Dietary Patterns and Cardiometabolic Risk: A Network Analysis Study in the Wuling Mountain Region of China

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The study aimed to examine the complex, multidimensional relationships among dietary patterns and cardiometabolic risk factors in middle-aged and older adults of the Miao ethnicity of China. Methods A cross-sectional study encompassed 261 middle-aged and older adults was conducted in area inhabited by the Miao ethnicity, located within the Wuling Mountain Area of China.Demographic characteristics and dietary intake were assessed via a questionnaire, while metabolic indicators were obtained through medical examination. Dietary patterns were derived using PCA, and a Gaussian Graphical Model was estimated to examine the conditional dependence relationships among dietary patterns, and cardiometabolic parameters. Results Diverse, Plant-based, Ethnic-specific, and High-starch and Sugar-sweetened Beverage patterns were indentified in middle-aged and older adults of the Miao ethnicity. A diversified dietary pattern is more common among individuals under 65, while a plant-based pattern is more prevalent in those aged 65 and older. Network analysis identified skeletal muscle mass (SMM) as the most key node, with highest degree centrality (9) and a high betweenness centrality (17), particularly among adults aged 65 years and older. Moreover, the Plant-based dietary pattern demonstrated the highest closeness centrality (0.963) and a relatively high betweenness centrality (17) in individuals under 65 years. The Diverse pattern with the highest closeness (0.635) and the second-highest betweenness (33) was a key secondary hub among adults aged 65 years and older. Conclusions Four major dietary patterns were found in middle-aged and older adults of the Miao ethnicity in the Wuling Mountain Area of China. SMM as the principal nexus between diet and cardiometabolic health, represented the potential key target for preventing cardiovascular risk. The diversified pattern may represent a promising intervention for addressing cardiometabolic risk in adults over 65 in this region. Network Analysis Dietary patterns Middle-aged and older Cardiometabolic risk Figures Figure 1 Figure 2 Figure 3 Figure 4 Introduction The increasing severity of global population aging and changes in lifestyle patterns have significantly amplified the health impact of cardiovascular disease (CVD), establishing it as the leading cause of global mortality and disability[ 1 ]. CVD risk factors encompass a range of conditions such as hypertension, dyslipidemia, diabetes, chronic kidney disease, and metabolic syndrome, as well as environmental factors like air pollution. Among these, modifiable cardiometabolic risk factors-specifically obesity, dyslipidemia, hyperglycemia, and hypertension-carry the highest population-attributable risk for CVD[ 2 ]. The mortality burden imputed to each individual factor ranges from approximately 500,000 to 2.4 million cardiovascular deaths per year in China[ 3 ]. Unhealthy dietary intake is strongly associated with the incidence of cardiometabolic risk factors and constitutes a major cause of CVD-related death globally. Globally, 11 million global deaths and 255 million total disability-adjusted life years were attributable to dietary intake. In China, more than half of CVD mortality are linked to unreasonable dietary intake[ 4 ].Furthermore, substantial studies confirm that appropriate dietary intake can significantly prevent the onset of CVD, and be recognized as the most effective and cost-efficient strategy[ 5 , 6 ]. However, the diet intake constitutes a complex and interconnected system of exposures, involving interactions among different foods, among various components within foods, and between the health status and the foods consumed[ 7 ]. Furthermore, there are also interactive effects among different cardiometabolic risk factors[ 8 , 9 ]. Nevertheless, previous studies have predominantly focused on the relationships between specific foods and cardiometabolic risk, often overlooking intricate relationships. Dietary patterns, which adopt a holistic perspective on food and nutrient intake, are widely used in research on the diet-cardiovascular disease relationship in recent years, because they can not only explore complex interactions but also analyze associations with health outcomes[ 10 ]. Moreover, network analysis as an emerging statistical method was further developed to study multiple variables simultaneously in health sciences, such as high-dimensional genetic and metabolomics data[ 11 ]. This approach can be used to explore the interactions between variables, as well as the associations among various factors and one or more diseases[ 12 ]. Wang et al. used network analysis to identify interactions among demographic factors, dietary behaviors, and cardiometabolic comorbidities in working-age adults. Their analysis pinpointed specific dietary behaviors—such as frequent meat consumption, dining out, and eating before bedtime—as key intervention targets[ 13 ]. The present study applied principal component analysis (PCA) to explore dietary patterns and network analysis to examine the complex, multidimensional relationships among dietary patterns and cardiometabolic risk factors in a sample of middle-aged and older adults of the Miao ethnicity in the Wuling Mountain Area. This method allows the visualization and quantification of interactions among diet intake and cardiometabolic risk factors, identifying key dietary pattern that may serve as potential intervention targets of cardiometabolic risk. Methods Study population This cross-sectional study encompassed middle-aged and older adults who were recruited from a township predominantly inhabited by the Miao ethnicity, located within the Wuling Mountain Area of China. Eligibility criteria included residents aged 35 years or older. Exclusion criteria excluded individuals who were frequently away, were pregnant or lactating, or had severe chronic conditions requiring strict dietary control, memory decline precluding valid questionnaire completion. Recruitment was conducted by locally trained undergraduate students from Hunan University of Medicine, who enrolled participants via convenience sampling in July 2025. A total of 261 participants was recruited. The study’s protocol was approved by the ethics committee of Hunan University of Medicine (Approval number: 2025-H03004)). Written informed consent was obtained from all participants. Sociodemographic factors Sociodemographic factors were assessed by a self designed questionnaire. These factors encompassed gender, age, occupation (classified into two categories:farmer and other), education level (primary school or higher, and illiterate), household income (less than 10,000 yuan, and 10,000 yuan and above). Age was further categorized into below 65 years, and 65years or above. Diet assessment Dietary intake was assessed by trained investigators through face-to-face interview with a validated food frequency questionnaire (FFQ). The questionnaire queried the frequency of consumption of 55 food items over the preceding 12 months. Food intake portion sizes and frequencies were estimated using photographs and standard portion sizes. Food intake frequency was assessed as times per day, week, month, or year, and portion sizes were expressed in grams or milliliters. The intake of each food item was calculated from portion size and intake frequency. Single-food intakes were collapsed into 17 food items according to similarities in nutrient profiles or food processing methods. Dietary patterns were derived using PCA, following our established methodology. To derive these patterns, the average daily intake of the 17 food groups was subjected to PCA after confirming sampling adequacy with the Kaiser-Meyer-Olkin (KMO) statistic. Pattern identification was based on a combination of criteria, including eigenvalues greater than 1, the scree plot, factor interpretability, and the proportion of variance explained. Food items with factor loadings exceeding |0.3| were considered significant contributors to a pattern. Finally, a standardized score for each dietary pattern was calculated for every participants by computing the intake-weighted sum of the foods based on their factor loadings. Higher scores indicated greater adherence to the respective dietary pattern. Cardiometabolic risk factors Body height and weight were measured using an electronic stadiometer and scale, while skeletal muscle mass (SMM) and body fat percentage (BFP) were assessed using a body composition analyzer. Body mass index (BMI) was calculated as weight in kilograms divided by the square of height in meters (kg/m²). A volume of 20–50 µL of capillary blood was collected from the fingertip following a 10–12 h fasting period. A Home-use monitors was used to detect blood biochemical parameters, including blood pressure, blood glucose (GLU), total cholesterol (TC), Triglycerides(TG), and uric acid (UA). Medical examinations were performed simultaneously with the face-to-face interviews by trained investigators. Statistical analysis Data were conducted by using SPSS version 25.0 software (SPSS, Chicago, Illinois) and R version 4.5.1 (Foundation for Statistical Computing, Vienna, Austria). Categorical variables, are presented as frequencies and percentages, stratified by age group. Differences were assessed using the chi-square test. Continuous variables was expressed by Mean and standard deviation(SD) for normality data, or Median and interquartile range (IQR). Independent-sample t tests were used to compare groups for normality data; otherwise, the Mann–Whitney U test was applied. Correlation analysis between dietary pattern factor scores and cardiometabolic risk factors was conducted by Spearman's rank correlation due to non-normally data. Statistical significance was set at a two-sided P value < 0.05. Network estimation A Gaussian Graphical Model (GGM) was estimated to examine the conditional dependence relationships among dietary patterns, body metrics, and cardiometabolic parameters. The analysis was implemented in R (version 4.5.1) using the bootnet package. Sparse partial correlation networks were constructed using the Graphical Least Absolute Shrinkage and Selection Operator (GLASSO) algorithm. The Extended Bayesian Information Criterion (EBIC) with hyperparameter γ = 0.2 was employed for model selection to balance network sparsity and sensitivity. The correlation matrix was computed using the cor_auto function to handle mixed data types appropriately. The resulting adjacency matrix was converted to an igraph object for topological analysis. Node centrality was quantified using three established metrics. Strength centrality: Weighted degree representing cumulative connection strength. Betweenness centrality: Proportion of shortest paths passing through each node. Closeness centrality: Inverse average shortest path distance to all other nodes. Network visualization was created using ggraph with a Davidson-Harel layout algorithm. Nodes were categorized and color-coded into four domains:Body Metrics (BMI, SMM, BFP), blood pressure (SBP, DBP), biochemical markers (Glu, UA, TC, TG), and dietary patterns. Edges were filtered to display only the top 50% of weights by magnitude, with width and transparency proportional to edge weights. All analyses used absolute partial correlation values to focus on connection strength regardless of direction. Results Sample characteristics As shown in Table 1 , a total of 261 middle-aged and older adults was included in the final analysis. Of the participants, 62.84% were female, 88.12% were employed as farmers, over 50% had attained an educational level of primary school or higher, and more than 90% reported an annual household income of less than 10,000 yuan. 47.51% were below 65 years of age, and 52.49% were 65 years or older. The younger participant group had higher educational level and household income than 65 years and above. The distribution of sex and occupation remained comparable on age group. Table 1 also summarizes cardiometabolic risk indicators. Notably, the younger group had higher mean levels of BMI, SMM, BFP, and DBP, whereas TG level was lower. Table 1 General demographic characteristics and cardiometabolic risk factors of participants Characteristics Total (N = 261) < 65 years (N = 124) ≥ 65 years (N = 137) χ 2 /t/Z P Sex 0.626 0.429 Male 97 (37.16) 43 (34.68) 54 (39.42) Female 164 (62.84) 81 (65.32) 83 (60.58) Occupation 1.572 0.210 Farmer 230 (88.12) 106 (85.48) 124 (90.51) Other 31 (11.88) 18 (14.52) 13 (9.49) Education 10.071 0.002 Primary school or higher 148 (56.70) 83 (66.94) 65 (47.45) Illiterate 113 (43.30) 41 (33.06) 72 (52.55) Household income Less than 10,000 yuan 238 (91.19) 106 (85.48) 132 (96.35) 9.564 0.002 10,000 yuan and above 23 (8.81) 18 (14.52) 5 (3.65) Body metrics BMI (mean(sd)) 24.34 (3.67) 25.38 (3.48) 23.41 (3.60) 4.481 < 0.001 SMM (kg,mean(sd))) 22.44 (5.43) 23.53 (5.33) 21.45 (5.35) 3.138 0.002 BFP (%,mean(sd))) 27.80 (8.25) 29.42 (7.65) 26.34 (8.53) 3.061 0.002 Blood pressure SBP (mmHg, mean(sd)) 123 (16.00) 121 (16.00) 125 (17.00) 1.603 0.104 DBP (mmHg,mean(sd))) 71 (10.00) 73 (10.00) 69 (10.00) 3.130 0.002 Biochemical markers Glu (mmol/L,mean(sd))) 8.40 (4.97) 8.18 (4.04) 8.61 (5.69) 0.689 0.492 UA(µmol/L,mean(sd))) 335 (78.00) 325 (74.00) 343 (81.00) 1.889 0.060 TC(mmol/L,mean(sd))) 4.44 (0.81) 4.42 (0.77) 4.45 (0.85) 0.345 0.730 TG(mmol/L,median(IQR)) 0.97 (0.72,1,33) 0.89 (0.70,1.20) 1.05 (0.79,1.40) 2.456 0.014 Dietary patterns Four representative dietary patterns were identified based on PCA (Table 2 ), explained 41.13% of the total variance the food group data. Dietary pattern 1 was characterised by high positive loadings for diverse foods items, including stem vegetables, eggs, leafy vegetables, dried and preserved vegetables, fruits, meats, dry beans, fish and shellfish, milk, and cereals, labeled as Diverse pattern. Dietary pattern 2 featured high loadings of plant-based food groups (e.g., fresh legumes, dry beans, potatoes), while demonstrating low loadings for animal-based groups, including meats, fish, and shellfish, named as the Plant-based pattern. Dietary pattern 3, termed Ethnic-specific dietary pattern, was defined by high consumption of alcoholic beverages, fresh legumes, snacks, and dry beans, alongside notably low consumption of dried, preserved and leafy vegetables. Dietary pattern 4 was characterised by high positive loadings for potatoes, sugar-sweetened beverages, eggs, and cereals, but low on fresh legume and dry beans, named as High-starch and Sugar-sweetened Beverage pattern. Table 2 Factor loadings of dietary patterns Food items Pattern1 Pattern2 Pattern3 Pattern4 Cereals 0.33 -0.09 0.22 0.33 Potatoes 0.28 0.38 0.19 0.53 Dry Beans 0.51 0.39 0.30 -0.31 Fresh legume 0.15 0.57 0.39 -0.32 Stem vegetables 0.68 0.17 -0.06 -0.24 Leafy vegetables 0.63 0.05 -0.44 -0.07 Fungi and algae 0.29 0.28 -0.12 -0.05 Dried and preserved vegetables 0.59 0.08 -0.31 0.05 Fruits 0.58 -0.29 0.03 0.19 Milk 0.34 -0.07 -0.10 -0.07 Meats 0.56 -0.41 0.05 -0.17 Fish and shellfish 0.46 -0.40 -0.05 -0.16 Eggs 0.68 0.11 0.14 0.35 Nuts and seeds 0.20 -0.26 0.14 -0.24 Snacks 0.06 -0.10 0.38 0.02 Alcoholic beverages 0.12 -0.36 0.58 0.15 Sugar-Sweetened beverages 0.03 0.14 -0.24 0.45 Dietary pattern scores were − 0.22(-0.55, 0.29) for the pattern 1, 0.03(-0.40, 0.27) for the pattern2, -0.13(-0.36, 0.21) for the pattern 3, and 0.03(-0.34, 0.25) for pattern 4, respectively(Table 3 ). Participants aged below 65 years tended to have significantly higher scores on dietary pattern 1 than 65 years and above. Participants aged 65 years and above tended to score significantly higher on dietary pattern 2 compared to age below 65 years. Table 3 The diet factor score of dietary patterns (Median(IQR)) Dietary patterns Total < 65 years ≥ 65 years Z P Pattern1 -0.22 (-0.55, 0.29) -0.15 (-0.48 , 0.34) -0.29 (-0.69, 0.24) -2.080 0.037 Pattern2 -0.03 (-0.40, 0.27) -0.13 (-0.45, 0.20) 0.01 (-0.27 , 0.39) -2.092 0.036 Pattern3 -0.13 (-0.36, 0.21) -0.12 (-0.32, 0.21) -0.15 (-0.39, 0.21) -0.778 0.436 Pattern4 -0.03 (-0.34, 0.25) -0.02 (-0.30, 0.25) -0.04 (-0.38, 0.32) -0.041 0.967 Dietary patterns and cardiometabolic risk indicators Correlations revealed that a higher pattern 1 score was correlated with lower SPB (r=-0.210, P = 0.001) and TC(r=-0.161, P = 0.009), but higher SMM(r = 0.225, P < 0.001) in the total population (Fig. 1 ). A higher pattern 2 score was correlated with lower BMI(r=-0.224, P < 0.001) and SMM(r=-0.192, P = 0.002), but higher UA(r = 0.152, P = 0.014) and TC(r = 0.200, P = 0.001). A higher pattern 3 score was correlated with lower BFP(r=-0.174, P = 0.005), but higher SMM(r = 0.200, P = 0.001). A higher pattern 4 score was correlated with higher SMM(r = 0.138, P = 0.026). In participant aged < 65 years, dietary pattern 1 (r = 0.187, P = 0.038) and 3 (r = 0.297, P = 0.001) factor scores were positively associated with SMM, while dietary pattern 2 factor scores were positively associated with TC(r = 0.235, P = 0.009). Among individuals aged ≥ 65 years, dietary pattern 1 was positively associated with SMM (r = 0.235, P = 0.006) and inversely with SBP (r=-0.297, P < 0.001) and TC(r=-0.221, P = 0.009); dietary pattern 2 was inversely associated with BMI (r=-0.305, P < 0.001) and SMM (r=-0.234, P = 0.006) but positively with UA(r = 0.172, P = 0.044); and dietary pattern 3 was inversely associated with BFP (r=-0.280, P = 0.001) and SBP(r=-0.271, P = 0.001) . Network structure and centrality measure analysis In the total population network, SMM had the highest degree centrality (9, nearly double that of DBP) and a high betweenness centrality (17). Dietary pattern 3 possessed the highest betweenness (27) and closeness (1.043) centralities, while dietary pattern 2 and DBP also showed elevated betweenness and closeness values (Fig. 2 ). In the network of the under-65 population, DBP and SMM formed a central core. SMM showed the highest degree centrality (7) and high betweenness (20), while DBP had the highest betweenness (23) and second-highest closeness centrality (0.916). Dietary pattern 2 demonstrated the highest closeness centrality (0.963) and a relatively high betweenness centrality (17). BFP, TC, and UA also demonstrated high betweenness centralities of 15 and 14, respectively (Fig. 3 ). In the over-65 network, SMM was the absolute core, marked by the highest degree (7) and betweenness (39) centralities. Dietary pattern 1 was a key secondary hub, demonstrating the highest closeness (0.635) and the second-highest betweenness (33). UA and SBP also served as notable intermediaries with high betweenness centralities (Fig. 4 ). Discussions This population-based cross-sectional study found that four major dietary patterns in middle-aged and older adults of the Miao ethnicity in the Wuling Mountain Area of China. A diversified dietary pattern is more common among individuals under 65, while a plant-based pattern is more prevalent in those aged 65 and older. Network analysis identified SMM as the most key node among cardiometabolic risk factors, particularly among adults aged 65 years and older. Moreover, the Plant-based dietary pattern exhibited the highest closeness centrality and a relatively high betweenness centrality in individuals under 65 years. The Diverse pattern with the highest closeness and the second-highest betweenness was a key secondary hub among adults aged 65 years and older. This study found that the diverse dietary pattern featured substantial dietary diversity, consistent with the recommendations as advised by the Dietary Guidelines for Chinese Residents[ 14 ]. The plant dietary pattern was characterized by a high intake of fresh legumes, dry beans, and potatoes, aligning with the typical characteristics of a plant-based diet among older adults in China[ 15 ]. The ethnic-specific dietary pattern characterized by high consumption of alcoholic drinks and snacks. This pattern is closely associated with the traditional drinking culture endemic to Southern China[ 16 , 17 ]. Additionally, the region is situated within the Wuling Mountain area, and the local residents show a distinct preference for traditional local snacks such as Ciba and Baba[ 18 ]. The High-Starch and Sugar-Sweetened Beverage pattern is characterized by energy-dense foods, including sugary beverages. This dietary pattern demonstrates a distinct tendency toward Western dietary pattern[ 19 ]. Moreover, middle-aged adults show a greater preference for diversified dietary patterns, whereas their older counterparts tend to adopt plant-based diets. This disparity may be attributable to the higher economic status of the middle-aged group, which