil., R I-H S C i S P Cardiovascular Program-ICCC, Barcelona, Spain., Diet and Cardiovascular Disease: Effects of Foods and Nutrients in Classical and Emerging Cardiovascular Risk Factors. Curr Med Chem. 2019;26:3639–51. Maneeth M, Madiha K, Lakshmi MK, Prachi D, Teja RD, Namratha E, Ravi SDEEPALIM. Abstract TAC134: Impact of Mediterranean Diet on Hypertension in Patients with High Cardiovascular disease Risk: A Systematic Review&Meta-Analysis of Randomized Controlled Trials, Hypertension 82 (2025). 10.1161/hyp.82.Suppl_1.Tac134 Correia PE, Bisi L, Zhang M, Sun Y, Martins BB, Porepp OSC, Colpani V, Kunzler LB, Teixeira PP, Ferrari GT, Zajdenverg L, Brietzke E, Socal MP, Gerchman F. Plant and Animal-Based Dietary Patterns and Cardiometabolic Diseases in the Brazilian Population: Cross-Sectional Analysis of the Brazilian National Health Survey. Nutrients. 2025;17:3448–3448. 10.3390/nu17213448 . Wang R, Yang Y, Lu J, Cui J, Xu W, Song L, Wu C, Zhang X, Dai H, Zhong H, Jin B, He W, Zhang Y, Yang H, Wang Y, Zhang X, Li X, Hu S. Cohort Profile: ChinaHEART (Health Evaluation And risk Reduction through nationwide Teamwork) Cohort, Int J Epidemiol 52 (2023) e273-e282. 10.1093/ije/dyad074 Lu J, Xuan S, Downing NS, Wu C, Li L, Krumholz HM, Jiang L. Protocol for the China PEACE (Patient-centered Evaluative Assessment of Cardiac Events) Million Persons Project pilot. BMJ Open. 2016;6:e010200. 10.1136/bmjopen-2015-010200 . Organization WH. International statistical classification of diseases and related health problems: alphabetical index: world health organization, (2004). Hu F, Qin W, Xu L. Association between Dietary Patterns and Cardiometabolic Multimorbidity among Chinese Rural Older Adults. Nutrients. 2024;16. 10.3390/nu16172830 . Vidal PM, Tsampasian V, Cassidy A, Zoccai GB, Chrysohoou C, Koskinas K, Verschuren WMM, Czapla M, Kavousi M, Kouvari M, Vassiliou VS, Panagiotakos D. Diet and nutrition in cardiovascular disease prevention: a scientific statement of the European Association of Preventive Cardiology and the Association of Cardiovascular Nursing & Allied Professions of the European Society of Cardiology. Eur J Prev Cardiol. 2025;32:1540–52. 10.1093/eurjpc/zwaf310 . Minnetti M, Barazzoni R, Batsis JA, Busetto L, Yumuk V, Poggiogalle E, Weijs PJM, Donini LM. The Integration of Lifestyle Modification Advice and Diet and Physical Exercise Interventions: Cornerstones in the Management of Obesity with Incretin Mimetics. Obes Facts. 2025;1–16. 10.1159/000548370 . Conceição AR, d LL, Juvanhol A, Marcadenti ÂCB, Ferreira B, Weber J, Bressan. Diet quality scores and incidence of cardiovascular events: A 4-year prospective study of patients in cardiology secondary care (BALANCE Program Trial). Br J Nutr. 2025;1–28. 10.1017/s000711452510559x . Castañeiras PM, Ortiz C, Baz NFdL, Lope V, Gordón GS, Moreno ER, Alonso I, Esquinas EG, Gómez BP, Barriuso RP, Galán I, Castelló A. Intake of fruit, vegetables and pulses, and all-cause, cardiovascular and cancer mortality: Results from a population-based prospective study. Public Health. 2025;239:169–78. 10.1016/j.Puhe.2024.12.014 . Xia L, Xu T, Zhan Z. Dietary cholesterol intake and egg consumption in relation to all-cause and cardiovascular mortality after stroke. Sci Rep. 2025;15:35163–35163. 10.1038/s41598-025-19028-0 . Buna B, Zhixin L, Sophia L, Rona M, Blessing A-I, John H, Xiaoqi F, Xiaoyue SAEX. Long-Term Consumption of 10 Food Groups and Cardiovascular Mortality: A Systematic Review and Dose Response Meta-Analysis of Prospective Cohort Studies. Adv Nutr. 2023;14:55–63. 10.1016/j.Advnut.2022.10.010 . Belokon CO, Palacios SG, Gabucio LMC, Cánovas AO, d l MG, Hera J, Vioque LT, Collado. Adherence to Three Mediterranean Dietary Indexes and All-Cause, Cardiovascular, and Cancer Mortality in an Older Mediterranean Population. Nutrients. 2025;17:2956–2956. 10.3390/nu17182956 . Sun J, Jiang X, Li Z, Shen Y. Impact of dietary patterns on the survival outcomes of patients with cardiovascular disease. Front Nutr. 2025;12:1535174–1535174. 10.3389/fnut.2025.1535174 . Wang X, Xuan J, Chen H, Lin B, Yuan C. Eastern healthy dietary patterns in relation to all-cause mortality. Eur J Nutr. 2025;64:286. 10.1007/s00394-025-03807-6 . Zhang W, Zou L, Chen J. The impact of physical activity and dietary habits on glycolipemic metabolism and inflammatory markers in the elderly: a cross-sectional study. Front Nutr. 2025;12:1600038–1600038. 10.3389/fnut.2025.1600038 . Arslan S, Aydın A. The weight-loss paradox in older adults: balancing fat loss with muscle preservation. Bull Natl Res Centre. 2025;49:79–79. 10.1186/s42269-025-01374-8 . Fioroni N, Rivier CM, Meudec E, Cheynier V, Boudard F, Hemery Y, Babot CL. Antioxidant Capacity of Polar and Non-Polar Extracts of Four African Green Leafy Vegetables and Correlation with Polyphenol and Carotenoid Contents, Antioxidants 12 (2023) 1726-. 10.3390/antiox12091726 Nishimoto M, Griffin K, Wynne BM, Fujita T. Salt-Sensitive Hypertension and the Kidney, Hypertension (Dallas, Tex.: 1979) (2024). 10.1161/hypertensionaha.123.21369 Xia Y, Wu X, Zhang C, Hong X. Distribution and influencing factors of cardiovascular health among community inhabitants: Based on the scale of life’s Essential 8. Chin Gen Pract J. 2025;2:100071–100071. 10.1016/j.Cgpj.2025.100071 . Park S, Kang S. Interaction of Genetics and Dietary Patterns Scored by the High Healthy Eating Index in Hyperhomocysteinaemia Influencing Cardiovascular Disease Risk. Nutr Bull. 2025;50:311–25. 10.1111/nbu.70007 . Additional Declarations No competing interests reported. Supplementary Files supplementalmaterial.docx SupplementaryTableS5.xlsx SupplementaryTableS8.xlsx SupplementaryTableS6.xlsx SupplementaryTableS7.xlsx SupplementaryTableS12.xlsx SupplementaryTableS11.xlsx SupplementaryTableS9.xlsx SupplementaryTableS10.xlsx Cite Share Download PDF Status: Under Review Version 1 posted Editorial decision: Revision requested 05 Mar, 2026 Reviews received at journal 03 Mar, 2026 Reviewers agreed at journal 27 Feb, 2026 Reviews received at journal 26 Feb, 2026 Reviewers agreed at journal 26 Feb, 2026 Reviewers agreed at journal 25 Feb, 2026 Reviewers invited by journal 25 Feb, 2026 Editor invited by journal 24 Feb, 2026 Editor assigned by journal 23 Feb, 2026 Submission checks completed at journal 23 Feb, 2026 First submitted to journal 23 Feb, 2026 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. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. 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-8943220","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":597664783,"identity":"ad1066ae-8fb4-428c-8443-75f12e46981d","order_by":0,"name":"Yilin Lv","email":"","orcid":"","institution":"Department of Epidemiology and Health Statistics, Xiangya School of Public Health, Central South University","correspondingAuthor":false,"prefix":"","firstName":"Yilin","middleName":"","lastName":"Lv","suffix":""},{"id":597664784,"identity":"47ddb82f-0545-43ca-97a7-d07f05c21003","order_by":1,"name":"Xian Xie","email":"","orcid":"","institution":"Department of Non-communicable Disease Prevention and Control, Hunan Provincial Center for Disease Control and Prevention","correspondingAuthor":false,"prefix":"","firstName":"Xian","middleName":"","lastName":"Xie","suffix":""},{"id":597664785,"identity":"e4743263-312d-4229-908d-54586ef26085","order_by":2,"name":"Yuan Liu","email":"","orcid":"","institution":"Department of Non-communicable Disease Prevention and Control, Hunan Provincial Center for Disease Control and Prevention","correspondingAuthor":false,"prefix":"","firstName":"Yuan","middleName":"","lastName":"Liu","suffix":""},{"id":597664786,"identity":"3b9bd996-bdc4-4256-95e4-05c657ea30bf","order_by":3,"name":"Lei Yin","email":"","orcid":"","institution":"Department of Non-communicable Disease Prevention and Control, Hunan Provincial Center for Disease Control and Prevention","correspondingAuthor":false,"prefix":"","firstName":"Lei","middleName":"","lastName":"Yin","suffix":""},{"id":597664787,"identity":"d18d2802-6da4-43e4-8c3b-4bd638b70190","order_by":4,"name":"Li Yin","email":"","orcid":"","institution":"Department of Non-communicable Disease Prevention and Control, Hunan Provincial Center for Disease Control and Prevention","correspondingAuthor":false,"prefix":"","firstName":"Li","middleName":"","lastName":"Yin","suffix":""},{"id":597664788,"identity":"16b46a9e-6e25-4f09-8114-9370006c4ea8","order_by":5,"name":"Xingli Li","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA00lEQVRIie3RsQrCMBCA4SuFdjnteoK+QyVQChV8lXZyKeLo4JCpLqKrvk0gYJe4u7o4Cy4iIoZWHE1GwfxLUriPJBTA5frBuj6AAEgxar99MwlaQtjj1qRdCGJhTUIcytmd+qyuzwTzrODhQRguFuRytyZMVJkQqEnBcZobiC9kZ6XJEQPyKllwwthAPN4Qtg3P5D2tiD4Fb3qMICHNbYh+S0ePkSpZmu8nrMLyO4kixa74GI2jZX06XhbZYBOq76TJq96bHD5/ytTDbszlcrn+tBfjGTiJikTLpwAAAABJRU5ErkJggg==","orcid":"","institution":"Department of Epidemiology and Health Statistics, Xiangya School of Public Health, Central South University","correspondingAuthor":true,"prefix":"","firstName":"Xingli","middleName":"","lastName":"Li","suffix":""}],"badges":[],"createdAt":"2026-02-23 05:39:23","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-8943220/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-8943220/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":103631571,"identity":"644f10e0-59ce-480f-8659-daeccda4d259","added_by":"auto","created_at":"2026-02-28 03:05:45","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":975133,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eFlow chart of selection of eligible participants\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"Figure1.png","url":"https://assets-eu.researchsquare.com/files/rs-8943220/v1/28b09d606405777e79bba27b.png"},{"id":104399487,"identity":"ac3e8aab-7881-4fa8-a7da-c6425f5db2c5","added_by":"auto","created_at":"2026-03-11 12:06:19","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":1257331,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eKaplan–Meier curves for all-cause mortality across dietary patterns in high-risk cardiovascular populations\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"Figure2.png","url":"https://assets-eu.researchsquare.com/files/rs-8943220/v1/abe9271cd0099d357de2d81a.png"},{"id":103631576,"identity":"6358783b-fcc9-4d31-a0a2-7cdd4349610f","added_by":"auto","created_at":"2026-02-28 03:05:45","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":919881,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eForest plots of hazard ratios (HRs) and 95% confidence intervals (CIs) for the associations between dietary patterns and different causes of death\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"3.png","url":"https://assets-eu.researchsquare.com/files/rs-8943220/v1/24e2e9efd25994495806dc1f.png"},{"id":104407694,"identity":"9dfe1424-7d3c-47a5-a88a-33fe45caa028","added_by":"auto","created_at":"2026-03-11 12:39:36","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":4963126,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-8943220/v1/0261bb0a-7e19-46ac-b01c-8cb38f4ea1d8.pdf"},{"id":104399577,"identity":"ea7c81eb-1c86-4c3d-bf39-35061dda27b7","added_by":"auto","created_at":"2026-03-11 