Dietary and Lifestyle Predictors of Obesity: A Structural Equation Model Approach

Dietary and Lifestyle Predictors of Obesity: A Structural Equation Model Approach | 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 Short Report Dietary and Lifestyle Predictors of Obesity: A Structural Equation Model Approach Somayeh Mollaei, Daem Roshani, Asma Salari-Moghddam, Yousef Moradi, and 1 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7049033/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Background Obesity and overweight, driven by complex interactions between socioeconomic, behavioral, and dietary factors, remain critical global health challenges. This study employs structural equation modeling to evaluate direct and indirect effects of these factors, mediated by dietary indices(Dietary Inflammatory Index and Healthy Eating Index), on obesity/overweight in western Iran. Methods A cross-sectional analysis of 3,169 adults from the Dehgolan Prospective Cohort Study(DehPCS) in western Iran was conducted. Data included anthropometric measures, socioeconomic status, lifestyle factors, chronic diseases, and dietary indices(HEI and DII). Structural equation modeling evaluated pathways linking latent variables and mediators to obesity/overweight. Model fit was assessed using CFI, RMSEA, and χ² statistics. Results The model explained 69% of obesity/overweight variance. Socioeconomic status directly reduced obesity risk (β=−0.21,p < 0.001), while chronic diseases increased it (β = 0.42,p = 0.002).Personal habits had a strong inverse association (β=−1.85,p < 0.001). DII elevated obesity likelihood (β = 0.65, p < 0.001), whereas HEI-improved diets reduced it (β=−1.14,p < 0.001). Socioeconomic status indirectly worsened outcomes via unhealthy dietary habits (DII: β = 0.01; HEI: β=−0.87), while chronic diseases indirectly lowered risk through improved HEI(β = 0.11,p = 0.03). Conclusion Obesity is mediated by dietary quality and inflammation, with socioeconomic disparities and chronic conditions amplifying risk. Public health strategies must prioritize anti-inflammatory diets and equitable access to nutritious foods to disrupt these pathways. Inflammatory Index Healthy Eating Index Obesity Overweight Figures Figure 1 Figure 2 Background Obesity and being overweight are significant global public health issues, with their prevalence rising dramatically in both developed and developing countries in recent years(1). These conditions are risk factors for numerous diseases, including diabetes, various types of cancer, hypertension, dyslipidemia, coronary heart disease, stroke, obstructive sleep apnea, and respiratory complications, as well as difficulties in physical functioning and a lower quality of life(2, 3). Obesity also contributes to premature death and disability in adulthood(4). People who are obese often face numerous challenges in society and their daily lives, in addition to health risks(5). After smoking, obesity and overweight are the second leading causes of preventable death in the United States(6). Each year, at least 2.8 million people die as a result of being overweight or obese(7). In low-income countries, obesity is more prevalent among middle-aged individuals, particularly women, in urban and affluent areas. In high-income countries, it affects both genders and all age groups, but is disproportionately more common among disadvantaged groups(8). The pathophysiology of obesity is multifactorial, involving complex interactions between genetic factors and social environmental influences, which may also be linked to other risk factors(9). A shift toward a sedentary lifestyle and unhealthy eating habits is the primary driver of the obesity epidemic(10). Several factors significantly contribute to the increasing prevalence of overweight and obesity, including genetic elements and lifestyle choices such as nutrition, socioeconomic status, and sleep duration. Among demographic variables, age, socioeconomic status, and marital status have been identified as predisposing factors for obesity and related health issues. Additionally, psychological factors can play a significant role in the development of obesity and its associated health consequences by promoting unhealthy behaviors like poor dietary choices and physical inactivity(1, 11). Furthermore, dietary patterns in human behavior represent an independent risk factor for overweight and obesity(12). Currently, various indices and patterns of overall dietary intake, such as the DII and the HEI, are used to assess the quality of dietary habits and their potential impact on health outcomes(11). Improving physical activity and adopting appropriate diets as modifiable risk factors are crucial components of effective weight management interventions(12). Therefore, a comprehensive understanding of the current situation and health determinants is essential for developing effective strategies(10). While the direct impact of each risk factor is significant, structural equation modeling(SEM) offers an approach to elucidate how various factors interact and contribute to overweight and obesity(13). To present study, no study has explored the relationship between various dietary indicators and their direct and indirect associations with overweight, obesity, and demographic variables. This study utilizes SEM to assess the direct and indirect effects of different risk factors on obesity and overweight among 3,169 individuals aged 35 to 70, using baseline data from the DehPCS. Methods Study Design and Participants This is a cross-sectional study that utilized data from the DehPCS conducted in western Iran in 2019. Data were collected during the registration phase of the study, which was coordinated by the Ministry of Health and Medical Education. The study included 4,000 adults. The Dehgolan Cohort Study focuses on the Kurdish population in Dehgolan County, located in the southern part of Kurdistan Province, and is still ongoing. Dehgolan County has a population of approximately 68,000, with Dehgolan City accounting for about 26,000 residents, nearly all of whom are Kurdish. Participants aged 35 to 70 years living in the urban area of Dehgolan and possessing Iranian nationality were selected for this study. Dehgolan City has one hospital, two urban comprehensive health service centers, four rural comprehensive service centers, and 41 health homes. Participants who met the inclusion criteria provided both oral and written informed consent. The exclusion criteria for the Dehgolan cohort included individuals who were blind, deaf, or mute, as well as those with mental disorders (such as untreated psychosis) that hindered their ability to participate in the questioning process. Additionally, individuals with cardiovascular disease, thyroid conditions, cancer, or who were pregnant were also excluded(14). The design and implementation of this study received approval from the Ethics Committee of Kurdistan University of Medical Sciences, which assigned it the ethical approval code IR.MUK.RE.1396.93. Measurement Educational attainment was categorized as follows: illiterate, 1-12 years (basic education), and 12 years and above (university education). Employment status (where a job is defined as working at least 8 hours per week, and seasonal work is also included) was classified as employed, retired, or unemployed. Socio-economic status(SES) was treated as a latent variable, incorporating indicators of wealth, education, and job. The Wealth score index (WSI) is separately estimated by multiple correspondence analysis (MCA) of the variables listed below: Access to a freezer, washing machine, dish washer, computer, internet, motorcycle, car, vacuum cleaner, color Tv type, owning a mobile, owning a PC or laptop, international trips in lifetime. Additionally, participants were classified based on their cigarette use (having smoked at least 100 cigarettes in their lifetime) into three groups: smokers, former smokers, and non-smokers. Depression was diagnosed by a physician. Cardiovascular disease including ischemic heart disease and heart failure, followed by confirmation of medication use and a physician's diagnosis. Joint pain was defined having pain in the joints of the bones in the past 30 days. sleep duration, measured in hours of sleep per day, as well as Circadian rhythm of food, specifically the number of meals consumed each day. Marital status was categorized as either Widow/divorced/single or married. Alcohol consumption defined as drinking approximately 200 ml of beer OR 45 ml of liquor, once per week for at least six months. Physical activity, which is measured using the Metabolic equivalent of task (MET) index calculated for 24 hours, is divided into low, average, High(15). Outcome variable For anthropometric measurements, participants were instructed to remove their shoes, heavy clothing, and accessories. A Seka hand scale and a Seka inelastic tape measure, accurate to 0.1 cm, were used for the measurements. WC, height (in cm), and weight (in kg) were measured following the national health protocol(16).The outcome variables in this study—overweight and obesity—were represented as latent variables, indicated by two factors: waist circumference and body mass index (weight/height²). In terms of BMI, a BMI of 30 or higher was classified as obesity, while a BMI ranging from 25 to 29.9 was categorized as overweight. For abdominal obesity, WC was selected as an indicator of obesity. WC was measured with a flexible measuring tape at a level midway between the lower rib margin and the iliac crest to the nearest 0.5 cm. Food Indicators healthy eating index Food-related data were collected using a 125-question food frequency questionnaire (FFQ)(17), and the Healthy Eating Index (HEI-2015) was employed to calculate the nutrition quality index. The quality of healthy nutrition is assessed through ten components: