Study on the Impact of Dietary Patterns on Cardiovascular Metabolic Comorbidities among Adults

Study on the Impact of Dietary Patterns on Cardiovascular Metabolic Comorbidities among Adults | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Study on the Impact of Dietary Patterns on Cardiovascular Metabolic Comorbidities among Adults Danhui Mao, Gongkui Li, Yajing Li, Shixun Wang, Mohan Zhang, Mingyan Ma, and 1 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-4451883/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Background The prevalence of cardiovascular metabolic comorbidities (CMM) among adults is relatively high, imposing a heavy burden on individuals, families, and society. Dietary patterns play a significant role in the occurrence and development of CMM. This study aimed to identify the combined types of CMM in adult populations and investigate the impact of dietary patterns on CMM. Methods Participants in this study were from the sixth wave of the China Health and Nutrition Survey (CHNS). Dietary intake was assessed using a three-day 24-hour dietary recall method among 4,963 participants. Latent profile analysis was used to determine dietary pattern types. Two-step cluster analysis was performed to identify the combined types of CMM based on the participants' conditions of hyperuricemia, dyslipidemia, diabetes, renal dysfunction, hypertension, and stroke. Logistic regression analysis with robust standard errors was used to determine the impact of dietary patterns on CMM. Results Participants were clustered into three dietary patterns (Pattern 1 to 3) and five CMM types (Class I to V). Class I combined six diseases, with a low proportion of diabetes. Class II also combined six diseases but with a high proportion of diabetes. Class III combined four diseases, with a high proportion of hypertension. Class IV combined three diseases, with the highest proportions of hyperuricemia, diabetes, and renal dysfunction. Class V combined two diseases, with high proportions of dyslipidemia and renal dysfunction. Patients with Class III CMM had a significantly higher average age than the other four classes ( P ≤ 0.05). Compared to those with isolated dyslipidemia, individuals with a low-grain, high-fruit, milk, and egg (LCHFM) dietary pattern had a higher risk of developing dyslipidemia combined with renal dysfunction (Class V CMM) with an odds ratio of 2.001 (95% CI : 1.011–3.960, P ≤ 0.05). Conclusion For individuals with isolated dyslipidemia, avoiding a low-grain, high-fruit, milk, and egg (LCHFM) dietary pattern may help reduce the risk of developing dyslipidemia combined with renal dysfunction (Class V CMM). dietary pattern cardiometabolic multimorbidity latent profile analysis adults Background Cardiovascular metabolic comorbidities (CMM) refers to the coexistence of two or more cardiovascular diseases or metabolic disorders in an individual, such as hypertension, diabetes, dyslipidemia, and stroke [ 1 ]. It is one of the most stable patterns of multimorbidity [ 2 ]. In a study by Chudasama et al. (2019), approximately 64%, 59%, 57%, and 54% of patients with diabetes, angina pectoris, stroke, and myocardial infarction were concurrently diagnosed with hypertension [ 3 ]. A 10-year follow-up study conducted by Zhao et al. in 2021 on a Chinese population of 461,047 individuals revealed that 18.7% of healthy individuals experienced their first onset of heart disease, stroke, and diabetes. Among these newly diagnosed patients, 16.2% further progressed to CMM, and 22.5% of these patients died [ 4 ]. An epidemiological survey in 2022 showed that the incidence of CMM was 14.4%. CMM can significantly impact disease prognosis and patients' quality of life, with studies indicating that the mortality risk among CMM patients is twice that of patients without multimorbidity [ 5 – 7 ]. As the population ages, the long duration and complex etiology of CMM pose greater risks of disability, death, as well as physical, psychological, and economic burdens on patients [ 7 , 8 ]. Therefore, preventing and controlling CMM is a critical task in primary healthcare services. According to statistics, CMM is closely associated with demographic characteristics. Research indicates that female gender, middle and old age, urban residency, and higher education levels are demographic risk factors for CMM [ 9 ]. Additionally, similar to single chronic diseases, studies have shown that smoking, excessive alcohol consumption, unhealthy dietary patterns, and physical inactivity are lifestyle risk factors for CMM [ 10 , 11 ]. Diet, as one of the modifiable lifestyle factors, can be specifically adjusted to reduce disease risk. In terms of cardiovascular diseases, reducing saturated fat intake and increasing dietary fiber intake can lower the risk of developing cardiovascular diseases. High-energy-density diets and obesity increase the risk of cardiovascular diseases. Appropriate intake of dietary fiber aids in insulin secretion, glucose control, blood lipid levels, and blood pressure maintenance at healthy levels. Furthermore, dietary patterns with high-energy density, high saturated fat, and low dietary fiber are associated with increased cardiometabolic risks in severely obese individuals [ 11 ]. Regarding metabolic diseases, reducing macronutrient intake can reduce the risk of developing metabolic diseases. High-energy-density diets and obesity also increase the risk of metabolic diseases. Appropriate intake of dietary fiber, vitamins, and minerals benefits insulin secretion, glucose control, and blood lipid levels, maintaining them at healthy levels and reducing the risk of metabolic diseases [ 12 – 14 ]. Moreover, previous studies have found that certain dietary patterns can simultaneously influence the cardiovascular or metabolic systems, such as the Mediterranean diet, the Dietary Approaches to Stop Hypertension (DASH) diet, and the Atkins diet, which can affect body weight and serum uric acid levels [ 15 – 17 ]. CMM exhibits a complex disease mechanism, with diseases being highly interrelated. To prevent and control CMM, it is essential to fully consider the correlations between disease combinations and explore preventive and therapeutic measures from a holistic perspective. As a qualitative approach for CMM, the combination types of CMM can represent the complex relationships between diseases in a holistic manner. Meanwhile, as modifiable factors in preventing and controlling multiple cardiovascular diseases or metabolic disorders, foods or nutrients coordinate their actions in a dynamic and complex system. Therefore, evaluating diet also requires a comprehensive consideration of the intricate relationships between foods and nutrients, analyzed from a holistic angle. Dietary patterns, as a qualitative approach for dietary habits, can represent the complex relationships between foods or nutrients in a holistic manner. Thus, understanding the impact of dietary patterns on CMM combination types may be of significant importance for the prevention and control of CMM. Currently, studies on the effects of dietary patterns on cardiovascular or metabolic diseases primarily focus on the relationships between single diseases and single dietary patterns, or multiple diseases and a single dietary pattern. However, there is a lack of exploration into the relationships between various CMM combination types and multiple dietary patterns within a population. Therefore, this study aims to determine the impact of major dietary patterns on primary CMM combination types, based on an understanding of the primary CMM combination types and major dietary patterns within the population. Materials and Methods Study population The participants in this study were derived from the sixth round of the China Health and Nutrition Survey (CHNS). For a detailed description of CHNS, please refer to https://www.cpc.unc.edu/projects/china . We excluded individuals with incomplete data, including socio-demographic characteristics, dietary and lifestyle habits, and disease characteristics. Therefore, the study encompassed a total of 4963 subjects (Table 1 ). The survey was approved by the ethics committee of the University of North Carolina at Chapel Hill and the National Institute for Nutrition and Health, Chinese Center for Disease Control and Prevention. Dietary Assessment For this study, ten types of foods were selected based on their daily average intake exceeding 10g and their common occurrence in research on adult dietary patterns, see Table 4 for details. Disease Assessment In this study, we assessed the presence of hyperuricemia, dyslipidemia, diabetes, chronic kidney disease, and hypertension among participants. Hyperuricemia was defined as serum uric acid levels above 420 µmol/L (7 mg/dl) for males and above 357 µmol/L (6 mg/dl) for females [ 18 ]. Dyslipidemia was defined as having at least one of the following conditions: (1) hypercholesterolemia (TC ≥ 5.18 mmol/L); (2) hypertriglyceridemia (TG ≥ 1.70 mmol/L); (3) low-density lipoprotein cholesterol (LDL-C) ≥ 3.37 mmol/L; and (4) high-density lipoprotein cholesterol (HDL-C) < 1.04 mmol/L [ 19 ]. Chronic kidney disease was identified as having at least one of the following conditions: (1) kidney dysfunction (abnormal kidney structure or function) lasting for more than 3 months; (2) glomerular filtration rate < 60 mL/min/1.73 m2 lasting for more than 3 months [ 20 ]. Diabetes was diagnosed if participants had either (1) fasting plasma glucose ≥ 7.0 mmol/L or 2-hour postprandial glucose ≥ 11.1 mmol/L; or (2) a prior diagnosis of diabetes by a physician [ 21 ]. Hypertension was defined as having either (1) an average systolic blood pressure > 140 mmHg or an average diastolic blood pressure > 90 mmHg; or (2) a prior diagnosis of hypertension by a physician [ 22 ]. Measurements of covariates The control variables in this study included demographic characteristics such as age and gender, as well as lifestyle features such as smoking status and physical activity. Smoking status was categorized as current or past smoker (yes = 1) and never smoker (no = 0). Additionally, we calculated the total weekly metabolic equivalents (MET) for physical activities, encompassing household, occupational, transportation, and recreational activities [ 23 – 25 ]. Statistical analysis Continuous variables, such as age, urbanization index, and physical activity, were described using mean ± standard deviation. Categorical variables, including gender, marital status, educational level, smoking status, and the presence of hypertension, stroke, diabetes, dyslipidemia, and renal dysfunction, were presented as frequencies (proportions). The differences in the distribution of demographic characteristics and disease status were analyzed using the t test, the F test, and the χ 2 test, with subsequent Bonferroni corrections for multiple comparisons. Latent Profile Analysis (LPA) was employed to identify subgroups of individuals based on their dietary patterns, and Two-Step Clustering (TSC) was used to categorize individuals according to their disease conditions. Finally, robust standard error Logistic regression models were applied to determine the influence of dietary patterns on chronic metabolic diseases (CMM). All tests were conducted using a two-sided significance level of 95%. Results Demographic characteristics A total of 4963 participants were included in this study, with 2658 females (53.6%) and 2305 males (46.4%). The participants' ages ranged from 18 to 94 years, with a mean age of 51 ± 15 years. Among all participants, 69.6% were from rural areas, 77.1% had an education level below junior high school, and 5.7% were never married. The average urbanization index of the participants' communities was 67.57 ± 19.01. The detailed information is presented in Table 5 . Disease Status and CMM Combination Types Two-Step Clustering (TSC) analysis was applied to categorize the disease status of the participants. Table 1 lists the fitting parameters of 15 different models (Models 1 to 15). Both Model 2 and Model 7 exhibited relatively high target distance measurement ratios (TDMR). However, Model 7 had a lower Bayesian Information Criterion (BIC) value compared to Model 2, indicating that Model 7 provided a superior classification ratio compared to the other models. Disease Prevalence Among Participants The prevalence of hyperuricemia, dyslipidemia, diabetes, renal dysfunction, hypertension, and stroke among the participants is presented in Table 2 . Specifically, 16.7% belonged to Class I, 9.9% to Class II, 15.0% to Class III, 8.8% to Class IV, 12.7% to Class V, 19.7% to Class VI, and 17.5% to Class VII. Participants with Class I and II disease patterns were characterized by having all six diseases to varying degrees. The multiple comparisons revealed that those with Class I were most likely to have stroke, followed by a relatively high probability of hyperuricemia, dyslipidemia, and renal dysfunction, but a lower likelihood of diabetes. In contrast, those with Class II showed a higher probability of dyslipidemia, diabetes, and renal dysfunction, but a lower likelihood of stroke. They also had a relatively high probability of hyperuricemia, dyslipidemia, and renal dysfunction, but a lower probability of hyperuricemia, hypertension, and stroke. Participants in Class III presented a pattern of having four diseases, with the highest likelihood of hypertension and moderate probabilities of dyslipidemia, renal dysfunction, and stroke. Class IV participants had three diseases, with the highest probabilities of hyperuricemia, diabetes, and renal dysfunction. Participants in Class V exhibited a two-disease pattern, with the highest likelihood of dyslipidemia