The Association Between Lifestyle Habits of Knowledge Workers and Carotid Atherosclerosis: A Latent Class Study Of 113,262 Chinese

The Association Between Lifestyle Habits of Knowledge Workers and Carotid Atherosclerosis: A Latent Class Study Of 113,262 Chinese | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article The Association Between Lifestyle Habits of Knowledge Workers and Carotid Atherosclerosis: A Latent Class Study Of 113,262 Chinese Huiyi Zhang, Xue He, Lijun Li, Ying Li, Yi Zhou, Jingying Wang, and 4 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-9028936/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 7 You are reading this latest preprint version Abstract Background Carotid atherosclerosis (CAS) underlies cardiovascular and cerebrovascular diseases and is linked to unhealthy lifestyles. Knowledge workers may face a higher risk of CAS due to certain special living habits, but evidence on CAS among knowledge workers is scarce because prior studies rarely classify lifestyle patterns. To address this gap, this study applies latent class analysis (LCA) to identify lifestyle patterns among 113,262 Chinese knowledge workers and assess their association with CAS risk. Methods This cross-sectional study analyzed data from the Health Management Center Database of a comprehensive Chinese hospital (2017–2024), including 113,262 knowledge workers. Lifestyle factors assessed included nocturnal eating, coffee and sugar-sweetened beverage consumption, smoking, alcohol use, sedentary behavior, and physical activity. CAS was evaluated via ultrasonography. Latent class analysis identified lifestyle patterns, and multivariable logistic regression assessed their associations with CAS risk. Stratified analysis was performed by gender, age, BMI, and marital status. Results The overall CAS prevalence among knowledge workers was 49.0%. Latent class analysis identified four lifestyle categories: basically healthy, mixed class 1, mixed class 2, and unhealthy. Compared to the basically healthy class, mixed class1 had a significantly increased CAS risk ( P < 0.05), while other classes showed inverse associations. Conclusions The findings reveal substantial heterogeneity in lifestyle behaviors among knowledge workers, which are closely associated with CAS risk. These results suggest that targeted interventions addressing specific lifestyle behaviors may help mitigate CAS risk, providing a scientific basis for the development of precise prevention strategies. Knowledge Workers Lifestyle Habits Carotid Atherosclerosis Latent Category Study Figures Figure 1 Figure 2 Introduction Atherosclerosis is a chronic inflammatory vascular disease characterized by lipid accumulation within the arterial walls, proliferation of smooth muscle cells, and fibrosis, leading to vascular thickening, hardening, and plaque formation(1). Carotid atherosclerosis (CAS), a key contributor to ischemic stroke and cardiovascular events(2), is rising globally, affecting ~2 billion people, including 270 million in China(3). CAS is characterized by increased carotid intima-media thickness (cIMT), plaque formation, and stenosis, and its progression is linked to cognitive decline, dementia, and stroke(4). Knowledge workers, also referred to as mental workers, are typically defined as individuals whose occupations primarily rely on cognitive abilities, information processing, decision-making, and creative thinking, rather than manual labor (e.g., engineers, professors)(5). Limited research exists on CAS prevalence and risk factors in this population. Prior CAS studies have focused on community and elderly cohorts, cardiometabolic high‑risk groups (hypertension, diabetes, metabolic syndrome), smokers, and certain occupational exposures, emphasizing conventional risk factors and the prognostic utility of carotid ultrasound metrics(6-9). Knowledge workers, however, display distinct lifestyle clusters—prolonged sedentary time, high caffeine consumption, psychosocial stress, and irregular eating habits—yet evidence linking these patterned behaviors to CAS is scarce(10-13). Studying knowledge workers is therefore warranted to identify modifiable, group‑specific risk phenotypes and enable tailored prevention(10, 12, 13). Latent Class Analysis (LCA) is a probabilistic clustering method that classifies individuals into homogeneous subgroups, revealing latent data structures(14). Unlike traditional regression that fails to capture multidimensional lifestyle interactions, LCA effectively identifies population subgroups and their behavior-health associations. We applied LCA to categorize knowledge workers' lifestyle patterns and evaluate CAS risk associations, overcoming single-factor analysis limitations. Given CAS's high prevalence and particular risk to knowledge workers, multidimensional risk factor analysis is imperative. This large-scale cross-sectional study of Chinese knowledge workers aims to: (a) Assess CAS prevalence and lifestyle factors (nighttime eating, coffee/sugar-sweetened beverage consumption, smoking, alcohol use, sedentariness, and physical activity); (b) Classify lifestyle patterns via LCA to identify population subgroups; (c) Investigate lifestyle-CAS risk associations. Combining lifestyle heterogeneity with advanced analytics, this study provides targeted prevention strategies, addressing a key research gap. Methods 2.1 Design and participants This cross-sectional study recruited participants from a tertiary hospital's health management database in Changsha (2017-2024). Data came from the hospital's resident health examination records. In this study, a self-designed staff-assisted lifestyle questionnaire was used to investigate residents' health-related information (supplementary file1). All participants provided written informed consent and completed: (a) a staff-assisted lifestyle questionnaire, and (b) carotid Doppler ultrasound(15). The cohort included knowledge workers of all ages, excluding those with cognitive/psychiatric impairments or communication barriers. The study protocol conforms to the ethical guidelines of the 1975 Declaration of Helsinki and the study protocol has been priorly approved by the Institution's ethics committee on research on humans. 2.2 Measures Based on preliminary literature reviews and expert panel focus group discussions, the following data collection components were identified: 2.2.1 Lifestyle habits This study selected seven lifestyle factors—nighttime eating, coffee consumption, sugar-sweetened beverage consumption, smoking, alcohol consumption, sedentary behavior, and physical activity—to construct a comprehensive lifestyle profile encompassing dietary habits, tobacco and alcohol use, and physical activity for analysis. 2.2.1.1 Nighttime eating Nighttime eating was assessed using two questions: (a) "Do you frequently eat after 9:00 PM?"(b) "How many times per week do you engage in nighttime eating?" Nighttime eating habits were categorized into two groups: healthy (no nighttime eating habit) and unhealthy (habitual nighttime eating). 2.2.1.2 Coffee consumption Coffee consumption was assessed using two questions: (a) "Do you like drinking coffee?" (b) "How many cups of coffee do you consume per week?" Based on weekly caffeine intake, participants were categorized into two groups: healthy (<400 mg) and unhealthy (≥400 mg). 2.2.1.3 Sugar-sweetened beverage consumption Sugar-sweetened beverage consumption was assessed using two questions: (a) "Do you consume sugar-sweetened beverages?" (b) "How many bottles of sugar-sweetened beverages do you consume per week?" Based on weekly sugar intake from beverages, participants were categorized into two groups: healthy (<175 g) and unhealthy (≥175 g). 2.2.1.4 Smoking Smoking status was assessed using a single-choice question: "Do you smoke? (Defined as continuous smoking for more than one year)." The four response options were: (a) No, I do not smoke. (b) Yes, I currently smoke. (c) I used to smoke. (d) I am a passive smoker (exposed for at least 15 minutes per day on more than one day per week). In this study, smoking status was categorized into two groups: healthy (non-smokers) and unhealthy (former smokers, current smokers, or passive smokers). 2.2.1.5 Alcohol consumption Alcohol consumption was assessed using the following four questions: (a) Do you drink alcohol? (b) What type of alcohol do you consume? (c) How many times per week do you drink? (d) How much alcohol do you consume per occasion? Based on the responses, alcohol consumption was categorized into two groups: healthy (<70 g per week) and unhealthy (≥70 g per week). 2.2.1.6 Sedentary behavior Sedentary behavior was categorized based on the daily sitting time excluding work or study hours. The two groups were: healthy (<2 hours) and unhealthy (≥2 hours). 2.2.1.7 Physical activity Physical activity was assessed using two questions: (a) How many times per week do you exercise? (b) How long do you exercise each time? (c) What type of exercise do you do? Based on the total weekly exercise duration, physical activity was categorized into two groups: unhealthy (<150 minutes/week) and healthy (≥150 minutes/week). 2.2.2 Carotid atherosclerosis (CAS) All participants underwent a comprehensive bilateral carotid artery ultrasound examination performed by trained and certified sonographers. To ensure consistency and minimize inter-operator variability across the study cohort, all sonographers followed a strictly standardized scanning protocol and underwent periodic retraining and quality assessment(16). High-resolution B-mode and color Doppler ultrasonography were performed using commercially available ultrasound systems, including the Philips EPIQ 7 (Philips Healthcare, Amsterdam, Netherlands) and Siemens Acuson Sequoia (Siemens Healthineers, Erlangen, Germany). A high-frequency linear array transducer (typically 7–12 MHz) was utilized for all examinations. During the procedure, participants were positioned in the supine position with their head slightly extended and rotated approximately 45 degrees contralateral to the side being examined to ensure optimal visualization of the carotid arteries. The examination systematically interrogated the bilateral common carotid arteries (CCA), the carotid bifurcation, and the proximal 1-2 cm segments of the internal carotid artery (ICA) and external carotid artery (ECA) in both longitudinal and transverse planes. Carotid Intima-Media Thickness (cIMT): The cIMT was measured on the far wall of a plaque-free segment of the distal CCA, approximately 1.0–1.5 cm proximal to the bifurcation. The measurement was defined as the distance between the lumen-intima interface and the media-adventitia interface. Automated or semi-automated edge-detection software was used to enhance precision. For each side, the mean cIMT was calculated from three distinct measurements obtained along a 10 mm segment. The maximum of the left and right mean cIMT values was used for analysis(16, 17). Carotid Plaque Assessment: The presence, location, and characteristics of atherosclerotic plaques were meticulously documented. A carotid plaque was defined according to the Mannheim consensus criteria as a focal structure that encroaches into the arterial lumen by at least 0.5 mm or 50% of the surrounding IMT value, or demonstrates a thickness >1.5 mm as measured from the media-adventitia interface to the intima-lumen interface(17). The presence of plaque was recorded for each of the six segments (CCA, bifurcation, and ICA on both sides). In this study, the degree of CAS was classified according to the following criteria(18): (a) clMT 1.5 mm was classified as carotid plaque; (d) Plaque thickness exceeding 50% of the lumen was considered arterial stenosis or occlusion. A CAS diagnosis was defined as meeting any one of the following carotid ultrasound criteria: (b), (c), or (d). 2.2.3 Socio-demographic information Demographic information included gender, age, marital status, and BMI. Age was categorized into four groups: 1 = < 18 years, 2 = 18–39 years, 3 = 40–59 years, 4 = ≥ 60 years. Marital status was divided into four categories: 1 = unmarried, 2 = married or cohabitating, 3 = divorced, 4 = widowed. BMI was calculated by dividing weight (kg) by the square of height (m²), and classified into four categories according to the Working Group on Obesity in China (WGOC) guidelines: underweight (<18.5 kg/m²), normal weight (18.5–23.9 kg/m²), overweight (24.0–27.9 kg/m²), and obese (≥28.0 kg/m²)(19). 2.3 Statistical analyses Statistical analyses were conducted using SPSS 27.0 and Mplus 8.3. For randomly missing data (1.6%-5.3%), multiple imputation(MI) with FCS generated five datasets, with results pooled for final estimates. Descriptive statistical analysis was conducted on the general demographic characteristics, lifestyle habits, and CAS status of knowledge workers. Continuous variables were expressed as mean ± standard deviation (x̄± s), while categorical variables were presented as frequencies and percentages (n%). Univariate analysis was used to explore the differences in CAS prevalence across knowledge workers with different characteristics, and the chi-square test (χ2) was used for comparisons of categorical data. A significance level of α = 0.05 was applied. LCA identified latent subgroups in multidimensional categorical data. We tested 1-9 class models, selecting the optimal number based on LL, BIC, AIC, aBIC, VLMR, and BLRT. While entropy>0.8 typically indicates good classification, its reliability decreases with large samples (>3000)(20). Therefore, we primarily used other statistical indicators. Although the 8-class model had the lowest BIC, classes with <0.5% frequency (from 5-class onward) were non-representative, leading to selection of the clinically meaningful 4-class model. The severity of CAS was used as the dependent variable, classified as follows: normal = 0, carotid intima-media thickening = 1, carotid plaque = 2, and arterial stenosis or occlusion = 3. Univariate-significant variables served as independent variables in collinearity diagnostics and multivariate logistic regression, with OR s (95% CIs) calculated. Parallelism test significance ( P <0.05) necessitated unordered multinomial logistic regression to examine lifestyle-CAS severity associations. Three models were built: (a) unadjusted; (b) age/sex-adjusted; (c) additionally adjusted for BMI and marital status. Two-tailed tests ( P <0.05) determined significance. Stratified analysis controls confounders and detects interactions by dividing samples by key variables (e.g., gender, age, BMI, marital status) and computing stratum-specific OR s. Post-multivariate regression, we stratified by sociodemographics, calculated stratum-specific CAS incidence by lifestyle category, then conducted separate logistic regressions (CAS occurrence: 0/1) per stratum. Significant between-stratum risk differences suggest potential interactions. Results 3.1 Socio-demographic characteristics of the sample A total of 113,262 Chinese knowledge workers were included in this study, aged between 12 and 94 years, with an average age of 42.55±10.78 years. Among them, 68,414 were male (60.4%) and 44,848 were female (39.6%). The majority of participants (87.1%) were married or cohabitating. More detailed information is presented in Table 1. Table1 Descriptive statistics and each latent class of healthy lifestyle behavioral patterns (n =113,262). Variables n(%) Basically Healthy class (n=50,055) Mixed class 1 (n=12,071) Mixed class 2 (n =33,751) Unhealthy class (n=17,385) P Gender <0.001 Male 68418(60.4) 25585(51.1) 11652(96.5) 15668(46.4) 15509(89.2) Female 44848(39.6) 24470(48.9) 419(3.5) 18083(53.6) 1876(10.8) Age(years)* <0.001 ＜18 90(0.1) 24(0.0) 0(0.0) 63(0.2) 3(0.0) 18-39 48421(42.8) 15373(30.7) 2679(22.2) 21583(64.0) 8786(50.5) 40-59 58946(52.0) 30759(61.5) 8643(71.6) 11434(33.9) 8110(46.7) ≥60 5802(5.1) 3899(7.8) 749(6.2) 669(2.0) 485(2.8) BMI † <0.001 Underweight 2839(2.6) 1191(2.5) 57(0.5) 1349(4.2) 242(1.5) Normal weight 50713(47.3) 24712(52.1) 3296(29.0) 17292(54.1) 5413(32.8) Overweight 40818(38.0) 17339(36.6) 5806(51.1) 9977(31.2) 7696(46.7) Obesity 12914(12.0) 4196(8.8) 2204(19.4) 3373(10.5) 3141(19.0) Marital status ‡ <0.001 Unmarried 11919(10.7) 3081(6.3) 420(3.5) 6457(19.6) 1943(11.3) Married or living together 98675(88.5) 45661(92.8) 11446(95.4) 26380(79.8) 15188(88.2) Divorced 714(0.6) 356(0.7) 106(0.9) 166(0.5) 86(0.5) Widowed 140(0.1) 93(0.2) 21(0.2) 18(0.1) 8(0.0) * Data on age were missing for 3 participants. † 5978(5.3%) missing information on BMI. ‡ 1814(1.6%) missing information on Marital status. 3.2 CAS Among the 113,262 knowledge workers, 55,495 cases were diagnosed with CAS, resulting in a detection rate of 49.0%. Of these, 44,099 cases (38.9%) had carotid plaques, 10,938 cases (9.7%) had carotid intima-media thickening, and 458 cases (0.4%) had arterial stenosis or occlusion. Chi-square test results showed that the incidence of CAS differed significantly across different genders, ages, marital statuses, and BMI categories ( P < 0.001). 3.3 Latent Class Analysis of Comprehensive Lifestyle Habits in Knowledge Workers In this study, knowledge workers' lifestyle habits, including nighttime eating, coffee consumption, sugar-sweetened beverage consumption, smoking, alcohol consumption, sedentary behavior, and physical activity, were considered as observable indicators and subjected to latent class analysis (LCA). The lifestyle habits were grouped into 1 to 9 classes, and the model fitting was conducted to determine the best model. The prevalence of the aforementioned habits were as follows: 43,532 (38.4%) for nighttime eating, 33,107 (29.2%) for coffee consumption, 52,920 (46.7%) for sugar-sweetened beverage consumption, 34,441 (30.4%) for smoking, 37,919 (33.5%) for alcohol consumption, 79,981 (70.6%) for sedentary behavior, and 52,564 (46.4%) for physical activity. The results shown in Table 2 indicate that as the number of categories increased from one to eight, the AIC, BIC, and aBIC values all decreased, with the lowest BIC value achieved in the eighth model. In models two to eight, both VLMR and BLRT tests showed statistical significance ( P < 0.001). However, starting from the fifth model, the proportion of the smallest group in each category was below 5%, resulting in low interpretability and a lack of credibility in generalization. Therefore, this study selected the four-class model as the best model for analyzing knowledge workers' lifestyle habits. Further details can be found in Table 2. Table2 Model fit statistics for the latent class analysis (n =113,262) Number of classes LL AIC BIC ABIC VLMR BLRT Entropy Category probability (%) 1C -510729.800 1021473.601 1021541.063 1021518.817 2C -497234.166 994498.332 994642.894 994595.224 ＜0.001 ＜0.001 0.508 0.48316/0.51684 3C -489475.308 978996.616 979218.278 979145.183 ＜0.001 ＜0.001 0.655 0.46437/0.35048/0.18515 4C* -487785.123 975632.246 975931.007 975832.488 ＜ 0.001 ＜ 0.001 0.660 0.44194/0.29799/0.15349/0.10658 5C -487107.561 974293.122 974668.983 974545.040 ＜0.001 ＜0.001 0.603 0.32245/0.04170/0.14756/0.11326/0.37503 6C -486872.380 973838.760 974291.721 974142.353 ＜0.001 ＜0.001 0.658 0.12861/0.03171/0.19476/0.11746/0.27228/0.25518 7C -486666.848 973443.695 973973.756 973798.964 ＜0.001 ＜0.001 0.697 0.17603/0.24047/0.24318/0.03593/0.08698/0.12468/0.09273 8C -486572.184 973270.368 973877.528 973677.312 ＜0.001 ＜0.001 0.729 0.10559/0.01948/0.10885/0.17077/0.17835/0.05996/0.20137/0.15563 9C -486551.875 973245.749 973930.009 973704.368 0.7904 0.7922 0.650 0.05221/0.10822/0.00304/0.04272/0.15907/0.18199/0.05419/0.31828/0.08028 * The final selected classification category. Figure 1 and 2 displays the four lifestyle habit categories of knowledge workers in China. Class 1, named the "Basically healthy Type" (n=50,055, 44.2%), is characterized by relatively healthy eating, smoking, alcohol consumption, and physical activity habits, with the only high-risk behavior being sedentary behavior (63.2%). Class 4, named the "Unhealthy Type" (n=17,385, 15.3%), exhibits high-risk behaviors in eating habits, smoking, alcohol consumption, and physical activity. Class 2, labeled as "Mixed class 1" (n=12,071, 10.7%), has relatively healthy eating habits but shows high-risk behaviors in smoking (77.5%), alcohol consumption (88.5%), and sedentary behavior (75.8%). Class 3, labeled as "Mixed class 2" (n=33,751, 29.8%), features low smoking and alcohol consumption, but exhibits high-risk behaviors in nighttime eating (58.4%) and coffee consumption (45.6%), very high-risk behaviors in sugar-sweetened beverage consumption (84.8%) and sedentary behavior (75.7%), as well as a lack of effective physical activity (29.9%).Table 3 presents descriptive statistics of each cluster sample and the conditional probabilities. Table3 Nomenclature of latent categories of mental workers lifestyles, their conditions and category probabilities Class NE CC SSBC Smoking AC SB PA n(%) Basically Healthy class 0.125 0.150 0.155 0.124 0.167 0.632 0.593 44.2% Mixed class 1 0.354 0.131 0.092 0.775 0.885 0.758 0.601 10.7% Mixed class 2 0.584 0.456 0.848 0.213 0.046 0.757 0.299 29.8% Unhealthy class 0.715 0.481 0.847 0.623 1.000 0.761 0.341 15.3% NE, nighttime eating; CC, coffee consumption; SSBC, sugar-sweetened beverage consumption; AC, alcohol consumption; SB, sedentary behavior; PA, physical activity. 