Associations between dietary inflammatory index and cardiovascular kidney metabolic syndrome: insights from NHANES 2005–2018

Associations between dietary inflammatory index and cardiovascular kidney metabolic syndrome: insights from NHANES 2005–2018 | 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 Article Associations between dietary inflammatory index and cardiovascular kidney metabolic syndrome: insights from NHANES 2005–2018 Tianyao Shi, Fanbo Mei, Xiao Bai, Chen Mo, Minghe Liu, Jiantang Guo, and 4 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-6224126/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 Cardiovascular–kidney–metabolic syndrome (CKM) is recognized as a dynamic systemic disorder. Inflammation is pivotal in CKM syndrome development. The dietary inflammatory index (DII) represents a well-validated tool to quantify the overall inflammatory potential of an individual diet. However, the association between DII and CKM syndrome remains undetermined. We analyzed data from 10,600 adults aged ≥ 20 years from the National Health and Nutrition Examination Survey (NHANES 2005–2018). The CKM stages were classified on the basis of metabolic risk factors, cardiovascular disease (CVD), and chronic kidney disease (CKD). Our findings indicated that advanced CKM stages overlapped with high-DII profiles. The findings derived from the four multivariable logistic regression analysis models revealed a significant positive correlation between a continuous DII and the incidence of advanced CKM syndrome. Additionally, the quartiles of the DII demonstrated a statistically significant association with an increased incidence of advanced CKM syndrome in the fully adjusted models (DII Q4 vs. Q1, odds ratio = 1.44, 95% confidence interval = 1.08–1.92, P =0.014). The results of restricted cubic spline (RCS) analysis suggested a linear and positive correlation between DII and advanced CKM syndrome. Subgroup analyses further revealed sex-, depression-, and sleep disorder-specific effects. This study indicates that the DII may be a modifiable lever in CKM syndrome management, bridging dietary inflammatory patterns with systemic metabolic and cardiovascular diseases. Health sciences/Health care/Nutrition Health sciences/Diseases/Cardiovascular diseases Dietary inflammatory index (DII) cardiovascular-kidney-metabolic (CKM) syndrome NHANES multivariable logistic regression RCS analysis Figures Figure 1 Figure 2 Figure 3 1. Introduction Cardiovascular-kidney-metabolic (CKM) syndrome, recently conceptualized by the American Heart Association (AHA), represents a systemic condition characterized by the bidirectional interplay among cardiovascular disease (CVD), chronic kidney disease (CKD), and metabolic disorders such as diabetes and obesity 1 – 3 . This syndrome involves interconnected pathophysiological mechanisms, particularly chronic inflammation 4 , oxidative stress, and endothelial dysfunction 5 – 7 , which drive multiorgan damage, increase the risk of adverse clinical outcomes 8 , and impact the morbidity and mortality of adults 9 – 12 . Systemic low-grade inflammation serves as a pivotal driver of CKM progression, functioning through its sustained activation of proinflammatory cascades that potentiate multiorgan dysfunction 4 , which exacerbates insulin resistance, renal fibrosis, and atherosclerotic processes, creating a vicious cycle of organ dysfunction 13 – 15 . Emerging evidence suggests that modifiable lifestyle factors, particularly diet, may play a pivotal role in modulating these inflammatory pathways and CKM progression 16 – 19 . The implementation of dietary interventions that focus on anti-inflammatory foods and proper nutrition could reverse some of the detrimental effects of chronic kidney disease and obesity, ultimately improving overall health outcomes. The dietary inflammatory index (DII) is an epidemiologically validated metric that quantifies the inflammatory potential of dietary patterns 20 . This study provides a strong methodological framework for investigating the relationship between inflammation and CKM syndrome. There are potential correlations between CKM syndrome and DII, particularly in terms of their shared inflammatory pathways. Proinflammatory diets rich in refined carbohydrates, saturated fats, and processed meats may aggravate systemic inflammation, thereby accelerating the progression of CKD (e.g., via albuminuria and glomerulosclerosis), CVD (e.g., plaque instability and heart failure), and metabolic dysregulation (e.g., insulin resistance) 21 , 22 . Conversely, anti-inflammatory diets rich in antioxidants, fiber, and omega-3 fatty acids could disrupt this cascade. In contrast, metabolic syndrome (MetS) and CKM share overlapping features, and the unique integration of renal and cardiovascular endpoints in CKM syndrome raises critical questions about whether dietary inflammation further amplifies cross-organ risk. Interventions targeting inflammation, such as the use of a nonsteroidal mineralocorticoid receptor antagonist, have shown efficacy in reducing cardiovascular and renal events in CKM patients, highlighting inflammation as a therapeutic target 21 . Similarly, dietary modifications that lower DII scores might synergize with pharmacological strategies to improve outcomes. However, current research on DII has focused predominantly on isolated MetS components or single-organ diseases, leaving a gap in understanding its role in the multiorgan context of CKM 22 . Our study uses data from the 2005–2018 NHANES, a United States database, to assess whether the DII is associated with the risk of CKM syndrome and its components. This paper aims to explore the conceptual frameworks of CKM syndrome and DII and investigate their potential interconnections to inform future research and clinical practice. 2. Methods 2.1. Study population This cross-sectional investigation employed data from the esteemed National Health and Nutrition Examination Survey (NHANES). The NHANES is a nationally representative survey conducted by the Centers for Disease Control and Prevention in the United States; it employs a stratified multistage probability sampling technique to assess nutritional and health conditions in the country. NHANES has received approval from the National Center for Health Statistics Ethics Review Committee, and all participants provided written informed consent. The secondary analysis did not require additional Institutional Review Board approval. The NHANES data are openly accessible and can be retrieved online at ( https://www.cdc.gov/nchs/nhanes/index.htm ). The data used in the present investigation were meticulously selected from seven consecutive cycles from 2005 through 2018. Figure 1 delineates the processes of inclusion and exclusion relevant to our study. (1) Initially, our analysis included data from 2005–2018, culminating in the recruitment of n = 70190 participants. (2) Subsequently, 30441 individuals under the age of 20 years and those with incomplete sample weights (WTSAF2YR) were systematically excluded from the analysis. (3) Furthermore, participants deficient in data concerning CKM syndrome and the Dietary Inflammatory Index (DII), along with associated covariates, were also excluded, ultimately yielding a cohort of 10600 eligible participants. Figure 1 provides a flowchart that elucidates the participant selection methodology. 2.2. Dietary inflammatory index (DII) The NHANES acquires dietary data at mobile examination centers (MECs) through interviews based on 24-hour dietary recalls. This investigation evaluated the nutrient consumption of participants, excluding dietary supplements and medications, by utilizing the average of two consistent 24-hour dietary recall assessments. The DII, introduced by Shivappa et al . in 2014 20 , serves as a metric for assessing the inflammatory potential inherent in an individual's dietary habits. Importantly, although the preliminary DII computation necessitated the inclusion of 45 distinct dietary components, the NHANES database featured only 27 components and accurately reflected dietary inflammation. In this study, a broad spectrum of dietary parameters, including a range of nutrients and components such as protein, carbohydrates, dietary fiber, cholesterol, polyunsaturated fatty acids (PUFAs), saturated fats, monounsaturated fatty acids (MUFAs), n-3 fatty acids, n-6 fatty acids, and alcohol, was examined. In addition to a spectrum of vitamins encompassing B2, B12, B6, A, C, and E, β-carotene, caffeine, folic acid, and essential minerals such as iron (Fe), magnesium (Mg), niacin, riboflavin, selenium (Se), thiamine, and zinc (Zn) were subjected to analysis. The subjects were stratified into quartiles according to their DII scores, thereby enabling a comprehensive investigation of the relationship between dietary inflammatory potential and CKM syndrome. The DII was calculated via the following Eq. 2 0 : 𝐷𝐼𝐼= (Z score′ × the inflammatory effect score of each dietary component) Z score= (Daily mean intake–Global daily mean intake)/Global standard deviation 𝑍 𝑠𝑐𝑜𝑟𝑒′=𝑍 𝑠𝑐𝑜𝑟𝑒→ (𝑐𝑜𝑛𝑣𝑒𝑟𝑡𝑒𝑑 𝑡𝑜 𝑎 𝑝𝑒𝑟𝑐𝑒𝑛𝑡𝑖𝑙𝑒 𝑠𝑐𝑜𝑟𝑒) ×2–1 2.3. CKM According to the definition provided by the AHA, CKM syndrome is delineated as a pathological condition engendered by interconnections among CVD, CKD, obesity, and diabetes and is systematically classified into five distinct stages 2 . In the context of our investigation, stage 0 is identified as a state of normal physiological function. Stage 1 is distinguished by the presence of one or more of the following criteria: (1). Body mass index (BMI) of ≥ 23 kg/m² for individuals of Asian descent and > 25 kg/m² for other demographic groups; (2). Increased waist circumference measurements of ≥ 80 cm for females and ≥ 90 cm for males in the Asian population; ≥88 cm for females and ≥ 102 cm for males in alternative populations; (3). Prediabetes is indicated by HbA1c levels ranging from 5.7–6.4%, fasting glucose levels between 100 and 125 mg/dL, and the absence of additional metabolic risk factors, CKD, or CVD. Stage 2 is characterized by the presence of multiple metabolic risk factors, which may include elevated triglycerides, hypertension, diabetes, or metabolic syndrome, as well as moderate-to-high-risk CKD per the Kidney Disease: Improving Global Outcomes (KDIGO) guidelines. Stages 3 and 4 are collectively categorized as advanced CKM syndrome, which includes individuals either diagnosed with or at elevated risk for developing cardiovascular disease 23 . Stage 3 includes subclinical CVD. equivalents, as assessed by the AHA’s PREVENT equations [11], and Stage 4 is marked by clinical manifestations of CVD with metabolic disorders. For detailed descriptions of the stage definitions, refer to reference 9 . 2.4. Covariates On the basis of the previous literature, factors that have been proven to be correlated with CKM and the dietary inflammatory index were included. The study investigated demographic characteristics, including sex, age (< 60, ≥ 60), race and ethnicity (Hispanics, White, Black, Asian, other races or ethnicities), educational level (less than 9th grade, 9-11th grade, high school grade, some college, college graduate or above), PIR (poverty income ratio: low- to middle-income level PIR < 5, and high-income level, PIR ≥ 5), and marital status; further information on lifestyle habits and comorbidities, such as smoking, alcohol consumption, and intensity of physical activity (low, high physical activity), was also collected 24 , 25 . Self-reported data concerning general health conditions, including hypertension, diabetes, depression, sleeping disorders, and hyperlipemia, were also collected. Physical and laboratory tests such as BMI (< 25, ≥ 25), waist count, high-density lipoprotein cholesterol (HDL-C), total cholesterol, low-density lipoprotein cholesterol (LDL-C), serum creatinine (SCR) and uric acid levels, the albumin/creatinine ratio (ACR), and the glomerular filtration rate (eGFR) were selected as potential confounders. Furthermore, the status of all-cause mortality, mortality attributable to cardiovascular diseases, and mortality related to cerebrovascular diseases was determined through probabilistic linkage to the National Death Index, with data collected until December 31, 2019. 2.5. Statistical analysis Data processing and analysis were performed via R version 4.4.0 (2024-04-24), along with the Storm Statistical Platform ( www.medsta.cn/software ). To account for disparities in sampling probabilities and instances of nonresponse, all participant data were weighted according to NHANES examination weights and fasting subsample weights. The baseline characteristics were systematically evaluated across the CKM syndrome stages within the two specified intervals via the Rao‒Scott chi-square test for categorical variables, alongside ANOVA and the Kruskal‒Wallis test, which were adjusted for sampling weights concerning continuous variables. Continuous variables were expressed as the means in conjunction with standard errors. In contrast, categorical variables were articulated as percentages, categorized on the basis of quartiles (Q1–Q4) of the DII index and stages of CKM syndrome. Descriptive analyses were executed employing weighted one-way ANOVA for continuous variables and weighted chi-square tests for categorical variables. Multivariate logistic regression analyses were performed to investigate the association between the DII and the progression of advanced CKM syndrome, resulting in the development of three distinct statistical inference models. The crude model (Model 1) was unadjusted; Model 2 was adjusted for age, sex, ethnicity, and education level; Model 3 was further adjusted for smoking status, marital status, drinking, physical activity, and PIR based on Model 2; and Model 4 was adjusted for hypertension, hyperlipidemia, diabetes disease, depression, sleep disorders, BMI, waist, HDL-C, LDL-C, cholesterol, SCR, and uric acid based on Model 3. Restricted cubic spline (RCS) analysis was used to explore the possible relationship between the DII and the likelihood of advanced CKM syndrome. Stratification and interaction analyses were conducted according to sex, age, race, educational level, PIR and physical activity. The results were considered statistically significant if P values were less than 0.05. 3. Results 3.1. Baseline characteristics of participants stratified by the quartiles of the DII. Table 1 presents the participants' key characteristics sorted by DII quartile. This study enrolled a total of 10600 participants, with 7194 (73.96%) under 60 years old and 3406 (26.04%) over 60 years. A total of 48.87% of them were male. A total of 2,359 individuals (22.25%) were in the DII quantile 1 group, 2,603 (24.56%) were in the DII quantile 2 group, 2,676 (25.25%) were in the DII quantile 3 group, and 2,962 (27.94%) were in the DII quantile 4 group. Statistically significant differences ( P < 0.05) were observed among the four groups for the following variables: BMI; waist count; HDL-C; uric acid; and covariates, including marital status, PIR, ACR, EGFR, DII, sex, race, and educational attainment during adulthood. In contrast, no statistically significant differences ( P > 0.05) were found for LDL-C, cholesterol, SCR, age, or smoking history. Compared with participants in the lowest DII quartile, those with a higher DII index were more likely to be female, White or Asian in ethnicity, widowed or separated, have a lower PIR, smoke, not drink, have a higher BMI, have a waist circumference, and be physically active. There was a higher all-cause mortality and cardiovascular disease mortality rate among participants in the highest quartile than in those in the lowest quartile. Analytical evaluations indicated that the incidence of chronic illnesses, including hypertension, diabetes mellitus, depression, and sleep disorders, was markedly elevated in the group exhibiting the highest DII compared with the lowest group. Table 1 Baseline characteristics classified by DII quartiles. The P value was calculated via the weighted chi-square test. Q: quartile; SE: standard error; F: ANOVA; χ²: chi-square test. Abbreviations: BMI: body mass index; PIR: poverty income ratio; HDL-C: high-density lipoprotein cholesterol; LDL-C: low-density lipoprotein cholesterol; SCR: serum creatinine; ACR: albumin-to-creatinine ratio; eGFR: estimated glomerular filtration rate. Characteristic Total (n = 10600) Quartiles of the DII index prevalence, % (95% CI) Statistic P Q1 (n = 2359) Q2 (n = 2603) Q3 (n = 2676) Q4(n = 2962) Age group (years) χ²=13.09 0.054 < 60 7194 (73.96) 1668 (76.44) 1796 (72.29) 1816 (73.91) 1914 (73.21) ≥ 60 3406 (26.04) 691 (23.56) 807 (27.71) 860 (26.09) 1048 (26.79) Sex, n (%)) χ²=438.03 < .001 Male 5215 (48.87) 1486 (62.52) 1396 (52.73) 1246 (45.51) 1087 (34.71) Female 5385 (51.13) 873 (37.48) 1207 (47.27) 1430 (54.49) 1875 (65.29) Race and ethnicity, n (%) χ²=105.38 < .001 Hispanics 1592 (7.62) 385 (8.02) 425 (8.23) 383 (7.39) 399 (6.86) White 1027 (5.19) 184 (4.14) 254 (5.19) 268 (5.02) 321 (6.40) Black 4977 (70.86) 1180 (74.83) 1214 (70.34) 1246 (70.59) 1337 (67.69) Asian 2059 (9.90) 359 (6.66) 438 (8.56) 563 (10.96) 699 (13.40) Other race or ethnicity 945 (6.44) 251 (6.36) 272 (7.68) 216 (6.03) 206 (5.66) Education levels, n (%) χ²=501.14 < .001 Less than 9th Grade 932 (4.61) 157 (3.28) 213 (4.22) 226 (4.63) 336 (6.31) 9-11th Grade 1477 (10.46) 238 (7.27) 316 (9.10) 400 (11.55) 523 (13.92) High school Grade 2400 (21.87) 432 (17.05) 542 (18.83) 641 (23.96) 785 (27.66) Some college 3109 (31.32) 645 (27.62) 791 (31.75) 814 (33.07) 859 (32.83) College graduate or above 2682 (31.74) 887 (44.78) 741 (36.10) 595 (26.78) 459 (19.28) Marriage status, n (%) χ²=147.56 < .001 Married 5553 (56.19) 1392 (61.66) 1429 (58.09) 1391 (56.79) 1341 (48.25) Widowed 768 (5.31) 103 (3.23) 171 (4.93) 200 (5.88) 294 (7.19) Divorced 1145 (10.46) 231 (9.48) 272 (10.30) 268 (9.62) 374 (12.43) Separated 332 (1.97) 52 (1.27) 67 (1.50) 105 (2.25) 108 (2.85) Never married 1923 (17.99) 388 (16.37) 443 (16.63) 513 (18.79) 579 (20.15) Living with partner 879 (8.09) 193 (8.00) 221 (8.54) 199 (6.68) 266 (9.14) PIR, n (%) χ²=243.63 < .001 <5 8648 (73.55) 1719 (64.32) 2067 (70.92) 2236 (76.62) 2626 (82.35) ≥5 1952 (26.45) 640 (35.68) 536 (29.08) 440 (23.38) 336 (17.65) Smoking status, n (%) χ²=26.35 0.002 No 5798 (54.08) 1304 (53.84) 1491 (57.80) 1444 (53.85) 1559 (50.81) Yes 4802 (45.92) 1055 (46.16) 1112 (42.20) 1232 (46.15) 1403 (49.19) Drinking, n (%) χ²=140.63 < .001 No 2856 (21.92) 472 (15.47) 626 (20.79) 731 (22.60) 1027 (28.82) Yes 7744 (78.08) 1887 (84.53) 1977 (79.21) 1945 (77.40) 1935 (71.18) Physical activity, n (%) χ²=211.19 < .001 Low physical activity 3921 (33.29) 636 (24.13) 912 (31.48) 1019 (34.92) 1354 (42.63) High physical activity 6679 (66.71) 1723 (75.87) 1691 (68.52) 1657 (65.08) 1608 (57.37) Hypertension, n (%) χ²=16.18 0.037 No 6321 (62.89) 1464 (64.79) 1619 (64.30) 1589 (62.48) 1649 (59.98) Yes 4279 (37.11) 895 (35.21) 984 (35.70) 1087 (37.52) 1313 (40.02) Diabetes, n (%) χ²=30.31 < .001 No 8466 (84.21) 1961 (86.80) 2123 (85.28) 2099 (82.98) 2283 (81.80) Yes 2134 (15.79) 398 (13.20) 480 (14.72) 577 (17.02) 679 (18.20) Depression, n (%) χ²=71.54 < .001 No 9745 (92.97) 2226 (94.78) 2429 (94.88) 2455 (92.51) 2635 (89.73) Yes 855 (7.03) 133 (5.22) 174 (5.12) 221 (7.49) 327 (10.27) Sleeping disorder, n (%) χ²=49.56 < .001 No 9018 (85.73) 2078 (88.78) 2231 (86.89) 2267 (84.95) 2442 (82.32) Yes 1582 (14.27) 281 (11.22) 372 (13.11) 409 (15.05) 520 (17.68) Hyperlipemia, n (%) χ²=22.45 0.002 No 3016 (28.84) 753 (32.35) 757 (28.50) 732 (27.41) 774 (27.13) Yes 7584 (71.16) 1606 (67.65) 1846 (71.50) 1944 (72.59) 2188 (72.87) All-cause mortality, n (%) χ²=36.94 < .001 No 9536 (92.58) 2186 (94.93) 2358 (92.76) 2397 (91.93) 2595 (90.68) Yes 1064 (7.42) 173 (5.07) 245 (7.24) 279 (8.07) 367 (9.32) Cardiovascular disease mortality, n (%) χ²=11.17 0.005 No 10342 (98.26) 2315 (98.75) 2546 (98.32) 2612 (98.37) 2869 (97.58) Yes 258 (1.74) 44 (1.25) 57 (1.68) 64 (1.63) 93 (2.42) Cerebrovascular disease mortality, n (%) χ²=0.63 0.841 No 10536 (99.61) 2345 (99.68) 2588 (99.55) 2659 (99.60) 2944 (99.62) Yes 64 (0.39) 14 (0.32) 15 (0.45) 17 (0.40) 18 (0.38) BMI, n (%) χ²=46.16 < .001 <25 3139 (30.89) 805 (35.80) 776 (31.06) 748 (28.77) 810 (27.96) ≥25 7461 (69.11) 1554 (64.20) 1827 (68.94) 1928 (71.23) 2152 (72.04) Waist (cm), Mean (SE) 98.86 (0.27) 97.73 (0.49) 98.56 (0.45) 99.36 (0.46) 99.79 (0.38) F = 12.92 < .001 HDL-C (mg/dl), Mean (SE) 54.93 (0.27) 55.52 (0.44) 54.99 (0.43) 55.34 (0.53) 53.86 (0.42) F = 5.81 0.018 LDL-C (mg/dl), Mean (SE) 114.82 (0.51) 113.50 (1.05) 115.13 (0.90) 115.86 (0.95) 114.78 (0.91) F = 1.13 0.292 Cholesterol (mg/dl) Mean (SE 193.53 (0.63) 192.45 (1.18) 193.46 (0.93) 195.67 (1.22) 192.53 (1.11) F = 0.25 0.621 SCR (mg/dl), Mean (SE) 0.88 (0.00) 0.89 (0.01) 0.88 (0.01) 0.87 (0.01) 0.87 (0.01) F = 2.45 0.121 Uric acid(mg/dl) Mean (SE) 5.48 (0.02) 5.54 (0.04) 5.50 (0.04) 5.52 (0.04) 5.36 (0.03) F = 9.06 0.003 ACR, Mean (SE) 30.63 (2.26) 21.32 (2.83) 32.46 (5.04) 28.01 (3.43) 40.72 (5.81) F = 7.32 0.008 EGFR, Mean (SE) 100.25 (0.35) 103.02 (0.65) 100.94 (0.59) 99.78 (0.57) 97.27 (0.58) F = 59.23 < .001 CKM, n (%) χ²=67.94 < .001 Stage 0 998 (11.29) 274 (13.97) 255 (11.82) 232 (10.13) 237 (9.27) Stage 1 2172 (22.36) 521 (23.49) 567 (22.85) 542 (22.10) 542 (21.00) Stage 2 5739 (54.11) 1264 (53.03) 1402 (53.08) 1488 (55.45) 1585 (54.86) Stage 3 573 (3.39) 102 (2.68) 134 (3.44) 140 (3.44) 197 (4.01) Stage 4 1118 (8.85) 198 (6.83) 245 (8.80) 274 (8.89) 401 (10.86) 3.2. Baseline characteristics of participants stratified by CKM syndrome stage Table 2 shows the baseline characteristics of the participants stratified by stage of CKM syndrome. The mean DII index observed in the study was 0.8 ± 0.04. A total of 998 individuals (9.42%) were classified as CKM-stage 0, 2,172 (20.49%) as stage 1, 5,739 (54.14%) as stage 2, 573 (5.41%) as stage 3, and 1,118 (10.55%) as stage 4. Statistically significant differences ( P < 0.05) were observed among the five groups for the following variables: BMI; waist, HDL-C, cholesterol, serum creatinine, ACR, and EGFR levels; and covariates, including age, marital status, PIR, sex, race, educational attainment during adulthood, and smoking history. However, no statistically significant difference ( P > 0.05) was found for LDL-C. Compared with participants in the low CKM stage, those in the advanced CKM syndrome stage tended to exhibit certain distinguishing characteristics. These characteristics included being older, female, having relatively lower education, Asian, widowed and separated, having a lower PIR, smoking and not drinking alcohol, and engaging in a lower intensity of physical activity. Furthermore, individuals in the advanced stage of CKM syndrome demonstrated a higher incidence of all-cause mortality, as well as mortality due to cardiovascular diseases, than did their counterparts in the lowest quartile. Analyses revealed that the prevalence of chronic illnesses, such as hypertension, diabetes mellitus, depression, and sleep disorders, was significantly greater in the highest CKM cohort than in the lowest cohort. Table 2 Baseline characteristics classified by CKM syndrome stage. P values were calculated via the weighted chi-square test. Q: quartile; SE: standard error; F: ANOVA; χ²: chi-square test. Abbreviations: BMI: body mass index; PIR: poverty income ratio; HDL-C: high-density lipoprotein cholesterol; LDL-C: low-density lipoprotein cholesterol; SCR: serum creatinine; ACR: albumin-to-creatinine ratio; eGFR: estimated glomerular filtration rate; Q: quartile. Characteristic Total (n = 10600) Cardiovascular kidney metabolic syndrome (CKM) stages Statistic P Stage 0 (n = 998) Stage 1 (n = 2172) Stage 2 (n = 5739) Stage 3 (n = 573) Stage 4 (n = 1118) Age group (years) χ²=2411.75 < .001 < 60 7194 (73.96) 948 (94.51) 1934 (89.46) 4007 (74.88) 20 (5.39) 285 (29.26) ≥ 60 3406 (26.04) 50 (5.49) 238 (10.54) 1732 (25.12) 553 (94.61) 833 (70.74) Sex, n (%)) χ²=144.74 < .001 Male 5215 (48.87) 356 (34.88) 996 (46.93) 2846 (50.63) 373 (60.28) 644 (56.45) Female 5385 (51.13) 642 (65.12) 1176 (53.07) 2893 (49.37) 200 (39.72) 474 (43.55) Race and ethnicity, n (%) χ²=89.48 < .001 Hispanics 1592 (7.62) 110 (5.26) 377 (9.42) 952 (8.12) 57 (4.22) 96 (4.38) White 1027 (5.19) 88 (4.43) 223 (6.10) 582 (5.33) 42 (3.80) 92 (3.48) Black 4977 (70.86) 522 (75.21) 945 (68.52) 2511 (69.62) 344 (78.74) 655 (75.74) Asian 2059 (9.90) 138 (7.00) 419 (10.21) 1168 (10.28) 110 (10.39) 224 (10.27) Other race or ethnicity 945 (6.44) 140 (8.09) 208 (5.75) 526 (6.65) 20 (2.85) 51 (6.14) Education levels, n (%) χ²=359.87 < .001 Less than 9th Grade 932 (4.61) 41 (2.38) 131 (3.20) 518 (4.72) 95 (10.45) 147 (8.14) 9-11th Grade 1477 (10.46) 91 (6.54) 265 (8.41) 827 (10.99) 104 (15.95) 190 (15.29) High school Grade 2400 (21.87) 174 (16.38) 432 (19.90) 1344 (22.29) 146 (30.96) 304 (27.84) Some college 3109 (31.32) 301 (28.87) 671 (31.59) 1725 (32.90) 131 (24.78) 281 (26.58) College graduate or above 2682 (31.74) 391 (45.82) 673 (36.90) 1325 (29.10) 97 (17.86) 196 (22.15) Marriage status, n (%) χ²=1068.32 < .001 Married 5553 (56.19) 423 (48.34) 1100 (55.49) 3088 (57.28) 336 (57.23) 606 (60.98) Widowed 768 (5.31) 12 (0.88) 46 (1.60) 361 (4.73) 152 (28.20) 197 (15.07) Divorced 1145 (10.46) 68 (5.72) 192 (9.19) 673 (11.77) 52 (9.89) 160 (11.91) Separated 332 (1.97) 18 (0.89) 63 (1.92) 204 (2.25) 11 (1.18) 36 (2.08) Never married 1923 (17.99) 372 (32.11) 534 (22.21) 932 (16.38) 13 (2.55) 72 (5.00) Living with partner 879 (8.09) 105 (12.06) 237 (9.59) 481 (7.60) 9 (0.94) 47 (4.96) PIR, n (%) χ²=77.10 < .001 <5 8648 (73.55) 776 (69.66) 1722 (72.27) 4666 (72.72) 509 (85.71) 975 (82.24) ≥5 1952 (26.45) 222 (30.34) 450 (27.73) 1073 (27.28) 64 (14.29) 143 (17.76) Smoking status, n (%) χ²=217.89 < .001 No 5798 (54.08) 655 (65.06) 1337 (59.15) 3121 (53.23) 247 (43.50) 438 (36.45) Yes 4802 (45.92) 343 (34.94) 835 (40.85) 2618 (46.77) 326 (56.50) 680 (63.55) Drinking, n (%) χ²=63.56 < .001 No 2856 (21.92) 231 (17.80) 501 (19.08) 1597 (22.46) 195 (33.22) 332 (26.73) Yes 7744 (78.08) 767 (82.20) 1671 (80.92) 4142 (77.54) 378 (66.78) 786 (73.27) Physical activity, n (%) χ²=258.68 < .001 Low physical activity 3921 (33.29) 261 (24.10) 613 (26.04) 2150 (34.72) 333 (56.26) 564 (45.81) High physical activity 6679 (66.71) 737 (75.90) 1559 (73.96) 3589 (65.28) 240 (43.74) 554 (54.19) Hypertension, n (%) χ²=3426.64 < .001 No 6321 (62.89) 998 (100.00) 2172 (100.00) 2766 (48.37) 122 (19.82) 263 (27.00) Yes 4279 (37.11) 0 (0.00) 0 (0.00) 2973 (51.63) 451 (80.18) 855 (73.00) Diabetes, n (%) χ²=1560.28 < .001 No 8466 (84.21) 998 (100.00) 2165 (99.71) 4457 (81.01) 236 (42.90) 610 (60.36) Yes 2134 (15.79) 0 (0.00) 7 (0.29) 1282 (18.99) 337 (57.10) 508 (39.64) Depression, n (%) χ²=86.07 < .001 No 9745 (92.97) 943 (96.00) 2057 (94.83) 5265 (92.51) 534 (94.06) 946 (86.83) Yes 855 (7.03) 55 (4.00) 115 (5.17) 474 (7.49) 39 (5.94) 172 (13.17) Sleeping disorder, n (%) χ²=77.42 < .001 No 9018 (85.73) 886 (89.20) 1935 (88.63) 4865 (85.02) 488 (86.83) 844 (77.94) Yes 1582 (14.27) 112 (10.80) 237 (11.37) 874 (14.98) 85 (13.17) 274 (22.06) Hyperlipemia, n (%) χ²=1623.29 < .001 No 3016 (28.84) 664 (66.14) 1031 (45.08) 1063 (17.79) 136 (22.73) 122 (10.17) Yes 7584 (71.16) 334 (33.86) 1141 (54.92) 4676 (82.21) 437 (77.27) 996 (89.83) All-cause mortality, n (%) χ²=1475.70 < .001 No 9536 (92.58) 987 (98.88) 2118 (98.26) 5370 (94.54) 315 (56.34) 746 (72.04) Yes 1064 (7.42) 11 (1.12) 54 (1.74) 369 (5.46) 258 (43.66) 372 (27.96) Cardiovascular disease mortality, n (%) χ²=471.24 0.005 No 10342 (98.26) 997 (99.94) 2164 (99.75) 5663 (98.93) 511 (89.46) 1007 (91.60) Yes 258 (1.74) 1 (0.06) 8 (0.25) 76 (1.07) 62 (10.54) 111 (8.40) Cerebrovascular disease mortality, n (%) χ²=149.07, < .001 No 10536 (99.61) 998 (100.00) 2171 (99.98) 5723 (99.81) 556 (96.92) 1088 (98.01) Yes 64 (0.39) 0 (0.00) 1 (0.02) 16 (0.19) 17 (3.08) 30 (1.99) BMI, n (%) χ²=3024.50 < .001 <25 3139 (30.89) 998 (100.00) 500 (22.87) 1238 (21.34) 162 (26.16) 241 (23.23) ≥25 7461 (69.11) 0 (0.00) 1672 (77.13) 4501 (78.66) 411 (73.84) 877 (76.77) Waist (cm), Mean (SE) 98.86 (0.27) 79.28 (0.26) 95.81 (0.36) 102.75 (0.29) 103.38 (0.83) 106.06 (0.73) F = 961.72 < .001 HDL-C (mg/dl), Mean (SE) 54.93 (0.27) 63.97 (0.68) 58.10 (0.53) 52.38 (0.33) 51.54 (0.79) 52.22 (0.76) F = 252.22 < .001 LDL-C (mg/dl), Mean (SE) 114.82 (0.51) 100.36 (1.25) 115.48 (0.86) 120.43 (0.75) 108.51 (2.56) 99.68 (1.21) F = 0.26 0.609 Cholesterol (mg/dl) Mean (SE) 193.53 (0.63) 178.53 (1.40) 190.00 (1.07) 201.07 (0.95) 185.93 (2.62) 178.39 (1.42) F = 6.04 0.016 SCR (mg/dl), Mean (SE) 0.88 (0.00) 0.81 (0.01) 0.84 (0.00) 0.86 (0.00) 1.21 (0.05) 1.03 (0.02) F = 226.59 < .001 Uric acid(mg/dl), Mean (SE) 5.48 (0.02) 4.62 (0.04) 5.15 (0.03) 5.69 (0.03) 5.94 (0.07) 5.91 (0.06) F = 445.88 < .001 ACR, Mean (SE) 30.63 (2.26) 7.54 (0.25) 6.54 (0.15) 27.71 (3.01) 166.35 (37.24) 86.83 (12.11) F = 85.08 < .001 EGFR, Mean (SE) 100.25 (0.35) 109.42 (0.86) 107.11 (0.58) 100.57 (0.48) 69.15 (1.64) 81.20 (0.85) F = 725.16 < .001 DII, Mean (SE) 0.80 (0.04) 0.46 (0.09) 0.70 (0.06) 0.84 (0.04) 1.06 (0.12) 1.10 (0.09) F = 38.45 < .001 DII quantile, n (%) χ²=67.94 < .001 Q1 2359 (24.98) 274 (30.90) 521 (26.24) 1264 (24.48) 102 (19.75) 198 (19.28) Q2 2603 (25.02) 255 (26.18) 567 (25.57) 1402 (24.55) 134 (25.35) 245 (24.91) Q3 2676 (25.00) 232 (22.41) 542 (24.71) 1488 (25.62) 140 (25.36) 274 (25.11) Q4 2962 (25.00) 237 (20.51) 542 (23.48) 1585 (25.35) 197 (29.54) 401 (30.70) Q1 2359 (24.98) 274 (30.90) 521 (26.24) 1264 (24.48) 102 (19.75) 198 (19.28) 3.3 Association of the DII with the risk of advanced CKM syndrome Multivariate logistic regression analysis was employed to evaluate the prospective correlation between the DII score and the progression of CKD syndrome, as illustrated in Table 3 . Through analysis of four logistic regression models, each integrating progressively higher degrees of adjustment for possible confounding variables, a persistent positive correlation was identified between heightened DII levels and the progression of CKD syndrome. In unadjusted Model 1, the DII was significantly linked to an increased probability of advanced CKM syndrome (odds ratio [OR] 1.10, 95% confidence interval [CI] 1.06–1.15; P < 0.001). This relationship remained significant, although slightly attenuated, after adjustments for age, sex, race and ethnicity; education level in Model 2 (OR 1.08, 95% CI 1.04–1.13, P < 0.001); and further adjustments in Model 3, which accounted for PIR status, lifestyle factors, and physical activity (OR 1.08, 95% CI 1.02–1.14, p P = 0.006). Model 4 was further adjusted for hypertension, hyperlipidemia, diabetes disease, depression, sleep disorders, BMI, waist circumference, HDL-C, LDL-C, cholesterol, SCR, uric acid, and EGFR (OR 1.07, 95% CI 1.01–1.14; P = 0.017). In order to ascertain the sensitivity of this finding, we conducted a quartile-based analysis of the DII compared with the first quartile (Q1) across all the models, which revealed a dose‒response relationship; notably, the fourth quartile (Q4) dose‒response consistently demonstrated a significant association with CKM syndrome and its components. In Model 4, which was fully adjusted, the ORs for CKM syndrome across DII quartiles Q2, Q3, and Q4 were 1.29 (0.98–1.70, P = 0.070), 1.31 (0.97–1.77, P = 0.077), and 1.44 (1.08–1.92, P = 0.014), respectively. In addition, our investigation provided insight into the potential linear association between the DII and vulnerability to advanced CKD syndrome through the application of restricted cubic spline (RCS) curves (Fig. 2 ). The results revealed a significant linear association ( P nonlinear = 0.941) in Model 4. Table 3 Multivariate regression analysis of DII with advanced CKM syndrome. Variables Model1 Model2 Model3 Model4 OR (95%CI) P OR (95%CI) P OR (95%CI) P OR (95%CI) P DII 1.10 (1.06 ~ 1.15) < .001 1.08 (1.04 ~ 1.13) < .001 1.08 (1.02 ~ 1.14) 0.006 1.07 (1.01 ~ 1.14) 0.017 DII quantile Q1 1.00 (Reference) 1.00 (Reference) 1.00 (Reference) 1.00 (Reference) Q2 1.33 (1.07 ~ 1.65) 0.013 1.33 (1.07 ~ 1.66) 0.012 1.26 (0.97 ~ 1.64) 0.083 1.29 (0.98 ~ 1.70) 0.070 Q3 1.34 (1.07 ~ 1.67) 0.012 1.27 (1.01 ~ 1.59) 0.041 1.28 (0.98 ~ 1.68) 0.074 1.31 (0.97 ~ 1.77) 0.077 Q4 1.66 (1.35 ~ 2.04) < .001 1.56 (1.27 ~ 1.90) < .001 1.52 (1.17 ~ 1.98) 0.002 1.44 (1.08 ~ 1.92) 0.014 Model 1: Crude (not adjusted). Model 2: adjusted for sex, age, race and ethnicity, and education level based on Model 1; Model 3: adjusted for smoking status, marital status, alcohol consumption, physical activity, and PIR based on Model 2; Model 4: adjusted for hypertension, hyperlipidemia, diabetes disease, depression, sleep disorders, BMI, waist circumference, HDL-C, LDL-C, cholesterol, SCR, uric acid, and EGFR; OR: odds ratio; CI: confidence interval. 3.4 Subgroup analysis Subgroup analyses and interaction tests were performed to evaluate the robustness of the association between the DII and the risk of advanced CKM syndrome across various population subgroups (Fig. 3 ). The results revealed a positive association between the DII index and advanced CKM syndrome in females (OR = 1.13, 95% CI 1.06–1.22; P < 0.001). Additionally, participants with depression disorders presented a more pronounced association (OR = 1.19, 95% CI 1.04–1.37, P = 0.015) than did those with no depression problems. Similarly, sleeping disorders (OR = 1.16, 95% CI 1.05–1.28, P = 0.06) were compared with no sleeping disorders. Interaction analysis revealed a significant interaction effect related to sex ( P for interaction = 0.003), depression ( P for interaction = 0.013) and sleep disorders ( P for interaction = 0.019). In contrast, no substantial interactions were observed within other subgroups, indicating that these variables might not meaningfully alter the relationship between the DII and advanced CKM syndrome. Discussion This large-scale study analyzed 10,600 adult participants and demonstrated a significant association between DII and the progression stages of CKM syndrome in the United States population based on data (2005--2018) from the NHANES database. Those in advanced CKM stages correlated with older age, female predominance, socioeconomic disadvantage, and inflammatory/metabolic dysregulation (reduced eGFR, elevated ACR), overlapping with high-DII profiles. Multivariate analyses confirmed DII's independent association with CKM progression, with this association demonstrating notable linear characteristics. Additionally, subgroup analyses revealed sex- and depression- and sleep disorder-specific interaction effects. To our knowledge, this is the first study to investigate the association between DII and CKM syndrome in the United States. CKM syndrome, recently defined by the AHA, represents a systemic pathophysiological disorder characterized by dysfunctional cross-organ communication between metabolic dysregulation (manifested as obesity, insulin resistance, and related metabolic risk factors), CKD, and CVD 2 . Current epidemiological studies indicate that CKM syndrome poses a substantial public health burden in the United States, with over 25% of adults exhibiting at least one component of the triad 26 . The AHA categorizes CKM syndrome into five clinical stages, ranging from stage 0, which represents a risk-free state, to stage 4b, which involves advanced multiorgan failure, reflecting the continuum from early metabolic disturbances to overt clinical manifestations 6 . This framework necessitates early clinical intervention, emphasizing the imperative to reconceptualize CKM through an integrative pathophysiological paradigm that supersedes traditional organ-specific nosography 8 . The pathophysiology of CKM syndrome is characterized by a complex interplay of multiple mechanisms 27,28 . Metabolic disturbances, such as hyperglycemia and insulin resistance, initiate vascular damage through endothelial dysfunction, oxidative stress, and advanced glycation end-product (AGE) accumulation 28-31 . These processes directly activate proinflammatory pathways, triggering leukocyte recruitment and cytokine release, which exacerbates vascular and glomerular injury 30,32 . Concurrently, CKD amplifies systemic inflammation through sodium retention, renin‒angiotensin‒aldosterone system (RAAS) overactivation, and uremic toxin buildup 33 . Notably, uremic toxins such as indoxyl sulfate stimulate the NLRP3 inflammasome, driving the production of IL-1β and IL-18, which promote renal and cardiovascular inflammation, fibrosis, and arrhythmias 34 . A proinflammatory milieu is central to the CKM axis. Adipose tissue dysfunction in obesity results in the release of free fatty acids and adipokines (e.g., leptin and resistin), which activate macrophages and induce systemic insulin resistance while generating TNF-α and IL-6[ 35,36 . These cytokines perpetuate endothelial dysfunction and tissue fibrosis by stimulating TGF-β signaling and collagen deposition in both renal and cardiac tissues 36 . Furthermore, immune cell infiltration and chronic low-grade inflammation in metabolic diseases create a feedforward cycle that accelerates organ damage. Emerging therapies targeting inflammatory pathways highlight their mechanistic importance. Finerenone, a nonsteroidal mineralocorticoid receptor antagonist, demonstrates renal and cardioprotective effects by suppressing macrophage-mediated inflammation and profibrotic signaling 37 38 . These findings align with recent studies advocating inflammation-focused strategies to disrupt the CKM cascade, particularly through NLRP3 inflammasome inhibition and cytokine neutralization 39 . Together, these mechanisms underscore inflammation as a critical driver and therapeutic lever in cardiorenal-metabolic disease progression. The relationship between diet and inflammation is a critical area of research, with evidence suggesting that dietary patterns significantly influence systemic inflammation in the human body and related chronic diseases 20 . Dietary components can either exacerbate or mitigate this inflammatory state. Studies indicate that higher intake of whole grains is correlated with lower CRP levels, likely due to their antioxidant and fiber contents, which counteract oxidative stress 40 . Similarly, omega-3 polyunsaturated fatty acids (PUFAs) from fish inhibit proinflammatory cytokines such as TNF-α and IL-6, as shown in randomized trials. Conversely, diets high in saturated fats, trans fats, and processed meats promote inflammation 41 . A meta-analysis revealed that saturated fatty acids (SFAs) increase CRP and IL-6 levels, contributing to chronic inflammation 42 . Additionally, the gut microbiome mediates diet-induced inflammatory responses by reducing Prevotella bacteria, which are linked to inflammatory diseases such as arthritis 43 . Zheng et al . 