Associations of Cigarette Smoking with General and Abdominal Obesity Risks among Men in Taiwan

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This study investigates the association between smoking behaviors and obesity among men in Taiwan, focusing on body mass index (BMI) and waist circumference (WC) as indicators of general and abdominal obesity. The sample consisted of 27,908 men categorized into five groups based on their smoking status: never smoking (NS), former smoking (FS), light-intensity smoking (LIS), moderate-intensity smoking (MIS), and heavy-intensity smoking (HIS). Our findings reveal a significant association between smoking and increased obesity risk, particularly among light- and moderate-intensity smokers. Socioeconomic factors such as education and income levels were also found to influence these behaviors. These results underscore the importance of integrated public health strategies that address both smoking cessation and obesity prevention. Introduction Obesity and smoking are two of the most significant public health challenges of the 21st century, both contributing substantially to the global burden of disease and mortality [ 1 , 2 ]. Obesity is a multifaceted condition associated with an increased risk of various chronic diseases, including cardiovascular disease, type 2 diabetes, and certain cancers [ 3 ]. Similarly, smoking is the leading cause of preventable death worldwide, linked to a myriad of adverse health outcomes such as lung cancer, respiratory diseases, and cardiovascular disorders [ 4 ]. Despite their distinct etiologies, recent research has begun to elucidate a complex relationship between smoking and obesity, highlighting the need for integrated public health strategies to address these dual epidemics [ 5 , 6 ]. The relationship between smoking and body weight is paradoxical. While smoking has long been perceived as a weight control tool due to its appetite-suppressing effects [ 7 ], evidence suggests that smokers may have higher levels of abdominal fat compared to non-smokers [ 8 ]. Additionally, smoking cessation is often associated with weight gain, further complicating the relationship between these two factors [ 9 ]. Understanding the nuanced interplay between smoking behaviors and obesity is crucial for developing effective interventions, particularly in populations with high smoking prevalence [ 10 , 11 ]. Taiwan, like many other countries, faces the dual burden of increasing obesity rates and persistent smoking prevalence. According to the Taiwan Ministry of Health and Welfare, approximately 20% of adult men are smokers, and the prevalence of obesity has been steadily rising [ 12 ]. The cultural and socioeconomic dynamics in Taiwan present unique challenges and opportunities for studying the intersection of smoking and obesity. Factors such as socioeconomic status, educational attainment, and self-perceived health play pivotal roles in shaping smoking behaviors and obesity risk [ 13 , 14 ]. The association between cigarette smoking and both general and abdominal obesity risks has garnered considerable attention in recent years, given its potential to exacerbate health inequalities and contribute to the rising prevalence of obesity-related diseases [ 15 ]. Previous studies have explored the individual impacts of smoking and obesity on health, but few have focused on their combined effects, particularly in the Taiwanese context. This study aims to fill this gap by examining the association between different smoking behaviors and obesity among Taiwanese men. By analyzing various smoking intensities and their relation to body mass index (BMI) and waist circumference (WC), we aim to provide insights into the public health implications of smoking on obesity and identify potential targets for intervention. Furthermore, this research seeks to explore the socioeconomic and educational factors contributing to these health behaviors, offering a comprehensive understanding of the complex interplay between smoking and obesity [ 16 , 17 ]. The findings from this study are expected to inform policymakers and public health professionals about the importance of integrating smoking cessation and obesity prevention efforts. By addressing both smoking and obesity simultaneously, we can work towards reducing the overall burden of disease and improving health outcomes in Taiwan and beyond [ 18 , 19 ]. Methods Study Design and Data Sources Data for this cross-sectional study were sourced from the Taiwan National Physical Fitness Survey (TNPFS) conducted by the Sports Administration, Ministry of Education, Taiwan. The TNPFS's established protocol and tool have been detailed in previous publications [ 20 , 21 ]. A summarized overview of the survey methods is provided here. Participants were recruited via age- and sex-stratified convenience sampling from 46 physical fitness test stations across 22 cities and counties in Taiwan, between October 2014 and March 2015. The survey involved a face-to-face interview, where trained examiners and medical professionals (usually nurses or doctors) administered a standardized structured questionnaire. This was followed by anthropometric measurements and health-related physical fitness tests. The collected TNPFS data, de-identified and managed by the Sports Cloud: Information and Application Research Center of Sports for All, Sports Administration, Ministry of Education, Taiwan, are publicly accessible for research. Detailed information about TNPFS can be found at Sports Cloud. This study adhered to the Declaration of Helsinki guidelines and received approval from the Institutional Review Board of Fu Jen Catholic University in Taiwan (FJU-IRB C110113). Eligibility Criteria for Study Participants Prior to TNPFS data collection, ineligible participants were excluded based on the following criteria: (1) systolic blood pressure ≥ 140 mmHg or diastolic blood pressure ≥ 90 mmHg; and (2) history or presence of heart disease, hypertension, chest pain, vertigo, or musculoskeletal disorders. Consequently, the TNPFS 2014–15 database includes 62,586 adults aged 23–64. For this study, female and non-Taiwanese participants were further excluded, along with records having missing data. Ultimately, 27,908 male Taiwanese adults aged 23–64 were included in the analysis. Data Collection In the face-to-face interviews conducted by trained examiners, data were collected on sociodemographic characteristics (age, sex, education, monthly income, marital status), cigarette smoking status, betel-nut chewing habits, perceived health, and anthropometric measurements. Education was categorized into three levels: elementary school or lower, junior or senior high school, and college or higher. Monthly income was classified into: ≤20,000 NTD, 20,001–40,000 NTD, and ≥ 40,001 NTD. Marital status was divided into married, never married, and divorced/separated/widowed. Betel-nut chewing habits were categorized as never users, former users, and current users. Health status was self-reported as excellent or good, fair, and very bad or poor. Anthropometric measurements included body weight (measured in light clothing to an accuracy of 0.1 kg using a scale), height (measured to an accuracy of 0.1 cm using a wall-mounted tape and headpiece, with shoes removed), waist circumference (WC), and hip circumference (HC), both measured to the nearest 0.1 cm. Body Mass Index (BMI) was calculated as weight (kg) divided by height (m²). Obesity was classified using the Health Promotion Administration, Taiwan's BMI cutoffs: underweight (BMI < 18.5 kg/m²), normal weight (18.5 ≤ BMI < 24 kg/m²), overweight (24 ≤ BMI < 27 kg/m²), or obese (BMI ≥ 27 kg/m²) [ 22 ]. WC and HC measurements were averaged from two readings, and Waist-Hip Ratio (WHR) was calculated as WC divided by HC. Cigarette Smoking Status Participants' smoking status was self-reported during the interview and categorized as never smokers (smoked 1 g of tobacco per day), and former smokers (those who had quit for more than 1 year) [ 23 ]. Statistical Analysis Data from 27,908 male participants meeting the sampling criteria were analyzed using SAS version 9.4 (SAS Institute., Cary, NC, USA). Descriptive analyses included means ± standard deviations (SD) for continuous variables and percentages for categorical variables. The Shapiro–Wilk test assessed the normal distribution of data. Demographic characteristics were compared using Chi-square tests and one-way ANOVA among different smoking status groups. Where significant F values (p < 0.05) were noted, Tukey's post hoc test identified differences between mean pairs. These differences were considered potential confounders for linear regression model adjustment. Multiple linear regression analysis evaluated the association between smoking status and general and abdominal obesity, adjusting for potential confounders like age, education, income, self-reported health, and betel nut chewing habits. All statistical tests were two-tailed, with significance set at p < 0.05. Results This study analyzed the association between different smoking behaviors and weight-related indicators among Taiwanese men. The total sample size was 27,908 men, divided into five groups: never smoking (NS, n = 19,674), former smoking (FS, n = 2,766), light-intensity smoking (LIS, n = 2,798), moderate-intensity smoking (MIS, n = 2,039), and heavy-intensity smoking (HIS, n = 631). The findings are as follows: Age Distribution There was a significant difference in age distribution among different smoking behaviors (p < 0.0001). In the 23–34 age group, the highest proportions were among never smokers (39.61%) and light-intensity smokers (40.35%), while the lowest was among former smokers (18.47%). In the 35–44 age group, heavy smokers (36.29%) had the highest proportion. In the 45–64 age group, former smokers had the highest proportion (51.45%), indicating that older individuals were more likely to be former smokers (Table 1 ). Table 1 Characteristics of the study participants according to cigarette smoking behaviors among men in Taiwan. Variables NS ( n = 19,674) FS ( n = 2,766) LIS ( n = 2,798) MIS ( n = 2,039) HIS ( n = 631) p Tukey’s post hoc test Age group (%) < 0.0001* 23–34 years 39.61 18.47 40.35 33.25 23.77 35–44 years 26.20 30.08 29.16 34.09 36.29 45–64 years 34.20 51.45 30.49 32.66 39.94 Height (cm) 170.67 (6.45) 170.32 (6.08) 171.12 (6.13) 170.93 (6.02) 170.76 (5.99) < 0.0001* Body weight (kg) 71.77 (10.47) 73.30 (9.94) 73.03 (10.75) 73.08 (10.91) 74.41 (11.00) < 0.0001* BMI (kg/m 2 ) 24.62 (3.22) 25.26 (3.13) 24.92 (3.33) 25.00 (3.43) 25.51 (3.50) < 0.0001* LIS = MIS, MIS = FS, HIS = FS WC (cm) 83.98 (8.64) 86.35 (8.34) 84.84 (8.88) 85.77 (8.94) 87.34 (9.09) < 0.0001* MIS = FS, HIS = FS HC (cm) 96.85 (6.26) 97.35 (5.83) 96.97 (6.43) 96.92 (6.32) 97.53 (6.67) 0.0003* NS < FS WHR 0.866 (0.058) 0.886 (0.056) 0.874 (0.058) 0.884 (0.057) 0.895 (0.058) < 0.0001* NS < LIS < MIS, FS < HIS Education level (%) < 0.0001* Elementary school or lower 1.68 1.54 1.78 1.59 3.99 Junior or senior school 19.31 32.16 31.12 43.16 47.92 College or higher 79.02 66.30 67.10 55.25 48.08 Income level (%) < 0.0001* ≦ 20,000 NTD 16.21 11.18 14.54 11.79 13.76 20,001–40,000 NTD 33.69 33.38 39.42 39.74 37.31 ≧ 40,001 NTD 50.10 55.44 46.04 48.46 48.92 Self-reported health status (%) < 0.0001* Excellent or good 64.10 60.16 60.37 52.95 48.25 Fair 30.07 32.97 33.51 39.18 37.46 Very bad or poor 5.83 6.87 6.12 7.87 14.29 Abbreviations: BMI, body mass index; FS, former smoking; HC, hip circumference; HIS, heavy-intensity smoking; LIS, light-intensity smoking; NTD, New Taiwan Dolloar; MIS, moderate-intensity smoking; NS, never smoking; SD, standard deviation; WC, waist circumference; WHR, waist-to-hip ratio; UW, underweight. Values are expressed as mean (SD) or %. * p < 0.05. Physical Characteristics Significant differences were found in height, body weight, BMI, waist circumference (WC), hip circumference (HC), and waist-to-hip ratio (WHR) among the groups: Height : Light-intensity smokers had the highest average height (171.12 cm), while former smokers had the lowest (170.32 cm). Body Weight : Body weight increased with smoking intensity, with heavy smokers having the highest weight (74.41 kg). BMI : Heavy smokers had the highest BMI (25.51 kg/m²), indicating a higher risk of obesity. Waist Circumference (WC) : Heavy smokers had the largest waist circumference (87.34 cm), suggesting a greater prevalence of abdominal obesity. Hip Circumference (HC) : Former smokers and heavy smokers had slightly larger mean hip circumferences. Waist-to-Hip Ratio (WHR) : Heavy smokers had the highest waist-to-hip ratio (0.895), indicating a higher risk of abdominal obesity. The differences in physical characteristics were statistically significant (p < 0.0001) (Table 1 ). Socioeconomic Status There is a significant association between socioeconomic status and smoking behaviors (p < 0.0001). In terms of education level, heavy smokers had the highest proportion with elementary school or lower education (3.99%), whereas never smokers had the highest proportion with college or higher education (79.02%). For income levels, former smokers had the highest proportion with incomes above 40,001 NTD (55.44%), while never smokers had the highest proportion with low incomes (≤ 20,000 NTD) (16.21%) (Table 1 ). Self-Reported Health Status There were significant differences in self-reported