Association Between Overweight/Obesity and Somatization Symptoms: A Large-Scale Cross-Sectional Study Among Health Examination Participants

Association Between Overweight/Obesity and Somatization Symptoms: A Large-Scale Cross-Sectional Study Among Health Examination Participants | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Association Between Overweight/Obesity and Somatization Symptoms: A Large-Scale Cross-Sectional Study Among Health Examination Participants YING CHE, Yaozong Wu, Jiayu Gao, Honghai He, Liyuan Tao, Ying Liang, and 1 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-9395618/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 According to World Health Organization data, the global obesity rate has nearly tripled since 1975, becoming a major public health concern. Obesity is closely associated not only with cardiovascular diseases and diabetes but also with mental health disorders such as depression and anxiety, though the underlying mechanisms remain incompletely understood. This study aimed to retrospectively investigate the relationship between obesity and mental health, as well as potential physiological mechanisms, within a health examination population. Methods We conducted a retrospective cross-sectional analysis using data from individuals who underwent routine health check-ups at a tertiary hospital in Beijing. Archived questionnaire data, including the Symptom Checklist-90 (SCL-90) and the Stress Self-Assessment Questionnaire-53 (SSQ-53), were used to assess mental health status. Archived hematological biomarkers were analyzed to evaluate physiological status. Statistical analyses included the Benjamini-Hochberg procedure for False Discovery Rate (FDR) correction, multiple linear regression, and binary logistic regression to identify factors associated with psychological symptoms. Results The analysis included 11,272 participants (6,041 normal weight, 3,797 overweight, 1,434 obese). The somatization subscale score increased significantly with BMI. The obesity group had a significantly higher positive rate for somatization (factor score ≥ 2) compared to the other groups (P = 0.011). Interestingly, the obese group reported lower levels of psychological and cognitive stress (FDR-adjusted P < 0.001) and more stable mood scores than the normal-weight group. Multivariable analysis confirmed that higher BMI, female gender, older age, and specific biochemical markers reflecting inflammation and metabolic dysregulation (e.g., total bilirubin, absolute lymphocyte count, all P < 0.001) were independently associated with more severe somatization symptoms. Conclusion In this retrospective health examination cohort, higher BMI was independently associated with increased somatization symptoms, linked to inflammatory and metabolic markers. Contrary to common assumption, the obese subgroup exhibited lower perceived stress and anxiety levels, which suggests a complex relationship between obesity and mental health. Further longitudinal research is needed to clarify causality. Obesity Mental Health Psychiatric Symptoms Influencing Factors Background According to data from the World Health Organization (WHO), the global obesity rate has nearly tripled since 1975 [ 1 ] . WHO defines overweight as a body mass index (BMI) ≥ 25 and obesity as a BMI ≥ 30 [ 2 ] . By 2008, approximately 34% of the U.S. population was classified as obese [ 3 ] . Overweight and obesity refer to abnormal or excessive fat accumulation, posing serious threats to both physical and mental health. This issue is not confined to adults, as children and adolescents also face significant health risks, and the various adverse physical and psychological consequences associated with obesity warrant heightened attention [ 4 ] . Obesity is closely linked to numerous physical diseases. A German study indicated that extremely obese adolescents have a higher incidence of physical health issues. Overweight children and adolescents not only exhibit more sleep problems but also report headaches, back pain, and functional gastrointestinal disorders more frequently [ 5 ] . Furthermore, research confirms associations between obesity and cardiovascular diseases, diabetes, hypertension, dyslipidemia, and cancer [ 6 ] . In the United States, substantial evidence suggests that the diabetes epidemic is closely tied to the rising rates of overweight and obesity [ 7 ] . As the primary risk factor for type 2 diabetes, abdominal obesity holds a central position within the concept of metabolic syndrome [ 8 ] . Additionally, Nicholson's research found a link between emotional stress and obesity [ 9 ] , highlighting a significant intrinsic connection between obesity and mental health. Consequently, research on obesity holds substantial scientific importance, as it can not only fill gaps in the field but also provide crucial guidance for other disciplines and clinical practice. Recent studies suggest that obesity may trigger a range of diseases by mediating inflammatory responses. It can act as a chronic systemic stressor, inducing inflammation that increases the risk of cardiovascular diseases and leads to elevated baseline levels of hormones, immune cells, and cytokines [ 10 ] . Chronic low-grade inflammation is a hallmark of obesity. White adipose tissue, infiltrated by immune cells like macrophages, produces pro-inflammatory cytokines [ 11 ] . Concurrently, hypertrophic adipose tissue, suffering from hypoperfusion, leads to hypoxia, dysfunction, and inflammation. Hypertrophic adipocytes are also more prone to rupture, triggering inflammatory cascades that affect the expression of various pro-inflammatory factors such as interleukins and tumor necrosis factor-alpha (TNF-α) [ 12 ] . These factors, upon reaching the brain, can exert effects through multiple pathways. For instance, they may regulate serotonin via indoleamine 2,3-dioxygenase and mitogen-activated protein kinases (MAPK), or inhibit glucocorticoid receptors by affecting the hypothalamic-pituitary-adrenal (HPA) axis [ 13 ] . They can also alter neurotrophic factors and modulate neuroplasticity [ 14 ] , thereby profoundly impacting neurotransmitter function, neuroendocrine activity, neuroplasticity, and brain circuits. This interference with brain signaling and emotional responses can ultimately lead to abnormal mental states in patients. The association between obesity and mental illness is complex, involving multifaceted mechanisms [ 2 ] . First, depression and obesity exhibit a bidirectional relationship, each potentially causing the other. Research has found that key brain regions regulating body weight and energy homeostasis overlap with those involved in mood regulation [ 15 ] . Furthermore, studies have identified over 50 reliable genetic loci associated with depression phenotypes. Some of these loci, with stronger signals, overlap with or are adjacent to specific sequences linked to high BMI and severe early-onset obesity, such as neuronal growth regulator 1 (NEGR1), olfactomedin 4 (OLFM4), and kinase suppressor of ras 2 (KSR2) [ 16 ],[ 17 ],[ 18 ] . For example, NEGR1 regulates synaptic plasticity in brain regions critical for emotion and appetite regulation, like the cortex, hippocampus, and hypothalamus, suggesting shared pathogenic mechanisms for depression and obesity. The high BMI in obese patients is often associated with fatigue and lack of energy, symptoms explainable by systemic low-grade inflammation. Patients also exhibit prominent psychological symptoms like "low mood" and "loss of interest," which are core features of major depressive disorder [ 19 ] . Substantial evidence confirms that immune-mediated inflammation can induce depression via cytokines and inflammatory factors through multiple pathways [ 14 ] . The comorbidity rate of mental disorders like depression is significantly higher in patients with inflammation-related chronic diseases such as rheumatoid arthritis, cancer, infectious diseases, autoimmune disorders, and cardiovascular diseases [ 20 ] . Gut microbiota plays a key role in the link between obesity and mental illness, with its connection to mental health, particularly depression, receiving increasing research attention. Studies show that the gut microbiota of obese individuals on high-fat diets undergoes alterations, subsequently contributing to systemic inflammation and dysregulation of mood [ 21 ] . Gut bacteria can mediate various gut-derived signals through interactions involving the vagus nerve, immune cells, and neurotransmitter signaling. This can lead to imbalances in active neurotransmitters like dopamine, serotonin, glutamate, and gamma-aminobutyric acid (GABA), as well as in tryptophan metabolites and short-chain fatty acids (SCFAs), thereby affecting signal transmission to the brain and ultimately disrupting emotional regulation [ 22 ] . This process also increases the risk of systemic inflammation and compromises intestinal barrier function, further exacerbating depressive symptoms [ 23 ] . Other research indicates that excessive intake of dietary saturated fatty acids and dyslipidemia caused by adipose tissue dysfunction can upregulate the expression of pattern recognition receptors (e.g., Toll-like receptors, TLRs) for pro-inflammatory cytokines like interleukin-6 (IL-6) and TNF-α in the peripheral circulation [ 24 ] . While the important association between obesity and mental illness is established, numerous underlying mechanisms remain to be elucidated [ 2 ] . Research confirms links between obesity and various psychiatric disorders, including bipolar disorder, personality disorders, attention-deficit/hyperactivity disorder, binge eating disorder, post-traumatic stress disorder, and schizophrenia [ 25 ] . One study showed that at least 42% of obese patients have at least one comorbid mental disorder [ 26 ] . Neglecting the issue of obesity will lead to persistent adverse effects on both physical and mental health, underscoring the practical necessity of research on obesity and mental health. In summary, while the adverse effects of obesity have garnered widespread attention in the medical field in recent years, its impact on human mental health remains an under-researched area. The interactive relationship and pathogenic mechanisms between the two are not yet fully understood. Obesity-related research in Asian countries has largely focused on physical impacts, with insufficient attention to psychological aspects. Mental disorders hold significant importance for the prognosis and treatment of obese patients, yet evidence regarding the prevalence of mental disorders in populations undergoing obesity treatment remains relatively scarce [ 26 ] . This study aims to investigate the mental health status of obese populations and analyze the underlying influencing factors, with the goal of advancing clinical and scientific development in this field and ultimately providing support for optimizing clinical guidance and improving patient prognosis. This study is expected to further reveal the intrinsic connection between obesity and mental health and elucidate the related biological mechanisms. Methods Research participants Participants were primarily recruited from individuals who underwent health examinations at the Health Management (Physical Examination) Center of Peking University Third Hospital between 2018 and 2023. Inclusion criteria were: (1) age ≥ 18 years; (2) completion of body composition testing; and (3) completion of psychological assessment. Exclusion criteria were: (1) diagnosis of any of the following conditions: heart disease, hyperlipidemia, epilepsy, stroke, immune system disorders, metabolism-related diseases, central nervous system diseases, or any other chronic physical illness; (2) use of medications that may affect metabolism, such as immunosuppressants, hormones, special supplements, antipsychotics, etc.; (3) meeting the diagnostic criteria for any mental disorder listed in the International Classification of Diseases, Tenth Revision (ICD-10); (4) participation in physical fitness training; (5) an irregular lifestyle in the previous three months, including excessive alcohol consumption, binge drinking, overeating, etc.; and (6) pregnancy. This study was conducted in accordance with the Declaration of Helsinki. The study protocol was reviewed and approved by the Institutional Ethics Committee of Peking University Third Hospital (Project No.IRB00006761-M20250131 ). This study is a retrospective cross-sectional study and was exempt from informed consent. Methords (1) General participant information, including age, gender and disease duration, was collected at the time of the physical examination. (2) Mental Health Assessment: The Symptom Checklist-90 (SCL-90) was used to assess participants' psychosomatic status. This scale consists of 90 items divided into 10 factors: somatization, obsessive-compulsive symptoms, interpersonal sensitivity, depression, anxiety, phobic anxiety, hostility, paranoid ideation, psychoticism, and additional items (primarily concerning sleep and eating conditions). A 5-point Likert scale was used, with higher scores indicating poorer health status. A participant's psychological assessment result was considered positive if the total score was ≥ 160 or if any single factor score was ≥ 2. Participants with SCL-90 total scores higher than the average score of the Chinese population norms were classified as having abnormal psychological status. The Stress Self-Assessment Questionnaire-53 (SSQ-53) was used to evaluate the degree of psychological stress. This questionnaire contains 53 items assessing the stress level an individual experienced in the last month, divided into five dimensions: overall stress, physiology, emotion, cognition, and behavior. The scoring is categorized into grades: 0–0.08 as grade 1, 0.09–0.19 as grade 2, 0.2–0.28 as grade 3, 0.29–0.42 as grade 4, 0.43–0.62 as grade 5, 0.63–0.91 as grade 6, 0.92–1.3 as grade 7, 1.31–1.72 as grade 8, 1.73–4 as grade 9, and a score of 1.73–4 points combined with a score of ≥ 2 on question 38 or ≥ 3 on questions 36 or 51 as grade 10. A grade of 7 or above was considered indicative of a stressed state. (3) Hematological Tests: Fasting antecubital venous blood was collected from participants. The samples were centrifuged at 3000 r/min for 15 minutes at 4°C. Complete blood count analysis was performed using the SYSMEX XN-2000 fully automated hematology analyzer. Blood biochemical parameters, including blood glucose, blood lipids, liver function, and kidney function, were analyzed using the Beckman Coulter AU5800 fully automatic biochemical analyzer. Thyroid-related markers, including thyroxine, triiodothyronine, free triiodothyronine, and thyroid-stimulating hormone, were measured using the Siemens Atellica analyzer. (4) Statistical Analysis: All data were analyzed using SPSS 26.0 or R 4.5.1 software. Normally distributed continuous data are presented as mean ± standard deviation (x̄ ± s), and linear trend comparisons among groups were performed using linear trend tests in one-way ANOVA.. Non-normally distributed continuous data are presented as median (first quartile, third quartile) [M (Q1, Q3)], and linear trend comparisons among groups were performed using the Jonckheere-Terpstra J test.. Categorical data are presented as number (percentage) [n (%)], and linear trend comparisons among groups were performed using the Cochran-Armitage trend test.. False Discovery Rate (FDR) correction was applied using the Benjamini–Hochberg procedure implemented in R software (version 4.5.1). Generalized linear models (Gamma distribution) were used when somatization was treated as a continuous variable, and binary logistic regression analysis was used when somatization was treated as a dichotomous variable. All variables with P 10 (indicating severe collinearity) were manually removed until all remaining variables had VIF < 10. Model 1 included only BMI groups (set as dummy variables and treated as an ordinal categorical variable). Model 2 adjusted for age, gender, marital status, and other demographic indicators based on Model 1. Model 3 further adjusted for white blood cells, red blood cells, hemoglobin, platelets, systolic blood pressure, diastolic blood pressure, fasting blood glucose, triglycerides, low-density lipoprotein cholesterol, high-density lipoprotein cholesterol, alpha-fetoprotein, carcinoembryonic antigen, uric acid, urea, creatinine, alanine aminotransferase, aspartate aminotransferase, total bilirubin, direct bilirubin, absolute lymphocyte count, and absolute eosinophil count based on Model 2. The statistical significance level was set at p < 0.05. For multiple comparison corrections, the significance threshold was set at an FDR-adjusted p-value < 0.10. Results Analysis of body composition indicators between the three groups This study included a total of 11,272 participants for comparative analysis. Among them, 6,041 participants (53.6%) were in the normal weight group (BMI < 24 kg/m²), with a mean age of 36.5 ± 10.0 years and a male-to-female ratio of 1:1.99. The overweight group (24 ≤ BMI < 28) comprised 3,797 participants (33.7%), with a mean age of 41.0 ± 10.7 years and a male-to-female ratio of 1:2.33. The obesity group (BMI ≥ 28 kg/m²) consisted of 1,434 participants (12.7%), with a mean age of 40.4 ± 10.8 years and a male-to-female ratio of 1:3.05. Details are presented in Table 1 . Table 1 Results of SCL-90 in three groups Variable BMI < 24 Group (n = 6041) 24 ≤ BMI < 28 Group (n = 3797) BMI ≥ 28 Group (n = 