facilitates their access to a wider variety of foods. Our analysis revealed that SMM demonstrated the highest degree centrality within the cardiometabolic risk factor network. Furthermore, in the network of adults over 65 years, SMM also exhibited the highest betweenness centrality.This study indicated SMM as the primary physiological indicator and the principal nexus between diet and cardiometabolic health, particularly among those over 65. Sarcopenia is characterized by the loss of skeletal muscle mass. Many studies exhibits a bidirectional relationship with cardiovascular disease. The progression of sarcopenia, through its contributions to adiposity, insulin resistance, and chronic inflammation, elevates cardiovascular risk, especially among the elderly. Conversely, in patients with cardiovascular disease, the prevalent states of chronic inflammation, malnutrition, and physical inactivity promote a catabolic condition that accelerates muscle loss, thereby exacerbating sarcopenia[ 20 , 21 ].The findings suggest that SMM could be a potential key target for preventing cardiovascular risk in the middle-aged and elderly population in this region. Furthermore, we found that in the overall population, DBP also exhibited elevated degree, betweenness, and closeness values. Notably, it demonstrated the highest betweenness value within the network of the subpopulation under 65 years of age. This suggests that DBP also serves as a pivotal node connecting diet, body composition, and other cardiometabolic indicators, functioning as a more critical bridging metric within the middle-aged population. A substantial body of evidence has established blood pressure as a leading risk factor for cardiovascular disease[ 22 ]. Our results highlight that controlling blood pressure is a key preventive strategy against cardiovascular disease among middle-aged individuals. Our findings further indicate that dietary patterns play a significant role within the network. In the overall population network, the Ethnic-specific and Plant-based Dietary Patterns exhibited the highest betweenness and closeness centrality, identifying them as critical hubs for cardiometabolic risk factors in this population. Numerous epidemiological studies have demonstrated that a diet rich in healthy plant-based foods—such as fruits, vegetables, legumes, whole grains, nuts, olive oil, and coffee—is associated with favorable changes in cardiometabolic markers[ 23 , 24 ].Our results indicate that the Plant-based pattern was correlated with lower SMM. This correlation may be attributable to the fact that the plant-based pattern in this study was predominantly characterized by fresh legumes and dry beans, with a lack of fruits, vegetables, and whole grains. Our findings further indicated an association between the Ethnic-specific pattern and a favorable body composition—namely, higher SMM coupled with lower BFP—which is likely to mitigate cardiometabolic risk. This pattern is high in alcoholic drinks. However, the relationship between alcohol and cardiovascular disease outcomes is not definitive. Indeed, some evidence supports a potential protective effect of moderate wine consumption against CVD[ 25 ]. Additionally, it was found through age-stratified analysis that a diversified diet acted as a key secondary hub within the over-65 network.Although such dietary patterns were positively associated with SMM and inversely associated with SBP and TC, individuals over 65 were less likely to adhere to them.This implies that a diversified dietary pattern may represent a promising intervention for addressing cardiometabolic risk in adults over 65 in this region. In the under-65 population network, the plant-based dietary pattern demonstrated the highest closeness centrality and a relatively high betweenness centrality. As this pattern was positively associated with TC, its optimization could be beneficial for reducing cardiometabolic risk in this population. The strengths of this study lie in the application of principal component analysis to identify dietary patterns in the middle-aged and elderly population of the Wuling Mountain area, followed by the use of network analysis to explore the multidimensional interactions between dietary patterns and cardiometabolic risk factors. This study offers insights for identifying intervention targets and formulating precise strategies to address cardiometabolic risks for this area. This study is subject to certain limitations. Firstly, its cross-sectional design inherently precludes definitive causal inferences, and future longitudinal studies are warranted to establish temporality and causality. Secondly, the recruitment of participants through convenience sampling may limit the external validity and generalizability of the results. Thirdly, the assessment of dietary intake via food frequency questionnaires is subject to potential recall bias. Conclusions Four major dietary patterns were found in middle-aged and older adults of the Miao ethnicity in the Wuling Mountain Area of China. SMM as the primary physiological indicator and the principal nexus between diet and cardiometabolic health, represented the potential key target for preventing cardiovascular risk. The diversified dietary pattern may represent a promising intervention for addressing cardiometabolic risk in adults over 65 in this region. Declarations Author Contribution Author Contributions StatementShi Yujun: Conceptualization, Data Curation, Investigation, Writing – Original Draft.Huang Zhi: Conceptualization, Writing – Review & Editing, Funding Acquisition.Pan Qiuxia: Formal Analysis, Visualization.Mu Ying: Validation, Resources, Software.Peng Xiaoyang: Supervision, Writing – Review & Editing.Zhen Aibing: Project Administration.Corresponding AuthorCorresponding author: Huang Zhi ( [email protected] ), responsible for the overall communication and final approval of the manuscript. References Sun D, Han Y, Lyu J, Li L. Current major public health challenges. Chin J Epidemiol. 2024;45(1):1–10. Yusuf S, Joseph P, Rangarajan S, Islam S, Mente A, Hystad P, Brauer M, Kutty VR, Gupta R, Wielgosz A, et al. Modifiable risk factors, cardiovascular disease, and mortality in 155 722 individuals from 21 high-income, middle-income, and low-income countries (PURE): a prospective cohort study. Lancet. 2020;395(10226):795–808. 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Huang Y, Li X, Zhang T, Zeng X, Li M, Li H, Yang H, Zhang C, Zhou Z, Zhu Y, et al. Associations of healthful and unhealthful plant-based diets with plasma markers of cardiometabolic risk. Eur J Nutr. 2023;62(6):2567–79. Krittanawong C, Isath A, Rosenson RS, Khawaja M, Wang Z, Fogg SE, Virani SS, Qi L, Cao Y, Long MT, et al. Alcohol Consumption and Cardiovascular Health. Am J Med. 2022;135(10):1213–e12301213. Additional Declarations No competing interests reported. Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-8384031","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":561672401,"identity":"65a74755-6086-43c9-86f6-3cd546c200d0","order_by":0,"name":"Shi Yujun","email":"","orcid":"","institution":"Hunan University of Medicine","correspondingAuthor":false,"prefix":"","firstName":"Shi","middleName":"","lastName":"Yujun","suffix":""},{"id":561672403,"identity":"cb31990e-fe85-4365-9602-0509710bc5cb","order_by":1,"name":"Pan Qiuxia","email":"","orcid":"","institution":"Hunan University of 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17:06:16","extension":"html","order_by":13,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":117979,"visible":true,"origin":"","legend":"","description":"","filename":"earlyproof.html","url":"https://assets-eu.researchsquare.com/files/rs-8384031/v1/3f6b3d9c7e660b69c7a913eb.html"},{"id":98492011,"identity":"3a82dcc4-0335-4c77-8c89-56961e9fedb1","added_by":"auto","created_at":"2025-12-18 08:10:06","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":244694,"visible":true,"origin":"","legend":"\u003cp\u003eCorrelation analysis between dietary pattern factor scores and cardiometabolic risk factors. r: Spearman's rank correlation coefficient; * indicates P\u0026lt;0.05.\u003c/p\u003e","description":"","filename":"1.png","url":"https://assets-eu.researchsquare.com/files/rs-8384031/v1/db38c02b1c02d2e8c31c86d7.png"},{"id":98624449,"identity":"9395ce52-28b3-4794-ace4-3f3647653a72","added_by":"auto","created_at":"2025-12-19 17:08:25","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":703575,"visible":true,"origin":"","legend":"\u003cp\u003eVisualization and centrality analysis of the network for dietary patterns and \u0026nbsp;cardiometabolic risk factors.As thicker the edge as stronger the connection weight. Nodes are plotted in colors depending on the food categories.\u003c/p\u003e","description":"","filename":"floatimage2.png","url":"https://assets-eu.researchsquare.com/files/rs-8384031/v1/095e4ff50110c8dbf358f9ad.png"},{"id":98492046,"identity":"d8558139-96c7-4e00-ad39-2f5c2c330da8","added_by":"auto","created_at":"2025-12-18 08:10:10","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":720917,"visible":true,"origin":"","legend":"\u003cp\u003eVisualization and centrality analysis of the network for dietary patterns and cardiometabolic risk factors among participants aged below 65 years. As thicker the edge as stronger the connection weight. Nodes are plotted in colors depending on the food categories.\u003c/p\u003e","description":"","filename":"floatimage3.png","url":"https://assets-eu.researchsquare.com/files/rs-8384031/v1/fe0f17fe22b7e92c5520dbc0.png"},{"id":98492013,"identity":"3bc0a63c-6ed6-43ad-933f-ae699e803242","added_by":"auto","created_at":"2025-12-18 08:10:06","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":634573,"visible":true,"origin":"","legend":"\u003cp\u003eVisualization and centrality analysis of the network for dietary patterns and cardiometabolic risk factors among participants aged 65 years and above. As thicker the edge as stronger the connection weight. Nodes are plotted in colors depending on the food categories.\u003c/p\u003e","description":"","filename":"floatimage4.png","url":"https://assets-eu.researchsquare.com/files/rs-8384031/v1/30975965d426236e937daca1.png"},{"id":98775008,"identity":"50158d1a-ca99-40f0-8233-038dead1f7d3","added_by":"auto","created_at":"2025-12-22 12:17:53","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":3175351,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-8384031/v1/142f1d35-6ae5-4bd6-957c-ff9283ee2ce5.