fruits, vegetables, whole grains, nuts and legumes, long-chain n-3 fatty acids (such as docosahexaenoic acid and eicosapentaenoic acid), polyunsaturated fatty acids (PUFAs), sugar-sweetened beverages and fruit juices, red and processed meats, trans fats, and sodium. To create the index, the energy contribution of these components was first determined using the residual method. In the next step, participants were categorized based on the decile categories of energy consumption from these components. To minimize the risk of misclassification, we utilized component deciles rather than alternative classification methods when scoring the index. Individuals in the highest deciles for fruits, vegetables, whole grains, nuts, legumes, long-chain n-3 fatty acids, and PUFAs received a score of 10, while those in the lowest deciles for these components were assigned a score of 1. Corresponding scores for the other deciles were awarded based on their relative positions. For components associated with negative health impacts, such as sugar-sweetened beverages, fruit juices, red and processed meats, trans fatty acids, and sodium, the scoring was reversed: individuals in the 1st decile received the highest score of 10, while those in the 10th decile received the lowest score of 1. Similarly, the other deciles were scored according to this inverse scale, encouraging higher consumption of beneficial components and lower consumption of detrimental ones(18). Dietary inflammatory index These dietary parameters were scored based on their potential to influence inflammation, utilizing six inflammatory indicators: IL-1β, IL-6, IL-4, TNF-α, C-reactive protein (CRP), and IL-10. Foods associated with an increase in inflammation received a score of +1, while those deemed neutral (having no effect on inflammation) scored 0. A specific inflammatory score was calculated for each food item by taking the percentile value assigned to it and multiplying it by the corresponding dietary data. This scoring methodology was based on data from human food consumption studies across eleven populations from various regions of the world, providing robust estimates of mean values and standard deviations for each parameter. This approach enhances the reliability of the inflammatory scoring system by reflecting diverse dietary practices and their impact on inflammation across different populations. These values were subsequently converted into coefficients to express an individual's exposure to the "global standard mean" of each parameter in the form of a z-score. The z-score was calculated by subtracting the "global standard mean" from the reported value and then dividing the result by the standard deviation. This process established a standardized criterion for each food parameter. Next, the z-scores were transformed into percentile scores. For each food parameter, the percentile score was calculated, multiplied by 2, and then subtracted by 1 to adjust the scale. The resulting value for each parameter was then multiplied by its corresponding inflammation score. Finally, all individual scores for the food parameters were aggregated to create a total DII score for each participant in the study. This total DII score reflects the overall inflammatory potential of an individual's diet, providing insights into the relationship between dietary patterns and inflammation(19). Patient and public involvement No patients or members of the public were involved in the design, implementation or dissemination phases of the study Statistical Analysis AMOS and IBM SPSS version 24 were utilized for data management and statistical analysis. The means, maximums, minimums, and standard deviations of nutritional indices, along with the frequency (%) of classification variables, were reported for the participants. The normality of the data was assessed using the Kolmogorov-Smirnov test. The adequacy of the sample was measured by the Kaiser-Meyer-Olkin statistic, which yielded a value of 0.572, and The Bartelt sphericity test, which assesses the correlation between variables, showed a significant level (P < 0.001) with 91 degrees of freedom. This indicates that the data is suitable for factor analysis and supports the creation of latent variables. SEM was employed to investigate both the direct and indirect effects of risk factors related to overweight and obesity. SEM is a primary method for analyzing complex data structures and exploring the relationships among a set of variables, distinguishing itself by demonstrating the simultaneous effects of these variables within a theory-based framework. We employed a two-step SEM approach: (1) Confirmatory Factor Analysis (CFA) to validate latent constructs, and (2) path analysis to quantify direct/indirect effects of predictors on obesity/overweight. The conceptual model of the research is illustrated in Figure 1. It features three latent variables, including the primary dependent variable—obesity and overweight—assessed through BMI and WC. Additionally, there are three other latent variables that function independently, including SES, These latent variables are represented by three indicators each: economic status (Wealth), education level, and employment status (Job). Additionally, variables related to chronic diseases include three indicators: depression, joint pain, and CVD. The model also incorporates individual habits, which consist of physical activity, sleep duration, and the number of meals. Other indicators, including the DII, HEI, marital status, and alcohol consumption, serve a mediating role. To assess model fit, criteria such as the Comparative Fit Index (CFI), Incremental Fit Index (IFI), and Normed Fit Index(NFI) were set at 0.90 or greater, along with a Root Mean Square Error of Approximation (RMSEA) of 0.07 or less. Model estimates were evaluated using Maximum Likelihood estimation. In all analyses, a P value of less than 0.05 was considered statistically significant. Results Study participants After applying the exclusion criteria, a total of 3,169 individuals with an average age of 48.4 ± 8.9 years were included in the study. Among the participants, 50.43% were women, and 92.11% were married. Additionally, 51.18% were employed. The average physical activity level was 8.7 ± 39.9, the average waist circumference was 97 ± 10 cm, the average hours of sleep per night was 1.4 ± 6.9, and the average BMI was 27.8 ± 4.5. Regarding education, 29.09% of participants were illiterate, 56.83% had a diploma or lower, and 14.07% held a bachelor's degree or higher. A history of smoking was reported by 17.1% of participants, while 13.73% had a history of alcohol consumption. Regarding SES, 25.62% were classified as level one, 13.82% as level two, 19.91% as level three, 20.48% as level four, and 20.16% as level five. The prevalence of overweight and obesity within the studied sample, categorized by various variables, is presented in Table 1 . Table 1 Characteristics of study participants according to weight Variables normal(%) Obesity and overweight (%) P-value The whole sample 818(25.81) 2531(74.19) Gender < 0.001 Female 247(15.46) 1351(84.54) Male 571(36.35) 1000(63.65) Age < 0.001 35–45 351(24.81) 1064(75.19) 46–60 331(24.37) 1027(75.63) 60< 136(34.34) 260(65.66) Educationlevel < 0.001 illiterate 192(20.82) 730(79.18) 1–12 years 486(26.99) 1315(73.01) University 140(31.39) 306(68.61) Wealth status < 0.001 Q 1(poorest) 240(29.56) 572(70.44) Q 2 137(31.28) 310(68.72) Q 3 155(24.56) 476(75.44) Q 4 148(22.8) 501(77.2) Q 5(wealthiest) 138(21.6) 501(78.4) Employmentstatus < 0.001 unemployed 249(17.34) 1187(82.66) employed 533(32.86) 1089(67.14) retired 36(32.34) 75(67.57) physicalactivity < 0.001 low 311(24.24) 972(75.76) Moderate 307(22.05) 1085(77.95) high 200(40.49) 294(59.51) smoking < 0.001 non smoker 510(21.74) 1836(78.26) Former smoker 75(26.69) 206(73.31) a smoker 233(42.99) 309(57.01) marital status0.405 married 759(26) 2160(74) Widow/divorced/single 59(23.6) 191(76.4) alcoholconsumption < 0.001 Yes 155(35.63) 280(64.37) No 663(24.25) 2071(75.75) Depression0.002 Yes 30(16.22) 155(83.78) No 788(26.41) 2196(73.59) Joint pain < 0.001 Yes 476(22.98) 1595(77.02) No 342(31.15) 756(68.85) Cardiovasculardisease0.013 Yes 44(18.97) 188(81.03) No 774(26.35) 2163(73.65) Confirmatory factor analysis In the CFA involving the model's latent variables, the correlation and fit indices were deemed satisfactory (IFI = 0.926, NFI = 0.921, CFI = 0.926, RMSEA = 0.064). The correlation between overweight/obesity and chronic diseases was 0.31, while the correlation between SES and chronic diseases was − 0.49. Additionally, the correlation between overweight/obesity and SES was − 0.30, between overweight/obesity and personal habits was − 0.26, and between SES and personal habits was − 0.49( Fig. 1 ) . Structural Equation Model In the final model, which demonstrated a good fit (see Table 2 ), the R² value for the dependent variable was 0.69. This R² value indicates the percentage of variance in overweight and obesity explained by the variables included in the model.