and a higher probability of renal dysfunction. Both Classes VI and VII were characterized by having only one disease, with Class VI showing a relatively higher probability of dyslipidemia and Class VII a relatively higher probability of stroke ( P < 0.05). Based on these characteristics, Class I was named Mixed-Low Diabetes (Mix-LDia), Class II was named Mixed-High Diabetes (Mix-HDia), Class III was named Four-Type Hypertension (Four-HHyp), Class IV was named Three-Type Hyperuricemia, Diabetes, and Renal Dysfunction (Three-HestHDRD), Class V was named Two-Type Hyperlipidemia and Renal Dysfunction (Two-HDRD), Class VI was named One-Type Hyperlipidemia (One-HDys), and Class VII was named One-Type High Stroke (One-HApo). Table 1 Fit indices for the class 1 through 10 Models Model BCI @ ΔBCI @ ΔBCI ratio @ TDMR @ 1 26340.205 2 20381.437 -5958.767 1 1.505 3 16438.648 -3942.789 0.662 1.541 4 13897.416 -2541.232 0.426 1.026 5 11422.636 -2474.78 0.415 1.160 6 9295.428 -2127.209 0.357 1.152 7 7455.201 -1840.227 0.309 1.883 8 6502.069 -953.132 0.16 1.283 9 5770.153 -731.916 0.123 1.231 10 5184.983 -585.17 0.098 1.004 11 4602.436 -582.547 0.098 1.094 12 4074.075 -528.361 0.089 1.126 13 3610.409 -463.666 0.078 1.141 14 3210.486 -399.923 0.067 1.247 15 2899.954 -310.532 0.052 1.400 @ BIC, Bayesian information criterion. ΔBCI, the change of Bayesian information criterion. ΔBCI ratio, the change ratio of Bayesian information criterion. TDMR, Target distance measurement ratio Between clusters between neighbors clusters. Table 2 Disease in the classes of CMM status Had Disease N% Class I Mix-LDia Class II Mix-HDia Class III Four-HHyp Class IV Three-HestHDRD Class V Two-HDRD Class VI One-HApo Class VII One-HDys All χ 2 P Hyperuricemia 781 121 0 902 0 0 0 1804 404.647 <0.001 Dyslipidemia 582 340 193 0 742 629 0 2486 3423.523 <0.001 Diabetes 2 489 0 491 0 0 0 982 4940.619 <0.001 Renal dysfunction 719 349 264 869 742 0 0 2943 3720.618 <0.001 Hypertension 121 113 435 0 0 0 0 669 3331.752 <0.001 Apoplexy 65 6 2 0 0 0 13 86 226.531 <0.001 Dietary Patterns and Food Intake Potential profile analysis was utilized in this study to classify the dietary patterns of participants, and the fitting parameters are presented in Table 3 . As shown in Table 3 , when comparing the Lo-Mendell-Rubin Likelihood Ratio Test (LMRT) and Bootstrap Likelihood Ratio Test (BLRT), the P -values for the model with three profiles were both less than 0.05. In the model with three profiles, the entropy value was 0.974, which is greater than 0.800, indicating that the classification accuracy was above 90.0% [ 26 ]. Therefore, the model with three profiles in this study was superior to others, suggesting that participants exhibited three distinct dietary patterns. The intake of various food types among participants with different dietary patterns is detailed in Table 4 . As can be seen from Table 4 , there were significant differences ( P < 0.05) in the intake of eight types of food among the different dietary patterns. Multiple comparisons revealed that, compared to participants with Pattern 2 and Pattern 3, participants with Pattern 1 had the lowest intake of cereals and cereal products, while their intake of fruits and fruit products, milk and milk products, as well as eggs and egg products, was the highest ( P < 0.001). In contrast, participants with Pattern 2 had the lowest intake of poultry and poultry products, fruits and fruit products, as well as aquatic products, compared to those with Pattern 1 and Pattern 3 ( P < 0.05). Additionally, compared to participants with Pattern 1 and Pattern 2, those with Pattern 3 had the highest intake of vegetables and vegetable products, as well as poultry and poultry products ( P < 0.01). Based on the characteristics of these three dietary pattern categories, Pattern 1 was named Low Cereal High Fruit Milk and Egg Diet (LCHFM), Pattern 2 was named Low Poultry Fruit and Fishery Diet (LPFF), and Pattern 3 was named High Vegetable and Poultry Diet (HVP). Table 3 Fit indices for the profile 2 through 8 models Profiles LogL AIC BIC aBIC Entropy LMRT BLRT 2 -279373.271 558808.541 559010.344 558911.837 0.999 0.476 <0.001 3 -278082.872 556249.744 556523.154 556389.692 0.974 0.024 <0.001 4 -277106.320 554318.639 554663.657 554495.241 0.966 0.080 <0.001 5 -276295.119 552718.239 553134.864 552931.494 0.971 0.204 <0.001 ^ LogL, Log likelihood. AIC, Akaike information criterion. BIC, Bayesian information criterion. aBIC, sample size-adjusted Bayesian information criterion. BLRT, Bootstrap likelihood ratio test. LMRT, Lo-Mendell-Rubin likelihood ratio test. LogL stands for log likelihood. AIC is a metric used to compare the relative quality of statistical models, balancing model fit and complexity to select an effective model that explains data without overfitting. BIC selects models from a finite set by penalizing complexity and emphasizing model fit. BLRT compares nested models in statistical modeling. LMRT compares different parameterizations in mixture models, aiding researchers in selecting the best model for their data. Table 4 Daily dietary food intakes in the latent profiles of dietary patterns Food intakes (mg/d) All Latent Dietary Patterns F P Mean ( SD ) Pattern 1 LCHFM Mean ( SD ) Pattern 2 LPFF Mean ( SD ) Pattern 3 HVP Mean ( SD ) Cereals and cereal products 386.852 (170.066) 297.048 (145.840) 390.395 (170.088) 409.348 (167.346) 40.742 <0.001 Tubers starches and products 30.584 (54.375) 29.804 (46.077) 31.169 (54.616) 23.833 (56.682) 2.963 0.052 Dried legumes and legume products 58.254 (75.964) 58.814 (64.550) 58.475 (76.500) 58.801 (77.158) 0.470 0.625 Vegetables and vegetable products 323.561 (172.517) 299.182 (183.426) 322.651 (171.879) 353.090 (168.753) 7.766 <0.001 Fruit and fruit products 51.786 (104.621) 133.305 (128.372) 46.093(101.158) 62.377 (101.776) 89.970 <0.001 Meat and meat products 75.082 (70.789) 80.869 (70.677) 73.271 (70.490) 93.449 (71.953) 14.078 <0.001 Poultry and poultry products 15.442 (34.850) 21.342 (38.343) 7.120 (16.897) 115.210 (42.585) 4156.778 <0.001 Milk and milk products 13.630 (53.208) 217.787 (84.131) 2.274 (12.888) 3.834 (17.666) 10628.239 <0.001 Eggs and egg products 29.716 (37.124) 47.460 (38.866) 28.8719 (37.014) 27.082 (33.756) 31.980 <0.001 Fish shellfish and mollusc 34.696 (60.795) 52.528 (71.109) 32.165 (58.878) 53.107 (70.194) 31.251 <0.001 The Impact of Dietary Patterns on CMM The distribution of CMM among participants is detailed in Table 5 . As can be seen from Table 5 , participants with Class III (Four-HHyp) CMM were slightly older than those in other groups, while participants with Class VI (One-HApo) and VII (One-HApo) CMM were slightly younger than those in other groups ( P < 0.05). Participants with Class II (Mix-HDia) and III (Four-HHyp) CMM resided in areas with higher urbanization indices. Additionally, there were associations between gender, marital status, education level, residence area, smoking status, and the categories of multiple dietary patterns, with χ² values of 559.905, 95.503, 19.104, 23.942, 229.620, and 34.967, respectively ( P < 0.01). The impact of Dietary Patterns on CMM is detailed in Table 6 . Due to the low incidence of hyperuricemia, diabetes, renal dysfunction, hypertension, and stroke in the Class VI (One-HDys, Type One Hyperlipidemia) group, the Class VI group was chosen as the reference group for comparative analysis. As shown in Table 6 , compared to participants with the HVP (High Vegetable and Poultry) dietary pattern, those with the CHFM (Low Cereal High Fruit Milk and Egg) dietary pattern were more likely to have Class V (Two-HDRD, Type Two Hyperlipidemia and Renal Dysfunction) CMM ( OR = 2.0011, 95% CI : 1.011–3.960, P ≤ 0.05). The OR values were statistically significant in both the model controlling only for demographic characteristics and the model controlling for both demographic and lifestyle characteristics ( P < 0.05), indicating the robustness of the model. Table 5 Characteristics of the subjects in different CMM status CMM status Mean ( SD ) OR N ( N% ) F/χ 2 P Class I Mix-LDia Class II Mix-HDia Class III Four-HHyp Class IV Three-HestHDRD Class V Two-HDRD Class VI One-HDys Class VII One-HApo All Age ^ 50.605 (15.029) 54.348 (14.567) 61.687 (11.439) 52.016 (14.516) 51.953 (13.919) 45.677 (15.209) 45.495 (14.699) 50.772 (15.106) 83.357 <0.001 Gender @ Females 592 (71.3%) 261 (53.4%) 212 (48.7%) 596 (86.6%) 496 (66.8%) 183 (29.1%) 318 (32.8%) 2658 (53.6%) 559.905 <0.001 Marital status @ Never married 41 (4.9%) 20 (4.1%) 1 (0.2%) 35 (4.4) 25 (3.4%) 66 (10.5%) 94 (9.7%) 282 (5.7%) 95.503 <0.001 Education level @ Below middle school 638 (76.9%) 385 (79.1%) 343 (78.9%) 695 (80.0%) 586 (79.0%) 473 (75.2%) 705 (72.8%) 3827 (77.1%) 19.104 0.004 Living areas @ Rural 579 (69.8%) 323 (66.1%) 265 (60.9%) 625 (71.9%) 522 (70.4%) 441 (70.1%) 699 (72.1%) 3454 (69.6) 23.942 0.001 Urbanindex ^ 67.827 (19.507) 71.440 (18.648) 71.982 (19.333) 65.394 (18.766) 67.105 (18.579) 67.568 (18.449) 65.710 (18.921) 67.567 (19.007) 10.961 <0.001 PA ^ 118.910 (678.353) 132.500 (681.375) 127.753 (732.710) 93.924 (641.655) 71.045 (565.911) 84.837 (551.904) 75.591 (500.051) 96.717 (614.144) 1.092 0.364 Smoking status Never smoke 660 (79.5%) 335 (68.5%) 286 (65.7%) 674 (77.6%) 565 (76.1%) 354 (56.3%) 531 (54.8%) 3405 (68.6%) 229.620 <0.001 Dietary Pattern @ Pattern 1 LCHFM Pattern 2 LPFF Pattern 3 HVP 44 (5.3%) 730 (728.5) 56 (6.7%) 35 (7.2%) 419(85.7%) 35 (7.2%) 38 (8.7%) 365(83.9%) 32 (7.4%) 41 (4.7%) 782(90.0%) 46 (5.3%) 41 (5.5%) 660(88.9%) 41 (5.5%) 22 (35%) 551(87.6%) 56 (8.9) 38 (3.9%) 849(87.6%) 82 (8.5%) 259 (5.2%) 4356(87.8%) 348 (7.0%) 34.967 <0.001 ^ The description of these variables were Mean ( SD ). @ The description of these variables were N ( N% ). Table 6 Association between dietary patterns and CMM status Discussion Characteristics of Different Cardiovascular Metabolic Disease (CMM) Combination Types in Various Populations In this study, five combined types of cardiovascular metabolic diseases (CMM, Class I to Class V) and two types of single cardiovascular or metabolic diseases (Class VI and Class VII) were identified through clustering analysis. Our findings indicate that the age of individuals with single diseases was significantly lower than those with CMM. This observation aligns with the study by Cheng et al. (2022), which reported the lowest CMM prevalence of 2.3% in the 20–39 age group and the highest of 42.9% in individuals aged ≥ 80 years [ 27 ]. Similarly, Otieno et al. (2023) found that compared to the 15–34 age group, the prevalence of CMM was higher in the 35–54 age group (PR = 3.9, 95%CI: 3.2–4.8) and the 55–69 age group (PR = 3.9, 95%CI: 3.2–4.8) [ 28 ]. Our study further reveals that participants with a higher probability of combined diabetes tend to be older than those with a lower probability. Specifically, participants with Class II (a combination of six diseases with a high probability of diabetes) were older than those with Class IV (dyslipidemia + diabetes + renal dysfunction), Class V (dyslipidemia + renal dysfunction), and Class I (a combination of six diseases with a low probability of diabetes). This suggests that dyslipidemia and renal dysfunction tend to occur earlier in middle-aged individuals, while the combination of dyslipidemia, hypertension, and diabetes emerges with advancing age. This finding is consistent with the results of a 5-year cohort study by Zhang et al. (2019), which showed that the prevalence of CMM with three-disease combinations increased nine times compared to baseline [ 29 ]. The potential reasons for this phenomenon may include the following. Dyslipidemia can lead to lipid deposition in islet cells, affecting glucose oxidation and causing β-cell apoptosis, resulting in insulin resistance. Additionally, dyslipidemia reduces glucose utilization and increases hepatic glycogenolysis, further exacerbating insulin resistance [ 30 , 31 ]. Insulin resistance, in turn, triggers inflammation, oxidative stress, insulin receptor mutations, endoplasmic reticulum stress, and mitochondrial dysfunction, impairing arterial endothelial function and leading to the development of hypertension and diabetes [ 32 , 33 ]. Another possible explanation is that dyslipidemia can lead to lipid deposition on blood vessel walls, causing inflammation and damaging pancreatic β-cells, interfering with glucose and lipid metabolism and leading to disorders such as insulin resistance and hyperglycemia [ 34 , 35 ]. As plaques increase, the vessel diameter narrows, potentially restricting blood flow. This sequence of events explains why CMM combinations with dyslipidemia and renal dysfunction tend to occur at younger ages, while those with hypertension and diabetes occur later. Therefore, controlling dyslipidemia in middle-aged individuals plays a crucial role in managing CMM. The Relationship between Dietary Patterns and CMM The current study identified three dietary patterns, Pattern I to III, through latent profile analysis (LPA). Specifically, Pattern I represents a low-cereal, high-fruit, dairy, and egg diet (LCHFM), Pattern II is a low-poultry, fruit, and fishery diet (LPFF), and Pattern III signifies a high-vegetable, poultry diet (HVP). Our findings indicate that individuals adhering to a low-grain, high-fruit, dairy, and egg diet (LCHFM) had a higher risk of developing Class V CMM (dyslipidemia + renal dysfunction) compared to those with Class VI CMM (solely dyslipidemia). This suggests that the LCHFM diet may contribute to the co-occurrence of dyslipidemia and renal dysfunction. In dietary interventions for individuals with impaired renal function, it is essential to ensure adequate intake of vegetables and dairy products. Therefore, the