health status among smoking behaviors (p < 0.0001). Never smokers reported the highest percentage of "excellent or good" health status (64.10%), while heavy smokers had the highest percentage of "very bad or poor" health status (14.29%) (Table 1 ). Association Between Smoking Behaviors and Obesity There is a significant association between smoking behaviors and the prevalence of general and abdominal obesity (p < 0.0001). In the 23–34 age group, heavy smokers had a significantly higher proportion of general obesity than other groups. With increasing age, the proportion of general and abdominal obesity increased across all groups (Table 2 ). Table 2 Prevalence of general and abdominal obesity according to cigarette smoking behaviors among men in Taiwan. Variables General obesity Abdominal obesity Underweight ( n = 309) Normal weight ( n = 4,957) Overweight ( n = 2,923) Obesity ( n = 2,071) p Abdominal obesity ( n = 2,142) Non-abdominal obesity ( n = 8,118) p 23–34 years < 0.0001* < 0.0001* LIS 2.27 1.09 1.47 2.22 1.96 1.33 MIS 6.47 6.48 5.95 7.87 8.45 6.12 HIS 13.59 10.29 10.91 12.46 12.32 10.66 FS 1.62 4.78 5.27 5.55 5.37 4.88 NS 76.05 77.37 76.39 71.90 71.90 77.01 35–44 years 0.0002* < 0.0001* LIS 2.78 2.27 3.02 3.86 4.38 2.33 MIS 5.56 8.96 8.54 9.78 10.06 8.52 HIS 12.50 9.21 11.38 11.25 11.10 10.32 FS 8.33 9.88 10.55 12.37 11.69 10.36 NS 70.83 69.68 66.51 62.74 62.77 68.48 45–64 years < 0.0001* < 0.0001* LIS 1.89 2.32 2.25 3.36 3.46 2.08 MIS 5.66 6.15 6.45 8.00 8.28 5.93 HIS 8.49 8.08 8.49 9.54 9.91 7.94 FS 10.38 12.36 15.34 15.88 16.67 13.18 NS 73.58 71.09 67.47 63.23 61.69 70.86 Abbreviations: FS, former smoking; HIS, heavy-intensity smoking; LIS, light-intensity smoking; MIS, moderate-intensity smoking; NS, never smoking. Values are expressed as or %. * p < 0.05. Multiple Regression Analysis After controlling for age, education level, income level, and health status, a significant positive association between smoking behaviors and both BMI and waist circumference was found: • BMI : Light-intensity smokers had the highest regression coefficient (β = 0.604, p < 0.0001), indicating a significantly higher BMI than never smokers. All smoking behavior groups showed a positive trend in BMI (trend test p < 0.0001) (Table 3 ). Table 3 Multiple regressions for the association of cigarette smoking behaviors with BMI and WC after adjustment for potential confounders Variables Cigarette smoking Model 1 (Age-adjusted) Model 2 (Multivariate-adjusted a ) β SE p β SE p BMI (kg/m 2 ) LIS 0.799 0.130 < 0.0001* 0.604 0.134 < 0.0001* MIS 0.356 0.075 < 0.0001* 0.281 0.077 < 0.0001* HIS 0.320 0.065 < 0.0001* 0.286 0.067 < 0.0001* FS 0.478 0.066 < 0.0001* 0.422 0.068 < 0.0001* NS 1.000 — — 1.000 — — Test for trend p < 0.0001* p < 0.0001* WC (cm) LIS 2.946 0.344 < 0.0001* 2.335 0.353 < 0.0001* MIS 1.700 0.198 < 0.0001* 1.532 0.204 < 0.0001* HIS 0.942 0.172 < 0.0001* 0.905 0.176 < 0.0001* FS 1.635 0.174 < 0.0001* 1.490 0.178 < 0.0001* NS 1.000 — — 1.000 — — Test for trend p < 0.0001* p < 0.0001* Abbreviations: β , regression coefficient; BMI, body mass index; CI, confidence interval; FS, former smoking; HIS, heavy-intensity smoking; LIS, light-intensity smoking; MIS, moderate-intensity smoking; NS, never smoking; SE, standard error; WC, waist circumference. * p < 0.05. a Adjusted for age group, educational levels, monthly income levels, and self-reported health status. • Waist Circumference (WC) : Light-intensity smokers also had the highest regression coefficient (β = 2.335, p < 0.0001), indicating a significantly larger waist circumference than never smokers. All smoking behavior groups showed a positive trend in waist circumference (trend test p < 0.0001) (Table 3 ). Multivariate Adjusted ORs for Obesity The multivariate adjusted odds ratios (ORs) demonstrate a significant association between smoking behaviors and general and abdominal obesity: • General Obesity : Light-intensity smokers had the highest OR for general obesity (OR = 1.566, 95% CI: 1.272–1.928, p < 0.0001). Former smokers and heavy smokers also had ORs significantly greater than never smokers (Table 4 ). Table 4 Multivariate adjusted ORs for general obesity in relation to cigarette smoking behaviors after adjustment for potential confounders. General obesity status Cigarette smoking Model 1 (Age-adjusted) Model 2 (Multivariate-adjusted a ) OR 95% CI p OR 95% CI p Obesity LIS 1.890 1.549–2.305 < 0.0001* 1.566 1.272–1.928 < 0.0001* MIS 1.350 1.203–1.514 < 0.0001* 1.242 1.101–1.402 0.0004* HIS 1.332 1.203–1.476 < 0.0001* 1.272 1.143–1.414 < 0.0001* FS 1.404 1.266–1.557 < 0.0001* 1.334 1.198–1.485 < 0.0001* NS 1.000 — — 1.000 — — Test for trend p < 0.0001* p < 0.0001* Overweight LIS 1.243 1.021–1.513 0.0303* 1.213 0.990–1.485 0.0620 MIS 1.026 0.921–1.145 0.6831 1.032 0.921–1.155 0.5895 HIS 1.153 1.049–1.266 0.0030* 1.153 1.047–1.270 0.0038* FS 1.214 1.105–1.335 < 0.0001* 1.204 1.092–1.328 0.0002* NS 1.000 — — 1.000 — — Test for trend p = 0.0015* p = 0.0029* Underweight LIS 1.414 0.762–2.625 0.2724 0.987 0.512–1.906 0.9696 MIS 0.880 0.602–1.287 0.5090 0.725 0.486–1.083 0.1167 HIS 1.272 0.960–1.685 0.0937 1.118 0.833–1.501 0.4567 FS 0.167 0.398–0.956 0.0307* 0.631 0.406–0.981 0.0408* NS 1.000 — — 1.000 — — Test for trend p = 0.5538 p = 0.3692 Abbreviations: CI, confidence interval; FS, former smoking; HIS, heavy-intensity smoking; LIS, light-intensity smoking; MIS, moderate-intensity smoking; NS, never smoking; OR, odds ratio. * p < 0.05. a Adjusted for age group, educational levels, monthly income levels, and self-reported health status. • Abdominal Obesity : All smoking behavior groups had higher ORs for abdominal obesity compared to never smokers, with statistical significance (p < 0.0001) (Table 5 ). Table 5 Multivariate adjusted ORs for abdominal obesity in relation to cigarette smoking behaviors after adjustment for potential confounders. Abdominal obesity status Cigarette smoking Model 1 (Age-adjusted) Model 2 (Multivariate-adjusted a ) OR 95% CI p OR 95% CI p Abdominal obesity LIS 1.922 1.634–2.262 < 0.0001* 1.634 1.378–1.937 < 0.0001* MIS 1.474 1.337–1.625 < 0.0001* 1.393 1.258–1.542 < 0.0001* HIS 1.291 1.183–1.408 < 0.0001* 1.271 1.161–1.391 < 0.0001* FS 1.340 1.229–1.460 < 0.0001* 1.282 1.173–1.401 < 0.0001* NS 1.000 — — 1.000 — — Test for trend p < 0.0001* p < 0.0001* Abbreviations: CI, confidence interval; FS, former smoking; HIS, heavy-intensity smoking; LIS, light-intensity smoking; MIS, moderate-intensity smoking; NS, never smoking; OR, odds ratio. * p < 0.05. a Adjusted for age group, educational levels, monthly income levels, and self-reported health status. Discussion This study provides compelling evidence of the complex relationship between cigarette smoking behaviors and obesity among men in Taiwan. Our findings demonstrate that smoking is significantly associated with both general and abdominal obesity risks, with light- and moderate-intensity smokers showing particularly high risks for increased BMI and waist circumference. This comprehensive examination of smoking behaviors reveals a nuanced understanding of how different intensities of smoking contribute to varying levels of obesity risk. Smoking and Obesity Contrary to the commonly held belief that smoking aids in weight control, our study reveals that smoking, especially at light and moderate intensities, is positively associated with increased obesity risk. This finding aligns with several studies that have documented similar associations between smoking and obesity [ 24 , 25 ]. For instance, Chiolero et al. demonstrated that smokers tend to have higher waist circumferences compared to non-smokers, which supports our observation of increased abdominal obesity among smokers [ 26 ]. One explanation for this paradoxical relationship is the role of smoking in altering metabolic processes and fat distribution. Smoking has been shown to decrease metabolic rate and increase appetite after cessation, potentially leading to weight gain [ 27 ]. Furthermore, nicotine can influence fat distribution, promoting visceral fat accumulation, which is more metabolically active and associated with higher cardiovascular risk [ 28 ]. Our findings underscore the necessity for public health interventions that challenge the misconception of smoking as a viable weight control method. The demonstrated association between smoking and both general and abdominal obesity risks in this study highlights the need for targeted education campaigns that address the harmful effects of smoking on body weight and fat distribution [ 29 , 30 ]. Socioeconomic Status and Education Our findings highlight the significant role of socioeconomic factors in smoking behaviors and their association with obesity. Individuals with lower education and income levels were more likely to be heavy smokers, a pattern consistent with global trends [ 31 , 32 ]. This socioeconomic gradient in smoking prevalence may exacerbate health inequalities, as individuals from lower socioeconomic backgrounds are already at greater risk for obesity and related comorbidities [ 33 ]. Education plays a critical role in shaping health behaviors, including smoking and diet [ 34 ]. The higher prevalence of smoking among those with lower education levels in our study underscores the need for targeted public health interventions that address these disparities. Health education programs focused on smoking cessation and obesity prevention could be particularly beneficial in reducing the dual burden of smoking and obesity in this population [ 35 ]. Health Perceptions and Smoking The study also reveals that smokers, particularly heavy smokers, are more likely to perceive their health as poor compared to non-smokers. This perception may contribute to a self-perpetuating cycle where individuals continue smoking despite awareness of its adverse effects on health [ 36 ]. Addressing this cognitive dissonance through motivational interviewing and behavioral interventions could enhance the effectiveness of smoking cessation efforts [ 37 ]. Implications for Public Health and Policy The significant association between smoking and obesity found in this study has important public health implications. First, it challenges the notion that smoking cessation invariably leads to weight gain, emphasizing the need for integrated interventions that address both smoking and weight management [ 38 ]. Secondly, our findings support the implementation of policies that reduce smoking prevalence and address obesity simultaneously. For example, tobacco taxation, combined with subsidies for healthy foods, may create synergistic effects in reducing smoking and promoting healthier lifestyles [ 39 ]. Moreover, the development of comprehensive health promotion strategies that target multiple risk factors, such as smoking, poor diet, and physical inactivity, could effectively combat the rising tide of obesity and its associated complications [ 40 ]. Such strategies should be culturally tailored to resonate with the specific beliefs and practices of the Taiwanese population, enhancing their acceptance and effectiveness [ 41 ]. Limitations and Future Research While this study provides valuable insights, it is not without limitations. The cross-sectional design limits our ability to infer causality, and the reliance on self-reported data may introduce reporting biases. Future longitudinal studies could provide more robust evidence of the causal relationship between smoking and obesity. Additionally, exploring the biological mechanisms underlying the observed associations could deepen our understanding of how smoking influences obesity risk and identify potential targets for intervention [ 42 ]. Conclusion In conclusion, our study underscores the complex interplay between smoking behaviors and general and abdominal obesity risks among men in Taiwan. It highlights the need for multifaceted public health strategies that address the dual burden of smoking and obesity, particularly among socioeconomically disadvantaged groups. By focusing on prevention and cessation efforts, policymakers can improve health outcomes and reduce the prevalence of these interrelated risk factors [ 43 ]. Declarations Human Ethics and Consent to Participate: This study adhered to the Declaration of Helsinki guidelines and received approval from the Institutional Review Board of Fu Jen Catholic University in Taiwan (FJU-IRB C110113). All volunteers participating in the project provided informed consent before their involvement in the study Consent for publication: Not applicable Availability of data and materials: The data and software underlying the findings of this study are available from the corresponding author upon reasonable request. Competing interests: The authors declare no conflicts of interest. Funding: No funding. Authors' contributions: Min-Chen Wu, Chien-Chang Ho , and Yung-Po Liaw contributed to the conception and design of the study., Chien-Chang Ho, and Yung-Po Liaw analyzed and interpreted data. Min-Chen Wu drafted the manuscript, assisted by Oswald Ndi Nfor. Chien-Chang Ho, Oswald Ndi Nfor, and Yung-Po Liaw critically reviewed the manuscript. All authors approved the final manuscript. References World Health Organization. Obesity and overweight. World Health Organization; 2020. U.S. Department of Health and Human Services. The health consequences of smoking—50 years of progress: A report of the Surgeon General. Atlanta, GA: Centers for Disease Control and Prevention; 2014. Mokdad AH, Marks JS, Stroup DF, Gerberding JL. Actual causes of death in the United States, 2000. JAMA. 2004;291(10):1238-45. Ng M, Fleming T, Robinson M, et al. 