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Dietary Patterns and Cardiometabolic Risk: A Network Analysis Study in the Wuling Mountain Region of China","fulltext":[{"header":"Introduction","content":"\u003cp\u003eThe increasing severity of global population aging and changes in lifestyle patterns have significantly amplified the health impact of cardiovascular disease (CVD), establishing it as the leading cause of global mortality and disability[\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e]. CVD risk factors encompass a range of conditions such as hypertension, dyslipidemia, diabetes, chronic kidney disease, and metabolic syndrome, as well as environmental factors like air pollution. Among these, modifiable cardiometabolic risk factors-specifically obesity, dyslipidemia, hyperglycemia, and hypertension-carry the highest population-attributable risk for CVD[\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e]. The mortality burden imputed to each individual factor ranges from approximately 500,000 to 2.4\u0026nbsp;million cardiovascular deaths per year in China[\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eUnhealthy dietary intake is strongly associated with the incidence of cardiometabolic risk factors and constitutes a major cause of CVD-related death globally. Globally, 11\u0026nbsp;million global deaths and 255\u0026nbsp;million total disability-adjusted life years were attributable to dietary intake. In China, more than half of CVD mortality are linked to unreasonable dietary intake[\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e].Furthermore, substantial studies confirm that appropriate dietary intake can significantly prevent the onset of CVD, and be recognized as the most effective and cost-efficient strategy[\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e, \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e]. However, the diet intake constitutes a complex and interconnected system of exposures, involving interactions among different foods, among various components within foods, and between the health status and the foods consumed[\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e]. Furthermore, there are also interactive effects among different cardiometabolic risk factors[\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e, \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e]. Nevertheless, previous studies have predominantly focused on the relationships between specific foods and cardiometabolic risk, often overlooking intricate relationships.\u003c/p\u003e \u003cp\u003eDietary patterns, which adopt a holistic perspective on food and nutrient intake, are widely used in research on the diet-cardiovascular disease relationship in recent years, because they can not only explore complex interactions but also analyze associations with health outcomes[\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e]. Moreover, network analysis as an emerging statistical method was further developed to study multiple variables simultaneously in health sciences, such as high-dimensional genetic and metabolomics data[\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e]. This approach can be used to explore the interactions between variables, as well as the associations among various factors and one or more diseases[\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e]. Wang et al. used network analysis to identify interactions among demographic factors, dietary behaviors, and cardiometabolic comorbidities in working-age adults. Their analysis pinpointed specific dietary behaviors\u0026mdash;such as frequent meat consumption, dining out, and eating before bedtime\u0026mdash;as key intervention targets[\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eThe present study applied principal component analysis (PCA) to explore dietary patterns and network analysis to examine the complex, multidimensional relationships among dietary patterns and cardiometabolic risk factors in a sample of middle-aged and older adults of the Miao ethnicity in the Wuling Mountain Area. This method allows the visualization and quantification of interactions among diet intake and cardiometabolic risk factors, identifying key dietary pattern that may serve as potential intervention targets of cardiometabolic risk.\u003c/p\u003e"},{"header":"Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003eStudy population\u003c/h2\u003e \u003cp\u003eThis cross-sectional study encompassed middle-aged and older adults who were recruited from a township predominantly inhabited by the Miao ethnicity, located within the Wuling Mountain Area of China. Eligibility criteria included residents aged 35 years or older. Exclusion criteria excluded individuals who were frequently away, were pregnant or lactating, or had severe chronic conditions requiring strict dietary control, memory decline precluding valid questionnaire completion. Recruitment was conducted by locally trained undergraduate students from Hunan University of Medicine, who enrolled participants via convenience sampling in July 2025. A total of 261 participants was recruited. The study\u0026rsquo;s protocol was approved by the ethics committee of Hunan University of Medicine (Approval number: 2025-H03004)). Written informed consent was obtained from all participants.\u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eSociodemographic factors\u003c/h3\u003e\n\u003cp\u003eSociodemographic factors were assessed by a self designed questionnaire. These factors encompassed gender, age, occupation (classified into two categories:farmer and other), education level (primary school or higher, and illiterate), household income (less than 10,000 yuan, and 10,000 yuan and above). Age was further categorized into below 65 years, and 65years or above.\u003c/p\u003e\n\u003ch3\u003eDiet assessment\u003c/h3\u003e\n\u003cp\u003eDietary intake was assessed by trained investigators through face-to-face interview with a validated food frequency questionnaire (FFQ). The questionnaire queried the frequency of consumption of 55 food items over the preceding 12 months. Food intake portion sizes and frequencies were estimated using photographs and standard portion sizes. Food intake frequency was assessed as times per day, week, month, or year, and portion sizes were expressed in grams or milliliters. The intake of each food item was calculated from portion size and intake frequency. Single-food intakes were collapsed into 17 food items according to similarities in nutrient profiles or food processing methods.\u003c/p\u003e \u003cp\u003eDietary patterns were derived using PCA, following our established methodology. To derive these patterns, the average daily intake of the 17 food groups was subjected to PCA after confirming sampling adequacy with the Kaiser-Meyer-Olkin (KMO) statistic. Pattern identification was based on a combination of criteria, including eigenvalues greater than 1, the scree plot, factor interpretability, and the proportion of variance explained. Food items with factor loadings exceeding |0.3| were considered significant contributors to a pattern. Finally, a standardized score for each dietary pattern was calculated for every participants by computing the intake-weighted sum of the foods based on their factor loadings. Higher scores indicated greater adherence to the respective dietary pattern.\u003c/p\u003e\n\u003ch3\u003eCardiometabolic risk factors\u003c/h3\u003e\n\u003cp\u003eBody height and weight were measured using an electronic stadiometer and scale, while skeletal muscle mass (SMM) and body fat percentage (BFP) were assessed using a body composition analyzer. Body mass index (BMI) was calculated as weight in kilograms divided by the square of height in meters (kg/m\u0026sup2;). A volume of 20\u0026ndash;50 \u0026micro;L of capillary blood was collected from the fingertip following a 10\u0026ndash;12 h fasting period. A Home-use monitors was used to detect blood biochemical parameters, including blood pressure, blood glucose (GLU), total cholesterol (TC), Triglycerides(TG), and uric acid (UA). Medical examinations were performed simultaneously with the face-to-face interviews by trained investigators.\u003c/p\u003e \u003cdiv id=\"Sec7\" class=\"Section2\"\u003e \u003ch2\u003eStatistical analysis\u003c/h2\u003e \u003cp\u003eData were conducted by using SPSS version 25.0 software (SPSS, Chicago, Illinois) and R version 4.5.1 (Foundation for Statistical Computing, Vienna, Austria). Categorical variables, are presented as frequencies and percentages, stratified by age group. Differences were assessed using the chi-square test. Continuous variables was expressed by Mean and standard deviation(SD) for normality data, or Median and interquartile range (IQR). Independent-sample t tests were used to compare groups for normality data; otherwise, the Mann\u0026ndash;Whitney U test was applied. Correlation analysis between dietary pattern factor scores and cardiometabolic risk factors was conducted by Spearman's rank correlation due to non-normally data. Statistical significance was set at a two-sided P value\u0026thinsp;\u0026lt;\u0026thinsp;0.05.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003eNetwork estimation\u003c/h2\u003e \u003cp\u003eA Gaussian Graphical Model (GGM) was estimated to examine the conditional dependence relationships among dietary patterns, body metrics, and cardiometabolic parameters. The analysis was implemented in R (version 4.5.1) using the bootnet package.\u003c/p\u003e \u003cp\u003eSparse partial correlation networks were constructed using the Graphical Least Absolute Shrinkage and Selection Operator (GLASSO) algorithm. The Extended Bayesian Information Criterion (EBIC) with hyperparameter γ\u0026thinsp;=\u0026thinsp;0.2 was employed for model selection to balance network sparsity and sensitivity. The correlation matrix was computed using the cor_auto function to handle mixed data types appropriately. The resulting adjacency matrix was converted to an igraph object for topological analysis.\u003c/p\u003e \u003cp\u003eNode centrality was quantified using three established metrics. Strength centrality: Weighted degree representing cumulative connection strength. Betweenness centrality: Proportion of shortest paths passing through each node. Closeness centrality: Inverse average shortest path distance to all other nodes.