association between renal dysfunction and a diet low in grains and high in fruits could be attributed to several factors. Grains are a significant source of glucose, and lower glucose levels can inhibit the reabsorption of serum uric acid in the proximal tubule, leading to a reduction in serum uric acid levels and thus reducing the metabolic burden on the kidneys, ultimately protecting renal function [ 36 – 41 ]. Conversely, excessive fruit intake can lead to the accumulation of uric acid precursors, increasing glyconeogenesis and the pentose phosphate pathway, ultimately resulting in elevated serum uric acid levels and renal dysfunction. Dietary recommendations for individuals with dyslipidemia often involve reducing macronutrient intake, particularly carbohydrates [ 42 ]. As grains are a primary source of carbohydrates, reducing their consumption is typically advised to manage dyslipidemia. However, our findings indicate that both excessively high and low grain intake may be detrimental. Therefore, it is crucial to maintain an appropriate level of grain consumption for individuals with dyslipidemia, as both extremes may hinder disease recovery. To reduce the incidence of dyslipidemia with renal dysfunction, a balanced intake of grains, fruits, and their derivatives is essential. This study provides valuable insights into the relationship between dietary patterns and CMM, particularly in the context of dyslipidemia and renal dysfunction, and highlights the importance of tailored dietary advice for individuals with these comorbidities. The current study has several limitations that need to be acknowledged. Firstly, the dietary data was collected through a three-day consecutive 24-hour dietary recall method, which may introduce measurement system errors compared to non-consecutive 24-hour dietary recall or food diary methods. Secondly, the analysis in this study is based on cross-sectional data, which cannot be used for causal inference. Future research should include experimental studies to draw causal conclusions and establish definitive formulations. Additionally, this study did not consider other potential factors that may influence CMM, such as genetic factors and medication use, which may have had an impact on the results. Therefore, in future studies, it is crucial to consider a wider range of potential influencing factors to arrive at more reliable conclusions. Conclusion The present study identified five distinct CMM combinations: Class I, a six-disease mixture with low diabetes risk (Mix-LDia); Class II, a six-disease mixture with high diabetes risk (Mix-HDia); Class III, a four-disease combination characterized by hypertension (Four-HHyp); Class IV, a three-disease combination encompassing hyperuricemia, diabetes, and renal dysfunction (Three-HestHDRD); and Class V, a two-disease mix of hyperlipidemia and renal dysfunction (Two-HDRD). The average age of patients with these five CMM combinations decreases in the following order: Class III, Class II, Class IV, Class V, and Class I. Three dietary patterns were also discerned: Pattern I, a low-cereal, high-fruit, dairy, and egg diet (LCHFM); Pattern II, a low-poultry, fruit, and fishery diet (LPFF); and Pattern III, a high-vegetable, poultry diet (HVP). Notably, individuals adhering to the low-grain, high-fruit, dairy, and egg diet (LCHFM) pattern were found to have a higher risk of developing hyperlipidemia combined with renal dysfunction compared to those with isolated hyperlipidemia. Future studies are warranted to explore the causal relationship between dietary patterns and the progression of CMM. Declarations Acknowledgements This research uses data from the China Health and Nutrition Survey (CHNS). We thank the National Institute of Nutrition and Food Safety, China Center for Disease Control and Prevention, Carolina Population Center, the University of North Carolina at Chapel Hill, the NIH (R01-HD30880, DK056350, and R01-HD38700) and the Fogarty International Center, NIH for financial support for the CHNS data collection and analysis files from 1989 to 2006 and both parties plus the China-Japan Friendship Hospital, Ministry of Health for support for CHNS 2009 and future surveys. Author Contributions Conceptualization, Danhui Mao and Xiaojun Ren; methodology, Danhui Mao and Shixun Wang; software, Danhui Mao, Mohan Zhang and Mingyan Ma; writing—original draft preparation, Xiaojun Ren, Mohan Zhang and Mingyan Ma; writing—review and editing, Gongkui Li and Yajing Li; visualization, Shixun Wang; supervision, Xiaojun Ren; re-editing, Xiaojun Ren. All authors have read and agreed to the published version of the manuscript. Funding This work was supported by the Postdoctoral Research Fund of Shanxi Bethune Hospital (Shanxi Academy of Medical Sciences) and the Doctorial Start-up Fund of Shanxi Medical University (XD2139). Data Availability Data are available from https://www.cpc.unc.edu/projects/china Ethics approval and consent to participate The survey was approved by the ethics committee of the University of North Carolina at Chapel Hill and the National Institute for Nutrition and Health, Chinese Center for Disease Control and Prevention. Consent for publication Not applicable. Competing interests The author(s) declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article. References Han Y, Hu Y, Yu C, et al. Lifestyle, cardiometabolic disease, and multimorbidity in a prospective Chinese study. Eur Heart J. 2021;42(34):3374–84. Busija L, Lim K, Szoeke C, et al. Do replicable profiles of multimorbidity exist? Systematic review and synthesis. Eur J Epidemiol. 2019;34:1025–53. Chudasama YV, Khunti KK, Zaccardi F, et al. Physical activity, multimorbidity, and life expectancy: a UK Biobank longitudinalstudy. BMCMed. 2019;17:108. Cheng X, Ouyang F, Ma T, et al. Association of Healthy Lifestyle and Life Expectancy in Patients With Cardiometabolic Multimorbidity: A Prospective Cohort Study of UK Biobank. Front Cardiovasc Med. 2022;9:830319. Dove A, Guo J, Marseglia A, et al. Cardiometabolic multimorbidity accelerates cognitive decline and dementia progression. Alzheimers Dement. 2023;19:821–30. Fan JN, Sun ZJ, Yu CQ, et al. Multimorbidity patterns and association with mortality in 0.5 million Chinese adults. Chin Med J (Engl). 2022;135:648–57. Zhang X, Padhi A, Wei T, et al. Community prevalence and dyad disease pattern of multimorbidity in China and India: a systematic review. BMJ Glob Health. 2022;7(9):e008880. Calderón-Larrañaga A, Vetrano DL, Ferrucci L, et al. Multimorbidity and functional impairment-bidirectional interplay, synergistic effects and common pathways. J Intern Med. 2019;285(3):255–71. Jia M, Peng J, Liu X, et al. Risk factors for cardiometabolic multimorbidity: a meta-analysis. Chin J Prev Med. 2023;35(9):790–5. Zhao D. Key roles of five lifestyle risk factors in the harmful transition froma healthy condition tomultimorbidity in the Chinese population. Eur Heart J. 2021;42:3385–7. Johns DJ, Lindroos AK, Jebb SA, et al. 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The effect of dietary approaches to stop hypertension (DASH) diet on weight and body composition in adults, a systematic review and meta-analysis of randomized controlled clinical trials. Obes Rev. 2016;1:442–54. Gardner CD, Kiazand A, Alhassan S, et al. Comparison of the Atkins, Zone, Ornish,and LEARN Diets for Change in Weight and Related Risk Factors Among Overweight Premenopausal Women. JAMA. 2007;29:969–79. Hu Y, Ding R. Expert Consensus on Diagnosis and Treatment of Asymptomatic Hyperuricemia Complicated with Cardiovascular Disease in China. Chin Gen Pract. 2010;13(11):1145–9. Joint Committee for the Revision of the Guidelines for Prevention and Treatment of Dyslipidemia in Chinese Adults. Guidelines for Prevention and Treatment of Dyslipidemia in Chinese Adults (2016 Revision). Chin J Cardiol. 2016;44(10):833–53. Chinese Diabetes Society of the Chinese Medical Association. Guidelines for the Prevention and Control of Type 2 Diabetes in China (2017 Edition). Chin J Practical Intern Med. 2018;38(4):53. National Kidney Foundation. K/DOQI clinical practice guidelines for chronic kidney disease: evaluation, classification, and stratification. AM J Kidney Dis. 2002;39(2 Suppl 1):S1–26. Revision Committee of the Chinese Guidelines for Hypertension Prevention and Control. Chinese Guidelines for Hypertension Prevention and Control (2018 Revision). Prevention and Treatment of Cardio-Cerebrovascular Diseases. 2019; 19(1): 44. Bassett DR. International physical activity questionnaire: 12-country reliability and validity. Med Sci Sport Exe. 2003;35:1381–95. Ainsworth BE, Haskell WL, Whitt MC, et al. Compendium of physical activities: an update of activity codes and MET intensities. Med Sci Sports Exerc. 2000;32:S498–504. Zuo H, Shi Z, Yuan B, et al. Interaction between physical activity and sleep duration in relation to insulin resistance among non-diabetic Chinese adults. BMC Public Health. 2012;12:247–247. Celeus G, Soromenho G. An entropy criterion for assessing the number of clusters in a mixture model. J Classif. 1996;13:195–212. Cheng X, Ma T, Ouyang F, et al. Trends in the Prevalence of Cardiometabolic Multimorbidity in the United States, 1999–2018. Int J Environ Res Public Health. 2022;19(8):4726. Otieno P, Asiki G, Wekesah F, et al. Multimorbidity of cardiometabolic diseases: a cross-sectional study of patterns, clusters and associated risk factors in sub-Saharan Africa. BMJ Open. 2023;13(2):e064275. Zhang D, Tang X, Shen P, et al. Multimorbidity of cardiometabolic diseases: prevalence and risk for mortality from one million Chinese adults in a longitudinal cohort study. BMJ Open. 2019;9:e024476. Newsholme P, Cruzat V, Arfuso F, et al. Nutrient regulation of insulin secretion and action. J Endocrinol. 2014;221:R105–20. Nolan CJ, Madiraju MS, Delghingaro-Augusto V, et al. Fatty acid signaling in the beta-cell and insulin secretion. Diabetes. 2006;55(Suppl 2):S16–23. Yaribeygi H, Farrokhi FR, Butler AE, et al. Insulin resistance: Review of the underlying molecular mechanisms. J Cell Physiol. 2019;234(6):8152–61. Sara JD, Taher R, Kolluri N, et al. Coronary microvascular dysfunction is associated with poor glycemic control amongst female diabetics with chest pain and non-obstructive coronary artery disease. Cardiovasc Diabetol. 2019;18(1):22. Vekic J, Zeljkovic A, Stefanovic A, et al. Obesity and dyslipidemia. Metabolism. 2019;92:71–81. Berbudi A, Rahmadika N, Tjahjadi AI, et al. Type 2 Diabetes and its Impact on the Immune System. Curr Diabetes Rev. 2020;16(5):442–9. Ebrahimpour-koujana S, Saneeib P, Larijanic B, et al. Consumption of sugar sweetened beverages and dietary fructose in relation to risk of gout and hyperuricemia: a systematic review and meta-analysis. Crit Rev Food Sci Nutr. 2020;60(1):1–10. Choi HK, Willett W, Curhan G. Fructose-rich beverages and risk of gout in women. JAMA. 2010;304(20):2270–8. Bae JBY, Chun PS, Park BY, et al. Higher consumption of sugar-sweetened soft drinks increases the risk of hyperuricemia in Korean population: the Korean multi-Rural Communities cohort study. Semin Arthritis Rheum. 2014;43(5):654–61. Brecher AS, Lehti MD. A hypothesis linking hypoglycemia, hyperuricemia, lactic acidemia, and reduced gluconeogenesis in alcoholics to inactivation of glucose-6-phosphatase activity by acetaldehyde. Alcohol. 1996;13(6):553–7. Choi HK, Curhan G. Soft drinks, fructose consumption, and the risk of gout in men: Prospective cohort study. BMJ. 2008;336(7639):309–12. Balakumar M, Raji L, Prabhu D, et al. High-fructose diet is as detrimental as high-fat diet in the induction of insulin resistance and diabetes mediated by hepatic/pancreatic endoplasmic reticulum (ER) stress. Mol Cell Biochem. 2016;423(1–2):93–104. Houttu V, Grefhorst A, Cohn DM, et al. Severe Dyslipidemia Mimicking Familial Hypercholesterolemia Induced by High-Fat, Low-Carbohydrate Diets: A Critical Review. Nutrients. 2023;15(4):962. Tables Table 6 is available in the Supplementary Files section. Additional Declarations No competing interests reported. Supplementary Files Table6.docx 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. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-4451883","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":305304145,"identity":"e3fb2811-520d-4686-9eac-867cb10999ae","order_by":0,"name":"Danhui Mao","email":"","orcid":"","institution":"Third Hospital of Shanxi Medical University, Shanxi Academy of Medical Sciences, Tongji Shanxi Hospital","correspondingAuthor":false,"prefix":"","firstName":"Danhui","middleName":"","lastName":"Mao","suffix":""},{"id":305304146,"identity":"993a7e80-a7c8-4cac-a603-2a896716ff7f","order_by":1,"name":"Gongkui Li","email":"","orcid":"","institution":"Third Hospital of Shanxi Medical University, Shanxi Academy of Medical Sciences, Tongji Shanxi Hospital","correspondingAuthor":false,"prefix":"","firstName":"Gongkui","middleName":"","lastName":"Li","suffix":""},{"id":305304147,"identity":"81ff73a4-83df-4c74-96a0-d416303a09c0","order_by":2,"name":"Yajing Li","email":"","orcid":"","institution":"Third Hospital of Shanxi Medical University, Shanxi Academy of Medical Sciences, Tongji Shanxi Hospital","correspondingAuthor":false,"prefix":"","firstName":"Yajing","middleName":"","lastName":"Li","suffix":""},{"id":305304148,"identity":"c336525a-8f23-44b3-b95c-18298e6d9341","order_by":3,"name":"Shixun Wang","email":"","orcid":"","institution":"Shanxi Medical University","correspondingAuthor":false,"prefix":"","firstName":"Shixun","middleName":"","lastName":"Wang","suffix":""},{"id":305304149,"identity":"b1be3f57-1a98-42b3-895a-aa8ba5a0ace5","order_by":4,"name":"Mohan Zhang","email":"","orcid":"","institution":"Shanxi Medical University","correspondingAuthor":false,"prefix":"","firstName":"Mohan","middleName":"","lastName":"Zhang","suffix":""},{"id":305304150,"identity":"1cffc205-a4f8-4eba-9bb9-76cffef5fd12","order_by":5,"name":"Mingyan Ma","email":"","orcid":"","institution":"Shanxi Medical University","correspondingAuthor":false,"prefix":"","firstName":"Mingyan","middleName":"","lastName":"Ma","suffix":""},{"id":305304152,"identity":"fe53b88a-bf83-4caf-837a-e5ac1e492a43","order_by":6,"name":"Xiaojun Ren","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA/ElEQVRIie3PsUrDQBzH8X84SJZ/6/o/CPgKVwrqEFof5UIgLnmADA6JB9dF3cX3cE4o1OWo60EcIl0d2kUcRJSC4NIko+B9pht+X/4cgOP8VRKAAXjl5iOP8CgoBidMCTRpyK+rwbcCTSO9jIQ9796dLm5W1F5GgWhKTWSeECx42112OAnNOhFylTLxXKszkTfo3ReM3z0cToiySSv9JRM2Lq00DbKw8tmoKzl+FZX83CcF1XqNPsmehHDSxnqfXPFSV4i9CWZTEd+mjNtYTcEkSFir7r8E5oS/v0XJ2F68bCCfzeePqt7uOpIfya+3V/Tvv80GrRzHcf6nL6iQUgoV6dikAAAAAElFTkSuQmCC","orcid":"","institution":"Third Hospital of Shanxi Medical University, Shanxi Academy of Medical Sciences, Tongji Shanxi Hospital","correspondingAuthor":true,"prefix":"","firstName":"Xiaojun","middleName":"","lastName":"Ren","suffix":""}],"badges":[],"createdAt":"2024-05-21 03:21:20","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-4451883/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-4451883/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":62061816,"identity":"cb69e3cf-9745-4b92-9331-2dc9ceba13d5","added_by":"auto","created_at":"2024-08-08 23:01:35","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":848351,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-4451883/v1/c72e64c1-6a53-462c-913a-cab00f93ffaf.pdf"},{"id":57589920,"identity":"a47a37de-f317-4f9f-9c12-caf78daa874c","added_by":"auto","created_at":"2024-06-03 04:37:53","extension":"docx","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":17924,"visible":true,"origin":"","legend":"","description":"","filename":"Table6.docx","url":"https://assets-eu.researchsquare.com/files/rs-4451883/v1/34bc7e8351b79eaff785d80b.docx"}],"financialInterests":"No competing interests reported.","formattedTitle":"Study on the Impact of Dietary Patterns on Cardiovascular Metabolic Comorbidities among Adults","fulltext":[{"header":"Background","content":"\u003cp\u003eCardiovascular metabolic comorbidities (CMM) refers to the coexistence of two or more cardiovascular diseases or metabolic disorders in an individual, such as hypertension, diabetes, dyslipidemia, and stroke [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e]. It is one of the most stable patterns of multimorbidity [\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e]. In a study by Chudasama et al. (2019), approximately 64%, 59%, 57%, and 54% of patients with diabetes, angina pectoris, stroke, and myocardial infarction were concurrently diagnosed with hypertension [\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e]. A 10-year follow-up study conducted by Zhao et al. in 2021 on a Chinese population of 461,047 individuals revealed that 18.7% of healthy individuals experienced their first onset of heart disease, stroke, and diabetes. Among these newly diagnosed patients, 16.2% further progressed to CMM, and 22.5% of these patients died [\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e]. An epidemiological survey in 2022 showed that the incidence of CMM was 14.4%. CMM can significantly impact disease prognosis and patients' quality of life, with studies indicating that the mortality risk among CMM patients is twice that of patients without multimorbidity [\u003cspan additionalcitationids=\"CR6\" citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e]. As the population ages, the long duration and complex etiology of CMM pose greater risks of disability, death, as well as physical, psychological, and economic burdens on patients [\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e, \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e]. Therefore, preventing and controlling CMM is a critical task in primary healthcare services.\u003c/p\u003e \u003cp\u003eAccording to statistics, CMM is closely associated with demographic characteristics. Research indicates that female gender, middle and old age, urban residency, and higher education levels are demographic risk factors for CMM [\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e]. Additionally, similar to single chronic diseases, studies have shown that smoking, excessive alcohol consumption, unhealthy dietary patterns, and physical inactivity are lifestyle risk factors for CMM [\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e, \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e]. Diet, as one of the modifiable lifestyle factors, can be specifically adjusted to reduce disease risk. In terms of cardiovascular diseases, reducing saturated fat intake and increasing dietary fiber intake can lower the risk of developing cardiovascular diseases. High-energy-density diets and obesity increase the risk of cardiovascular diseases. Appropriate intake of dietary fiber aids in insulin secretion, glucose control, blood lipid levels, and blood pressure maintenance at healthy levels. Furthermore, dietary patterns with high-energy density, high saturated fat, and low dietary fiber are associated with increased cardiometabolic risks in severely obese individuals [\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e]. Regarding metabolic diseases, reducing macronutrient intake can reduce the risk of developing metabolic diseases. High-energy-density diets and obesity also increase the risk of metabolic diseases. Appropriate intake of dietary fiber, vitamins, and minerals benefits insulin secretion, glucose control, and blood lipid levels, maintaining them at healthy levels and reducing the risk of metabolic diseases [\u003cspan additionalcitationids=\"CR13\" citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e]. Moreover, previous studies have found that certain dietary patterns can simultaneously influence the cardiovascular or metabolic systems, such as the Mediterranean diet, the Dietary Approaches to Stop Hypertension (DASH) diet, and the Atkins diet, which can affect body weight and serum uric acid levels [\u003cspan additionalcitationids=\"CR16\" citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eCMM exhibits a complex disease mechanism, with diseases being highly interrelated. To prevent and control CMM, it is essential to fully consider the correlations between disease combinations and explore preventive and therapeutic measures from a holistic perspective. As a qualitative approach for CMM, the combination types of CMM can represent the complex relationships between diseases in a holistic manner. Meanwhile, as modifiable factors in preventing and controlling multiple cardiovascular diseases or metabolic disorders, foods or nutrients coordinate their actions in a dynamic and complex system. Therefore, evaluating diet also requires a comprehensive consideration of the intricate relationships between foods and nutrients, analyzed from a holistic angle. Dietary patterns, as a qualitative approach for dietary habits, can represent the complex relationships between foods or nutrients in a holistic manner. Thus, understanding the impact of dietary patterns on CMM combination types may be of significant importance for the prevention and control of CMM. Currently, studies on the effects of dietary patterns on cardiovascular or metabolic diseases primarily focus on the relationships between single diseases and single dietary patterns, or multiple diseases and a single dietary pattern. However, there is a lack of exploration into the relationships between various CMM combination types and multiple dietary patterns within a population. Therefore, this study aims to determine the impact of major dietary patterns on primary CMM combination types, based on an understanding of the primary CMM combination types and major dietary patterns within the population.\u003c/p\u003e"},{"header":"Materials and Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003eStudy population\u003c/h2\u003e \u003cp\u003eThe participants in this study were derived from the sixth round of the China Health and Nutrition Survey (CHNS). For a detailed description of CHNS, please refer to \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.cpc.unc.edu/projects/china\u003c/span\u003e\u003cspan address=\"https://www.cpc.unc.edu/projects/china\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e. We excluded individuals with incomplete data, including socio-demographic characteristics, dietary and lifestyle habits, and disease characteristics. Therefore, the study encompassed a total of 4963 subjects (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). The survey was approved by the ethics committee of the University of North Carolina at Chapel Hill and the National Institute for Nutrition and Health, Chinese Center for Disease Control and Prevention.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003eDietary Assessment\u003c/h2\u003e \u003cp\u003eFor this study, ten types of foods were selected based on their daily average intake exceeding 10g and their common occurrence in research on adult dietary patterns, see Table\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e for details.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec5\" class=\"Section2\"\u003e \u003ch2\u003eDisease Assessment\u003c/h2\u003e \u003cp\u003eIn this study, we assessed the presence of hyperuricemia, dyslipidemia, diabetes, chronic kidney disease, and hypertension among participants. Hyperuricemia was defined as serum uric acid levels above 420 \u0026micro;mol/L (7 mg/dl) for males and above 357 \u0026micro;mol/L (6 mg/dl) for females [\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e]. Dyslipidemia was defined as having at least one of the following conditions: (1) hypercholesterolemia (TC\u0026thinsp;\u0026ge;\u0026thinsp;5.18 mmol/L); (2) hypertriglyceridemia (TG\u0026thinsp;\u0026ge;\u0026thinsp;1.70 mmol/L); (3) low-density lipoprotein cholesterol (LDL-C)\u0026thinsp;\u0026ge;\u0026thinsp;3.37 mmol/L; and (4) high-density lipoprotein cholesterol (HDL-C)\u0026thinsp;\u0026lt;\u0026thinsp;1.04 mmol/L [\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e]. Chronic kidney disease was identified as having at least one of the following conditions: (1) kidney dysfunction (abnormal kidney structure or function) lasting for more than 3 months; (2) glomerular filtration rate\u0026thinsp;\u0026lt;\u0026thinsp;60 mL/min/1.73 m2 lasting for more than 3 months [\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e]. Diabetes was diagnosed if participants had either (1) fasting plasma glucose\u0026thinsp;\u0026ge;\u0026thinsp;7.0 mmol/L or 2-hour postprandial glucose\u0026thinsp;\u0026ge;\u0026thinsp;11.1 mmol/L; or (2) a prior diagnosis of diabetes by a physician [\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e]. Hypertension was defined as having either (1) an average systolic blood pressure\u0026thinsp;\u0026gt;\u0026thinsp;140 mmHg or an average diastolic blood pressure\u0026thinsp;\u0026gt;\u0026thinsp;90 mmHg; or (2) a prior diagnosis of hypertension by a physician [\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e].\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec6\" class=\"Section2\"\u003e \u003ch2\u003eMeasurements of covariates\u003c/h2\u003e \u003cp\u003eThe control variables in this study included demographic characteristics such as age and gender, as well as lifestyle features such as smoking status and physical activity. Smoking status was categorized as current or past smoker (yes\u0026thinsp;=\u0026thinsp;1) and never smoker (no\u0026thinsp;=\u0026thinsp;0). Additionally, we calculated the total weekly metabolic equivalents (MET) for physical activities, encompassing household, occupational, transportation, and recreational activities [\u003cspan additionalcitationids=\"CR24\" citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e].\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec7\" class=\"Section2\"\u003e \u003ch2\u003eStatistical analysis\u003c/h2\u003e \u003cp\u003eContinuous variables, such as age, urbanization index, and physical activity, were described using mean\u0026thinsp;\u0026plusmn;\u0026thinsp;standard deviation. Categorical variables, including gender, marital status, educational level, smoking status, and the presence of hypertension, stroke, diabetes, dyslipidemia, and renal dysfunction, were presented as frequencies (proportions). The differences in the distribution of demographic characteristics and disease status were analyzed using the \u003cem\u003et\u003c/em\u003e test, the \u003cem\u003eF\u003c/em\u003e test, and the \u003cem\u003eχ\u003c/em\u003e\u003csup\u003e\u003cem\u003e2\u003c/em\u003e\u003c/sup\u003e test, with subsequent Bonferroni corrections for multiple comparisons. Latent Profile Analysis (LPA) was employed to identify subgroups of individuals based on their dietary patterns, and Two-Step Clustering (TSC) was used to categorize individuals according to their disease conditions. Finally, robust standard error Logistic regression models were applied to determine the influence of dietary patterns on chronic metabolic diseases (CMM). All tests were conducted using a two-sided significance level of 95%.