12:06:42","extension":"docx","order_by":0,"title":"","display":"","copyAsset":false,"role":"supplement","size":26934,"visible":true,"origin":"","legend":"","description":"","filename":"supplementalmaterial.docx","url":"https://assets-eu.researchsquare.com/files/rs-8943220/v1/b4d99ec6f28adf26eef43a43.docx"},{"id":104399079,"identity":"7d29a043-01df-4fb5-a01e-fffcd07671da","added_by":"auto","created_at":"2026-03-11 12:04:42","extension":"xlsx","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":18822,"visible":true,"origin":"","legend":"","description":"","filename":"SupplementaryTableS5.xlsx","url":"https://assets-eu.researchsquare.com/files/rs-8943220/v1/4547dcbe906c97d889031331.xlsx"},{"id":103631574,"identity":"c3b61c1a-e3c2-4c84-badc-07a779d1e06f","added_by":"auto","created_at":"2026-02-28 03:05:45","extension":"xlsx","order_by":2,"title":"","display":"","copyAsset":false,"role":"supplement","size":18873,"visible":true,"origin":"","legend":"","description":"","filename":"SupplementaryTableS8.xlsx","url":"https://assets-eu.researchsquare.com/files/rs-8943220/v1/74847660d244d005781e989b.xlsx"},{"id":103631577,"identity":"373a6ec4-d17f-4420-803d-4effe84305d8","added_by":"auto","created_at":"2026-02-28 03:05:46","extension":"xlsx","order_by":3,"title":"","display":"","copyAsset":false,"role":"supplement","size":18971,"visible":true,"origin":"","legend":"","description":"","filename":"SupplementaryTableS6.xlsx","url":"https://assets-eu.researchsquare.com/files/rs-8943220/v1/45004e71bb7aaf3fb1983e89.xlsx"},{"id":104399520,"identity":"9e2863ed-0402-4ab7-84f0-253bd2296088","added_by":"auto","created_at":"2026-03-11 12:06:28","extension":"xlsx","order_by":4,"title":"","display":"","copyAsset":false,"role":"supplement","size":18807,"visible":true,"origin":"","legend":"","description":"","filename":"SupplementaryTableS7.xlsx","url":"https://assets-eu.researchsquare.com/files/rs-8943220/v1/8040838615d259c4ca61bea8.xlsx"},{"id":104399425,"identity":"d6be4cba-e90b-4914-bb8e-9c3a9916b2f9","added_by":"auto","created_at":"2026-03-11 12:06:04","extension":"xlsx","order_by":5,"title":"","display":"","copyAsset":false,"role":"supplement","size":17399,"visible":true,"origin":"","legend":"","description":"","filename":"SupplementaryTableS12.xlsx","url":"https://assets-eu.researchsquare.com/files/rs-8943220/v1/a16c8cd333ddf00ae3567a65.xlsx"},{"id":103631581,"identity":"6ec6421b-bfc3-4759-9d9d-15cc44014cdd","added_by":"auto","created_at":"2026-02-28 03:05:46","extension":"xlsx","order_by":6,"title":"","display":"","copyAsset":false,"role":"supplement","size":17003,"visible":true,"origin":"","legend":"","description":"","filename":"SupplementaryTableS11.xlsx","url":"https://assets-eu.researchsquare.com/files/rs-8943220/v1/b1cb2d4ea4751189f29765b4.xlsx"},{"id":103631582,"identity":"90516714-e6d8-4c38-a69b-50c5e36fcb43","added_by":"auto","created_at":"2026-02-28 03:05:46","extension":"xlsx","order_by":7,"title":"","display":"","copyAsset":false,"role":"supplement","size":17402,"visible":true,"origin":"","legend":"","description":"","filename":"SupplementaryTableS9.xlsx","url":"https://assets-eu.researchsquare.com/files/rs-8943220/v1/5b88528b4806266346c4b5cc.xlsx"},{"id":103631579,"identity":"22b089b3-2980-4ca2-b9ea-aa07906b9ac5","added_by":"auto","created_at":"2026-02-28 03:05:46","extension":"xlsx","order_by":8,"title":"","display":"","copyAsset":false,"role":"supplement","size":18499,"visible":true,"origin":"","legend":"","description":"","filename":"SupplementaryTableS10.xlsx","url":"https://assets-eu.researchsquare.com/files/rs-8943220/v1/d8c90985201276a79037c30d.xlsx"}],"financialInterests":"No competing interests reported.","formattedTitle":"Association of Dietary Patterns with All-Cause and Cause-Specific Mortality in a High Cardiovascular Risk Population in China","fulltext":[{"header":"1. Introduction","content":"\u003cp\u003eCardiovascular disease (CVD) remains one of the most important causes of mortality and disease burden worldwide\u003csup\u003e[\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e]\u003c/sup\u003e. By 2023, an estimated 18\u0026nbsp;million deaths annually were attributable to cardiovascular diseases, accounting for approximately 32% of all global deaths\u003csup\u003e[\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e]\u003c/sup\u003e. The China Cardiovascular Health and Disease Report 2024 indicates that the number of individuals living with cardiovascular disease in China has exceeded 350 million\u003csup\u003e[\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e]\u003c/sup\u003e. Driven by population aging and the increasing prevalence of unhealthy lifestyles, both the prevalence of cardiovascular disease and the associated mortality risk in China remain high\u003csup\u003e[\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e]\u003c/sup\u003e. Moreover, the burden of cardiovascular disease in central and western regions has been reported to exceed that in eastern coastal areas\u003csup\u003e[\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e]\u003c/sup\u003e. Epidemiological evidence further indicates that the burden of noncommunicable diseases, including cardiovascular disease and diabetes, attributable to elevated body mass index (BMI\u0026thinsp;\u0026ge;\u0026thinsp;25.0 kg/m\u0026sup2;) has steadily increased in Hunan Province over the past three decades\u003csup\u003e[\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e]\u003c/sup\u003e. Therefore, strengthening early prevention strategies and health management for populations at high risk of cardiovascular disease is of critical importance for the effective prevention and control of chronic cardiovascular diseases in China\u003csup\u003e[\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e]\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eHigh-risk cardiovascular individuals are defined as those with a substantially elevated risk of future major adverse cardiovascular events (MACE) or cardiovascular mortality\u003csup\u003e[\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e]\u003c/sup\u003e. This population is typically characterized by heightened chronic inflammation, metabolic dysfunction, and vascular impairment and constitutes a major source of cardiovascular mortality and recurrent events\u003csup\u003e[\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e]\u003c/sup\u003e. Updated U.S. dietary guidelines emphasize diet as a key strategy for reducing the burden of chronic diseases\u003csup\u003e[\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e]\u003c/sup\u003e. As a safe, cost-effective, and sustainable strategy, adherence to healthy dietary patterns has been shown to improve blood pressure and lipid profiles\u003csup\u003e[\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e]\u003c/sup\u003e, thereby reducing the risks of cardiovascular and cerebrovascular events\u003csup\u003e[\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e]\u003c/sup\u003e and alleviating overall disease burden\u003csup\u003e[\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e]\u003c/sup\u003e. Moreover, dietary pattern\u0026ndash;based interventions have also been recognized as having important implications for the primary prevention of cardiovascular disease among populations without established high cardiovascular risk\u003csup\u003e[\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e]\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eHunan Province, located in central-southern China, is characterized by a humid and hot climate. The local diet is predominantly rice-based, with cooking methods involving substantial use of oil and salt, often accompanied by pickled and high-fat foods. Previous studies have indicated that unhealthy dietary patterns\u0026mdash;marked by excessive intake of oil, salt, fat, and sugar, alongside low consumption of vegetables and fruits\u0026mdash;not only contribute to overweight and obesity\u003csup\u003e[\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e]\u003c/sup\u003e, but also promote chronic inflammation, impair vascular endothelial function, and increase the risk of cardiovascular mortality\u003csup\u003e[\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e]\u003c/sup\u003e. Dietary patterns such as the Mediterranean diet\u003csup\u003e[\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e]\u003c/sup\u003e, Dietary Approaches to Stop Hypertension (DASH) diet\u003csup\u003e[\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e]\u003c/sup\u003e and plant-based diets\u003csup\u003e[\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e]\u003c/sup\u003e have been shown to reduce the incidence and mortality of cardiovascular disease by improving cardiometabolic indicators and exerting anti-inflammatory and antioxidant effects. However, the majority of research on these dietary patterns has been conducted in Western populations. The effects of the Hunan dietary pattern\u0026mdash;characterized by rice as the staple food and cuisine high in oil, salt, and spices\u0026mdash;on cardiovascular disease remain largely unexplored. Moreover, existing studies have primarily focused on the general population, with limited evidence available regarding dietary patterns in high-risk cardiovascular groups.\u003c/p\u003e \u003cp\u003eIn this context, a large-scale population cohort was utilized to characterize dietary patterns among high-risk cardiovascular populations in Hunan Province, China. Associations between different dietary patterns and risks of all-cause and cause-specific mortality were examined, providing evidence to inform personalized dietary intervention strategies for high-risk cardiovascular populations.\u003c/p\u003e"},{"header":"2. Materials and methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003e2.1 Study population\u003c/h2\u003e \u003cp\u003eThis study was conducted based on the China Health Evaluation And risk Reduction through nationwide Teamwork (ChinaHEART) project, a nationwide initiative aimed at early screening and comprehensive intervention among populations at high risk of cardiovascular disease in China\u003csup\u003e[\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e]\u003c/sup\u003e. The project was implemented in 14 prefecture-level cities and autonomous prefectures across Hunan Province between 2014 and 2023. This study population consisted of permanent residents aged 35\u0026ndash;75 years who had lived in their communities for at least six months during the 12 months preceding enrollment.\u003c/p\u003e \u003cp\u003eBaseline data, including questionnaires, physical examinations, and laboratory tests, were collected from all enrolled participants. Cardiovascular high-risk individuals were identified based on predefined criteria. After excluding those with missing dietary records (\u003cem\u003en\u003c/em\u003e\u0026thinsp;=\u0026thinsp;354) or missing survival records (\u003cem\u003en\u003c/em\u003e\u0026thinsp;=\u0026thinsp;328) within the high-risk group, a total of 46239 eligible participants were included in the final analysis (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003e\u003cb\u003e2.2 Definition of high-risk cardiovascular individuals\u003c/b\u003e\u003c/h2\u003e \u003cp\u003eAccording to the technical specifications of the national project, individuals meeting any of the following criteria were classified as being at high risk for cardiovascular disease: (1) History of major cardiovascular events; (2) Abnormal blood pressure or lipid levels; (3) A 10-year predicted risk of cardiovascular disease\u0026thinsp;\u0026ge;\u0026thinsp;20% as estimated using the World Health Organization (WHO) 2008 cardiovascular disease risk prediction chart; or (4) Family history of cardiovascular disease or hereditary cardiovascular conditions. Detailed criteria for the identification of high-risk cardiovascular individuals are provided in Table \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003e.