3.4 The relationship between lifestyle habits and CAS Collinearity diagnostics indicated that no multicollinearity was present among the independent variables ( VIF: 1.125-1.252 < 5,Tolerance:0.799-0.889＞0.1).Table 4 shows a significant association between different lifestyle habit categories and the severity of CAS. In the unadjusted model, Mixed class 1 was associated with an increased risk of CAS, but after adjusting for gender, age, marital status, and BMI, this association weakened, particularly in arterial stenosis or occlusion. Mixed class 2 consistently demonstrated paradoxically lower risk across models. The Unhealthy class exhibited inverse associations for early CAS stages, though confounding adjustments partially diminished this trend. Table4 Associations between lifestyle habits and CAS (n =113,262). Characteristic CAS, odds ratio (95 %) a normal carotid intima-media thickening carotid plaque arterial stenosis or occlusion Basically Healthy class No. of cases 23308(46.6) 5188(10.4) 21325(42.6) 234(0.5) Model 1 1[Reference] 1[Reference] 1[Reference] 1[Reference] Model 2 1[Reference] 1[Reference] 1[Reference] 1[Reference] Model 3 1[Reference] 1[Reference] 1[Reference] 1[Reference] Mixed class 1 No. of cases 4595(38.1) 1513(12.5) 5884(48.7) 79(0.7) Model 1 1[Reference] 1.479(1.386，1.579)*** 1.400(1.341,1.461)*** 1.713(1.324，2.214)*** Model 2 1[Reference] 1.091(1.018,1.169)** 1.201(1.147,1.258)*** 1.222(0.934,1.598)* Model 3 1[Reference] 1.067(0.993,1.145)* 1.176(1.121,1.234)*** 1.138(0.862,1.501)* Mixed class 2 No. of cases 20528(60.8) 2429(7.2) 10717(31.8) 77(0.2) Model 1 1[Reference] 0.532(0.505，0.560)*** 0.571(0.554,0.588)*** 0.374(0.289，0.484)*** Model 2 1[Reference] 0.807(0.764,0.852)*** 0.817(0.791,0.844)*** 0.731(0.562,0.951)** Model 3 1[Reference] 0.809(0.765,0.856)*** 0.798(0.772,0.826)*** 0.734(0.562,0.959)** Unhealthy class No. of cases 9336(53.7) 1808(10.4) 6173(35.5) 68(0.4) Model 1 1[Reference] 0.870(0.820,0.932)*** 0.723(0.696，0.750)*** 0.725(0.553，0.951)** Model 2 1[Reference] 0.916(0.861,0.976)** 0.829(0.796,.863)*** 0.869(0.655,1.152)* Model 3 1[Reference] 0.915(0.857,0.976)** 0.825(0.791,0.861)*** 0.841(0.631,1.123)* Model 1 with no adjustments; Model 2 with gender and age adjustments; Model 3 with gender, age, marital status and BMI adjustments; CI, confidence interval. a Combined results using multiple imputation. *** P <0.001. ** P 0.05. 3.5 Stratified Analysis of Knowledge Workers' Lifestyle Habits and CAS Across Different Sociodemographic Characteristics The stratified analysis revealed that gender, age, BMI, and marital status modified the relationship between lifestyle categories and CAS risk (Table 5,6). Table 5 Incidence of CAS Across Different Lifestyle Categories Under Various Sociodemographic Characteristics Variables Incidence of CAS(%) Basically Healthy class Mixed class 1 Mixed class 2 Unhealthy class Gender Male 60.0 62.4 44.2 47.2 Female 46.6 48.4 34.9 38.8 Age(years) ＜18 58.3 0.0 33.3 33.3 18-39 34.4 37.0 30.8 32.6 40-59 59.0 67.3 52.4 58.8 ≥60 84.3 89.7 83.7 85.4 BMI Underweight 39.7 50.9 34.0 35.5 Normal weight 48.9 60.4 35.5 42.0 Overweight 58.3 61.9 42.7 48.2 Obesity 60.0 61.8 42.3 48.2 Marital status Unmarried 37.2 46.4 30.4 36.1 Married or living together 54.3 62.4 41.0 47.6 Divorced 68.8 73.6 53.6 46.5 Widowed 81.7 66.7 61.1 75.0 Table 6 Relationship Between Lifestyle Habits and CAS Under Different Sociodemographic Characteristics Variables β SE Walds P Odds Ratio (95 %) Gender Male Basically Healthy class 1585.473 ＜0.001 Mixed class 1 0.518 0.021 635.362 ＜0.001 1.678(1.612,1.747) Mixed class 2 0.619 0.025 614.007 ＜0.001 1.858(1.769,1.951) Unhealthy class -0.122 0.023 28.867 ＜0.001 0.885(0.846,0.925) Female Basically Healthy class 598.440 ＜0.001 Mixed class 1 0.318 0.049 41.965 ＜0.001 1.374(1.248,1.513) Mixed class 2 0.393 0.109 13.115 ＜0.001 1.482(1.198,1.834) Unhealthy class -0.170 0.050 11.634 ＜0.001 0.844(0.765,0.930) Age(years) ＜18 Basically Healthy class 4.424 ＞0.05 Mixed class 1 1.030 1.293 0.634 ＞0.05 2.800(0.222,35.288) Mixed class 2 0.000 1.254 0.000 ＞0.05 1.000(0.086,11.669) Unhealthy class / / / / / 18-39 Basically Healthy class 77.568 ＜0.001 Mixed class 1 0.081 0.028 8.223 ＜0.05 1.085(1.026,1.147) Mixed class 2 0.194 0.046 17.733 ＜0.001 1.214(1.109,1.329) Unhealthy class -0.082 0.027 9.208 ＜0.05 0.921(0.873,0.971) 40-59 Basically Healthy class 446.478 ＜0.001 Mixed class 1 0.009 0.025 0.132 ＞0.05 1.009(0.960,1.061) Mixed class 2 0.364 0.032 128.059 ＜0.001 1.439(1.351,1.533) Unhealthy class -0.261 0.029 79.026 ＜0.001 0.771(0.728,0.816) ≥60 Basically Healthy class 15.258 ＜0.05 Mixed class 1 -0.082 0.136 0.366 ＞0.05 0.921(0.706,1.202) Mixed class 2 0.403 0.176 5.250 ＜0.05 1.497(1.060,2.113) Unhealthy class -0.127 0.166 0.584 ＞0.05 0.881(0.637,1.219) BMI Underweight Basically Healthy class 13.926 ＜0.05 Mixed class 1 0.178 0.147 1.473 ＞0.05 1.195(0.896,1.593) Mixed class 2 0.631 0.297 4.507 ＜0.05 1.879(1.050,3.363) Unhealthy class -0.070 0.146 0.229 ＞0.05 0.932(0.700,1.242) Normal weight Basically Healthy class 1089.424 ＜0.001 Mixed class 1 0.278 0.030 83.983 ＜0.001 1.320(1.244,1.401) Mixed class 2 0.746 0.045 274.573 ＜0.001 2.109(1.931,2.303) Unhealthy class -0.273 0.032 73.825 ＜0.001 0.761(0.715,0.810) Overweight Basically Healthy class 867.513 ＜0.001 Mixed class 1 0.409 0.028 220.953 ＜0.001 1.506(1.426,1.589) Mixed class 2 0.560 0.035 250.342 ＜0.001 1.750(1.633,1.876) Unhealthy class -0.219 0.030 51.728 ＜0.001 0.803(0.756,0.853) Obesity Basically Healthy class 331.679 ＜0.001 Mixed class 1 0.478 0.048 100.793 ＜0.001 1.613(1.469,1.771) Mixed class 2 0.554 0.057 96.065 ＜0.001 1.741(1.558,1.945) Unhealthy class -0.238 0.050 22.778 ＜0.001 0.788(0.715,0.869) Marital status Unmarried Basically Healthy class 83.992 ＜0.001 Mixed class 1 0.050 0.060 0.678 ＞0.05 1.051(0.934,1.182) Mixed class 2 0.429 0.109 15.581 ＜0.001 1.536(1.241,1.900) Unhealthy class -0.258 0.054 22.490 ＜0.001 0.773(0.694,0.859) Married or living together Basically Healthy class 1902.449 ＜0.001 Mixed class 1 0.270 0.019 207.569 ＜0.001 1.310(1.263,1.360) Mixed class 2 0.603 0.025 571.070 ＜0.001 1.827(1.739,1.920) Unhealthy class -0.269 0.021 172.092 ＜0.001 0.764(0.734,0.795) Divorced 25.993 Basically Healthy class 0.931 0.245 14.502 ＜0.001 Mixed class 1 1.164 0.309 14.228 ＜0.001 2.538(1.572,4.100) Mixed class 2 0.285 0.266 1.141 ＜0.001 3.204(1.749,5.866) Unhealthy class ＞0.05 1.329(0.789,2.241) Widowed Basically Healthy class 4.734 ＞0.05 Mixed class 1 0.399 0.859 0.215 ＞0.05 1.490(0.276,8.032) Mixed class 2 -0.405 0.939 0.187 ＞0.05 0.667(0.106,4.196) Unhealthy class -0.647 0.949 0.464 ＞0.05 0.524(0.082,3.364) Gender Stratification: Differences in CAS incidence were observed between males and females across different lifestyle categories, though the overall trend remained consistent. The highest CAS risk was associated with the Mixed class 2 (male OR = 1.858, female OR = 1.482), whereas the Unhealthy class had a lower CAS risk (male OR = 0.885, female OR = 0.844). The effect size was weaker in females. Age Stratification: The incidence of CAS increases with age, and among individuals aged ≥60 years, the Unhealthy class has a higher CAS incidence than the Basically healthy class. Binary logistic regression analysis indicates that in the 18–39 and 40–59 age groups, the Mixed class 2 group exhibits a significantly increased CAS risk ( OR = 1.214 and OR = 1.439), while the Unhealthy class shows a lower CAS risk ( OR = 0.921 and OR = 0.771). In the ≥60 age group, only the Mixed class 2 group has an elevated CAS risk ( OR = 1.497), while differences among other groups are not statistically significant. BMI Stratification: CAS incidence increased with BMI. Across all BMI categories, individuals in the Mixed class 2 group exhibited an elevated CAS risk, whereas those in the Unhealthy classgroup had a lower CAS risk (normal weight OR = 0.761, overweight OR = 0.803, obesity OR = 0.788). Marital Status Stratification: Among married or cohabiting individuals, the CAS risk was significantly higher in the Mixed class 2 group ( OR = 1.827) and significantly lower in the Unhealthy class group ( OR = 0.764). Among divorced individuals, both Mixed class 1 ( OR = 2.538) and Mixed class 2 ( OR = 3.204) groups showed significantly increased CAS risk, whereas the Unhealthy class group did not show a statistically significant difference ( P > 0.05). Among unmarried individuals, the CAS risk was elevated in the Mixed class 2 group ( OR = 1.536) but reduced in the Unhealthy class group ( OR = 0.773). Discussion To the best of our knowledge, this is the first large-scale study exploring the relationship between lifestyle habits and CAS among knowledge workers. The results show that the prevalence of CAS among Chinese knowledge workers is 49.0%. Four distinct lifestyle patterns were identified, with significant CAS correlations. Mixed lifestyle groups showed higher CAS risk than healthy-lifestyle individuals. Our findings highlight lifestyle's crucial role in CAS development and inform targeted prevention strategies. CAS prevalence among Chinese knowledge workers was 49.0% (cIMT thickening: 9.7%, plaques: 38.9%, stenosis/occlusion: 0.4%), significantly higher than general adults(21), likely due to occupational stress and unhealthy lifestyles. Consistent with previous findings, CAS varied by demographics - males, obesity, divorced and older individuals showed higher risk(21). Estrogen's cardiovascular protection in women(22) and age-related vascular dysfunction(23) combined with prolonged exposure to metabolic risk factors(24) explain these patterns. Normal-BMI knowledge workers showed lower CAS risk than overweight and obese peers(25), likely due to reduced visceral fat, better insulin sensitivity, and lower inflammatory markers (IL-6, CRP)(26). Divorced and widowed individuals had higher CAS risk, potentially from stress-induced sympathetic activation and elevated inflammatory responses(27). The study identified four lifestyle patterns: Healthy, Mixed1 (smoking/drinking), Mixed2 (diet/activity), and Unhealthy. Analysis showed Mixed1 had highest CAS risk versus Healthy type (adjusted OR = 1.32, 95%CI:1.15-1.52), confirming smoking/alcohol as key risk factors(28-30). Surprisingly, Mixed2 and Unhealthy classes showed lower CAS risk, suggesting inverse associations from dietary/activity factors - contradicting previous findings(31). Several potential mechanisms may explain the unexpected inverse associations observed in the 'Mixed class 2' and 'Unhealthy' groups. First, age differences likely play a confounding role; the 'Mixed class 2' (mean age 37.5±9.7 years) and 'Unhealthy' (40.7±9.7 years) groups were significantly younger than the 'Basically healthy' group (45.5±10.7 years). Given that our stratified analysis confirms CAS incidence increases with age, the lower risk in these groups may partially reflect their younger demographic. Second, the 'healthy worker effect' and survivor bias may be present, where individuals with severe health consequences from long-term unhealthy behaviors may have already left the workforce or suffered premature mortality, excluding them from the study. Third, reverse causality is a significant consideration; knowledge workers with existing health concerns may have recently adopted healthier habits (e.g., improving diet or activity) post-diagnosis (32). This behavioral change could result in high-risk individuals being classified into the 'Basically healthy' group, paradoxically inflating its associated risk. Finally, the moderate entropy (>0.5) indicates some degree of overlap between latent classes, which may blur the distinctions in risk profiles. Collectively, these factors suggest that the CAS risk may be underestimated in the 'Unhealthy' class and overestimated in the 'Basically healthy' class. Multilevel analysis results indicate that individuals in the Mixed class 2 generally exhibit a higher CAS risk, while those in the Unhealthy class show relatively lower risk. This pattern demonstrates a certain degree of heterogeneity across different populations. Men showed greater lifestyle-CAS risk sensitivity than women. The reduced risk in unhealthy males ( OR = 0.885) may reflect the Healthy Survivor Effect, where long-term unhealthy individuals experience earlier mortality(33). Women demonstrated smaller OR variations, potentially due to better healthcare engagement and health consciousness(34) mitigating lifestyle-related risk differences. Stratified analyses revealed age and BMI-dependent lifestyle-CAS risk relationships: Mixed2 showed elevated risk in younger (18-39 years) and higher BMI groups, while Unhealthy class demonstrated lower risk, potentially due to greater metabolic resilience in these populations. However, long-term CVD risk remains concerning. Notably, Unhealthy individuals >40y showed reduced CAS risk, suggesting health-motivated lifestyle modifications may complicate risk associations. Stratified analysis by marital status reveals that divorced individuals in the Mixed class 1 and Mixed class 2 groups exhibit a significantly higher CAS risk, potentially due to lack of social support, lifestyle instability, and psychological stress. In contrast, among married or cohabiting individuals, the Mixed class 2 group continues to show an elevated CAS risk, suggesting that despite maintaining a stable marital relationship, these individuals may still be affected by unhealthy lifestyle factors. Marital status stratification showed divorced individuals in Mixed 1 and 2 classes had highest CAS risk, likely from psychosocial stressors. Married Mixed 2 individuals maintained elevated risk, indicating lifestyle factors outweigh marital stability benefits. This study employs a cross-sectional design, which limits the ability to establish causal relationships. Although an association between lifestyle habit categories and CAS severity was observed, it cannot be concluded that lifestyle habits directly cause the onset or progression of CAS. Future longitudinal or interventional studies are needed to verify causal relationships. Secondly, we used the Chinese specific BMI cut-off points rather than WHO criteria, which may limit the direct comparability of our findings with studies involving non-Chinese populations. Additionally, the assessment of lifestyle habits relies on self-reported data, which may introduce recall bias or social desirability bias. Then, participants were recruited from a single health management center database, which might limit the generalizability to all Chinese knowledge workers or other populations. Lastly, future research should further explore long-term follow-up data to clarify the causal relationship between lifestyle and CAS and develop individualized intervention strategies for high-risk populations. Conclusions This study conducted a latent class analysis of lifestyle habits among 113,262 knowledge workers and found a significant association between different lifestyle patterns and CAS risk. The results indicate that subgroups engaging in unhealthy behaviors such as smoking, alcohol consumption, and prolonged sedentary behavior have a significantly higher risk of CAS. Although some findings contradict previous research—such as the inverse associations between CAS and nighttime eating—these results require further investigation. Nevertheless, the authors recommend that knowledge workers should maintain healthy lifestyle habits in three key aspects - diet, tobacco/alcohol consumption, and physical activity. Specifically, this includes: reducing late-night eating, decreasing consumption of coffee and sugar-sweetened beverages, quitting smoking and alcohol, avoiding prolonged sitting while maintaining adequate physical activity, thereby reducing the risk of CAS. Abbreviations Abbreviation Full English Name CAS Carotid Atherosclerosis LCA Latent Class Analysis cIMT Carotid Intima-Media Thickness MI Multiple Imputation SPSS Statistical Package for the Social Sciences AIC Akaike Information Criterion BIC Bayesian Information Criterion aBIC Adjusted Bayesian Information Criterion VLMR Vuong-Lo-Mendell-Rubin Likelihood Ratio Test BLRT Bootstrapped Likelihood Ratio Test OR Odds Ratio CI Confidence Interval NE Nighttime eating CC Coffee Consumption SSBC Sugar-Sweetened Beverage Consumption AC Alcohol Consumption SB Sedentary Behavior PA Physical Activity BMI Body Mass Index CVD Cardiovascular Disease IL-6 Interleukin-6 CRP C-Reactive Protein Declarations 【 Ethics approval and consent to participate 】 This study was conducted according to the guidelines laid down in the Declaration of Helsinki and all procedures involving research study participants were approved by the The IRB of Third Xiangya Hospital, Central South University. Written informed consent was obtained from all patients. 【 Consent for publication 】 Not applicable. 【 Availability of data and materials 】 The datasets used and/or analysed during the current study are available from the corresponding author on reasonable request. 【 Competing interests 】 The authors declare that they have no competing interests. 【 Funding 】 This work was supported by the Special Funding for Chenzhou Resident Health Science Popularization Platform (NO.2023sfq13) 【 Authors' contributions 】 ZHY and HX conceived and designed the work, acquired data, played an important role in interpreting the results, drafted the manuscript, approved the final version, and agreed to be accountable for all aspects of the work. LLJ, LY, ZY, WJY, WXX, ZJY contributed to data acquisition, participated in result interpretation, revised the manuscript, approved the final version, and agreed to be accountable for all aspects of the work. LL (corresponding author) oversaw the conception and design of the work, played a key role in result interpretation, revised the manuscript critically, approved the final version, and agreed to be accountable for all aspects of the work. PHFN provided feedback on result interpretation, revised the manuscript, approved the final version, and agreed to be accountable for all aspects of the work. 【 Acknowledgments 】 Thanks to Professor Xie Jianfei for her guidance in the writing and revision of this article. References Libby P, Ridker PM, Hansson GK. Inflammation in atherosclerosis: from pathophysiology to practice. J Am Coll Cardiol. 2009;54(23):2129-38. Byrne RA, Rossello X, Coughlan JJ, Barbato E, Berry C, Chieffo A, et al. 2023 ESC Guidelines for the management of acute coronary syndromes: Developed by the task force on the management of acute coronary syndromes of the European Society of Cardiology (ESC). European Heart Journal-Acute Cardiovascular Care. 2024;13(1):55-161. 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Supplementary Files supplementaryfile1QuestionnaireonPersonalBasicInformation.pdf Cite Share Download PDF Status: Under Review Version 1 posted Reviews received at journal 19 Mar, 2026 Reviewers agreed at journal 16 Mar, 2026 Reviewers invited by journal 16 Mar, 2026 Editor invited by journal 05 Mar, 2026 Editor assigned by journal 05 Mar, 2026 Submission checks completed at journal 05 Mar, 2026 First submitted to journal 04 Mar, 2026 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. 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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-9028936","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":607645051,"identity":"f893bb13-8810-4ba4-ae49-e144bb44da0e","order_by":0,"name":"Huiyi Zhang","email":"","orcid":"","institution":"The Third Xiangya Hospital, Central South University","correspondingAuthor":false,"prefix":"","firstName":"Huiyi","middleName":"","lastName":"Zhang","suffix":""},{"id":607645052,"identity":"455104bb-f706-4769-86e1-62784c9a1f0d","order_by":1,"name":"Xue He","email":"","orcid":"","institution":"The Third Xiangya Hospital, Central South University","correspondingAuthor":false,"prefix":"","firstName":"Xue","middleName":"","lastName":"He","suffix":""},{"id":607645053,"identity":"3e107e4b-47d3-480e-8fb0-5e43df1ce432","order_by":2,"name":"Lijun Li","email":"","orcid":"","institution":"Monash University","correspondingAuthor":false,"prefix":"","firstName":"Lijun","middleName":"","lastName":"Li","suffix":""},{"id":607645054,"identity":"65f1a78b-c637-414b-b2b5-7e16c472e49b","order_by":3,"name":"Ying Li","email":"","orcid":"","institution":"The Third Xiangya Hospital, Central South University","correspondingAuthor":false,"prefix":"","firstName":"Ying","middleName":"","lastName":"Li","suffix":""},{"id":607645055,"identity":"b3d024a9-30a3-4791-8f5a-1f5eac31c416","order_by":4,"name":"Yi Zhou","email":"","orcid":"","institution":"The Third Xiangya Hospital, Central South University","correspondingAuthor":false,"prefix":"","firstName":"Yi","middleName":"","lastName":"Zhou","suffix":""},{"id":607645056,"identity":"64b11240-555e-406d-bca0-6e98ac3c8c79","order_by":5,"name":"Jingying Wang","email":"","orcid":"","institution":"Central South University","correspondingAuthor":false,"prefix":"","firstName":"Jingying","middleName":"","lastName":"Wang","suffix":""},{"id":607645057,"identity":"0686a548-49e9-4dff-9d0e-b90a73669a55","order_by":6,"name":"Xingxing Wang","email":"","orcid":"","institution":"Central South University","correspondingAuthor":false,"prefix":"","firstName":"Xingxing","middleName":"","lastName":"Wang","suffix":""},{"id":607645058,"identity":"4ffb3060-def0-462f-ab07-1c8d4088d793","order_by":7,"name":"Jiayi Zhu","email":"","orcid":"","institution":"Central South University","correspondingAuthor":false,"prefix":"","firstName":"Jiayi","middleName":"","lastName":"Zhu","suffix":""},{"id":607645059,"identity":"bfe85a4b-5aff-4328-bb36-261a816d3ea8","order_by":8,"name":"Li Liang","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA6UlEQVRIie3RsWrDMBCA4QOBuohqPZOQvMKVDElpt76IRIozBTK6EKiNgzy03f0YHjvGCNRF3TMmj9AtQwlJ5wbb2TLon++DOw4gFLrC+NB+7TF5ZFJm2VYly3Zyi1zhxMc3UWlz2nrXTgYoCF+MlZTOTLRbsQ6L9d6JNtz2CGqT6JSDLN5UM+m7hS7FbDRmmdnozz6g/66aCUwri/gwvU/rE/EcCOdtRN3lv8Req7U2C21YB4LPI0D1xOhEoBsRLgZcxywq6xyVd6L1lmGRO4gOf68sdj/7ZDmQxUcz+Ze4bDwUCoVCZzsCk/RK2fpYD7MAAAAASUVORK5CYII=","orcid":"","institution":"The Third Xiangya Hospital, Central South University","correspondingAuthor":true,"prefix":"","firstName":"Li","middleName":"","lastName":"Liang","suffix":""},{"id":607645060,"identity":"952e63f5-e626-4b9d-910c-a397af337d24","order_by":9,"name":"Peter Ng","email":"","orcid":"","institution":"Hong Kong Polytechnic University","correspondingAuthor":false,"prefix":"","firstName":"Peter","middleName":"","lastName":"Ng","suffix":""}],"badges":[],"createdAt":"2026-03-04 10:11:23","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-9028936/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-9028936/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":104914585,"identity":"9c681dc4-8997-4665-9ddb-4806874a05ef","added_by":"auto","created_at":"2026-03-18 15:55:49","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":78309,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eFour clusters of lifestyle habits (n =113,262).