1434) Z/χ² value P for trend FDR-adjusted P-value Somatization 1.17(1.08,1.50) 1.17(1.08,1.50) 1.25(1.08,1.50) 3.127 0.002 0.005 Obsessive-compulsive 1.40(1.20,1.80) 1.40(1.20,1.80) 1.40(1.20,1.80) -0.090 0.928 0.928 Interpersonal sensitivity 1.22(1.11,1.56) 1.22(1.11,1.67) 1.33(1.11,1.67) 1.391 0.164 0.205 Depression 1.20(1.07,1.53) 1.20(1.07,1.53) 1.20(1.07,1.53) -0.820 0.412 0.458 Anxiety 1.30(1.10,1.50) 1.20(1.10,1.50) 1.20(1.10,1.50) -3.565 < 0.001 0.003 Hostility 1.17(1.00,1.50) 1.17(1.00,1.50) 1.17(1.00,1.67) 2.237 0.025 0.042 Phobic anxiety 1.00(1.00,1.14) 1.00(1.00,1.14) 1.00(1.00,1.14) -2.936 0.003 0.006 Paranoid ideation 1.17(1.00,1.33) 1.17(1.00,1.33) 1.17(1.00,1.50) 4.247 < 0.001 0.003 Psychoticism 1.10(1.00,1.40) 1.10(1.00,1.40) 1.10(1.00,1.40) 1.755 0.079 0.113 Additional items (Sleep/Eating) 1.40(1.00,1.80) 1.40(1.20,1.80) 1.40(1.20,2.00) 9.059 < 0.001 0.003 Total score ​ 113.0(100.0,136.0) 113.0(100.0,137.0) 113.0(101.0,139.0) 1.208 0.227 Average score ​ 1.26(1.11,1.51) 1.26(1.11,1.52) 1.26(1.12,1.54) 1.208 0.227 Somatization (≥ 2), n(%) ​ 479(7.9%) 330(8.7%) 143(10.0%) 6.519 0.011 0.028 Hostility (≥ 2), n(%) ​ 650(10.8%) 481(12.7%) 213(14.9%) 21.476 < 0.001 0.003 Paranoid ideation (≥ 2), n(%) 410(6.8%) 318(8.4%) 133(9.3%) 14.219 < 0.001 0.003 Add. items (≥ 2), n(%) ​ 1137(18.8%) 867(22.8%) 363(25.3%) 40.324 < 0.001 0.003 Mental health abnormality (Mild/Moderate/Severe), n(%) ​ 1984(32.8%) 1315(34.6%) 556(38.8%) 17.307 < 0.001 Analysis of SCL-90 data between the three groups The SCL-90 total score for the BMI < 24 group was 113.0 (100.0, 136.0), with a total average score of 1.26 (1.11, 1.51). The SCL-90 total score for the BMI 24 ~ 28 group was 113.0 (100.0, 137.0), with a total average score of 1.26 (1.11, 1.52). The SCL-90 total score for the BMI ≥ 28 group was 113.0 (101.0, 139.0), with a total average score of 1.26 (1.12, 1.54). There was no statistically significant difference in the total and average scores among the three groups (P > 0.05) (Table 2 ). Table 2 Results of SSQ-53 in three groups Variable BMI < 24 Group (n = 5156) 24 ≤ BMI < 28 Group (n = 3233) BMI ≥ 28 Group (n = 1207) Z/χ² value P for trend FDR-adjusted P-value Overall stress 0.23(0.09,0.49) 0.21(0.08,0.47) 0.23(0.08,0.47) -3.177 0.001 0.002 Physiological 0.30(0.10,0.55) 0.25(0.10,0.50) 0.25(0.10,0.55) -3.438 0.001 0.002 Emotional 0.17(0.00,0.50) 0.11(0.00,0.44) 0.11(0.00,0.44) -4.726 < 0.001 0.002 Cognitive 0.25(0.00,0.75) 0.25(0.00,0.75) 0.25(0.00,0.75) -0.602 0.547 0.547 Behavioral 0.18(0.00,0.36) 0.18(0.00,0.36) 0.18(0.00,0.45) 1.786 0.074 0.093 High behavioral stress level (7–10), n(%) 294(5.7%) 203(6.3%) 91(7.5%) 5.574 0.018 0.018 Note: Stress scores are presented as M(Q1, Q3); a stress grade of 7-10 is defined as the high-stress level. Based on the total scores, participants were categorized into a mentally healthy/sub-healthy group and a mentally unhealthy group (mild, moderate, and severe). In the BMI < 24 group, there were 4,057 cases (67.2%) in the mentally healthy/sub-healthy group and 1,984 cases (32.8%) in the mentally unhealthy group. In the BMI 24 ~ 28 group, there were 2,482 cases (65.4%) in the mentally healthy/sub-healthy group and 1,315 cases (34.6%) in the mentally unhealthy group. In the BMI ≥ 28 group, there were 878 cases (61.2%) in the mentally healthy/sub-healthy group and 556 cases (38.8%) in the mentally unhealthy group. Statistical analysis revealed a significant difference in the distribution of mental health categories among the three groups (P < 0.001) (Table 1 ). Among the ten factors of the SCL-90, the scores for somatization, hostility, paranoid ideation, and the additional items (primarily sleep and eating conditions) in the BMI ≥ 28 group were significantly higher than those in the BMI < 24 group (P < 0.05). Compared to the BMI < 24 group, the BMI ≥ 28 group had a significantly higher proportion of participants with positive somatization factor scores (factor score ≥ 2), with 143 individuals (10.0%) (P = 0.011). Similarly, the proportions for positive hostility factor scores (213 individuals, 14.9%; P < 0.001), positive paranoid ideation factor scores (133 individuals, 9.3%; P < 0.001), and positive additional items factor scores (363 individuals, 25.3%; P < 0.001) were all significantly higher in the BMI ≥ 28 group (Table 1 ). Analysis of psychological stress between the three groups Results of the Stress Self-Assessment Questionnaire-53 (SSQ-53):​ The scores for overall stress, physiological stress, and emotional stress in the BMI 24 ~ 28 group (i.e., the overweight group) were all significantly lower than those in the BMI < 24 group (normal weight group) (P ≤ 0.001). Among these, there was no statistically significant difference in overall stress and physiological stress scores between the BMI ≥ 28 group and the BMI < 24 group, while the emotional stress score in the BMI ≥ 28 group was also significantly lower than that in the BMI < 24 group (P < 0.001). Regarding behavioral stress, the proportion of individuals with a behavioral stress level of 7–10 (high stress) differed among the three groups, with the BMI ≥ 28 group (7.5%) showing a higher proportion than the 24 ≤ BMI < 28 group (6.3%) and the BMI < 24 group (5.7%) (P = 0.018) (Table 2 ). In summary, this study indicates that as body mass index (BMI) increases, an individual's overall mental health level (as measured by SCL-90 total score) showed no significant change. However, more pronounced manifestations were observed in specific psychological symptom dimensions such as somatization, hostility, paranoid ideation, and sleep/eating problems. Furthermore, the proportion of mentally unhealthy individuals (mild, moderate, and severe) tends to increase. On the other hand, regarding subjective stress perception, both overweight (24 ≤ BMI < 28) and obese (BMI ≥ 28) individuals reported lower levels of subjective and emotional stress. Nevertheless, the obese group demonstrated a higher tendency for behavioral stress reactions. Analysis of the individuals' hematological test results between the two groups Analysis of stress-related hematological indicators among the three groups revealed that participants in the BMI ≥ 28 group had significantly higher levels of fasting blood glucose (GLU), serum total cholesterol (TC), triglycerides (TG), low-density lipoprotein cholesterol (LDL-C), uric acid (UA), alanine aminotransferase (ALT), and gamma-glutamyl transferase (GGT) compared to both the BMI < 24 group and the BMI 24 ~ 28 group (all FDR-adjusted P-values < 0.001). Furthermore, the white blood cell count (WBC) in this group was also significantly higher than in the BMI < 24 group (P = 0.035). The level of high-density lipoprotein cholesterol (HDL-C) was significantly higher in the BMI < 24 group compared to the other two groups (P 0.05). These results indicate that obese participants in the BMI ≥ 28 group exhibit a comprehensive profile of physiological abnormalities characterized by glucose and lipid metabolism disorders, increased hepatic and renal functional load, and a potential state of low-grade inflammation (Table 3 ). Table 3 Comparison of Other Physical Examination Indicators Among Participants Stratified by BMI Groups Variable BMI < 24 Group (n = 6041) 24 ≤ BMI < 28 Group (n = 3797) BMI ≥ 28 Group (n = 1434) Z/F/χ² value P for trend Age (years), x̄±s 36.5 ± 10.0 41.0 ± 10.7 40.4 ± 10.8 167.123 < 0.001 Male, n(%) 2017(33.4%) 2658(70.0%) 1080(75.3%) 1637.727 < 0.001 Married, n(%) 4145(69.4%) 3104(83.6%) 1146(81.6%) 282.189 < 0.001 White blood cell count (×10⁹/L), x̄±s 5.63 ± 1.37 6.13 ± 1.41 6.68 ± 1.62 622.088 < 0.001 High-sensitivity CRP (mg/L), M(Q1,Q3) 0.38(0.20,0.80) 0.80(0.43,1.56) 1.37(0.70,2.66) 22.643 < 0.001 Triglycerides (mmol/L), M(Q1,Q3) 0.91(0.70,1.25) 1.39(0.98,2.01) 1.75(1.25,2.47) 47.833 < 0.001 HDL-C (mmol/L), x̄±s 1.45 ± 0.32 1.21 ± 0.26 1.12 ± 0.23 1514.443 < 0.001 Uric acid (µmol/L), x̄±s 306.0 ± 78.1 369.0 ± 86.9 406.3 ± 89.8 1677.2 < 0.001 Creatinine (µmol/L), x̄±s 75.2 ± 13.0 82.9 ± 14.0 84.2 ± 13.2 523.321 < 0.001 ALT (U/L), M(Q1,Q3) 14(11,20) 22(16,31) 29(20,44) 48.044 < 0.001 Free T3 (pmol/L), x̄±s 3.25 ± 0.52 3.40 ± 0.40 3.45 ± 0.42 177.420 < 0.001 Multiple conditional logistic regression After controlling for relevant confounding factors, a binary logistic regression analysis was conducted using the somatization factor positivity as the dependent variable. Independent variables included age, gender, marital status, BMI group (with the BMI < 24 group as the reference), and the following hematological indicators: systolic blood pressure, diastolic blood pressure, white blood cell count, hemoglobin, triglycerides, high-density lipoprotein cholesterol, low-density lipoprotein cholesterol, fasting blood glucose, creatinine, uric acid, alanine aminotransferase, aspartate aminotransferase, gamma-glutamyl transferase, homocysteine, and thyroid-stimulating hormone. The results showed that female gender was a significant risk factor for somatization symptoms (P < 0.001; OR = 3.280, 95% CI: 2.749–3.914). Increasing age was a significant risk factor for somatization symptoms (P < 0.001; OR = 1.020, 95% CI: 1.013–1.027). Elevated creatinine level was a significant risk factor for somatization symptoms (P < 0.001; OR = 1.020, 95% CI: 1.010–1.031). High-density lipoprotein cholesterol was a protective factor against somatization symptoms (P = 0.001; OR = 0.502, 95% CI: 0.336–0.751). BMI group was an independent factor associated with somatization symptoms. Compared to the normal weight group (BMI < 24), the overweight group (24 ≤ BMI < 28) had a significantly higher risk of somatization symptoms (P = 0.014; OR = 1.243, 95% CI: 1.045–1.479), and the obesity group (BMI ≥ 28) had a significantly higher risk of somatization symptoms (P = 0.002; OR = 1.407, 95% CI: 1.130–1.752) (Table 5 ). Table 5 Binary Logistic Regression Analysis with SCL-90 Somatization Factor Positivity (Score ≥ 2) as the Dependent Variable Model and Variables B Std. Error Wald χ² P-value OR 95% CI Model 1: BMI category only ​ BMI category (Overweight vs. Normal) 0.100 0.075 1.792 0.181 1.105 0.955–1.279 BMI category (Obesity vs. Normal) 0.252 0.100 6.312 0.012 1.286 1.057–1.565 Intercept -2.424 0.044 3076.569 < 0.001 0.089 Model 2: Adjusted for age, sex, marital status ​ BMI category (Overweight vs. Normal) 0.200 0.076 6.895 0.009 1.221 1.052–1.417 BMI category (Obesity vs. Normal) 0.401 0.103 15.223 < 0.001 1.493 1.221–1.825 Age group (Middle-aged vs. Young) 0.543 0.086 40.235 < 0.001 1.721 1.455–2.036 Age group (Old vs. Young) 0.816 0.171 22.804 < 0.001 2.262 1.619–3.161 Sex (Female vs. Male) 0.768 0.076 102.928 < 0.001 2.155 1.858–2.499 Marital status (Married vs. Unmarried) 0.012 0.093 0.016 0.901 1.012 0.844–1.214 Intercept -3.187 0.099 1036.723 < 0.001 0.041 Model 3: Model 2 + comprehensive physiological indicators ​ BMI category (Overweight vs. Normal) 0.217 0.089 5.968 0.015 1.243 1.043–1.480 BMI category (Obesity vs. Normal) 0.341 0.127 7.244 0.007 1.407 1.096–1.805 Age group (Middle-aged vs. Young) 0.562 0.090 38.703 < 0.001 1.754 1.469–2.095 Age group (Old vs. Young) 0.888 0.187 22.669 < 0.001 2.431 1.687–3.504 Sex (Female vs. Male) 0.611 0.130 22.004 < 0.001 1.842 1.427–2.378 Marital status (Married vs. Unmarried) -0.073 0.109 0.443 0.506 0.930 0.751–1.151 Physiological indicators (selected significant variables) White blood cell count (×10⁹/L) 0.072 0.028 6.790 0.009 1.075 1.018–1.135 Total bilirubin (µmol/L) -0.030 0.011 7.415 0.006 0.970 0.950–0.991 Absolute lymphocyte count (×10⁹/L) -0.294 0.091 10.356 0.001 0.745 0.624–0.890 Intercept -2.442 0.701 12.129 0.001 0.087 Notes : 1.Reference Groups:The "normal weight (BMI < 24)" group was used as the reference for BMI categories. The "young" age group was the reference for age categories. "Male" was the reference for gender. "Unmarried" was the reference for marital status. 2.After adjusting for age, gender, marital status, and a series of physiological and biochemical indicators, the overweight group (24 ≤ BMI < 28) had a 1.243-fold higher risk of positive somatization symptoms compared to the normal weight group (OR = 1.243, 95% CI: 1.048–1.487, P = 0.014). The obesity group (BMI ≥ 28) had a 1.407-fold higher risk compared to the normal weight group (OR = 1.407, 95% CI: 1.153–1.905, P = 0.002). Furthermore, elevated white blood cell count and decreased absolute lymphocyte count were identified as independent risk factors for somatization symptoms. 3.CI: Confidence Interval. The Hosmer-Lemeshow test was used to evaluate the goodness-of-fit of the model. The result showed P = 0.195 (P > 0.05), indicating a good model fit with no significant difference between the predicted and the actual observed values. Generalized linear models After controlling for relevant confounding factors, a Generalized linear models(Gamma distribution) analysis was conducted using the somatization factor score as the dependent variable. Independent variables included BMI group, age, gender, marital status, and key hematological indicators selected following collinearity diagnosis. To evaluate the overall goodness-of-fit of the model, the Omnibus test was employed. The results for the final model (including BMI group, demographic indicators, and the adjusted hematological indicators) showed an Omnibus test P-value < 0.001. This indicates that all the independent variables included in the model, as a whole, significantly predict the variation in the dependent variable (somatization score), and the model is statistically significant. For assessing model fit, the Akaike Information Criterion (AIC) and the Bayesian Information Criterion (BIC) were used. The final model had an AIC of 7666.485, a finite-sample corrected AIC (AICc) of 7666.638, a BIC of 7870.088, and a consistent AIC (CAIC) of 7898.088. The values for these criteria are all within an acceptable range and, together with the results of other model tests, support the usability of the model (Table 4 ).After progressively adjusting for confounding factors, overweight and obesity status remained independent predictors of significantly higher somatization symptom scores. Furthermore, female sex, increasing age, and a series of blood indicators reflecting inflammation (e.g., elevated white blood cell count), dyslipidemia (e.g., elevated triglycerides, decreased high-density lipoprotein cholesterol), and hepatic/renal load (e.g., elevated creatinine and total bilirubin) were also independently associated with more severe somatization symptoms. The overall model demonstrated high statistical significance. Table 4 Generalized Linear Model Analysis with SCL-90 Somatization Factor Score as the Dependent Variable Model and Variables B Std. Error Wald χ² P-value Exp(B) 95% CI Model 1: BMI category only ​ (Intercept) 0.207 0.015 190.007 < 0.001 1.230 1.193–1.268 BMI category (Overweight vs. Normal) 0.029 0.009 10.209 0.001 1.029 1.012–1.046 BMI category (Obesity vs. Normal) 0.043 0.013 11.379 0.001 1.044 1.018–1.070 Model 2: Adjusted for age, sex, marital status ​ (Intercept) 0.354 0.022 250.786 < 0.001 1.425 1.364–1.489 BMI category (Overweight vs. Normal) 0.054 0.009 34.016 < 0.001 1.055 1.037–1.074 BMI category (Obesity vs. Normal) 0.078 0.014 31.834 < 0.001 1.081 1.053–1.110 Sex (Female vs. Male) 0.114 0.007 279.865 < 0.001 1.121 1.106–1.136 Age (per 1-year increase) 0.004 0.000 138.331 < 0.001 1.004 1.003–1.004 Marital status (Married vs. Unmarried) 0.003 0.008 0.013 0.909 1.003 0.987–1.019 Model 3: Model 2 + comprehensive physiological indicators ​ (Intercept) 1.108 0.241 21.153 < 0.001 3.028 1.889–4.854 BMI category (Overweight vs. Normal) 0.038 0.010 15.573 < 0.001 1.039 1.019–1.059 BMI category (Obesity vs. Normal) 0.087 0.016 28.747 < 0.001 1.091 1.056–1.126 Sex (Female vs. Male) 0.082 0.010 66.423 < 0.001 1.085 1.065–1.106 Age (per 1-year increase) 0.004 0.000 118.169 < 0.001 1.004 1.003–1.004 Marital status (Married vs. Unmarried) -0.037 0.008 22.276 < 0.001 0.964 0.949–0.979 Physiological indicators (selected significant variables) White blood cell count (×10⁹/L) 0.028 0.010 8.616 0.003 1.028 1.010–1.047 Triglycerides (mmol/L) 0.007 0.002 11.658 0.001 1.007 1.003–1.011 HDL-C (mmol/L) -0.041 0.013 10.354 0.001 0.960 0.936–0.984 Creatinine (µmol/L) 0.002 0.001 7.621 0.006 1.002 1.001–1.004 Total bilirubin (µmol/L) 0.003 0.001 17.526 < 0.001 1.003 1.002–1.004 Absolute lymphocyte count (×10⁹/L) -0.054 0.013 17.038 < 0.001 0.947 0.924–0.971 Absolute eosinophil count (×10⁹/L) -0.065 0.023 7.822 0.005 0.937 0.896–0.980 Notes : 1.In Model 3, in addition to the significant variables listed in the table above, indicators such as red blood cell count, hemoglobin, platelet count, blood pressure, fasting blood glucose, low-density lipoprotein cholesterol, alpha-fetoprotein, carcinoembryonic antigen, uric acid, urea, aminotransferases, and direct bilirubin were also included. However, the P-values for these variables in the final model were all > 0.05. 2.Exp(B), or the exponentiated coefficient, can be interpreted as the risk ratio (RR). It represents the expected fold change in the dependent variable (somatization score) for each one-unit increase in the independent variable (or for a given category compared to the reference category). 