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec5\" class=\"Section2\"\u003e \u003ch2\u003e2.3 Dietary assessment\u003c/h2\u003e \u003cp\u003eDietary information was collected using a semi-quantitative food frequency questionnaire\u003csup\u003e[\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e]\u003c/sup\u003e. Common foods were categorized into 12 major groups: rice, wheat, grain, meat, poultry, seafood, eggs, fresh vegetables, pickle, fresh fruits, bean, and dairy. Consumption frequency for each food group was recorded on a five-level scale: \"daily\u0026thinsp;=\u0026thinsp;1\", \"4\u0026ndash;6 days per week\u0026thinsp;=\u0026thinsp;2\", \"1\u0026ndash;3 days per week\u0026thinsp;=\u0026thinsp;3\", \"1\u0026ndash;3 days per month\u0026thinsp;=\u0026thinsp;4\" and \"never or rarely consumed\u0026thinsp;=\u0026thinsp;5\". These scores were subsequently used for dietary pattern analysis.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec6\" class=\"Section2\"\u003e \u003ch2\u003e2.4 Mortality outcomes\u003c/h2\u003e \u003cp\u003eCauses of death were classified according to the International Classification of Diseases, 10th Revision (ICD-10) \u003csup\u003e[\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e]\u003c/sup\u003e. Cause-specific mortality was classified into three categories according to ICD-10 codes: acute cardiovascular events (I21\u0026ndash;I22, I46, I49.0, I60\u0026ndash;I63), chronic cardiovascular disease (I20, I50, I30\u0026ndash;I52, I70\u0026ndash;I79), and non-cardiovascular causes (non\u0026ndash;I00\u0026ndash;I99). The detailed classification of disease categories used in this study is provided in Table \u003cspan refid=\"MOESM2\" class=\"InternalRef\"\u003eS2\u003c/span\u003e.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec7\" class=\"Section2\"\u003e \u003ch2\u003e2.5 Statistical analysis\u003c/h2\u003e \u003cp\u003eTo account for differences in measurement scales, dietary scores were standardized prior to analysis. Exploratory factor analysis was performed using principal components methods in SPSS 25.0, with orthogonal (maximum variance) rotation applied to improve factor interpretability and independence. The number of factors (dietary patterns) was determined based on eigenvalues greater than 1, examination of scree plots, and cumulative variance explained exceeding 50%. Four dietary patterns were extracted and subsequently labeled according to the food groups with factor loadings greater than 0.5 on each dimension.\u003c/p\u003e \u003cp\u003eBaseline characteristics of participants across the four dietary patterns were analyzed using RStudio 4.4.3. Categorical variables were summarized as frequencies and percentages, whereas continuous variables were expressed as means\u0026thinsp;\u0026plusmn;\u0026thinsp;standard deviations or medians with interquartile ranges, as appropriate. Kaplan-Meier survival curves were constructed to compare survival probabilities among high-risk individuals across dietary patterns, with the \"Rice Pattern\" selected as the reference due to the predominance of rice-based dietary habits in Hunan Province. Associations between dietary patterns and all-cause or cause-specific mortality were evaluated using Cox proportional hazards regression models. An unadjusted model and three sequentially adjusted models were fitted, with adjustments for demographic characteristics, lifestyle factors, and physiological indicators, respectively, to account for potential confounding. Results are reported as hazard ratios (HRs) with corresponding 95% confidence intervals (CIs). Further stratified analyses were performed according to age (\u0026gt;\u0026thinsp;60 years vs. \u0026le;60 years), sex (male vs. female), smoking status (yes vs. no), drinking status (yes vs. no), and BMI (\u0026ge;\u0026thinsp;28 vs. \u0026lt;28), and interaction terms were tested to evaluate the consistency of associations across subgroups.\u003c/p\u003e \u003c/div\u003e"},{"header":"3. Results","content":"\u003cdiv id=\"Sec9\" class=\"Section2\"\u003e \u003ch2\u003e3.1 Types and Characteristics of Dietary Patterns\u003c/h2\u003e \u003cp\u003eThe results of the exploratory factor analysis indicated that the Kaiser-Meyer-Olkin (KMO) value was 0.809 (\u0026gt;\u0026thinsp;0.6), and Bartlett's sphericity test was significant (\u003cem\u003eX\u0026sup2;\u003c/em\u003e = 106204.556, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001), demonstrating that the data were suitable for factor analysis. Four common factors with eigenvalues greater than 1 were extracted (3.393, 1.482, 1.046, and 1.038), cumulatively accounting for 57.99% of the total variance.\u003c/p\u003e \u003cp\u003eBased on factor loadings derived from the 12 food groups, four distinct dietary patterns were identified (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). The \"Balanced Pattern\" was characterized by higher intakes of wheat, grain, poultry, fresh fruits, bean, and dairy, reflecting a relatively balanced nutritional profile. The \"Meat, Eggs, and Fresh Vegetables Pattern\" was characterized by higher consumption of meat, seafood, eggs, and fresh vegetables, corresponding to increased protein and dietary fiber intake. The \"Pickle Pattern\" was predominantly defined by pickled food consumption, whereas the \u0026ldquo;Rice Pattern\u0026rdquo; was primarily characterized by high rice intake.\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\u003eFactor-loading matric for the major dietary patterns identified by factor analysis\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=\"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=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eBalanced Pattern\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eMeat, Eggs, and Fresh Vegetables Pattern\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003ePickle Pattern\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eRice Pattern\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003erice\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.089\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.039\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e-0.012\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e0.931\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003ewheat\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003e0.567\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.210\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e-0.267\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e-0.092\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003egrain\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003e0.713\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e-0.193\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.279\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.061\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003emeat\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.063\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003e0.681\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.261\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.274\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003epoultry\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003e0.569\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.297\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.367\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.054\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eseafood\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.241\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003e0.531\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.316\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e-0.279\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eeggs\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.465\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003e0.570\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e-0.171\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e-0.066\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003efresh vegetables\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e-0.190\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003e0.628\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e-0.093\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e-0.006\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003epickle\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.123\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.053\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e0.827\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e-0.020\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003efresh fruits\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003e0.671\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.114\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e-0.028\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.098\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003ebean\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003e0.729\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.028\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.229\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e-0.006\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003edairy\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003e0.737\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e-0.072\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.100\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.012\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec10\" class=\"Section2\"\u003e \u003ch2\u003e3.2 Baseline characteristics\u003c/h2\u003e \u003cp\u003eA total of 46239 participants were included in the analysis, of whom 41% were male, and the mean age of the cohort was 61 years. Baseline characteristics of the participants are summarized in Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e. Stratified by dietary pattern, the \"Balanced Pattern\" accounted for the largest proportion of participants (approximately 30%) and was characterized by a higher representation of rural residents, individuals with lower educational attainment and income levels, and those who reported smoking or alcohol consumption. Participants in the \"Meat, Eggs, and Fresh Vegetables Pattern\" were predominantly urban residents, had higher educational attainment, and were more likely to be non-smokers and non-drinkers. The \"Pickle Pattern\" was primarily composed of Han Chinese participants and exhibited a relatively higher proportion of individuals with high income compared with other groups. The \"Rice Pattern\" accounted for the smallest proportion of participants (18%) and was characterized by relatively higher educational levels and a greater prevalence of habitual salt addition during meals.