(Fig. 2 ). Table 2 Model fit fit index Abbreviation Desired amount estimate interpretation Chi-square \(\:X^2\) - 817.580 Good fit degree of freedom df - 70 Good fit Probability value for chi-square P-value P-value > 0.05 .09 0.920 Good fit Normed Fit Index NFI NFI > 0.9 0.914 Good fit Root Mean Square Error of Approximation RMSEA RMSEA 0.9 0.920 Good fit Lower SES predicted DII, while high physical activit and healthy eating reduced obesity risk, while marital status and chronic diseases had a negative impact. Additionally, the direct effects of socioeconomic level, personal habits, marital status, and alcohol consumption on overweight and obesity were negative, whereas chronic diseases had a positive effect. Personal habits, SES, and alcohol consumption played a mediating role, indirectly contributing positively to overweight and obesity. In contrast, chronic diseases and marital status had an indirect negative effect. Overall, SES, personal habits, marital status, and alcohol consumption negatively influenced overweight and obesity, while chronic diseases had a positive impact(see Table 3 ). Table 3 Direct, indirect, and totale effect between predictors and responses in Figur2 Predictor Response Direct effect Indirect effect Total effect Socioeconomic status Dietary inflammatory index 0.009 0.012 0.021 healthy eating index 0.013 -0.013 0.00 overweight and obesity -0.213 0.004 -0.209 alcohol consumption 0.275 0.005 0.280 Marital status -0.213 -0.213 Personal habits Dietary inflammatory index 0.808 0.808 healthy eating index -0.867 -0867 overweight and obesity -1.852 1.517 -0.335 Chronic disease Dietary inflammatory index -0.138 -0.001 -0.139 healthy eating index 0.111 0.111 overweight and obesity 0.419 -0.222 0.197 Marital status 0.067 0.067 alcohol consumption -0.002 -0.002 Marital status Dietary inflammatory index -0.014 -0.001 -0.014 healthy eating index 0.001 0.001 0.002 overweight and obesity -0.062 -0.01 -0.072 alcohol consumption -0.025 -0.025 alcohol consumption Dietary inflammatory index 0.032 0.032 healthy eating index -0.047 -0.047 overweight and obesity -0.083 0.075 -0.008 Dietary inflammatory index overweight and obesity 0.654 0.654 healthy eating index overweight and obesity -1.141 -1.141 Discussion The findings of this study indicate that socioeconomic status, personal habits, chronic diseases, marital status, and alcohol consumption have varying direct and indirect effects on overweight and obesity. The DII and the HEI serve as mediators for the influence of these factors on overweight and obesity. Furthermore, the relationship between the DII and the HEI shows opposing effects on overweight and obesity. higher SES demonstrates a direct negative effect on overweight and obesity. This aligns with several studies that have found that lower education levels, unemployment, income insecurity, and overall lower SES are directly associated with higher rates of overweight and obesity. However, some studies have reported contradictory or no significant results regarding these associations(20–25). However, SES appears to have minimal indirect effects, likely due to unhealthy dietary habits among individuals with higher SES. While SES directly and positively influences the HEI, it exerts a negative indirect effect through the mediation of marital status and alcohol consumption. This finding is consistent with a study conducted in Brazil, which also observed similar patterns(25). The study's results indicated that chronic conditions, such as depression, heart disease, and joint pain, are linked to obesity and being overweight, a finding that aligns with earlier research(26–28). However, it has an indirect negative effect on overweight and obesity, as evidenced by its direct influence on improving the quality of a healthy diet and reducing dietary inflammation. This likely reflects adherence to the diet among individuals with health issues. It's important to note that this relationship was assessed using a cross-sectional design, meaning we cannot establish the timeline between dietary patterns and the variables involved. Nevertheless, previous studies have shown that an increase in the DII—indicating a pro-inflammatory diet rather than an anti-inflammatory one—is associated with higher rates of depression and heart disease, as well as exacerbated symptoms of depression(29–31). A person’s habits of increasing sleep hours, reducing the number of meals consumed per day, and increasing daily physical activity have a direct negative effect on overweight and obesity. While some studies support these findings, others present conflicting results, highlighting the complexity of this relationship. Longer sleep durations are linked to depression and blood pressure, both of which are also associated with obesity. Interestingly, there is also an indirect positive effect on overweight and obesity; for example, increased physical activity may lead to greater food consumption, potentially resulting in obesity. Several studies have noted a correlation between increased physical activity and reductions in WC, BMI, abdominal obesity, general obesity, and fat mass(32–34). According to the results of this study, for every unit increase in the DII, the likelihood of overweight and obesity also rises. This effect is mediated by the diet's impact on the gut microbiome, which can lead to low-grade systemic inflammation and contribute to chronic obesity(35, 36). increase of each unit in the HEI reduces the likelihood of overweight and obesity, due to the cumulative protective effects of its components, which include fruits, vegetables, and unsaturated fatty acids(37). Limitations and strengths of the study One notable limitation is that obesity and overweight are multifactorial disorders, with genetics playing a significant role. Other studies estimate that genetic factors account for nearly 70% of the risk(38). While SEM explained 69% of obesity variance, unmeasured confounders (e.g., genetic factors, food insecurity) and the cross-sectional design limit causal inference. Future longitudinal studies should validate these pathways. Additionally, due to the non-linearity of the variables between men and women, the AMOS software encountered limitations in analyzing this variable. One of the strengths of this study was the use of advanced predictive models, including confirmatory factor analysis (CFA), path analysis, and structural equation modeling (SEM), along with a large sample size. Furthermore, this research is the first of its kind conducted on Kurdish ethnic groups in Iran, providing a valuable reference for future studies on other ethnic groups and enabling comparisons between them. Conclusion Our study advances prior work by quantifying how socioeconomic deprivation exacerbates obesity via inflammatory diets, while HEI mediates protective effects—a pathway underexplored in Middle Eastern populations. This highlights the complex interplay between diet and weight-related outcomes, suggesting that improving dietary quality and its components is crucial in combating obesity. Targeting pro-inflammatory diets and improving access to nutrient-rich foods in socioeconomically disadvantaged groups may disrupt obesity pathways, as demonstrated by the mediating role of HEI/DII. Declarations Author Contribution Abstract written by Somayeh MollaeiBackground written by Daem RoshaniMethods written by Yousef MoradiMeasurement written by Asma Salari- MoghaddamStatistical Analysis written by Farhad MoradpourAbstract written by Somayeh Mollaei Acknowledgement We thank the people who participated in the study and members of the Dehgolan cohort center in Dehgolan, Iran Data Availability The data that support the fundings of this study are available from the corresponding author upon reasonable request References Khodarahmi M, Asghari-Jafarabadi M, Abbasalizad Farhangi M. A structural equation modeling approach for the association of a healthy eating index with metabolic syndrome and cardio-metabolic risk factors among obese individuals. PLoS One. 2019;14(7):e0219193. 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Association of physical activity with body mass index, waist circumference and incidence of obesity in older adults. Eur J Public Health. 2018;28(5):944-50. Tan X, Chapman CD, Cedernaes J, Benedict C. Association between long sleep duration and increased risk of obesity and type 2 diabetes: A review of possible mechanisms. Sleep Med Rev. 2018;40:127-34. Pfisterer J, Rausch C, Wohlfarth D, Bachert P, Jekauc D, Wunsch K. Effectiveness of Physical-Activity-Based Interventions Targeting Overweight and Obesity among University Students-A Systematic Review. Int J Environ Res Public Health. 2022;19(15). Cuevas-Sierra A, Ramos-Lopez O, Riezu-Boj JI, Milagro FI, Martinez JA. Diet, Gut Microbiota, and Obesity: Links with Host Genetics and Epigenetics and Potential Applications. Adv Nutr. 2019;10(suppl_1):S17-s30. Citronberg JS, Curtis KR, White E, Newcomb PA, Newton K, Atkinson C, et al. Association of gut microbial communities with plasma lipopolysaccharide-binding protein (LBP) in premenopausal women. Isme j. 2018;12(7):1631-41. Poursalehi D, Bahrami G, Mirzaei S, Asadi A, Akhlaghi M, Saneei P. Association between alternative healthy eating index (AHEI) with metabolic health status in adolescents with overweight and obesity. BMC Public Health. 2024;24(1):42. Farooqi IS, O'Rahilly S. Mutations in ligands and receptors of the leptin-melanocortin pathway that lead to obesity. Nat Clin Pract Endocrinol Metab. 2008;4(10):569-77. Additional Declarations No competing interests reported. Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. 