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Prescription for a healthy nation: A new approach to improving our lives by fixing our everyday world. Beacon Press; 2005. Barendregt JJ, Veerman JL. Causal inference in public health: The role of mediation analysis. Int J Public Health. 2010;55(1):177-82. Steenland K, Armstrong B. An overview of methods for calculating the burden of disease due to specific risk factors. Epidemiology. 2006;17(5):512-9. Marmot M, Wilkinson RG. Social determinants of health: The solid facts. 2nd ed. World Health Organization; 2003. Mirowsky J, Ross CE. Education, social status, and health. Transaction Publishers; 2003. National Institutes of Health. Clinical guidelines on the identification, evaluation, and treatment of overweight and obesity in adults: The evidence report. Obes Res. 1998;6(Suppl 2):51S-209S. Prochaska JJ, Das S, Young-Wolff KC. Smoking, mental illness, and public health.Annu Rev Public Health. 2017;38:165-85. Miller WR, Rollnick S. Motivational interviewing: Preparing people for change. 2nd ed. Guilford Press; 2002. Pisinger C, Jørgensen T. Weight concerns and smoking in a general population: The interplay with smoking status, obesity, gender and age. Prev Med. 2007;45(2-3):283-7. Jha P, Chaloupka FJ. Curbing the epidemic: Governments and the economics of tobacco control. World Bank Publications; 1999. Kumanyika SK, Obarzanek E, Stettler N, et al. Population-based prevention of obesity: The need for comprehensive promotion of healthful eating, physical activity, and energy balance. Circulation. 2008;118(4):428-64. Lau DC, Douketis JD, Morrison KM, et al. 2006 Canadian clinical practice guidelines on the management and prevention of obesity in adults and children. CMAJ. 2007;176(8) LaRowe TL, Wubben DP, Karanja N, et al. Development of a culturally appropriate, home-based nutrition and physical activity curriculum for Wisconsin American Indian families. Prev Chronic Dis. 2007;4(4) Aldrich R, Zwi AB, Short S, et al. Setting priorities for health research: Lessons from developing countries. Health Policy Plan. 2000;15(2):130-6. Additional Declarations No competing interests reported. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-4900878","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":354834926,"identity":"6243552d-5b92-419a-94e9-b3d428f085d4","order_by":0,"name":"Min-Chen Wu","email":"","orcid":"","institution":"Chung Yuan Christian University","correspondingAuthor":false,"prefix":"","firstName":"Min-Chen","middleName":"","lastName":"Wu","suffix":""},{"id":354834927,"identity":"6ec978d5-2911-454b-9066-5ac3acf69070","order_by":1,"name":"Oswald Ndi Nfor","email":"","orcid":"","institution":"Chung Shan Medical University","correspondingAuthor":false,"prefix":"","firstName":"Oswald","middleName":"Ndi","lastName":"Nfor","suffix":""},{"id":354834928,"identity":"f6562e08-415b-478c-9e7b-aec48eefcff8","order_by":2,"name":"Yung-Po Liaw","email":"","orcid":"","institution":"Chung Shan Medical University","correspondingAuthor":false,"prefix":"","firstName":"Yung-Po","middleName":"","lastName":"Liaw","suffix":""},{"id":354834929,"identity":"9ffc4705-364a-4347-bfdc-dd7345a40aab","order_by":3,"name":"Chien-Chang Ho","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAAyElEQVRIiWNgGAWjYFACHgaGhAobHn4QO6GAWC0PzqTJSTaAtBgQqYXxYdthY4MDIA4xWvjbzx58kNiWlrj5/OrEDw8MGOT5xQ7g1yJxJi/ZIOGcTeK2G283SwAdZjhzdgJ+LQYSPGYSCWVpQC1nN4C0JBjcJkoL2+HEzTPObv5BghaQ9/l7txFni8SZHGODBGAgS9zg3WaRYCBB2C/87WcMH/4ARWX/2c03gQx5fmkCWpDsA6uUIFY52L4DpKgeBaNgFIyCkQQAXvxFlr67PicAAAAASUVORK5CYII=","orcid":"","institution":"Fu Jen Catholic University","correspondingAuthor":true,"prefix":"","firstName":"Chien-Chang","middleName":"","lastName":"Ho","suffix":""}],"badges":[],"createdAt":"2024-08-12 13:26:49","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-4900878/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-4900878/v1","draftVersion":[],"editorialEvents":[{"content":"https://doi.org/10.1186/s12889-025-21821-5","type":"published","date":"2025-02-17T15:57:28+00:00"}],"editorialNote":"","failedWorkflow":false,"files":[{"id":77053816,"identity":"9bc61d45-ae12-4b14-b3d1-6a93adc4712b","added_by":"auto","created_at":"2025-02-24 16:30:28","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1366711,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-4900878/v1/53650842-3830-4d9d-b8a5-b621c84ff4fa.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Associations of Cigarette Smoking with General and Abdominal Obesity Risks among Men in Taiwan","fulltext":[{"header":"Introduction","content":"\u003cp\u003eObesity and smoking are two of the most significant public health challenges of the 21st century, both contributing substantially to the global burden of disease and mortality [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e, \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e]. Obesity is a multifaceted condition associated with an increased risk of various chronic diseases, including cardiovascular disease, type 2 diabetes, and certain cancers [\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e]. Similarly, smoking is the leading cause of preventable death worldwide, linked to a myriad of adverse health outcomes such as lung cancer, respiratory diseases, and cardiovascular disorders [\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e]. Despite their distinct etiologies, recent research has begun to elucidate a complex relationship between smoking and obesity, highlighting the need for integrated public health strategies to address these dual epidemics [\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e, \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eThe relationship between smoking and body weight is paradoxical. While smoking has long been perceived as a weight control tool due to its appetite-suppressing effects [\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e], evidence suggests that smokers may have higher levels of abdominal fat compared to non-smokers [\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e]. Additionally, smoking cessation is often associated with weight gain, further complicating the relationship between these two factors [\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e]. Understanding the nuanced interplay between smoking behaviors and obesity is crucial for developing effective interventions, particularly in populations with high smoking prevalence [\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e, \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eTaiwan, like many other countries, faces the dual burden of increasing obesity rates and persistent smoking prevalence. According to the Taiwan Ministry of Health and Welfare, approximately 20% of adult men are smokers, and the prevalence of obesity has been steadily rising [\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e]. The cultural and socioeconomic dynamics in Taiwan present unique challenges and opportunities for studying the intersection of smoking and obesity. Factors such as socioeconomic status, educational attainment, and self-perceived health play pivotal roles in shaping smoking behaviors and obesity risk [\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e, \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eThe association between cigarette smoking and both general and abdominal obesity risks has garnered considerable attention in recent years, given its potential to exacerbate health inequalities and contribute to the rising prevalence of obesity-related diseases [\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e]. Previous studies have explored the individual impacts of smoking and obesity on health, but few have focused on their combined effects, particularly in the Taiwanese context. This study aims to fill this gap by examining the association between different smoking behaviors and obesity among Taiwanese men. By analyzing various smoking intensities and their relation to body mass index (BMI) and waist circumference (WC), we aim to provide insights into the public health implications of smoking on obesity and identify potential targets for intervention. Furthermore, this research seeks to explore the socioeconomic and educational factors contributing to these health behaviors, offering a comprehensive understanding of the complex interplay between smoking and obesity [\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e, \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eThe findings from this study are expected to inform policymakers and public health professionals about the importance of integrating smoking cessation and obesity prevention efforts. By addressing both smoking and obesity simultaneously, we can work towards reducing the overall burden of disease and improving health outcomes in Taiwan and beyond [\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e, \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e].\u003c/p\u003e"},{"header":"Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003eStudy Design and Data Sources\u003c/h2\u003e \u003cp\u003eData for this cross-sectional study were sourced from the Taiwan National Physical Fitness Survey (TNPFS) conducted by the Sports Administration, Ministry of Education, Taiwan. The TNPFS's established protocol and tool have been detailed in previous publications [\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e, \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e]. A summarized overview of the survey methods is provided here. Participants were recruited via age- and sex-stratified convenience sampling from 46 physical fitness test stations across 22 cities and counties in Taiwan, between October 2014 and March 2015. The survey involved a face-to-face interview, where trained examiners and medical professionals (usually nurses or doctors) administered a standardized structured questionnaire. This was followed by anthropometric measurements and health-related physical fitness tests. The collected TNPFS data, de-identified and managed by the Sports Cloud: Information and Application Research Center of Sports for All, Sports Administration, Ministry of Education, Taiwan, are publicly accessible for research. Detailed information about TNPFS can be found at Sports Cloud. This study adhered to the Declaration of Helsinki guidelines and received approval from the Institutional Review Board of Fu Jen Catholic University in Taiwan (FJU-IRB C110113).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003eEligibility Criteria for Study Participants\u003c/h2\u003e \u003cp\u003ePrior to TNPFS data collection, ineligible participants were excluded based on the following criteria: (1) systolic blood pressure\u0026thinsp;\u0026ge;\u0026thinsp;140 mmHg or diastolic blood pressure\u0026thinsp;\u0026ge;\u0026thinsp;90 mmHg; and (2) history or presence of heart disease, hypertension, chest pain, vertigo, or musculoskeletal disorders. Consequently, the TNPFS 2014\u0026ndash;15 database includes 62,586 adults aged 23\u0026ndash;64. For this study, female and non-Taiwanese participants were further excluded, along with records having missing data. Ultimately, 27,908 male Taiwanese adults aged 23\u0026ndash;64 were included in the analysis.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec5\" class=\"Section2\"\u003e \u003ch2\u003eData Collection\u003c/h2\u003e \u003cp\u003eIn the face-to-face interviews conducted by trained examiners, data were collected on sociodemographic characteristics (age, sex, education, monthly income, marital status), cigarette smoking status, betel-nut chewing habits, perceived health, and anthropometric measurements. Education was categorized into three levels: elementary school or lower, junior or senior high school, and college or higher. Monthly income was classified into: \u0026le;20,000 NTD, 20,001\u0026ndash;40,000 NTD, and \u0026ge;\u0026thinsp;40,001 NTD. Marital status was divided into married, never married, and divorced/separated/widowed. Betel-nut chewing habits were categorized as never users, former users, and current users. Health status was self-reported as excellent or good, fair, and very bad or poor. Anthropometric measurements included body weight (measured in light clothing to an accuracy of 0.1 kg using a scale), height (measured to an accuracy of 0.1 cm using a wall-mounted tape and headpiece, with shoes removed), waist circumference (WC), and hip circumference (HC), both measured to the nearest 0.1 cm. Body Mass Index (BMI) was calculated as weight (kg) divided by height (m\u0026sup2;). Obesity was classified using the Health Promotion Administration, Taiwan's BMI cutoffs: underweight (BMI\u0026thinsp;\u0026lt;\u0026thinsp;18.5 kg/m\u0026sup2;), normal weight (18.5\u0026thinsp;\u0026le;\u0026thinsp;BMI\u0026thinsp;\u0026lt;\u0026thinsp;24 kg/m\u0026sup2;), overweight (24\u0026thinsp;\u0026le;\u0026thinsp;BMI\u0026thinsp;\u0026lt;\u0026thinsp;27 kg/m\u0026sup2;), or obese (BMI\u0026thinsp;\u0026ge;\u0026thinsp;27 kg/m\u0026sup2;) [\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e]. WC and HC measurements were averaged from two readings, and Waist-Hip Ratio (WHR) was calculated as WC divided by HC.