\u003c/p\u003e \u003c/div\u003e"},{"header":"Results","content":"\u003cdiv id=\"Sec9\" class=\"Section2\"\u003e \u003ch2\u003eDemographic characteristics\u003c/h2\u003e \u003cp\u003eA total of 4963 participants were included in this study, with 2658 females (53.6%) and 2305 males (46.4%). The participants' ages ranged from 18 to 94 years, with a mean age of 51\u0026thinsp;\u0026plusmn;\u0026thinsp;15 years. Among all participants, 69.6% were from rural areas, 77.1% had an education level below junior high school, and 5.7% were never married. The average urbanization index of the participants' communities was 67.57\u0026thinsp;\u0026plusmn;\u0026thinsp;19.01. The detailed information is presented in Table\u0026nbsp;\u003cspan refid=\"Tab5\" class=\"InternalRef\"\u003e5\u003c/span\u003e.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec10\" class=\"Section2\"\u003e \u003ch2\u003eDisease Status and CMM Combination Types\u003c/h2\u003e \u003cp\u003eTwo-Step Clustering (TSC) analysis was applied to categorize the disease status of the participants. Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e lists the fitting parameters of 15 different models (Models 1 to 15). Both Model 2 and Model 7 exhibited relatively high target distance measurement ratios (TDMR). However, Model 7 had a lower Bayesian Information Criterion (BIC) value compared to Model 2, indicating that Model 7 provided a superior classification ratio compared to the other models.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003eDisease Prevalence Among Participants\u003c/h2\u003e \u003cp\u003eThe prevalence of hyperuricemia, dyslipidemia, diabetes, renal dysfunction, hypertension, and stroke among the participants is presented in Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e. Specifically, 16.7% belonged to Class I, 9.9% to Class II, 15.0% to Class III, 8.8% to Class IV, 12.7% to Class V, 19.7% to Class VI, and 17.5% to Class VII.\u003c/p\u003e \u003cp\u003eParticipants with Class I and II disease patterns were characterized by having all six diseases to varying degrees. The multiple comparisons revealed that those with Class I were most likely to have stroke, followed by a relatively high probability of hyperuricemia, dyslipidemia, and renal dysfunction, but a lower likelihood of diabetes. In contrast, those with Class II showed a higher probability of dyslipidemia, diabetes, and renal dysfunction, but a lower likelihood of stroke. They also had a relatively high probability of hyperuricemia, dyslipidemia, and renal dysfunction, but a lower probability of hyperuricemia, hypertension, and stroke. Participants in Class III presented a pattern of having four diseases, with the highest likelihood of hypertension and moderate probabilities of dyslipidemia, renal dysfunction, and stroke. Class IV participants had three diseases, with the highest probabilities of hyperuricemia, diabetes, and renal dysfunction. Participants in Class V exhibited a two-disease pattern, with the highest likelihood of dyslipidemia and a higher probability of renal dysfunction. Both Classes VI and VII were characterized by having only one disease, with Class VI showing a relatively higher probability of dyslipidemia and Class VII a relatively higher probability of stroke (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05).\u003c/p\u003e \u003cp\u003eBased on these characteristics, Class I was named Mixed-Low Diabetes (Mix-LDia), Class II was named Mixed-High Diabetes (Mix-HDia), Class III was named Four-Type Hypertension (Four-HHyp), Class IV was named Three-Type Hyperuricemia, Diabetes, and Renal Dysfunction (Three-HestHDRD), Class V was named Two-Type Hyperlipidemia and Renal Dysfunction (Two-HDRD), Class VI was named One-Type Hyperlipidemia (One-HDys), and Class VII was named One-Type High Stroke (One-HApo).\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\u003eFit indices for the class 1 through 10 Models\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"5\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eModel\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eBCI\u003csup\u003e@\u003c/sup\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eΔBCI\u003csup\u003e@\u003c/sup\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eΔBCI ratio\u003csup\u003e@\u003c/sup\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eTDMR\u003csup\u003e@\u003c/sup\u003e\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e26340.205\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 \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003e2\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003e20381.437\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003e-5958.767\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e1\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e1.505\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e16438.648\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e-3942.789\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.662\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1.541\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e13897.416\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e-2541.232\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.426\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1.026\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e11422.636\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e-2474.78\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.415\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1.160\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e9295.428\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e-2127.209\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.357\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1.152\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003e7\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003e7455.201\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003e-1840.227\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e0.309\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e1.883\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e6502.069\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e-953.132\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.16\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1.283\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e5770.153\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e-731.916\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.123\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1.231\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e5184.983\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e-585.17\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.098\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1.004\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e11\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e4602.436\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e-582.547\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.098\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1.094\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e12\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e4074.075\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e-528.361\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.089\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1.126\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e13\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e3610.409\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e-463.666\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.078\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1.141\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e14\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e3210.486\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e-399.923\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.067\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1.247\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e15\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e2899.954\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e-310.532\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.052\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1.400\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003csup\u003e@\u003c/sup\u003e BIC, Bayesian information criterion. ΔBCI, the change of Bayesian information criterion. ΔBCI ratio, the change ratio of Bayesian information criterion. TDMR, Target distance measurement ratio Between clusters between neighbors clusters.\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\u003eDisease in the classes of CMM status\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"11\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c9\" colnum=\"9\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c10\" colnum=\"10\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c11\" colnum=\"11\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHad Disease N%\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003eClass I\u003c/em\u003e\u003c/p\u003e \u003cp\u003e\u003cem\u003eMix-LDia\u003c/em\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cem\u003eClass II\u003c/em\u003e\u003c/p\u003e \u003cp\u003e\u003cem\u003eMix-HDia\u003c/em\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cem\u003eClass III\u003c/em\u003e\u003c/p\u003e \u003cp\u003e\u003cem\u003eFour-HHyp\u003c/em\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cem\u003eClass IV\u003c/em\u003e\u003c/p\u003e \u003cp\u003e\u003cem\u003eThree-HestHDRD\u003c/em\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u003cem\u003eClass V\u003c/em\u003e\u003c/p\u003e \u003cp\u003e\u003cem\u003eTwo-HDRD\u003c/em\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u003cem\u003eClass VI\u003c/em\u003e\u003c/p\u003e \u003cp\u003e\u003cem\u003eOne-HApo\u003c/em\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c8\"\u003e \u003cp\u003e\u003cem\u003eClass VII\u003c/em\u003e\u003c/p\u003e \u003cp\u003e\u003cem\u003eOne-HDys\u003c/em\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c9\"\u003e \u003cp\u003e\u003cem\u003eAll\u003c/em\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c10\"\u003e \u003cp\u003e\u003cem\u003eχ\u003c/em\u003e\u003csup\u003e\u003cem\u003e2\u003c/em\u003e\u003c/sup\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c11\"\u003e \u003cp\u003e\u003cem\u003eP\u003c/em\u003e\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHyperuricemia\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e781\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e121\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e902\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e1804\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e404.647\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e \u003cp\u003e\u0026lt;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDyslipidemia\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e582\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e340\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e193\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e742\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e629\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e2486\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e3423.523\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e \u003cp\u003e\u0026lt;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDiabetes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e489\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e491\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e982\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e4940.619\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e \u003cp\u003e\u0026lt;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRenal dysfunction\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e719\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e349\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e264\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e869\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e742\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e2943\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e3720.618\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e \u003cp\u003e\u0026lt;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHypertension\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e121\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e113\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e435\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e669\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e3331.752\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e \u003cp\u003e\u0026lt;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eApoplexy\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e65\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e13\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e86\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e226.531\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e \u003cp\u003e\u0026lt;0.001\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 Food Intake\u003c/h2\u003e \u003cp\u003ePotential profile analysis was utilized in this study to classify the dietary patterns of participants, and the fitting parameters are presented in Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e. As shown in Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e, when comparing the Lo-Mendell-Rubin Likelihood Ratio Test (LMRT) and Bootstrap Likelihood Ratio Test (BLRT), the \u003cem\u003eP\u003c/em\u003e-values for the model with three profiles were both less than 0.05. In the model with three profiles, the entropy value was 0.974, which is greater than 0.800, indicating that the classification accuracy was above 90.0% [\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e]. Therefore, the model with three profiles in this study was superior to others, suggesting that participants exhibited three distinct dietary patterns.