44 demonstrated that inflammatory biomarkers mediate the association between triglyceride‒glucose‒body mass index and future CVD risk in early-stage CKM patients, suggesting a potential role for dietary interventions in primary prevention. Our study revealed intricate associations between DII and CKM syndrome components, highlighting the multifaceted pathophysiological effects of systemic inflammation. In metabolic tissues such as adipose and liver tissue, oxidative stress disrupts mitochondrial respiration, leading to disrupted insulin signaling, exacerbating dyslipidemia, and accelerating renal injury through glomerular hyperfiltration and fibrosis 29 , which aligns with the elevated ACR and reduced eGFR in the high-DII groups. Simultaneously, high-DII diets also dysregulate the hypothalamic–pituitary–adrenal (HPA) axis, increasing cortisol secretion and glucocorticoid receptor resistance. Excess cortisol promotes gluconeogenesis and lipolysis, exacerbating hyperglycemia and dyslipidemia, which is consistent with our findings of elevated BMI, waist, and HDL-C. Subsequently, we elucidated its association with CKM prognosis, demonstrating that varying DIIs are linked to statistically significant disparities in all-cause mortality and cardiovascular disease mortality among adult patients. The DII helps quantify the effect of the food consumed on the body’s inflammatory state, exploring the relationship between diet and CKM syndrome with a comprehensive assessment of a person’s dietary habits rather than a single food component, which provides a new perspective for interpreting the healthy diet associated with CKM symptoms. Our investigation has numerous strengths. This is the first large-scale observational study with data derived from the NHANES database to evaluate the association between DII and CKM syndrome innovatively, providing novel insights into diet-driven multiorgan pathology with high confidence. Second, we adjusted for covariates, including demographic variables, laboratory assessments, and comorbidities, and conducted subgroup analyses to validate the robustness of our findings. Despite its strengths, our study has certain limitations. First, although adjustments have been made for multiple potential confounding covariates, the potential for residual confounding remains plausible, which could influence the findings. Second, the DII was calculated from the 24-hour dietary recall interview, which can represent only habitual diet to a certain extent. Some recall bias is unavoidable and may not capture long-term dietary patterns. Third, the DII calculation excluded certain bioactive compounds (e.g., polyphenols) because of data constraints, potentially underestimating dietary anti-inflammatory potential. Ultimately, the present investigation predominantly relies on individuals hailing from the United States, and geographical limitations may constrain the generalizability and relevance of the research findings owing to elements such as environmental conditions and nutritional practices. Overall, a proinflammatory diet is associated with advanced CKM syndrome with increased all-cause and cardiovascular disease mortality in patients. The DII helps quantify the effect of the food consumed on the body’s inflammatory state and explores the relationship between diet and CKM syndrome, with a comprehensive assessment suggesting that bidirectional diet‒CKM interactions are mediated by chronic inflammation, oxidative stress, and endothelial dysfunction. Conclusion In conclusion, this study positions DII as a modifiable lever in CKM syndrome management, bridging dietary patterns with systemic metabolic and cardiovascular diseases. Its implications extend beyond observational epidemiology, urging translational efforts to harness anti-inflammatory nutrition as a therapeutic cornerstone in multimorbidity prevention. Declarations Data availability: The data used in this study were obtained from the NHANES, which was conducted between 2005 and 2018. The NHANES data are publicly available and can be accessed at the following URL: https://www.cdc.gov/nchs/nhanes/index.htm. Acknowledgments: All the authors express gratitude to the participants and staff of the NHANES for their contributions. Author contributions: TY. S were involved in the study design and wrote and edited the manuscript. FB.M. performed the data analysis. SF. F and YQ.Z. reviewed the manuscript and provided critical suggestions. All the authors have read and agreed to the published version of the manuscript. Conflict of interest: The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as potential conflicts of interest. Funding: The authors have not declared a specific grant for this research from any funding agency in the public, commercial or not-for-profit sectors. Ethics approval and consent to participate: The study involves the use of a publicly available dataset (NHANES), which was collected under ethical standards, including informed consent from all participants. All methods were carried out in accordance with the relevant guidelines and regulations. References Sebastian, S. A., Padda, I. & Johal, G. Cardiovascular-Kidney-Metabolic (CKM) syndrome: A state-of-the-art review. Curr Probl Cardiol 49 , 102344, doi:10.1016/j.cpcardiol.2023.102344 (2024). Larkin, H. Here's What to Know About Cardiovascular-Kidney-Metabolic Syndrome, Newly Defined by the AHA. 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Association of leukocyte telomere length with chronic kidney disease in East Asians with type 2 diabetes: a Mendelian randomization study. Clin Kidney J 14 , 2371-2376, doi:10.1093/ckj/sfab067 (2021). Lim, H. S., MacFadyen, R. J. & Lip, G. Y. Diabetes mellitus, the renin-angiotensin-aldosterone system, and the heart. Arch Intern Med 164 , 1737-1748, doi:10.1001/archinte.164.16.1737 (2004). Yamaguchi, K. et al. Indoxyl Sulfate Activates NLRP3 Inflammasome to Induce Cardiac Contractile Dysfunction Accompanied by Myocardial Fibrosis and Hypertrophy. Cardiovasc Toxicol 22 , 365-377, doi:10.1007/s12012-021-09718-2 (2022). Tilg, H., Ianiro, G., Gasbarrini, A. & Adolph, T. E. Adipokines: masterminds of metabolic inflammation. Nat Rev Immunol , doi:10.1038/s41577-024-01103-8 (2024). Kawai, T., Autieri, M. V. & Scalia, R. Adipose tissue inflammation and metabolic dysfunction in obesity. Am J Physiol Cell Physiol 320 , C375-C391, doi:10.1152/ajpcell.00379.2020 (2021). Ravid, J. D. & Laffin, L. J. Effects of Finerenone, a Novel Nonsteroidal Mineralocorticoid Receptor Antagonist, on Cardiovascular Disease, Chronic Kidney Disease, and Blood Pressure. Curr Cardiol Rep 24 , 1251-1259, doi:10.1007/s11886-022-01750-0 (2022). Gonzalez-Juanatey, J. R. et al. Cardiorenal benefits of finerenone: protecting kidney and heart. Ann Med 55 , 502-513, doi:10.1080/07853890.2023.2171110 (2023). Speer, T., Dimmeler, S., Schunk, S. J., Fliser, D. & Ridker, P. M. Targeting innate immunity-driven inflammation in CKD and cardiovascular disease. Nat Rev Nephrol 18 , 762-778, doi:10.1038/s41581-022-00621-9 (2022). Taskinen, R. E., Hantunen, S., Tuomainen, T. P. & Virtanen, J. K. The associations between whole grain and refined grain intakes and serum C-reactive protein. Eur J Clin Nutr 76 , 544-550, doi:10.1038/s41430-021-00996-1 (2022). Wall, R., Ross, R. P., Fitzgerald, G. F. & Stanton, C. Fatty acids from fish: the anti-inflammatory potential of long-chain omega-3 fatty acids. Nutr Rev 68 , 280-289, doi:10.1111/j.1753-4887.2010.00287.x (2010). Santos, S., Oliveira, A. & Lopes, C. Systematic review of saturated fatty acids on inflammation and circulating levels of adipokines. Nutr Res 33 , 687-695, doi:10.1016/j.nutres.2013.07.002 (2013). Yang, W. & Cong, Y. Gut microbiota-derived metabolites in the regulation of host immune responses and immune-related inflammatory diseases. Cellular & Molecular Immunology 18 , 866-877, doi:10.1038/s41423-021-00661-4 (2021). Wu, L. & Huang, Z. Elevated triglyceride glucose index is associated with advanced cardiovascular kidney metabolic syndrome. Scientific Reports 14 , 31352, doi:10.1038/s41598-024-82881-y (2024). Additional Declarations No competing interests reported. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-6224126","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":431288805,"identity":"568cca5b-e203-47dd-a7e8-8e4ecb797dfa","order_by":0,"name":"Tianyao Shi","email":"","orcid":"","institution":"Beijing Institute of Pharmacology and Toxicology","correspondingAuthor":false,"prefix":"","firstName":"Tianyao","middleName":"","lastName":"Shi","suffix":""},{"id":431288806,"identity":"0054d4fe-95d5-44fe-82d5-6e380359270e","order_by":1,"name":"Fanbo Mei","email":"","orcid":"","institution":"Chinese PLA General Hospital","correspondingAuthor":false,"prefix":"","firstName":"Fanbo","middleName":"","lastName":"Mei","suffix":""},{"id":431288807,"identity":"7659342e-c22f-4404-bca3-e2d3cb5684b9","order_by":2,"name":"Xiao Bai","email":"","orcid":"","institution":"Chinese PLA General Hospital","correspondingAuthor":false,"prefix":"","firstName":"Xiao","middleName":"","lastName":"Bai","suffix":""},{"id":431288808,"identity":"59b521b4-ca23-4abd-98ae-123771a689df","order_by":3,"name":"Chen Mo","email":"","orcid":"","institution":"Chinese PLA General Hospital","correspondingAuthor":false,"prefix":"","firstName":"Chen","middleName":"","lastName":"Mo","suffix":""},{"id":431288809,"identity":"e11c6a8a-3b6b-418b-9aaf-295876906480","order_by":4,"name":"Minghe Liu","email":"","orcid":"","institution":"Chinese PLA General Hospital","correspondingAuthor":false,"prefix":"","firstName":"Minghe","middleName":"","lastName":"Liu","suffix":""},{"id":431288810,"identity":"37453764-4759-42de-b672-764663d43efa","order_by":5,"name":"Jiantang Guo","email":"","orcid":"","institution":"Chinese PLA General Hospital","correspondingAuthor":false,"prefix":"","firstName":"Jiantang","middleName":"","lastName":"Guo","suffix":""},{"id":431288811,"identity":"50519d55-96b5-4fee-9fd0-21e2635d262f","order_by":6,"name":"Li Xu","email":"","orcid":"","institution":"Chinese PLA General Hospital","correspondingAuthor":false,"prefix":"","firstName":"Li","middleName":"","lastName":"Xu","suffix":""},{"id":431288812,"identity":"c03cb313-57d9-47ae-9366-a0d0ebabdf47","order_by":7,"name":"Jing Zhao","email":"","orcid":"","institution":"Chinese PLA General Hospital","correspondingAuthor":false,"prefix":"","firstName":"Jing","middleName":"","lastName":"Zhao","suffix":""},{"id":431288813,"identity":"234580f5-69ee-4d5c-86d6-7959b4933cd3","order_by":8,"name":"Yongqing Zhang","email":"","orcid":"","institution":"Chinese PLA General Hospital","correspondingAuthor":false,"prefix":"","firstName":"Yongqing","middleName":"","lastName":"Zhang","suffix":""},{"id":431288814,"identity":"9713411b-c15f-4ea0-be0b-b15931e3c6e0","order_by":9,"name":"Shufang Feng","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA00lEQVRIiWNgGAWjYDACCQaGDyCavb2x8cEHIrUwzgDRPGcONxvOIE3LjfQ2aQ5idMjP7jFs+LmjloFH8mGDNAODnZxuAwEtBnfOGDb2njnOwCOd2GBcwJBsbHaAkBaJHPMHvG3HGOyBWpJnMBxI3EZIi/yMHMPGv0AtPJIHGw7zEKOF4UaOYTNvWw0DjwRjYzNRWgxupBU2y7YdYODhSWxmnGFAhF/kZyRvbHzbVsfAw378+Y8PFXZyBLVAweH6BoilxCkHgTrilY6CUTAKRsHIAwAgNUQ6sHWaDAAAAABJRU5ErkJggg==","orcid":"","institution":"Chinese PLA General Hospital","correspondingAuthor":true,"prefix":"","firstName":"Shufang","middleName":"","lastName":"Feng","suffix":""}],"badges":[],"createdAt":"2025-03-14 07:08:14","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-6224126/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-6224126/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":78883978,"identity":"027e4266-fc05-4cb4-9902-62a46a3e8f49","added_by":"auto","created_at":"2025-03-20 09:15:57","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":204134,"visible":true,"origin":"","legend":"\u003cp\u003eFlow chart of study population selection.\u003c/p\u003e","description":"","filename":"Figure1.png","url":"https://assets-eu.researchsquare.com/files/rs-6224126/v1/42efa5361e0dc56c27a84305.png"},{"id":78882618,"identity":"2bfdf7d9-5305-4b2a-8690-1e0019c4bfb8","added_by":"auto","created_at":"2025-03-20 08:59:57","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":2082046,"visible":true,"origin":"","legend":"\u003cp\u003eMultivariable-adjusted restricted cubic spline model for the association between DII and advancedCKM syndrome.\u003c/p\u003e","description":"","filename":"Figure2.png","url":"https://assets-eu.researchsquare.com/files/rs-6224126/v1/182bbf98d2551ded32d40ef4.png"},{"id":78882620,"identity":"770e1dc0-18c9-4947-9647-02ee07a6bede","added_by":"auto","created_at":"2025-03-20 08:59:57","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":5420089,"visible":true,"origin":"","legend":"\u003cp\u003eAssociations between DII and advancedCKM syndrome in different subgroups.\u003c/p\u003e","description":"","filename":"Figure.3.png","url":"https://assets-eu.researchsquare.com/files/rs-6224126/v1/5ae07e8eb0e90e0f213311e9.png"},{"id":86121854,"identity":"5ed27ca5-b821-49cd-bc6c-3de0e9ee0df4","added_by":"auto","created_at":"2025-07-07 04:01:37","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":10578104,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-6224126/v1/1c7713c5-fe9a-4b5b-92f6-907f1fe1b09e.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Associations between dietary inflammatory index and cardiovascular kidney metabolic syndrome: insights from NHANES 2005–2018","fulltext":[{"header":"1. Introduction","content":"\u003cp\u003eCardiovascular-kidney-metabolic (CKM) syndrome, recently conceptualized by the American Heart Association (AHA), represents a systemic condition characterized by the bidirectional interplay among cardiovascular disease (CVD), chronic kidney disease (CKD), and metabolic disorders such as diabetes and obesity\u003csup\u003e\u003cspan additionalcitationids=\"CR2\" citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e\u003c/sup\u003e. This syndrome involves interconnected pathophysiological mechanisms, particularly chronic inflammation \u003csup\u003e\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e\u003c/sup\u003e, oxidative stress, and endothelial dysfunction\u003csup\u003e\u003cspan additionalcitationids=\"CR6\" citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e\u003c/sup\u003e, which drive multiorgan damage, increase the risk of adverse clinical outcomes\u003csup\u003e\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e\u003c/sup\u003e, and impact the morbidity and mortality of adults\u003csup\u003e\u003cspan additionalcitationids=\"CR10 CR11\" citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e\u003c/sup\u003e. Systemic low-grade inflammation serves as a pivotal driver of CKM progression, functioning through its sustained activation of proinflammatory cascades that potentiate multiorgan dysfunction\u003csup\u003e\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e\u003c/sup\u003e, which exacerbates insulin resistance, renal fibrosis, and atherosclerotic processes, creating a vicious cycle of organ dysfunction\u003csup\u003e\u003cspan additionalcitationids=\"CR14\" citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e\u003c/sup\u003e. Emerging evidence suggests that modifiable lifestyle factors, particularly diet, may play a pivotal role in modulating these inflammatory pathways and CKM progression\u003csup\u003e\u003cspan additionalcitationids=\"CR17 CR18\" citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e\u003c/sup\u003e. The implementation of dietary interventions that focus on anti-inflammatory foods and proper nutrition could reverse some of the detrimental effects of chronic kidney disease and obesity, ultimately improving overall health outcomes.\u003c/p\u003e \u003cp\u003eThe dietary inflammatory index (DII) is an epidemiologically validated metric that quantifies the inflammatory potential of dietary patterns\u003csup\u003e\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e\u003c/sup\u003e. This study provides a strong methodological framework for investigating the relationship between inflammation and CKM syndrome. There are potential correlations between CKM syndrome and DII, particularly in terms of their shared inflammatory pathways. Proinflammatory diets rich in refined carbohydrates, saturated fats, and processed meats may aggravate systemic inflammation, thereby accelerating the progression of CKD (e.g., via albuminuria and glomerulosclerosis), CVD (e.g., plaque instability and heart failure), and metabolic dysregulation (e.g., insulin resistance)\u003csup\u003e\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e,\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e\u003c/sup\u003e. Conversely, anti-inflammatory diets rich in antioxidants, fiber, and omega-3 fatty acids could disrupt this cascade. In contrast, metabolic syndrome (MetS) and CKM share overlapping features, and the unique integration of renal and cardiovascular endpoints in CKM syndrome raises critical questions about whether dietary inflammation further amplifies cross-organ risk. Interventions targeting inflammation, such as the use of a nonsteroidal mineralocorticoid receptor antagonist, have shown efficacy in reducing cardiovascular and renal events in CKM patients, highlighting inflammation as a therapeutic target\u003csup\u003e\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e\u003c/sup\u003e. Similarly, dietary modifications that lower DII scores might synergize with pharmacological strategies to improve outcomes. However, current research on DII has focused predominantly on isolated MetS components or single-organ diseases, leaving a gap in understanding its role in the multiorgan context of CKM\u003csup\u003e\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eOur study uses data from the 2005\u0026ndash;2018 NHANES, a United States database, to assess whether the DII is associated with the risk of CKM syndrome and its components. This paper aims to explore the conceptual frameworks of CKM syndrome and DII and investigate their potential interconnections to inform future research and clinical practice.\u003c/p\u003e"},{"header":"2. Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e\n \u003ch2\u003e2.1. Study population\u003c/h2\u003e\n \u003cp\u003eThis cross-sectional investigation employed data from the esteemed National Health and Nutrition Examination Survey (NHANES). The NHANES is a nationally representative survey conducted by the Centers for Disease Control and Prevention in the United States; it employs a stratified multistage probability sampling technique to assess nutritional and health conditions in the country. NHANES has received approval from the National Center for Health Statistics Ethics Review Committee, and all participants provided written informed consent. The secondary analysis did not require additional Institutional Review Board approval. The NHANES data are openly accessible and can be retrieved online at (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.cdc.gov/nchs/nhanes/index.htm\u003c/span\u003e\u003c/span\u003e). The data used in the present investigation were meticulously selected from seven consecutive cycles from 2005 through 2018. Figure \u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003e delineates the processes of inclusion and exclusion relevant to our study. (1) Initially, our analysis included data from 2005\u0026ndash;2018, culminating in the recruitment of n\u0026thinsp;=\u0026thinsp;70190 participants. (2) Subsequently, 30441 individuals under the age of 20 years and those with incomplete sample weights (WTSAF2YR) were systematically excluded from the analysis. (3) Furthermore, participants deficient in data concerning CKM syndrome and the Dietary Inflammatory Index (DII), along with associated covariates, were also excluded, ultimately yielding a cohort of 10600 eligible participants. Figure \u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003e provides a flowchart that elucidates the participant selection methodology.\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec4\" class=\"Section2\"\u003e\n \u003ch2\u003e2.2. Dietary inflammatory index (DII)\u003c/h2\u003e\n \u003cp\u003eThe NHANES acquires dietary data at mobile examination centers (MECs) through interviews based on 24-hour dietary recalls. This investigation evaluated the nutrient consumption of participants, excluding dietary supplements and medications, by utilizing the average of two consistent 24-hour dietary recall assessments. The DII, introduced by Shivappa \u003cem\u003eet al\u003c/em\u003e. in 2014\u003csup\u003e20\u003c/sup\u003e, serves as a metric for assessing the inflammatory potential inherent in an individual\u0026apos;s dietary habits. Importantly, although the preliminary DII computation necessitated the inclusion of 45 distinct dietary components, the NHANES database featured only 27 components and accurately reflected dietary inflammation. In this study, a broad spectrum of dietary parameters, including a range of nutrients and components such as protein, carbohydrates, dietary fiber, cholesterol, polyunsaturated fatty acids (PUFAs), saturated fats, monounsaturated fatty acids (MUFAs), n-3 fatty acids, n-6 fatty acids, and alcohol, was examined. In addition to a spectrum of vitamins encompassing B2, B12, B6, A, C, and E, \u0026beta;-carotene, caffeine, folic acid, and essential minerals such as iron (Fe), magnesium (Mg), niacin, riboflavin, selenium (Se), thiamine, and zinc (Zn) were subjected to analysis. The subjects were stratified into quartiles according to their DII scores, thereby enabling a comprehensive investigation of the relationship between dietary inflammatory potential and CKM syndrome. The DII was calculated via the following Eq.