3.CI: Confidence Interval. Discussion The complex relationship between mental health disturbances and obesity is well-documented. However, detailed investigations across different BMI categories remain valuable, and research on the association between overweight/obesity and specific mental health issues is of significant importance due to their high prevalence and the potential for confusion with metabolic abnormalities alone. This study provides evidence that overweight and obesity status are closely associated with specific mental health conditions, particularly somatization symptoms, and evaluates the multidimensional physiological profile associated with this status. To investigate the mental health status and influencing factors among populations with different Body Mass Index (BMI), this study recruited a large-scale physical examination cohort and categorized participants into normal weight (BMI < 24, n = 6041), overweight (24 ≤ BMI < 28, n = 3797), and obesity (BMI ≥ 28, n = 1434) groups. The study confirms a significant and independent association between overweight/obesity status and specific mental health problems, especially somatization symptoms. Obesity has been confirmed to negatively impact mental health through various mechanisms, such as promoting abnormal inflammatory responses, inducing metabolic disorders, or causing endocrine dysfunction [ 27 ] .Several studies have attempted to unravel the complex relationship between obesity and somatic symptoms, involving various inflammatory and neuroendocrine mechanisms [ 28 ] . Visceral adipose tissue is closely associated with chronic low-grade inflammation, and inflammatory markers such as C-reactive protein and interleukin-6 may mediate the link between obesity and neuropsychiatric symptoms [ 29 ] .This study provides support for this: obese individuals exhibited significantly elevated white blood cell counts and high-sensitivity C-reactive protein levels, and white blood cell count was an independent predictor of somatic symptoms. This suggests that obesity-related systemic inflammation may contribute to the development of somatic symptoms by affecting neuroimmune regulation, neurotransmitter metabolism, or neuroendocrine function. However, some studies have indicated that depressive symptoms in obese patients with metabolic syndrome are not associated with C-reactive protein [ 30 ] . In this study, white blood cell count (WBC), reflecting participants' inflammatory levels, was significantly higher in the obese group (6.68 ± 1.62) than in the normal-weight group (5.63 ± 1.37, P < 0.001). Multivariate analysis further revealed that white blood cell count was an independent risk factor for somatic symptoms (P = 0.003). This indicates that obesity may be associated with somatic symptoms through mechanisms similar to chronic low-grade inflammation.This study found that the obesity group (BMI ≥ 28) had significantly higher scores on the Somatization, Hostility, Paranoid Ideation, and Additional Items (primarily sleep and diet) factors of the Symptom Checklist-90 (SCL-90). The proportion of participants with positive somatization factor scores (factor score ≥ 2) reached 10.0% in the obesity group, significantly higher than the 7.9% in the normal weight group (P = 0.011). Concurrently, the proportion of individuals with poor mental health (mild, moderate, or severe psychological abnormality) increased significantly with rising BMI. This proportion was 38.8% in the obesity group, higher than the 34.6% in the overweight group and 32.8% in the normal weight group (P < 0.001).Results from the Stress Self-Assessment Questionnaire-53 (SSQ-53) revealed a nuanced stress profile. The overweight and obesity groups reported significantly lower scores for emotional stress compared to the normal weight group. However, the proportion of individuals with high behavioral stress levels (grades 7–10) was significantly higher in the obesity group (7.5%) than in the overweight (6.3%) and normal weight (5.7%) groups (P = 0.018). This discrepancy between subjective emotional experience and behavioral response suggests potential alterations in HPA axis regulation or stress-coping strategies among individuals with obesity [ 31 ] . The study also identified widespread abnormalities in hematological indicators among overweight and obese participants, including significant differences in blood cell counts, blood glucose, blood lipids, liver and kidney function, and thyroid hormones compared to the normal weight group. These abnormalities were characterized by elevated levels of triglycerides, LDL-C, uric acid, alanine aminotransferase, and creatinine, along with reduced levels of HDL-C. Simultaneously, uric acid, as a product of purine metabolism, has been reported to be associated with an increased risk of anxiety and depression [ 32 ],[ 33 ] . This study also found significantly elevated serum uric acid levels in the obese group, suggesting that abnormal purine metabolism may be another potential pathway linking obesity to psychological symptoms.Furthermore, Multivariate analysis confirmed that elevated WBC, elevated triglycerides, reduced HDL-C, and elevated creatinine were independently associated with somatization symptoms. This suggests that obesity status may also influence mental health by mediating lipid metabolism disorders and increasing renal load. Disorders of thyroid hormone levels have been demonstrated to be associated with mood disorders [ 34 ] , cognitive impairment, attention deficits [ 35 ] , and other conditions.The significant elevation of blood uric acid and free triiodothyronine levels in the obesity group points to purine metabolism and thyroid axis dysfunction as other potential pathways linking obesity to psychological symptoms. Multifactorial binary logistic regression analysis, with somatization symptoms as the dependent variable and adjustments for age, gender, marital status, and a series of physiological and biochemical indicators, demonstrated that overweight and obesity status are independent risk factors. Compared to the normal weight group, the overweight group had a 1.243-fold increased risk (OR = 1.243, 95%CI: 1.048–1.487, P = 0.014), and the obesity group had a 1.407-fold increased risk (OR = 1.407, 95%CI: 1.153–1.905, P = 0.002) of positive somatization symptoms. Notably, the regression model confirmed that female gender, increasing age, and elevated creatinine were significant risk factors, while higher HDL-C was a protective factor against somatization symptoms. Importantly, the association between BMI group and somatization risk remained significant even after adjusting for these and other physiological variables, indicating that overweight and obesity confer an independent risk beyond measurable inflammatory and metabolic disturbances. Obesity has been confirmed to negatively impact mental health through various mechanisms, such as promoting abnormal inflammatory responses, inducing metabolic disorders, or causing endocrine dysfunction. The elevated WBC and its identification as an independent predictor of somatization symptoms in this study support the role of obesity-related systemic low-grade inflammation, which may participate in the formation of somatization symptoms by affecting neuroimmune regulation, neurotransmitter metabolism, or neuroendocrine function. The observed "low emotional stress perception - high behavioral stress reaction" dissociation aligns with descriptions of potential HPA axis dysregulation or altered stress coping in individuals with obesity. The comprehensive profile of physiological abnormalities—characterized by metabolic, inflammatory, and endocrine dysregulation—observed in overweight and obese participants suggests this state may be closely linked to the development and progression of psychiatric symptoms. The persistence of overweight and obesity as independent risk factors for somatization symptoms after adjusting for multiple physiological indicators implies the involvement of other, not yet fully elucidated mechanisms, such as adipokine secretion abnormalities or gut microbiota-brain axis disturbances [ 36 ] . The correlation between changes in absolute lymphocyte count and somatization symptoms also hints that the balance of immune cell subsets may play a role in neuroimmune regulation, offering a new direction for future mechanistic research. As a cross-sectional study, it cannot establish causality between overweight/obesity and psychiatric symptoms. Further research is needed to clarify the causal pathways, which could aid in improving early psychosomatic intervention and secondary prevention for this population. While the cross-sectional design limits causal inference, our findings provide a critical foundation for future longitudinal studies. Subsequent research should employ longitudinal designs combined with multi-omics technologies to clarify the causal pathways between physiological indicators and psychological symptoms, explore individual differences, and investigate the moderating effects of social-behavioral factors (such as diet, exercise, and social support) to advance the development of personalized psychosomatic medicine intervention strategies. The easily detectable laboratory indicators associated with somatization symptoms in this study (e.g., WBC, triglycerides, HDL-C, creatinine) show promise as potential biomarkers for identifying individuals at increased psychological risk, facilitating early identification and proactive clinical intervention.The findings of this study indicate that overweight and obesity status are significantly associated with more pronounced specific psychiatric symptoms, particularly somatization, hostility, and sleep/eating disturbances. Concurrently, individuals with overweight or obesity reported lower levels of subjective emotional stress but exhibited a higher tendency towards behavioral stress reactions. Multivariate analyses, after adjusting for age, gender, and a range of hematological indicators, further revealed that not only are overweight and obesity independent risk factors for somatization symptoms, but these symptoms are also broadly associated with female gender, increasing age, and abnormalities in physiological markers reflecting metabolic disorders, hepatic/renal functional load, and alterations in immune-inflammatory status, such as triglycerides, creatinine, white blood cell count, and lymphocyte parameters. Abbreviations SCL-90,Symptom Checklist-90 SSQ-53, Stress Self-assessment Questionnaire-53 BMI,Body Mass Index Declarations This study was conducted in strict accordance with the principles of the Declaration of Helsinki. The study protocol involving human participants was reviewed and approved by the Institutional Ethics Committee of Peking University Third Hospital (Project No.: IRB00006761-M20250131). Ethical approval This study strictly adhered to the principles outlined in the Declaration of Helsinki. All research protocols involving human participants were reviewed and approved by the Institutional Ethics Committee of Peking University Third Hospital (Approval No.: IRB00006761-M20250131). Consent to participate All participants, after being fully informed of the study's purpose and methods, signed an informed consent form and agreed to the use of their current health survey data, relevant physical examination results, and hematological test results for this study. Consent for publication The raw data supporting the conclusions of this article will be made available by the authors, without undue reservation. Competing interests The authors declare that they have no known competing financial interests or personal relationships that could have influenced the work reported in this paper. Availability of data and materials The data that support the findings of this study are available from Peking University Third Hospital but restrictions apply to the availability of those data, which were used under license for the current study, and so are not publicly available. Data are however available from the corresponding authors upon reasonable request and with permission of Peking University Third Hospital. Funding This work was supported by grants funded by the Peking University Third Hospital Nursing Seed Fund (BYSYHL2023009). This work was supported by the National Science and Technology Innovation 2030, Noncommunicable Chronic Diseases-National Science and Technology Major Project (Grant No. 2024ZD0524300, 2024ZD0524301) Authors' contributions Ying Che collected the relevant data, designed the study, analyzed the data, and wrote the manuscript. Yaozong Wu wrote the manuscript. Jiayu Gao edited the manuscript. Honghai He and Liyuan Tao analyzed the data. Ying Liang designed the study, wrote the manuscript. Peng Wang provided financial support. The author(s) read and approved the final manuscript. Acknowledgements We would like to thank the support of Peking University Third Hospital. Clinical trial number Not applicable. References World Health Organization. Obesity and overweight [Internet]. World Health Organization. 2022. https://www.who.int/news-room/fact-sheets/detail/obesity-and-overweight . Accessed 16 Feb 2022. Avila C, Holloway AC, Hahn MK, Morrison KM, Restivo M, Anglin R, Taylor VH. 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PMID: 37855318; PMCID: PMC11031221. Chaker L, Papaleontiou M, Hypothyroidism A, Review JAMA. 2025 Sep 3. 10.1001/jama.2025.13559 . Epub ahead of print. Erratum in: JAMA. 2025;334(13):1203. doi: 10.1001/jama.2025.17740. Erratum in: JAMA. 2025;334(15):1397. doi: 10.1001/jama.2025.18724. PMID: 40900603. Verma A, Inslicht SS, Bhargava A, Gut-Brain, Axis. Role of Microbiome, Metabolomics, Hormones, and Stress in Mental Health Disorders. Cells. 2024;13(17):1436. 10.3390/cells13171436 . PMID: 39273008; PMCID: PMC11394554. 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-9395618","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":634146879,"identity":"9f2bdf00-b315-44ab-9c16-ba382f13438f","order_by":0,"name":"YING CHE","email":"","orcid":"","institution":"Peking University Third Hospital","correspondingAuthor":false,"prefix":"","firstName":"YING","middleName":"","lastName":"CHE","suffix":""},{"id":634146880,"identity":"4ab41192-4c1a-462a-a754-d423d8c0ea3f","order_by":1,"name":"Yaozong Wu","email":"","orcid":"","institution":"Peking University Sixth Hospital","correspondingAuthor":false,"prefix":"","firstName":"Yaozong","middleName":"","lastName":"Wu","suffix":""},{"id":634146881,"identity":"e64806c5-d3bc-4151-9f37-acc2f9f0bb48","order_by":2,"name":"Jiayu Gao","email":"","orcid":"","institution":"Henan University of Science and Technology","correspondingAuthor":false,"prefix":"","firstName":"Jiayu","middleName":"","lastName":"Gao","suffix":""},{"id":634146882,"identity":"8ae5dbb6-bebc-4f62-a9e1-d491ffe54aa0","order_by":3,"name":"Honghai He","email":"","orcid":"","institution":"Peking University Third Hospital","correspondingAuthor":false,"prefix":"","firstName":"Honghai","middleName":"","lastName":"He","suffix":""},{"id":634146883,"identity":"284afd53-ac8e-438e-9010-1fc6c96f6abc","order_by":4,"name":"Liyuan Tao","email":"","orcid":"","institution":"Peking University Third Hospital","correspondingAuthor":false,"prefix":"","firstName":"Liyuan","middleName":"","lastName":"Tao","suffix":""},{"id":634146884,"identity":"7cb6c65c-0316-484f-8a1d-939dc330c88f","order_by":5,"name":"Ying Liang","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAABEElEQVRIiWNgGAWjYBACPmYwJcHAwMx88EFCxQEInwePFja4Fna2ZIMPZ4jRAmfx85hJzmw7wEBYCzvzs4df2yzy5J15jI15591h152RwPjgbRuDvDlOh7GZG8u2SRQbHmYrfMy77Rmz2Y0EZsO5bQyGOxtw+sVMWrJNInFjM/NmY95th0Fa2KR52xgSDA7g0sL+DaoFqJd3DlgL+2/8WoC+/gjUMp+ZBej9BogtzAS0lEkznJNI3MAMCuRjQL+cedgsOeechOEGHFr4+Y9vk/xRVpc4v/8wMCpr7iSbHU8++OFNmY08LltAgJkXGDswBckMDIwNDODIxQMYf/xhYJBvgHDs8CodBaNgFIyCEQkACmpXXjNwyjgAAAAASUVORK5CYII=","orcid":"","institution":"Peking University Sixth Hospital","correspondingAuthor":true,"prefix":"","firstName":"Ying","middleName":"","lastName":"Liang","suffix":""},{"id":634146885,"identity":"3fdc2c9b-ec63-4271-b5c5-eb915f69c557","order_by":6,"name":"Peng Wang","email":"","orcid":"","institution":"Peking University Third Hospital","correspondingAuthor":false,"prefix":"","firstName":"Peng","middleName":"","lastName":"Wang","suffix":""}],"badges":[],"createdAt":"2026-04-12 16:08:55","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-9395618/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-9395618/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":108940418,"identity":"7df5fe4d-6953-4d96-bc6b-a988f1fa4c98","added_by":"auto","created_at":"2026-05-11 05:12:42","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":632800,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-9395618/v1/b3cc4445-d0f1-4556-82ab-7f825ce33c9f.