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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-7049033","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Short Report","associatedPublications":[],"authors":[{"id":492138057,"identity":"1c38ddac-b47b-42e1-8257-b570681ce479","order_by":0,"name":"Somayeh Mollaei","email":"","orcid":"","institution":"Kurdistan University of Medical Sciences","correspondingAuthor":false,"prefix":"","firstName":"Somayeh","middleName":"","lastName":"Mollaei","suffix":""},{"id":492138058,"identity":"de66f475-265a-4338-9c2c-c3f3af0f6c89","order_by":1,"name":"Daem Roshani","email":"","orcid":"","institution":"Kurdistan University of Medical Sciences","correspondingAuthor":false,"prefix":"","firstName":"Daem","middleName":"","lastName":"Roshani","suffix":""},{"id":492138062,"identity":"2fa9cabf-7526-44e5-9a85-b49de68f49a5","order_by":2,"name":"Asma Salari-Moghddam","email":"","orcid":"","institution":"Ilam University of Medical Sciences","correspondingAuthor":false,"prefix":"","firstName":"Asma","middleName":"","lastName":"Salari-Moghddam","suffix":""},{"id":492138064,"identity":"92f4b155-5ae2-4593-a909-88ecf96731d6","order_by":3,"name":"Yousef Moradi","email":"","orcid":"","institution":"Kurdistan University of Medical Sciences","correspondingAuthor":false,"prefix":"","firstName":"Yousef","middleName":"","lastName":"Moradi","suffix":""},{"id":492138066,"identity":"ae2c875f-435f-43c7-9844-e6b84f582030","order_by":4,"name":"Farhad Moradpour","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA5UlEQVRIiWNgGAWjYBAC+wMMbAwgxMDMYPiAgeEAYS0GDAgtxgYkamFgMJMgTgv72WcPfpRtkzNnZ95WzVNzR46fgfnhoxt4tNjzpJsb9py7bWzZzFZ2m+fYM2PJBjZj4xy8Dktjk+Btu5244TCP2W0etsOJGw7wsEnj1cL/jE3yb9vtepCWYp5/xGiRSGOTBtqSYADUwszbRpSWZ2zSMuduG+5sZiuWnNt32FiymZBf+NPYJN+U3ZY35z+88cObb4fl+NmbHz7GpwWhF4iZeEAsZmKUw7Qw/iBW9SgYBaNgFIwoAAA1mEj58yRWbwAAAABJRU5ErkJggg==","orcid":"","institution":"Kurdistan University of Medical Sciences","correspondingAuthor":true,"prefix":"","firstName":"Farhad","middleName":"","lastName":"Moradpour","suffix":""}],"badges":[],"createdAt":"2025-07-04 18:38:05","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-7049033/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-7049033/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":87811586,"identity":"0db52aaf-e45c-44f3-bf61-0a73ccf04672","added_by":"auto","created_at":"2025-07-29 09:26:02","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":154054,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eConfirmatory factor analysis.\u003c/strong\u003e \u003cstrong\u003eMeasurement model of the latent construct of diease, personal habit, socioeconomic status(ses), overweight/obesity\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"1.png","url":"https://assets-eu.researchsquare.com/files/rs-7049033/v1/a4e8dc2e0b1bf63ad573d03b.png"},{"id":87811585,"identity":"6518b797-0189-45ae-98e7-422322defde2","added_by":"auto","created_at":"2025-07-29 09:26:02","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":215076,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eFinal structural model.\u003c/strong\u003e \u003cstrong\u003eThe path standardized coefficients of variables are presented on pathways\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"2.png","url":"https://assets-eu.researchsquare.com/files/rs-7049033/v1/42277596996d63db5996f3f2.png"},{"id":97136214,"identity":"c0566a59-5516-44bc-a17a-dce1e88854bc","added_by":"auto","created_at":"2025-12-01 09:56:03","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1164216,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-7049033/v1/e291c72c-94e9-4742-a63e-b68511f6f037.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Dietary and Lifestyle Predictors of Obesity: A Structural Equation Model Approach","fulltext":[{"header":"Background","content":"\u003cp\u003eObesity and being overweight are significant global public health issues, with their prevalence rising dramatically in both developed and developing countries in recent years(1). These conditions are risk factors for numerous diseases, including diabetes, various types of cancer, hypertension, dyslipidemia, coronary heart disease, stroke, obstructive sleep apnea, and respiratory complications, as well as difficulties in physical functioning and a lower quality of life(2, 3). Obesity also contributes to premature death and disability in adulthood(4). People who are obese often face numerous challenges in society and their daily lives, in addition to health risks(5).\u003c/p\u003e\n\u003cp\u003eAfter smoking, obesity and overweight are the second leading causes of preventable death in the United States(6). Each year, at least 2.8 million people die as a result of being overweight or obese(7).\u003c/p\u003e\n\u003cp\u003eIn low-income countries, obesity is more prevalent among middle-aged individuals, particularly women, in urban and affluent areas. In high-income countries, it affects both genders and all age groups, but is disproportionately more common among disadvantaged groups(8).\u003c/p\u003e\n\u003cp\u003eThe pathophysiology of obesity is multifactorial, involving complex interactions between genetic factors and social environmental influences, which may also be linked to other risk factors(9). A shift toward a sedentary lifestyle and unhealthy eating habits is the primary driver of the obesity epidemic(10). Several factors significantly contribute to the increasing prevalence of overweight and obesity, including genetic elements and lifestyle choices such as nutrition, socioeconomic status, and sleep duration. Among demographic variables, age, socioeconomic status, and marital status have been identified as predisposing factors for obesity and related health issues. Additionally, psychological factors can play a significant role in the development of obesity and its associated health consequences by promoting unhealthy behaviors like poor dietary choices and physical inactivity(1, 11). Furthermore, dietary patterns in human behavior represent an independent risk factor for overweight and obesity(12). Currently, various indices and patterns of overall dietary intake, such as the DII and the HEI, are used to assess the quality of dietary habits and their potential impact on health outcomes(11).\u003c/p\u003e\n\u003cp\u003eImproving physical activity and adopting appropriate diets as modifiable risk factors are crucial components of effective weight management interventions(12). Therefore, a comprehensive understanding of the current situation and health determinants is essential for developing effective strategies(10). While the direct impact of each risk factor is significant, structural equation modeling(SEM) offers an approach to elucidate how various factors interact and contribute to overweight and obesity(13). To present study, no study has explored the relationship between various dietary indicators and their direct and indirect associations with overweight, obesity, and demographic variables. This study utilizes SEM to assess the direct and indirect effects of different risk factors on obesity and overweight among 3,169 individuals aged 35 to 70, using baseline data from the DehPCS.\u003c/p\u003e"},{"header":"Methods","content":"\u003cp\u003e\u003cstrong\u003eStudy Design and Participants\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis is a cross-sectional study that utilized data from the DehPCS conducted in western Iran in 2019. Data were collected during the registration phase of the study, which was coordinated by the Ministry of Health and Medical Education. The study included 4,000 adults. The Dehgolan Cohort Study focuses on the Kurdish population in Dehgolan County, located in the southern part of Kurdistan Province, and is still ongoing. Dehgolan County has a population of approximately 68,000, with Dehgolan City accounting for about 26,000 residents, nearly all of whom are Kurdish. Participants aged 35 to 70 years living in the urban area of Dehgolan and possessing Iranian nationality were selected for this study. Dehgolan City has one hospital, two urban comprehensive health service centers, four rural comprehensive service centers, and 41 health homes. Participants who met the inclusion criteria provided both oral and written informed consent. The exclusion criteria for the Dehgolan cohort included individuals who were blind, deaf, or mute, as well as those with mental disorders (such as untreated psychosis) that hindered their ability to participate in the questioning process. Additionally, individuals with cardiovascular disease, thyroid conditions, cancer, or who were pregnant were also excluded(14).\u003c/p\u003e\n\u003cp\u003eThe design and implementation of this study received approval from the Ethics Committee of Kurdistan University of Medical Sciences, which assigned it the ethical approval code IR.MUK.RE.1396.93.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eMeasurement\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eEducational attainment was categorized as follows: illiterate, 1-12 years (basic education), and 12 years and above (university education). Employment status (where a job is defined as working at least 8 hours per week, and seasonal work is also included) was classified as employed, retired, or unemployed. Socio-economic status(SES) was treated as a latent variable, incorporating indicators of wealth, education, and job. The Wealth score index (WSI) is separately estimated by multiple correspondence analysis (MCA) of the variables listed below: Access to a freezer, washing machine, dish washer, computer, internet, motorcycle, car, vacuum cleaner, color Tv type, owning a mobile, owning a PC or laptop, international trips in lifetime. Additionally, participants were classified based on their cigarette use (having smoked at least 100 cigarettes in their lifetime) into three groups: smokers, former smokers, and non-smokers. Depression was diagnosed by a physician. Cardiovascular disease including ischemic heart disease and heart failure, followed by confirmation of medication use and a physician\u0026apos;s diagnosis. Joint pain was defined having pain in the joints of the bones in the past 30 days. sleep duration, measured in hours of sleep per day, as well as Circadian rhythm of food, specifically the number of meals consumed each day. Marital status was categorized as either Widow/divorced/single or married.\u0026nbsp;Alcohol consumption defined as drinking approximately 200 ml of beer OR 45 ml of liquor, once per week for at least six months.\u0026nbsp;Physical activity, which is measured using the Metabolic equivalent of task (MET) index calculated for 24 hours, is divided into low, average, High(15).