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec6\" class=\"Section2\"\u003e \u003ch2\u003eCigarette Smoking Status\u003c/h2\u003e \u003cp\u003eParticipants' smoking status was self-reported during the interview and categorized as never smokers (smoked\u0026thinsp;\u0026lt;\u0026thinsp;1 g of tobacco per day), current smokers (\u0026gt;\u0026thinsp;1 g of tobacco per day), and former smokers (those who had quit for more than 1 year) [\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e].\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec7\" class=\"Section2\"\u003e \u003ch2\u003eStatistical Analysis\u003c/h2\u003e \u003cp\u003eData from 27,908 male participants meeting the sampling criteria were analyzed using SAS version 9.4 (SAS Institute., Cary, NC, USA). Descriptive analyses included means\u0026thinsp;\u0026plusmn;\u0026thinsp;standard deviations (SD) for continuous variables and percentages for categorical variables. The Shapiro\u0026ndash;Wilk test assessed the normal distribution of data. Demographic characteristics were compared using Chi-square tests and one-way ANOVA among different smoking status groups. Where significant F values (p\u0026thinsp;\u0026lt;\u0026thinsp;0.05) were noted, Tukey's post hoc test identified differences between mean pairs. These differences were considered potential confounders for linear regression model adjustment. Multiple linear regression analysis evaluated the association between smoking status and general and abdominal obesity, adjusting for potential confounders like age, education, income, self-reported health, and betel nut chewing habits. All statistical tests were two-tailed, with significance set at p\u0026thinsp;\u0026lt;\u0026thinsp;0.05.\u003c/p\u003e \u003c/div\u003e"},{"header":"Results","content":"\u003cp\u003eThis study analyzed the association between different smoking behaviors and weight-related indicators among Taiwanese men. The total sample size was 27,908 men, divided into five groups: never smoking (NS, n\u0026thinsp;=\u0026thinsp;19,674), former smoking (FS, n\u0026thinsp;=\u0026thinsp;2,766), light-intensity smoking (LIS, n\u0026thinsp;=\u0026thinsp;2,798), moderate-intensity smoking (MIS, n\u0026thinsp;=\u0026thinsp;2,039), and heavy-intensity smoking (HIS, n\u0026thinsp;=\u0026thinsp;631). The findings are as follows:\u003c/p\u003e\n\u003cdiv id=\"Sec9\"\u003e\n \u003ch2\u003eAge Distribution\u003c/h2\u003e\n \u003cp\u003eThere was a significant difference in age distribution among different smoking behaviors (p\u0026thinsp;\u0026lt;\u0026thinsp;0.0001). In the 23\u0026ndash;34 age group, the highest proportions were among never smokers (39.61%) and light-intensity smokers (40.35%), while the lowest was among former smokers (18.47%). In the 35\u0026ndash;44 age group, heavy smokers (36.29%) had the highest proportion. In the 45\u0026ndash;64 age group, former smokers had the highest proportion (51.45%), indicating that older individuals were more likely to be former smokers (Table \u003cspan\u003e1\u003c/span\u003e).\u003c/p\u003e\n \u003cdiv\u003e\n \u003ctable id=\"Tab1\" border=\"1\"\u003e\n \u003ccaption language=\"En\"\u003e\n \u003cdiv\u003eTable 1\u003c/div\u003e\n \u003cdiv\u003e\n \u003cp\u003eCharacteristics of the study participants according to cigarette smoking behaviors among men in Taiwan.\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003ccolgroup cols=\"8\"\u003e\u003c/colgroup\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eVariables\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eNS\u003c/p\u003e\n \u003cp\u003e(\u003cem\u003en\u003c/em\u003e\u0026thinsp;=\u0026thinsp;19,674)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eFS\u003c/p\u003e\n \u003cp\u003e(\u003cem\u003en\u003c/em\u003e\u0026thinsp;=\u0026thinsp;2,766)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eLIS\u003c/p\u003e\n \u003cp\u003e(\u003cem\u003en\u003c/em\u003e\u0026thinsp;=\u0026thinsp;2,798)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eMIS\u003c/p\u003e\n \u003cp\u003e(\u003cem\u003en\u003c/em\u003e\u0026thinsp;=\u0026thinsp;2,039)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eHIS\u003c/p\u003e\n \u003cp\u003e(\u003cem\u003en\u003c/em\u003e\u0026thinsp;=\u0026thinsp;631)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003e\u003cem\u003ep\u003c/em\u003e\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eTukey\u0026rsquo;s post hoc test\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eAge group (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.0001*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e23\u0026ndash;34 years\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e39.61\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e18.47\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e40.35\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e33.25\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e23.77\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e35\u0026ndash;44 years\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e26.20\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e30.08\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e29.16\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e34.09\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e36.29\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e45\u0026ndash;64 years\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e34.20\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e51.45\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e30.49\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e32.66\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e39.94\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eHeight (cm)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e170.67 (6.45)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e170.32 (6.08)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e171.12 (6.13)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e170.93 (6.02)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e170.76 (5.99)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.0001*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eBody weight (kg)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e71.77 (10.47)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e73.30 (9.94)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e73.03 (10.75)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e73.08 (10.91)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e74.41 (11.00)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.0001*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eBMI (kg/m\u003csup\u003e2\u003c/sup\u003e)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e24.62 (3.22)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e25.26 (3.13)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e24.92 (3.33)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e25.00 (3.43)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e25.51 (3.50)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.0001*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eLIS\u0026thinsp;=\u0026thinsp;MIS, MIS\u0026thinsp;=\u0026thinsp;FS, HIS\u0026thinsp;=\u0026thinsp;FS\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eWC (cm)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e83.98 (8.64)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e86.35 (8.34)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e84.84 (8.88)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e85.77 (8.94)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e87.34 (9.09)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.0001*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eMIS\u0026thinsp;=\u0026thinsp;FS, HIS\u0026thinsp;=\u0026thinsp;FS\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eHC (cm)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e96.85 (6.26)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e97.35 (5.83)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e96.97 (6.43)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e96.92 (6.32)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e97.53 (6.67)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.0003*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNS\u0026thinsp;\u0026lt;\u0026thinsp;FS\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eWHR\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.866 (0.058)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.886 (0.056)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.874 (0.058)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.884 (0.057)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.895 (0.058)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.0001*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNS\u0026thinsp;\u0026lt;\u0026thinsp;LIS\u0026thinsp;\u0026lt;\u0026thinsp;MIS, FS\u0026thinsp;\u0026lt;\u0026thinsp;HIS\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eEducation level (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.0001*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eElementary school or lower\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1.68\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1.54\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1.78\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1.59\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e3.99\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eJunior or senior school\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e19.31\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e32.16\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e31.12\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e43.16\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e47.92\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eCollege or higher\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e79.02\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e66.30\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e67.10\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e55.25\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e48.08\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eIncome level (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.0001*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e≦ 20,000 NTD\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e16.21\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e11.18\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e14.54\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e11.79\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e13.76\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e20,001\u0026ndash;40,000 NTD\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e33.69\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e33.38\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e39.42\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e39.74\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e37.31\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e≧ 40,001 NTD\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e50.10\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e55.44\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e46.04\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e48.46\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e48.92\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eSelf-reported health status (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.0001*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eExcellent or good\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e64.10\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e60.16\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e60.37\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e52.95\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e48.25\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eFair\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e30.07\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e32.97\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e33.51\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e39.18\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e37.46\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eVery bad or poor\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e5.83\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e6.87\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e6.12\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e7.87\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e14.29\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n \u003c/div\u003e\n \u003cp\u003e\u003cem\u003eAbbreviations:\u0026nbsp;\u003c/em\u003eBMI, body mass index; FS, former smoking; HC, hip circumference; HIS, heavy-intensity smoking; LIS, light-intensity smoking; NTD, New Taiwan Dolloar; MIS, moderate-intensity smoking; NS, never smoking; SD, standard deviation; WC, waist circumference; WHR, waist-to-hip ratio; UW, underweight.\u0026nbsp;\u003c/p\u003e\n \u003cp\u003eValues are expressed as mean (SD) or %. \u003csup\u003e*\u003c/sup\u003e\u003cem\u003ep\u003c/em\u003e \u0026lt; 0.05.