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Association Between Overweight/Obesity and Somatization Symptoms: A Large-Scale Cross-Sectional Study Among Health Examination Participants","fulltext":[{"header":"Background","content":"\u003cp\u003eAccording to data from the World Health Organization (WHO), the global obesity rate has nearly tripled since 1975\u003csup\u003e[\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e]\u003c/sup\u003e. WHO defines overweight as a body mass index (BMI)\u0026thinsp;\u0026ge;\u0026thinsp;25 and obesity as a BMI\u0026thinsp;\u0026ge;\u0026thinsp;30\u003csup\u003e[\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e]\u003c/sup\u003e. By 2008, approximately 34% of the U.S. population was classified as obese\u003csup\u003e[\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e]\u003c/sup\u003e. Overweight and obesity refer to abnormal or excessive fat accumulation, posing serious threats to both physical and mental health. This issue is not confined to adults, as children and adolescents also face significant health risks, and the various adverse physical and psychological consequences associated with obesity warrant heightened attention\u003csup\u003e[\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e]\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eObesity is closely linked to numerous physical diseases. A German study indicated that extremely obese adolescents have a higher incidence of physical health issues. Overweight children and adolescents not only exhibit more sleep problems but also report headaches, back pain, and functional gastrointestinal disorders more frequently\u003csup\u003e[\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e]\u003c/sup\u003e. Furthermore, research confirms associations between obesity and cardiovascular diseases, diabetes, hypertension, dyslipidemia, and cancer\u003csup\u003e[\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e]\u003c/sup\u003e. In the United States, substantial evidence suggests that the diabetes epidemic is closely tied to the rising rates of overweight and obesity\u003csup\u003e[\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e]\u003c/sup\u003e. As the primary risk factor for type 2 diabetes, abdominal obesity holds a central position within the concept of metabolic syndrome\u003csup\u003e[\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e]\u003c/sup\u003e. Additionally, Nicholson's research found a link between emotional stress and obesity\u003csup\u003e[\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e]\u003c/sup\u003e, highlighting a significant intrinsic connection between obesity and mental health. Consequently, research on obesity holds substantial scientific importance, as it can not only fill gaps in the field but also provide crucial guidance for other disciplines and clinical practice.\u003c/p\u003e \u003cp\u003eRecent studies suggest that obesity may trigger a range of diseases by mediating inflammatory responses. It can act as a chronic systemic stressor, inducing inflammation that increases the risk of cardiovascular diseases and leads to elevated baseline levels of hormones, immune cells, and cytokines\u003csup\u003e[\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e]\u003c/sup\u003e. Chronic low-grade inflammation is a hallmark of obesity. White adipose tissue, infiltrated by immune cells like macrophages, produces pro-inflammatory cytokines\u003csup\u003e[\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e]\u003c/sup\u003e. Concurrently, hypertrophic adipose tissue, suffering from hypoperfusion, leads to hypoxia, dysfunction, and inflammation. Hypertrophic adipocytes are also more prone to rupture, triggering inflammatory cascades that affect the expression of various pro-inflammatory factors such as interleukins and tumor necrosis factor-alpha (TNF-α)\u003csup\u003e[\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e]\u003c/sup\u003e. These factors, upon reaching the brain, can exert effects through multiple pathways. For instance, they may regulate serotonin via indoleamine 2,3-dioxygenase and mitogen-activated protein kinases (MAPK), or inhibit glucocorticoid receptors by affecting the hypothalamic-pituitary-adrenal (HPA) axis\u003csup\u003e[\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e]\u003c/sup\u003e. They can also alter neurotrophic factors and modulate neuroplasticity\u003csup\u003e[\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e]\u003c/sup\u003e, thereby profoundly impacting neurotransmitter function, neuroendocrine activity, neuroplasticity, and brain circuits. This interference with brain signaling and emotional responses can ultimately lead to abnormal mental states in patients.\u003c/p\u003e \u003cp\u003eThe association between obesity and mental illness is complex, involving multifaceted mechanisms\u003csup\u003e[\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e]\u003c/sup\u003e. First, depression and obesity exhibit a bidirectional relationship, each potentially causing the other. Research has found that key brain regions regulating body weight and energy homeostasis overlap with those involved in mood regulation\u003csup\u003e[\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e]\u003c/sup\u003e. Furthermore, studies have identified over 50 reliable genetic loci associated with depression phenotypes. Some of these loci, with stronger signals, overlap with or are adjacent to specific sequences linked to high BMI and severe early-onset obesity, such as neuronal growth regulator 1 (NEGR1), olfactomedin 4 (OLFM4), and kinase suppressor of ras 2 (KSR2)\u003csup\u003e[\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e],[\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e],[\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e]\u003c/sup\u003e. For example, NEGR1 regulates synaptic plasticity in brain regions critical for emotion and appetite regulation, like the cortex, hippocampus, and hypothalamus, suggesting shared pathogenic mechanisms for depression and obesity. The high BMI in obese patients is often associated with fatigue and lack of energy, symptoms explainable by systemic low-grade inflammation. Patients also exhibit prominent psychological symptoms like \"low mood\" and \"loss of interest,\" which are core features of major depressive disorder\u003csup\u003e[\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e]\u003c/sup\u003e. Substantial evidence confirms that immune-mediated inflammation can induce depression via cytokines and inflammatory factors through multiple pathways\u003csup\u003e[\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e]\u003c/sup\u003e. The comorbidity rate of mental disorders like depression is significantly higher in patients with inflammation-related chronic diseases such as rheumatoid arthritis, cancer, infectious diseases, autoimmune disorders, and cardiovascular diseases\u003csup\u003e[\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e]\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eGut microbiota plays a key role in the link between obesity and mental illness, with its connection to mental health, particularly depression, receiving increasing research attention. Studies show that the gut microbiota of obese individuals on high-fat diets undergoes alterations, subsequently contributing to systemic inflammation and dysregulation of mood\u003csup\u003e[\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e]\u003c/sup\u003e. Gut bacteria can mediate various gut-derived signals through interactions involving the vagus nerve, immune cells, and neurotransmitter signaling. This can lead to imbalances in active neurotransmitters like dopamine, serotonin, glutamate, and gamma-aminobutyric acid (GABA), as well as in tryptophan metabolites and short-chain fatty acids (SCFAs), thereby affecting signal transmission to the brain and ultimately disrupting emotional regulation\u003csup\u003e[\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e]\u003c/sup\u003e. This process also increases the risk of systemic inflammation and compromises intestinal barrier function, further exacerbating depressive symptoms\u003csup\u003e[\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e]\u003c/sup\u003e. Other research indicates that excessive intake of dietary saturated fatty acids and dyslipidemia caused by adipose tissue dysfunction can upregulate the expression of pattern recognition receptors (e.g., Toll-like receptors, TLRs) for pro-inflammatory cytokines like interleukin-6 (IL-6) and TNF-α in the peripheral circulation\u003csup\u003e[\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e]\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eWhile the important association between obesity and mental illness is established, numerous underlying mechanisms remain to be elucidated\u003csup\u003e[\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e]\u003c/sup\u003e. Research confirms links between obesity and various psychiatric disorders, including bipolar disorder, personality disorders, attention-deficit/hyperactivity disorder, binge eating disorder, post-traumatic stress disorder, and schizophrenia\u003csup\u003e[\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e]\u003c/sup\u003e. One study showed that at least 42% of obese patients have at least one comorbid mental disorder\u003csup\u003e[\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e]\u003c/sup\u003e. Neglecting the issue of obesity will lead to persistent adverse effects on both physical and mental health, underscoring the practical necessity of research on obesity and mental health.\u003c/p\u003e \u003cp\u003eIn summary, while the adverse effects of obesity have garnered widespread attention in the medical field in recent years, its impact on human mental health remains an under-researched area. The interactive relationship and pathogenic mechanisms between the two are not yet fully understood. Obesity-related research in Asian countries has largely focused on physical impacts, with insufficient attention to psychological aspects. Mental disorders hold significant importance for the prognosis and treatment of obese patients, yet evidence regarding the prevalence of mental disorders in populations undergoing obesity treatment remains relatively scarce\u003csup\u003e[\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e]\u003c/sup\u003e. This study aims to investigate the mental health status of obese populations and analyze the underlying influencing factors, with the goal of advancing clinical and scientific development in this field and ultimately providing support for optimizing clinical guidance and improving patient prognosis. This study is expected to further reveal the intrinsic connection between obesity and mental health and elucidate the related biological mechanisms.\u003c/p\u003e"},{"header":"Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003eResearch participants\u003c/h2\u003e \u003cp\u003e Participants were primarily recruited from individuals who underwent health examinations at the Health Management (Physical Examination) Center of Peking University Third Hospital between 2018 and 2023. Inclusion criteria were: (1) age\u0026thinsp;\u0026ge;\u0026thinsp;18 years; (2) completion of body composition testing; and (3) completion of psychological assessment. Exclusion criteria were: (1) diagnosis of any of the following conditions: heart disease, hyperlipidemia, epilepsy, stroke, immune system disorders, metabolism-related diseases, central nervous system diseases, or any other chronic physical illness; (2) use of medications that may affect metabolism, such as immunosuppressants, hormones, special supplements, antipsychotics, etc.; (3) meeting the diagnostic criteria for any mental disorder listed in the International Classification of Diseases, Tenth Revision (ICD-10); (4) participation in physical fitness training; (5) an irregular lifestyle in the previous three months, including excessive alcohol consumption, binge drinking, overeating, etc.; and (6) pregnancy.\u003c/p\u003e \u003cp\u003e This study was conducted in accordance with the Declaration of Helsinki. The study protocol was reviewed and approved by the Institutional Ethics Committee of Peking University Third Hospital (Project No.IRB00006761-M20250131 ). This study is a retrospective cross-sectional study and was exempt from informed consent.\u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eMethords\u003c/h3\u003e\n\u003cp\u003e(1) General participant information, including age, gender and disease duration, was collected at the time of the physical examination.\u003c/p\u003e \u003cp\u003e(2) Mental Health Assessment: The Symptom Checklist-90 (SCL-90) was used to assess participants' psychosomatic status. This scale consists of 90 items divided into 10 factors: somatization, obsessive-compulsive symptoms, interpersonal sensitivity, depression, anxiety, phobic anxiety, hostility, paranoid ideation, psychoticism, and additional items (primarily concerning sleep and eating conditions). A 5-point Likert scale was used, with higher scores indicating poorer health status. A participant's psychological assessment result was considered positive if the total score was \u0026ge;\u0026thinsp;160 or if any single factor score was \u0026ge;\u0026thinsp;2. Participants with SCL-90 total scores higher than the average score of the Chinese population norms were classified as having abnormal psychological status. The Stress Self-Assessment Questionnaire-53 (SSQ-53) was used to evaluate the degree of psychological stress. This questionnaire contains 53 items assessing the stress level an individual experienced in the last month, divided into five dimensions: overall stress, physiology, emotion, cognition, and behavior. The scoring is categorized into grades: 0\u0026ndash;0.08 as grade 1, 0.09\u0026ndash;0.19 as grade 2, 0.2\u0026ndash;0.28 as grade 3, 0.29\u0026ndash;0.42 as grade 4, 0.43\u0026ndash;0.62 as grade 5, 0.63\u0026ndash;0.91 as grade 6, 0.92\u0026ndash;1.3 as grade 7, 1.31\u0026ndash;1.72 as grade 8, 1.73\u0026ndash;4 as grade 9, and a score of 1.73\u0026ndash;4 points combined with a score of \u0026ge;\u0026thinsp;2 on question 38 or \u0026ge;\u0026thinsp;3 on questions 36 or 51 as grade 10. A grade of 7 or above was considered indicative of a stressed state.\u003c/p\u003e \u003cp\u003e(3) Hematological Tests: Fasting antecubital venous blood was collected from participants. The samples were centrifuged at 3000 r/min for 15 minutes at 4\u0026deg;C. Complete blood count analysis was performed using the SYSMEX XN-2000 fully automated hematology analyzer. Blood biochemical parameters, including blood glucose, blood lipids, liver function, and kidney function, were analyzed using the Beckman Coulter AU5800 fully automatic biochemical analyzer. Thyroid-related markers, including thyroxine, triiodothyronine, free triiodothyronine, and thyroid-stimulating hormone, were measured using the Siemens Atellica analyzer.\u003c/p\u003e \u003cp\u003e(4) Statistical Analysis: All data were analyzed using SPSS 26.0 or R 4.5.1 software. Normally distributed continuous data are presented as mean\u0026thinsp;\u0026plusmn;\u0026thinsp;standard deviation (x̄ \u0026plusmn; s), and linear trend comparisons among groups were performed using linear trend tests in one-way ANOVA.. Non-normally distributed continuous data are presented as median (first quartile, third quartile) [M (Q1, Q3)], and linear trend comparisons among groups were performed using the Jonckheere-Terpstra J test.. Categorical data are presented as number (percentage) [n (%)], and linear trend comparisons among groups were performed using the Cochran-Armitage trend test.. False Discovery Rate (FDR) correction was applied using the Benjamini\u0026ndash;Hochberg procedure implemented in R software (version 4.5.1). Generalized linear models (Gamma distribution) were used when somatization was treated as a continuous variable, and binary logistic regression analysis was used when somatization was treated as a dichotomous variable. All variables with P\u0026thinsp;\u0026lt;\u0026thinsp;0.05 in univariate analysis, except demographic indicators, were included in collinearity diagnostics. Variables with variance inflation factor (VIF)\u0026thinsp;\u0026gt;\u0026thinsp;10 (indicating severe collinearity) were manually removed until all remaining variables had VIF\u0026thinsp;\u0026lt;\u0026thinsp;10. Model 1 included only BMI groups (set as dummy variables and treated as an ordinal categorical variable). Model 2 adjusted for age, gender, marital status, and other demographic indicators based on Model 1. Model 3 further adjusted for white blood cells, red blood cells, hemoglobin, platelets, systolic blood pressure, diastolic blood pressure, fasting blood glucose, triglycerides, low-density lipoprotein cholesterol, high-density lipoprotein cholesterol, alpha-fetoprotein, carcinoembryonic antigen, uric acid, urea, creatinine, alanine aminotransferase, aspartate aminotransferase, total bilirubin, direct bilirubin, absolute lymphocyte count, and absolute eosinophil count based on Model 2. The statistical significance level was set at p\u0026thinsp;\u0026lt;\u0026thinsp;0.05. For multiple comparison corrections, the significance threshold was set at an FDR-adjusted p-value\u0026thinsp;\u0026lt;\u0026thinsp;0.10.