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eOutcome variable\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eFor anthropometric measurements, participants were instructed to remove their shoes, heavy clothing, and accessories. A Seka hand scale and a Seka inelastic tape measure, accurate to 0.1 cm, were used for the measurements. WC, height (in cm), and weight (in kg) were measured following the national health protocol(16).The outcome variables in this study\u0026mdash;overweight and obesity\u0026mdash;were represented as latent variables, indicated by two factors: waist circumference and body mass index (weight/height\u0026sup2;).\u003cspan dir=\"RTL\"\u003e\u0026nbsp;\u003c/span\u003eIn terms of BMI, a BMI of 30 or higher was classified as obesity, while a BMI ranging from 25 to 29.9 was categorized as overweight. For abdominal obesity, WC was selected as an indicator of obesity. WC was measured with a flexible measuring tape at a level midway between the lower rib margin and the iliac crest to the nearest 0.5 cm.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFood Indicators\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003ehealthy eating index\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eFood-related data were collected using a 125-question food frequency questionnaire (FFQ)(17), and the Healthy Eating Index (HEI-2015) was employed to calculate the nutrition quality index. The quality of healthy nutrition is assessed through ten components: fruits, vegetables, whole grains, nuts and legumes, long-chain n-3 fatty acids (such as docosahexaenoic acid and eicosapentaenoic acid), polyunsaturated fatty acids (PUFAs), sugar-sweetened beverages and fruit juices, red and processed meats, trans fats, and sodium. To create the index, the energy contribution of these components was first determined using the residual method. In the next step, participants were categorized based on the decile categories of energy consumption from these components. To minimize the risk of misclassification, we utilized component deciles rather than alternative classification methods when scoring the index. Individuals in the highest deciles for fruits, vegetables, whole grains, nuts, legumes, long-chain n-3 fatty acids, and PUFAs received a score of 10, while those in the lowest deciles for these components were assigned a score of 1. Corresponding scores for the other deciles were awarded based on their relative positions. For components associated with negative health impacts, such as sugar-sweetened beverages, fruit juices, red and processed meats, trans fatty acids, and sodium, the scoring was reversed: individuals in the 1st decile received the highest score of 10, while those in the 10th decile received the lowest score of 1. Similarly, the other deciles were scored according to this inverse scale, encouraging higher consumption of beneficial components and lower consumption of detrimental ones(18).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eDietary inflammatory index\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThese dietary parameters were scored based on their potential to influence inflammation, utilizing six inflammatory indicators: IL-1\u0026beta;, IL-6, IL-4, TNF-\u0026alpha;, C-reactive protein (CRP), and IL-10. Foods associated with an increase in inflammation received a score of +1, while those deemed neutral (having no effect on inflammation) scored 0. A specific inflammatory score was calculated for each food item by taking the percentile value assigned to it and multiplying it by the corresponding dietary data. This scoring methodology was based on data from human food consumption studies across eleven populations from various regions of the world, providing robust estimates of mean values and standard deviations for each parameter. This approach enhances the reliability of the inflammatory scoring system by reflecting diverse dietary practices and their impact on inflammation across different populations. These values were subsequently converted into coefficients to express an individual\u0026apos;s exposure to the \u0026quot;global standard mean\u0026quot; of each parameter in the form of a z-score. The z-score was calculated by subtracting the \u0026quot;global standard mean\u0026quot; from the reported value and then dividing the result by the standard deviation. This process established a standardized criterion for each food parameter. Next, the z-scores were transformed into percentile scores. For each food parameter, the percentile score was calculated, multiplied by 2, and then subtracted by 1 to adjust the scale. The resulting value for each parameter was then multiplied by its corresponding inflammation score. Finally, all individual scores for the food parameters were aggregated to create a total DII score for each participant in the study. This total DII score reflects the overall inflammatory potential of an individual\u0026apos;s diet, providing insights into the relationship between dietary patterns and inflammation(19).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003ePatient and public involvement\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNo patients or members of the public were involved in the design, implementation or dissemination phases of the study\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eStatistical Analysis\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAMOS and IBM SPSS version 24 were utilized for data management and statistical analysis. The means, maximums, minimums, and standard deviations of nutritional indices, along with the frequency (%) of classification variables, were reported for the participants. The normality of the data was assessed using the Kolmogorov-Smirnov test. The adequacy of the sample was measured by the Kaiser-Meyer-Olkin statistic, which yielded a value of 0.572, and The Bartelt sphericity test, which assesses the correlation between variables, showed a significant level (P \u0026lt; 0.001) with 91 degrees of freedom. This indicates that the data is suitable for factor analysis and supports the creation of latent variables. SEM was employed to investigate both the direct and indirect effects of risk factors related to overweight and obesity. SEM is a primary method for analyzing complex data structures and exploring the relationships among a set of variables, distinguishing itself by demonstrating the simultaneous effects of these variables within a theory-based framework. We employed a two-step SEM approach: (1) Confirmatory Factor Analysis (CFA) to validate latent constructs, and (2) path analysis to quantify direct/indirect effects of predictors on obesity/overweight. The conceptual model of the research is illustrated in Figure 1. It features three latent variables, including the primary dependent variable\u0026mdash;obesity and overweight\u0026mdash;assessed through BMI and WC. Additionally, there are three other latent variables that function independently, including SES, These latent variables are represented by three indicators each: economic status (Wealth), education level, and employment status (Job). Additionally, variables related to chronic diseases include three indicators: depression, joint pain, and CVD. The model also incorporates individual habits, which consist of physical activity, sleep duration, and the number of meals. Other indicators, including the DII, HEI, marital status, and alcohol consumption, serve a mediating role. To assess model fit, criteria such as the Comparative Fit Index (CFI), Incremental Fit Index (IFI), and Normed Fit Index(NFI) were set at 0.90 or greater, along with a Root Mean Square Error of Approximation (RMSEA) of 0.07 or less. Model estimates were evaluated using Maximum Likelihood estimation. In all analyses, a P value of less than 0.05 was considered statistically significant.\u003c/p\u003e"},{"header":"Results","content":"\u003cp\u003e\u003cb\u003eStudy participants\u003c/b\u003e\u003c/p\u003e\u003cp\u003eAfter applying the exclusion criteria, a total of 3,169 individuals with an average age of 48.4\u0026thinsp;\u0026plusmn;\u0026thinsp;8.9 years were included in the study. Among the participants, 50.43% were women, and 92.11% were married. Additionally, 51.18% were employed. The average physical activity level was 8.7\u0026thinsp;\u0026plusmn;\u0026thinsp;39.9, the average waist circumference was 97\u0026thinsp;\u0026plusmn;\u0026thinsp;10 cm, the average hours of sleep per night was 1.4\u0026thinsp;\u0026plusmn;\u0026thinsp;6.9, and the average BMI was 27.8\u0026thinsp;\u0026plusmn;\u0026thinsp;4.5. Regarding education, 29.09% of participants were illiterate, 56.83% had a diploma or lower, and 14.07% held a bachelor's degree or higher. A history of smoking was reported by 17.1% of participants, while 13.73% had a history of alcohol consumption. Regarding SES, 25.62% were classified as level one, 13.82% as level two, 19.91% as level three, 20.48% as level four, and 20.16% as level five. The prevalence of overweight and obesity within the studied sample, categorized by various variables, is presented in Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e.