\u003c/p\u003e \u003cp\u003eNetwork visualization was created using ggraph with a Davidson-Harel layout algorithm. Nodes were categorized and color-coded into four domains:Body Metrics (BMI, SMM, BFP), blood pressure (SBP, DBP), biochemical markers (Glu, UA, TC, TG), and dietary patterns. Edges were filtered to display only the top 50% of weights by magnitude, with width and transparency proportional to edge weights. All analyses used absolute partial correlation values to focus on connection strength regardless of direction.\u003c/p\u003e \u003c/div\u003e"},{"header":"Results","content":"\u003cdiv id=\"Sec10\" class=\"Section2\"\u003e \u003ch2\u003eSample characteristics\u003c/h2\u003e \u003cp\u003eAs shown in Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e, a total of 261 middle-aged and older adults was included in the final analysis. Of the participants, 62.84% were female, 88.12% were employed as farmers, over 50% had attained an educational level of primary school or higher, and more than 90% reported an annual household income of less than 10,000 yuan. 47.51% were below 65 years of age, and 52.49% were 65 years or older. The younger participant group had higher educational level and household income than 65 years and above. The distribution of sex and occupation remained comparable on age group. Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e also summarizes cardiometabolic risk indicators. Notably, the younger group had higher mean levels of BMI, SMM, BFP, and DBP, whereas TG level was lower.\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\u003eGeneral demographic characteristics and cardiometabolic risk factors of participants\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"10\"\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 \u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c9\" colnum=\"9\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c10\" colnum=\"10\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003eCharacteristics\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e \u003cp\u003eTotal\u003c/p\u003e \u003cp\u003e(N\u0026thinsp;=\u0026thinsp;261)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c6\" namest=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;65 years\u003c/p\u003e \u003cp\u003e(N\u0026thinsp;=\u0026thinsp;124)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c8\" namest=\"c7\"\u003e \u003cp\u003e\u0026ge;\u0026thinsp;65 years\u003c/p\u003e \u003cp\u003e(N\u0026thinsp;=\u0026thinsp;137)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c9\"\u003e \u003cp\u003eχ\u003csup\u003e2\u003c/sup\u003e/t/Z\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c10\"\u003e \u003cp\u003eP\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003eSex\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 \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.626\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e0.429\u003c/p\u003e \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\u003eMale\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e97\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e(37.16)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e43\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e(34.68)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e54\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e(39.42)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\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\u003eFemale\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e164\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e(62.84)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e81\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e(65.32)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e83\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e(60.58)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003eOccupation\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 \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e1.572\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e0.210\u003c/p\u003e \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\u003eFarmer\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e230\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e(88.12)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e106\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e(85.48)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e124\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e(90.51)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\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\u003eOther\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e31\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e(11.88)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e18\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e(14.52)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e13\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e(9.49)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eEducation\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e10.071\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e0.002\u003c/p\u003e \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\u003ePrimary school or higher\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e148\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e(56.70)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e83\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e(66.94)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e65\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e(47.45)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\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\u003eIlliterate\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e113\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e(43.30)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e41\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e(33.06)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e72\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e(52.55)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003eHousehold income\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 \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\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\u003eLess than 10,000 yuan\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e238\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e(91.19)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e106\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e(85.48)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e132\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e(96.35)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e9.564\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e0.002\u003c/p\u003e \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\u003e10,000 yuan and above\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\u003e(8.81)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e18\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e(14.52)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e(3.65)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003eBody metrics\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 \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003eBMI (mean(sd))\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e24.34\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e(3.67)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e25.38\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e(3.48)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e23.41\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e(3.60)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e4.481\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003e\u003cb\u003eSMM (kg,mean(sd)))\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003e22.44\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e(5.43)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e23.53\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u003cb\u003e(5.33)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u003cb\u003e21.45\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e\u003cb\u003e(5.35)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e\u003cb\u003e3.138\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e\u003cb\u003e0.002\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003e\u003cb\u003eBFP (%,mean(sd)))\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e27.80\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e(8.25)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e29.42\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e(7.65)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e26.34\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e(8.53)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e3.061\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e0.002\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003eBlood pressure\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 \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003eSBP (mmHg, mean(sd))\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e123\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e(16.00)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e121\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e(16.00)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e125\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e(17.00)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e1.603\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e0.104\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003eDBP (mmHg,mean(sd)))\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e71\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e(10.00)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e73\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e(10.00)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e69\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e(10.00)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e3.130\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e0.002\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003eBiochemical markers\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 \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003eGlu (mmol/L,mean(sd)))\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e8.40\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e(4.97)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e8.18\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e(4.04)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e8.61\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e(5.69)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.689\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e0.492\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003eUA(\u0026micro;mol/L,mean(sd)))\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e335\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e(78.00)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e325\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e(74.00)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e343\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e(81.00)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e1.889\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e0.060\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003eTC(mmol/L,mean(sd)))\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e4.44\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e(0.81)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e4.42\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e(0.77)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e4.45\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e(0.85)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.345\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e0.730\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003eTG(mmol/L,median(IQR))\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.97\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e(0.72,1,33)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.89\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e(0.70,1.20)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e1.05\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e(0.79,1.40)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e2.456\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e0.014\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\u003eDietary patterns\u003c/h2\u003e \u003cp\u003eFour representative dietary patterns were identified based on PCA (Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e), explained 41.13% of the total variance the food group data. Dietary pattern 1 was characterised by high positive loadings for diverse foods items, including stem vegetables, eggs, leafy vegetables, dried and preserved vegetables, fruits, meats, dry beans, fish and shellfish, milk, and cereals, labeled as Diverse pattern. Dietary pattern 2 featured high loadings of plant-based food groups (e.g., fresh legumes, dry beans, potatoes), while demonstrating low loadings for animal-based groups, including meats, fish, and shellfish, named as the Plant-based pattern. Dietary pattern 3, termed Ethnic-specific dietary pattern, was defined by high consumption of alcoholic beverages, fresh legumes, snacks, and dry beans, alongside notably low consumption of dried, preserved and leafy vegetables. Dietary pattern 4 was characterised by high positive loadings for potatoes, sugar-sweetened beverages, eggs, and cereals, but low on fresh legume and dry beans, named as High-starch and Sugar-sweetened Beverage pattern.\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\u003eFactor loadings of 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 \u003cp\u003eFood items\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePattern1\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003ePattern2\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003ePattern3\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003ePattern4\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCereals\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003e0.33\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-0.09\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.22\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e0.33\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePotatoes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.28\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003e0.38\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.19\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e0.53\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDry Beans\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003e0.51\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003e0.39\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e0.30\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e-0.31\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFresh legume\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.15\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003e0.57\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e0.39\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e-0.32\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eStem vegetables\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003e0.68\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.17\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-0.06\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-0.24\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLeafy vegetables\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003e0.63\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.05\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e-0.44\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-0.07\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFungi and algae\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.29\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.28\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-0.12\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-0.05\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDried and preserved vegetables\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003e0.59\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.08\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e-0.31\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.05\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFruits\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003e0.58\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-0.29\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.03\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.19\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMilk\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003e0.34\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-0.07\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-0.10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-0.07\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMeats\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003e0.56\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003e-0.41\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.05\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-0.17\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFish and shellfish\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003e0.46\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003e-0.40\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-0.05\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-0.16\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eEggs\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003e0.68\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.11\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.14\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e0.35\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNuts and seeds\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.20\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-0.26\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.14\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-0.24\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSnacks\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.06\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-0.10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e0.38\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.02\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAlcoholic beverages\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.12\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003e-0.36\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e0.58\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.15\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSugar-Sweetened beverages\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.03\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.14\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-0.24\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e0.45\u003c/b\u003e\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\u003eDietary pattern scores were \u0026minus;\u0026thinsp;0.22(-0.55, 0.29) for the pattern 1, 0.03(-0.40, 0.27) for the pattern2, -0.13(-0.36, 0.21) for the pattern 3, and 0.03(-0.34, 0.25) for pattern 4, respectively(Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e). Participants aged below 65 years tended to have significantly higher scores on dietary pattern 1 than 65 years and above. Participants aged 65 years and above tended to score significantly higher on dietary pattern 2 compared to age below 65 years.