\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNE, nighttime eating; CC, coffee consumption; SSBC, sugar-sweetened beverage consumption; AC, alcohol consumption; SB, sedentary behavior; PA, physical activity.\u003c/p\u003e","description":"","filename":"1.png","url":"https://assets-eu.researchsquare.com/files/rs-9028936/v1/4a2e219e86d1576909b2d445.png"},{"id":104914579,"identity":"432834b6-ec19-4c54-888a-6729405893f1","added_by":"auto","created_at":"2026-03-18 15:55:44","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":141218,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003ecategory: 0=healthy;1=unhealthy.\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNE, nighttime eating; CC, coffee consumption; SSBC, sugar-sweetened beverage consumption; AC, alcohol consumption; SB, sedentary behavior; PA, physical activity.\u003c/p\u003e","description":"","filename":"2.png","url":"https://assets-eu.researchsquare.com/files/rs-9028936/v1/08f999e8f172ddb1b07cd844.png"},{"id":104914644,"identity":"1e5d826c-87db-4c95-abd8-6dc61b9dfe2f","added_by":"auto","created_at":"2026-03-18 15:56:17","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1522519,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-9028936/v1/0b6d49b8-f2eb-4fc3-aafa-f9090ea43e58.pdf"},{"id":104914626,"identity":"832fc0b0-0a18-4032-a360-d7d43fd3497c","added_by":"auto","created_at":"2026-03-18 15:56:02","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"supplement","size":79727,"visible":true,"origin":"","legend":"","description":"","filename":"supplementaryfile1QuestionnaireonPersonalBasicInformation.pdf","url":"https://assets-eu.researchsquare.com/files/rs-9028936/v1/3017f64fcc3a066d0356beda.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"The Association Between Lifestyle Habits of Knowledge Workers and Carotid Atherosclerosis: A Latent Class Study Of 113,262 Chinese","fulltext":[{"header":"Introduction","content":"\u003cp\u003eAtherosclerosis is a chronic inflammatory vascular disease characterized by lipid accumulation within the arterial walls, proliferation of smooth muscle cells, and fibrosis, leading to vascular thickening, hardening, and plaque formation(1). Carotid atherosclerosis (CAS), a key contributor to ischemic stroke and cardiovascular events(2), is rising globally, affecting ~2 billion people, including 270 million in China(3). CAS is characterized by increased carotid intima-media thickness (cIMT), plaque formation, and stenosis, and its progression is linked to cognitive decline, dementia, and stroke(4).\u003c/p\u003e\n\u003cp\u003eKnowledge workers, also referred to as mental workers, are typically defined as individuals whose occupations primarily rely on cognitive abilities, information processing, decision-making, and creative thinking, rather than manual labor (e.g., engineers, professors)(5). Limited research exists on CAS prevalence and risk factors in this population. \u0026nbsp;Prior CAS studies have focused on community and elderly cohorts, cardiometabolic high‑risk groups (hypertension, diabetes, metabolic syndrome), smokers, and certain occupational exposures, emphasizing conventional risk factors and the prognostic utility of carotid ultrasound metrics(6-9). Knowledge workers, however, display distinct lifestyle clusters\u0026mdash;prolonged sedentary time, high caffeine consumption, psychosocial stress, and irregular eating habits\u0026mdash;yet evidence linking these patterned behaviors to CAS is scarce(10-13). Studying knowledge workers is therefore warranted to identify modifiable, group‑specific risk phenotypes and enable tailored prevention(10, 12, 13).\u003c/p\u003e\n\u003cp\u003eLatent Class Analysis (LCA) is a probabilistic clustering method that classifies individuals into homogeneous subgroups, revealing latent data structures(14). Unlike traditional regression that fails to capture multidimensional lifestyle interactions, LCA effectively identifies population subgroups and their behavior-health associations. We applied LCA to categorize knowledge workers\u0026apos; lifestyle patterns and evaluate CAS risk associations, overcoming single-factor analysis limitations.\u003c/p\u003e\n\u003cp\u003eGiven CAS\u0026apos;s high prevalence and particular risk to knowledge workers, multidimensional risk factor analysis is imperative. This large-scale cross-sectional study of Chinese knowledge workers aims to: (a) Assess CAS prevalence and lifestyle factors (nighttime eating, coffee/sugar-sweetened beverage consumption, smoking, alcohol use, sedentariness, and physical activity); (b) Classify lifestyle patterns via LCA to identify population subgroups; (c) Investigate lifestyle-CAS risk associations. Combining lifestyle heterogeneity with advanced analytics, this study provides targeted prevention strategies, addressing a key research gap.\u003c/p\u003e"},{"header":"Methods","content":"\u003cp\u003e2.1 Design and participants\u003c/p\u003e\n\u003cp\u003eThis cross-sectional study recruited participants from a tertiary hospital\u0026apos;s health management database in Changsha (2017-2024). Data came from the hospital\u0026apos;s resident health examination records. In this study, a self-designed staff-assisted lifestyle questionnaire was used to investigate residents\u0026apos; health-related information (supplementary file1). All participants provided written informed consent and completed: (a) a staff-assisted lifestyle questionnaire, and (b) carotid Doppler ultrasound(15). The cohort included knowledge workers of all ages, excluding those with cognitive/psychiatric impairments or communication barriers. The study protocol conforms to the ethical guidelines of the 1975 Declaration of Helsinki and the study protocol has been priorly approved by the Institution\u0026apos;s ethics committee on research on humans.\u003c/p\u003e\n\u003cp\u003e2.2 Measures\u003c/p\u003e\n\u003cp\u003eBased on preliminary literature reviews and expert panel focus group discussions, the following data collection components were identified:\u003c/p\u003e\n\u003cp\u003e2.2.1 Lifestyle habits\u003c/p\u003e\n\u003cp\u003eThis study selected seven lifestyle factors\u0026mdash;nighttime eating, coffee consumption, sugar-sweetened beverage consumption, smoking, alcohol consumption, sedentary behavior, and physical activity\u0026mdash;to construct a comprehensive lifestyle profile encompassing dietary habits, tobacco and alcohol use, and physical activity for analysis.\u003c/p\u003e\n\u003cp\u003e2.2.1.1 Nighttime eating\u003c/p\u003e\n\u003cp\u003eNighttime eating was assessed using two questions: (a) \u0026quot;Do you frequently eat after 9:00 PM?\u0026quot;(b) \u0026quot;How many times per week do you engage in nighttime eating?\u0026quot; Nighttime eating habits were categorized into two groups: healthy (no nighttime eating habit) and unhealthy (habitual nighttime eating).\u003c/p\u003e\n\u003cp\u003e2.2.1.2 Coffee consumption\u003c/p\u003e\n\u003cp\u003eCoffee consumption was assessed using two questions: (a) \u0026quot;Do you like drinking coffee?\u0026quot; (b) \u0026quot;How many cups of coffee do you consume per week?\u0026quot; Based on weekly caffeine intake, participants were categorized into two groups: healthy (\u0026lt;400 mg) and unhealthy (\u0026ge;400 mg).\u003c/p\u003e\n\u003cp\u003e2.2.1.3 Sugar-sweetened beverage consumption\u003c/p\u003e\n\u003cp\u003eSugar-sweetened beverage consumption was assessed using two questions: (a) \u0026quot;Do you consume sugar-sweetened beverages?\u0026quot; (b) \u0026quot;How many bottles of sugar-sweetened beverages do you consume per week?\u0026quot; Based on weekly sugar intake from beverages, participants were categorized into two groups: healthy (\u0026lt;175 g) and unhealthy (\u0026ge;175 g).\u003c/p\u003e\n\u003cp\u003e2.2.1.4 Smoking\u003c/p\u003e\n\u003cp\u003eSmoking status was assessed using a single-choice question: \u0026quot;Do you smoke? (Defined as continuous smoking for more than one year).\u0026quot; The four response options were: (a) No, I do not smoke. (b) Yes, I currently smoke. (c) I used to smoke. (d) I am a passive smoker (exposed for at least 15 minutes per day on more than one day per week). In this study, smoking status was categorized into two groups: healthy (non-smokers) and unhealthy (former smokers, current smokers, or passive smokers).\u003c/p\u003e\n\u003cp\u003e2.2.1.5 Alcohol consumption\u003c/p\u003e\n\u003cp\u003eAlcohol consumption was assessed using the following four questions: (a) Do you drink alcohol? (b) What type of alcohol do you consume? (c) How many times per week do you drink? (d) How much alcohol do you consume per occasion? Based on the responses, alcohol consumption was categorized into two groups: healthy (\u0026lt;70 g per week) and unhealthy (\u0026ge;70 g per week).\u003c/p\u003e\n\u003cp\u003e2.2.1.6 Sedentary behavior\u003c/p\u003e\n\u003cp\u003eSedentary behavior was categorized based on the daily sitting time excluding work or study hours. The two groups were: healthy (\u0026lt;2 hours) and unhealthy (\u0026ge;2 hours).\u003c/p\u003e\n\u003cp\u003e2.2.1.7 Physical activity\u003c/p\u003e\n\u003cp\u003ePhysical activity was assessed using two questions: (a) How many times per week do you exercise? (b) How long do you exercise each time? (c) What type of exercise do you do? Based on the total weekly exercise duration, physical activity was categorized into two groups: unhealthy (\u0026lt;150 minutes/week) and healthy (\u0026ge;150 minutes/week).\u003c/p\u003e\n\u003cp\u003e2.2.2 Carotid atherosclerosis (CAS)\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eAll participants underwent a comprehensive bilateral carotid artery ultrasound examination performed by trained and certified sonographers. To ensure consistency and minimize inter-operator variability across the study cohort, all sonographers followed a strictly standardized scanning protocol and underwent periodic retraining and quality assessment(16).\u003c/p\u003e\n\u003cp\u003eHigh-resolution B-mode and color Doppler ultrasonography were performed using commercially available ultrasound systems, including the Philips EPIQ 7 (Philips Healthcare, Amsterdam, Netherlands) and Siemens Acuson Sequoia (Siemens Healthineers, Erlangen, Germany). A high-frequency linear array transducer (typically 7\u0026ndash;12 MHz) was utilized for all examinations. During the procedure, participants were positioned in the supine position with their head slightly extended and rotated approximately 45 degrees contralateral to the side being examined to ensure optimal visualization of the carotid arteries. The examination systematically interrogated the bilateral common carotid arteries (CCA), the carotid bifurcation, and the proximal 1-2 cm segments of the internal carotid artery (ICA) and external carotid artery (ECA) in both longitudinal and transverse planes.\u003c/p\u003e\n\u003cp\u003eCarotid Intima-Media Thickness (cIMT): The cIMT was measured on the far wall of a plaque-free segment of the distal CCA, approximately 1.0\u0026ndash;1.5 cm proximal to the bifurcation. The measurement was defined as the distance between the lumen-intima interface and the media-adventitia interface. Automated or semi-automated edge-detection software was used to enhance precision. For each side, the mean cIMT was calculated from three distinct measurements obtained along a 10 mm segment. The maximum of the left and right mean cIMT values was used for analysis(16, 17).\u003c/p\u003e\n\u003cp\u003eCarotid Plaque Assessment: The presence, location, and characteristics of atherosclerotic plaques were meticulously documented. A carotid plaque was defined according to the Mannheim consensus criteria as a focal structure that encroaches into the arterial lumen by at least 0.5 mm or 50% of the surrounding IMT value, or demonstrates a thickness \u0026gt;1.5 mm as measured from the media-adventitia interface to the intima-lumen interface(17). The presence of plaque was recorded for each of the six segments (CCA, bifurcation, and ICA on both sides).\u003c/p\u003e\n\u003cp\u003eIn this study, the degree of CAS was classified according to the following criteria(18): (a) clMT \u0026lt; 1.0 mm with no carotid plaque was considered normal; (b) cIMT between 1.0 to 1.5 mm was classified as carotid intima-media thickening; (c) clMT \u0026gt; 1.5 mm was classified as carotid plaque; (d) Plaque thickness exceeding 50% of the lumen was considered arterial stenosis or occlusion. A CAS diagnosis was defined as meeting any one of the following carotid ultrasound criteria: (b), (c), or (d).\u003c/p\u003e\n\u003cp\u003e2.2.3 Socio-demographic information\u003c/p\u003e\n\u003cp\u003eDemographic information included gender, age, marital status, and BMI. Age was categorized into four groups: 1 = \u0026lt; 18 years, 2 = 18\u0026ndash;39 years, 3 = 40\u0026ndash;59 years, 4 = \u0026ge; 60 years. Marital status was divided into four categories: 1 = unmarried, 2 = married or cohabitating, 3 = divorced, 4 = widowed. BMI was calculated by dividing weight (kg) by the square of height (m\u0026sup2;), and classified into four categories according to the Working Group on Obesity in China (WGOC) guidelines: underweight (\u0026lt;18.5 kg/m\u0026sup2;), normal weight (18.5\u0026ndash;23.9 kg/m\u0026sup2;), overweight (24.0\u0026ndash;27.9 kg/m\u0026sup2;), and obese (\u0026ge;28.0 kg/m\u0026sup2;)(19).\u003c/p\u003e\n\u003cp\u003e2.3 Statistical analyses\u003c/p\u003e\n\u003cp\u003eStatistical analyses were conducted using SPSS 27.0 and Mplus 8.3. For randomly missing data (1.6%-5.3%), multiple imputation(MI) with FCS generated five datasets, with results pooled for final estimates. Descriptive statistical analysis was conducted on the general demographic characteristics, lifestyle habits, and CAS status of knowledge workers. Continuous variables were expressed as mean \u0026plusmn; standard deviation (x̄\u0026plusmn; s), while categorical variables were presented as frequencies and percentages (n%). Univariate analysis was used to explore the differences in CAS prevalence across knowledge workers with different characteristics, and the chi-square test (\u0026chi;2) was used for comparisons of categorical data. A significance level of \u0026alpha; = 0.05 was applied. LCA identified latent subgroups in multidimensional categorical data. We tested 1-9 class models, selecting the optimal number based on LL, BIC, AIC, aBIC, VLMR, and BLRT. While entropy\u0026gt;0.8 typically indicates good classification, its reliability decreases with large samples (\u0026gt;3000)(20). Therefore, we primarily used other statistical indicators. Although the 8-class model had the lowest BIC, classes with \u0026lt;0.5% frequency (from 5-class onward) were non-representative, leading to selection of the clinically meaningful 4-class model.\u003c/p\u003e\n\u003cp\u003eThe severity of CAS was used as the dependent variable, classified as follows: normal = 0, carotid intima-media thickening = 1, carotid plaque = 2, and arterial stenosis or occlusion = 3. Univariate-significant variables served as independent variables in collinearity diagnostics and multivariate logistic regression, with \u003cem\u003eOR\u003c/em\u003es (95% CIs) calculated. Parallelism test significance (\u003cem\u003eP\u003c/em\u003e\u0026lt;0.05) necessitated unordered multinomial logistic regression to examine lifestyle-CAS severity associations. Three models were built: (a) unadjusted; (b) age/sex-adjusted; (c) additionally adjusted for BMI and marital status. Two-tailed tests (\u003cem\u003eP\u003c/em\u003e\u0026lt;0.05) determined significance.\u003c/p\u003e\n\u003cp\u003eStratified analysis controls confounders and detects interactions by dividing samples by key variables (e.g., gender, age, BMI, marital status) and computing stratum-specific \u003cem\u003eOR\u003c/em\u003es. Post-multivariate regression, we stratified by sociodemographics, calculated stratum-specific CAS incidence by lifestyle category, then conducted separate logistic regressions (CAS occurrence: 0/1) per stratum. Significant between-stratum risk differences suggest potential interactions.\u003c/p\u003e"},{"header":"Results","content":"\u003cp\u003e3.1 Socio-demographic characteristics of the sample\u003c/p\u003e\n\u003cp\u003eA total of 113,262 Chinese knowledge workers were included in this study, aged between 12 and 94 years, with an average age of 42.55\u0026plusmn;10.78 years. Among them, 68,414 were male (60.4%) and 44,848 were female (39.6%). The majority of participants (87.1%) were married or cohabitating. More detailed information is presented in Table 1.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable1 Descriptive statistics and each latent class of healthy lifestyle behavioral patterns (n =113,262).\u003c/strong\u003e\u003c/p\u003e\n\u003ctable border=\"0\" cellspacing=\"0\" cellpadding=\"0\" width=\"111%\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 17px;\"\u003e\n \u003cp\u003eVariables\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14px;\"\u003e\n \u003cp\u003en(%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14px;\"\u003e\n \u003cp\u003eBasically Healthy class (n=50,055)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14px;\"\u003e\n \u003cp\u003eMixed class 1 (n=12,071)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 15px;\"\u003e\n \u003cp\u003eMixed class 2\u003cbr\u003e\u0026nbsp;(n =33,751)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14px;\"\u003e\n \u003cp\u003eUnhealthy class (n=17,385)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 8px;\"\u003e\n \u003cp\u003e\u003cem\u003eP\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 17px;\"\u003e\n \u003cp\u003eGender\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 15px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 8px;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 17px;\"\u003e\n \u003cp\u003eMale\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14px;\"\u003e\n \u003cp\u003e68418(60.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14px;\"\u003e\n \u003cp\u003e25585(51.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14px;\"\u003e\n \u003cp\u003e11652(96.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 15px;\"\u003e\n \u003cp\u003e15668(46.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14px;\"\u003e\n \u003cp\u003e15509(89.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 8px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 17px;\"\u003e\n \u003cp\u003eFemale\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14px;\"\u003e\n \u003cp\u003e44848(39.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14px;\"\u003e\n \u003cp\u003e24470(48.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14px;\"\u003e\n \u003cp\u003e419(3.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 15px;\"\u003e\n \u003cp\u003e18083(53.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14px;\"\u003e\n \u003cp\u003e1876(10.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 8px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 17px;\"\u003e\n \u003cp\u003eAge(years)*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 15px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 8px;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 17px;\"\u003e\n \u003cp\u003e＜18\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14px;\"\u003e\n \u003cp\u003e90(0.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14px;\"\u003e\n \u003cp\u003e24(0.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14px;\"\u003e\n \u003cp\u003e0(0.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 15px;\"\u003e\n \u003cp\u003e63(0.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14px;\"\u003e\n \u003cp\u003e3(0.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 8px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 17px;\"\u003e\n \u003cp\u003e18-39\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14px;\"\u003e\n \u003cp\u003e48421(42.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14px;\"\u003e\n \u003cp\u003e15373(30.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14px;\"\u003e\n \u003cp\u003e2679(22.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 15px;\"\u003e\n \u003cp\u003e21583(64.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14px;\"\u003e\n \u003cp\u003e8786(50.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 8px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 17px;\"\u003e\n \u003cp\u003e40-59\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14px;\"\u003e\n \u003cp\u003e58946(52.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14px;\"\u003e\n \u003cp\u003e30759(61.