\u003c/p\u003e\u0026nbsp;\n\u003c/div\u003e\n\u003cdiv id=\"Sec10\"\u003e\n \u003ch2\u003ePhysical Characteristics\u003c/h2\u003e\n \u003cp\u003eSignificant differences were found in height, body weight, BMI, waist circumference (WC), hip circumference (HC), and waist-to-hip ratio (WHR) among the groups:\u003c/p\u003e\n \u003cul\u003e\n \u003cli\u003e\n \u003cp\u003e\u003cstrong\u003eHeight\u003c/strong\u003e: Light-intensity smokers had the highest average height (171.12 cm), while former smokers had the lowest (170.32 cm).\u003c/p\u003e\n \u003c/li\u003e\n \u003cli\u003e\n \u003cp\u003e\u003cstrong\u003eBody Weight\u003c/strong\u003e: Body weight increased with smoking intensity, with heavy smokers having the highest weight (74.41 kg).\u003c/p\u003e\n \u003c/li\u003e\n \u003cli\u003e\n \u003cp\u003e\u003cstrong\u003eBMI\u003c/strong\u003e: Heavy smokers had the highest BMI (25.51 kg/m\u0026sup2;), indicating a higher risk of obesity.\u003c/p\u003e\n \u003c/li\u003e\n \u003cli\u003e\n \u003cp\u003e\u003cstrong\u003eWaist Circumference (WC)\u003c/strong\u003e: Heavy smokers had the largest waist circumference (87.34 cm), suggesting a greater prevalence of abdominal obesity.\u003c/p\u003e\n \u003c/li\u003e\n \u003cli\u003e\n \u003cp\u003e\u003cstrong\u003eHip Circumference (HC)\u003c/strong\u003e: Former smokers and heavy smokers had slightly larger mean hip circumferences.\u003c/p\u003e\n \u003c/li\u003e\n \u003cli\u003e\n \u003cp\u003e\u003cstrong\u003eWaist-to-Hip Ratio (WHR)\u003c/strong\u003e: Heavy smokers had the highest waist-to-hip ratio (0.895), indicating a higher risk of abdominal obesity.\u003c/p\u003e\n \u003c/li\u003e\n \u003c/ul\u003e\n \u003cp\u003eThe differences in physical characteristics were statistically significant (p\u0026thinsp;\u0026lt;\u0026thinsp;0.0001) (Table \u003cspan\u003e1\u003c/span\u003e).\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec11\"\u003e\n \u003ch2\u003eSocioeconomic Status\u003c/h2\u003e\n \u003cp\u003eThere is a significant association between socioeconomic status and smoking behaviors (p\u0026thinsp;\u0026lt;\u0026thinsp;0.0001). In terms of education level, heavy smokers had the highest proportion with elementary school or lower education (3.99%), whereas never smokers had the highest proportion with college or higher education (79.02%). For income levels, former smokers had the highest proportion with incomes above 40,001 NTD (55.44%), while never smokers had the highest proportion with low incomes (\u0026le;\u0026thinsp;20,000 NTD) (16.21%) (Table \u003cspan\u003e1\u003c/span\u003e).\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec12\"\u003e\n \u003ch2\u003eSelf-Reported Health Status\u003c/h2\u003e\n \u003cp\u003eThere were significant differences in self-reported health status among smoking behaviors (p\u0026thinsp;\u0026lt;\u0026thinsp;0.0001). Never smokers reported the highest percentage of \u0026quot;excellent or good\u0026quot; health status (64.10%), while heavy smokers had the highest percentage of \u0026quot;very bad or poor\u0026quot; health status (14.29%) (Table \u003cspan\u003e1\u003c/span\u003e).\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec13\"\u003e\n \u003ch2\u003eAssociation Between Smoking Behaviors and Obesity\u003c/h2\u003e\n \u003cp\u003eThere is a significant association between smoking behaviors and the prevalence of general and abdominal obesity (p\u0026thinsp;\u0026lt;\u0026thinsp;0.0001). In the 23\u0026ndash;34 age group, heavy smokers had a significantly higher proportion of general obesity than other groups. With increasing age, the proportion of general and abdominal obesity increased across all groups (Table \u003cspan\u003e2\u003c/span\u003e).\u003c/p\u003e\n \u003cdiv\u003e\n \u003ctable id=\"Tab2\" border=\"1\"\u003e\n \u003ccaption language=\"En\"\u003e\n \u003cdiv\u003eTable 2\u003c/div\u003e\n \u003cdiv\u003e\n \u003cp\u003ePrevalence of general and abdominal obesity according to cigarette smoking behaviors among men in Taiwan.\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003ccolgroup cols=\"10\"\u003e\u003c/colgroup\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\" rowspan=\"2\"\u003e\n \u003cp\u003eVariables\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" colspan=\"5\"\u003e\n \u003cp\u003eGeneral obesity\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\u0026nbsp;\u003c/th\u003e\n \u003cth align=\"left\" colspan=\"3\"\u003e\n \u003cp\u003eAbdominal obesity\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eUnderweight\u003c/p\u003e\n \u003cp\u003e(\u003cem\u003en\u003c/em\u003e\u0026thinsp;=\u0026thinsp;309)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eNormal weight\u003c/p\u003e\n \u003cp\u003e(\u003cem\u003en\u003c/em\u003e\u0026thinsp;=\u0026thinsp;4,957)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eOverweight\u003c/p\u003e\n \u003cp\u003e(\u003cem\u003en\u003c/em\u003e\u0026thinsp;=\u0026thinsp;2,923)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eObesity\u003c/p\u003e\n \u003cp\u003e(\u003cem\u003en\u003c/em\u003e\u0026thinsp;=\u0026thinsp;2,071)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003e\u003cem\u003ep\u003c/em\u003e\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\u0026nbsp;\u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eAbdominal obesity\u003c/p\u003e\n \u003cp\u003e(\u003cem\u003en\u003c/em\u003e\u0026thinsp;=\u0026thinsp;2,142)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eNon-abdominal obesity (\u003cem\u003en\u003c/em\u003e\u0026thinsp;=\u0026thinsp;8,118)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003e\u003cem\u003ep\u003c/em\u003e\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e23\u0026ndash;34 years\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.0001*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.0001*\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eLIS\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e2.27\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1.09\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1.47\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e2.22\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1.96\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1.33\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eMIS\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e6.47\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e6.48\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e5.95\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e7.87\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e8.45\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e6.12\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eHIS\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e13.59\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e10.29\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e10.91\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e12.46\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e12.32\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e10.66\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eFS\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1.62\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e4.78\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e5.27\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e5.55\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e5.37\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e4.88\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNS\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e76.05\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e77.37\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e76.39\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e71.90\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e71.90\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e77.01\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e35\u0026ndash;44 years\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.0002*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.0001*\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eLIS\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e2.78\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e2.27\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e3.02\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e3.86\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e4.38\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e2.33\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eMIS\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e5.56\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e8.96\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e8.54\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e9.78\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e10.06\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e8.52\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eHIS\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e12.50\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e9.21\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e11.38\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e11.25\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e11.10\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e10.32\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eFS\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e8.33\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e9.88\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e10.55\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e12.37\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e11.69\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e10.36\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNS\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e70.83\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e69.68\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e66.51\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e62.74\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e62.77\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e68.48\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e45\u0026ndash;64 years\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.0001*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.0001*\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eLIS\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1.89\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e2.32\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e2.25\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e3.36\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e3.46\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e2.08\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eMIS\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e5.66\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e6.15\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e6.45\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e8.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e8.28\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e5.93\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eHIS\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e8.49\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e8.08\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e8.49\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e9.54\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e9.91\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e7.94\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eFS\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e10.38\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e12.36\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e15.34\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e15.88\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e16.67\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e13.18\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNS\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e73.58\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e71.09\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e67.47\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e63.23\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e61.69\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e70.86\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n \u003c/div\u003e\n \u003cp\u003e\u003cem\u003eAbbreviations:\u0026nbsp;\u003c/em\u003eFS, former smoking; HIS, heavy-intensity smoking; LIS, light-intensity smoking; MIS, moderate-intensity smoking; NS, never smoking.\u0026nbsp;\u003c/p\u003e\n \u003cp\u003eValues are expressed as or %.\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e\u003csup\u003e*\u003c/sup\u003e\u003cem\u003ep\u003c/em\u003e \u0026lt; 0.05.\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec14\"\u003e\n \u003ch2\u003eMultiple Regression Analysis\u003c/h2\u003e\n \u003cp\u003eAfter controlling for age, education level, income level, and health status, a significant positive association between smoking behaviors and both BMI and waist circumference was found:\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec15\"\u003e\n \u003ch2\u003e\u0026bull; \u003cstrong\u003eBMI\u003c/strong\u003e:\u003c/h2\u003e\n \u003cul\u003e\n \u003cli\u003e\n \u003cp\u003eLight-intensity smokers had the highest regression coefficient (\u0026beta;\u0026thinsp;=\u0026thinsp;0.604, p\u0026thinsp;\u0026lt;\u0026thinsp;0.0001), indicating a significantly higher BMI than never smokers.