\u0026nbsp;2\u003csup\u003e0\u003c/sup\u003e:\u003c/p\u003e\n \u003cp\u003e𝐷𝐼𝐼= (Z score\u0026prime; \u0026times; the inflammatory effect score of each dietary component)\u003c/p\u003e\n \u003cp\u003eZ score= (Daily mean intake\u0026ndash;Global daily mean intake)/Global standard deviation\u003c/p\u003e\n \u003cp\u003e𝑍 𝑠𝑐𝑜𝑟𝑒\u0026prime;=𝑍 𝑠𝑐𝑜𝑟𝑒\u0026rarr; (𝑐𝑜𝑛𝑣𝑒𝑟𝑡𝑒𝑑 𝑡𝑜 𝑎 𝑝𝑒𝑟𝑐𝑒𝑛𝑡𝑖𝑙𝑒 𝑠𝑐𝑜𝑟𝑒) \u0026times;2\u0026ndash;1\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec5\" class=\"Section2\"\u003e\n \u003ch2\u003e2.3. CKM\u003c/h2\u003e\n \u003cp\u003eAccording to the definition provided by the AHA, CKM syndrome is delineated as a pathological condition engendered by interconnections among CVD, CKD, obesity, and diabetes and is systematically classified into five distinct stages\u003csup\u003e\u003cspan class=\"CitationRef\"\u003e2\u003c/span\u003e\u003c/sup\u003e. In the context of our investigation, stage 0 is identified as a state of normal physiological function. Stage 1 is distinguished by the presence of one or more of the following criteria: (1). Body mass index (BMI) of \u0026ge;\u0026thinsp;23 kg/m\u0026sup2; for individuals of Asian descent and \u0026gt;\u0026thinsp;25 kg/m\u0026sup2; for other demographic groups; (2). Increased waist circumference measurements of \u0026ge;\u0026thinsp;80 cm for females and \u0026ge;\u0026thinsp;90 cm for males in the Asian population; \u0026ge;88 cm for females and \u0026ge;\u0026thinsp;102 cm for males in alternative populations; (3). Prediabetes is indicated by HbA1c levels ranging from 5.7\u0026ndash;6.4%, fasting glucose levels between 100 and 125 mg/dL, and the absence of additional metabolic risk factors, CKD, or CVD. Stage 2 is characterized by the presence of multiple metabolic risk factors, which may include elevated triglycerides, hypertension, diabetes, or metabolic syndrome, as well as moderate-to-high-risk CKD per the Kidney Disease: Improving Global Outcomes (KDIGO) guidelines. Stages 3 and 4 are collectively categorized as advanced CKM syndrome, which includes individuals either diagnosed with or at elevated risk for developing cardiovascular disease \u003csup\u003e\u003cspan class=\"CitationRef\"\u003e23\u003c/span\u003e\u003c/sup\u003e. Stage 3 includes subclinical CVD. equivalents, as assessed by the AHA\u0026rsquo;s PREVENT equations [11], and Stage 4 is marked by clinical manifestations of CVD with metabolic disorders. For detailed descriptions of the stage definitions, refer to reference \u003csup\u003e\u003cspan class=\"CitationRef\"\u003e9\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec6\" class=\"Section2\"\u003e\n \u003ch2\u003e2.4. Covariates\u003c/h2\u003e\n \u003cp\u003eOn the basis of the previous literature, factors that have been proven to be correlated with CKM and the dietary inflammatory index were included. The study investigated demographic characteristics, including sex, age (\u0026lt;\u0026thinsp;60, \u0026ge;\u0026thinsp;60), race and ethnicity (Hispanics, White, Black, Asian, other races or ethnicities), educational level (less than 9th grade, 9-11th grade, high school grade, some college, college graduate or above), PIR (poverty income ratio: low- to middle-income level PIR\u0026thinsp;\u0026lt;\u0026thinsp;5, and high-income level, PIR\u0026thinsp;\u0026ge;\u0026thinsp;5), and marital status; further information on lifestyle habits and comorbidities, such as smoking, alcohol consumption, and intensity of physical activity (low, high physical activity), was also collected \u003csup\u003e\u003cspan class=\"CitationRef\"\u003e24\u003c/span\u003e,\u003cspan class=\"CitationRef\"\u003e25\u003c/span\u003e\u003c/sup\u003e. Self-reported data concerning general health conditions, including hypertension, diabetes, depression, sleeping disorders, and hyperlipemia, were also collected. Physical and laboratory tests such as BMI (\u0026lt;\u0026thinsp;25, \u0026ge;\u0026thinsp;25), waist count, high-density lipoprotein cholesterol (HDL-C), total cholesterol, low-density lipoprotein cholesterol (LDL-C), serum creatinine (SCR) and uric acid levels, the albumin/creatinine ratio (ACR), and the glomerular filtration rate (eGFR) were selected as potential confounders. Furthermore, the status of all-cause mortality, mortality attributable to cardiovascular diseases, and mortality related to cerebrovascular diseases was determined through probabilistic linkage to the National Death Index, with data collected until December 31, 2019.\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec7\" class=\"Section2\"\u003e\n \u003ch2\u003e2.5. Statistical analysis\u003c/h2\u003e\n \u003cp\u003eData processing and analysis were performed via R version 4.4.0 (2024-04-24), along with the Storm Statistical Platform (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ewww.medsta.cn/software\u003c/span\u003e\u003c/span\u003e). To account for disparities in sampling probabilities and instances of nonresponse, all participant data were weighted according to NHANES examination weights and fasting subsample weights. The baseline characteristics were systematically evaluated across the CKM syndrome stages within the two specified intervals via the Rao‒Scott chi-square test for categorical variables, alongside ANOVA and the Kruskal‒Wallis test, which were adjusted for sampling weights concerning continuous variables. Continuous variables were expressed as the means in conjunction with standard errors. In contrast, categorical variables were articulated as percentages, categorized on the basis of quartiles (Q1\u0026ndash;Q4) of the DII index and stages of CKM syndrome. Descriptive analyses were executed employing weighted one-way ANOVA for continuous variables and weighted chi-square tests for categorical variables. Multivariate logistic regression analyses were performed to investigate the association between the DII and the progression of advanced CKM syndrome, resulting in the development of three distinct statistical inference models. The crude model (Model 1) was unadjusted; Model 2 was adjusted for age, sex, ethnicity, and education level; Model 3 was further adjusted for smoking status, marital status, drinking, physical activity, and PIR based on Model 2; and Model 4 was adjusted for hypertension, hyperlipidemia, diabetes disease, depression, sleep disorders, BMI, waist, HDL-C, LDL-C, cholesterol, SCR, and uric acid based on Model 3. Restricted cubic spline (RCS) analysis was used to explore the possible relationship between the DII and the likelihood of advanced CKM syndrome. Stratification and interaction analyses were conducted according to sex, age, race, educational level, PIR and physical activity. The results were considered statistically significant if \u003cem\u003eP\u003c/em\u003e values were less than 0.05.\u003c/p\u003e\n\u003c/div\u003e"},{"header":"3. Results","content":"\u003cdiv id=\"Sec9\" class=\"Section2\"\u003e\n \u003ch2\u003e3.1. Baseline characteristics of participants stratified by the quartiles of the DII.\u003c/h2\u003e\n \u003cp\u003eTable \u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003e presents the participants\u0026apos; key characteristics sorted by DII quartile. This study enrolled a total of 10600 participants, with 7194 (73.96%) under 60 years old and 3406 (26.04%) over 60 years. A total of 48.87% of them were male. A total of 2,359 individuals (22.25%) were in the DII quantile 1 group, 2,603 (24.56%) were in the DII quantile 2 group, 2,676 (25.25%) were in the DII quantile 3 group, and 2,962 (27.94%) were in the DII quantile 4 group. Statistically significant differences (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05) were observed among the four groups for the following variables: BMI; waist count; HDL-C; uric acid; and covariates, including marital status, PIR, ACR, EGFR, DII, sex, race, and educational attainment during adulthood. In contrast, no statistically significant differences (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026gt;\u0026thinsp;0.05) were found for LDL-C, cholesterol, SCR, age, or smoking history. Compared with participants in the lowest DII quartile, those with a higher DII index were more likely to be female, White or Asian in ethnicity, widowed or separated, have a lower PIR, smoke, not drink, have a higher BMI, have a waist circumference, and be physically active. There was a higher all-cause mortality and cardiovascular disease mortality rate among participants in the highest quartile than in those in the lowest quartile. Analytical evaluations indicated that the incidence of chronic illnesses, including hypertension, diabetes mellitus, depression, and sleep disorders, was markedly elevated in the group exhibiting the highest DII compared with the lowest group.\u003c/p\u003e\n \u003cdiv class=\"gridtable\"\u003e\u0026nbsp;\u003ctable id=\"Tab1\" border=\"1\"\u003e\n \u003ccaption language=\"En\"\u003e\n \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e\n \u003cdiv class=\"CaptionContent\"\u003e\n \u003cp\u003eBaseline characteristics classified by DII quartiles. The P value was calculated via the weighted chi-square test. Q: quartile; SE: standard error; F: ANOVA; \u0026chi;\u0026sup2;: chi-square test. Abbreviations: BMI: body mass index; PIR: poverty income ratio; HDL-C: high-density lipoprotein cholesterol; LDL-C: low-density lipoprotein cholesterol; SCR: serum creatinine; ACR: albumin-to-creatinine ratio; eGFR: estimated glomerular filtration rate.\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\" rowspan=\"2\"\u003e\n \u003cp\u003eCharacteristic\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" colspan=\"2\" rowspan=\"2\"\u003e\n \u003cp\u003eTotal (n\u0026thinsp;=\u0026thinsp;10600)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" colspan=\"4\"\u003e\n \u003cp\u003eQuartiles of the DII index prevalence, % (95% CI)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" rowspan=\"2\"\u003e\n \u003cp\u003eStatistic\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" colspan=\"2\" rowspan=\"2\"\u003e\n \u003cp\u003e\u003cem\u003eP\u003c/em\u003e\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eQ1 (n\u0026thinsp;=\u0026thinsp;2359)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eQ2 (n\u0026thinsp;=\u0026thinsp;2603)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eQ3 (n\u0026thinsp;=\u0026thinsp;2676)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eQ4(n\u0026thinsp;=\u0026thinsp;2962)\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003eAge group (years)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e\u0026chi;\u0026sup2;=13.09\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.054\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;60\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e7194 (73.96)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1668 (76.44)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1796 (72.29)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1816 (73.91)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1914 (73.21)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e\u0026ge;\u0026thinsp;60\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3406 (26.04)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e691 (23.56)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e807 (27.71)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e860 (26.09)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1048 (26.79)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003eSex, n (%))\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e\u0026chi;\u0026sup2;=438.03\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026lt;\u0026thinsp;.001\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003eMale\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e5215 (48.87)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1486 (62.52)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1396 (52.73)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1246 (45.51)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1087 (34.71)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003eFemale\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e5385 (51.13)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e873 (37.48)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1207 (47.27)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1430 (54.49)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1875 (65.29)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003eRace and ethnicity, n (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e\u0026chi;\u0026sup2;=105.38\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026lt;\u0026thinsp;.001\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003eHispanics\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1592 (7.62)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e385 (8.02)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e425 (8.23)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e383 (7.39)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e399 (6.86)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003eWhite\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1027 (5.19)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e184 (4.14)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e254 (5.19)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e268 (5.02)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e321 (6.40)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003eBlack\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e4977 (70.86)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1180 (74.83)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1214 (70.34)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1246 (70.59)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1337 (67.69)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003eAsian\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2059 (9.90)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e359 (6.66)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e438 (8.56)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e563 (10.96)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e699 (13.40)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003eOther race or ethnicity\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e945 (6.44)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e251 (6.36)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e272 (7.68)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e216 (6.03)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e206 (5.66)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003eEducation levels, n (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e\u0026chi;\u0026sup2;=501.14\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026lt;\u0026thinsp;.001\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003eLess than 9th Grade\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e932 (4.61)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e157 (3.28)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e213 (4.22)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e226 (4.63)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e336 (6.31)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e9-11th Grade\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1477 (10.46)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e238 (7.27)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e316 (9.10)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e400 (11.55)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e523 (13.92)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003eHigh school Grade\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2400 (21.87)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e432 (17.05)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e542 (18.83)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e641 (23.96)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e785 (27.66)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003eSome college\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3109 (31.32)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e645 (27.62)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e791 (31.75)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e814 (33.07)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e859 (32.83)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003eCollege graduate or above\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2682 (31.74)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e887 (44.78)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e741 (36.10)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e595 (26.78)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e459 (19.28)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003eMarriage status, n (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e\u0026chi;\u0026sup2;=147.56\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026lt;\u0026thinsp;.001\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003eMarried\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e5553 (56.19)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1392 (61.66)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1429 (58.09)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1391 (56.79)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1341 (48.25)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003eWidowed\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e768 (5.31)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e103 (3.23)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e171 (4.93)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e200 (5.88)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e294 (7.19)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003eDivorced\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1145 (10.46)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e231 (9.48)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e272 (10.30)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e268 (9.62)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e374 (12.43)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003eSeparated\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e332 (1.97)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e52 (1.27)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e67 (1.50)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e105 (2.25)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e108 (2.85)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003eNever married\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1923 (17.99)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e388 (16.37)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e443 (16.63)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e513 (18.79)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e579 (20.15)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003eLiving with partner\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e879 (8.09)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e193 (8.00)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e221 (8.54)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e199 (6.68)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e266 (9.14)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003ePIR, n (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e\u0026chi;\u0026sup2;=243.63\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026lt;\u0026thinsp;.001\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e\u0026lt;5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e8648 (73.55)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1719 (64.32)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2067 (70.92)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2236 (76.62)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2626 (82.35)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e\u0026ge;5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1952 (26.45)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e640 (35.68)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e536 (29.08)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e440 (23.38)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e336 (17.65)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003eSmoking status, n (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e\u0026chi;\u0026sup2;=26.35\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.002\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003eNo\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e5798 (54.08)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1304 (53.84)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1491 (57.80)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1444 (53.85)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1559 (50.81)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e4802 (45.92)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1055 (46.16)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1112 (42.20)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1232 (46.15)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1403 (49.19)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003eDrinking, n (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e\u0026chi;\u0026sup2;=140.63\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026lt;\u0026thinsp;.001\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003eNo\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2856 (21.92)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e472 (15.47)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e626 (20.79)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e731 (22.60)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1027 (28.82)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e7744 (78.08)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1887 (84.53)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1977 (79.21)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1945 (77.40)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1935 (71.18)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003ePhysical activity, n (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e\u0026chi;\u0026sup2;=211.19\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026lt;\u0026thinsp;.001\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003eLow physical activity\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3921 (33.29)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e636 (24.13)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e912 (31.48)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1019 (34.92)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1354 (42.63)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003eHigh physical activity\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e6679 (66.71)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1723 (75.87)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1691 (68.52)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1657 (65.08)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1608 (57.37)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003eHypertension, n (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e\u0026chi;\u0026sup2;=16.18\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.037\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003eNo\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e6321 (62.89)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1464 (64.79)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1619 (64.30)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1589 (62.48)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1649 (59.98)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e4279 (37.11)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e895 (35.21)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e984 (35.70)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1087 (37.52)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1313 (40.02)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003eDiabetes, n (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e\u0026chi;\u0026sup2;=30.31\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026lt;\u0026thinsp;.001\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003eNo\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e8466 (84.21)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1961 (86.80)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2123 (85.28)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2099 (82.98)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2283 (81.80)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2134 (15.79)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e398 (13.20)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e480 (14.72)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e577 (17.02)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e679 (18.20)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003eDepression, n (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e\u0026chi;\u0026sup2;=71.54\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026lt;\u0026thinsp;.001\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003eNo\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e9745 (92.97)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2226 (94.78)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2429 (94.88)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2455 (92.51)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2635 (89.73)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e855 (7.03)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e133 (5.22)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e174 (5.12)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e221 (7.49)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e327 (10.27)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003eSleeping disorder, n (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e\u0026chi;\u0026sup2;=49.56\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026lt;\u0026thinsp;.001\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003eNo\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e9018 (85.73)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2078 (88.78)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2231 (86.89)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2267 (84.95)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2442 (82.32)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1582 (14.27)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e281 (11.22)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e372 (13.11)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e409 (15.05)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e520 (17.68)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003eHyperlipemia, n (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e\u0026chi;\u0026sup2;=22.45\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.002\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003eNo\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3016 (28.84)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e753 (32.35)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e757 (28.50)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e732 (27.41)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e774 (27.13)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e7584 (71.16)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1606 (67.65)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1846 (71.50)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1944 (72.59)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2188 (72.87)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003eAll-cause mortality, n (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e\u0026chi;\u0026sup2;=36.94\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026lt;\u0026thinsp;.001\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003eNo\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e9536 (92.58)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2186 (94.93)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2358 (92.76)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2397 (91.93)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2595 (90.68)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1064 (7.42)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e173 (5.07)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e245 (7.24)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e279 (8.07)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e367 (9.32)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colspan=\"7\"\u003e\n \u003cp\u003eCardiovascular disease mortality, n (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e\u0026chi;\u0026sup2;=11.17\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.005\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003eNo\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e10342 (98.26)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2315 (98.75)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2546 (98.32)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2612 (98.37)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2869 (97.58)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e258 (1.74)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e44 (1.25)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e57 (1.68)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e64 (1.63)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e93 (2.42)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colspan=\"7\"\u003e\n \u003cp\u003eCerebrovascular disease mortality, n (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e\u0026chi;\u0026sup2;=0.63\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.841\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003eNo\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e10536 (99.61)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2345 (99.68)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2588 (99.55)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2659 (99.60)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2944 (99.62)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e64 (0.39)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e14 (0.32)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e15 (0.45)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e17 (0.40)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e18 (0.38)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003eBMI, n (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e\u0026chi;\u0026sup2;=46.16\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026lt;\u0026thinsp;.001\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e\u0026lt;25\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3139 (30.89)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e805 (35.80)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e776 (31.06)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e748 (28.77)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e810 (27.96)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e\u0026ge;25\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e7461 (69.11)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1554 (64.20)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1827 (68.94)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1928 (71.23)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2152 (72.04)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003eWaist (cm), Mean (SE)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e98.86 (0.27)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e97.73 (0.49)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e98.56 (0.45)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e99.36 (0.46)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e99.79 (0.38)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003eF\u0026thinsp;=\u0026thinsp;12.92\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026lt;\u0026thinsp;.001\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003eHDL-C (mg/dl), Mean (SE)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e54.93 (0.27)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e55.52 (0.44)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e54.99 (0.43)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e55.34 (0.53)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e53.86 (0.42)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003eF\u0026thinsp;=\u0026thinsp;5.81\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.018\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003eLDL-C (mg/dl), Mean (SE)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e114.82 (0.51)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e113.50 (1.05)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e115.13 (0.90)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e115.86 (0.95)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e114.78 (0.91)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003eF\u0026thinsp;=\u0026thinsp;1.13\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.292\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003eCholesterol (mg/dl) Mean (SE\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e193.53 (0.63)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e192.45 (1.18)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e193.46 (0.93)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e195.67 (1.22)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e192.53 (1.11)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003eF\u0026thinsp;=\u0026thinsp;0.25\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.621\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003eSCR (mg/dl), Mean (SE)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.88 (0.00)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.89 (0.01)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.88 (0.01)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.87 (0.01)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.87 (0.01)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003eF\u0026thinsp;=\u0026thinsp;2.45\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.121\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003eUric acid(mg/dl) Mean (SE)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e5.48 (0.02)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e5.54 (0.04)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e5.50 (0.04)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e5.52 (0.04)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e5.36 (0.03)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003eF\u0026thinsp;=\u0026thinsp;9.06\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.003\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003eACR, Mean (SE)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e30.63 (2.26)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e21.32 (2.83)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e32.46 (5.04)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e28.01 (3.43)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e40.72 (5.81)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003eF\u0026thinsp;=\u0026thinsp;7.32\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.008\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003eEGFR, Mean (SE)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e100.25 (0.35)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e103.02 (0.65)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e100.94 (0.59)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e99.78 (0.57)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e97.27 (0.58)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003eF\u0026thinsp;=\u0026thinsp;59.23\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026lt;\u0026thinsp;.001\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003eCKM, n (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e\u0026chi;\u0026sup2;=67.94\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026lt;\u0026thinsp;.001\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003eStage 0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e998 (11.29)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e274 (13.97)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e255 (11.82)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e232 (10.13)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e237 (9.27)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003eStage 1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2172 (22.36)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e521 (23.49)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e567 (22.85)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e542 (22.10)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e542 (21.00)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003eStage 2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e5739 (54.11)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1264 (53.03)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1402 (53.08)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1488 (55.45)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1585 (54.86)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003eStage 3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e573 (3.39)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e102 (2.68)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e134 (3.44)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e140 (3.44)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e197 (4.01)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003eStage 4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1118 (8.85)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e198 (6.83)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e245 (8.80)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e274 (8.89)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e401 (10.86)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n \u003c/div\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec10\" class=\"Section2\"\u003e\n \u003ch2\u003e3.2. Baseline characteristics of participants stratified by CKM syndrome stage\u003c/h2\u003e\n \u003cp\u003eTable \u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003e shows the baseline characteristics of the participants stratified by stage of CKM syndrome. The mean DII index observed in the study was 0.8\u0026thinsp;\u0026plusmn;\u0026thinsp;0.04. A total of 998 individuals (9.42%) were classified as CKM-stage 0, 2,172 (20.49%) as stage 1, 5,739 (54.14%) as stage 2, 573 (5.41%) as stage 3, and 1,118 (10.55%) as stage 4. Statistically significant differences (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05) were observed among the five groups for the following variables: BMI; waist, HDL-C, cholesterol, serum creatinine, ACR, and EGFR levels; and covariates, including age, marital status, PIR, sex, race, educational attainment during adulthood, and smoking history. However, no statistically significant difference (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026gt;\u0026thinsp;0.05) was found for LDL-C. Compared with participants in the low CKM stage, those in the advanced CKM syndrome stage tended to exhibit certain distinguishing characteristics. These characteristics included being older, female, having relatively lower education, Asian, widowed and separated, having a lower PIR, smoking and not drinking alcohol, and engaging in a lower intensity of physical activity. Furthermore, individuals in the advanced stage of CKM syndrome demonstrated a higher incidence of all-cause mortality, as well as mortality due to cardiovascular diseases, than did their counterparts in the lowest quartile. Analyses revealed that the prevalence of chronic illnesses, such as hypertension, diabetes mellitus, depression, and sleep disorders, was significantly greater in the highest CKM cohort than in the lowest cohort.