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14px;\"\u003e\n \u003cp\u003e8643(71.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 15px;\"\u003e\n \u003cp\u003e11434(33.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14px;\"\u003e\n \u003cp\u003e8110(46.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 8px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 17px;\"\u003e\n \u003cp\u003e\u0026ge;60\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14px;\"\u003e\n \u003cp\u003e5802(5.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14px;\"\u003e\n \u003cp\u003e3899(7.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14px;\"\u003e\n \u003cp\u003e749(6.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 15px;\"\u003e\n \u003cp\u003e669(2.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14px;\"\u003e\n \u003cp\u003e485(2.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 8px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 17px;\"\u003e\n \u003cp\u003eBMI\u003csup\u003e\u0026dagger;\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 15px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 8px;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 17px;\"\u003e\n \u003cp\u003eUnderweight\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14px;\"\u003e\n \u003cp\u003e2839(2.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14px;\"\u003e\n \u003cp\u003e1191(2.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14px;\"\u003e\n \u003cp\u003e57(0.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 15px;\"\u003e\n \u003cp\u003e1349(4.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14px;\"\u003e\n \u003cp\u003e242(1.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 8px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 17px;\"\u003e\n \u003cp\u003eNormal weight\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14px;\"\u003e\n \u003cp\u003e50713(47.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14px;\"\u003e\n \u003cp\u003e24712(52.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14px;\"\u003e\n \u003cp\u003e3296(29.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 15px;\"\u003e\n \u003cp\u003e17292(54.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14px;\"\u003e\n \u003cp\u003e5413(32.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 8px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 17px;\"\u003e\n \u003cp\u003eOverweight\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14px;\"\u003e\n \u003cp\u003e40818(38.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14px;\"\u003e\n \u003cp\u003e17339(36.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14px;\"\u003e\n \u003cp\u003e5806(51.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 15px;\"\u003e\n \u003cp\u003e9977(31.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14px;\"\u003e\n \u003cp\u003e7696(46.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 8px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 17px;\"\u003e\n \u003cp\u003eObesity\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14px;\"\u003e\n \u003cp\u003e12914(12.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14px;\"\u003e\n \u003cp\u003e4196(8.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14px;\"\u003e\n \u003cp\u003e2204(19.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 15px;\"\u003e\n \u003cp\u003e3373(10.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14px;\"\u003e\n \u003cp\u003e3141(19.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 8px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 17px;\"\u003e\n \u003cp\u003eMarital status\u003csup\u003e\u0026Dagger;\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 15px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 8px;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 17px;\"\u003e\n \u003cp\u003eUnmarried\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14px;\"\u003e\n \u003cp\u003e11919(10.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14px;\"\u003e\n \u003cp\u003e3081(6.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14px;\"\u003e\n \u003cp\u003e420(3.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 15px;\"\u003e\n \u003cp\u003e6457(19.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14px;\"\u003e\n \u003cp\u003e1943(11.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 8px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 17px;\"\u003e\n \u003cp\u003eMarried or living together\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14px;\"\u003e\n \u003cp\u003e98675(88.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14px;\"\u003e\n \u003cp\u003e45661(92.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14px;\"\u003e\n \u003cp\u003e11446(95.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 15px;\"\u003e\n \u003cp\u003e26380(79.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14px;\"\u003e\n \u003cp\u003e15188(88.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 8px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 17px;\"\u003e\n \u003cp\u003eDivorced\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14px;\"\u003e\n \u003cp\u003e714(0.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14px;\"\u003e\n \u003cp\u003e356(0.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14px;\"\u003e\n \u003cp\u003e106(0.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 15px;\"\u003e\n \u003cp\u003e166(0.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14px;\"\u003e\n \u003cp\u003e86(0.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 8px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 17px;\"\u003e\n \u003cp\u003eWidowed\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14px;\"\u003e\n \u003cp\u003e140(0.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14px;\"\u003e\n \u003cp\u003e93(0.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14px;\"\u003e\n \u003cp\u003e21(0.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 15px;\"\u003e\n \u003cp\u003e18(0.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14px;\"\u003e\n \u003cp\u003e8(0.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 8px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e* Data on age were missing for 3 participants.\u003c/p\u003e\n\u003cp\u003e\u003csup\u003e\u0026dagger;\u003c/sup\u003e\u003csup\u003e\u0026nbsp;\u003c/sup\u003e5978(5.3%) missing information on BMI.\u003c/p\u003e\n\u003cp\u003e\u003csup\u003e\u0026Dagger;\u003c/sup\u003e\u003csup\u003e\u0026nbsp;\u003c/sup\u003e1814(1.6%) missing information on Marital status.\u003c/p\u003e\n\u003cp\u003e3.2 CAS\u003c/p\u003e\n\u003cp\u003eAmong the 113,262 knowledge workers, 55,495 cases were diagnosed with CAS, resulting in a detection rate of 49.0%. Of these, 44,099 cases (38.9%) had carotid plaques, 10,938 cases (9.7%) had carotid intima-media thickening, and 458 cases (0.4%) had arterial stenosis or occlusion. Chi-square test results showed that the incidence of CAS differed significantly across different genders, ages, marital statuses, and BMI categories (\u003cem\u003eP\u0026nbsp;\u003c/em\u003e\u0026lt; 0.001).\u003c/p\u003e\n\u003cp\u003e3.3 Latent Class Analysis of Comprehensive Lifestyle Habits in Knowledge Workers\u003c/p\u003e\n\u003cp\u003eIn this study, knowledge workers\u0026apos; lifestyle habits, including nighttime eating, coffee consumption, sugar-sweetened beverage consumption, smoking, alcohol consumption, sedentary behavior, and physical activity, were considered as observable indicators and subjected to latent class analysis (LCA). The lifestyle habits were grouped into 1 to 9 classes, and the model fitting was conducted to determine the best model. The prevalence of the aforementioned habits were as follows: 43,532 (38.4%) for nighttime eating, 33,107 (29.2%) for coffee consumption, 52,920 (46.7%) for sugar-sweetened beverage consumption, 34,441 (30.4%) for smoking, 37,919 (33.5%) for alcohol consumption, 79,981 (70.6%) for sedentary behavior, and 52,564 (46.4%) for physical activity.\u003c/p\u003e\n\u003cp\u003eThe results shown in Table 2 indicate that as the number of categories increased from one to eight, the AIC, BIC, and aBIC values all decreased, with the lowest BIC value achieved in the eighth model. In models two to eight, both VLMR and BLRT tests showed statistical significance (\u003cem\u003eP\u003c/em\u003e \u0026lt; 0.001). However, starting from the fifth model, the proportion of the smallest group in each category was below 5%, resulting in low interpretability and a lack of credibility in generalization. Therefore, this study selected the four-class model as the best model for analyzing knowledge workers\u0026apos; lifestyle habits. Further details can be found in Table 2.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable2 Model fit statistics for the latent class analysis (n =113,262)\u003c/strong\u003e\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" width=\"948\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 101px;\"\u003e\n \u003cp\u003eNumber of classes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 74px;\"\u003e\n \u003cp\u003eLL\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 76px;\"\u003e\n \u003cp\u003eAIC\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 76px;\"\u003e\n \u003cp\u003eBIC\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 76px;\"\u003e\n \u003cp\u003eABIC\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 59px;\"\u003e\n \u003cp\u003eVLMR\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 57px;\"\u003e\n \u003cp\u003eBLRT\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 56px;\"\u003e\n \u003cp\u003eEntropy\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 374px;\"\u003e\n \u003cp\u003eCategory probability (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 101px;\"\u003e\n \u003cp\u003e1C\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 74px;\"\u003e\n \u003cp\u003e-510729.800\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003e1021473.601\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003e1021541.063\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003e1021518.817\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 59px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 57px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 56px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 374px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 101px;\"\u003e\n \u003cp\u003e2C\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 74px;\"\u003e\n \u003cp\u003e-497234.166\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003e994498.332\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003e994642.894\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003e994595.224\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 59px;\"\u003e\n \u003cp\u003e＜0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 57px;\"\u003e\n \u003cp\u003e＜0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 56px;\"\u003e\n \u003cp\u003e0.508\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 374px;\"\u003e\n \u003cp\u003e0.48316/0.51684\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 101px;\"\u003e\n \u003cp\u003e3C\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 74px;\"\u003e\n \u003cp\u003e-489475.308\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003e978996.616\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003e979218.278\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003e979145.183\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 59px;\"\u003e\n \u003cp\u003e＜0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 57px;\"\u003e\n \u003cp\u003e＜0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 56px;\"\u003e\n \u003cp\u003e0.655\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 374px;\"\u003e\n \u003cp\u003e0.46437/0.35048/0.18515\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 101px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e4C*\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 74px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e-487785.123\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e975632.246\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e975931.007\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e975832.488\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 59px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e＜\u003c/strong\u003e\u003cstrong\u003e0.001\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 57px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e＜\u003c/strong\u003e\u003cstrong\u003e0.001\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 56px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.660\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 374px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.44194/0.29799/0.15349/0.10658\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 101px;\"\u003e\n \u003cp\u003e5C\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 74px;\"\u003e\n \u003cp\u003e-487107.561\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003e974293.122\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003e974668.983\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003e974545.040\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 59px;\"\u003e\n \u003cp\u003e＜0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 57px;\"\u003e\n \u003cp\u003e＜0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 56px;\"\u003e\n \u003cp\u003e0.603\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 374px;\"\u003e\n \u003cp\u003e0.32245/0.04170/0.14756/0.11326/0.37503\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 101px;\"\u003e\n \u003cp\u003e6C\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 74px;\"\u003e\n \u003cp\u003e-486872.380\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003e973838.760\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003e974291.721\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003e974142.353\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 59px;\"\u003e\n \u003cp\u003e＜0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 57px;\"\u003e\n \u003cp\u003e＜0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 56px;\"\u003e\n \u003cp\u003e0.658\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 374px;\"\u003e\n \u003cp\u003e0.12861/0.03171/0.19476/0.11746/0.27228/0.25518\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 101px;\"\u003e\n \u003cp\u003e7C\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 74px;\"\u003e\n \u003cp\u003e-486666.848\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003e973443.695\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003e973973.756\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003e973798.964\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 59px;\"\u003e\n \u003cp\u003e＜0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 57px;\"\u003e\n \u003cp\u003e＜0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 56px;\"\u003e\n \u003cp\u003e0.697\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 374px;\"\u003e\n \u003cp\u003e0.17603/0.24047/0.24318/0.03593/0.08698/0.12468/0.09273\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 101px;\"\u003e\n \u003cp\u003e8C\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 74px;\"\u003e\n \u003cp\u003e-486572.184\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003e973270.368\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003e973877.528\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003e973677.312\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 59px;\"\u003e\n \u003cp\u003e＜0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 57px;\"\u003e\n \u003cp\u003e＜0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 56px;\"\u003e\n \u003cp\u003e0.729\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 374px;\"\u003e\n \u003cp\u003e0.10559/0.01948/0.10885/0.17077/0.17835/0.05996/0.20137/0.15563\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 101px;\"\u003e\n \u003cp\u003e9C\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 74px;\"\u003e\n \u003cp\u003e-486551.875\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003e973245.749\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003e973930.009\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003e973704.368\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 59px;\"\u003e\n \u003cp\u003e0.7904\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 57px;\"\u003e\n \u003cp\u003e0.7922\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 56px;\"\u003e\n \u003cp\u003e0.650\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 374px;\"\u003e\n \u003cp\u003e0.05221/0.10822/0.00304/0.04272/0.15907/0.18199/0.05419/0.31828/0.08028\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e* The final selected classification category.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eFigure 1 and 2 displays the four lifestyle habit categories of knowledge workers in China. Class 1, named the \u0026quot;Basically healthy Type\u0026quot; (n=50,055, 44.2%), is characterized by relatively healthy eating, smoking, alcohol consumption, and physical activity habits, with the only high-risk behavior being sedentary behavior (63.2%). Class 4, named the \u0026quot;Unhealthy Type\u0026quot; (n=17,385, 15.3%), exhibits high-risk behaviors in eating habits, smoking, alcohol consumption, and physical activity. Class 2, labeled as \u0026quot;Mixed class 1\u0026quot; (n=12,071, 10.7%), has relatively healthy eating habits but shows high-risk behaviors in smoking (77.5%), alcohol consumption (88.5%), and sedentary behavior (75.8%). Class 3, labeled as \u0026quot;Mixed class 2\u0026quot; (n=33,751, 29.8%), features low smoking and alcohol consumption, but exhibits high-risk behaviors in nighttime eating (58.4%) and coffee consumption (45.6%), very high-risk behaviors in sugar-sweetened beverage consumption (84.8%) and sedentary behavior (75.7%), as well as a lack of effective physical activity (29.9%).Table 3 presents descriptive statistics of each cluster sample and the conditional probabilities.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable3 Nomenclature of latent categories of mental workers lifestyles, their conditions and category probabilities\u003c/strong\u003e\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" width=\"661\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 156px;\"\u003e\n \u003cp\u003eClass\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 61px;\"\u003e\n \u003cp\u003eNE\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 61px;\"\u003e\n \u003cp\u003eCC\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 61px;\"\u003e\n \u003cp\u003eSSBC\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003eSmoking\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 54px;\"\u003e\n \u003cp\u003eAC\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 53px;\"\u003e\n \u003cp\u003eSB\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 61px;\"\u003e\n \u003cp\u003ePA\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003en(%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 156px;\"\u003e\n \u003cp\u003eBasically Healthy class\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 61px;\"\u003e\n \u003cp\u003e0.125\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 61px;\"\u003e\n \u003cp\u003e0.150\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 61px;\"\u003e\n \u003cp\u003e0.155\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003e0.124\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 54px;\"\u003e\n \u003cp\u003e0.167\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 53px;\"\u003e\n \u003cp\u003e0.632\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 61px;\"\u003e\n \u003cp\u003e0.593\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003e44.2%\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 156px;\"\u003e\n \u003cp\u003eMixed class 1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 61px;\"\u003e\n \u003cp\u003e0.354\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 61px;\"\u003e\n \u003cp\u003e0.131\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 61px;\"\u003e\n \u003cp\u003e0.092\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003e0.775\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 54px;\"\u003e\n \u003cp\u003e0.885\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 53px;\"\u003e\n \u003cp\u003e0.758\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 61px;\"\u003e\n \u003cp\u003e0.601\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003e10.7%\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 156px;\"\u003e\n \u003cp\u003eMixed class 2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 61px;\"\u003e\n \u003cp\u003e0.584\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 61px;\"\u003e\n \u003cp\u003e0.456\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 61px;\"\u003e\n \u003cp\u003e0.848\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003e0.213\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 54px;\"\u003e\n \u003cp\u003e0.046\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 53px;\"\u003e\n \u003cp\u003e0.757\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 61px;\"\u003e\n \u003cp\u003e0.299\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003e29.8%\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 156px;\"\u003e\n \u003cp\u003eUnhealthy class\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 61px;\"\u003e\n \u003cp\u003e0.715\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 61px;\"\u003e\n \u003cp\u003e0.481\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 61px;\"\u003e\n \u003cp\u003e0.847\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003e0.623\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 54px;\"\u003e\n \u003cp\u003e1.000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 53px;\"\u003e\n \u003cp\u003e0.761\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 61px;\"\u003e\n \u003cp\u003e0.341\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003e15.3%\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003eNE, nighttime eating; CC, coffee consumption; SSBC, sugar-sweetened beverage consumption; AC, alcohol consumption; SB, sedentary behavior; PA, physical activity.