\u003c/p\u003e"},{"header":"Results","content":"\u003cdiv id=\"Sec6\" class=\"Section2\"\u003e \u003ch2\u003eAnalysis of body composition indicators between the three groups\u003c/h2\u003e \u003cp\u003eThis study included a total of 11,272 participants for comparative analysis. Among them, 6,041 participants (53.6%) were in the normal weight group (BMI\u0026thinsp;\u0026lt;\u0026thinsp;24 kg/m\u0026sup2;), with a mean age of 36.5\u0026thinsp;\u0026plusmn;\u0026thinsp;10.0 years and a male-to-female ratio of 1:1.99. The overweight group (24\u0026thinsp;\u0026le;\u0026thinsp;BMI\u0026thinsp;\u0026lt;\u0026thinsp;28) comprised 3,797 participants (33.7%), with a mean age of 41.0\u0026thinsp;\u0026plusmn;\u0026thinsp;10.7 years and a male-to-female ratio of 1:2.33. The obesity group (BMI\u0026thinsp;\u0026ge;\u0026thinsp;28 kg/m\u0026sup2;) consisted of 1,434 participants (12.7%), with a mean age of 40.4\u0026thinsp;\u0026plusmn;\u0026thinsp;10.8 years and a male-to-female ratio of 1:3.05. Details are 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\u003eResults of SCL-90 in three groups\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"7\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eVariable\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eBMI\u0026thinsp;\u0026lt;\u0026thinsp;24 Group (n\u0026thinsp;=\u0026thinsp;6041)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003e24\u0026thinsp;\u0026le;\u0026thinsp;BMI\u0026thinsp;\u0026lt;\u0026thinsp;28 Group (n\u0026thinsp;=\u0026thinsp;3797)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eBMI\u0026thinsp;\u0026ge;\u0026thinsp;28 Group (n\u0026thinsp;=\u0026thinsp;1434)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eZ/χ\u0026sup2; value\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eP for trend\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003eFDR-adjusted P-value\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSomatization\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.17(1.08,1.50)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.17(1.08,1.50)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.25(1.08,1.50)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e3.127\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.002\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.005\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eObsessive-compulsive\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.40(1.20,1.80)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.40(1.20,1.80)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.40(1.20,1.80)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e-0.090\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.928\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.928\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eInterpersonal sensitivity\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.22(1.11,1.56)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.22(1.11,1.67)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.33(1.11,1.67)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1.391\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.164\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.205\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDepression\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.20(1.07,1.53)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.20(1.07,1.53)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.20(1.07,1.53)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e-0.820\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.412\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.458\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAnxiety\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.30(1.10,1.50)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.20(1.10,1.50)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.20(1.10,1.50)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e-3.565\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.003\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHostility\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.17(1.00,1.50)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.17(1.00,1.50)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.17(1.00,1.67)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e2.237\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.025\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.042\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePhobic anxiety\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.00(1.00,1.14)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.00(1.00,1.14)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.00(1.00,1.14)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e-2.936\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.003\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.006\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eParanoid ideation\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.17(1.00,1.33)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.17(1.00,1.33)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.17(1.00,1.50)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e4.247\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.003\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePsychoticism\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.10(1.00,1.40)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.10(1.00,1.40)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.10(1.00,1.40)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1.755\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.079\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.113\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAdditional items (Sleep/Eating)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.40(1.00,1.80)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.40(1.20,1.80)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.40(1.20,2.00)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e9.059\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.003\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eTotal score\u003c/b\u003e​\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e113.0(100.0,136.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e113.0(100.0,137.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e113.0(101.0,139.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1.208\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.227\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eAverage score\u003c/b\u003e​\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.26(1.11,1.51)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.26(1.11,1.52)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.26(1.12,1.54)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1.208\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.227\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eSomatization (\u0026ge;\u0026thinsp;2), n(%)\u003c/b\u003e​\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e479(7.9%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e330(8.7%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e143(10.0%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e6.519\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.011\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.028\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eHostility (\u0026ge;\u0026thinsp;2), n(%)\u003c/b\u003e​\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e650(10.8%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e481(12.7%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e213(14.9%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e21.476\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.003\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eParanoid ideation (\u0026ge;\u0026thinsp;2), n(%)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e410(6.8%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e318(8.4%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e133(9.3%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e14.219\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.003\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eAdd. items (\u0026ge;\u0026thinsp;2), n(%)\u003c/b\u003e​\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1137(18.8%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e867(22.8%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e363(25.3%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e40.324\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.003\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eMental health abnormality (Mild/Moderate/Severe), n(%)\u003c/b\u003e​\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1984(32.8%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1315(34.6%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e556(38.8%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e17.307\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eAnalysis of SCL-90 data between the three groups\u003c/h3\u003e\n\u003cp\u003eThe SCL-90 total score for the BMI\u0026thinsp;\u0026lt;\u0026thinsp;24 group was 113.0 (100.0, 136.0), with a total average score of 1.26 (1.11, 1.51). The SCL-90 total score for the BMI 24\u0026thinsp;~\u0026thinsp;28 group was 113.0 (100.0, 137.0), with a total average score of 1.26 (1.11, 1.52). The SCL-90 total score for the BMI\u0026thinsp;\u0026ge;\u0026thinsp;28 group was 113.0 (101.0, 139.0), with a total average score of 1.26 (1.12, 1.54). There was no statistically significant difference in the total and average scores among the three groups (P\u0026thinsp;\u0026gt;\u0026thinsp;0.05) (Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\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\u003eResults of SSQ-53 in three groups\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"7\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eVariable\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eBMI\u0026thinsp;\u0026lt;\u0026thinsp;24 Group (n\u0026thinsp;=\u0026thinsp;5156)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003e24\u0026thinsp;\u0026le;\u0026thinsp;BMI\u0026thinsp;\u0026lt;\u0026thinsp;28 Group (n\u0026thinsp;=\u0026thinsp;3233)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eBMI\u0026thinsp;\u0026ge;\u0026thinsp;28 Group (n\u0026thinsp;=\u0026thinsp;1207)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eZ/χ\u0026sup2; value\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eP for trend\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003eFDR-adjusted P-value\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eOverall stress\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.23(0.09,0.49)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.21(0.08,0.47)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.23(0.08,0.47)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e-3.177\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.002\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePhysiological\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.30(0.10,0.55)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.25(0.10,0.50)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.25(0.10,0.55)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e-3.438\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.002\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eEmotional\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.17(0.00,0.50)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.11(0.00,0.44)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.11(0.00,0.44)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e-4.726\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.002\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCognitive\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.25(0.00,0.75)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.25(0.00,0.75)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.25(0.00,0.75)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e-0.602\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.547\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.547\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBehavioral\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.18(0.00,0.36)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.18(0.00,0.36)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.18(0.00,0.45)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1.786\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.074\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.093\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHigh behavioral stress level (7\u0026ndash;10), n(%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e294(5.7%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e203(6.3%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e91(7.5%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e5.574\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.018\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.018\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e\u003cstrong\u003eNote:\u003c/strong\u003e Stress scores are presented as M(Q1, Q3); a stress grade of 7-10 is defined as the high-stress level.\u003c/p\u003e\u003cp\u003eBased on the total scores, participants were categorized into a mentally healthy/sub-healthy group and a mentally unhealthy group (mild, moderate, and severe). In the BMI\u0026thinsp;\u0026lt;\u0026thinsp;24 group, there were 4,057 cases (67.2%) in the mentally healthy/sub-healthy group and 1,984 cases (32.8%) in the mentally unhealthy group. In the BMI 24\u0026thinsp;~\u0026thinsp;28 group, there were 2,482 cases (65.4%) in the mentally healthy/sub-healthy group and 1,315 cases (34.6%) in the mentally unhealthy group. In the BMI\u0026thinsp;\u0026ge;\u0026thinsp;28 group, there were 878 cases (61.2%) in the mentally healthy/sub-healthy group and 556 cases (38.8%) in the mentally unhealthy group. Statistical analysis revealed a significant difference in the distribution of mental health categories among the three groups (P\u0026thinsp;\u0026lt;\u0026thinsp;0.001) (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eAmong the ten factors of the SCL-90, the scores for somatization, hostility, paranoid ideation, and the additional items (primarily sleep and eating conditions) in the BMI\u0026thinsp;\u0026ge;\u0026thinsp;28 group were significantly higher than those in the BMI\u0026thinsp;\u0026lt;\u0026thinsp;24 group (P\u0026thinsp;\u0026lt;\u0026thinsp;0.05). Compared to the BMI\u0026thinsp;\u0026lt;\u0026thinsp;24 group, the BMI\u0026thinsp;\u0026ge;\u0026thinsp;28 group had a significantly higher proportion of participants with positive somatization factor scores (factor score\u0026thinsp;\u0026ge;\u0026thinsp;2), with 143 individuals (10.0%) (P\u0026thinsp;=\u0026thinsp;0.011). Similarly, the proportions for positive hostility factor scores (213 individuals, 14.9%; P\u0026thinsp;\u0026lt;\u0026thinsp;0.001), positive paranoid ideation factor scores (133 individuals, 9.3%; P\u0026thinsp;\u0026lt;\u0026thinsp;0.001), and positive additional items factor scores (363 individuals, 25.3%; P\u0026thinsp;\u0026lt;\u0026thinsp;0.001) were all significantly higher in the BMI\u0026thinsp;\u0026ge;\u0026thinsp;28 group (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e).\u003c/p\u003e \u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003eAnalysis of psychological stress between the three groups\u003c/h2\u003e \u003cp\u003eResults of the Stress Self-Assessment Questionnaire-53 (SSQ-53):​ The scores for overall stress, physiological stress, and emotional stress in the BMI 24\u0026thinsp;~\u0026thinsp;28 group (i.e., the overweight group) were all significantly lower than those in the BMI\u0026thinsp;\u0026lt;\u0026thinsp;24 group (normal weight group) (P\u0026thinsp;\u0026le;\u0026thinsp;0.001). Among these, there was no statistically significant difference in overall stress and physiological stress scores between the BMI\u0026thinsp;\u0026ge;\u0026thinsp;28 group and the BMI\u0026thinsp;\u0026lt;\u0026thinsp;24 group, while the emotional stress score in the BMI\u0026thinsp;\u0026ge;\u0026thinsp;28 group was also significantly lower than that in the BMI\u0026thinsp;\u0026lt;\u0026thinsp;24 group (P\u0026thinsp;\u0026lt;\u0026thinsp;0.001). Regarding behavioral stress, the proportion of individuals with a behavioral stress level of 7\u0026ndash;10 (high stress) differed among the three groups, with the BMI\u0026thinsp;\u0026ge;\u0026thinsp;28 group (7.5%) showing a higher proportion than the 24\u0026thinsp;\u0026le;\u0026thinsp;BMI\u0026thinsp;\u0026lt;\u0026thinsp;28 group (6.3%) and the BMI\u0026thinsp;\u0026lt;\u0026thinsp;24 group (5.7%) (P\u0026thinsp;=\u0026thinsp;0.018) (Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eIn summary, this study indicates that as body mass index (BMI) increases, an individual's overall mental health level (as measured by SCL-90 total score) showed no significant change. However, more pronounced manifestations were observed in specific psychological symptom dimensions such as somatization, hostility, paranoid ideation, and sleep/eating problems. Furthermore, the proportion of mentally unhealthy individuals (mild, moderate, and severe) tends to increase. On the other hand, regarding subjective stress perception, both overweight (24\u0026thinsp;\u0026le;\u0026thinsp;BMI\u0026thinsp;\u0026lt;\u0026thinsp;28) and obese (BMI\u0026thinsp;\u0026ge;\u0026thinsp;28) individuals reported lower levels of subjective and emotional stress. Nevertheless, the obese group demonstrated a higher tendency for behavioral stress reactions.\u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eAnalysis of the individuals' hematological test results between the two groups\u003c/h3\u003e\n\u003cp\u003eAnalysis of stress-related hematological indicators among the three groups revealed that participants in the BMI\u0026thinsp;\u0026ge;\u0026thinsp;28 group had significantly higher levels of fasting blood glucose (GLU), serum total cholesterol (TC), triglycerides (TG), low-density lipoprotein cholesterol (LDL-C), uric acid (UA), alanine aminotransferase (ALT), and gamma-glutamyl transferase (GGT) compared to both the BMI\u0026thinsp;\u0026lt;\u0026thinsp;24 group and the BMI 24\u0026thinsp;~\u0026thinsp;28 group (all FDR-adjusted P-values\u0026thinsp;\u0026lt;\u0026thinsp;0.001). Furthermore, the white blood cell count (WBC) in this group was also significantly higher than in the BMI\u0026thinsp;\u0026lt;\u0026thinsp;24 group (P\u0026thinsp;=\u0026thinsp;0.035). The level of high-density lipoprotein cholesterol (HDL-C) was significantly higher in the BMI\u0026thinsp;\u0026lt;\u0026thinsp;24 group compared to the other two groups (P\u0026thinsp;\u0026lt;\u0026thinsp;0.001). Other indicators, such as thyroid-stimulating hormone (TSH) and homocysteine (Hcy) levels, did not show statistically significant differences among the three groups (P\u0026thinsp;\u0026gt;\u0026thinsp;0.05).