\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eCharacteristics of study participants according to weight\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"4\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eVariables\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003enormal(%)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eObesity and overweight (%)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003eP-value\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eThe whole sample\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e818(25.81)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e2531(74.19)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"4\" nameend=\"c4\" namest=\"c1\"\u003e\u003cp\u003eGender\u0026thinsp;\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eFemale\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e247(15.46)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1351(84.54)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eMale\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e571(36.35)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1000(63.65)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"4\" nameend=\"c4\" namest=\"c1\"\u003e\u003cp\u003eAge\u0026thinsp;\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e35\u0026ndash;45\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e351(24.81)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1064(75.19)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e46\u0026ndash;60\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e331(24.37)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1027(75.63)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e60\u0026lt;\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e136(34.34)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e260(65.66)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"4\" nameend=\"c4\" namest=\"c1\"\u003e\u003cp\u003eEducationlevel\u0026thinsp;\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eilliterate\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e192(20.82)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e730(79.18)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e1\u0026ndash;12 years\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e486(26.99)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1315(73.01)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eUniversity\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e140(31.39)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e306(68.61)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"4\" nameend=\"c4\" namest=\"c1\"\u003e\u003cp\u003eWealth status \u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eQ 1(poorest)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e240(29.56)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e572(70.44)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eQ 2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e137(31.28)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e310(68.72)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eQ 3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e155(24.56)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e476(75.44)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eQ 4\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e148(22.8)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e501(77.2)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eQ 5(wealthiest)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e138(21.6)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e501(78.4)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"4\" nameend=\"c4\" namest=\"c1\"\u003e\u003cp\u003eEmploymentstatus\u0026thinsp;\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eunemployed\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e249(17.34)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1187(82.66)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eemployed\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e533(32.86)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1089(67.14)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eretired\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e36(32.34)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e75(67.57)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"4\" nameend=\"c4\" namest=\"c1\"\u003e\u003cp\u003ephysicalactivity\u0026thinsp;\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003elow\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e311(24.24)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e972(75.76)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eModerate\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e307(22.05)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1085(77.95)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ehigh\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e200(40.49)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e294(59.51)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"4\" nameend=\"c4\" namest=\"c1\"\u003e\u003cp\u003esmoking\u0026thinsp;\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003enon smoker\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e510(21.74)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1836(78.26)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eFormer smoker\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e75(26.69)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e206(73.31)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ea smoker\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e233(42.99)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e309(57.01)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"4\" nameend=\"c4\" namest=\"c1\"\u003e\u003cp\u003emarital status0.405\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003emarried\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e759(26)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e2160(74)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eWidow/divorced/single\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e59(23.6)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e191(76.4)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"4\" nameend=\"c4\" namest=\"c1\"\u003e\u003cp\u003ealcoholconsumption\u0026thinsp;\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eYes\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e155(35.63)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e280(64.37)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eNo\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e663(24.25)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e2071(75.75)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"4\" nameend=\"c4\" namest=\"c1\"\u003e\u003cp\u003eDepression0.002\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eYes\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e30(16.22)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e155(83.78)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eNo\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e788(26.41)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e2196(73.59)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"4\" nameend=\"c4\" namest=\"c1\"\u003e\u003cp\u003eJoint pain\u0026thinsp;\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eYes\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e476(22.98)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1595(77.02)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eNo\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e342(31.15)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e756(68.85)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"4\" nameend=\"c4\" namest=\"c1\"\u003e\u003cp\u003eCardiovasculardisease0.013\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eYes\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e44(18.97)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e188(81.03)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eNo\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e774(26.35)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e2163(73.65)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003e\u003cb\u003eConfirmatory factor analysis\u003c/b\u003e\u003c/p\u003e\u003cp\u003eIn the CFA involving the model's latent variables, the correlation and fit indices were deemed satisfactory (IFI\u0026thinsp;=\u0026thinsp;0.926, NFI\u0026thinsp;=\u0026thinsp;0.921, CFI\u0026thinsp;=\u0026thinsp;0.926, RMSEA\u0026thinsp;=\u0026thinsp;0.064). The correlation between overweight/obesity and chronic diseases was 0.31, while the correlation between SES and chronic diseases was \u0026minus;\u0026thinsp;0.49. Additionally, the correlation between overweight/obesity and SES was \u0026minus;\u0026thinsp;0.30, between overweight/obesity and personal habits was \u0026minus;\u0026thinsp;0.26, and between SES and personal habits was \u0026minus;\u0026thinsp;0.49( Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e) .\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003e\u003cb\u003eStructural Equation Model\u003c/b\u003e\u003c/p\u003e\u003cp\u003eIn the final model, which demonstrated a good fit (see Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e), the R\u0026sup2; value for the dependent variable was 0.69. This R\u0026sup2; value indicates the percentage of variance in overweight and obesity explained by the variables included in the model.(Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e).