\u003c/p\u003e\n\u003cp\u003e3.4 The relationship between lifestyle habits and CAS\u003c/p\u003e\n\u003cp\u003eCollinearity diagnostics indicated that no multicollinearity was present among the independent variables (\u003cem\u003eVIF:\u003c/em\u003e1.125-1.252 \u0026lt; 5,Tolerance:0.799-0.889＞0.1).Table 4 shows a significant association between different lifestyle habit categories and the severity of CAS. In the unadjusted model, Mixed class 1 was associated with an increased risk of CAS, but after adjusting for gender, age, marital status, and BMI, this association weakened, particularly in arterial stenosis or occlusion. Mixed class 2 consistently demonstrated paradoxically lower risk across models. The Unhealthy class exhibited inverse associations for early CAS stages, though confounding adjustments partially diminished this trend.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable4 Associations between lifestyle habits and CAS (n =113,262).\u003c/strong\u003e\u003c/p\u003e\n\u003ctable border=\"0\" cellspacing=\"0\" cellpadding=\"0\" width=\"100%\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"2\" style=\"width: 17px;\"\u003e\n \u003cp\u003eCharacteristic\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"4\" style=\"width: 82px;\"\u003e\n \u003cp\u003e\u0026nbsp;CAS, odds ratio (95 %)\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 12px;\"\u003e\n \u003cp\u003enormal\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 23px;\"\u003e\n \u003cp\u003ecarotid intima-media thickening\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 22px;\"\u003e\n \u003cp\u003ecarotid plaque\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 23px;\"\u003e\n \u003cp\u003earterial stenosis or occlusion\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 17px;\"\u003e\n \u003cp\u003eBasically Healthy class\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 23px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 22px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 23px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 17px;\"\u003e\n \u003cp\u003eNo. of cases\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12px;\"\u003e\n \u003cp\u003e23308(46.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 23px;\"\u003e\n \u003cp\u003e5188(10.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 22px;\"\u003e\n \u003cp\u003e21325(42.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 23px;\"\u003e\n \u003cp\u003e234(0.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 17px;\"\u003e\n \u003cp\u003eModel 1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12px;\"\u003e\n \u003cp\u003e1[Reference]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 23px;\"\u003e\n \u003cp\u003e1[Reference]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 22px;\"\u003e\n \u003cp\u003e1[Reference]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 23px;\"\u003e\n \u003cp\u003e1[Reference]\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 17px;\"\u003e\n \u003cp\u003eModel 2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12px;\"\u003e\n \u003cp\u003e1[Reference]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 23px;\"\u003e\n \u003cp\u003e1[Reference]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 22px;\"\u003e\n \u003cp\u003e1[Reference]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 23px;\"\u003e\n \u003cp\u003e1[Reference]\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 17px;\"\u003e\n \u003cp\u003eModel 3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12px;\"\u003e\n \u003cp\u003e1[Reference]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 23px;\"\u003e\n \u003cp\u003e1[Reference]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 22px;\"\u003e\n \u003cp\u003e1[Reference]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 23px;\"\u003e\n \u003cp\u003e1[Reference]\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 17px;\"\u003e\n \u003cp\u003eMixed class 1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 23px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 22px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 23px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 17px;\"\u003e\n \u003cp\u003eNo. of cases\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12px;\"\u003e\n \u003cp\u003e4595(38.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 23px;\"\u003e\n \u003cp\u003e1513(12.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 22px;\"\u003e\n \u003cp\u003e5884(48.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 23px;\"\u003e\n \u003cp\u003e79(0.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 17px;\"\u003e\n \u003cp\u003eModel 1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12px;\"\u003e\n \u003cp\u003e1[Reference]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 23px;\"\u003e\n \u003cp\u003e1.479(1.386，1.579)***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 22px;\"\u003e\n \u003cp\u003e1.400(1.341,1.461)***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 23px;\"\u003e\n \u003cp\u003e1.713(1.324，2.214)***\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 17px;\"\u003e\n \u003cp\u003eModel 2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12px;\"\u003e\n \u003cp\u003e1[Reference]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 23px;\"\u003e\n \u003cp\u003e1.091(1.018,1.169)**\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 22px;\"\u003e\n \u003cp\u003e1.201(1.147,1.258)***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 23px;\"\u003e\n \u003cp\u003e1.222(0.934,1.598)*\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 17px;\"\u003e\n \u003cp\u003eModel 3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12px;\"\u003e\n \u003cp\u003e1[Reference]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 23px;\"\u003e\n \u003cp\u003e1.067(0.993,1.145)*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 22px;\"\u003e\n \u003cp\u003e1.176(1.121,1.234)***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 23px;\"\u003e\n \u003cp\u003e1.138(0.862,1.501)*\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 17px;\"\u003e\n \u003cp\u003eMixed class 2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 23px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 22px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 23px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 17px;\"\u003e\n \u003cp\u003eNo. of cases\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12px;\"\u003e\n \u003cp\u003e20528(60.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 23px;\"\u003e\n \u003cp\u003e2429(7.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 22px;\"\u003e\n \u003cp\u003e10717(31.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 23px;\"\u003e\n \u003cp\u003e77(0.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 17px;\"\u003e\n \u003cp\u003eModel 1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12px;\"\u003e\n \u003cp\u003e1[Reference]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 23px;\"\u003e\n \u003cp\u003e0.532(0.505，0.560)***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 22px;\"\u003e\n \u003cp\u003e0.571(0.554,0.588)***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 23px;\"\u003e\n \u003cp\u003e0.374(0.289，0.484)***\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 17px;\"\u003e\n \u003cp\u003eModel 2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12px;\"\u003e\n \u003cp\u003e1[Reference]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 23px;\"\u003e\n \u003cp\u003e0.807(0.764,0.852)***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 22px;\"\u003e\n \u003cp\u003e0.817(0.791,0.844)***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 23px;\"\u003e\n \u003cp\u003e0.731(0.562,0.951)**\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 17px;\"\u003e\n \u003cp\u003eModel 3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12px;\"\u003e\n \u003cp\u003e1[Reference]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 23px;\"\u003e\n \u003cp\u003e0.809(0.765,0.856)***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 22px;\"\u003e\n \u003cp\u003e0.798(0.772,0.826)***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 23px;\"\u003e\n \u003cp\u003e0.734(0.562,0.959)**\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 17px;\"\u003e\n \u003cp\u003eUnhealthy class\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 23px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 22px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 23px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 17px;\"\u003e\n \u003cp\u003eNo. of cases\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12px;\"\u003e\n \u003cp\u003e9336(53.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 23px;\"\u003e\n \u003cp\u003e1808(10.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 22px;\"\u003e\n \u003cp\u003e6173(35.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 23px;\"\u003e\n \u003cp\u003e68(0.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 17px;\"\u003e\n \u003cp\u003eModel 1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12px;\"\u003e\n \u003cp\u003e1[Reference]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 23px;\"\u003e\n \u003cp\u003e0.870(0.820,0.932)***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 22px;\"\u003e\n \u003cp\u003e0.723(0.696，0.750)***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 23px;\"\u003e\n \u003cp\u003e0.725(0.553，0.951)**\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 17px;\"\u003e\n \u003cp\u003eModel 2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12px;\"\u003e\n \u003cp\u003e1[Reference]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 23px;\"\u003e\n \u003cp\u003e0.916(0.861,0.976)**\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 22px;\"\u003e\n \u003cp\u003e0.829(0.796,.863)***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 23px;\"\u003e\n \u003cp\u003e0.869(0.655,1.152)*\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 17px;\"\u003e\n \u003cp\u003eModel 3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12px;\"\u003e\n \u003cp\u003e1[Reference]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 23px;\"\u003e\n \u003cp\u003e0.915(0.857,0.976)**\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 22px;\"\u003e\n \u003cp\u003e0.825(0.791,0.861)***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 23px;\"\u003e\n \u003cp\u003e0.841(0.631,1.123)*\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003eModel 1 with no adjustments; Model 2 with gender and age adjustments; Model 3 with gender, age, marital status and BMI adjustments;\u003c/p\u003e\n\u003cp\u003eCI, confidence interval.\u003c/p\u003e\n\u003cp\u003ea Combined results using multiple imputation.\u003c/p\u003e\n\u003cp\u003e***\u003cem\u003eP\u003c/em\u003e \u0026lt;0.001.\u003c/p\u003e\n\u003cp\u003e**\u003cem\u003eP\u0026nbsp;\u003c/em\u003e\u0026lt;0.05.\u003c/p\u003e\n\u003cp\u003e*\u003cem\u003eP \u0026gt;\u003c/em\u003e0.05.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e3.5 Stratified Analysis of Knowledge Workers\u0026apos; Lifestyle Habits and CAS Across Different Sociodemographic Characteristics\u003c/p\u003e\n\u003cp\u003eThe stratified analysis revealed that gender, age, BMI, and marital status modified the relationship between lifestyle categories and CAS risk (Table 5,6).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 5 Incidence of CAS Across Different Lifestyle Categories Under Various Sociodemographic Characteristics\u003c/strong\u003e\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" width=\"100%\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"2\" valign=\"top\" style=\"width: 26px;\"\u003e\n \u003cp\u003eVariables\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"4\" valign=\"top\" style=\"width: 73px;\"\u003e\n \u003cp\u003eIncidence of CAS(%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 18px;\"\u003e\n \u003cp\u003eBasically Healthy class\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 18px;\"\u003e\n \u003cp\u003eMixed class 1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 18px;\"\u003e\n \u003cp\u003eMixed class 2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 18px;\"\u003e\n \u003cp\u003eUnhealthy class\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 26px;\"\u003e\n \u003cp\u003eGender\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 18px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 18px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 18px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 18px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 26px;\"\u003e\n \u003cp\u003eMale\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 18px;\"\u003e\n \u003cp\u003e60.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 18px;\"\u003e\n \u003cp\u003e62.4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 18px;\"\u003e\n \u003cp\u003e44.2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 18px;\"\u003e\n \u003cp\u003e47.2\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 26px;\"\u003e\n \u003cp\u003eFemale\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 18px;\"\u003e\n \u003cp\u003e46.6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 18px;\"\u003e\n \u003cp\u003e48.4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 18px;\"\u003e\n \u003cp\u003e34.9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 18px;\"\u003e\n \u003cp\u003e38.8\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 26px;\"\u003e\n \u003cp\u003eAge(years)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 18px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 18px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 18px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 18px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 26px;\"\u003e\n \u003cp\u003e＜18\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 18px;\"\u003e\n \u003cp\u003e58.3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 18px;\"\u003e\n \u003cp\u003e0.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 18px;\"\u003e\n \u003cp\u003e33.3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 18px;\"\u003e\n \u003cp\u003e33.3\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 26px;\"\u003e\n \u003cp\u003e18-39\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 18px;\"\u003e\n \u003cp\u003e34.4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 18px;\"\u003e\n \u003cp\u003e37.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 18px;\"\u003e\n \u003cp\u003e30.8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 18px;\"\u003e\n \u003cp\u003e32.6\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 26px;\"\u003e\n \u003cp\u003e40-59\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 18px;\"\u003e\n \u003cp\u003e59.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 18px;\"\u003e\n \u003cp\u003e67.3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 18px;\"\u003e\n \u003cp\u003e52.4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 18px;\"\u003e\n \u003cp\u003e58.8\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 26px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026ge;60\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 18px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e84.3\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 18px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e89.7\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 18px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e83.7\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 18px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e85.4\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 26px;\"\u003e\n \u003cp\u003eBMI\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 18px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 18px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 18px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 18px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 26px;\"\u003e\n \u003cp\u003eUnderweight\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 18px;\"\u003e\n \u003cp\u003e39.7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 18px;\"\u003e\n \u003cp\u003e50.9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 18px;\"\u003e\n \u003cp\u003e34.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 18px;\"\u003e\n \u003cp\u003e35.5\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 26px;\"\u003e\n \u003cp\u003eNormal weight\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 18px;\"\u003e\n \u003cp\u003e48.9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 18px;\"\u003e\n \u003cp\u003e60.4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 18px;\"\u003e\n \u003cp\u003e35.5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 18px;\"\u003e\n \u003cp\u003e42.0\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 26px;\"\u003e\n \u003cp\u003eOverweight\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 18px;\"\u003e\n \u003cp\u003e58.3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 18px;\"\u003e\n \u003cp\u003e61.9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 18px;\"\u003e\n \u003cp\u003e42.7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 18px;\"\u003e\n \u003cp\u003e48.2\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 26px;\"\u003e\n \u003cp\u003eObesity\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 18px;\"\u003e\n \u003cp\u003e60.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 18px;\"\u003e\n \u003cp\u003e61.8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 18px;\"\u003e\n \u003cp\u003e42.3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 18px;\"\u003e\n \u003cp\u003e48.2\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 26px;\"\u003e\n \u003cp\u003eMarital status\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 18px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 18px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 18px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 18px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 26px;\"\u003e\n \u003cp\u003eUnmarried\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 18px;\"\u003e\n \u003cp\u003e37.2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 18px;\"\u003e\n \u003cp\u003e46.4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 18px;\"\u003e\n \u003cp\u003e30.4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 18px;\"\u003e\n \u003cp\u003e36.1\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 26px;\"\u003e\n \u003cp\u003eMarried or living together\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 18px;\"\u003e\n \u003cp\u003e54.3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 18px;\"\u003e\n \u003cp\u003e62.4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 18px;\"\u003e\n \u003cp\u003e41.