\u003c/p\u003e\n \u003c/li\u003e\n \u003cli\u003e\n \u003cp\u003eAll smoking behavior groups showed a positive trend in BMI (trend test p\u0026thinsp;\u0026lt;\u0026thinsp;0.0001) (Table \u003cspan\u003e3\u003c/span\u003e).\u003c/p\u003e\n \u003c/li\u003e\n \u003c/ul\u003e\n \u003cdiv\u003e\n \u003ctable id=\"Tab3\" border=\"1\"\u003e\n \u003ccaption language=\"En\"\u003e\n \u003cdiv\u003eTable 3\u003c/div\u003e\n \u003cdiv\u003e\n \u003cp\u003eMultiple regressions for the association of cigarette smoking behaviors with BMI and WC after adjustment for potential confounders\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003ccolgroup cols=\"9\"\u003e\u003c/colgroup\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\" rowspan=\"2\"\u003e\n \u003cp\u003eVariables\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" rowspan=\"2\"\u003e\n \u003cp\u003eCigarette smoking\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" colspan=\"3\"\u003e\n \u003cp\u003eModel 1 (Age-adjusted)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\u0026nbsp;\u003c/th\u003e\n \u003cth align=\"left\" colspan=\"3\"\u003e\n \u003cp\u003eModel 2 (Multivariate-adjusted\u003csup\u003ea\u003c/sup\u003e)\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003e\u003cem\u003e\u0026beta;\u003c/em\u003e\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eSE\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003e\u003cem\u003ep\u003c/em\u003e\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\u0026nbsp;\u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003e\u003cem\u003e\u0026beta;\u003c/em\u003e\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eSE\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003e\u003cem\u003ep\u003c/em\u003e\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" rowspan=\"5\"\u003e\n \u003cp\u003eBMI (kg/m\u003csup\u003e2\u003c/sup\u003e)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eLIS\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.799\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.130\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.0001*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.604\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.134\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.0001*\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eMIS\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.356\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.075\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.0001*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.281\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.077\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.0001*\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eHIS\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.320\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.065\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.0001*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.286\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.067\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.0001*\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eFS\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.478\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.066\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.0001*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.422\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.068\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.0001*\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNS\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026mdash;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026mdash;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026mdash;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026mdash;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003eTest for trend\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"3\"\u003e\n \u003cp\u003e\u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.0001*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"3\"\u003e\n \u003cp\u003e\u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.0001*\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" rowspan=\"5\"\u003e\n \u003cp\u003eWC (cm)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eLIS\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2.946\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.344\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.0001*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2.335\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.353\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.0001*\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eMIS\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.700\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.198\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.0001*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.532\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.204\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.0001*\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eHIS\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.942\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.172\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.0001*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.905\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.176\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.0001*\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eFS\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.635\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.174\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.0001*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.490\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.178\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.0001*\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNS\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026mdash;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026mdash;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026mdash;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026mdash;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003eTest for trend\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"3\"\u003e\n \u003cp\u003e\u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.0001*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"3\"\u003e\n \u003cp\u003e\u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.0001*\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003ctfoot\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"9\"\u003e\u003cem\u003eAbbreviations: \u0026beta;\u003c/em\u003e, regression coefficient; BMI, body mass index; CI, confidence interval; FS, former smoking; HIS, heavy-intensity smoking; LIS, light-intensity smoking; MIS, moderate-intensity smoking; NS, never smoking; SE, standard error; WC, waist circumference.\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"9\"\u003e*\u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05.\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"9\"\u003e\u003csup\u003ea\u003c/sup\u003eAdjusted for age group, educational levels, monthly income levels, and self-reported health status.\u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tfoot\u003e\n \u003c/table\u003e\n \u003c/div\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec16\"\u003e\n \u003ch2\u003e\u0026bull; \u003cstrong\u003eWaist Circumference (WC)\u003c/strong\u003e:\u003c/h2\u003e\n \u003cul\u003e\n \u003cli\u003e\n \u003cp\u003eLight-intensity smokers also had the highest regression coefficient (\u0026beta;\u0026thinsp;=\u0026thinsp;2.335, p\u0026thinsp;\u0026lt;\u0026thinsp;0.0001), indicating a significantly larger waist circumference than never smokers.\u003c/p\u003e\n \u003c/li\u003e\n \u003cli\u003e\n \u003cp\u003eAll smoking behavior groups showed a positive trend in waist circumference (trend test p\u0026thinsp;\u0026lt;\u0026thinsp;0.0001) (Table \u003cspan\u003e3\u003c/span\u003e).\u003c/p\u003e\n \u003c/li\u003e\n \u003c/ul\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec17\"\u003e\n \u003ch2\u003eMultivariate Adjusted ORs for Obesity\u003c/h2\u003e\n \u003cp\u003eThe multivariate adjusted odds ratios (ORs) demonstrate a significant association between smoking behaviors and general and abdominal obesity:\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec18\"\u003e\n \u003ch2\u003e\u0026bull; \u003cstrong\u003eGeneral Obesity\u003c/strong\u003e:\u003c/h2\u003e\n \u003cul\u003e\n \u003cli\u003e\n \u003cp\u003eLight-intensity smokers had the highest OR for general obesity (OR\u0026thinsp;=\u0026thinsp;1.566, 95% CI: 1.272\u0026ndash;1.928, p\u0026thinsp;\u0026lt;\u0026thinsp;0.0001).\u003c/p\u003e\n \u003c/li\u003e\n \u003cli\u003e\n \u003cp\u003eFormer smokers and heavy smokers also had ORs significantly greater than never smokers (Table \u003cspan\u003e4\u003c/span\u003e).\u003c/p\u003e\n \u003c/li\u003e\n \u003c/ul\u003e\n \u003cdiv\u003e\n \u003ctable id=\"Tab4\" border=\"1\"\u003e\n \u003ccaption language=\"En\"\u003e\n \u003cdiv\u003eTable 4\u003c/div\u003e\n \u003cdiv\u003e\n \u003cp\u003eMultivariate adjusted ORs for general obesity in relation to cigarette smoking behaviors after adjustment for potential confounders.\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003ccolgroup cols=\"9\"\u003e\u003c/colgroup\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\" rowspan=\"2\"\u003e\n \u003cp\u003eGeneral obesity status\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" rowspan=\"2\"\u003e\n \u003cp\u003eCigarette smoking\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" colspan=\"3\"\u003e\n \u003cp\u003eModel 1 (Age-adjusted)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\u0026nbsp;\u003c/th\u003e\n \u003cth align=\"left\" colspan=\"3\"\u003e\n \u003cp\u003eModel 2 (Multivariate-adjusted\u003csup\u003ea\u003c/sup\u003e)\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eOR\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003e95% CI\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003e\u003cem\u003ep\u003c/em\u003e\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\u0026nbsp;\u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eOR\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003e95% CI\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003e\u003cem\u003ep\u003c/em\u003e\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" rowspan=\"5\"\u003e\n \u003cp\u003eObesity\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eLIS\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.890\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.549\u0026ndash;2.305\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.0001*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.566\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.272\u0026ndash;1.928\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.0001*\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eMIS\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.350\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.203\u0026ndash;1.514\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.0001*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.242\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.101\u0026ndash;1.402\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.0004*\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eHIS\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.332\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.203\u0026ndash;1.476\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.0001*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.272\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.143\u0026ndash;1.414\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.0001*\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eFS\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.404\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.266\u0026ndash;1.557\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.0001*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.334\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.198\u0026ndash;1.485\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.0001*\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNS\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026mdash;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026mdash;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026mdash;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026mdash;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003eTest for trend\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"3\"\u003e\n \u003cp\u003e\u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.0001*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"3\"\u003e\n \u003cp\u003e\u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.0001*\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" rowspan=\"5\"\u003e\n \u003cp\u003eOverweight\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eLIS\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.243\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.021\u0026ndash;1.513\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.0303*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.213\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.990\u0026ndash;1.485\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.0620\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eMIS\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.026\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.921\u0026ndash;1.145\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.6831\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.032\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.921\u0026ndash;1.155\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.5895\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eHIS\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.153\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.049\u0026ndash;1.266\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.0030*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.153\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.047\u0026ndash;1.270\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.0038*\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eFS\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.214\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.105\u0026ndash;1.335\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.0001*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.204\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.092\u0026ndash;1.328\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.0002*\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNS\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026mdash;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026mdash;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026mdash;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026mdash;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003eTest for trend\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"3\"\u003e\n \u003cp\u003e\u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.0015*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"3\"\u003e\n \u003cp\u003e\u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.0029*\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" rowspan=\"5\"\u003e\n \u003cp\u003eUnderweight\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eLIS\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.414\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.762\u0026ndash;2.625\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.2724\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.987\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.512\u0026ndash;1.906\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.9696\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eMIS\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.880\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.602\u0026ndash;1.287\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.5090\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.725\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.486\u0026ndash;1.083\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.1167\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eHIS\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.272\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.960\u0026ndash;1.685\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.0937\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.118\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.833\u0026ndash;1.501\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.4567\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eFS\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.167\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.398\u0026ndash;0.956\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.0307*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.631\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.406\u0026ndash;0.981\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.0408*\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNS\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026mdash;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026mdash;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026mdash;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026mdash;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003eTest for trend\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"3\"\u003e\n \u003cp\u003e\u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.5538\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"3\"\u003e\n \u003cp\u003e\u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.3692\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n \u003c/div\u003e\n \u003cp\u003e\u003cem\u003eAbbreviations:\u003c/em\u003e CI, confidence interval;\u0026nbsp;FS,\u0026nbsp;former smoking; HIS,\u0026nbsp;heavy-intensity smoking; LIS,\u0026nbsp;light-intensity smoking; MIS, moderate-intensity smoking; NS, never smoking; OR, odds ratio.\u003c/p\u003e\n \u003cp\u003e*\u003cem\u003ep\u003c/em\u003e \u0026lt; 0.05.\u003c/p\u003e\n \u003cp\u003e\u003csup\u003ea\u003c/sup\u003eAdjusted for age group, educational levels, monthly income levels, and self-reported health status.\u003c/p\u003e\u0026nbsp;\u0026nbsp;\n\u003c/div\u003e\n\u003cdiv id=\"Sec19\"\u003e\n \u003ch2\u003e\u0026bull; \u003cstrong\u003eAbdominal Obesity\u003c/strong\u003e:\u003c/h2\u003e\n \u003cul\u003e\n \u003cli\u003e\n \u003cp\u003eAll smoking behavior groups had higher ORs for abdominal obesity compared to never smokers, with statistical significance (p\u0026thinsp;\u0026lt;\u0026thinsp;0.0001) (Table \u003cspan\u003e5\u003c/span\u003e).\u003c/p\u003e\n \u003c/li\u003e\n \u003c/ul\u003e\n \u003cdiv\u003e\n \u003ctable id=\"Tab5\" border=\"1\"\u003e\n \u003ccaption language=\"En\"\u003e\n \u003cdiv\u003eTable 5\u003c/div\u003e\n \u003cdiv\u003e\n \u003cp\u003eMultivariate adjusted ORs for abdominal obesity in relation to cigarette smoking behaviors after adjustment for potential confounders.\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003ccolgroup cols=\"9\"\u003e\u003c/colgroup\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\" rowspan=\"2\"\u003e\n \u003cp\u003eAbdominal obesity status\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" rowspan=\"2\"\u003e\n \u003cp\u003eCigarette smoking\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" colspan=\"3\"\u003e\n \u003cp\u003eModel 1 (Age-adjusted)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\u0026nbsp;\u003c/th\u003e\n \u003cth align=\"left\" colspan=\"3\"\u003e\n \u003cp\u003eModel 2 (Multivariate-adjusted\u003csup\u003ea\u003c/sup\u003e)\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eOR\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003e95% CI\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003e\u003cem\u003ep\u003c/em\u003e\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\u0026nbsp;\u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eOR\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003e95% CI\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003e\u003cem\u003ep\u003c/em\u003e\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" rowspan=\"5\"\u003e\n \u003cp\u003eAbdominal obesity\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eLIS\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.922\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.634\u0026ndash;2.262\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.0001*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.634\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.378\u0026ndash;1.937\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.0001*\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eMIS\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.474\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.337\u0026ndash;1.625\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.0001*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.393\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.258\u0026ndash;1.542\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.0001*\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eHIS\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.291\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.183\u0026ndash;1.408\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.0001*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.271\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.161\u0026ndash;1.391\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.0001*\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eFS\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.340\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.229\u0026ndash;1.460\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.0001*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.282\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.173\u0026ndash;1.401\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.0001*\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNS\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026mdash;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026mdash;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026mdash;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026mdash;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003eTest for trend\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"3\"\u003e\n \u003cp\u003e\u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.0001*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"3\"\u003e\n \u003cp\u003e\u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.0001*\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n \u003c/div\u003e\n \u003cp\u003e\u003cem\u003eAbbreviations:\u003c/em\u003e CI, confidence interval;\u0026nbsp;FS,\u0026nbsp;former smoking; HIS,\u0026nbsp;heavy-intensity smoking; LIS,\u0026nbsp;light-intensity smoking; MIS, moderate-intensity smoking; NS, never smoking; OR, odds ratio.\u003c/p\u003e\n \u003cp\u003e*\u003cem\u003ep\u003c/em\u003e \u0026lt; 0.05.\u003c/p\u003e\n \u003cp\u003e\u003csup\u003ea\u003c/sup\u003eAdjusted for age group, educational levels, monthly income levels, and self-reported health status.\u003c/p\u003e\n\u003c/div\u003e"},{"header":"Discussion","content":"\u003cp\u003eThis study provides compelling evidence of the complex relationship between cigarette smoking behaviors and obesity among men in Taiwan. Our findings demonstrate that smoking is significantly associated with both general and abdominal obesity risks, with light- and moderate-intensity smokers showing particularly high risks for increased BMI and waist circumference. This comprehensive examination of smoking behaviors reveals a nuanced understanding of how different intensities of smoking contribute to varying levels of obesity risk.\u003c/p\u003e \u003cdiv id=\"Sec21\" class=\"Section2\"\u003e \u003ch2\u003eSmoking and Obesity\u003c/h2\u003e \u003cp\u003eContrary to the commonly held belief that smoking aids in weight control, our study reveals that smoking, especially at light and moderate intensities, is positively associated with increased obesity risk. This finding aligns with several studies that have documented similar associations between smoking and obesity [\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e, \u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e]. For instance, Chiolero et al. demonstrated that smokers tend to have higher waist circumferences compared to non-smokers, which supports our observation of increased abdominal obesity among smokers [\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eOne explanation for this paradoxical relationship is the role of smoking in altering metabolic processes and fat distribution. Smoking has been shown to decrease metabolic rate and increase appetite after cessation, potentially leading to weight gain [\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e]. Furthermore, nicotine can influence fat distribution, promoting visceral fat accumulation, which is more metabolically active and associated with higher cardiovascular risk [\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eOur findings underscore the necessity for public health interventions that challenge the misconception of smoking as a viable weight control method. The demonstrated association between smoking and both general and abdominal obesity risks in this study highlights the need for targeted education campaigns that address the harmful effects of smoking on body weight and fat distribution [\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e, \u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e].\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec22\" class=\"Section2\"\u003e \u003ch2\u003eSocioeconomic Status and Education\u003c/h2\u003e \u003cp\u003eOur findings highlight the significant role of socioeconomic factors in smoking behaviors and their association with obesity. Individuals with lower education and income levels were more likely to be heavy smokers, a pattern consistent with global trends [\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e, \u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e]. This socioeconomic gradient in smoking prevalence may exacerbate health inequalities, as individuals from lower socioeconomic backgrounds are already at greater risk for obesity and related comorbidities [\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eEducation plays a critical role in shaping health behaviors, including smoking and diet [\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e]. The higher prevalence of smoking among those with lower education levels in our study underscores the need for targeted public health interventions that address these disparities. Health education programs focused on smoking cessation and obesity prevention could be particularly beneficial in reducing the dual burden of smoking and obesity in this population [\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e].