\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\u003eBasic characteristics of the study population for the four dietary patterns\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"6\"\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=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eOverall\u003c/p\u003e \u003cp\u003e(\u003cem\u003en\u003c/em\u003e\u0026thinsp;=\u0026thinsp;46239)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eBalanced Pattern (\u003cem\u003en\u003c/em\u003e\u0026thinsp;=\u0026thinsp;13,796)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eMeat, Eggs, and Fresh Vegetables Pattern\u003c/p\u003e \u003cp\u003e(\u003cem\u003en\u003c/em\u003e\u0026thinsp;=\u0026thinsp;10,900)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003ePickle Pattern (\u003cem\u003en\u003c/em\u003e\u0026thinsp;=\u0026thinsp;13,217)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eRice Pattern\u003c/p\u003e \u003cp\u003e(\u003cem\u003en\u003c/em\u003e\u0026thinsp;=\u0026thinsp;8,326)\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003eAge, median (IQR), year\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\" colname=\"c5\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e61.00(54.00\u0026ndash;68.00)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e62.00 (54.00\u0026ndash;67.00)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e62.00(54.00\u0026ndash;68.00)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e61.00(54.00\u0026ndash;67.00)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e60.00(53.00\u0026ndash;67.00)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eSex, n (%)\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 \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eMale\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e18815(41%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e5,613 (41%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e4,312 (40%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e5,440 (41%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e3,450 (41%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eFemale\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e27424(59%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e8,183 (59%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e6,588 (60%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e7,777 (59%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e4,876 (59%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003e\u003cb\u003eUrbanity, n (%)\u003c/b\u003e\u003c/p\u003e \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 \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eRural\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e34517(75%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e12,298 (89%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e6,747 (62%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e9,517 (72%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e5,955 (72%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eUrban\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e11722(25%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1,498 (11%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e4,153 (38%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e3,700 (28%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e2,371 (28%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003e\u003cb\u003eEthnicity, n (%)\u003c/b\u003e\u003c/p\u003e \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 \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eHan\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e42952(93%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e12,745 (93%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e9,334 (86%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e12,848 (98%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e8,025 (97%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eEthnic minority\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e3112(6.8%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e998 (7.3%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1,540 (14%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e314 (2.4%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e260 (3.1%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003e\u003cb\u003eAnnual income (CNY), n (%)\u003c/b\u003e\u003c/p\u003e \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 \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003e\u0026gt;50000\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e10280(22%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2,154 (16%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2,685 (25%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e3,662 (28%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1,779 (21%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003e≦50000\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e35959(78%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e11,642 (84%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e8,215 (75%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e9,555 (72%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e6,547 (79%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003e\u003cb\u003eEducation level, n (%)\u003c/b\u003e\u003c/p\u003e \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 \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eBelow high school\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e39468(85%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e12,564 (91%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e9,143 (84%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e10,982 (83%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e6,779 (81%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eHigh school or above\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e6771(15%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1,232 (8.9%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1,757 (16%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e2,235 (17%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1,547 (19%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003e\u003cb\u003eSmoking status, n (%)\u003c/b\u003e\u003c/p\u003e \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 \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eNo\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e35530(77%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e10,381 (75%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e8,510 (78%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e10,319 (78%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e6,320 (76%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eYes\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e10709(23%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e3,415 (25%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2,390 (22%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e2,898 (22%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e2,006 (24%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003e\u003cb\u003eAlcohol consumption, n (%)\u003c/b\u003e\u003c/p\u003e \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 \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eNo\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e41592(90%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e12,195 (88%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e10,007 (92%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e11,912 (90%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e7,478 (90%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eYes\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e4647(10%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1,601 (12%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e893 (8.2%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1,305 (9.9%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e848 (10%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003e\u003cb\u003eAdditional salt added, n (%)\u003c/b\u003e\u003c/p\u003e \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 \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eNo\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e38691(84%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e12,135 (88%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e9,002 (83%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e11,077 (84%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e6,477 (78%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eYes\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e7434(16%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1,651 (12%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1,876 (17%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e2,116 (16%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1,791 (22%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003e\u003cb\u003eBMI, n (%)\u003c/b\u003e\u003c/p\u003e \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 \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003e\u0026lt;28\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e38630(84%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e11,331 (82%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e9,198 (84%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e11,121 (84%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e6,980 (84%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003e\u0026ge;28\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e7609(16%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2,465 (18%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1,702 (16%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e2,096 (16%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1,346 (16%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003e\u003cb\u003eBP, median (IQR), mmHg\u003c/b\u003e\u003c/p\u003e \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 \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eSBP\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e159.00(136.00-169.00)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e161.00 (139.00-171.00)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e158.50 (135.00-169.00)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e159.00 (135.00-169.00)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e154.00 (134.00-167.50)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eDBP\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e86.50(77.50\u0026ndash;96.00)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e87.00 (78.50\u0026ndash;96.50)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e86.00 (77.00-95.50)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e86.50 (77.00\u0026ndash;96.00)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e86.50 (78.00-96.50)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003e\u003cb\u003eTC, median (IQR), mmol/L\u003c/b\u003e\u003c/p\u003e \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 \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e5.12(4.28\u0026ndash;6.19)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e5.10 (4 .26-6.11)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e5.09 (4.22\u0026ndash;6.20)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e5.08 (4.27\u0026ndash;6.14)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e5.26 (4.38\u0026ndash;6.38)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003e\u003cb\u003eGlu, median (IQR), mmol/L\u003c/b\u003e\u003c/p\u003e \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 \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e5.80(5.30\u0026ndash;6.70)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e5.80 (5.30\u0026ndash;6.70)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e5.90 (5.30\u0026ndash;6.70)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e5.80 (5.30\u0026ndash;6.60)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e5.80 (5.30\u0026ndash;6.70)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003e\u003cb\u003eWC, median (IQR), cm\u003c/b\u003e\u003c/p\u003e \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 \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e84.00(78.00\u0026ndash;90.00)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e84.00 (78.00\u0026ndash;90.00)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e84.00 (79.00\u0026ndash;90.00)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e84.00 (78.00\u0026ndash;90.00)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e84.00 (78.00\u0026ndash;90.00)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003e\u003cb\u003eHeight, median (IQR), cm\u003c/b\u003e\u003c/p\u003e \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 \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e156.50(151.50-162.50)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e155.50 (155.00-161.50)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e157.00(151.00-162.80)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e157.00(152.00-162.50)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e158.00(152.50-163.10)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003e\u003cb\u003eWeight, median (IQR), kg\u003c/b\u003e\u003c/p\u003e \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 \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e60.20(54.00\u0026ndash;68.00)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e60.00 (53.40, 67.40)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e60.00 (53.90, 67.80)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e60.20 (54.00, 68.00)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e61.10 (54.50, 68.90)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003e3.3 Survival analysis of all-cause mortality\u003c/h2\u003e \u003cp\u003eDuring follow-up, 792 all-cause deaths were observed among 46239 participants over a median duration of 45 months (range: 3\u0026ndash;81 months). Kaplan-Meier survival analysis demonstrated statistically significant differences in survival curves among the four dietary patterns (log-rank test, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.0001) (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). During the median follow-up period of 45 months, restricted mean survival time (RMST) analysis indicated that, compared with the \"Rice Pattern\", the RMST was prolonged by 0.05 months for the \"Balanced Pattern\", by 0.03 months for the \"Meat, Eggs, and Fresh Vegetables Pattern\", and by 0.10 months for the \"Pickle Pattern\" (Table \u003cspan refid=\"MOESM3\" class=\"InternalRef\"\u003eS3\u003c/span\u003e). The \"Meat, Eggs, and Fresh Vegetables Pattern\" exhibited the highest cumulative survival rates among the four dietary patterns at both the 3-year and 5-year follow-up time points, reaching 99.25% (95% CI: 99.07%\u0026ndash;99.53%) and 98.17% (95% CI: 97.48%\u0026ndash;98.97%), respectively. The survival rate for the \"Pickle Pattern\" was higher than that of the reference group (\"Rice Pattern\") during years 1\u0026ndash;4, but lower during years 5\u0026ndash;6 (Table \u003cspan refid=\"MOESM4\" class=\"InternalRef\"\u003eS4\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec12\" class=\"Section2\"\u003e \u003ch2\u003e3.4 Association between dietary patterns and mortality\u003c/h2\u003e \u003cp\u003eMultivariate Cox proportional hazards models were applied to estimate hazard ratios and corresponding 95% confidence intervals for the associations between the four dietary patterns and cause-specific mortality. The \"Meat, Eggs, and Fresh Vegetables Pattern\" was associated with significantly lower risks of all-cause mortality (HR\u0026thinsp;=\u0026thinsp;0.64, 95% CI: 0.50\u0026ndash;0.82, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001), Chronic CVD mortality (HR\u0026thinsp;=\u0026thinsp;0.44, 95% CI: 0.24\u0026ndash;0.77, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.01), and non-CVD mortality (HR\u0026thinsp;=\u0026thinsp;0.71, 95% CI: 0.51\u0026ndash;0.98, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05) (Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e). After full adjustment, the \"Balanced Pattern\" was also associated with a reduced risk of non-CVD mortality (HR\u0026thinsp;=\u0026thinsp;0.70, 95% CI: 0.53\u0026ndash;0.93, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05).\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\u003eHazard ratio (95% CI) for all-cause mortality and cause-specific mortality across different dietary patterns.\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=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eBalanced Pattern\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eMeat, Eggs, and Fresh Vegetables Pattern\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003ePickle Pattern\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eRice Pattern\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAll-cause mortality\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"4\" nameend=\"c5\" namest=\"c2\"\u003e\u0026nbsp;\u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNo. of deaths\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e304\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e124\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e229\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e135\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eModel 1\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.08(0.88\u0026ndash;1.33)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.66(0.51\u0026ndash;0.84)\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.99(0.80\u0026ndash;1.22)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1(Ref.)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eModel 2 \u003csup\u003eb\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.82(0.67\u0026ndash;1.01)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.64(0.50\u0026ndash;0.83)\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.90(0.73\u0026ndash;1.12)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1(Ref.)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eModel 3 \u003csup\u003ec\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.82(0.67\u0026ndash;1.01)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.64(0.50\u0026ndash;0.83)\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.90(0.73\u0026ndash;1.12)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1(Ref.)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eModel 4 \u003csup\u003ed\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.83(0.68\u0026ndash;1.02)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.64(0.50\u0026ndash;0.82)\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.91(0.74\u0026ndash;1.13)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1(Ref.)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eAcute CVD mortality\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"4\" nameend=\"c5\" namest=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNo. of deaths\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e89\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e27\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e59\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e28\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eModel 1\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.54(1.01\u0026ndash;2.35)\u003csup\u003e*\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.68(0.40\u0026ndash;1.15)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.22 (0.78\u0026ndash;1.92)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1(Ref.)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eModel 2 \u003csup\u003eb\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.19(0.77\u0026ndash;1.84)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.67(0.39\u0026ndash;1.15)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.14(0.72\u0026ndash;1.78)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1(Ref.)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eModel 3 \u003csup\u003ec\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.20(0.78\u0026ndash;1.84)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.67(0.39\u0026ndash;1.15)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.13(0.72\u0026ndash;1.78)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1(Ref.)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eModel 4 \u003csup\u003ed\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.20(0.78\u0026ndash;1.85)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.69(0.40\u0026ndash;1.17)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.14(0.73\u0026ndash;1.80)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1(Ref.)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eChronic CVD mortality\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"4\" nameend=\"c5\" namest=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNo. of deaths\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e71\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e23\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e45\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e28\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eModel 1\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.24(0.80\u0026ndash;1.92)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.56(0.32\u0026ndash;0.98)\u003csup\u003e*\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.93(0.58\u0026ndash;1.49)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1(Ref.)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eModel 2 \u003csup\u003eb\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.80(0.51\u0026ndash;1.26)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.45(0.26\u0026ndash;0.80)\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.80(0.50\u0026ndash;1.29)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1(Ref.)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eModel 3 \u003csup\u003ec\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.82(0.52\u0026ndash;1.28)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.50(0.25\u0026ndash;0.79)\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.80(0.50\u0026ndash;1.28)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1(Ref.)