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 18px;\"\u003e\n \u003cp\u003e47.6\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 26px;\"\u003e\n \u003cp\u003eDivorced\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 18px;\"\u003e\n \u003cp\u003e68.8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 18px;\"\u003e\n \u003cp\u003e73.6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 18px;\"\u003e\n \u003cp\u003e53.6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 18px;\"\u003e\n \u003cp\u003e46.5\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 26px;\"\u003e\n \u003cp\u003eWidowed\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 18px;\"\u003e\n \u003cp\u003e81.7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 18px;\"\u003e\n \u003cp\u003e66.7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 18px;\"\u003e\n \u003cp\u003e61.1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 18px;\"\u003e\n \u003cp\u003e75.0\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 6 Relationship Between Lifestyle Habits and CAS Under Different Sociodemographic Characteristics\u003c/strong\u003e\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" width=\"99%\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 33px;\"\u003e\n \u003cp\u003eVariables\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9px;\"\u003e\n \u003cp\u003e\u003cem\u003e\u0026beta;\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 10px;\"\u003e\n \u003cp\u003e\u003cem\u003eSE\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12px;\"\u003e\n \u003cp\u003eWalds\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 10px;\"\u003e\n \u003cp\u003e\u003cem\u003eP\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 23px;\"\u003e\n \u003cp\u003eOdds Ratio (95 %)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 33px;\"\u003e\n \u003cp\u003eGender\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 10px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 10px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 23px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 33px;\"\u003e\n \u003cp\u003eMale\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 10px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 10px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 23px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 33px;\"\u003e\n \u003cp\u003eBasically Healthy class\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 10px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12px;\"\u003e\n \u003cp\u003e1585.473\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 10px;\"\u003e\n \u003cp\u003e＜0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 23px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 33px;\"\u003e\n \u003cp\u003eMixed class 1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9px;\"\u003e\n \u003cp\u003e0.518\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 10px;\"\u003e\n \u003cp\u003e0.021\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12px;\"\u003e\n \u003cp\u003e635.362\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 10px;\"\u003e\n \u003cp\u003e＜0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 23px;\"\u003e\n \u003cp\u003e1.678(1.612,1.747)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 33px;\"\u003e\n \u003cp\u003eMixed class 2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9px;\"\u003e\n \u003cp\u003e0.619\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 10px;\"\u003e\n \u003cp\u003e0.025\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12px;\"\u003e\n \u003cp\u003e614.007\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 10px;\"\u003e\n \u003cp\u003e＜0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 23px;\"\u003e\n \u003cp\u003e1.858(1.769,1.951)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 33px;\"\u003e\n \u003cp\u003eUnhealthy class\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9px;\"\u003e\n \u003cp\u003e-0.122\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 10px;\"\u003e\n \u003cp\u003e0.023\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12px;\"\u003e\n \u003cp\u003e28.867\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 10px;\"\u003e\n \u003cp\u003e＜0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 23px;\"\u003e\n \u003cp\u003e0.885(0.846,0.925)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 33px;\"\u003e\n \u003cp\u003eFemale\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 10px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 10px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 23px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 33px;\"\u003e\n \u003cp\u003eBasically Healthy class\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 10px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12px;\"\u003e\n \u003cp\u003e598.440\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 10px;\"\u003e\n \u003cp\u003e＜0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 23px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 33px;\"\u003e\n \u003cp\u003eMixed class 1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9px;\"\u003e\n \u003cp\u003e0.318\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 10px;\"\u003e\n \u003cp\u003e0.049\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12px;\"\u003e\n \u003cp\u003e41.965\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 10px;\"\u003e\n \u003cp\u003e＜0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 23px;\"\u003e\n \u003cp\u003e1.374(1.248,1.513)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 33px;\"\u003e\n \u003cp\u003eMixed class 2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9px;\"\u003e\n \u003cp\u003e0.393\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 10px;\"\u003e\n \u003cp\u003e0.109\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12px;\"\u003e\n \u003cp\u003e13.115\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 10px;\"\u003e\n \u003cp\u003e＜0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 23px;\"\u003e\n \u003cp\u003e1.482(1.198,1.834)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 33px;\"\u003e\n \u003cp\u003eUnhealthy class\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9px;\"\u003e\n \u003cp\u003e-0.170\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 10px;\"\u003e\n \u003cp\u003e0.050\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12px;\"\u003e\n \u003cp\u003e11.634\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 10px;\"\u003e\n \u003cp\u003e＜0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 23px;\"\u003e\n \u003cp\u003e0.844(0.765,0.930)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 33px;\"\u003e\n \u003cp\u003eAge(years)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 10px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 10px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 23px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 33px;\"\u003e\n \u003cp\u003e＜18\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 10px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 10px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 23px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 33px;\"\u003e\n \u003cp\u003eBasically Healthy class\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 10px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12px;\"\u003e\n \u003cp\u003e4.424\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 10px;\"\u003e\n \u003cp\u003e＞0.05\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 23px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 33px;\"\u003e\n \u003cp\u003eMixed class 1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9px;\"\u003e\n \u003cp\u003e1.030\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 10px;\"\u003e\n \u003cp\u003e1.293\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12px;\"\u003e\n \u003cp\u003e0.634\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 10px;\"\u003e\n \u003cp\u003e＞0.05\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 23px;\"\u003e\n \u003cp\u003e2.800(0.222,35.288)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 33px;\"\u003e\n \u003cp\u003eMixed class 2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9px;\"\u003e\n \u003cp\u003e0.000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 10px;\"\u003e\n \u003cp\u003e1.254\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12px;\"\u003e\n \u003cp\u003e0.000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 10px;\"\u003e\n \u003cp\u003e＞0.05\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 23px;\"\u003e\n \u003cp\u003e1.000(0.086,11.669)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 33px;\"\u003e\n \u003cp\u003eUnhealthy class\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9px;\"\u003e\n \u003cp\u003e/\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 10px;\"\u003e\n \u003cp\u003e/\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12px;\"\u003e\n \u003cp\u003e/\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 10px;\"\u003e\n \u003cp\u003e/\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 23px;\"\u003e\n \u003cp\u003e/\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 33px;\"\u003e\n \u003cp\u003e18-39\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 10px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 10px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 23px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 33px;\"\u003e\n \u003cp\u003eBasically Healthy class\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 10px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12px;\"\u003e\n \u003cp\u003e77.568\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 10px;\"\u003e\n \u003cp\u003e＜0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 23px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 33px;\"\u003e\n \u003cp\u003eMixed class 1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9px;\"\u003e\n \u003cp\u003e0.081\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 10px;\"\u003e\n \u003cp\u003e0.028\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12px;\"\u003e\n \u003cp\u003e8.223\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 10px;\"\u003e\n \u003cp\u003e＜0.05\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 23px;\"\u003e\n \u003cp\u003e1.085(1.026,1.147)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 33px;\"\u003e\n \u003cp\u003eMixed class 2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9px;\"\u003e\n \u003cp\u003e0.194\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 10px;\"\u003e\n \u003cp\u003e0.046\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12px;\"\u003e\n \u003cp\u003e17.733\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 10px;\"\u003e\n \u003cp\u003e＜0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 23px;\"\u003e\n \u003cp\u003e1.214(1.109,1.329)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 33px;\"\u003e\n \u003cp\u003eUnhealthy class\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9px;\"\u003e\n \u003cp\u003e-0.082\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 10px;\"\u003e\n \u003cp\u003e0.027\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12px;\"\u003e\n \u003cp\u003e9.208\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 10px;\"\u003e\n \u003cp\u003e＜0.05\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 23px;\"\u003e\n \u003cp\u003e0.921(0.873,0.971)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 33px;\"\u003e\n \u003cp\u003e40-59\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 10px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 10px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 23px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 33px;\"\u003e\n \u003cp\u003eBasically Healthy class\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 10px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12px;\"\u003e\n \u003cp\u003e446.478\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 10px;\"\u003e\n \u003cp\u003e＜0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 23px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 33px;\"\u003e\n \u003cp\u003eMixed class 1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9px;\"\u003e\n \u003cp\u003e0.009\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 10px;\"\u003e\n \u003cp\u003e0.025\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12px;\"\u003e\n \u003cp\u003e0.132\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 10px;\"\u003e\n \u003cp\u003e＞0.05\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 23px;\"\u003e\n \u003cp\u003e1.009(0.960,1.061)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 33px;\"\u003e\n \u003cp\u003eMixed class 2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9px;\"\u003e\n \u003cp\u003e0.364\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 10px;\"\u003e\n \u003cp\u003e0.032\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12px;\"\u003e\n \u003cp\u003e128.059\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 10px;\"\u003e\n \u003cp\u003e＜0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 23px;\"\u003e\n \u003cp\u003e1.439(1.351,1.533)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 33px;\"\u003e\n \u003cp\u003eUnhealthy class\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9px;\"\u003e\n \u003cp\u003e-0.261\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 10px;\"\u003e\n \u003cp\u003e0.029\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12px;\"\u003e\n \u003cp\u003e79.026\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 10px;\"\u003e\n \u003cp\u003e＜0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 23px;\"\u003e\n \u003cp\u003e0.771(0.728,0.816)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 33px;\"\u003e\n \u003cp\u003e\u0026ge;60\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 10px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 10px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 23px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 33px;\"\u003e\n \u003cp\u003eBasically Healthy class\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 10px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12px;\"\u003e\n \u003cp\u003e15.258\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 10px;\"\u003e\n \u003cp\u003e＜0.05\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 23px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 33px;\"\u003e\n \u003cp\u003eMixed class 1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9px;\"\u003e\n \u003cp\u003e-0.082\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 10px;\"\u003e\n \u003cp\u003e0.136\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12px;\"\u003e\n \u003cp\u003e0.366\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 10px;\"\u003e\n \u003cp\u003e＞0.05\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 23px;\"\u003e\n \u003cp\u003e0.921(0.706,1.202)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 33px;\"\u003e\n \u003cp\u003eMixed class 2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9px;\"\u003e\n \u003cp\u003e0.403\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 10px;\"\u003e\n \u003cp\u003e0.176\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12px;\"\u003e\n \u003cp\u003e5.250\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 10px;\"\u003e\n \u003cp\u003e＜0.05\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 23px;\"\u003e\n \u003cp\u003e1.497(1.060,2.113)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 33px;\"\u003e\n \u003cp\u003eUnhealthy class\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9px;\"\u003e\n \u003cp\u003e-0.127\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 10px;\"\u003e\n \u003cp\u003e0.166\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12px;\"\u003e\n \u003cp\u003e0.584\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 10px;\"\u003e\n \u003cp\u003e＞0.05\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 23px;\"\u003e\n \u003cp\u003e0.881(0.637,1.219)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 33px;\"\u003e\n \u003cp\u003eBMI\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 10px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 10px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 23px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 33px;\"\u003e\n \u003cp\u003eUnderweight\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 10px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 10px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 23px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 33px;\"\u003e\n \u003cp\u003eBasically Healthy class\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 10px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12px;\"\u003e\n \u003cp\u003e13.926\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 10px;\"\u003e\n \u003cp\u003e＜0.05\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 23px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 33px;\"\u003e\n \u003cp\u003eMixed class 1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9px;\"\u003e\n \u003cp\u003e0.178\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 10px;\"\u003e\n \u003cp\u003e0.147\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12px;\"\u003e\n \u003cp\u003e1.473\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 10px;\"\u003e\n \u003cp\u003e＞0.05\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 23px;\"\u003e\n \u003cp\u003e1.195(0.896,1.593)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 33px;\"\u003e\n \u003cp\u003eMixed class 2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9px;\"\u003e\n \u003cp\u003e0.631\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 10px;\"\u003e\n \u003cp\u003e0.297\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12px;\"\u003e\n \u003cp\u003e4.507\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 10px;\"\u003e\n \u003cp\u003e＜0.05\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 23px;\"\u003e\n \u003cp\u003e1.879(1.050,3.363)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 33px;\"\u003e\n \u003cp\u003eUnhealthy class\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9px;\"\u003e\n \u003cp\u003e-0.070\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 10px;\"\u003e\n \u003cp\u003e0.146\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12px;\"\u003e\n \u003cp\u003e0.229\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 10px;\"\u003e\n \u003cp\u003e＞0.05\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 23px;\"\u003e\n \u003cp\u003e0.932(0.700,1.242)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 33px;\"\u003e\n \u003cp\u003eNormal weight\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 10px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 10px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 23px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 33px;\"\u003e\n \u003cp\u003eBasically Healthy class\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 10px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12px;\"\u003e\n \u003cp\u003e1089.424\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 10px;\"\u003e\n \u003cp\u003e＜0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 23px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 33px;\"\u003e\n \u003cp\u003eMixed class 1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9px;\"\u003e\n \u003cp\u003e0.278\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 10px;\"\u003e\n \u003cp\u003e0.030\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12px;\"\u003e\n \u003cp\u003e83.983\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 10px;\"\u003e\n \u003cp\u003e＜0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 23px;\"\u003e\n \u003cp\u003e1.320(1.244,1.401)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 33px;\"\u003e\n \u003cp\u003eMixed class 2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9px;\"\u003e\n \u003cp\u003e0.746\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 10px;\"\u003e\n \u003cp\u003e0.045\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12px;\"\u003e\n \u003cp\u003e274.573\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 10px;\"\u003e\n \u003cp\u003e＜0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 23px;\"\u003e\n \u003cp\u003e2.109(1.931,2.303)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 33px;\"\u003e\n \u003cp\u003eUnhealthy class\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9px;\"\u003e\n \u003cp\u003e-0.273\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 10px;\"\u003e\n \u003cp\u003e0.032\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12px;\"\u003e\n \u003cp\u003e73.825\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 10px;\"\u003e\n \u003cp\u003e＜0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 23px;\"\u003e\n \u003cp\u003e0.761(0.715,0.810)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 33px;\"\u003e\n \u003cp\u003eOverweight\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 10px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 10px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 23px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 33px;\"\u003e\n \u003cp\u003eBasically Healthy class\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 10px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12px;\"\u003e\n \u003cp\u003e867.513\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 10px;\"\u003e\n \u003cp\u003e＜0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 23px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 33px;\"\u003e\n \u003cp\u003eMixed class 1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9px;\"\u003e\n \u003cp\u003e0.409\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 10px;\"\u003e\n \u003cp\u003e0.028\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12px;\"\u003e\n \u003cp\u003e220.953\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 10px;\"\u003e\n \u003cp\u003e＜0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 23px;\"\u003e\n \u003cp\u003e1.506(1.426,1.589)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 33px;\"\u003e\n \u003cp\u003eMixed class 2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9px;\"\u003e\n \u003cp\u003e0.560\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 10px;\"\u003e\n \u003cp\u003e0.035\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12px;\"\u003e\n \u003cp\u003e250.342\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 10px;\"\u003e\n \u003cp\u003e＜0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 23px;\"\u003e\n \u003cp\u003e1.750(1.633,1.876)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 33px;\"\u003e\n \u003cp\u003eUnhealthy class\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9px;\"\u003e\n \u003cp\u003e-0.219\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 10px;\"\u003e\n \u003cp\u003e0.030\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12px;\"\u003e\n \u003cp\u003e51.728\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 10px;\"\u003e\n \u003cp\u003e＜0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 23px;\"\u003e\n \u003cp\u003e0.803(0.756,0.853)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 33px;\"\u003e\n \u003cp\u003eObesity\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 10px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 10px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 23px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 33px;\"\u003e\n \u003cp\u003eBasically Healthy class\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 