\u0026nbsp;\u003c/p\u003e\n \u003ctable id=\"Tab2\" border=\"1\"\u003e\n \u003ccaption language=\"En\"\u003e\n \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e\n \u003cdiv class=\"CaptionContent\"\u003e\n \u003cp\u003eBaseline characteristics classified by CKM syndrome stage. P values were calculated via the weighted chi-square test. Q: quartile; SE: standard error; F: ANOVA; \u0026chi;\u0026sup2;: chi-square test. Abbreviations: BMI: body mass index; PIR: poverty income ratio; HDL-C: high-density lipoprotein cholesterol; LDL-C: low-density lipoprotein cholesterol; SCR: serum creatinine; ACR: albumin-to-creatinine ratio; eGFR: estimated glomerular filtration rate; Q: quartile.\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\" rowspan=\"2\"\u003e\n \u003cp\u003eCharacteristic\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" colspan=\"2\" rowspan=\"2\"\u003e\n \u003cp\u003eTotal (n\u0026thinsp;=\u0026thinsp;10600)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" colspan=\"5\"\u003e\n \u003cp\u003eCardiovascular kidney metabolic syndrome (CKM) stages\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" rowspan=\"2\"\u003e\n \u003cp\u003eStatistic\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" colspan=\"2\" rowspan=\"2\"\u003e\n \u003cp\u003e\u003cem\u003eP\u003c/em\u003e\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eStage 0\u003c/p\u003e\n \u003cp\u003e(n\u0026thinsp;=\u0026thinsp;998)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eStage 1\u003c/p\u003e\n \u003cp\u003e(n\u0026thinsp;=\u0026thinsp;2172)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eStage 2\u003c/p\u003e\n \u003cp\u003e(n\u0026thinsp;=\u0026thinsp;5739)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eStage 3\u003c/p\u003e\n \u003cp\u003e(n\u0026thinsp;=\u0026thinsp;573)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eStage 4 (n\u0026thinsp;=\u0026thinsp;1118)\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003eAge group (years)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e\u0026chi;\u0026sup2;=2411.75\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026lt;\u0026thinsp;.001\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;60\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e7194 (73.96)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e948 (94.51)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1934 (89.46)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e4007 (74.88)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e20 (5.39)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e285 (29.26)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e\u0026ge;\u0026thinsp;60\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3406 (26.04)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e50 (5.49)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e238 (10.54)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1732 (25.12)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e553 (94.61)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e833 (70.74)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003eSex, n (%))\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e\u0026chi;\u0026sup2;=144.74\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026lt;\u0026thinsp;.001\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003eMale\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e5215 (48.87)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e356 (34.88)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e996 (46.93)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2846 (50.63)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e373 (60.28)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e644 (56.45)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003eFemale\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e5385 (51.13)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e642 (65.12)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1176 (53.07)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2893 (49.37)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e200 (39.72)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e474 (43.55)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003eRace and ethnicity, n (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e\u0026chi;\u0026sup2;=89.48\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026lt;\u0026thinsp;.001\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003eHispanics\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1592 (7.62)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e110 (5.26)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e377 (9.42)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e952 (8.12)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e57 (4.22)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e96 (4.38)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003eWhite\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1027 (5.19)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e88 (4.43)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e223 (6.10)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e582 (5.33)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e42 (3.80)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e92 (3.48)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003eBlack\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e4977 (70.86)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e522 (75.21)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e945 (68.52)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2511 (69.62)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e344 (78.74)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e655 (75.74)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003eAsian\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2059 (9.90)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e138 (7.00)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e419 (10.21)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1168 (10.28)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e110 (10.39)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e224 (10.27)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003eOther race or ethnicity\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e945 (6.44)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e140 (8.09)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e208 (5.75)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e526 (6.65)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e20 (2.85)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e51 (6.14)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003eEducation levels, n (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e\u0026chi;\u0026sup2;=359.87\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026lt;\u0026thinsp;.001\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003eLess than 9th Grade\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e932 (4.61)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e41 (2.38)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e131 (3.20)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e518 (4.72)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e95 (10.45)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e147 (8.14)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e9-11th Grade\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1477 (10.46)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e91 (6.54)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e265 (8.41)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e827 (10.99)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e104 (15.95)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e190 (15.29)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003eHigh school Grade\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2400 (21.87)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e174 (16.38)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e432 (19.90)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1344 (22.29)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e146 (30.96)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e304 (27.84)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003eSome college\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3109 (31.32)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e301 (28.87)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e671 (31.59)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1725 (32.90)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e131 (24.78)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e281 (26.58)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003eCollege graduate or above\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2682 (31.74)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e391 (45.82)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e673 (36.90)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1325 (29.10)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e97 (17.86)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e196 (22.15)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003eMarriage status, n (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e\u0026chi;\u0026sup2;=1068.32\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026lt;\u0026thinsp;.001\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003eMarried\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e5553 (56.19)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e423 (48.34)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1100 (55.49)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3088 (57.28)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e336 (57.23)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e606 (60.98)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003eWidowed\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e768 (5.31)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e12 (0.88)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e46 (1.60)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e361 (4.73)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e152 (28.20)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e197 (15.07)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003eDivorced\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1145 (10.46)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e68 (5.72)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e192 (9.19)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e673 (11.77)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e52 (9.89)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e160 (11.91)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003eSeparated\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e332 (1.97)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e18 (0.89)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e63 (1.92)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e204 (2.25)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e11 (1.18)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e36 (2.08)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003eNever married\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1923 (17.99)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e372 (32.11)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e534 (22.21)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e932 (16.38)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e13 (2.55)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e72 (5.00)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003eLiving with partner\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e879 (8.09)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e105 (12.06)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e237 (9.59)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e481 (7.60)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e9 (0.94)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e47 (4.96)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003ePIR, n (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e\u0026chi;\u0026sup2;=77.10\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026lt;\u0026thinsp;.001\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e\u0026lt;5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e8648 (73.55)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e776 (69.66)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1722 (72.27)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e4666 (72.72)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e509 (85.71)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e975 (82.24)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e\u0026ge;5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1952 (26.45)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e222 (30.34)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e450 (27.73)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1073 (27.28)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e64 (14.29)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e143 (17.76)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003eSmoking status, n (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e\u0026chi;\u0026sup2;=217.89\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026lt;\u0026thinsp;.001\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003eNo\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e5798 (54.08)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e655 (65.06)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1337 (59.15)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3121 (53.23)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e247 (43.50)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e438 (36.45)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e4802 (45.92)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e343 (34.94)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e835 (40.85)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2618 (46.77)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e326 (56.50)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e680 (63.55)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003eDrinking, n (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e\u0026chi;\u0026sup2;=63.56\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026lt;\u0026thinsp;.001\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003eNo\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2856 (21.92)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e231 (17.80)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e501 (19.08)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1597 (22.46)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e195 (33.22)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e332 (26.73)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e7744 (78.08)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e767 (82.20)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1671 (80.92)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e4142 (77.54)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e378 (66.78)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e786 (73.27)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003ePhysical activity, n (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e\u0026chi;\u0026sup2;=258.68\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026lt;\u0026thinsp;.001\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003eLow physical activity\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3921 (33.29)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e261 (24.10)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e613 (26.04)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2150 (34.72)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e333 (56.26)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e564 (45.81)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003eHigh physical activity\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e6679 (66.71)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e737 (75.90)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1559 (73.96)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3589 (65.28)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e240 (43.74)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e554 (54.19)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003eHypertension, n (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e\u0026chi;\u0026sup2;=3426.64\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026lt;\u0026thinsp;.001\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003eNo\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e6321 (62.89)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e998 (100.00)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2172 (100.00)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2766 (48.37)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e122 (19.82)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e263 (27.00)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e4279 (37.11)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0 (0.00)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0 (0.00)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2973 (51.63)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e451 (80.18)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e855 (73.00)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003eDiabetes, n (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e\u0026chi;\u0026sup2;=1560.28\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026lt;\u0026thinsp;.001\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003eNo\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e8466 (84.21)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e998 (100.00)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2165 (99.71)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e4457 (81.01)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e236 (42.90)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e610 (60.36)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2134 (15.79)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0 (0.00)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e7 (0.29)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1282 (18.99)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e337 (57.10)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e508 (39.64)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003eDepression, n (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e\u0026chi;\u0026sup2;=86.07\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026lt;\u0026thinsp;.001\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003eNo\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e9745 (92.97)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e943 (96.00)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2057 (94.83)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e5265 (92.51)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e534 (94.06)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e946 (86.83)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e855 (7.03)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e55 (4.00)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e115 (5.17)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e474 (7.49)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e39 (5.94)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e172 (13.17)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003eSleeping disorder, n (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e\u0026chi;\u0026sup2;=77.42\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026lt;\u0026thinsp;.001\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003eNo\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e9018 (85.73)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e886 (89.20)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1935 (88.63)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e4865 (85.02)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e488 (86.83)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e844 (77.94)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1582 (14.27)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e112 (10.80)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e237 (11.37)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e874 (14.98)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e85 (13.17)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e274 (22.06)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003eHyperlipemia, n (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e\u0026chi;\u0026sup2;=1623.29\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026lt;\u0026thinsp;.001\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003eNo\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3016 (28.84)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e664 (66.14)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1031 (45.08)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1063 (17.79)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e136 (22.73)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e122 (10.17)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e7584 (71.16)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e334 (33.86)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1141 (54.92)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e4676 (82.21)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e437 (77.27)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e996 (89.83)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003eAll-cause mortality, n (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e\u0026chi;\u0026sup2;=1475.70\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026lt;\u0026thinsp;.001\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003eNo\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e9536 (92.58)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e987 (98.88)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2118 (98.26)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e5370 (94.54)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e315 (56.34)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e746 (72.04)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1064 (7.42)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e11 (1.12)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e54 (1.74)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e369 (5.46)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e258 (43.66)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e372 (27.96)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colspan=\"7\"\u003e\n \u003cp\u003eCardiovascular disease mortality, n (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e\u0026chi;\u0026sup2;=471.24\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.005\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003eNo\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e10342 (98.26)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e997 (99.94)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2164 (99.75)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e5663 (98.93)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e511 (89.46)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1007 (91.60)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e258 (1.74)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1 (0.06)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e8 (0.25)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e76 (1.07)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e62 (10.54)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e111 (8.40)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colspan=\"7\"\u003e\n \u003cp\u003eCerebrovascular disease mortality, n (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e\u0026chi;\u0026sup2;=149.07,\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026lt;\u0026thinsp;.001\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003eNo\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e10536 (99.61)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e998 (100.00)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2171 (99.98)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e5723 (99.81)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e556 (96.92)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1088 (98.01)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e64 (0.39)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0 (0.00)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1 (0.02)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e16 (0.19)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e17 (3.08)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e30 (1.99)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003eBMI, n (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e\u0026chi;\u0026sup2;=3024.50\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026lt;\u0026thinsp;.001\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e\u0026lt;25\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3139 (30.89)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e998 (100.00)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e500 (22.87)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1238 (21.34)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e162 (26.16)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e241 (23.23)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e\u0026ge;25\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e7461 (69.11)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0 (0.00)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1672 (77.13)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e4501 (78.66)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e411 (73.84)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e877 (76.77)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003eWaist (cm), Mean (SE)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e98.86 (0.27)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e79.28 (0.26)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e95.81 (0.36)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e102.75 (0.29)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e103.38 (0.83)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e106.06 (0.73)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003eF\u0026thinsp;=\u0026thinsp;961.72\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026lt;\u0026thinsp;.001\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003eHDL-C (mg/dl), Mean (SE)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e54.93 (0.27)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e63.97 (0.68)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e58.10 (0.53)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e52.38 (0.33)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e51.54 (0.79)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e52.22 (0.76)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003eF\u0026thinsp;=\u0026thinsp;252.22\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026lt;\u0026thinsp;.001\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003eLDL-C (mg/dl), Mean (SE)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e114.82 (0.51)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e100.36 (1.25)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e115.48 (0.86)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e120.43 (0.75)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e108.51 (2.56)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e99.68 (1.21)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003eF\u0026thinsp;=\u0026thinsp;0.26\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.609\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003eCholesterol (mg/dl) Mean (SE)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e193.53 (0.63)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e178.53 (1.40)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e190.00 (1.07)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e201.07 (0.95)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e185.93 (2.62)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e178.39 (1.42)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003eF\u0026thinsp;=\u0026thinsp;6.04\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.016\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003eSCR (mg/dl), Mean (SE)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.88 (0.00)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.81 (0.01)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.84 (0.00)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.86 (0.00)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.21 (0.05)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.03 (0.02)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003eF\u0026thinsp;=\u0026thinsp;226.59\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026lt;\u0026thinsp;.001\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003eUric acid(mg/dl), Mean (SE)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e5.48 (0.02)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e4.62 (0.04)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e5.15 (0.03)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e5.69 (0.03)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e5.94 (0.07)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e5.91 (0.06)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003eF\u0026thinsp;=\u0026thinsp;445.88\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026lt;\u0026thinsp;.001\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003eACR, Mean (SE)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e30.63 (2.26)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e7.54 (0.25)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e6.54 (0.15)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e27.71 (3.01)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e166.35 (37.24)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e86.83 (12.11)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003eF\u0026thinsp;=\u0026thinsp;85.08\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026lt;\u0026thinsp;.001\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003eEGFR, Mean (SE)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e100.25 (0.35)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e109.42 (0.86)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e107.11 (0.58)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e100.57 (0.48)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e69.15 (1.64)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e81.20 (0.85)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003eF\u0026thinsp;=\u0026thinsp;725.16\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026lt;\u0026thinsp;.001\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003eDII, Mean (SE)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.80 (0.04)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.46 (0.09)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.70 (0.06)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.84 (0.04)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.06 (0.12)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.10 (0.09)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003eF\u0026thinsp;=\u0026thinsp;38.45\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026lt;\u0026thinsp;.001\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003eDII quantile, n (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e\u0026chi;\u0026sup2;=67.94\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026lt;\u0026thinsp;.001\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003eQ1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2359 (24.98)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e274 (30.90)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e521 (26.24)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1264 (24.48)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e102 (19.75)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e198 (19.28)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003eQ2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2603 (25.02)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e255 (26.18)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e567 (25.57)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1402 (24.55)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e134 (25.35)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e245 (24.91)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003eQ3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2676 (25.00)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e232 (22.41)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e542 (24.71)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1488 (25.62)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e140 (25.36)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e274 (25.11)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003eQ4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2962 (25.00)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e237 (20.51)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e542 (23.48)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1585 (25.35)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e197 (29.54)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e401 (30.70)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003eQ1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2359 (24.98)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e274 (30.90)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e521 (26.24)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1264 (24.48)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e102 (19.75)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e198 (19.28)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n \u003cp\u003e\u003cbr\u003e\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec11\" class=\"Section2\"\u003e\n \u003ch2\u003e3.3 Association of the DII with the risk of advanced CKM syndrome\u003c/h2\u003e\n \u003cp\u003eMultivariate logistic regression analysis was employed to evaluate the prospective correlation between the DII score and the progression of CKD syndrome, as illustrated in Table \u003cspan class=\"InternalRef\"\u003e3\u003c/span\u003e. Through analysis of four logistic regression models, each integrating progressively higher degrees of adjustment for possible confounding variables, a persistent positive correlation was identified between heightened DII levels and the progression of CKD syndrome. In unadjusted Model 1, the DII was significantly linked to an increased probability of advanced CKM syndrome (odds ratio [OR] 1.10, 95% confidence interval [CI] 1.06\u0026ndash;1.15; \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001). This relationship remained significant, although slightly attenuated, after adjustments for age, sex, race and ethnicity; education level in Model 2 (OR 1.08, 95% CI 1.04\u0026ndash;1.13, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001); and further adjustments in Model 3, which accounted for PIR status, lifestyle factors, and physical activity (OR 1.08, 95% CI 1.02\u0026ndash;1.14, p \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.006). Model 4 was further adjusted for hypertension, hyperlipidemia, diabetes disease, depression, sleep disorders, BMI, waist circumference, HDL-C, LDL-C, cholesterol, SCR, uric acid, and EGFR (OR 1.07, 95% CI 1.01\u0026ndash;1.14; \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.017). In order to ascertain the sensitivity of this finding, we conducted a quartile-based analysis of the DII compared with the first quartile (Q1) across all the models, which revealed a dose‒response relationship; notably, the fourth quartile (Q4) dose‒response consistently demonstrated a significant association with CKM syndrome and its components. In Model 4, which was fully adjusted, the ORs for CKM syndrome across DII quartiles Q2, Q3, and Q4 were 1.29 (0.98\u0026ndash;1.70, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.070), 1.31 (0.97\u0026ndash;1.77, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.077), and 1.44 (1.08\u0026ndash;1.92, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.014), respectively. In addition, our investigation provided insight into the potential linear association between the DII and vulnerability to advanced CKD syndrome through the application of restricted cubic spline (RCS) curves (Fig. \u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003e). The results revealed a significant linear association (\u003cem\u003eP\u003c/em\u003e nonlinear\u0026thinsp;=\u0026thinsp;0.941) in Model 4.\u003c/p\u003e\n \u003cdiv class=\"gridtable\"\u003e\n \u003ctable id=\"Tab3\" border=\"1\"\u003e\n \u003ccaption language=\"En\"\u003e\n \u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e\n \u003cdiv class=\"CaptionContent\"\u003e\n \u003cp\u003eMultivariate regression analysis of DII with advanced CKM syndrome.