\u003c/p\u003e \u003cdiv id=\"Sec23\" class=\"Section3\"\u003e \u003ch2\u003eHealth Perceptions and Smoking\u003c/h2\u003e \u003cp\u003eThe study also reveals that smokers, particularly heavy smokers, are more likely to perceive their health as poor compared to non-smokers. This perception may contribute to a self-perpetuating cycle where individuals continue smoking despite awareness of its adverse effects on health [\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e]. Addressing this cognitive dissonance through motivational interviewing and behavioral interventions could enhance the effectiveness of smoking cessation efforts [\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e].\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv id=\"Sec24\" class=\"Section2\"\u003e \u003ch2\u003eImplications for Public Health and Policy\u003c/h2\u003e \u003cp\u003eThe significant association between smoking and obesity found in this study has important public health implications. First, it challenges the notion that smoking cessation invariably leads to weight gain, emphasizing the need for integrated interventions that address both smoking and weight management [\u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e]. Secondly, our findings support the implementation of policies that reduce smoking prevalence and address obesity simultaneously. For example, tobacco taxation, combined with subsidies for healthy foods, may create synergistic effects in reducing smoking and promoting healthier lifestyles [\u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eMoreover, the development of comprehensive health promotion strategies that target multiple risk factors, such as smoking, poor diet, and physical inactivity, could effectively combat the rising tide of obesity and its associated complications [\u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e40\u003c/span\u003e]. Such strategies should be culturally tailored to resonate with the specific beliefs and practices of the Taiwanese population, enhancing their acceptance and effectiveness [\u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e41\u003c/span\u003e].\u003c/p\u003e \u003cdiv id=\"Sec25\" class=\"Section3\"\u003e \u003ch2\u003eLimitations and Future Research\u003c/h2\u003e \u003cp\u003eWhile this study provides valuable insights, it is not without limitations. The cross-sectional design limits our ability to infer causality, and the reliance on self-reported data may introduce reporting biases. Future longitudinal studies could provide more robust evidence of the causal relationship between smoking and obesity. Additionally, exploring the biological mechanisms underlying the observed associations could deepen our understanding of how smoking influences obesity risk and identify potential targets for intervention [\u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e42\u003c/span\u003e].\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e"},{"header":"Conclusion","content":"\u003cp\u003eIn conclusion, our study underscores the complex interplay between smoking behaviors and general and abdominal obesity risks among men in Taiwan. It highlights the need for multifaceted public health strategies that address the dual burden of smoking and obesity, particularly among socioeconomically disadvantaged groups. By focusing on prevention and cessation efforts, policymakers can improve health outcomes and reduce the prevalence of these interrelated risk factors [\u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e43\u003c/span\u003e].\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eHuman Ethics and Consent to Participate:\u003c/strong\u003e This study adhered to the Declaration of Helsinki guidelines and received approval from the Institutional Review Board of Fu Jen Catholic University in Taiwan (FJU-IRB C110113).\u0026nbsp;All volunteers participating in the project provided informed consent before their involvement in the study\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent for publication:\u0026nbsp;\u003c/strong\u003eNot applicable\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAvailability of data and materials:\u003c/strong\u003e The data and software underlying the findings of this study are available from the corresponding author upon reasonable request.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting interests:\u0026nbsp;\u003c/strong\u003eThe authors declare no conflicts of interest.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding:\u003c/strong\u003e No funding.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthors\u0026apos; contributions:\u0026nbsp;\u003c/strong\u003eMin-Chen Wu,\u0026nbsp;Chien-Chang Ho\u003csub\u003e,\u0026nbsp;\u003c/sub\u003eand Yung-Po Liaw contributed to the conception and design of the study., Chien-Chang Ho, and Yung-Po Liaw analyzed and interpreted data. Min-Chen Wu drafted the manuscript, assisted by Oswald Ndi Nfor. Chien-Chang Ho, Oswald Ndi Nfor, and Yung-Po Liaw critically reviewed the manuscript. All authors approved the final manuscript.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eWorld Health Organization. Obesity and overweight. World Health Organization; 2020.\u003c/li\u003e\n\u003cli\u003eU.S. Department of Health and Human Services. The health consequences of smoking\u0026mdash;50 years of progress: A report of the Surgeon General. Atlanta, GA: Centers for Disease Control and Prevention; 2014.\u003c/li\u003e\n\u003cli\u003eMokdad AH, Marks JS, Stroup DF, Gerberding JL. Actual causes of death in the United States, 2000. JAMA. 2004;291(10):1238-45.\u003c/li\u003e\n\u003cli\u003eNg M, Fleming T, Robinson M, et al. 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JAMA. 2003;289(1):76-9.\u003c/li\u003e\n\u003cli\u003eFarley TA, Cohen DA. Prescription for a healthy nation: A new approach to improving our lives by fixing our everyday world. Beacon Press; 2005.\u003c/li\u003e\n\u003cli\u003eBarendregt JJ, Veerman JL. Causal inference in public health: The role of mediation analysis. Int J Public Health. 2010;55(1):177-82.\u003c/li\u003e\n\u003cli\u003eSteenland K, Armstrong B. An overview of methods for calculating the burden of disease due to specific risk factors. Epidemiology. 2006;17(5):512-9.\u003c/li\u003e\n\u003cli\u003eMarmot M, Wilkinson RG. Social determinants of health: The solid facts. 2nd ed. World Health Organization; 2003.\u003c/li\u003e\n\u003cli\u003eMirowsky J, Ross CE. Education, social status, and health. Transaction Publishers; 2003.\u003c/li\u003e\n\u003cli\u003eNational Institutes of Health. Clinical guidelines on the identification, evaluation, and treatment of overweight and obesity in adults: The evidence report. Obes Res. 1998;6(Suppl 2):51S-209S.\u003c/li\u003e\n\u003cli\u003eProchaska JJ, Das S, Young-Wolff KC. Smoking, mental illness, and public health.Annu Rev Public Health. 2017;38:165-85.\u003c/li\u003e\n\u003cli\u003eMiller WR, Rollnick S. Motivational interviewing: Preparing people for change. 2nd ed. Guilford Press; 2002.\u003c/li\u003e\n\u003cli\u003ePisinger C, J\u0026oslash;rgensen T. Weight concerns and smoking in a general population: The interplay with smoking status, obesity, gender and age. Prev Med. 2007;45(2-3):283-7.\u003c/li\u003e\n\u003cli\u003eJha P, Chaloupka FJ. Curbing the epidemic: Governments and the economics of tobacco control. World Bank Publications; 1999.\u003c/li\u003e\n\u003cli\u003eKumanyika SK, Obarzanek E, Stettler N, et al. Population-based prevention of obesity: The need for comprehensive promotion of healthful eating, physical activity, and energy balance. Circulation. 2008;118(4):428-64.\u003c/li\u003e\n\u003cli\u003eLau DC, Douketis JD, Morrison KM, et al. 2006 Canadian clinical practice guidelines on the management and prevention of obesity in adults and children. CMAJ. 2007;176(8)\u003c/li\u003e\n\u003cli\u003eLaRowe TL, Wubben DP, Karanja N, et al. Development of a culturally appropriate, home-based nutrition and physical activity curriculum for Wisconsin American Indian families. Prev Chronic Dis. 2007;4(4)\u003c/li\u003e\n\u003cli\u003eAldrich R, Zwi AB, Short S, et al. Setting priorities for health research: Lessons from developing countries. Health Policy Plan. 2000;15(2):130-6.\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"bmc-public-health","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"pubh","sideBox":"Learn more about [BMC Public Health](http://bmcpublichealth.biomedcentral.com/)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/pubh/default.aspx","title":"BMC Public Health","twitterHandle":"@BMC_series","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"em","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"","lastPublishedDoi":"10.21203/rs.3.rs-4900878/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-4900878/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eObesity and smoking are two major public health challenges, both contributing significantly to morbidity and mortality worldwide. This study investigates the association between smoking behaviors and obesity among men in Taiwan, focusing on body mass index (BMI) and waist circumference (WC) as indicators of general and abdominal obesity. The sample consisted of 27,908 men categorized into five groups based on their smoking status: never smoking (NS), former smoking (FS), light-intensity smoking (LIS), moderate-intensity smoking (MIS), and heavy-intensity smoking (HIS). Our findings reveal a significant association between smoking and increased obesity risk, particularly among light- and moderate-intensity smokers. Socioeconomic factors such as education and income levels were also found to influence these behaviors. These results underscore the importance of integrated public health strategies that address both smoking cessation and obesity prevention.\u003c/p\u003e","manuscriptTitle":"Associations of Cigarette Smoking with General and Abdominal Obesity Risks among Men in Taiwan","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-09-17 10:04:46","doi":"10.21203/rs.3.rs-4900878/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2024-10-28T03:39:05+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2024-10-27T10:02:53+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"317054469390373132872232448052257156640","date":"2024-10-16T19:42:26+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"299927933987509753856073812807800904406","date":"2024-10-08T05:11:21+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2024-09-24T07:08:07+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"14728060730713285049008738509404284399","date":"2024-09-12T03:27:29+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"301672633791958230593974983860335126503","date":"2024-09-11T17:43:43+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"115052597876851196911588399288403492497","date":"2024-09-11T10:52:41+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"120487553525878628554600317972669515468","date":"2024-09-01T12:15:38+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2024-08-30T10:27:57+00:00","index":"","fulltext":""},{"type":"editorInvited","content":"","date":"2024-08-21T04:41:03+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2024-08-20T04:33:36+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2024-08-20T04:32:27+00:00","index":"","fulltext":""},{"type":"submitted","content":"BMC Public Health","date":"2024-08-12T13:25:24+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"bmc-public-health","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"pubh","sideBox":"Learn more about [BMC Public Health](http://bmcpublichealth.biomedcentral.com/)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/pubh/default.aspx","title":"BMC Public Health","twitterHandle":"@BMC_series","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"em","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"904e85b3-1b95-4f60-b639-640244338b3f","owner":[],"postedDate":"September 17th, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"published-in-journal","subjectAreas":[],"tags":[],"updatedAt":"2025-02-24T16:24:18+00:00","versionOfRecord":{"articleIdentity":"rs-4900878","link":"https://doi.org/10.1186/s12889-025-21821-5","journal":{"identity":"bmc-public-health","isVorOnly":false,"title":"BMC Public Health"},"publishedOn":"2025-02-17 15:57:28","publishedOnDateReadable":"February 17th, 2025"},"versionCreatedAt":"2024-09-17 10:04:46","video":"","vorDoi":"10.1186/s12889-025-21821-5","vorDoiUrl":"https://doi.org/10.1186/s12889-025-21821-5","workflowStages":[]},"version":"v1","identity":"rs-4900878","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-4900878","identity":"rs-4900878","version":["v1"]},"buildId":"qtupq5eGEP_6zYnWcrvyt","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}