10px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12px;\"\u003e\n \u003cp\u003e331.679\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 10px;\"\u003e\n \u003cp\u003e＜0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 23px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 33px;\"\u003e\n \u003cp\u003eMixed class 1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9px;\"\u003e\n \u003cp\u003e0.478\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 10px;\"\u003e\n \u003cp\u003e0.048\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12px;\"\u003e\n \u003cp\u003e100.793\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 10px;\"\u003e\n \u003cp\u003e＜0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 23px;\"\u003e\n \u003cp\u003e1.613(1.469,1.771)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 33px;\"\u003e\n \u003cp\u003eMixed class 2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9px;\"\u003e\n \u003cp\u003e0.554\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 10px;\"\u003e\n \u003cp\u003e0.057\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12px;\"\u003e\n \u003cp\u003e96.065\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 10px;\"\u003e\n \u003cp\u003e＜0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 23px;\"\u003e\n \u003cp\u003e1.741(1.558,1.945)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 33px;\"\u003e\n \u003cp\u003eUnhealthy class\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9px;\"\u003e\n \u003cp\u003e-0.238\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 10px;\"\u003e\n \u003cp\u003e0.050\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12px;\"\u003e\n \u003cp\u003e22.778\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 10px;\"\u003e\n \u003cp\u003e＜0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 23px;\"\u003e\n \u003cp\u003e0.788(0.715,0.869)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 33px;\"\u003e\n \u003cp\u003eMarital status\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 10px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 10px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 23px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 33px;\"\u003e\n \u003cp\u003eUnmarried\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 10px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 10px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 23px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 33px;\"\u003e\n \u003cp\u003eBasically Healthy class\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 10px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12px;\"\u003e\n \u003cp\u003e83.992\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 10px;\"\u003e\n \u003cp\u003e＜0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 23px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 33px;\"\u003e\n \u003cp\u003eMixed class 1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9px;\"\u003e\n \u003cp\u003e0.050\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 10px;\"\u003e\n \u003cp\u003e0.060\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12px;\"\u003e\n \u003cp\u003e0.678\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 10px;\"\u003e\n \u003cp\u003e＞0.05\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 23px;\"\u003e\n \u003cp\u003e1.051(0.934,1.182)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 33px;\"\u003e\n \u003cp\u003eMixed class 2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9px;\"\u003e\n \u003cp\u003e0.429\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 10px;\"\u003e\n \u003cp\u003e0.109\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12px;\"\u003e\n \u003cp\u003e15.581\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 10px;\"\u003e\n \u003cp\u003e＜0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 23px;\"\u003e\n \u003cp\u003e1.536(1.241,1.900)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 33px;\"\u003e\n \u003cp\u003eUnhealthy class\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9px;\"\u003e\n \u003cp\u003e-0.258\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 10px;\"\u003e\n \u003cp\u003e0.054\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12px;\"\u003e\n \u003cp\u003e22.490\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 10px;\"\u003e\n \u003cp\u003e＜0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 23px;\"\u003e\n \u003cp\u003e0.773(0.694,0.859)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 33px;\"\u003e\n \u003cp\u003eMarried or living together\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 10px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 10px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 23px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 33px;\"\u003e\n \u003cp\u003eBasically Healthy class\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 10px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12px;\"\u003e\n \u003cp\u003e1902.449\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 10px;\"\u003e\n \u003cp\u003e＜0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 23px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 33px;\"\u003e\n \u003cp\u003eMixed class 1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9px;\"\u003e\n \u003cp\u003e0.270\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 10px;\"\u003e\n \u003cp\u003e0.019\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12px;\"\u003e\n \u003cp\u003e207.569\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 10px;\"\u003e\n \u003cp\u003e＜0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 23px;\"\u003e\n \u003cp\u003e1.310(1.263,1.360)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 33px;\"\u003e\n \u003cp\u003eMixed class 2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9px;\"\u003e\n \u003cp\u003e0.603\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 10px;\"\u003e\n \u003cp\u003e0.025\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12px;\"\u003e\n \u003cp\u003e571.070\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 10px;\"\u003e\n \u003cp\u003e＜0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 23px;\"\u003e\n \u003cp\u003e1.827(1.739,1.920)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 33px;\"\u003e\n \u003cp\u003eUnhealthy class\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9px;\"\u003e\n \u003cp\u003e-0.269\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 10px;\"\u003e\n \u003cp\u003e0.021\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12px;\"\u003e\n \u003cp\u003e172.092\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 10px;\"\u003e\n \u003cp\u003e＜0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 23px;\"\u003e\n \u003cp\u003e0.764(0.734,0.795)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 33px;\"\u003e\n \u003cp\u003eDivorced\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 10px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12px;\"\u003e\n \u003cp\u003e25.993\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 10px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 23px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 33px;\"\u003e\n \u003cp\u003eBasically Healthy class\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9px;\"\u003e\n \u003cp\u003e0.931\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 10px;\"\u003e\n \u003cp\u003e0.245\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12px;\"\u003e\n \u003cp\u003e14.502\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 10px;\"\u003e\n \u003cp\u003e＜0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 23px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 33px;\"\u003e\n \u003cp\u003eMixed class 1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9px;\"\u003e\n \u003cp\u003e1.164\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 10px;\"\u003e\n \u003cp\u003e0.309\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12px;\"\u003e\n \u003cp\u003e14.228\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 10px;\"\u003e\n \u003cp\u003e＜0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 23px;\"\u003e\n \u003cp\u003e2.538(1.572,4.100)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 33px;\"\u003e\n \u003cp\u003eMixed class 2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9px;\"\u003e\n \u003cp\u003e0.285\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 10px;\"\u003e\n \u003cp\u003e0.266\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12px;\"\u003e\n \u003cp\u003e1.141\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 10px;\"\u003e\n \u003cp\u003e＜0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 23px;\"\u003e\n \u003cp\u003e3.204(1.749,5.866)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 33px;\"\u003e\n \u003cp\u003eUnhealthy class\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 10px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 10px;\"\u003e\n \u003cp\u003e＞0.05\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 23px;\"\u003e\n \u003cp\u003e1.329(0.789,2.241)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 33px;\"\u003e\n \u003cp\u003eWidowed\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 10px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 10px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 23px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 33px;\"\u003e\n \u003cp\u003eBasically Healthy class\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 10px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12px;\"\u003e\n \u003cp\u003e4.734\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 10px;\"\u003e\n \u003cp\u003e＞0.05\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 23px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 33px;\"\u003e\n \u003cp\u003eMixed class 1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9px;\"\u003e\n \u003cp\u003e0.399\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 10px;\"\u003e\n \u003cp\u003e0.859\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12px;\"\u003e\n \u003cp\u003e0.215\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 10px;\"\u003e\n \u003cp\u003e＞0.05\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 23px;\"\u003e\n \u003cp\u003e1.490(0.276,8.032)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 33px;\"\u003e\n \u003cp\u003eMixed class 2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9px;\"\u003e\n \u003cp\u003e-0.405\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 10px;\"\u003e\n \u003cp\u003e0.939\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12px;\"\u003e\n \u003cp\u003e0.187\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 10px;\"\u003e\n \u003cp\u003e＞0.05\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 23px;\"\u003e\n \u003cp\u003e0.667(0.106,4.196)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 33px;\"\u003e\n \u003cp\u003eUnhealthy class\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9px;\"\u003e\n \u003cp\u003e-0.647\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 10px;\"\u003e\n \u003cp\u003e0.949\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12px;\"\u003e\n \u003cp\u003e0.464\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 10px;\"\u003e\n \u003cp\u003e＞0.05\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 23px;\"\u003e\n \u003cp\u003e0.524(0.082,3.364)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003eGender Stratification: Differences in CAS incidence were observed between males and females across different lifestyle categories, though the overall trend remained consistent. The highest CAS risk was associated with the Mixed class 2 (male \u003cem\u003eOR\u003c/em\u003e = 1.858, female \u003cem\u003eOR\u003c/em\u003e = 1.482), whereas the Unhealthy class had a lower CAS risk (male \u003cem\u003eOR\u003c/em\u003e = 0.885, female \u003cem\u003eOR\u003c/em\u003e = 0.844). The effect size was weaker in females.\u003c/p\u003e\n\u003cp\u003eAge Stratification: The incidence of CAS increases with age, and among individuals aged \u0026ge;60 years, the Unhealthy class has a higher CAS incidence than the Basically healthy class. Binary logistic regression analysis indicates that in the 18\u0026ndash;39 and 40\u0026ndash;59 age groups, the Mixed class 2 group exhibits a significantly increased CAS risk (\u003cem\u003eOR\u003c/em\u003e = 1.214 and \u003cem\u003eOR\u003c/em\u003e = 1.439), while the Unhealthy class shows a lower CAS risk (\u003cem\u003eOR\u003c/em\u003e = 0.921 and \u003cem\u003eOR\u003c/em\u003e = 0.771). In the \u0026ge;60 age group, only the Mixed class 2 group has an elevated CAS risk (\u003cem\u003eOR\u003c/em\u003e = 1.497), while differences among other groups are not statistically significant.\u003c/p\u003e\n\u003cp\u003eBMI Stratification: CAS incidence increased with BMI. Across all BMI categories, individuals in the Mixed class 2 group exhibited an elevated CAS risk, whereas those in the Unhealthy classgroup had a lower CAS risk (normal weight \u003cem\u003eOR\u003c/em\u003e = 0.761, overweight \u003cem\u003eOR\u003c/em\u003e = 0.803, obesity \u003cem\u003eOR\u003c/em\u003e = 0.788).\u003c/p\u003e\n\u003cp\u003eMarital Status Stratification: Among married or cohabiting individuals, the CAS risk was significantly higher in the Mixed class 2 group (\u003cem\u003eOR\u003c/em\u003e = 1.827) and significantly lower in the Unhealthy class group (\u003cem\u003eOR\u003c/em\u003e = 0.764). Among divorced individuals, both Mixed class 1 (\u003cem\u003eOR\u003c/em\u003e = 2.538) and Mixed class 2 (\u003cem\u003eOR\u003c/em\u003e = 3.204) groups showed significantly increased CAS risk, whereas the Unhealthy class group did not show a statistically significant difference (\u003cem\u003eP\u003c/em\u003e \u0026gt; 0.05). Among unmarried individuals, the CAS risk was elevated in the Mixed class 2 group (\u003cem\u003eOR\u003c/em\u003e = 1.536) but reduced in the Unhealthy class group (\u003cem\u003eOR\u003c/em\u003e = 0.773).\u003c/p\u003e"},{"header":"Discussion","content":"\u003cp\u003eTo the best of our knowledge, this is the first large-scale study exploring the relationship between lifestyle habits and CAS among knowledge workers. The results show that the prevalence of CAS among Chinese knowledge workers is 49.0%. Four distinct lifestyle patterns were identified, with significant CAS correlations. Mixed lifestyle groups showed higher CAS risk than healthy-lifestyle individuals. Our findings highlight lifestyle\u0026apos;s crucial role in CAS development and inform targeted prevention strategies.\u003c/p\u003e\n\u003cp\u003eCAS prevalence among Chinese knowledge workers was 49.0% (cIMT thickening: 9.7%, plaques: 38.9%, stenosis/occlusion: 0.4%), significantly higher than general adults(21), likely due to occupational stress and unhealthy lifestyles. Consistent with previous findings, CAS varied by demographics - males, obesity, divorced and older individuals showed higher risk(21). Estrogen\u0026apos;s cardiovascular protection in women(22) and age-related vascular dysfunction(23) combined with prolonged exposure to metabolic risk factors(24) explain these patterns.\u003c/p\u003e\n\u003cp\u003eNormal-BMI knowledge workers showed lower CAS risk than overweight and obese peers(25), likely due to reduced visceral fat, better insulin sensitivity, and lower inflammatory markers (IL-6, CRP)(26). Divorced and widowed individuals had higher CAS risk, potentially from stress-induced sympathetic activation and elevated inflammatory responses(27).\u003c/p\u003e\n\u003cp\u003eThe study identified four lifestyle patterns: Healthy, Mixed1 (smoking/drinking), Mixed2 (diet/activity), and Unhealthy. Analysis showed Mixed1 had highest CAS risk versus Healthy type (adjusted \u003cem\u003eOR\u0026nbsp;\u003c/em\u003e= 1.32, 95%CI:1.15-1.52), confirming smoking/alcohol as key risk factors(28-30). Surprisingly, Mixed2 and Unhealthy classes showed lower CAS risk, suggesting inverse associations from dietary/activity factors - contradicting previous findings(31). Several potential mechanisms may explain the unexpected inverse associations observed in the \u0026apos;Mixed class 2\u0026apos; and \u0026apos;Unhealthy\u0026apos; groups. First, age differences likely play a confounding role; the \u0026apos;Mixed class 2\u0026apos; (mean age 37.5\u0026plusmn;9.7 years) and \u0026apos;Unhealthy\u0026apos; (40.7\u0026plusmn;9.7 years) groups were significantly younger than the \u0026apos;Basically healthy\u0026apos; group (45.5\u0026plusmn;10.7 years). Given that our stratified analysis confirms CAS incidence increases with age, the lower risk in these groups may partially reflect their younger demographic. Second, the \u0026apos;healthy worker effect\u0026apos; and survivor bias may be present, where individuals with severe health consequences from long-term unhealthy behaviors may have already left the workforce or suffered premature mortality, excluding them from the study. Third, reverse causality is a significant consideration; knowledge workers with existing health concerns may have recently adopted healthier habits (e.g., improving diet or activity) post-diagnosis\u0026nbsp;(32). This behavioral change could result in high-risk individuals being classified into the \u0026apos;Basically healthy\u0026apos; group, paradoxically inflating its associated risk. Finally, the moderate entropy (\u0026gt;0.5) indicates some degree of overlap between latent classes, which may blur the distinctions in risk profiles. Collectively, these factors suggest that the CAS risk may be underestimated in the \u0026apos;Unhealthy\u0026apos; class and overestimated in the \u0026apos;Basically healthy\u0026apos; class.\u003c/p\u003e\n\u003cp\u003eMultilevel analysis results indicate that individuals in the Mixed class 2 generally exhibit a higher CAS risk, while those in the Unhealthy class show relatively lower risk. This pattern demonstrates a certain degree of heterogeneity across different populations.\u003c/p\u003e\n\u003cp\u003eMen showed greater lifestyle-CAS risk sensitivity than women. The reduced risk in unhealthy males (\u003cem\u003eOR\u0026nbsp;\u003c/em\u003e= 0.885) may reflect the Healthy Survivor Effect, where long-term unhealthy individuals experience earlier mortality(33). Women demonstrated smaller OR variations, potentially due to better healthcare engagement and health consciousness(34)\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003emitigating lifestyle-related risk differences.\u003c/p\u003e\n\u003cp\u003eStratified analyses revealed age and BMI-dependent lifestyle-CAS risk relationships: Mixed2 showed elevated risk in younger (18-39 years) and higher BMI groups, while Unhealthy class demonstrated lower risk, potentially due to greater metabolic resilience in these populations. However, long-term CVD risk remains concerning. Notably, Unhealthy individuals \u0026gt;40y showed reduced CAS risk, suggesting health-motivated lifestyle modifications may complicate risk associations.\u003c/p\u003e\n\u003cp\u003eStratified analysis by marital status reveals that divorced individuals in the Mixed class 1 and Mixed class 2 groups exhibit a significantly higher CAS risk, potentially due to lack of social support, lifestyle instability, and psychological stress. In contrast, among married or cohabiting individuals, the Mixed class 2 group continues to show an elevated CAS risk, suggesting that despite maintaining a stable marital relationship, these individuals may still be affected by unhealthy lifestyle factors.\u003c/p\u003e\n\u003cp\u003eMarital status stratification showed divorced individuals in Mixed 1 and 2 classes had highest CAS risk, likely from psychosocial stressors. Married Mixed 2 individuals maintained elevated risk, indicating lifestyle factors outweigh marital stability benefits.\u003c/p\u003e\n\u003cp\u003eThis study employs a cross-sectional design, which limits the ability to establish causal relationships. Although an association between lifestyle habit categories and CAS severity was observed, it cannot be concluded that lifestyle habits directly cause the onset or progression of CAS. Future longitudinal or interventional studies are needed to verify causal relationships. Secondly, we used the Chinese specific BMI cut-off points rather than WHO criteria, which may limit the direct comparability of our findings with studies involving non-Chinese populations.\u003c/p\u003e\n\u003cp\u003eAdditionally, the assessment of lifestyle habits relies on self-reported data, which may introduce recall bias or social desirability bias. Then, participants were recruited from a single health management center database, which might limit the generalizability to all Chinese knowledge workers or other populations.\u003c/p\u003e\n\u003cp\u003eLastly, future research should further explore long-term follow-up data to clarify the causal relationship between lifestyle and CAS and develop individualized intervention strategies for high-risk populations.\u003c/p\u003e"},{"header":"Conclusions","content":"\u003cp\u003eThis study conducted a latent class analysis of lifestyle habits among 113,262 knowledge workers and found a significant association between different lifestyle patterns and CAS risk. The results indicate that subgroups engaging in unhealthy behaviors such as smoking, alcohol consumption, and prolonged sedentary behavior have a significantly higher risk of CAS. Although some findings contradict previous research\u0026mdash;such as the inverse associations between CAS and nighttime eating\u0026mdash;these results require further investigation. Nevertheless, the authors recommend that knowledge workers should maintain healthy lifestyle habits in three key aspects - diet, tobacco/alcohol consumption, and physical activity. Specifically, this includes: reducing late-night eating, decreasing consumption of coffee and sugar-sweetened beverages, quitting smoking and alcohol, avoiding prolonged sitting while maintaining adequate physical activity, thereby reducing the risk of CAS.