\u003c/p\u003e \u003cp\u003eThe intake of various food types among participants with different dietary patterns is detailed in Table\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e. As can be seen from Table\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e, there were significant differences (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05) in the intake of eight types of food among the different dietary patterns. Multiple comparisons revealed that, compared to participants with Pattern 2 and Pattern 3, participants with Pattern 1 had the lowest intake of cereals and cereal products, while their intake of fruits and fruit products, milk and milk products, as well as eggs and egg products, was the highest (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001). In contrast, participants with Pattern 2 had the lowest intake of poultry and poultry products, fruits and fruit products, as well as aquatic products, compared to those with Pattern 1 and Pattern 3 (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05). Additionally, compared to participants with Pattern 1 and Pattern 2, those with Pattern 3 had the highest intake of vegetables and vegetable products, as well as poultry and poultry products (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.01).\u003c/p\u003e \u003cp\u003eBased on the characteristics of these three dietary pattern categories, Pattern 1 was named Low Cereal High Fruit Milk and Egg Diet (LCHFM), Pattern 2 was named Low Poultry Fruit and Fishery Diet (LPFF), and Pattern 3 was named High Vegetable and Poultry Diet (HVP).\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\u003eFit indices for the profile 2 through 8 models\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"8\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eProfiles\u003c/em\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003eLogL\u003c/em\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cem\u003eAIC\u003c/em\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cem\u003eBIC\u003c/em\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cem\u003eaBIC\u003c/em\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u003cem\u003eEntropy\u003c/em\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u003cem\u003eLMRT\u003c/em\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c8\"\u003e \u003cp\u003e\u003cem\u003eBLRT\u003c/em\u003e\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e-279373.271\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e558808.541\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e559010.344\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e558911.837\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.999\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.476\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e\u0026lt;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e-278082.872\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e556249.744\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e556523.154\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e556389.692\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.974\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.024\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e\u0026lt;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e-277106.320\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e554318.639\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e554663.657\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e554495.241\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.966\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.080\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e\u0026lt;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e-276295.119\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e552718.239\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e553134.864\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e552931.494\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.971\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.204\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e\u0026lt;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003csup\u003e^\u003c/sup\u003e LogL, Log likelihood. AIC, Akaike information criterion. BIC, Bayesian information criterion. aBIC, sample size-adjusted Bayesian information criterion. BLRT, Bootstrap likelihood ratio test. LMRT, Lo-Mendell-Rubin likelihood ratio test. LogL stands for log likelihood. AIC is a metric used to compare the relative quality of statistical models, balancing model fit and complexity to select an effective model that explains data without overfitting. BIC selects models from a finite set by penalizing complexity and emphasizing model fit. BLRT compares nested models in statistical modeling. LMRT compares different parameterizations in mixture models, aiding researchers in selecting the best model for their data.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab4\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 4\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eDaily dietary food intakes in the latent profiles of dietary patterns\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"7\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eFood intakes (mg/d)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003eAll\u003c/em\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"3\" nameend=\"c5\" namest=\"c3\"\u003e \u003cp\u003e\u003cem\u003eLatent Dietary Patterns\u003c/em\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e\u003cem\u003eF\u003c/em\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e\u003cem\u003eP\u003c/em\u003e\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003eMean\u003c/em\u003e (\u003cem\u003eSD\u003c/em\u003e)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cem\u003ePattern 1\u003c/em\u003e\u003c/p\u003e \u003cp\u003e\u003cem\u003eLCHFM\u003c/em\u003e\u003c/p\u003e \u003cp\u003e\u003cem\u003eMean\u003c/em\u003e (\u003cem\u003eSD\u003c/em\u003e)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cem\u003ePattern 2\u003c/em\u003e\u003c/p\u003e \u003cp\u003e\u003cem\u003eLPFF\u003c/em\u003e\u003c/p\u003e \u003cp\u003e\u003cem\u003eMean\u003c/em\u003e (\u003cem\u003eSD\u003c/em\u003e)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cem\u003ePattern 3\u003c/em\u003e\u003c/p\u003e \u003cp\u003e\u003cem\u003eHVP\u003c/em\u003e\u003c/p\u003e \u003cp\u003e\u003cem\u003eMean\u003c/em\u003e (\u003cem\u003eSD\u003c/em\u003e)\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCereals and cereal products\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e386.852 (170.066)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e297.048 (145.840)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e390.395 (170.088)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e409.348 (167.346)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e40.742\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e\u0026lt;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTubers starches and products\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e30.584 (54.375)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e29.804 (46.077)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e31.169 (54.616)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e23.833 (56.682)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e2.963\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.052\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDried legumes and legume products\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e58.254 (75.964)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e58.814 (64.550)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e58.475 (76.500)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e58.801 (77.158)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.470\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.625\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eVegetables and vegetable products\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e323.561 (172.517)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e299.182 (183.426)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e322.651 (171.879)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e353.090 (168.753)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e7.766\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e\u0026lt;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFruit and fruit products\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e51.786 (104.621)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e133.305 (128.372)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e46.093(101.158)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e62.377 (101.776)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e89.970\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e\u0026lt;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMeat and meat products\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e75.082 (70.789)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e80.869 (70.677)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e73.271 (70.490)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e93.449 (71.953)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e14.078\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e\u0026lt;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePoultry and poultry products\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e15.442 (34.850)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e21.342 (38.343)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e7.120 (16.897)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e115.210 (42.585)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e4156.778\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e\u0026lt;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMilk and milk products\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e13.630 (53.208)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e217.787 (84.131)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e2.274 (12.888)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e3.834 (17.666)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e10628.239\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e\u0026lt;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eEggs and egg products\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e29.716 (37.124)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e47.460 (38.866)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e28.8719 (37.014)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e27.082 (33.756)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e31.980\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e\u0026lt;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFish shellfish and mollusc\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e34.696 (60.795)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e52.528 (71.109)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e32.165 (58.878)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e53.107 (70.194)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e31.251\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e\u0026lt;0.001\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=\"Sec13\" class=\"Section2\"\u003e \u003ch2\u003eThe Impact of Dietary Patterns on CMM\u003c/h2\u003e \u003cp\u003eThe distribution of CMM among participants is detailed in Table\u0026nbsp;\u003cspan refid=\"Tab5\" class=\"InternalRef\"\u003e5\u003c/span\u003e. As can be seen from Table\u0026nbsp;\u003cspan refid=\"Tab5\" class=\"InternalRef\"\u003e5\u003c/span\u003e, participants with Class III (Four-HHyp) CMM were slightly older than those in other groups, while participants with Class VI (One-HApo) and VII (One-HApo) CMM were slightly younger than those in other groups (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05). Participants with Class II (Mix-HDia) and III (Four-HHyp) CMM resided in areas with higher urbanization indices. Additionally, there were associations between gender, marital status, education level, residence area, smoking status, and the categories of multiple dietary patterns, with χ\u0026sup2; values of 559.905, 95.503, 19.104, 23.942, 229.620, and 34.967, respectively (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.01).\u003c/p\u003e \u003cp\u003eThe impact of Dietary Patterns on CMM is detailed in Table\u0026nbsp;\u003cspan refid=\"Tab6\" class=\"InternalRef\"\u003e6\u003c/span\u003e. Due to the low incidence of hyperuricemia, diabetes, renal dysfunction, hypertension, and stroke in the Class VI (One-HDys, Type One Hyperlipidemia) group, the Class VI group was chosen as the reference group for comparative analysis. As shown in Table\u0026nbsp;\u003cspan refid=\"Tab6\" class=\"InternalRef\"\u003e6\u003c/span\u003e, compared to participants with the HVP (High Vegetable and Poultry) dietary pattern, those with the CHFM (Low Cereal High Fruit Milk and Egg) dietary pattern were more likely to have Class V (Two-HDRD, Type Two Hyperlipidemia and Renal Dysfunction) CMM (\u003cem\u003eOR\u003c/em\u003e\u0026thinsp;=\u0026thinsp;2.0011, 95% \u003cem\u003eCI\u003c/em\u003e: 1.011\u0026ndash;3.960, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026le;\u0026thinsp;0.05). The OR values were statistically significant in both the model controlling only for demographic characteristics and the model controlling for both demographic and lifestyle characteristics (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05), indicating the robustness of the model.