\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eModel fit\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"5\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003efit index\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eAbbreviation\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eDesired amount\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003eestimate\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u003cp\u003einterpretation\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eChi-square\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:X^2\\)\u003c/span\u003e\u003c/span\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e817.580\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eGood fit\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003edegree of freedom\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003edf\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e70\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eGood fit\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eProbability value for chi-square\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eP-value\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eP-value\u0026thinsp;\u0026gt;\u0026thinsp;0.05\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eGood fit\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eComparative Fit Index\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eCFI\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eCFI\u0026thinsp;\u0026gt;\u0026thinsp;.09\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.920\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eGood fit\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eNormed Fit Index\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eNFI\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eNFI\u0026thinsp;\u0026gt;\u0026thinsp;0.9\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.914\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eGood fit\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eRoot Mean Square Error of Approximation\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eRMSEA\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eRMSEA\u0026thinsp;\u0026lt;\u0026thinsp;0.1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.058\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eGood fit\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eIncremental Fit Index\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eIFI\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eIFI\u0026thinsp;\u0026gt;\u0026thinsp;0.9\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.920\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eGood fit\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003eLower SES predicted DII, while high physical activit and healthy eating reduced obesity risk, while marital status and chronic diseases had a negative impact. Additionally, the direct effects of socioeconomic level, personal habits, marital status, and alcohol consumption on overweight and obesity were negative, whereas chronic diseases had a positive effect. Personal habits, SES, and alcohol consumption played a mediating role, indirectly contributing positively to overweight and obesity. In contrast, chronic diseases and marital status had an indirect negative effect. Overall, SES, personal habits, marital status, and alcohol consumption negatively influenced overweight and obesity, while chronic diseases had a positive impact(see Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e).\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab3\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eDirect, indirect, and totale effect between predictors and responses in Figur2\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"5\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003ePredictor\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eResponse\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eDirect effect\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003eIndirect effect\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u003cp\u003eTotal effect\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\" morerows=\"4\" rowspan=\"5\"\u003e\u003cp\u003eSocioeconomic status\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eDietary inflammatory index\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.009\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.012\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.021\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003ehealthy eating index\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.013\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e-0.013\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.00\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eoverweight and obesity\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e-0.213\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.004\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e-0.209\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003ealcohol consumption\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.275\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.005\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.280\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eMarital status\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e-0.213\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e-0.213\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\" morerows=\"2\" rowspan=\"3\"\u003e\u003cp\u003ePersonal habits\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eDietary inflammatory index\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.808\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.808\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003ehealthy eating index\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e-0.867\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e-0867\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eoverweight and obesity\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e-1.852\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e1.517\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e-0.335\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\" morerows=\"4\" rowspan=\"5\"\u003e\u003cp\u003eChronic disease\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eDietary inflammatory index\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e-0.138\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e-0.001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e-0.139\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003ehealthy eating index\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.111\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.111\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eoverweight and obesity\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.419\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e-0.222\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.197\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eMarital status\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.067\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.067\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003ealcohol consumption\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e-0.002\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e-0.002\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\" morerows=\"3\" rowspan=\"4\"\u003e\u003cp\u003eMarital status\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eDietary inflammatory index\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e-0.014\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e-0.001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e-0.014\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003ehealthy eating index\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.002\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eoverweight and obesity\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e-0.062\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e-0.01\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e-0.072\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003ealcohol consumption\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e-0.025\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e-0.025\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\" morerows=\"2\" rowspan=\"3\"\u003e\u003cp\u003ealcohol consumption\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eDietary inflammatory index\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.032\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.032\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003ehealthy eating index\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e-0.047\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e-0.047\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eoverweight and obesity\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e-0.083\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.075\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e-0.008\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eDietary inflammatory index\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eoverweight and obesity\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.654\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.654\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ehealthy eating index\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eoverweight and obesity\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e-1.141\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e-1.141\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e"},{"header":"Discussion","content":"\u003cp\u003eThe findings of this study indicate that socioeconomic status, personal habits, chronic diseases, marital status, and alcohol consumption have varying direct and indirect effects on overweight and obesity. The DII and the HEI serve as mediators for the influence of these factors on overweight and obesity. Furthermore, the relationship between the DII and the HEI shows opposing effects on overweight and obesity. higher SES demonstrates a direct negative effect on overweight and obesity. This aligns with several studies that have found that lower education levels, unemployment, income insecurity, and overall lower SES are directly associated with higher rates of overweight and obesity. However, some studies have reported contradictory or no significant results regarding these associations(20\u0026ndash;25). However, SES appears to have minimal indirect effects, likely due to unhealthy dietary habits among individuals with higher SES. While SES directly and positively influences the HEI, it exerts a negative indirect effect through the mediation of marital status and alcohol consumption. This finding is consistent with a study conducted in Brazil, which also observed similar patterns(25). The study's results indicated