\u003c/p\u003e \u003cp\u003eThese results indicate that obese participants in the BMI\u0026thinsp;\u0026ge;\u0026thinsp;28 group exhibit a comprehensive profile of physiological abnormalities characterized by glucose and lipid metabolism disorders, increased hepatic and renal functional load, and a potential state of low-grade inflammation (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\u003eComparison of Other Physical Examination Indicators Among Participants Stratified by BMI Groups\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"6\"\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=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eVariable\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eBMI\u0026thinsp;\u0026lt;\u0026thinsp;24 Group (n\u0026thinsp;=\u0026thinsp;6041)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003e24\u0026thinsp;\u0026le;\u0026thinsp;BMI\u0026thinsp;\u0026lt;\u0026thinsp;28 Group (n\u0026thinsp;=\u0026thinsp;3797)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eBMI\u0026thinsp;\u0026ge;\u0026thinsp;28 Group (n\u0026thinsp;=\u0026thinsp;1434)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eZ/F/χ\u0026sup2; value\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eP for trend\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAge (years), x̄\u0026plusmn;s\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e36.5\u0026thinsp;\u0026plusmn;\u0026thinsp;10.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e41.0\u0026thinsp;\u0026plusmn;\u0026thinsp;10.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e40.4\u0026thinsp;\u0026plusmn;\u0026thinsp;10.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e167.123\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMale, n(%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2017(33.4%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2658(70.0%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1080(75.3%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1637.727\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMarried, n(%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e4145(69.4%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e3104(83.6%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1146(81.6%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e282.189\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eWhite blood cell count (\u0026times;10⁹/L), x̄\u0026plusmn;s\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e5.63\u0026thinsp;\u0026plusmn;\u0026thinsp;1.37\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e6.13\u0026thinsp;\u0026plusmn;\u0026thinsp;1.41\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e6.68\u0026thinsp;\u0026plusmn;\u0026thinsp;1.62\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e622.088\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHigh-sensitivity CRP (mg/L), M(Q1,Q3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.38(0.20,0.80)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.80(0.43,1.56)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.37(0.70,2.66)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e22.643\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTriglycerides (mmol/L), M(Q1,Q3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.91(0.70,1.25)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.39(0.98,2.01)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.75(1.25,2.47)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e47.833\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHDL-C (mmol/L), x̄\u0026plusmn;s\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.45\u0026thinsp;\u0026plusmn;\u0026thinsp;0.32\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.21\u0026thinsp;\u0026plusmn;\u0026thinsp;0.26\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.12\u0026thinsp;\u0026plusmn;\u0026thinsp;0.23\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1514.443\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eUric acid (\u0026micro;mol/L), x̄\u0026plusmn;s\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e306.0\u0026thinsp;\u0026plusmn;\u0026thinsp;78.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e369.0\u0026thinsp;\u0026plusmn;\u0026thinsp;86.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e406.3\u0026thinsp;\u0026plusmn;\u0026thinsp;89.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1677.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCreatinine (\u0026micro;mol/L), x̄\u0026plusmn;s\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e75.2\u0026thinsp;\u0026plusmn;\u0026thinsp;13.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e82.9\u0026thinsp;\u0026plusmn;\u0026thinsp;14.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e84.2\u0026thinsp;\u0026plusmn;\u0026thinsp;13.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e523.321\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eALT (U/L), M(Q1,Q3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e14(11,20)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e22(16,31)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e29(20,44)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e48.044\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFree T3 (pmol/L), x̄\u0026plusmn;s\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e3.25\u0026thinsp;\u0026plusmn;\u0026thinsp;0.52\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e3.40\u0026thinsp;\u0026plusmn;\u0026thinsp;0.40\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e3.45\u0026thinsp;\u0026plusmn;\u0026thinsp;0.42\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e177.420\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e\n\u003ch3\u003eMultiple conditional logistic regression\u003c/h3\u003e\n\u003cp\u003eAfter controlling for relevant confounding factors, a binary logistic regression analysis was conducted using the somatization factor positivity as the dependent variable. Independent variables included age, gender, marital status, BMI group (with the BMI\u0026thinsp;\u0026lt;\u0026thinsp;24 group as the reference), and the following hematological indicators: systolic blood pressure, diastolic blood pressure, white blood cell count, hemoglobin, triglycerides, high-density lipoprotein cholesterol, low-density lipoprotein cholesterol, fasting blood glucose, creatinine, uric acid, alanine aminotransferase, aspartate aminotransferase, gamma-glutamyl transferase, homocysteine, and thyroid-stimulating hormone.\u003c/p\u003e \u003cp\u003eThe results showed that female gender was a significant risk factor for somatization symptoms (P\u0026thinsp;\u0026lt;\u0026thinsp;0.001; OR\u0026thinsp;=\u0026thinsp;3.280, 95% CI: 2.749\u0026ndash;3.914). Increasing age was a significant risk factor for somatization symptoms (P\u0026thinsp;\u0026lt;\u0026thinsp;0.001; OR\u0026thinsp;=\u0026thinsp;1.020, 95% CI: 1.013\u0026ndash;1.027). Elevated creatinine level was a significant risk factor for somatization symptoms (P\u0026thinsp;\u0026lt;\u0026thinsp;0.001; OR\u0026thinsp;=\u0026thinsp;1.020, 95% CI: 1.010\u0026ndash;1.031). High-density lipoprotein cholesterol was a protective factor against somatization symptoms (P\u0026thinsp;=\u0026thinsp;0.001; OR\u0026thinsp;=\u0026thinsp;0.502, 95% CI: 0.336\u0026ndash;0.751). BMI group was an independent factor associated with somatization symptoms. Compared to the normal weight group (BMI\u0026thinsp;\u0026lt;\u0026thinsp;24), the overweight group (24\u0026thinsp;\u0026le;\u0026thinsp;BMI\u0026thinsp;\u0026lt;\u0026thinsp;28) had a significantly higher risk of somatization symptoms (P\u0026thinsp;=\u0026thinsp;0.014; OR\u0026thinsp;=\u0026thinsp;1.243, 95% CI: 1.045\u0026ndash;1.479), and the obesity group (BMI\u0026thinsp;\u0026ge;\u0026thinsp;28) had a significantly higher risk of somatization symptoms (P\u0026thinsp;=\u0026thinsp;0.002; OR\u0026thinsp;=\u0026thinsp;1.407, 95% CI: 1.130\u0026ndash;1.752) (Table\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e5\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab4\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 5\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eBinary Logistic Regression Analysis with SCL-90 Somatization Factor Positivity (Score\u0026thinsp;\u0026ge;\u0026thinsp;2) as the Dependent Variable\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"7\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eModel and Variables\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eB\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eStd. Error\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eWald χ\u0026sup2;\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eP-value\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eOR\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003e95% CI\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eModel 1: BMI category only\u003c/b\u003e​\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBMI category (Overweight vs. Normal)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.100\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.075\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.792\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.181\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e1.105\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.955\u0026ndash;1.279\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBMI category (Obesity vs. Normal)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.252\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.100\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e6.312\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.012\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e1.286\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e1.057\u0026ndash;1.565\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eIntercept\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e-2.424\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.044\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e3076.569\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.089\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eModel 2: Adjusted for age, sex, marital status\u003c/b\u003e​\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBMI category (Overweight vs. Normal)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.200\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.076\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e6.895\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.009\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e1.221\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e1.052\u0026ndash;1.417\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBMI category (Obesity vs. Normal)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.401\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.103\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e15.223\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e1.493\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e1.221\u0026ndash;1.825\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAge group (Middle-aged vs. Young)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.543\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.086\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e40.235\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e1.721\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e1.455\u0026ndash;2.036\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAge group (Old vs. Young)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.816\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.171\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e22.804\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e2.262\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e1.619\u0026ndash;3.161\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSex (Female vs. Male)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.768\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.076\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e102.928\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e2.155\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e1.858\u0026ndash;2.499\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMarital status (Married vs. Unmarried)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.012\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.093\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.016\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.901\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e1.012\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.844\u0026ndash;1.214\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eIntercept\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e-3.187\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.099\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1036.723\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.041\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eModel 3: Model 2\u0026thinsp;+\u0026thinsp;comprehensive physiological indicators\u003c/b\u003e​\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBMI category (Overweight vs. Normal)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.217\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.089\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e5.968\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.015\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e1.243\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e1.043\u0026ndash;1.480\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBMI category (Obesity vs. Normal)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.341\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.127\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e7.244\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.007\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e1.407\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e1.096\u0026ndash;1.805\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAge group (Middle-aged vs. Young)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.562\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.090\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e38.703\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e1.754\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e1.469\u0026ndash;2.095\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAge group (Old vs. Young)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.888\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.187\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e22.669\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e2.431\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e1.687\u0026ndash;3.504\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSex (Female vs. Male)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.611\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.130\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e22.004\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e1.842\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e1.427\u0026ndash;2.378\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMarital status (Married vs. Unmarried)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e-0.073\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.109\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.443\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.506\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.930\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.751\u0026ndash;1.151\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003ePhysiological indicators (selected significant variables)\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eWhite blood cell count (\u0026times;10⁹/L)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.072\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.028\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e6.790\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.009\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e1.075\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e1.018\u0026ndash;1.135\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTotal bilirubin (\u0026micro;mol/L)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e-0.030\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.011\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e7.415\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.006\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.970\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.950\u0026ndash;0.991\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAbsolute lymphocyte count (\u0026times;10⁹/L)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e-0.294\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.091\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e10.356\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.745\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.624\u0026ndash;0.890\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eIntercept\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e-2.442\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.701\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e12.129\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.087\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"7\"\u003e\u003cb\u003eNotes\u003c/b\u003e:\u003c/td\u003e\u003c/tr\u003e \u003ctr\u003e\u003ctd colspan=\"7\"\u003e1.Reference Groups:The \"normal weight (BMI\u0026thinsp;\u0026lt;\u0026thinsp;24)\" group was used as the reference for BMI categories. The \"young\" age group was the reference for age categories. \"Male\" was the reference for gender. \"Unmarried\" was the reference for marital status.