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eModel 4 \u003csup\u003ed\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.81(0.52\u0026ndash;1.28)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.44(0.24\u0026ndash;0.77)\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.81(0.50\u0026ndash;1.30)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1(Ref.)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eNon-CVD mortality\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 \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNo. of deaths\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e144\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e74\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e125\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e79\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eModel 1\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.87(0.66\u0026ndash;1.14)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.68(0.50\u0026ndash;0.94)\u003csup\u003e*\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.92(0.70\u0026ndash;1.22)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1(Ref.)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eModel 2 \u003csup\u003eb\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.69(0.52\u0026ndash;0.91)\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.72(0.52\u0026ndash;0.99)\u003csup\u003e*\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.86(0.65\u0026ndash;1.14)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1(Ref.)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eModel 3 \u003csup\u003ec\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.69(0.52\u0026ndash;0.91)\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.72(0.52\u0026ndash;0.99)\u003csup\u003e*\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.86 (0.65\u0026ndash;1.14)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1(Ref.)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eModel 4 \u003csup\u003ed\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.70(0.53\u0026ndash;0.93)\u003csup\u003e*\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.71(0.51\u0026ndash;0.98)\u003csup\u003e*\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.86(0.65\u0026ndash;1.15)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1(Ref.)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e*: \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05, **: \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.01, ***\u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001.\u003c/p\u003e \u003cp\u003e \u003csup\u003ea\u003c/sup\u003e Model 1: No adjustments made.\u003c/p\u003e \u003cp\u003e \u003csup\u003eb\u003c/sup\u003e Model 2: Adjusted for age, sex, urbanity, ethnicity, annual household income, and education level.\u003c/p\u003e \u003cp\u003e \u003csup\u003ec\u003c/sup\u003e Model 3: Model 2 further adjusted for smoking status, and alcohol consumption.\u003c/p\u003e \u003cp\u003e \u003csup\u003ed\u003c/sup\u003e Model 4: Model 3 further adjusted for BMI, hypertension, dyslipidemia, and diabetes.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec13\" class=\"Section2\"\u003e \u003ch2\u003e3.5 Subgroup and interaction analyses\u003c/h2\u003e \u003cp\u003eSubgroup analyses were performed using the fully adjusted Model 4 in which all covariates were included except for the stratification variable corresponding to the subgroup of interest. The direction of the associations between the four dietary patterns and the risks of all-cause and cause-specific mortality was generally consistent across subgroups and was concordant with the findings observed in the overall population (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eAcross subgroups defined by basic demographic characteristics, selected lifestyle factors, and metabolism-related variables, the \"Meat, Eggs, and Fresh Vegetables Pattern\" was consistently associated with lower risks of all-cause mortality and chronic cardiovascular disease mortality, demonstrating robust and stable protective effects across different population strata.\u003c/p\u003e \u003cp\u003eFurther interaction analyses showed no statistically significant interactions between dietary patterns and age, sex, urbanity, annual income, education level, smoking status, alcohol consumption, hypertension, dyslipidemia, diabetes, physical activity, waist circumference, or BMI for all-cause, acute, chronic, and non-cardiovascular mortality (\u003cem\u003eP\u003c/em\u003e interaction\u0026thinsp;\u0026gt;\u0026thinsp;0.05) (Table \u003cspan refid=\"MOESM5\" class=\"InternalRef\"\u003eS5\u003c/span\u003e\u0026ndash;S8). These results indicate that the observed associations were generally consistent across different population subgroups and causes of death.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec14\" class=\"Section2\"\u003e \u003ch2\u003e3.6 Sensitivity analyses\u003c/h2\u003e \u003cp\u003eAfter excluding participants with follow-up durations of less than one year, no substantial changes were observed in the estimated risk of all-cause mortality, acute cardiovascular mortality, chronic cardiovascular mortality, or non-cardiovascular mortality across the four dietary patterns (Table \u003cspan refid=\"MOESM9\" class=\"InternalRef\"\u003eS9\u003c/span\u003e\u0026ndash;S12).\u003c/p\u003e \u003c/div\u003e"},{"header":"4. Discussion","content":"\u003cp\u003eFour major dietary patterns were identified based on 12 dietary components among high-risk cardiovascular populations in Hunan Province. \"Meat, Eggs, and Fresh Vegetables Pattern\" was associated with a longer median survival time as well as higher 3-year and 5-year survival rates. Compared with the reference group (\"Rice Pattern\"), adherence to this dietary pattern was associated with significantly lower risks of all-cause mortality, chronic cardiovascular disease progression, and non-CVD mortality, findings that are consistent with previous studies\u003csup\u003e[\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e, \u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e]\u003c/sup\u003e. These associations were consistently observed across multiple subgroups and remained robust after excluding participants with a follow-up duration of less than one year.\u003c/p\u003e \u003cp\u003eGreater cardiovascular health benefits have been reported for the \"Balanced Pattern\" compared with single dietary components or isolated dietary patterns\u003csup\u003e[\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e]\u003c/sup\u003e。Among individuals at high cardiovascular risk, dietary approaches characterized by increased intake of plant-based foods and high-quality protein, while overall dietary balance is maintained, have been considered particularly important\u003csup\u003e[\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e]\u003c/sup\u003e. The dietary pattern extracted in the present study\u0026mdash;characterized by high consumption of fresh vegetables\u003csup\u003e[\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e]\u003c/sup\u003e、eggs\u003csup\u003e[\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e]\u003c/sup\u003e、meat and moderate grains\u003csup\u003e[\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e]\u003c/sup\u003e\u0026mdash;was found to share core features with established cardioprotective dietary models, including the Mediterranean diet\u003csup\u003e[\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e]\u003c/sup\u003e、DASH diet\u003csup\u003e[\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e]\u003c/sup\u003e and Oriental Healthy Dietary Pattern (OHDP)\u003csup\u003e[\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e]\u003c/sup\u003e. Furthermore, consistency with the principles outlined in the updated U.S. Dietary Guidelines, which emphasize higher-quality protein intake and the consumption of minimally processed foods, was observed\u003csup\u003e[\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e]\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eThe \"Meat, Eggs, and Fresh Vegetables Pattern\" was characterized by high-quality protein and fresh vegetables, and potential cardiovascular benefits were suggested to be mediated through multiple biological pathways. Improved glycemic control, favorable lipid profiles\u003csup\u003e[\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e]\u003c/sup\u003e, and maintenance of skeletal muscle mass\u003csup\u003e[\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e]\u003c/sup\u003e, were observed in association with diets combining high-quality protein and low intake of refined carbohydrates, which may contribute to reduced metabolic burden. Systemic inflammation and oxidative stress were reported to be attenuated by polyphenols and antioxidants abundant in fresh vegetables, both of which are recognized contributors to cardiovascular disease progression\u003csup\u003e[\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e]\u003c/sup\u003e. In contrast, pickled vegetables, a traditional dietary component in Hunan Province, were reported to be associated with elevated blood pressure, which may be induced by increased sodium intake, plasma volume expansion, and activation of the renin\u0026ndash;angiotensin system\u003csup\u003e[\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e]\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eBased on region-specific evidence from high-risk cardiovascular populations in Hunan Province, adherence to the \"Meat, Eggs, and Fresh Vegetables Pattern\" was associated with more favorable survival outcomes. In addition, the implementation of healthy lifestyle interventions, including tobacco control, alcohol restriction, and appropriate physical activity, in combination with a healthy diet, was suggested to confer potential benefits for cardiovascular health\u003csup\u003e[\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e]\u003c/sup\u003e. However, several limitations were identified. First, dietary intake was assessed only at baseline, which may have failed to capture dietary changes during follow-up and may have introduced exposure misclassification. Second, causes of death were determined using registration systems, which may have introduced outcome misclassification. Further studies integrating repeated dietary assessments, dietary quality indices, objective biomarkers, and interventional designs are warranted to validate and clarify potential causal associations\u003csup\u003e[\u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e]\u003c/sup\u003e.\u003c/p\u003e"},{"header":"5. Conclusion","content":"\u003cp\u003eIn summary, four dietary patterns were identified, and their dietary characteristics were delineated among high-risk cardiovascular populations in Hunan Province. The \"Meat, Eggs, and Fresh Vegetables\" dietary pattern was significantly associated with reduced mortality risk and aligned with the principles of the Oriental Healthy Dietary Pattern. These findings provide evidence-based support for dietary interventions in high-risk cardiovascular populations, underscoring the feasibility and potential benefits of adopting dietary practices to reduce the overall burden of cardiovascular disease.