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab5\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 5\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eCharacteristics of the subjects in different CMM status\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"11\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c9\" colnum=\"9\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c10\" colnum=\"10\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c11\" colnum=\"11\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colspan=\"8\" nameend=\"c9\" namest=\"c2\"\u003e \u003cp\u003eCMM status\u003c/p\u003e \u003cp\u003e\u003cem\u003eMean\u003c/em\u003e (\u003cem\u003eSD\u003c/em\u003e) \u003cem\u003eOR N\u003c/em\u003e (\u003cem\u003eN%\u003c/em\u003e)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c10\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e\u003cem\u003eF/χ\u003c/em\u003e\u003csup\u003e\u003cem\u003e2\u003c/em\u003e\u003c/sup\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c11\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e\u003cem\u003eP\u003c/em\u003e\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003eClass I\u003c/em\u003e\u003c/p\u003e \u003cp\u003e\u003cem\u003eMix-LDia\u003c/em\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cem\u003eClass II\u003c/em\u003e\u003c/p\u003e \u003cp\u003e\u003cem\u003eMix-HDia\u003c/em\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cem\u003eClass III\u003c/em\u003e\u003c/p\u003e \u003cp\u003e\u003cem\u003eFour-HHyp\u003c/em\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cem\u003eClass IV\u003c/em\u003e\u003c/p\u003e \u003cp\u003e\u003cem\u003eThree-HestHDRD\u003c/em\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u003cem\u003eClass V\u003c/em\u003e\u003c/p\u003e \u003cp\u003e\u003cem\u003eTwo-HDRD\u003c/em\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u003cem\u003eClass VI\u003c/em\u003e\u003c/p\u003e \u003cp\u003e\u003cem\u003eOne-HDys\u003c/em\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c8\"\u003e \u003cp\u003e\u003cem\u003eClass VII\u003c/em\u003e\u003c/p\u003e \u003cp\u003e\u003cem\u003eOne-HApo\u003c/em\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c9\"\u003e \u003cp\u003e\u003cem\u003eAll\u003c/em\u003e\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAge\u003csup\u003e^\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e50.605 (15.029)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e54.348 (14.567)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e61.687 (11.439)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e52.016 (14.516)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e51.953 (13.919)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e45.677 (15.209)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e45.495 (14.699)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e50.772 (15.106)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e83.357\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e \u003cp\u003e\u0026lt;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGender\u003csup\u003e@\u003c/sup\u003e\u003c/p\u003e \u003cp\u003eFemales\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e592 (71.3%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e261 (53.4%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e212 (48.7%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e596 (86.6%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e496 (66.8%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e183 (29.1%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e318 (32.8%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e2658 (53.6%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e559.905\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e \u003cp\u003e\u0026lt;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMarital status\u003csup\u003e@\u003c/sup\u003e\u003c/p\u003e \u003cp\u003eNever married\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e41 (4.9%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e20 (4.1%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1 (0.2%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e35 (4.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e25 (3.4%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e66 (10.5%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e94 (9.7%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e282 (5.7%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e95.503\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e \u003cp\u003e\u0026lt;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eEducation level\u003csup\u003e@\u003c/sup\u003e\u003c/p\u003e \u003cp\u003eBelow middle school\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e638 (76.9%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e385 (79.1%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e343 (78.9%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e695 (80.0%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e586 (79.0%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e473 (75.2%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e705 (72.8%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e3827 (77.1%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e19.104\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e \u003cp\u003e0.004\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLiving areas\u003csup\u003e@\u003c/sup\u003e\u003c/p\u003e \u003cp\u003eRural\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e579 (69.8%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e323 (66.1%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e265 (60.9%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e625 (71.9%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e522 (70.4%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e441 (70.1%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e699 (72.1%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e3454 (69.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e23.942\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e \u003cp\u003e0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eUrbanindex \u003csup\u003e^\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e67.827 (19.507)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e71.440 (18.648)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e71.982 (19.333)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e65.394 (18.766)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e67.105 (18.579)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e67.568 (18.449)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e65.710 (18.921)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e67.567 (19.007)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e10.961\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e \u003cp\u003e\u0026lt;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePA\u003csup\u003e^\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e118.910 (678.353)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e132.500 (681.375)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e127.753 (732.710)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e93.924 (641.655)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e71.045 (565.911)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e84.837 (551.904)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e75.591 (500.051)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e96.717 (614.144)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e1.092\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e \u003cp\u003e0.364\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSmoking status\u003c/p\u003e \u003cp\u003eNever smoke\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e660 (79.5%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e335 (68.5%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e286 (65.7%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e674 (77.6%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e565 (76.1%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e354 (56.3%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e531 (54.8%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e3405 (68.6%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e229.620\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e \u003cp\u003e\u0026lt;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDietary Pattern\u003csup\u003e@\u003c/sup\u003e\u003c/p\u003e \u003cp\u003e\u003cem\u003ePattern 1 LCHFM\u003c/em\u003e\u003c/p\u003e \u003cp\u003e\u003cem\u003ePattern 2 LPFF\u003c/em\u003e\u003c/p\u003e \u003cp\u003e\u003cem\u003ePattern 3 HVP\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e44 (5.3%)\u003c/p\u003e \u003cp\u003e730 (728.5)\u003c/p\u003e \u003cp\u003e56 (6.7%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e35 (7.2%)\u003c/p\u003e \u003cp\u003e419(85.7%)\u003c/p\u003e \u003cp\u003e35 (7.2%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e38 (8.7%)\u003c/p\u003e \u003cp\u003e365(83.9%)\u003c/p\u003e \u003cp\u003e32 (7.4%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e41 (4.7%)\u003c/p\u003e \u003cp\u003e782(90.0%)\u003c/p\u003e \u003cp\u003e46 (5.3%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e41 (5.5%)\u003c/p\u003e \u003cp\u003e660(88.9%)\u003c/p\u003e \u003cp\u003e41 (5.5%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e22 (35%)\u003c/p\u003e \u003cp\u003e551(87.6%)\u003c/p\u003e \u003cp\u003e56 (8.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e38 (3.9%)\u003c/p\u003e \u003cp\u003e849(87.6%)\u003c/p\u003e \u003cp\u003e82 (8.5%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e259 (5.2%)\u003c/p\u003e \u003cp\u003e4356(87.8%)\u003c/p\u003e \u003cp\u003e348 (7.0%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e34.967\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e \u003cp\u003e\u0026lt;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003csup\u003e^\u003c/sup\u003eThe description of these variables were \u003cem\u003eMean\u003c/em\u003e (\u003cem\u003eSD\u003c/em\u003e).\u003c/p\u003e \u003cp\u003e \u003csup\u003e@\u003c/sup\u003eThe description of these variables were \u003cem\u003eN\u003c/em\u003e (\u003cem\u003eN%\u003c/em\u003e).\u003c/p\u003e \u003cp\u003e\u003cstrong\u003eTable 6\u0026nbsp;\u003c/strong\u003eAssociation between dietary patterns and CMM status\u003c/p\u003e"},{"header":"Discussion","content":"\u003cdiv id=\"Sec15\" class=\"Section2\"\u003e \u003ch2\u003eCharacteristics of Different Cardiovascular Metabolic Disease (CMM) Combination Types in Various Populations\u003c/h2\u003e \u003cp\u003eIn this study, five combined types of cardiovascular metabolic diseases (CMM, Class I to Class V) and two types of single cardiovascular or metabolic diseases (Class VI and Class VII) were identified through clustering analysis. Our findings indicate that the age of individuals with single diseases was significantly lower than those with CMM. This observation aligns with the study by Cheng et al. (2022), which reported the lowest CMM prevalence of 2.3% in the 20\u0026ndash;39 age group and the highest of 42.9% in individuals aged\u0026thinsp;\u0026ge;\u0026thinsp;80 years [\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e]. Similarly, Otieno et al. (2023) found that compared to the 15\u0026ndash;34 age group, the prevalence of CMM was higher in the 35\u0026ndash;54 age group (PR\u0026thinsp;=\u0026thinsp;3.9, 95%CI: 3.2\u0026ndash;4.8) and the 55\u0026ndash;69 age group (PR\u0026thinsp;=\u0026thinsp;3.9, 95%CI: 3.2\u0026ndash;4.8) [\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e]. Our study further reveals that participants with a higher probability of combined diabetes tend to be older than those with a lower probability. Specifically, participants with Class II (a combination of six diseases with a high probability of diabetes) were older than those with Class IV (dyslipidemia\u0026thinsp;+\u0026thinsp;diabetes\u0026thinsp;+\u0026thinsp;renal dysfunction), Class V (dyslipidemia\u0026thinsp;+\u0026thinsp;renal dysfunction), and Class I (a combination of six diseases with a low probability of diabetes). This suggests that dyslipidemia and renal dysfunction tend to occur earlier in middle-aged individuals, while the combination of dyslipidemia, hypertension, and diabetes emerges with advancing age. This finding is consistent with the results of a 5-year cohort study by Zhang et al. (2019), which showed that the prevalence of CMM with three-disease combinations increased nine times compared to baseline [\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e]. The potential reasons for this phenomenon may include the following. Dyslipidemia can lead to lipid deposition in islet cells, affecting glucose oxidation and causing β-cell apoptosis, resulting in insulin resistance. Additionally, dyslipidemia reduces glucose utilization and increases hepatic glycogenolysis, further exacerbating insulin resistance [\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e, \u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e]. Insulin resistance, in turn, triggers inflammation, oxidative stress, insulin receptor mutations, endoplasmic reticulum stress, and mitochondrial dysfunction, impairing arterial endothelial function and leading to the development of hypertension and diabetes [\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e, \u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e]. Another possible explanation is that dyslipidemia can lead to lipid deposition on blood vessel walls, causing inflammation and damaging pancreatic β-cells, interfering with glucose and lipid metabolism and leading to disorders such as insulin resistance and hyperglycemia [\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e, \u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e]. As plaques increase, the vessel diameter narrows, potentially restricting blood flow. This sequence of events explains why CMM combinations with dyslipidemia and renal dysfunction tend to occur at younger ages, while those with hypertension and diabetes occur later. Therefore, controlling dyslipidemia in middle-aged individuals plays a crucial role in managing CMM.