\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\u003eThe diet factor score of dietary patterns (Median(IQR))\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"12\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"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 \u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c9\" colnum=\"9\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c10\" colnum=\"10\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c11\" colnum=\"11\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c12\" colnum=\"12\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDietary patterns\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"3\" nameend=\"c4\" namest=\"c2\"\u003e \u003cp\u003eTotal\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"3\" nameend=\"c7\" namest=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;65 years\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"3\" nameend=\"c10\" namest=\"c8\"\u003e \u003cp\u003e\u0026ge;\u0026thinsp;65 years\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c11\"\u003e \u003cp\u003eZ\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c12\"\u003e \u003cp\u003eP\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePattern1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-0.22\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e(-0.55,\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.29)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e-0.15\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u003cb\u003e(-0.48\u003c/b\u003e,\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u003cb\u003e0.34)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e-0.29\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e(-0.69,\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e0.24)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e-2.080\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e0.037\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePattern2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-0.03\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e(-0.40,\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.27)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-0.13\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e(-0.45,\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.20)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e\u003cb\u003e0.01\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e\u003cb\u003e(-0.27\u003c/b\u003e,\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e\u003cb\u003e0.39)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e-2.092\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e0.036\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePattern3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-0.13\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e(-0.36,\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.21)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-0.12\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e(-0.32,\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.21)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e-0.15\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e(-0.39,\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e0.21)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e-0.778\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e0.436\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePattern4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-0.03\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e(-0.34,\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.25)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-0.02\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e(-0.30,\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.25)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e-0.04\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e(-0.38,\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e0.32)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e-0.041\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e0.967\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=\"Sec12\" class=\"Section2\"\u003e \u003ch2\u003eDietary patterns and cardiometabolic risk indicators\u003c/h2\u003e \u003cp\u003eCorrelations revealed that a higher pattern 1 score was correlated with lower SPB (r=-0.210, P\u0026thinsp;=\u0026thinsp;0.001) and TC(r=-0.161, P\u0026thinsp;=\u0026thinsp;0.009), but higher SMM(r\u0026thinsp;=\u0026thinsp;0.225, P\u0026thinsp;\u0026lt;\u0026thinsp;0.001) in the total population (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). A higher pattern 2 score was correlated with lower BMI(r=-0.224, P\u0026thinsp;\u0026lt;\u0026thinsp;0.001) and SMM(r=-0.192, P\u0026thinsp;=\u0026thinsp;0.002), but higher UA(r\u0026thinsp;=\u0026thinsp;0.152, P\u0026thinsp;=\u0026thinsp;0.014) and TC(r\u0026thinsp;=\u0026thinsp;0.200, P\u0026thinsp;=\u0026thinsp;0.001). A higher pattern 3 score was correlated with lower BFP(r=-0.174, P\u0026thinsp;=\u0026thinsp;0.005), but higher SMM(r\u0026thinsp;=\u0026thinsp;0.200, P\u0026thinsp;=\u0026thinsp;0.001). A higher pattern 4 score was correlated with higher SMM(r\u0026thinsp;=\u0026thinsp;0.138, P\u0026thinsp;=\u0026thinsp;0.026). In participant aged\u0026thinsp;\u0026lt;\u0026thinsp;65 years, dietary pattern 1 (r\u0026thinsp;=\u0026thinsp;0.187, P\u0026thinsp;=\u0026thinsp;0.038) and 3 (r\u0026thinsp;=\u0026thinsp;0.297, P\u0026thinsp;=\u0026thinsp;0.001) factor scores were positively associated with SMM, while dietary pattern 2 factor scores were positively associated with TC(r\u0026thinsp;=\u0026thinsp;0.235, P\u0026thinsp;=\u0026thinsp;0.009). Among individuals aged\u0026thinsp;\u0026ge;\u0026thinsp;65 years, dietary pattern 1 was positively associated with SMM (r\u0026thinsp;=\u0026thinsp;0.235, P\u0026thinsp;=\u0026thinsp;0.006) and inversely with SBP (r=-0.297, P\u0026thinsp;\u0026lt;\u0026thinsp;0.001) and TC(r=-0.221, P\u0026thinsp;=\u0026thinsp;0.009); dietary pattern 2 was inversely associated with BMI (r=-0.305, P\u0026thinsp;\u0026lt;\u0026thinsp;0.001) and SMM (r=-0.234, P\u0026thinsp;=\u0026thinsp;0.006) but positively with UA(r\u0026thinsp;=\u0026thinsp;0.172, P\u0026thinsp;=\u0026thinsp;0.044); and dietary pattern 3 was inversely associated with BFP (r=-0.280, P\u0026thinsp;=\u0026thinsp;0.001) and SBP(r=-0.271, P\u0026thinsp;=\u0026thinsp;0.001) .\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec13\" class=\"Section2\"\u003e \u003ch2\u003eNetwork structure and centrality measure analysis\u003c/h2\u003e \u003cp\u003eIn the total population network, SMM had the highest degree centrality (9, nearly double that of DBP) and a high betweenness centrality (17). Dietary pattern 3 possessed the highest betweenness (27) and closeness (1.043) centralities, while dietary pattern 2 and DBP also showed elevated betweenness and closeness values (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eIn the network of the under-65 population, DBP and SMM formed a central core. SMM showed the highest degree centrality (7) and high betweenness (20), while DBP had the highest betweenness (23) and second-highest closeness centrality (0.916). Dietary pattern 2 demonstrated the highest closeness centrality (0.963) and a relatively high betweenness centrality (17). BFP, TC, and UA also demonstrated high betweenness centralities of 15 and 14, respectively (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eIn the over-65 network, SMM was the absolute core, marked by the highest degree (7) and betweenness (39) centralities. Dietary pattern 1 was a key secondary hub, demonstrating the highest closeness (0.635) and the second-highest betweenness (33). UA and SBP also served as notable intermediaries with high betweenness centralities (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e"},{"header":"Discussions","content":"\u003cp\u003eThis population-based cross-sectional study found that four major dietary patterns in middle-aged and older adults of the Miao ethnicity in the Wuling Mountain Area of China. A diversified dietary pattern is more common among individuals under 65, while a plant-based pattern is more prevalent in those aged 65 and older. Network analysis identified SMM as the most key node among cardiometabolic risk factors, particularly among adults aged 65 years and older. Moreover, the Plant-based dietary pattern exhibited the highest closeness centrality and a relatively high betweenness centrality in individuals under 65 years. The Diverse pattern with the highest closeness and the second-highest betweenness was a key secondary hub among adults aged 65 years and older.\u003c/p\u003e \u003cp\u003eThis study found that the diverse dietary pattern featured substantial dietary diversity, consistent with the recommendations as advised by the Dietary Guidelines for Chinese Residents[\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e]. The plant dietary pattern was characterized by a high intake of fresh legumes, dry beans, and potatoes, aligning with the typical characteristics of a plant-based diet among older adults in China[\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e]. The ethnic-specific dietary pattern characterized by high consumption of alcoholic drinks and snacks. This pattern is closely associated with the traditional drinking culture endemic to Southern China[\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e, \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e]. Additionally, the region is situated within the Wuling Mountain area, and the local residents show a distinct preference for traditional local snacks such as Ciba and Baba[\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e]. The High-Starch and Sugar-Sweetened Beverage pattern is characterized by energy-dense foods, including sugary beverages. This dietary pattern demonstrates a distinct tendency toward Western dietary pattern[\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e]. Moreover, middle-aged adults show a greater preference for diversified dietary patterns, whereas their older counterparts tend to adopt plant-based diets. This disparity may be attributable to the higher economic status of the middle-aged group, which facilitates their access to a wider variety of foods.\u003c/p\u003e \u003cp\u003eOur analysis revealed that SMM demonstrated the highest degree centrality within the cardiometabolic risk factor network. Furthermore, in the network of adults over 65 years, SMM also exhibited the highest betweenness centrality.This study indicated SMM as the primary physiological indicator and the principal nexus between diet and cardiometabolic health, particularly among those over 65. Sarcopenia is characterized by the loss of skeletal muscle mass. Many studies exhibits a bidirectional relationship with cardiovascular disease. The progression of sarcopenia, through its contributions to adiposity, insulin resistance, and chronic inflammation, elevates cardiovascular risk, especially among the elderly. Conversely, in patients with cardiovascular disease, the prevalent states of chronic inflammation, malnutrition, and physical inactivity promote a catabolic condition that accelerates muscle loss, thereby exacerbating sarcopenia[\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e, \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e].The findings suggest that SMM could be a potential key target for preventing cardiovascular risk in the middle-aged and elderly population in this region.