\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\" rowspan=\"2\"\u003e\n \u003cp\u003eVariables\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003eModel1\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" rowspan=\"2\"\u003e\u0026nbsp;\u003c/th\u003e\n \u003cth align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003eModel2\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" rowspan=\"2\"\u003e\u0026nbsp;\u003c/th\u003e\n \u003cth align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003eModel3\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" rowspan=\"2\"\u003e\u0026nbsp;\u003c/th\u003e\n \u003cth align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003eModel4\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eOR (95%CI)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003e\u003cem\u003eP\u003c/em\u003e\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eOR (95%CI)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003e\u003cem\u003eP\u003c/em\u003e\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eOR (95%CI)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003e\u003cem\u003eP\u003c/em\u003e\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eOR (95%CI)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003e\u003cem\u003eP\u003c/em\u003e\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eDII\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.10 (1.06\u0026thinsp;~\u0026thinsp;1.15)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026lt;\u0026thinsp;.001\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.08 (1.04\u0026thinsp;~\u0026thinsp;1.13)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026lt;\u0026thinsp;.001\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.08 (1.02\u0026thinsp;~\u0026thinsp;1.14)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.006\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.07 (1.01\u0026thinsp;~\u0026thinsp;1.14)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.017\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eDII quantile\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eQ1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.00 (Reference)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.00 (Reference)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.00 (Reference)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.00 (Reference)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eQ2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.33 (1.07\u0026thinsp;~\u0026thinsp;1.65)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.013\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.33 (1.07\u0026thinsp;~\u0026thinsp;1.66)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.012\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.26 (0.97\u0026thinsp;~\u0026thinsp;1.64)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.083\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.29 (0.98\u0026thinsp;~\u0026thinsp;1.70)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.070\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eQ3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.34 (1.07\u0026thinsp;~\u0026thinsp;1.67)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.012\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.27 (1.01\u0026thinsp;~\u0026thinsp;1.59)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.041\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.28 (0.98\u0026thinsp;~\u0026thinsp;1.68)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.074\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.31 (0.97\u0026thinsp;~\u0026thinsp;1.77)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.077\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eQ4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.66 (1.35\u0026thinsp;~\u0026thinsp;2.04)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026lt;\u0026thinsp;.001\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.56 (1.27\u0026thinsp;~\u0026thinsp;1.90)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026lt;\u0026thinsp;.001\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.52 (1.17\u0026thinsp;~\u0026thinsp;1.98)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.002\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.44 (1.08\u0026thinsp;~\u0026thinsp;1.92)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.014\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003ctfoot\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"12\"\u003eModel 1: Crude (not adjusted).\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"12\"\u003eModel 2: adjusted for sex, age, race and ethnicity, and education level based on Model 1;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"12\"\u003eModel 3: adjusted for smoking status, marital status, alcohol consumption, physical activity, and PIR based on Model 2;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"12\"\u003eModel 4: adjusted for hypertension, hyperlipidemia, diabetes disease, depression, sleep disorders, BMI, waist circumference, HDL-C, LDL-C, cholesterol, SCR, uric acid, and EGFR; OR: odds ratio; CI: confidence interval.\u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tfoot\u003e\n \u003c/table\u003e\n \u003c/div\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec12\" class=\"Section2\"\u003e\n \u003ch2\u003e3.4 Subgroup analysis\u003c/h2\u003e\n \u003cp\u003eSubgroup analyses and interaction tests were performed to evaluate the robustness of the association between the DII and the risk of advanced CKM syndrome across various population subgroups (Fig. \u003cspan class=\"InternalRef\"\u003e3\u003c/span\u003e). The results revealed a positive association between the DII index and advanced CKM syndrome in females (OR\u0026thinsp;=\u0026thinsp;1.13, 95% CI 1.06\u0026ndash;1.22; \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001). Additionally, participants with depression disorders presented a more pronounced association (OR\u0026thinsp;=\u0026thinsp;1.19, 95% CI 1.04\u0026ndash;1.37, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.015) than did those with no depression problems. Similarly, sleeping disorders (OR\u0026thinsp;=\u0026thinsp;1.16, 95% CI 1.05\u0026ndash;1.28, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.06) were compared with no sleeping disorders. Interaction analysis revealed a significant interaction effect related to sex (\u003cem\u003eP\u003c/em\u003e for interaction\u0026thinsp;=\u0026thinsp;0.003), depression (\u003cem\u003eP\u003c/em\u003e for interaction\u0026thinsp;=\u0026thinsp;0.013) and sleep disorders (\u003cem\u003eP\u003c/em\u003e for interaction\u0026thinsp;=\u0026thinsp;0.019). In contrast, no substantial interactions were observed within other subgroups, indicating that these variables might not meaningfully alter the relationship between the DII and advanced CKM syndrome.\u003c/p\u003e\n\u003c/div\u003e"},{"header":"Discussion","content":"\u003cp\u003eThis large-scale study analyzed 10,600 adult participants and demonstrated a significant association between DII and the progression stages of CKM syndrome in the United States population based on data (2005--2018) from the NHANES database. Those in advanced CKM stages correlated with older age, female predominance, socioeconomic disadvantage, and inflammatory/metabolic dysregulation (reduced eGFR, elevated ACR), overlapping with high-DII profiles. Multivariate analyses confirmed DII's independent association with CKM progression, with this association demonstrating notable linear characteristics. Additionally, subgroup analyses revealed sex- and depression- and sleep disorder-specific interaction effects. To our knowledge, this is the first study to investigate the association between DII and CKM syndrome in the United States.\u003c/p\u003e\n\u003cp\u003eCKM syndrome, recently defined by the AHA, represents a systemic pathophysiological disorder characterized by dysfunctional cross-organ communication between metabolic dysregulation (manifested as obesity, insulin resistance, and related metabolic risk factors), CKD, and CVD\u003csup\u003e2\u003c/sup\u003e. Current epidemiological studies indicate that CKM syndrome poses a substantial public health burden in the United States, with over 25% of adults exhibiting at least one component of the triad\u003csup\u003e26\u003c/sup\u003e. The AHA categorizes CKM syndrome into five clinical stages, ranging from\u0026nbsp;stage 0, which represents a risk-free state, to\u0026nbsp;stage 4b, which involves advanced multiorgan failure, reflecting the continuum from early metabolic disturbances to overt clinical manifestations\u003csup\u003e6\u003c/sup\u003e. This framework necessitates early clinical intervention, emphasizing the imperative to reconceptualize CKM through an integrative pathophysiological paradigm that supersedes traditional organ-specific nosography\u003csup\u003e8\u003c/sup\u003e. The pathophysiology of CKM syndrome is characterized by a complex interplay of multiple mechanisms\u003csup\u003e27,28\u003c/sup\u003e. Metabolic disturbances,\u0026nbsp;such as hyperglycemia and insulin resistance,\u0026nbsp;initiate vascular damage through endothelial dysfunction, oxidative stress, and advanced glycation end-product (AGE) accumulation\u003csup\u003e28-31\u003c/sup\u003e. These processes directly activate proinflammatory pathways, triggering leukocyte recruitment and cytokine release, which exacerbates\u0026nbsp;vascular and glomerular injury\u003csup\u003e30,32\u003c/sup\u003e. Concurrently, CKD amplifies systemic inflammation through sodium retention,\u0026nbsp;renin‒angiotensin‒aldosterone\u0026nbsp;system (RAAS) overactivation, and uremic toxin buildup\u003csup\u003e33\u003c/sup\u003e. Notably, uremic toxins\u0026nbsp;such as\u0026nbsp;indoxyl sulfate stimulate the NLRP3 inflammasome, driving\u0026nbsp;the production of\u0026nbsp;IL-1β and IL-18, which promote\u0026nbsp;renal and cardiovascular inflammation, fibrosis, and arrhythmias\u003csup\u003e34\u003c/sup\u003e. A proinflammatory milieu is central to the CKM axis. Adipose tissue dysfunction in obesity\u0026nbsp;results in the release of\u0026nbsp;free fatty acids and adipokines (e.g., leptin\u0026nbsp;and\u0026nbsp;resistin), which activate macrophages and induce systemic insulin resistance while generating TNF-α and IL-6[\u003csup\u003e35,36\u003c/sup\u003e. These cytokines perpetuate endothelial dysfunction and tissue fibrosis by stimulating TGF-β signaling and collagen deposition in both renal and cardiac tissues\u003csup\u003e36\u003c/sup\u003e. Furthermore, immune cell infiltration and chronic low-grade inflammation in metabolic diseases create a\u0026nbsp;feedforward\u0026nbsp;cycle that accelerates organ damage. Emerging therapies targeting inflammatory pathways highlight their mechanistic importance. Finerenone, a nonsteroidal mineralocorticoid receptor antagonist, demonstrates renal and cardioprotective effects by suppressing macrophage-mediated inflammation and profibrotic signaling\u003csup\u003e37\u003c/sup\u003e \u003csup\u003e38\u003c/sup\u003e.\u0026nbsp;These findings align with recent studies advocating inflammation-focused strategies to disrupt the CKM cascade, particularly through NLRP3 inflammasome inhibition and cytokine neutralization\u003csup\u003e39\u003c/sup\u003e. Together, these mechanisms underscore inflammation as a critical driver and therapeutic lever in cardiorenal-metabolic disease progression.\u003c/p\u003e\n\u003cp\u003eThe relationship between diet and inflammation is a critical area of research, with evidence suggesting that dietary patterns significantly influence systemic inflammation\u0026nbsp;in the human body and related chronic diseases\u003csup\u003e20\u003c/sup\u003e. Dietary components can either exacerbate or mitigate this inflammatory state. Studies indicate that higher intake of whole grains is correlated with lower CRP levels, likely due to their antioxidant and fiber contents, which counteract oxidative stress\u003csup\u003e40\u003c/sup\u003e. Similarly, omega-3 polyunsaturated fatty acids (PUFAs) from fish inhibit proinflammatory cytokines such as TNF-α and IL-6, as shown in randomized trials. Conversely, diets high in saturated fats, trans fats, and processed meats promote inflammation\u003csup\u003e41\u003c/sup\u003e. A meta-analysis revealed that saturated fatty acids (SFAs) increase CRP and IL-6 levels, contributing to chronic inflammation\u003csup\u003e42\u003c/sup\u003e. Additionally, the gut microbiome mediates diet-induced inflammatory responses by reducing \u003cem\u003ePrevotella\u003c/em\u003e bacteria, which are linked to inflammatory diseases such as arthritis\u003csup\u003e43\u003c/sup\u003e. Zheng \u003cem\u003eet al\u003c/em\u003e. \u003csup\u003e44\u003c/sup\u003edemonstrated that inflammatory biomarkers mediate the association between\u0026nbsp;triglyceride‒glucose‒body\u0026nbsp;mass index and future CVD risk in early-stage CKM\u0026nbsp;patients, suggesting a potential role for dietary interventions in primary prevention.\u003c/p\u003e\n\u003cp\u003eOur study revealed intricate associations between DII and CKM syndrome components, highlighting the multifaceted pathophysiological effects of systemic inflammation. In metabolic tissues such as adipose and liver tissue, oxidative stress disrupts mitochondrial respiration, leading to disrupted insulin signaling, exacerbating dyslipidemia, and accelerating renal injury through glomerular hyperfiltration and fibrosis\u003csup\u003e29\u003c/sup\u003e, which aligns with the elevated ACR and reduced eGFR in the high-DII groups. Simultaneously, high-DII diets also dysregulate the\u0026nbsp;hypothalamic–pituitary–adrenal (HPA) axis, increasing cortisol secretion and glucocorticoid receptor resistance. Excess cortisol promotes gluconeogenesis and lipolysis, exacerbating hyperglycemia and dyslipidemia, which is consistent with our findings of elevated BMI, waist, and HDL-C. Subsequently, we elucidated its association with CKM prognosis, demonstrating that varying DIIs are linked to statistically significant disparities in all-cause mortality and cardiovascular disease mortality among adult patients. The DII helps quantify the effect of the food consumed on the body’s inflammatory state, exploring the relationship between diet and CKM syndrome with a comprehensive assessment of a person’s dietary habits rather than a single food component, which provides a new perspective for interpreting the healthy diet associated with CKM symptoms.\u003c/p\u003e\n\u003cp\u003eOur investigation has numerous strengths. This is the first large-scale observational study with data derived from the NHANES database to evaluate the association between DII and CKM syndrome innovatively, providing novel insights into diet-driven multiorgan pathology with high confidence. Second, we adjusted for covariates, including demographic variables, laboratory assessments, and comorbidities, and conducted subgroup analyses to validate the robustness of our findings. Despite its strengths, our study has certain limitations. First, although adjustments have been made for multiple potential confounding covariates, the potential for residual confounding remains plausible, which could influence the findings. Second, the DII was calculated from the 24-hour dietary recall interview, which can represent only habitual diet to a certain extent. Some recall bias is unavoidable and may not capture long-term dietary patterns. Third, the DII calculation excluded certain bioactive compounds (e.g., polyphenols) because of data constraints, potentially underestimating dietary anti-inflammatory potential. Ultimately, the present investigation predominantly relies on individuals hailing from the United States, and geographical limitations may constrain the generalizability and relevance of the research findings owing to elements such as environmental conditions and nutritional practices.\u003c/p\u003e\n\u003cp\u003eOverall, a proinflammatory diet is associated with advanced CKM syndrome with increased all-cause and cardiovascular disease mortality in patients. The DII helps quantify the effect of the food consumed on the body’s inflammatory state and explores the relationship between diet and CKM syndrome, with a comprehensive assessment suggesting that bidirectional diet‒CKM interactions are mediated by chronic inflammation, oxidative stress, and endothelial dysfunction.\u003c/p\u003e"},{"header":"Conclusion","content":"\u003cp\u003eIn conclusion, this study positions DII as a modifiable lever in CKM syndrome management, bridging dietary patterns with systemic metabolic and cardiovascular diseases. Its implications extend beyond observational epidemiology, urging translational efforts to harness anti-inflammatory nutrition as a therapeutic cornerstone in multimorbidity prevention.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eData availability:\u0026nbsp;\u003c/strong\u003eThe data used in this study were obtained from the NHANES, which was conducted between 2005 and 2018. The NHANES data are publicly available and can be accessed at the following URL: https://www.cdc.gov/nchs/nhanes/index.htm.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAcknowledgments:\u0026nbsp;\u003c/strong\u003eAll the authors express gratitude to the participants and staff of the NHANES for their contributions.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthor contributions:\u0026nbsp;\u003c/strong\u003eTY. S were involved in the study design and wrote and edited the manuscript. FB.M. performed the data analysis. SF. F and YQ.Z. reviewed the manuscript and provided critical suggestions. All the authors have read and agreed to the published version of the manuscript.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConflict of interest:\u0026nbsp;\u003c/strong\u003eThe authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as potential conflicts of interest.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding:\u0026nbsp;\u003c/strong\u003eThe authors have not declared a specific grant for this research from any funding agency in the public, commercial or not-for-profit sectors.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEthics approval and consent to participate:\u0026nbsp;\u003c/strong\u003eThe study involves the use of a publicly available dataset (NHANES), which was collected under ethical standards, including informed consent from all participants. All methods were carried out in accordance with the relevant guidelines and regulations.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n \u003cli\u003eSebastian, S. A., Padda, I. \u0026amp; Johal, G. Cardiovascular-Kidney-Metabolic (CKM) syndrome: A state-of-the-art review. \u003cem\u003eCurr Probl Cardiol\u003c/em\u003e \u003cstrong\u003e49\u003c/strong\u003e, 102344, doi:10.1016/j.cpcardiol.2023.102344 (2024).\u003c/li\u003e\n \u003cli\u003eLarkin, H. Here\u0026apos;s What to Know About Cardiovascular-Kidney-Metabolic Syndrome, Newly Defined by the AHA. \u003cem\u003eJAMA\u003c/em\u003e \u003cstrong\u003e330\u003c/strong\u003e, 2042-2043, doi:10.1001/jama.2023.22276 (2023).\u003c/li\u003e\n \u003cli\u003eHyun, Y. Y. 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Elevated triglyceride glucose index is associated with advanced cardiovascular kidney metabolic syndrome. \u003cem\u003eScientific Reports\u003c/em\u003e \u003cstrong\u003e14\u003c/strong\u003e, 31352, doi:10.1038/s41598-024-82881-y (2024).\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":true,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"Dietary inflammatory index (DII), cardiovascular-kidney-metabolic (CKM) syndrome, NHANES, multivariable logistic regression, RCS analysis ","lastPublishedDoi":"10.21203/rs.3.rs-6224126/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-6224126/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eCardiovascular–kidney–metabolic syndrome (CKM) is recognized as a dynamic systemic disorder. Inflammation is pivotal in CKM syndrome development. The dietary inflammatory index (DII) represents a well-validated tool to quantify the overall inflammatory potential of an individual diet. However, the association between DII and CKM syndrome remains undetermined. We analyzed data from 10,600 adults aged ≥ 20 years from the National Health and Nutrition Examination Survey (NHANES 2005–2018). The CKM stages were classified on the basis of metabolic risk factors, cardiovascular disease (CVD), and chronic kidney disease (CKD). Our findings indicated that advanced CKM stages overlapped with high-DII profiles. The findings derived from the four multivariable logistic regression analysis models revealed a significant positive correlation between a continuous DII and the incidence of advanced CKM syndrome. Additionally, the quartiles of the DII demonstrated a statistically significant association with an increased incidence of advanced CKM syndrome in the fully adjusted models (DII Q4 vs. Q1, odds ratio = 1.44, 95% confidence interval = 1.08–1.92, \u003cem\u003eP\u003c/em\u003e=0.014). The results of restricted cubic spline (RCS) analysis suggested a linear and positive correlation between DII and advanced CKM syndrome. Subgroup analyses further revealed sex-, depression-, and sleep disorder-specific effects. This study indicates that the DII may be a modifiable lever in CKM syndrome management, bridging dietary inflammatory patterns with systemic metabolic and cardiovascular diseases.\u003c/p\u003e","manuscriptTitle":"Associations between dietary inflammatory index and cardiovascular kidney metabolic syndrome: insights from NHANES 2005–2018","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-03-20 08:59:52","doi":"10.21203/rs.3.rs-6224126/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":"bd575af2-b01e-40df-bdb7-6c5181660b05","owner":[],"postedDate":"March 20th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[{"id":45937483,"name":"Health sciences/Health care/Nutrition"},{"id":45937484,"name":"Health sciences/Diseases/Cardiovascular diseases"}],"tags":[],"updatedAt":"2025-07-07T03:53:12+00:00","versionOfRecord":[],"versionCreatedAt":"2025-03-20 08:59:52","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-6224126","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-6224126","identity":"rs-6224126","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}