that chronic conditions, such as depression, heart disease, and joint pain, are linked to obesity and being overweight, a finding that aligns with earlier research(26\u0026ndash;28). However, it has an indirect negative effect on overweight and obesity, as evidenced by its direct influence on improving the quality of a healthy diet and reducing dietary inflammation. This likely reflects adherence to the diet among individuals with health issues. It's important to note that this relationship was assessed using a cross-sectional design, meaning we cannot establish the timeline between dietary patterns and the variables involved. Nevertheless, previous studies have shown that an increase in the DII\u0026mdash;indicating a pro-inflammatory diet rather than an anti-inflammatory one\u0026mdash;is associated with higher rates of depression and heart disease, as well as exacerbated symptoms of depression(29\u0026ndash;31). A person\u0026rsquo;s habits of increasing sleep hours, reducing the number of meals consumed per day, and increasing daily physical activity have a direct negative effect on overweight and obesity. While some studies support these findings, others present conflicting results, highlighting the complexity of this relationship. Longer sleep durations are linked to depression and blood pressure, both of which are also associated with obesity. Interestingly, there is also an indirect positive effect on overweight and obesity; for example, increased physical activity may lead to greater food consumption, potentially resulting in obesity. Several studies have noted a correlation between increased physical activity and reductions in WC, BMI, abdominal obesity, general obesity, and fat mass(32\u0026ndash;34). According to the results of this study, for every unit increase in the DII, the likelihood of overweight and obesity also rises. This effect is mediated by the diet's impact on the gut microbiome, which can lead to low-grade systemic inflammation and contribute to chronic obesity(35, 36). increase of each unit in the HEI reduces the likelihood of overweight and obesity, due to the cumulative protective effects of its components, which include fruits, vegetables, and unsaturated fatty acids(37).\u003c/p\u003e\u003cp\u003e\u003cb\u003eLimitations and strengths of the study\u003c/b\u003e\u003c/p\u003e\u003cp\u003eOne notable limitation is that obesity and overweight are multifactorial disorders, with genetics playing a significant role. Other studies estimate that genetic factors account for nearly 70% of the risk(38). While SEM explained 69% of obesity variance, unmeasured confounders (e.g., genetic factors, food insecurity) and the cross-sectional design limit causal inference. Future longitudinal studies should validate these pathways. Additionally, due to the non-linearity of the variables between men and women, the AMOS software encountered limitations in analyzing this variable. One of the strengths of this study was the use of advanced predictive models, including confirmatory factor analysis (CFA), path analysis, and structural equation modeling (SEM), along with a large sample size. Furthermore, this research is the first of its kind conducted on Kurdish ethnic groups in Iran, providing a valuable reference for future studies on other ethnic groups and enabling comparisons between them.\u003c/p\u003e"},{"header":"Conclusion","content":"\u003cp\u003eOur study advances prior work by quantifying how socioeconomic deprivation exacerbates obesity via inflammatory diets, while HEI mediates protective effects\u0026mdash;a pathway underexplored in Middle Eastern populations. This highlights the complex interplay between diet and weight-related outcomes, suggesting that improving dietary quality and its components is crucial in combating obesity. Targeting pro-inflammatory diets and improving access to nutrient-rich foods in socioeconomically disadvantaged groups may disrupt obesity pathways, as demonstrated by the mediating role of HEI/DII.\u003c/p\u003e"},{"header":"Declarations","content":"\u003ch2\u003eAuthor Contribution\u003c/h2\u003e\u003cp\u003eAbstract written by Somayeh MollaeiBackground written by Daem RoshaniMethods written by Yousef MoradiMeasurement written by Asma Salari- MoghaddamStatistical Analysis written by Farhad MoradpourAbstract written by Somayeh Mollaei\u003c/p\u003e\u003ch2\u003eAcknowledgement\u003c/h2\u003e\u003cp\u003eWe thank the people who participated in the study and members of the Dehgolan cohort center in Dehgolan, Iran\u003c/p\u003e\u003ch2\u003eData Availability\u003c/h2\u003e\u003cp\u003eThe data that support the fundings of this study are available from the corresponding author upon reasonable request\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eKhodarahmi M, Asghari-Jafarabadi M, Abbasalizad Farhangi M. 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Front Nutr. 2022;9:1044329.\u003c/li\u003e\n\u003cli\u003eC\u0026aacute;rdenas Fuentes G, Bawaked RA, Mart\u0026iacute;nez Gonz\u0026aacute;lez M, Corella D, Subirana Cachinero I, Salas-Salvad\u0026oacute; J, et al. Association of physical activity with body mass index, waist circumference and incidence of obesity in older adults. Eur J Public Health. 2018;28(5):944-50.\u003c/li\u003e\n\u003cli\u003eTan X, Chapman CD, Cedernaes J, Benedict C. Association between long sleep duration and increased risk of obesity and type 2 diabetes: A review of possible mechanisms. Sleep Med Rev. 2018;40:127-34.\u003c/li\u003e\n\u003cli\u003ePfisterer J, Rausch C, Wohlfarth D, Bachert P, Jekauc D, Wunsch K. Effectiveness of Physical-Activity-Based Interventions Targeting Overweight and Obesity among University Students-A Systematic Review. Int J Environ Res Public Health. 2022;19(15).\u003c/li\u003e\n\u003cli\u003eCuevas-Sierra A, Ramos-Lopez O, Riezu-Boj JI, Milagro FI, Martinez JA. Diet, Gut Microbiota, and Obesity: Links with Host Genetics and Epigenetics and Potential Applications. Adv Nutr. 2019;10(suppl_1):S17-s30.\u003c/li\u003e\n\u003cli\u003eCitronberg JS, Curtis KR, White E, Newcomb PA, Newton K, Atkinson C, et al. Association of gut microbial communities with plasma lipopolysaccharide-binding protein (LBP) in premenopausal women. Isme j. 2018;12(7):1631-41.\u003c/li\u003e\n\u003cli\u003ePoursalehi D, Bahrami G, Mirzaei S, Asadi A, Akhlaghi M, Saneei P. Association between alternative healthy eating index (AHEI) with metabolic health status in adolescents with overweight and obesity. BMC Public Health. 2024;24(1):42.\u003c/li\u003e\n\u003cli\u003eFarooqi IS, O\u0026apos;Rahilly S. Mutations in ligands and receptors of the leptin-melanocortin pathway that lead to obesity. Nat Clin Pract Endocrinol Metab. 2008;4(10):569-77.\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"Inflammatory Index, Healthy Eating Index, Obesity, Overweight","lastPublishedDoi":"10.21203/rs.3.rs-7049033/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-7049033/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eBackground\u003c/h2\u003e\u003cp\u003eObesity and overweight, driven by complex interactions between socioeconomic, behavioral, and dietary factors, remain critical global health challenges. This study employs structural equation modeling to evaluate direct and indirect effects of these factors, mediated by dietary indices(Dietary Inflammatory Index and Healthy Eating Index), on obesity/overweight in western Iran.\u003c/p\u003e\u003ch2\u003eMethods\u003c/h2\u003e\u003cp\u003eA cross-sectional analysis of 3,169 adults from the Dehgolan Prospective Cohort Study(DehPCS) in western Iran was conducted. Data included anthropometric measures, socioeconomic status, lifestyle factors, chronic diseases, and dietary indices(HEI and DII). Structural equation modeling evaluated pathways linking latent variables and mediators to obesity/overweight. Model fit was assessed using CFI, RMSEA, and χ\u0026sup2; statistics.\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e\u003cp\u003eThe model explained 69% of obesity/overweight variance. Socioeconomic status directly reduced obesity risk (β=\u0026minus;0.21,p\u0026thinsp;\u0026lt;\u0026thinsp;0.001), while chronic diseases increased it (β\u0026thinsp;=\u0026thinsp;0.42,p\u0026thinsp;=\u0026thinsp;0.002).Personal habits had a strong inverse association (β=\u0026minus;1.85,p\u0026thinsp;\u0026lt;\u0026thinsp;0.001). DII elevated obesity likelihood (β\u0026thinsp;=\u0026thinsp;0.65, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001), whereas HEI-improved diets reduced it (β=\u0026minus;1.14,p\u0026thinsp;\u0026lt;\u0026thinsp;0.001). Socioeconomic status indirectly worsened outcomes via unhealthy dietary habits (DII: β\u0026thinsp;=\u0026thinsp;0.01; HEI: β=\u0026minus;0.87), while chronic diseases indirectly lowered risk through improved HEI(β\u0026thinsp;=\u0026thinsp;0.11,p\u0026thinsp;=\u0026thinsp;0.03).\u003c/p\u003e\u003ch2\u003eConclusion\u003c/h2\u003e\u003cp\u003eObesity is mediated by dietary quality and inflammation, with socioeconomic disparities and chronic conditions amplifying risk. Public health strategies must prioritize anti-inflammatory diets and equitable access to nutritious foods to disrupt these pathways.\u003c/p\u003e","manuscriptTitle":"Dietary and Lifestyle Predictors of Obesity: A Structural Equation Model Approach","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-07-29 09:25:58","doi":"10.21203/rs.3.rs-7049033/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"96023b7e-d836-4952-8201-6712eea8272f","owner":[],"postedDate":"July 29th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2025-11-27T14:24:43+00:00","versionOfRecord":[],"versionCreatedAt":"2025-07-29 09:25:58","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-7049033","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-7049033","identity":"rs-7049033","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}