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eEthics approval and consent to participate\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe ChinaHEART project has been approved by the Central Ethics Committee of Fuwai Hospital (2014-574) and registered on ClinicalTrials.gov (NCT02536456). All participants provided written informed consent through registration.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent for publication\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAll authors have given consent for publishing this study.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCRediT authorship contribution statement\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eYilin Lv:\u003c/strong\u003e Writing \u0026ndash; original draft, Visualization, Formal analysis, Data curation. \u003cstrong\u003eXian Xie:\u0026nbsp;\u003c/strong\u003eValidation, Data curation, Visualization. \u003cstrong\u003eYuan Liu:\u003c/strong\u003e Project administration, Conceptualization, Software. \u003cstrong\u003eLei Yin:\u003c/strong\u003e Resources, Supervision. \u003cstrong\u003eLi Yin:\u003c/strong\u003e Writing - review and editing, Data curation. \u003cstrong\u003eXingli Li\u003c/strong\u003e: Conceptualization, Funding acquisition, Supervision, Writing \u0026ndash; review and editing.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eDeclaration of competing interest\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAcknowledgements\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors thank all participants and staff involved in the China Health Evaluation And risk Reduction through nationwide Teamwork (ChinaHEART) project for their contributions to this study.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eData availability\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eData will be made available on request.\u003c/p\u003e\n"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eAngela P, Danilo NG. The burden of hypercholesterolemia and ischemic heart disease in an ageing world. Pharmacol Res. 2023;193:106814\u0026ndash;106814.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eA A W. WTC, A Z I., A C A.M. A, Pankaj AC, L., B S C M, B A Z., B A K., B A E. CM, Yvonne EMSV, Chete EKREN, Setri F, Giuliano G, Swapnil HDGH, Rizwan HJEK, Darae KDSK, Junxiu LDAL, Jun M, M E D. MJW, P N I. MMENSD, Remy P, Mary RH, R G A., S, N S., S, O M Pierre TEL, Yuh VSSVJHWN, Kristine Y. M S S., Heart Disease and Stroke Statistics\u0026mdash;2023 Update: A Report From the American Heart Association, Circulation (2023).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLiu MB, He XY, Yang XH, Wang ZW. Interpretation of Annual Report on Cardiovascular Health and Diseases in China 2024, Biomedical and environmental sciences: BES 38 (2025) 893\u0026ndash;917. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.3967/bes2025.099\u003c/span\u003e\u003cspan address=\"10.3967/bes2025.099\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eCenter For Cardiovascular Diseases The Writing Committee Of The Report On Cardiovascular H. China, Report on Cardiovascular Health and Diseases in China 2023: An Updated Summary. Biomed Environ Sci. 2024;37:949\u0026ndash;92. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.3967/bes2024.162\u003c/span\u003e\u003cspan address=\"10.3967/bes2024.162\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eZheng C, Wang X, Gu R, Wang W, Tian Y, Pei X, Jia Q, Song Q, Cao X, Tian Y, Tuerdi N, Sun M, Liu S, Zhang Y, Wang Z. The Burden of Cardiovascular Disease in China: Incidence Estimates From National Surveillance (2023). J Am Coll Cardiol. 2025. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1016/j.jacc.2025.08.060\u003c/span\u003e\u003cspan address=\"10.1016/j.jacc.2025.08.060\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eHe X, Zhang X, Mao F, Yao Y, Zhang R, Li J. Disease burden of non-communicable diseases attributable to high body mass index in China and its provinces, 1990\u0026ndash;2021: A systematic analysis for the Global Burden of Disease Study 2021. Chin Med J (Engl). 2025. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1097/cm9.0000000000003900\u003c/span\u003e\u003cspan address=\"10.1097/cm9.0000000000003900\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLi Y, Zhang C, Chen F, Zhang J. Prevalence and influencing factors of subjects at high risk for cardiovascular disease in different regions of Gansu province, China: a cross-sectional study of 100,725 residents from 2017 to 2022, Frontiers in Cardiovascular Medicine 11 (2024) 1373123\u0026ndash;1373123. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.3389/fcvm.2024.1373123\u003c/span\u003e\u003cspan address=\"10.3389/fcvm.2024.1373123\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMach F, Baigent C, Catapano AL, Koskinas KC, Casula M, Badimon L, Chapman MJ, De Backer GG, Delgado V, Ference BA, Graham IM, Halliday A, Landmesser U, Mihaylova B, Pedersen TR, Riccardi G, Richter DJ, Sabatine MS, Taskinen MR, Tokgozoglu L. Wiklund, 2019 ESC/EAS Guidelines for the management of dyslipidaemias: lipid modification to reduce cardiovascular risk. Eur Heart J. 2020;41:111\u0026ndash;88. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1093/eurheartj/ehz455\u003c/span\u003e\u003cspan address=\"10.1093/eurheartj/ehz455\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eNurul Izzati A, Fatin Syazwani AM, Kahar F, Amrina MA, Izzudin MP, Sazlina SG, Sabariah MN. Integrating CCL2 and TNF-α into the Framingham Risk Score for cardiovascular risk prediction: a cross-sectional study in a Malaysian cohort. Med J Malaysia. 2025;80:133\u0026ndash;40.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eServices US, D o. A a U S D o H a H, Dietary Guidelines for Americans, 2025\u0026ndash;2030, Washington, DC, 2026.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eTian W, K C M C, Sophie M, Sayan RRV, Shane J, Andrius M, Luigi SAMF. Vegetarian Dietary Patterns and Cardiometabolic Risk in People With or at High Risk of Cardiovascular Disease: A Systematic Review and Meta-analysis. JAMA Netw open. 2023;6:e2325658\u0026ndash;2325658. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1001/jamanetworkopen.2023.25658\u003c/span\u003e\u003cspan address=\"10.1001/jamanetworkopen.2023.25658\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eCherian L, Agarwal P, Agrawal S, James BD, Yang D, Wagner M, Leurgans SE, Bennett DA, Aggarwal NT, Schneider JA. Dietary Patterns Associated With Risk of Intracranial Atherosclerosis in Older Adults With Hypertension or Myocardial Infarction. Neurology. 2025;105:e214147. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1212/wnl.0000000000214147\u003c/span\u003e\u003cspan address=\"10.1212/wnl.0000000000214147\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eShreyaa R, Twisha P, k, sai V, Vaidheesh SS, AHMED P, Ridham K, Gunjan P, Anusha DH. Dineshbhai, s deepika, Abstract 4371831: Global Burden and Trends in Cardiovascular Disease Attributable to Dietary Risk Factors Among Women from 1990\u0026ndash;2021: A Global Benchmarking Systematic Analysis, Circulation 152 (2025) A4371831-A4371831. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1161/circ.152.Suppl_3.4371831\u003c/span\u003e\u003cspan address=\"10.1161/circ.152.Suppl_3.4371831\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eDaneshzad E, Goodarzi S, Ghoreshi R, Shahrestanaki E, Hatami SE, Assar O, Abdar ZE, Sadeghi T, Zarif Yeganeh M, Mahmoodi Z, Qorbani M. The effect of a lifestyle intervention package on cardiovascular risk factors in the first grade relatives of hospitalized patients with cardiovascular diseases: a protocol of parallel-randomized trial. J Diabetes Metab Disord. 2025;24:188. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1007/s40200-025-01697-3\u003c/span\u003e\u003cspan address=\"10.1007/s40200-025-01697-3\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eHu Y, Zhang Y, Zhong J, Wang Y, Zhou E, Hong F. Association between obesity phenotypes and dietary patterns: A two-step cluster analysis based on the China multi-ethnic cohort study. Prev Med. 2024;187:108100. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1016/j.ypmed.2024.108100\u003c/span\u003e\u003cspan address=\"10.1016/j.ypmed.2024.108100\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eWilcox NS, Amit U, Reibel JB, Berlin E, Howell K, Ky B. Cardiovascular disease and cancer: shared risk factors and mechanisms, Nature reviews. Cardiology. 2024;21:617\u0026ndash;31.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eCardiovascular Program-ICCC R I-H S C i S P, Barcelona, Spain. MCIBERCV, Instituto de Salud Carlos III, Spain., R I-H S, C i. S P Cardiovascular Program-ICCC, Barcelona, Spain., D o Food, U F d S M Nutrition, Bolsista do CNPq, Brazil., R I-H S C i S P Cardiovascular Program-ICCC, Barcelona, Spain., Diet and Cardiovascular Disease: Effects of Foods and Nutrients in Classical and Emerging Cardiovascular Risk Factors. Curr Med Chem. 2019;26:3639\u0026ndash;51.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eManeeth M, Madiha K, Lakshmi MK, Prachi D, Teja RD, Namratha E, Ravi SDEEPALIM. Abstract TAC134: Impact of Mediterranean Diet on Hypertension in Patients with High Cardiovascular disease Risk: A Systematic Review\u0026amp;Meta-Analysis of Randomized Controlled Trials, Hypertension 82 (2025). \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1161/hyp.82.Suppl_1.Tac134\u003c/span\u003e\u003cspan address=\"10.1161/hyp.82.Suppl_1.Tac134\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eCorreia PE, Bisi L, Zhang M, Sun Y, Martins BB, Porepp OSC, Colpani V, Kunzler LB, Teixeira PP, Ferrari GT, Zajdenverg L, Brietzke E, Socal MP, Gerchman F. Plant and Animal-Based Dietary Patterns and Cardiometabolic Diseases in the Brazilian Population: Cross-Sectional Analysis of the Brazilian National Health Survey. Nutrients. 2025;17:3448\u0026ndash;3448. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.3390/nu17213448\u003c/span\u003e\u003cspan address=\"10.3390/nu17213448\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eWang R, Yang Y, Lu J, Cui J, Xu W, Song L, Wu C, Zhang X, Dai H, Zhong H, Jin B, He W, Zhang Y, Yang H, Wang Y, Zhang X, Li X, Hu S. Cohort Profile: ChinaHEART (Health Evaluation And risk Reduction through nationwide Teamwork) Cohort, Int J Epidemiol 52 (2023) e273-e282. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1093/ije/dyad074\u003c/span\u003e\u003cspan address=\"10.1093/ije/dyad074\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLu J, Xuan S, Downing NS, Wu C, Li L, Krumholz HM, Jiang L. Protocol for the China PEACE (Patient-centered Evaluative Assessment of Cardiac Events) Million Persons Project pilot. BMJ Open. 2016;6:e010200. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1136/bmjopen-2015-010200\u003c/span\u003e\u003cspan address=\"10.1136/bmjopen-2015-010200\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eOrganization WH. International statistical classification of diseases and related health problems: alphabetical index: world health organization, (2004).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eHu F, Qin W, Xu L. Association between Dietary Patterns and Cardiometabolic Multimorbidity among Chinese Rural Older Adults. Nutrients. 2024;16. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.3390/nu16172830\u003c/span\u003e\u003cspan address=\"10.3390/nu16172830\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eVidal PM, Tsampasian V, Cassidy A, Zoccai GB, Chrysohoou C, Koskinas K, Verschuren WMM, Czapla M, Kavousi M, Kouvari M, Vassiliou VS, Panagiotakos D. Diet and nutrition in cardiovascular disease prevention: a scientific statement of the European Association of Preventive Cardiology and the Association of Cardiovascular Nursing \u0026amp; Allied Professions of the European Society of Cardiology. Eur J Prev Cardiol. 2025;32:1540\u0026ndash;52. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1093/eurjpc/zwaf310\u003c/span\u003e\u003cspan address=\"10.1093/eurjpc/zwaf310\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMinnetti M, Barazzoni R, Batsis JA, Busetto L, Yumuk V, Poggiogalle E, Weijs PJM, Donini LM. The Integration of Lifestyle Modification Advice and Diet and Physical Exercise Interventions: Cornerstones in the Management of Obesity with Incretin Mimetics. Obes Facts. 2025;1\u0026ndash;16. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1159/000548370\u003c/span\u003e\u003cspan address=\"10.1159/000548370\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eConcei\u0026ccedil;\u0026atilde;o AR, d LL, Juvanhol A, Marcadenti \u0026Acirc;CB, Ferreira B, Weber J, Bressan. Diet quality scores and incidence of cardiovascular events: A 4-year prospective study of patients in cardiology secondary care (BALANCE Program Trial). Br J Nutr. 2025;1\u0026ndash;28. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1017/s000711452510559x\u003c/span\u003e\u003cspan address=\"10.1017/s000711452510559x\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eCasta\u0026ntilde;eiras PM, Ortiz C, Baz NFdL, Lope V, Gord\u0026oacute;n GS, Moreno ER, Alonso I, Esquinas EG, G\u0026oacute;mez BP, Barriuso RP, Gal\u0026aacute;n I, Castell\u0026oacute; A. Intake of fruit, vegetables and pulses, and all-cause, cardiovascular and cancer mortality: Results from a population-based prospective study. Public Health. 2025;239:169\u0026ndash;78. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1016/j.Puhe.2024.12.014\u003c/span\u003e\u003cspan address=\"10.1016/j.Puhe.2024.12.014\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eXia L, Xu T, Zhan Z. Dietary cholesterol intake and egg consumption in relation to all-cause and cardiovascular mortality after stroke. Sci Rep. 2025;15:35163\u0026ndash;35163. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1038/s41598-025-19028-0\u003c/span\u003e\u003cspan address=\"10.1038/s41598-025-19028-0\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBuna B, Zhixin L, Sophia L, Rona M, Blessing A-I, John H, Xiaoqi F, Xiaoyue SAEX. Long-Term Consumption of 10 Food Groups and Cardiovascular Mortality: A Systematic Review and Dose Response Meta-Analysis of Prospective Cohort Studies. Adv Nutr. 2023;14:55\u0026ndash;63. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1016/j.Advnut.2022.10.010\u003c/span\u003e\u003cspan address=\"10.1016/j.Advnut.2022.10.010\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBelokon CO, Palacios SG, Gabucio LMC, C\u0026aacute;novas AO, d l MG, Hera J, Vioque LT, Collado. Adherence to Three Mediterranean Dietary Indexes and All-Cause, Cardiovascular, and Cancer Mortality in an Older Mediterranean Population. Nutrients. 2025;17:2956\u0026ndash;2956. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.3390/nu17182956\u003c/span\u003e\u003cspan address=\"10.3390/nu17182956\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSun J, Jiang X, Li Z, Shen Y. Impact of dietary patterns on the survival outcomes of patients with cardiovascular disease. Front Nutr. 2025;12:1535174\u0026ndash;1535174. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.3389/fnut.2025.1535174\u003c/span\u003e\u003cspan address=\"10.3389/fnut.2025.1535174\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eWang X, Xuan J, Chen H, Lin B, Yuan C. Eastern healthy dietary patterns in relation to all-cause mortality. Eur J Nutr. 2025;64:286. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1007/s00394-025-03807-6\u003c/span\u003e\u003cspan address=\"10.1007/s00394-025-03807-6\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eZhang W, Zou L, Chen J. The impact of physical activity and dietary habits on glycolipemic metabolism and inflammatory markers in the elderly: a cross-sectional study. Front Nutr. 2025;12:1600038\u0026ndash;1600038. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.3389/fnut.2025.1600038\u003c/span\u003e\u003cspan address=\"10.3389/fnut.2025.1600038\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eArslan S, Aydın A. The weight-loss paradox in older adults: balancing fat loss with muscle preservation. Bull Natl Res Centre. 2025;49:79\u0026ndash;79. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1186/s42269-025-01374-8\u003c/span\u003e\u003cspan address=\"10.1186/s42269-025-01374-8\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eFioroni N, Rivier CM, Meudec E, Cheynier V, Boudard F, Hemery Y, Babot CL. Antioxidant Capacity of Polar and Non-Polar Extracts of Four African Green Leafy Vegetables and Correlation with Polyphenol and Carotenoid Contents, Antioxidants 12 (2023) 1726-. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.3390/antiox12091726\u003c/span\u003e\u003cspan address=\"10.3390/antiox12091726\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eNishimoto M, Griffin K, Wynne BM, Fujita T. Salt-Sensitive Hypertension and the Kidney, Hypertension (Dallas, Tex.: 1979) (2024). \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1161/hypertensionaha.123.21369\u003c/span\u003e\u003cspan address=\"10.1161/hypertensionaha.123.21369\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eXia Y, Wu X, Zhang C, Hong X. Distribution and influencing factors of cardiovascular health among community inhabitants: Based on the scale of life\u0026rsquo;s Essential 8. Chin Gen Pract J. 2025;2:100071\u0026ndash;100071. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1016/j.Cgpj.2025.100071\u003c/span\u003e\u003cspan address=\"10.1016/j.Cgpj.2025.100071\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003ePark S, Kang S. Interaction of Genetics and Dietary Patterns Scored by the High Healthy Eating Index in Hyperhomocysteinaemia Influencing Cardiovascular Disease Risk. Nutr Bull. 2025;50:311\u0026ndash;25. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1111/nbu.70007\u003c/span\u003e\u003cspan address=\"10.1111/nbu.70007\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"bmc-cardiovascular-disorders","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"bcar","sideBox":"Learn more about [BMC Cardiovascular Disorders](http://bmccardiovascdisord.biomedcentral.com/)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/bcar/default.aspx","title":"BMC Cardiovascular Disorders","twitterHandle":"BMC_series","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"em","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"Cardiovascular diseases, High-risk population, Dietary patterns, All-cause mortality, Specific cause mortality","lastPublishedDoi":"10.21203/rs.3.rs-8943220/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-8943220/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003ePurpose\u003c/h2\u003e \u003cp\u003eThis study investigated dietary patterns among populations at high risk for cardiovascular disease (CVD) in Hunan Province and examined their associations with mortality.\u003c/p\u003e\u003ch2\u003eMethods\u003c/h2\u003e \u003cp\u003eData were obtained from the China Health Evaluation and Risk Reduction through Nationwide Teamwork (ChinaHEART) project conducted between 2014 and 2023. A total of 46,239 individuals aged 35\u0026ndash;75 years who were identified as being at high risk for CVD were included in the analysis. Major dietary patterns were derived using exploratory factor analysis. Baseline characteristics, survival analyses, and Cox proportional hazards regression models were performed using SPSS version 25.0 and R version 4.4.3 to estimate hazard ratios (HRs) and corresponding 95% confidence intervals (CIs).\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e \u003cp\u003eCompared with the \"Rice Pattern\" greater adherence to the \"Meat, Eggs, and Fresh Vegetables Pattern\" was associated with significantly lower risks of all-cause mortality (HR\u0026thinsp;=\u0026thinsp;0.64, 95% CI: 0.50\u0026ndash;0.82; \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001) and chronic cardiovascular disease mortality (HR\u0026thinsp;=\u0026thinsp;0.44, 95% CI: 0.24\u0026ndash;0.77; \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.01). These associations were consistently observed across population subgroups in stratified analyses and remained robust in sensitivity analyses.\u003c/p\u003e\u003ch2\u003eConclusion\u003c/h2\u003e \u003cp\u003eThese findings provide evidence-based support for dietary guidance in high-risk populations and have important implications for the prevention and management of cardiovascular disease.\u003c/p\u003e","manuscriptTitle":"Association of Dietary Patterns with All-Cause and Cause-Specific Mortality in a High Cardiovascular Risk Population in China","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2026-02-28 03:05:41","doi":"10.21203/rs.3.rs-8943220/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2026-03-05T07:15:47+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2026-03-03T17:27:42+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"133786277264608616477081115505069095721","date":"2026-02-27T14:09:52+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2026-02-26T15:42:49+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"248082994168583724032372137789732227959","date":"2026-02-26T15:20:25+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"200462339519510080656676458580854160342","date":"2026-02-26T01:35:38+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2026-02-25T13:55:33+00:00","index":"","fulltext":""},{"type":"editorInvited","content":"","date":"2026-02-24T12:18:02+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2026-02-24T02:28:21+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2026-02-24T02:27:38+00:00","index":"","fulltext":""},{"type":"submitted","content":"BMC Cardiovascular Disorders","date":"2026-02-23T05:32:07+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"bmc-cardiovascular-disorders","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"bcar","sideBox":"Learn more about [BMC Cardiovascular Disorders](http://bmccardiovascdisord.biomedcentral.com/)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/bcar/default.aspx","title":"BMC Cardiovascular Disorders","twitterHandle":"BMC_series","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"em","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"e3b8f9ef-da22-477b-b39d-27ebe76ce7a3","owner":[],"postedDate":"February 28th, 2026","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"under-review","subjectAreas":[],"tags":[],"updatedAt":"2026-05-12T13:13:02+00:00","versionOfRecord":[],"versionCreatedAt":"2026-02-28 03:05:41","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-8943220","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-8943220","identity":"rs-8943220","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}