\u003c/td\u003e\u003c/tr\u003e \u003ctr\u003e\u003ctd colspan=\"7\"\u003e2.After adjusting for age, gender, marital status, and a series of physiological and biochemical indicators, the overweight group (24\u0026thinsp;\u0026le;\u0026thinsp;BMI\u0026thinsp;\u0026lt;\u0026thinsp;28) had a 1.243-fold higher risk of positive somatization symptoms compared to the normal weight group (OR\u0026thinsp;=\u0026thinsp;1.243, 95% CI: 1.048\u0026ndash;1.487, P\u0026thinsp;=\u0026thinsp;0.014). The obesity group (BMI\u0026thinsp;\u0026ge;\u0026thinsp;28) had a 1.407-fold higher risk compared to the normal weight group (OR\u0026thinsp;=\u0026thinsp;1.407, 95% CI: 1.153\u0026ndash;1.905, P\u0026thinsp;=\u0026thinsp;0.002). Furthermore, elevated white blood cell count and decreased absolute lymphocyte count were identified as independent risk factors for somatization symptoms.\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e3.CI: Confidence Interval.\u003c/p\u003e\u003cp\u003eThe Hosmer-Lemeshow test was used to evaluate the goodness-of-fit of the model. The result showed P\u0026thinsp;=\u0026thinsp;0.195 (P\u0026thinsp;\u0026gt;\u0026thinsp;0.05), indicating a good model fit with no significant difference between the predicted and the actual observed values.\u003c/p\u003e \u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003eGeneralized linear models\u003c/h2\u003e \u003cp\u003eAfter controlling for relevant confounding factors, a Generalized linear models(Gamma distribution) analysis was conducted using the somatization factor score as the dependent variable. Independent variables included BMI group, age, gender, marital status, and key hematological indicators selected following collinearity diagnosis.\u003c/p\u003e \u003cp\u003eTo evaluate the overall goodness-of-fit of the model, the Omnibus test was employed. The results for the final model (including BMI group, demographic indicators, and the adjusted hematological indicators) showed an Omnibus test P-value\u0026thinsp;\u0026lt;\u0026thinsp;0.001. This indicates that all the independent variables included in the model, as a whole, significantly predict the variation in the dependent variable (somatization score), and the model is statistically significant.\u003c/p\u003e \u003cp\u003eFor assessing model fit, the Akaike Information Criterion (AIC) and the Bayesian Information Criterion (BIC) were used. The final model had an AIC of 7666.485, a finite-sample corrected AIC (AICc) of 7666.638, a BIC of 7870.088, and a consistent AIC (CAIC) of 7898.088. The values for these criteria are all within an acceptable range and, together with the results of other model tests, support the usability of the model (Table\u0026nbsp;\u003cspan refid=\"Tab5\" class=\"InternalRef\"\u003e4\u003c/span\u003e).After progressively adjusting for confounding factors, overweight and obesity status remained independent predictors of significantly higher somatization symptom scores. Furthermore, female sex, increasing age, and a series of blood indicators reflecting inflammation (e.g., elevated white blood cell count), dyslipidemia (e.g., elevated triglycerides, decreased high-density lipoprotein cholesterol), and hepatic/renal load (e.g., elevated creatinine and total bilirubin) were also independently associated with more severe somatization symptoms. The overall model demonstrated high statistical significance.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab5\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 4\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eGeneralized Linear Model Analysis with SCL-90 Somatization Factor Score as the Dependent Variable\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"7\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eModel and Variables\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eB\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eStd. Error\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eWald χ\u0026sup2;\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eP-value\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eExp(B)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003e95% CI\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eModel 1: BMI category only\u003c/b\u003e​\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e(Intercept)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.207\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.015\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e190.007\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e1.230\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e1.193\u0026ndash;1.268\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBMI category (Overweight vs. Normal)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.029\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\u003e10.209\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e1.029\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e1.012\u0026ndash;1.046\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBMI category (Obesity vs. Normal)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.043\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\u003e11.379\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e1.044\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e1.018\u0026ndash;1.070\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eModel 2: Adjusted for age, sex, marital status\u003c/b\u003e​\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e(Intercept)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.354\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.022\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e250.786\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e1.425\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e1.364\u0026ndash;1.489\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBMI category (Overweight vs. Normal)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.054\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\u003e34.016\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e1.055\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e1.037\u0026ndash;1.074\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBMI category (Obesity vs. Normal)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.078\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.014\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e31.834\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e1.081\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e1.053\u0026ndash;1.110\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSex (Female vs. Male)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.114\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.007\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e279.865\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e1.121\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e1.106\u0026ndash;1.136\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAge (per 1-year increase)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.004\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e138.331\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e1.004\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e1.003\u0026ndash;1.004\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMarital status (Married vs. Unmarried)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.003\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.008\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.013\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.909\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e1.003\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.987\u0026ndash;1.019\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eModel 3: Model 2\u0026thinsp;+\u0026thinsp;comprehensive physiological indicators\u003c/b\u003e​\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e(Intercept)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.108\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.241\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e21.153\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e3.028\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e1.889\u0026ndash;4.854\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBMI category (Overweight vs. Normal)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.038\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.010\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e15.573\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e1.039\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e1.019\u0026ndash;1.059\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBMI category (Obesity vs. Normal)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.087\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.016\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e28.747\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e1.091\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e1.056\u0026ndash;1.126\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSex (Female vs. Male)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.082\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.010\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e66.423\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e1.085\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e1.065\u0026ndash;1.106\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAge (per 1-year increase)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.004\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e118.169\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e1.004\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e1.003\u0026ndash;1.004\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMarital status (Married vs. Unmarried)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e-0.037\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.008\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e22.276\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.964\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.949\u0026ndash;0.979\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003ePhysiological indicators (selected significant variables)\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eWhite blood cell count (\u0026times;10⁹/L)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.028\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.010\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e8.616\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.003\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e1.028\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e1.010\u0026ndash;1.047\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTriglycerides (mmol/L)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.007\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.002\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e11.658\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e1.007\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e1.003\u0026ndash;1.011\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHDL-C (mmol/L)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e-0.041\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\u003e10.354\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.960\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.936\u0026ndash;0.984\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCreatinine (\u0026micro;mol/L)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.002\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\u003e7.621\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.006\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e1.002\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e1.001\u0026ndash;1.004\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTotal bilirubin (\u0026micro;mol/L)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.003\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\u003e17.526\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e1.003\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e1.002\u0026ndash;1.004\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAbsolute lymphocyte count (\u0026times;10⁹/L)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e-0.054\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\u003e17.038\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.947\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.924\u0026ndash;0.971\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAbsolute eosinophil count (\u0026times;10⁹/L)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e-0.065\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.023\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e7.822\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.005\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.937\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.896\u0026ndash;0.980\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"7\"\u003e\u003cb\u003eNotes\u003c/b\u003e:\u003c/td\u003e\u003c/tr\u003e \u003ctr\u003e\u003ctd colspan=\"7\"\u003e1.In Model 3, in addition to the significant variables listed in the table above, indicators such as red blood cell count, hemoglobin, platelet count, blood pressure, fasting blood glucose, low-density lipoprotein cholesterol, alpha-fetoprotein, carcinoembryonic antigen, uric acid, urea, aminotransferases, and direct bilirubin were also included. However, the P-values for these variables in the final model were all \u0026gt;\u0026thinsp;0.05.\u003c/td\u003e\u003c/tr\u003e \u003ctr\u003e\u003ctd colspan=\"7\"\u003e2.Exp(B), or the exponentiated coefficient, can be interpreted as the risk ratio (RR). It represents the expected fold change in the dependent variable (somatization score) for each one-unit increase in the independent variable (or for a given category compared to the reference category).\u003c/td\u003e\u003c/tr\u003e \u003ctr\u003e\u003ctd colspan=\"7\"\u003e3.CI: Confidence Interval.\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e"},{"header":"Discussion","content":"\u003cp\u003eThe complex relationship between mental health disturbances and obesity is well-documented. However, detailed investigations across different BMI categories remain valuable, and research on the association between overweight/obesity and specific mental health issues is of significant importance due to their high prevalence and the potential for confusion with metabolic abnormalities alone. This study provides evidence that overweight and obesity status are closely associated with specific mental health conditions, particularly somatization symptoms, and evaluates the multidimensional physiological profile associated with this status. To investigate the mental health status and influencing factors among populations with different Body Mass Index (BMI), this study recruited a large-scale physical examination cohort and categorized participants into normal weight (BMI\u0026thinsp;\u0026lt;\u0026thinsp;24, n\u0026thinsp;=\u0026thinsp;6041), overweight (24\u0026thinsp;\u0026le;\u0026thinsp;BMI\u0026thinsp;\u0026lt;\u0026thinsp;28, n\u0026thinsp;=\u0026thinsp;3797), and obesity (BMI\u0026thinsp;\u0026ge;\u0026thinsp;28, n\u0026thinsp;=\u0026thinsp;1434) groups. The study confirms a significant and independent association between overweight/obesity status and specific mental health problems, especially somatization symptoms.