\u003c/p\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec16\" class=\"Section2\"\u003e \u003ch2\u003eThe Relationship between Dietary Patterns and CMM\u003c/h2\u003e \u003cp\u003e The current study identified three dietary patterns, Pattern I to III, through latent profile analysis (LPA). Specifically, Pattern I represents a low-cereal, high-fruit, dairy, and egg diet (LCHFM), Pattern II is a low-poultry, fruit, and fishery diet (LPFF), and Pattern III signifies a high-vegetable, poultry diet (HVP). Our findings indicate that individuals adhering to a low-grain, high-fruit, dairy, and egg diet (LCHFM) had a higher risk of developing Class V CMM (dyslipidemia\u0026thinsp;+\u0026thinsp;renal dysfunction) compared to those with Class VI CMM (solely dyslipidemia). This suggests that the LCHFM diet may contribute to the co-occurrence of dyslipidemia and renal dysfunction. In dietary interventions for individuals with impaired renal function, it is essential to ensure adequate intake of vegetables and dairy products. Therefore, the association between renal dysfunction and a diet low in grains and high in fruits could be attributed to several factors. Grains are a significant source of glucose, and lower glucose levels can inhibit the reabsorption of serum uric acid in the proximal tubule, leading to a reduction in serum uric acid levels and thus reducing the metabolic burden on the kidneys, ultimately protecting renal function [\u003cspan additionalcitationids=\"CR37 CR38 CR39 CR40\" citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e41\u003c/span\u003e]. Conversely, excessive fruit intake can lead to the accumulation of uric acid precursors, increasing glyconeogenesis and the pentose phosphate pathway, ultimately resulting in elevated serum uric acid levels and renal dysfunction. Dietary recommendations for individuals with dyslipidemia often involve reducing macronutrient intake, particularly carbohydrates [\u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e42\u003c/span\u003e]. As grains are a primary source of carbohydrates, reducing their consumption is typically advised to manage dyslipidemia. However, our findings indicate that both excessively high and low grain intake may be detrimental. Therefore, it is crucial to maintain an appropriate level of grain consumption for individuals with dyslipidemia, as both extremes may hinder disease recovery. To reduce the incidence of dyslipidemia with renal dysfunction, a balanced intake of grains, fruits, and their derivatives is essential. This study provides valuable insights into the relationship between dietary patterns and CMM, particularly in the context of dyslipidemia and renal dysfunction, and highlights the importance of tailored dietary advice for individuals with these comorbidities.\u003c/p\u003e \u003cp\u003eThe current study has several limitations that need to be acknowledged. Firstly, the dietary data was collected through a three-day consecutive 24-hour dietary recall method, which may introduce measurement system errors compared to non-consecutive 24-hour dietary recall or food diary methods. Secondly, the analysis in this study is based on cross-sectional data, which cannot be used for causal inference. Future research should include experimental studies to draw causal conclusions and establish definitive formulations. Additionally, this study did not consider other potential factors that may influence CMM, such as genetic factors and medication use, which may have had an impact on the results. Therefore, in future studies, it is crucial to consider a wider range of potential influencing factors to arrive at more reliable conclusions.\u003c/p\u003e \u003c/div\u003e"},{"header":"Conclusion","content":"\u003cp\u003eThe present study identified five distinct CMM combinations: Class I, a six-disease mixture with low diabetes risk (Mix-LDia); Class II, a six-disease mixture with high diabetes risk (Mix-HDia); Class III, a four-disease combination characterized by hypertension (Four-HHyp); Class IV, a three-disease combination encompassing hyperuricemia, diabetes, and renal dysfunction (Three-HestHDRD); and Class V, a two-disease mix of hyperlipidemia and renal dysfunction (Two-HDRD). The average age of patients with these five CMM combinations decreases in the following order: Class III, Class II, Class IV, Class V, and Class I. Three dietary patterns were also discerned: Pattern I, a low-cereal, high-fruit, dairy, and egg diet (LCHFM); Pattern II, a low-poultry, fruit, and fishery diet (LPFF); and Pattern III, a high-vegetable, poultry diet (HVP). Notably, individuals adhering to the low-grain, high-fruit, dairy, and egg diet (LCHFM) pattern were found to have a higher risk of developing hyperlipidemia combined with renal dysfunction compared to those with isolated hyperlipidemia. Future studies are warranted to explore the causal relationship between dietary patterns and the progression of CMM.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eAcknowledgements\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis research uses data from the China Health and Nutrition Survey (CHNS). We thank the National Institute of Nutrition and Food Safety, China Center for Disease Control and Prevention, Carolina Population Center, the University of North Carolina at Chapel Hill, the NIH (R01-HD30880, DK056350, and R01-HD38700) and the Fogarty International Center, NIH for financial support for the CHNS data collection and analysis files from 1989 to 2006 and both parties plus the China-Japan Friendship Hospital, Ministry of Health for support for CHNS 2009 and future surveys.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthor Contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eConceptualization, Danhui Mao and\u0026nbsp;Xiaojun\u0026nbsp;Ren; methodology, Danhui Mao\u0026nbsp;and\u0026nbsp;Shixun Wang; software, Danhui Mao,\u0026nbsp;Mohan Zhang\u0026nbsp;and\u0026nbsp;Mingyan Ma; writing\u0026mdash;original draft preparation,\u0026nbsp;Xiaojun\u0026nbsp;Ren, Mohan Zhang and Mingyan Ma; writing\u0026mdash;review and editing,\u0026nbsp;Gongkui Li\u0026nbsp;and\u0026nbsp;Yajing Li; visualization, Shixun Wang; supervision,\u0026nbsp;Xiaojun\u0026nbsp;Ren; re-editing,\u0026nbsp;Xiaojun\u0026nbsp;Ren. All authors have read and agreed to the published version of the manuscript.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis work was supported by the Postdoctoral Research Fund of Shanxi Bethune Hospital \u0026nbsp; (Shanxi Academy of Medical Sciences) and the Doctorial Start-up Fund of Shanxi Medical University (XD2139).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eData Availability\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eData are available from https://www.cpc.unc.edu/projects/china\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEthics approval and consent to participate\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe survey was approved by the ethics committee of \u0026nbsp;the University of North Carolina at Chapel Hill and the National Institute for Nutrition and Health, Chinese Center for Disease Control and Prevention.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent for publication\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting interests\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe author(s) declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eHan Y, Hu Y, Yu C, et al. Lifestyle, cardiometabolic disease, and multimorbidity in a prospective Chinese study. Eur Heart J. 2021;42(34):3374\u0026ndash;84.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBusija L, Lim K, Szoeke C, et al. Do replicable profiles of multimorbidity exist? Systematic review and synthesis. Eur J Epidemiol. 2019;34:1025\u0026ndash;53.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eChudasama YV, Khunti KK, Zaccardi F, et al. Physical activity, multimorbidity, and life expectancy: a UK Biobank longitudinalstudy. BMCMed. 2019;17:108.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eCheng X, Ouyang F, Ma T, et al. Association of Healthy Lifestyle and Life Expectancy in Patients With Cardiometabolic Multimorbidity: A Prospective Cohort Study of UK Biobank. Front Cardiovasc Med. 2022;9:830319.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eDove A, Guo J, Marseglia A, et al. 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Guidelines for the Prevention and Control of Type 2 Diabetes in China (2017 Edition). Chin J Practical Intern Med. 2018;38(4):53.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eNational Kidney Foundation. K/DOQI clinical practice guidelines for chronic kidney disease: evaluation, classification, and stratification. AM J Kidney Dis. 2002;39(2 Suppl 1):S1\u0026ndash;26.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eRevision Committee of the Chinese Guidelines for Hypertension Prevention and Control. Chinese Guidelines for Hypertension Prevention and Control (2018 Revision). Prevention and Treatment of Cardio-Cerebrovascular Diseases. 2019; 19(1): 44.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBassett DR. International physical activity questionnaire: 12-country reliability and validity. Med Sci Sport Exe. 2003;35:1381\u0026ndash;95.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eAinsworth BE, Haskell WL, Whitt MC, et al. 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Nutrients. 2023;15(4):962.\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"},{"header":"Tables","content":"\u003cp\u003eTable 6 is available in the Supplementary Files section.\u003c/p\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"dietary pattern, cardiometabolic multimorbidity, latent profile analysis, adults","lastPublishedDoi":"10.21203/rs.3.rs-4451883/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-4451883/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eBackground\u003c/h2\u003e \u003cp\u003eThe prevalence of cardiovascular metabolic comorbidities (CMM) among adults is relatively high, imposing a heavy burden on individuals, families, and society. Dietary patterns play a significant role in the occurrence and development of CMM. This study aimed to identify the combined types of CMM in adult populations and investigate the impact of dietary patterns on CMM.\u003c/p\u003e\u003ch2\u003eMethods\u003c/h2\u003e \u003cp\u003eParticipants in this study were from the sixth wave of the China Health and Nutrition Survey (CHNS). Dietary intake was assessed using a three-day 24-hour dietary recall method among 4,963 participants. Latent profile analysis was used to determine dietary pattern types. Two-step cluster analysis was performed to identify the combined types of CMM based on the participants' conditions of hyperuricemia, dyslipidemia, diabetes, renal dysfunction, hypertension, and stroke. Logistic regression analysis with robust standard errors was used to determine the impact of dietary patterns on CMM.\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e \u003cp\u003eParticipants were clustered into three dietary patterns (Pattern 1 to 3) and five CMM types (Class I to V). Class I combined six diseases, with a low proportion of diabetes. Class II also combined six diseases but with a high proportion of diabetes. Class III combined four diseases, with a high proportion of hypertension. Class IV combined three diseases, with the highest proportions of hyperuricemia, diabetes, and renal dysfunction. Class V combined two diseases, with high proportions of dyslipidemia and renal dysfunction. Patients with Class III CMM had a significantly higher average age than the other four classes (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026le;\u0026thinsp;0.05). Compared to those with isolated dyslipidemia, individuals with a low-grain, high-fruit, milk, and egg (LCHFM) dietary pattern had a higher risk of developing dyslipidemia combined with renal dysfunction (Class V CMM) with an odds ratio of 2.001 (95% \u003cem\u003eCI\u003c/em\u003e: 1.011\u0026ndash;3.960, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026le;\u0026thinsp;0.05).\u003c/p\u003e\u003ch2\u003eConclusion\u003c/h2\u003e \u003cp\u003eFor individuals with isolated dyslipidemia, avoiding a low-grain, high-fruit, milk, and egg (LCHFM) dietary pattern may help reduce the risk of developing dyslipidemia combined with renal dysfunction (Class V CMM).\u003c/p\u003e","manuscriptTitle":"Study on the Impact of Dietary Patterns on Cardiovascular Metabolic Comorbidities among Adults","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-06-03 04:37:48","doi":"10.21203/rs.3.rs-4451883/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":"c6b084ff-8e64-424d-8aa3-63c4b3bf62c5","owner":[],"postedDate":"June 3rd, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2024-08-10T18:08:18+00:00","versionOfRecord":[],"versionCreatedAt":"2024-06-03 04:37:48","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-4451883","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-4451883","identity":"rs-4451883","version":["v1"]},"buildId":"qtupq5eGEP_6zYnWcrvyt","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}