\u003c/p\u003e"},{"header":"Abbreviations","content":"\u003cdiv align=\"\"\u003e\n \u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 99px;\"\u003e\n \u003cp\u003eAbbreviation\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 428px;\"\u003e\n \u003cp\u003eFull English Name\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 99px;\"\u003e\n \u003cp\u003eCAS\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 428px;\"\u003e\n \u003cp\u003eCarotid Atherosclerosis\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 99px;\"\u003e\n \u003cp\u003eLCA\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 428px;\"\u003e\n \u003cp\u003eLatent Class Analysis\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 99px;\"\u003e\n \u003cp\u003ecIMT\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 428px;\"\u003e\n \u003cp\u003eCarotid Intima-Media Thickness\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 99px;\"\u003e\n \u003cp\u003eMI\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 428px;\"\u003e\n \u003cp\u003eMultiple Imputation\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 99px;\"\u003e\n \u003cp\u003eSPSS\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 428px;\"\u003e\n \u003cp\u003eStatistical Package for the Social Sciences\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 99px;\"\u003e\n \u003cp\u003eAIC\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 428px;\"\u003e\n \u003cp\u003eAkaike Information Criterion\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 99px;\"\u003e\n \u003cp\u003eBIC\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 428px;\"\u003e\n \u003cp\u003eBayesian Information Criterion\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 99px;\"\u003e\n \u003cp\u003eaBIC\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 428px;\"\u003e\n \u003cp\u003eAdjusted Bayesian Information Criterion\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 99px;\"\u003e\n \u003cp\u003eVLMR\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 428px;\"\u003e\n \u003cp\u003eVuong-Lo-Mendell-Rubin Likelihood Ratio Test\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 99px;\"\u003e\n \u003cp\u003eBLRT\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 428px;\"\u003e\n \u003cp\u003eBootstrapped Likelihood Ratio Test\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 99px;\"\u003e\n \u003cp\u003eOR\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 428px;\"\u003e\n \u003cp\u003eOdds Ratio\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 99px;\"\u003e\n \u003cp\u003eCI\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 428px;\"\u003e\n \u003cp\u003eConfidence Interval\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 99px;\"\u003e\n \u003cp\u003eNE\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 428px;\"\u003e\n \u003cp\u003eNighttime eating\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 99px;\"\u003e\n \u003cp\u003eCC\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 428px;\"\u003e\n \u003cp\u003eCoffee Consumption\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 99px;\"\u003e\n \u003cp\u003eSSBC\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 428px;\"\u003e\n \u003cp\u003eSugar-Sweetened Beverage Consumption\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 99px;\"\u003e\n \u003cp\u003eAC\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 428px;\"\u003e\n \u003cp\u003eAlcohol Consumption\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 99px;\"\u003e\n \u003cp\u003eSB\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 428px;\"\u003e\n \u003cp\u003eSedentary Behavior\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 99px;\"\u003e\n \u003cp\u003ePA\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 428px;\"\u003e\n \u003cp\u003ePhysical Activity\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 99px;\"\u003e\n \u003cp\u003eBMI\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 428px;\"\u003e\n \u003cp\u003eBody Mass Index\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 99px;\"\u003e\n \u003cp\u003eCVD\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 428px;\"\u003e\n \u003cp\u003eCardiovascular Disease\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 99px;\"\u003e\n \u003cp\u003eIL-6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 428px;\"\u003e\n \u003cp\u003eInterleukin-6\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 99px;\"\u003e\n \u003cp\u003eCRP\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 428px;\"\u003e\n \u003cp\u003eC-Reactive Protein\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n\u003c/div\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003e【\u003c/strong\u003e\u003cstrong\u003eEthics approval and consent to participate\u003c/strong\u003e\u003cstrong\u003e】\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis study was conducted according to the guidelines laid down in the Declaration of Helsinki and all procedures involving research study participants were approved by the The IRB of Third Xiangya Hospital, Central South University. Written informed consent was obtained from all patients.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e【\u003c/strong\u003e\u003cstrong\u003eConsent for publication\u003c/strong\u003e\u003cstrong\u003e】\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e【\u003c/strong\u003e\u003cstrong\u003eAvailability of data and materials\u003c/strong\u003e\u003cstrong\u003e】\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe datasets used and/or analysed during the current study are available from the corresponding author on reasonable request.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e【\u003c/strong\u003e\u003cstrong\u003eCompeting interests\u003c/strong\u003e\u003cstrong\u003e】\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare that they have no competing interests.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e【\u003c/strong\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003cstrong\u003e】\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis work was supported by the Special Funding for Chenzhou Resident Health Science Popularization Platform (NO.2023sfq13)\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e【\u003c/strong\u003e\u003cstrong\u003eAuthors\u0026apos; contributions\u003c/strong\u003e\u003cstrong\u003e】\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eZHY and HX conceived and designed the work, acquired data, played an important role in interpreting the results, drafted the manuscript, approved the final version, and agreed to be accountable for all aspects of the work.\u003c/p\u003e\n\u003cp\u003eLLJ, LY, ZY, WJY, WXX, ZJY contributed to data acquisition, participated in result interpretation, revised the manuscript, approved the final version, and agreed to be accountable for all aspects of the work.\u003c/p\u003e\n\u003cp\u003eLL (corresponding author) oversaw the conception and design of the work, played a key role in result interpretation, revised the manuscript critically, approved the final version, and agreed to be accountable for all aspects of the work.\u003c/p\u003e\n\u003cp\u003ePHFN provided feedback on result interpretation, revised the manuscript, approved the final version, and agreed to be accountable for all aspects of the work.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e【\u003c/strong\u003e\u003cstrong\u003eAcknowledgments\u003c/strong\u003e\u003cstrong\u003e】\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThanks to Professor Xie Jianfei for her guidance in the writing and revision of this article.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eLibby P, Ridker PM, Hansson GK. Inflammation in atherosclerosis: from pathophysiology to practice. J Am Coll Cardiol. 2009;54(23):2129-38.\u003c/li\u003e\n\u003cli\u003eByrne RA, Rossello X, Coughlan JJ, Barbato E, Berry C, Chieffo A, et al. 2023 ESC Guidelines for the management of acute coronary syndromes: Developed by the task force on the management of acute coronary syndromes of the European Society of Cardiology (ESC). European Heart Journal-Acute Cardiovascular Care. 2024;13(1):55-161.\u003c/li\u003e\n\u003cli\u003eSong P, Fang Z, Wang H, Cai Y, Rahimi K, Zhu Y, et al. Global and regional prevalence, burden, and risk factors for carotid atherosclerosis: a systematic review, meta-analysis, and modelling study. Lancet Glob Health. 2020;8(5):e721-e9.\u003c/li\u003e\n\u003cli\u003eBenjamin EJ, Muntner P, Alonso A, Bittencourt MS, Callaway CW, Carson AP, et al. Heart Disease and Stroke Statistics-2019 Update: A Report From the American Heart Association. Circulation. 2019;139(10):e56-e528.\u003c/li\u003e\n\u003cli\u003eDrucker PF. The post-capitalist executive. Interview by T George Harris. Harv Bus Rev. 1993;71(3):114-22.\u003c/li\u003e\n\u003cli\u003eNaqvi TZ, Lee MS. Carotid intima-media thickness and plaque in cardiovascular risk assessment. JACC Cardiovasc Imaging. 2014;7(10):1025-38.\u003c/li\u003e\n\u003cli\u003eWannarong T, Parraga G, Buchanan D, Fenster A, House AA, Hackam DG, et al. Progression of carotid plaque volume predicts cardiovascular events. Stroke. 2013;44(7):1859-65.\u003c/li\u003e\n\u003cli\u003eNambi V, Chambless L, Folsom AR, He M, Hu Y, Mosley T, et al. Carotid intima-media thickness and presence or absence of plaque improves prediction of coronary heart disease risk: the ARIC (Atherosclerosis Risk In Communities) study. J Am Coll Cardiol. 2010;55(15):1600-7.\u003c/li\u003e\n\u003cli\u003eSong P, Xia W, Zhu Y, Wang M, Chang X, Jin S, et al. Prevalence of carotid atherosclerosis and carotid plaque in Chinese adults: A systematic review and meta-regression analysis. Atherosclerosis. 2018;276:67-73.\u003c/li\u003e\n\u003cli\u003ePatterson R, McNamara E, Tainio M, de S\u0026aacute; TH, Smith AD, Sharp SJ, et al. Sedentary behaviour and risk of all-cause, cardiovascular and cancer mortality, and incident type 2 diabetes: a systematic review and dose response meta-analysis. Eur J Epidemiol. 2018;33(9):811-29.\u003c/li\u003e\n\u003cli\u003eDutheil F, Baker JS, Mermillod M, De Cesare M, Vidal A, Moustafa F, et al. Shift work, and particularly permanent night shifts, promote dyslipidaemia: A systematic review and meta-analysis. Atherosclerosis. 2020;313:156-69.\u003c/li\u003e\n\u003cli\u003eItani O, Jike M, Watanabe N, Kaneita Y. Short sleep duration and health outcomes: a systematic review, meta-analysis, and meta-regression. Sleep Med. 2017;32:246-56.\u003c/li\u003e\n\u003cli\u003eFranssen WMA, Nieste I, Verboven K, Eijnde BO. Sedentary behaviour and cardiometabolic health: Integrating the potential underlying molecular health aspects. Metabolism. 2025;170:156320.\u003c/li\u003e\n\u003cli\u003eLanza ST, Rhoades BL. Latent class analysis: an alternative perspective on subgroup analysis in prevention and treatment. Prev Sci. 2013;14(2):157-68.\u003c/li\u003e\n\u003cli\u003eNaylor R, Rantner B, Ancetti S, de Borst GJ, De Carlo M, Halliday A, et al. Editor\u0026apos;s Choice - European Society for Vascular Surgery (ESVS) 2023 Clinical Practice Guidelines on the Management of Atherosclerotic Carotid and Vertebral Artery Disease. Eur J Vasc Endovasc Surg. 2023;65(1):7-111.\u003c/li\u003e\n\u003cli\u003eStein JH, Korcarz CE, Hurst RT, Lonn E, Kendall CB, Mohler ER, et al. Use of carotid ultrasound to identify subclinical vascular disease and evaluate cardiovascular disease risk: a consensus statement from the American Society of Echocardiography Carotid Intima-Media Thickness Task Force. Endorsed by the Society for Vascular Medicine. J Am Soc Echocardiogr. 2008;21(2):93-111; quiz 89-90.\u003c/li\u003e\n\u003cli\u003eTouboul PJ, Hennerici MG, Meairs S, Adams H, Amarenco P, Bornstein N, et al. Mannheim carotid intima-media thickness and plaque consensus (2004-2006-2011). An update on behalf of the advisory board of the 3rd, 4th and 5th watching the risk symposia, at the 13th, 15th and 20th European Stroke Conferences, Mannheim, Germany, 2004, Brussels, Belgium, 2006, and Hamburg, Germany, 2011. Cerebrovasc Dis. 2012;34(4):290-6.\u003c/li\u003e\n\u003cli\u003eAboyans V, Ricco JB, Bartelink MEL, Bj\u0026ouml;rck M, Brodmann M, Cohnert T, et al. 2017 ESC Guidelines on the Diagnosis and Treatment of Peripheral Arterial Diseases, in collaboration with the European Society for Vascular Surgery (ESVS): Document covering atherosclerotic disease of extracranial carotid and vertebral, mesenteric, renal, upper and lower extremity arteriesEndorsed by: the European Stroke Organization (ESO)The Task Force for the Diagnosis and Treatment of Peripheral Arterial Diseases of the European Society of Cardiology (ESC) and of the European Society for Vascular Surgery (ESVS). Eur Heart J. 2018;39(9):763-816.\u003c/li\u003e\n\u003cli\u003eGuidelines for medical nutrition treatment of overweight/obesity in China (2021). Asia Pac J Clin Nutr. 2022;31(3):450-82.\u003c/li\u003e\n\u003cli\u003eHausser J, Strimmer K. Entropy inference and the james-stein estimator, with application to nonlinear gene association networks. Journal of Machine Learning Research. 2009;10:1469-84.\u003c/li\u003e\n\u003cli\u003eFu J, Deng Y, Ma Y, Man S, Yang X, Yu C, et al. National and Provincial-Level Prevalence and Risk Factors of Carotid Atherosclerosis in Chinese Adults. JAMA Netw Open. 2024;7(1):e2351225.\u003c/li\u003e\n\u003cli\u003eVisseren FLJ, Mach F, Smulders YM, Carballo D, Koskinas KC, B\u0026auml;ck M, et al. 2021 ESC Guidelines on cardiovascular disease prevention in clinical practice. Eur Heart J. 2021;42(34):3227-337.\u003c/li\u003e\n\u003cli\u003eAlGhatrif M, Cingolani O, Lakatta EG. The Dilemma of Coronavirus Disease 2019, Aging, and Cardiovascular Disease: Insights From Cardiovascular Aging Science. JAMA Cardiol. 2020;5(7):747-8.\u003c/li\u003e\n\u003cli\u003eLakatta EG, Levy D. Arterial and cardiac aging: major shareholders in cardiovascular disease enterprises: Part I: aging arteries: a \u0026quot;set up\u0026quot; for vascular disease. Circulation. 2003;107(1):139-46.\u003c/li\u003e\n\u003cli\u003eLiang DK, Bai XJ, Wu B, Han LL, Wang XN, Yang J, et al. Associations between bone mineral density and subclinical atherosclerosis: a cross-sectional study of a Chinese population. J Clin Endocrinol Metab. 2014;99(2):469-77.\u003c/li\u003e\n\u003cli\u003ePowell-Wiley TM, Poirier P, Burke LE, Despr\u0026eacute;s JP, Gordon-Larsen P, Lavie CJ, et al. Obesity and Cardiovascular Disease: A Scientific Statement From the American Heart Association. Circulation. 2021;143(21):e984-e1010.\u003c/li\u003e\n\u003cli\u003eRidker PM, Everett BM, Thuren T, MacFadyen JG, Chang WH, Ballantyne C, et al. Antiinflammatory Therapy with Canakinumab for Atherosclerotic Disease. N Engl J Med. 2017;377(12):1119-31.\u003c/li\u003e\n\u003cli\u003eWang Y, Li L, Li Y, Liu M, Gan G, Zhou Y, et al. The Impact of Dietary Diversity, Lifestyle, and Blood Lipids on Carotid Atherosclerosis: A Cross-Sectional Study. Nutrients. 2022;14(4).\u003c/li\u003e\n\u003cli\u003eZhang X, Wu Y, Na M, Lichtenstein AH, Xing A, Chen S, et al. Habitual Night Eating Was Positively Associated With Progress of Arterial Stiffness in Chinese Adults. J Am Heart Assoc. 2020;9(19):e016455.\u003c/li\u003e\n\u003cli\u003ePacheco LS, Lacey JV, Jr., Martinez ME, Lemus H, Araneta MRG, Sears DD, et al. Sugar-Sweetened Beverage Intake and Cardiovascular Disease Risk in the California Teachers Study. J Am Heart Assoc. 2020;9(10):e014883.\u003c/li\u003e\n\u003cli\u003eEstruch R, Ros E, Salas-Salvad\u0026oacute; J, Covas MI, Corella D, Ar\u0026oacute;s F, et al. Primary Prevention of Cardiovascular Disease with a Mediterranean Diet Supplemented with Extra-Virgin Olive Oil or Nuts. N Engl J Med. 2018;378(25):e34.\u003c/li\u003e\n\u003cli\u003eKivim\u0026auml;ki M, Luukkonen R, Batty GD, Ferrie JE, Pentti J, Nyberg ST, et al. Body mass index and risk of dementia: Analysis of individual-level data from 1.3 million individuals. Alzheimers Dement. 2018;14(5):601-9.\u003c/li\u003e\n\u003cli\u003eBrown DM, Picciotto S, Costello S, Neophytou AM, Izano MA, Ferguson JM, et al. The Healthy Worker Survivor Effect: Target Parameters and Target Populations. Curr Environ Health Rep. 2017;4(3):364-72.\u003c/li\u003e\n\u003cli\u003eAzad AD, Charles AG, Ding Q, Trickey AW, Wren SM. Publisher Correction to: The gender gap and healthcare: associations between gender roles and factors affecting healthcare access in Central Malawi, June-August 2017. Arch Public Health. 2021;79(1):19.\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"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":"bmc-public-health","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"pubh","sideBox":"Learn more about [BMC Public Health](http://bmcpublichealth.biomedcentral.com/)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/pubh/default.aspx","title":"BMC Public Health","twitterHandle":"@BMC_series","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"em","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"Knowledge Workers, Lifestyle Habits, Carotid Atherosclerosis, Latent Category Study","lastPublishedDoi":"10.21203/rs.3.rs-9028936/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-9028936/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003e\u003cstrong\u003eBackground\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eCarotid atherosclerosis (CAS) underlies cardiovascular and cerebrovascular diseases and is linked to unhealthy lifestyles. Knowledge workers may face a higher risk of CAS due to certain special living habits, but evidence on CAS among knowledge workers is scarce because prior studies rarely classify lifestyle patterns. To address this gap, this study applies latent class analysis (LCA) to identify lifestyle patterns among 113,262 Chinese knowledge workers and assess their association with CAS risk.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eMethods\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis cross-sectional study analyzed data from the Health Management Center Database of a comprehensive Chinese hospital (2017–2024), including 113,262 knowledge workers. Lifestyle factors assessed included nocturnal eating, coffee and sugar-sweetened beverage consumption, smoking, alcohol use, sedentary behavior, and physical activity. CAS was evaluated via ultrasonography. Latent class analysis identified lifestyle patterns, and multivariable logistic regression assessed their associations with CAS risk. Stratified analysis was performed by gender, age, BMI, and marital status.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eResults\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe overall CAS prevalence among knowledge workers was 49.0%. Latent class analysis identified four lifestyle categories: basically healthy, mixed class 1, mixed class 2, and unhealthy. Compared to the basically healthy class, mixed class1 had a significantly increased CAS risk (\u003cem\u003eP \u003c/em\u003e\u0026lt; 0.05), while other classes showed inverse associations.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConclusions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe findings reveal substantial heterogeneity in lifestyle behaviors among knowledge workers, which are closely associated with CAS risk. These results suggest that targeted interventions addressing specific lifestyle behaviors may help mitigate CAS risk, providing a scientific basis for the development of precise prevention strategies.\u003c/p\u003e","manuscriptTitle":"The Association Between Lifestyle Habits of Knowledge Workers and Carotid Atherosclerosis: A Latent Class Study Of 113,262 Chinese","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2026-03-18 15:55:12","doi":"10.21203/rs.3.rs-9028936/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"editorInvitedReview","content":"","date":"2026-03-19T05:40:12+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"322428006948313242623670983935469760961","date":"2026-03-16T12:51:38+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2026-03-16T12:47:31+00:00","index":"","fulltext":""},{"type":"editorInvited","content":"","date":"2026-03-05T13:51:20+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2026-03-05T10:59:32+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2026-03-05T10:58:29+00:00","index":"","fulltext":""},{"type":"submitted","content":"BMC Public Health","date":"2026-03-04T10:01:06+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"bmc-public-health","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"pubh","sideBox":"Learn more about [BMC Public Health](http://bmcpublichealth.biomedcentral.com/)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/pubh/default.aspx","title":"BMC Public Health","twitterHandle":"@BMC_series","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"em","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"bc50052d-a262-4031-bef1-b41d1c5e4905","owner":[],"postedDate":"March 18th, 2026","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"under-review","subjectAreas":[],"tags":[],"updatedAt":"2026-03-18T15:55:12+00:00","versionOfRecord":[],"versionCreatedAt":"2026-03-18 15:55:12","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-9028936","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-9028936","identity":"rs-9028936","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}