\u003c/p\u003e \u003cp\u003eObesity has been confirmed to negatively impact mental health through various mechanisms, such as promoting abnormal inflammatory responses, inducing metabolic disorders, or causing endocrine dysfunction\u003csup\u003e[\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e]\u003c/sup\u003e.Several studies have attempted to unravel the complex relationship between obesity and somatic symptoms, involving various inflammatory and neuroendocrine mechanisms\u003csup\u003e[\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e]\u003c/sup\u003e. Visceral adipose tissue is closely associated with chronic low-grade inflammation, and inflammatory markers such as C-reactive protein and interleukin-6 may mediate the link between obesity and neuropsychiatric symptoms\u003csup\u003e[\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e]\u003c/sup\u003e.This study provides support for this: obese individuals exhibited significantly elevated white blood cell counts and high-sensitivity C-reactive protein levels, and white blood cell count was an independent predictor of somatic symptoms. This suggests that obesity-related systemic inflammation may contribute to the development of somatic symptoms by affecting neuroimmune regulation, neurotransmitter metabolism, or neuroendocrine function. However, some studies have indicated that depressive symptoms in obese patients with metabolic syndrome are not associated with C-reactive protein\u003csup\u003e[\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e]\u003c/sup\u003e. In this study, white blood cell count (WBC), reflecting participants' inflammatory levels, was significantly higher in the obese group (6.68\u0026thinsp;\u0026plusmn;\u0026thinsp;1.62) than in the normal-weight group (5.63\u0026thinsp;\u0026plusmn;\u0026thinsp;1.37, P\u0026thinsp;\u0026lt;\u0026thinsp;0.001). Multivariate analysis further revealed that white blood cell count was an independent risk factor for somatic symptoms (P\u0026thinsp;=\u0026thinsp;0.003). This indicates that obesity may be associated with somatic symptoms through mechanisms similar to chronic low-grade inflammation.This study found that the obesity group (BMI\u0026thinsp;\u0026ge;\u0026thinsp;28) had significantly higher scores on the Somatization, Hostility, Paranoid Ideation, and Additional Items (primarily sleep and diet) factors of the Symptom Checklist-90 (SCL-90). The proportion of participants with positive somatization factor scores (factor score\u0026thinsp;\u0026ge;\u0026thinsp;2) reached 10.0% in the obesity group, significantly higher than the 7.9% in the normal weight group (P\u0026thinsp;=\u0026thinsp;0.011). Concurrently, the proportion of individuals with poor mental health (mild, moderate, or severe psychological abnormality) increased significantly with rising BMI. This proportion was 38.8% in the obesity group, higher than the 34.6% in the overweight group and 32.8% in the normal weight group (P\u0026thinsp;\u0026lt;\u0026thinsp;0.001).Results from the Stress Self-Assessment Questionnaire-53 (SSQ-53) revealed a nuanced stress profile. The overweight and obesity groups reported significantly lower scores for emotional stress compared to the normal weight group. However, the proportion of individuals with high behavioral stress levels (grades 7\u0026ndash;10) was significantly higher in the obesity group (7.5%) than in the overweight (6.3%) and normal weight (5.7%) groups (P\u0026thinsp;=\u0026thinsp;0.018). This discrepancy between subjective emotional experience and behavioral response suggests potential alterations in HPA axis regulation or stress-coping strategies among individuals with obesity\u003csup\u003e[\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e]\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eThe study also identified widespread abnormalities in hematological indicators among overweight and obese participants, including significant differences in blood cell counts, blood glucose, blood lipids, liver and kidney function, and thyroid hormones compared to the normal weight group. These abnormalities were characterized by elevated levels of triglycerides, LDL-C, uric acid, alanine aminotransferase, and creatinine, along with reduced levels of HDL-C. Simultaneously, uric acid, as a product of purine metabolism, has been reported to be associated with an increased risk of anxiety and depression\u003csup\u003e[\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e],[\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e]\u003c/sup\u003e. This study also found significantly elevated serum uric acid levels in the obese group, suggesting that abnormal purine metabolism may be another potential pathway linking obesity to psychological symptoms.Furthermore, Multivariate analysis confirmed that elevated WBC, elevated triglycerides, reduced HDL-C, and elevated creatinine were independently associated with somatization symptoms. This suggests that obesity status may also influence mental health by mediating lipid metabolism disorders and increasing renal load. Disorders of thyroid hormone levels have been demonstrated to be associated with mood disorders\u003csup\u003e[\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e]\u003c/sup\u003e, cognitive impairment, attention deficits\u003csup\u003e[\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e]\u003c/sup\u003e, and other conditions.The significant elevation of blood uric acid and free triiodothyronine levels in the obesity group points to purine metabolism and thyroid axis dysfunction as other potential pathways linking obesity to psychological symptoms.\u003c/p\u003e \u003cp\u003eMultifactorial binary logistic regression analysis, with somatization symptoms as the dependent variable and adjustments for age, gender, marital status, and a series of physiological and biochemical indicators, demonstrated that overweight and obesity status are independent risk factors. Compared to the normal weight group, the overweight group had a 1.243-fold increased risk (OR\u0026thinsp;=\u0026thinsp;1.243, 95%CI: 1.048\u0026ndash;1.487, P\u0026thinsp;=\u0026thinsp;0.014), and the obesity group had a 1.407-fold increased risk (OR\u0026thinsp;=\u0026thinsp;1.407, 95%CI: 1.153\u0026ndash;1.905, P\u0026thinsp;=\u0026thinsp;0.002) of positive somatization symptoms. Notably, the regression model confirmed that female gender, increasing age, and elevated creatinine were significant risk factors, while higher HDL-C was a protective factor against somatization symptoms. Importantly, the association between BMI group and somatization risk remained significant even after adjusting for these and other physiological variables, indicating that overweight and obesity confer an independent risk beyond measurable inflammatory and metabolic disturbances.\u003c/p\u003e \u003cp\u003eObesity has been confirmed to negatively impact mental health through various mechanisms, such as promoting abnormal inflammatory responses, inducing metabolic disorders, or causing endocrine dysfunction. The elevated WBC and its identification as an independent predictor of somatization symptoms in this study support the role of obesity-related systemic low-grade inflammation, which may participate in the formation of somatization symptoms by affecting neuroimmune regulation, neurotransmitter metabolism, or neuroendocrine function. The observed \"low emotional stress perception - high behavioral stress reaction\" dissociation aligns with descriptions of potential HPA axis dysregulation or altered stress coping in individuals with obesity.\u003c/p\u003e \u003cp\u003eThe comprehensive profile of physiological abnormalities\u0026mdash;characterized by metabolic, inflammatory, and endocrine dysregulation\u0026mdash;observed in overweight and obese participants suggests this state may be closely linked to the development and progression of psychiatric symptoms. The persistence of overweight and obesity as independent risk factors for somatization symptoms after adjusting for multiple physiological indicators implies the involvement of other, not yet fully elucidated mechanisms, such as adipokine secretion abnormalities or gut microbiota-brain axis disturbances\u003csup\u003e[\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e]\u003c/sup\u003e. The correlation between changes in absolute lymphocyte count and somatization symptoms also hints that the balance of immune cell subsets may play a role in neuroimmune regulation, offering a new direction for future mechanistic research.\u003c/p\u003e \u003cp\u003eAs a cross-sectional study, it cannot establish causality between overweight/obesity and psychiatric symptoms. Further research is needed to clarify the causal pathways, which could aid in improving early psychosomatic intervention and secondary prevention for this population. While the cross-sectional design limits causal inference, our findings provide a critical foundation for future longitudinal studies. Subsequent research should employ longitudinal designs combined with multi-omics technologies to clarify the causal pathways between physiological indicators and psychological symptoms, explore individual differences, and investigate the moderating effects of social-behavioral factors (such as diet, exercise, and social support) to advance the development of personalized psychosomatic medicine intervention strategies. The easily detectable laboratory indicators associated with somatization symptoms in this study (e.g., WBC, triglycerides, HDL-C, creatinine) show promise as potential biomarkers for identifying individuals at increased psychological risk, facilitating early identification and proactive clinical intervention.The findings of this study indicate that overweight and obesity status are significantly associated with more pronounced specific psychiatric symptoms, particularly somatization, hostility, and sleep/eating disturbances. Concurrently, individuals with overweight or obesity reported lower levels of subjective emotional stress but exhibited a higher tendency towards behavioral stress reactions. Multivariate analyses, after adjusting for age, gender, and a range of hematological indicators, further revealed that not only are overweight and obesity independent risk factors for somatization symptoms, but these symptoms are also broadly associated with female gender, increasing age, and abnormalities in physiological markers reflecting metabolic disorders, hepatic/renal functional load, and alterations in immune-inflammatory status, such as triglycerides, creatinine, white blood cell count, and lymphocyte parameters.\u003c/p\u003e"},{"header":"Abbreviations","content":"\u003cp\u003eSCL-90,Symptom Checklist-90\u003c/p\u003e\n\u003cp\u003eSSQ-53, Stress Self-assessment Questionnaire-53\u003c/p\u003e\n\u003cp\u003eBMI,Body Mass Index\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003eThis study was conducted in strict accordance with the principles of the Declaration of Helsinki. The study protocol involving human participants was reviewed and approved by the Institutional Ethics Committee of Peking University Third Hospital (Project No.: IRB00006761-M20250131).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEthical approval\u003c/strong\u003e\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eThis study strictly adhered to the principles outlined in the Declaration of Helsinki. All research protocols involving human participants were reviewed and approved by the Institutional Ethics Committee of Peking University Third Hospital (Approval No.: IRB00006761-M20250131).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent to participate\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAll participants, after being fully informed of the study's purpose and methods, signed an informed consent form and agreed to the use of their current health survey data, relevant physical examination results, and hematological test results for this study.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent for publication\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe raw data supporting the conclusions of this article will be made available by the authors, without undue reservation.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting interests\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare that they have no known competing financial interests or personal relationships that could have influenced the work reported in this paper.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAvailability of data and materials\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe data that support the findings of this study are available from Peking University Third Hospital but restrictions apply to the availability of those data, which were used under license for the current study, and so are not publicly available. Data are however available from the corresponding authors upon reasonable request and with permission of Peking University Third Hospital.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis work was supported by grants funded by the Peking University Third Hospital Nursing Seed Fund (BYSYHL2023009). This work was supported by the National Science and Technology Innovation 2030, Noncommunicable Chronic Diseases-National Science and Technology Major Project (Grant No. 2024ZD0524300, 2024ZD0524301)\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthors' contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eYing Che collected the relevant data, designed the study, analyzed the data, and wrote the manuscript. Yaozong Wu wrote the manuscript. Jiayu Gao edited the manuscript. Honghai He and Liyuan Tao analyzed the data. Ying Liang designed the study, wrote the manuscript. Peng Wang provided financial support. The author(s) read and approved the final manuscript.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAcknowledgements\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWe would like to thank the support of Peking University Third Hospital.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eClinical trial number\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eWorld Health Organization. Obesity and overweight [Internet]. World Health Organization. 2022. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.who.int/news-room/fact-sheets/detail/obesity-and-overweight\u003c/span\u003e\u003cspan address=\"https://www.who.int/news-room/fact-sheets/detail/obesity-and-overweight\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e. 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PMID: 39273008; PMCID: PMC11394554.\u003c/span\u003e\u003c/li\u003e\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":"Obesity, Mental Health, Psychiatric Symptoms, Influencing Factors","lastPublishedDoi":"10.21203/rs.3.rs-9395618/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-9395618/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eBackground\u003c/h2\u003e \u003cp\u003eAccording to World Health Organization data, the global obesity rate has nearly tripled since 1975, becoming a major public health concern. Obesity is closely associated not only with cardiovascular diseases and diabetes but also with mental health disorders such as depression and anxiety, though the underlying mechanisms remain incompletely understood. This study aimed to retrospectively investigate the relationship between obesity and mental health, as well as potential physiological mechanisms, within a health examination population.\u003c/p\u003e\u003ch2\u003eMethods\u003c/h2\u003e \u003cp\u003eWe conducted a retrospective cross-sectional analysis using data from individuals who underwent routine health check-ups at a tertiary hospital in Beijing. Archived questionnaire data, including the Symptom Checklist-90 (SCL-90) and the Stress Self-Assessment Questionnaire-53 (SSQ-53), were used to assess mental health status. Archived hematological biomarkers were analyzed to evaluate physiological status. Statistical analyses included the Benjamini-Hochberg procedure for False Discovery Rate (FDR) correction, multiple linear regression, and binary logistic regression to identify factors associated with psychological symptoms.\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e \u003cp\u003eThe analysis included 11,272 participants (6,041 normal weight, 3,797 overweight, 1,434 obese). The somatization subscale score increased significantly with BMI. The obesity group had a significantly higher positive rate for somatization (factor score\u0026thinsp;\u0026ge;\u0026thinsp;2) compared to the other groups (P\u0026thinsp;=\u0026thinsp;0.011). Interestingly, the obese group reported lower levels of psychological and cognitive stress (FDR-adjusted P\u0026thinsp;\u0026lt;\u0026thinsp;0.001) and more stable mood scores than the normal-weight group. Multivariable analysis confirmed that higher BMI, female gender, older age, and specific biochemical markers reflecting inflammation and metabolic dysregulation (e.g., total bilirubin, absolute lymphocyte count, all P\u0026thinsp;\u0026lt;\u0026thinsp;0.001) were independently associated with more severe somatization symptoms.\u003c/p\u003e\u003ch2\u003eConclusion\u003c/h2\u003e \u003cp\u003eIn this retrospective health examination cohort, higher BMI was independently associated with increased somatization symptoms, linked to inflammatory and metabolic markers. Contrary to common assumption, the obese subgroup exhibited lower perceived stress and anxiety levels, which suggests a complex relationship between obesity and mental health. Further longitudinal research is needed to clarify causality.\u003c/p\u003e","manuscriptTitle":"Association Between Overweight/Obesity and Somatization Symptoms: A Large-Scale Cross-Sectional Study Among Health Examination Participants","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2026-05-05 10:23:15","doi":"10.21203/rs.3.rs-9395618/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":"bff471ea-3313-4e24-af23-d0d64dd26c3b","owner":[],"postedDate":"May 5th, 2026","published":true,"recentEditorialEvents":[{"type":"editorInvitedReview","content":"","date":"2026-05-03T08:41:48+00:00","index":23,"fulltext":""},{"type":"editorInvitedReview","content":"","date":"2026-04-30T03:11:35+00:00","index":22,"fulltext":""}],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2026-05-11T05:11:14+00:00","versionOfRecord":[],"versionCreatedAt":"2026-05-05 10:23:15","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-9395618","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-9395618","identity":"rs-9395618","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}