\u003c/p\u003e \u003cp\u003eFurthermore, we found that in the overall population, DBP also exhibited elevated degree, betweenness, and closeness values. Notably, it demonstrated the highest betweenness value within the network of the subpopulation under 65 years of age. This suggests that DBP also serves as a pivotal node connecting diet, body composition, and other cardiometabolic indicators, functioning as a more critical bridging metric within the middle-aged population. A substantial body of evidence has established blood pressure as a leading risk factor for cardiovascular disease[\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e]. Our results highlight that controlling blood pressure is a key preventive strategy against cardiovascular disease among middle-aged individuals.\u003c/p\u003e \u003cp\u003eOur findings further indicate that dietary patterns play a significant role within the network. In the overall population network, the Ethnic-specific and Plant-based Dietary Patterns exhibited the highest betweenness and closeness centrality, identifying them as critical hubs for cardiometabolic risk factors in this population. Numerous epidemiological studies have demonstrated that a diet rich in healthy plant-based foods\u0026mdash;such as fruits, vegetables, legumes, whole grains, nuts, olive oil, and coffee\u0026mdash;is associated with favorable changes in cardiometabolic markers[\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e, \u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e].Our results indicate that the Plant-based pattern was correlated with lower SMM. This correlation may be attributable to the fact that the plant-based pattern in this study was predominantly characterized by fresh legumes and dry beans, with a lack of fruits, vegetables, and whole grains. Our findings further indicated an association between the Ethnic-specific pattern and a favorable body composition\u0026mdash;namely, higher SMM coupled with lower BFP\u0026mdash;which is likely to mitigate cardiometabolic risk. This pattern is high in alcoholic drinks. However, the relationship between alcohol and cardiovascular disease outcomes is not definitive. Indeed, some evidence supports a potential protective effect of moderate wine consumption against CVD[\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eAdditionally, it was found through age-stratified analysis that a diversified diet acted as a key secondary hub within the over-65 network.Although such dietary patterns were positively associated with SMM and inversely associated with SBP and TC, individuals over 65 were less likely to adhere to them.This implies that a diversified dietary pattern may represent a promising intervention for addressing cardiometabolic risk in adults over 65 in this region. In the under-65 population network, the plant-based dietary pattern demonstrated the highest closeness centrality and a relatively high betweenness centrality. As this pattern was positively associated with TC, its optimization could be beneficial for reducing cardiometabolic risk in this population.\u003c/p\u003e \u003cp\u003eThe strengths of this study lie in the application of principal component analysis to identify dietary patterns in the middle-aged and elderly population of the Wuling Mountain area, followed by the use of network analysis to explore the multidimensional interactions between dietary patterns and cardiometabolic risk factors. This study offers insights for identifying intervention targets and formulating precise strategies to address cardiometabolic risks for this area. This study is subject to certain limitations. Firstly, its cross-sectional design inherently precludes definitive causal inferences, and future longitudinal studies are warranted to establish temporality and causality. Secondly, the recruitment of participants through convenience sampling may limit the external validity and generalizability of the results. Thirdly, the assessment of dietary intake via food frequency questionnaires is subject to potential recall bias.\u003c/p\u003e"},{"header":"Conclusions","content":"\u003cp\u003eFour major dietary patterns were found in middle-aged and older adults of the Miao ethnicity in the Wuling Mountain Area of China. SMM as the primary physiological indicator and the principal nexus between diet and cardiometabolic health, represented the potential key target for preventing cardiovascular risk. The diversified dietary pattern may represent a promising intervention for addressing cardiometabolic risk in adults over 65 in this region.\u003c/p\u003e"},{"header":"Declarations","content":"\u003ch2\u003eAuthor Contribution\u003c/h2\u003e\u003cp\u003eAuthor Contributions StatementShi Yujun: Conceptualization, Data Curation, Investigation, Writing \u0026ndash; Original Draft.Huang Zhi: Conceptualization, Writing \u0026ndash; Review \u0026amp; Editing, Funding Acquisition.Pan Qiuxia: Formal Analysis, Visualization.Mu Ying: Validation, Resources, Software.Peng Xiaoyang: Supervision, Writing \u0026ndash; Review \u0026amp; Editing.Zhen Aibing: Project Administration.Corresponding AuthorCorresponding author: Huang Zhi ([email protected]), responsible for the overall communication and final approval of the manuscript.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eSun D, Han Y, Lyu J, Li L. Current major public health challenges. 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J Cachexia Sarcopenia Muscle. 2023;14(3):1183\u0026ndash;98.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eStevens SL, Wood S, Koshiaris C, Law K, Glasziou P, Stevens RJ, McManus RJ. Blood pressure variability and cardiovascular disease: systematic review and meta-analysis. BMJ. 2016;354:i4098.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eWang XJ, Steur M, Kavousi M, Voortman T. Adherence to plant-based diets and long-term changes in cardiometabolic markers: a longitudinal analysis in a population-based cohort. Am J Clin Nutr. 2025;122(2):424\u0026ndash;32.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eHuang Y, Li X, Zhang T, Zeng X, Li M, Li H, Yang H, Zhang C, Zhou Z, Zhu Y, et al. Associations of healthful and unhealthful plant-based diets with plasma markers of cardiometabolic risk. Eur J Nutr. 2023;62(6):2567\u0026ndash;79.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eKrittanawong C, Isath A, Rosenson RS, Khawaja M, Wang Z, Fogg SE, Virani SS, Qi L, Cao Y, Long MT, et al. Alcohol Consumption and Cardiovascular Health. Am J Med. 2022;135(10):1213\u0026ndash;e12301213.\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":true,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"Network Analysis, Dietary patterns, Middle-aged and older, Cardiometabolic risk","lastPublishedDoi":"10.21203/rs.3.rs-8384031/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-8384031/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eBackground\u003c/h2\u003e \u003cp\u003eUnhealthy dietary intake is strongly associated with the incidence of cardiometabolic risk factors and constitutes a major cause of cardiovascular disease (CVD) related death globally. The study aimed to examine the complex, multidimensional relationships among dietary patterns and cardiometabolic risk factors in middle-aged and older adults of the Miao ethnicity of China.\u003c/p\u003e\u003ch2\u003eMethods\u003c/h2\u003e \u003cp\u003eA cross-sectional study encompassed 261 middle-aged and older adults was conducted in area inhabited by the Miao ethnicity, located within the Wuling Mountain Area of China.Demographic characteristics and dietary intake were assessed via a questionnaire, while metabolic indicators were obtained through medical examination. Dietary patterns were derived using PCA, and a Gaussian Graphical Model was estimated to examine the conditional dependence relationships among dietary patterns, and cardiometabolic parameters.\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e \u003cp\u003eDiverse, Plant-based, Ethnic-specific, and High-starch and Sugar-sweetened Beverage patterns were indentified in middle-aged and older adults of the Miao ethnicity. A diversified dietary pattern is more common among individuals under 65, while a plant-based pattern is more prevalent in those aged 65 and older. Network analysis identified skeletal muscle mass (SMM) as the most key node, with highest degree centrality (9) and a high betweenness centrality (17), particularly among adults aged 65 years and older. Moreover, the Plant-based dietary pattern demonstrated the highest closeness centrality (0.963) and a relatively high betweenness centrality (17) in individuals under 65 years. The Diverse pattern with the highest closeness (0.635) and the second-highest betweenness (33) was a key secondary hub among adults aged 65 years and older.\u003c/p\u003e\u003ch2\u003eConclusions\u003c/h2\u003e \u003cp\u003eFour major dietary patterns were found in middle-aged and older adults of the Miao ethnicity in the Wuling Mountain Area of China. SMM as the principal nexus between diet and cardiometabolic health, represented the potential key target for preventing cardiovascular risk. The diversified pattern may represent a promising intervention for addressing cardiometabolic risk in adults over 65 in this region.\u003c/p\u003e","manuscriptTitle":"Dietary Patterns and Cardiometabolic Risk: A Network Analysis Study in the Wuling Mountain Region of China","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-12-18 08:09:07","doi":"10.21203/rs.3.rs-8384031/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"2cc52564-567d-4898-8d62-afeef2644f20","owner":[],"postedDate":"December 18th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2025-12-19T14:09:00+00:00","versionOfRecord":[],"versionCreatedAt":"2025-12-18 08:09:07","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-8384031","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-8384031","identity":"rs-8384031","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}