Alcohol use disorder across the weight spectrum: a cross-sectional study from a Korean national survey

Alcohol use disorder across the weight spectrum: a cross-sectional study from a Korean national survey | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Alcohol use disorder across the weight spectrum: a cross-sectional study from a Korean national survey Su Jeong Seong, Bong-Jin Hahm, Jee Eun Park, Hwa Yeon Seo, Sung Man Chang, and 6 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7912946/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 12 You are reading this latest preprint version Abstract Background Findings on the relationship between weight status and alcohol use disorder (AUD) have been inconsistent. Therefore, this cross-sectional study investigated the relationship between AUD and body mass index (BMI) in a nationally representative sample of South Korean adults. Methods We analysed the data of 5,511 adults aged 18–79 years who participated in the 2021 National Mental Health Survey of Korea. AUD was diagnosed using the Korean version of the Composite International Diagnostic Interview. BMI was calculated using self-reported height and weight. Participants were categorised according to the World Health Organization Asia-Pacific criteria as underweight (< 18.5), normal weight (18.5–23), overweight (23–25), and obese (≥ 25). Gender-stratified multivariate logistic regression analyses were conducted after adjusting for socio-demographic variables and drinking patterns. Potential nonlinear associations were evaluated using quadratic terms and generalised additive models. Results Among 3,468 past-year drinkers, 144 (4.2%) met the AUD criteria. AUD prevalence was highest in the underweight group and decreased with increasing BMI in both genders. In men, overweight and obesity were associated with significantly lower odds of AUD (OR 0.537, 95% CI 0.327–0.882; OR 0.353, 95% CI 0.196–0.634, respectively). No significant association was found in women after adjustment. Supplementary analyses suggested a nonlinear U-shaped association between AUD and BMI in men. Conclusions The association between BMI and AUD differed by gender. In men, overweight and obesity were associated with lower odds of AUD, whereas a potential U-shaped pattern with increased odds at both BMI extremes. No significant associations were observed among women. alcohol use disorder weight status body mass index underweight obesity Figures Figure 1 Figure 2 Background Alcohol consumption poses substantial health risks and contributes to more than 200 diseases including infectious diseases (e.g. tuberculosis and human immunodeficiency virus), non-communicable diseases (e.g. liver disease, cardiovascular disease, and cancer), and psychiatric disorders (e.g. depression and alcohol use disorder [AUD])[ 1 ]. Despite the harmful effects, alcohol consumption remains widespread. Nearly half (47.5%) of the American population aged 12 years and older reported alcohol consumption in the past year[ 2 ]. Consequently, the burden of alcohol-related disease is considerable. In 2019, alcohol use was responsible for approximately 2.6 million deaths worldwide, accounting for 4.7% of global mortality and contributing to 6.9% of global disability-adjusted life years (DALYs) among men and 2.0% among women[ 3 ]. AUD is characterised by a maladaptive pattern of alcohol consumption leading to clinically significant impairment or distress. It affects approximately 400 million individuals worldwide—roughly 7% of the global population aged 15 years and above[ 3 ]. AUD frequently co-occurs with other psychiatric conditions and is associated with an approximately twofold increased risk of major depressive disorder and 1.5-fold increased risk of anxiety disorders[ 4 ]. It is also strongly associated with suicide. Approximately 25% of individuals who die by suicide have a history of AUD[ 5 ]. Thus, identifying the factors associated with AUD is critical for the early detection and development of targeted prevention and intervention strategies. Body mass index (BMI) is another factor associated with diverse health outcomes. Being overweight and obese increase the risk of diabetes, cancer, cardiovascular disease, and all-cause mortality[ 6 ]. Conversely, low BMI has been linked to increased mortality from various diseases, including cancer, and cardiovascular and respiratory diseases[ 6 – 8 ] Evidence on the association between alcohol consumption, including both quantity and frequency, and BMI is inconsistent. Some studies have found that high alcohol intake is associated with high BMI[ 9 , 10 ]. However, other studies have reported inverse associations. For example, Shaikh et al. reported the lowest BMI among heavy drinkers[ 11 ], and Kleiner et al. observed a negative association between alcohol intake and BMI in women[ 12 ]. A recent meta-analysis suggests a sex-divergent pattern, indicating that the amount of alcohol consumption was associated with a higher BMI in men but a lower BMI in women[ 13 ], and that drinking frequency, as opposed to quantity, showed an inverse relationship with BMI[ 10 , 14 – 16 ]. The relationship between AUD and weight status appears to be equally complex. Several reports have indicated a positive association between being overweight and AUD. In men, class III obesity was associated with significantly higher odds of AUD than lower obesity classes or normal weight, even after adjusting for drinking patterns[ 17 ]. Among U.S. veterans, being overweight was more prevalent in those with probable AUD[ 18 ]. Similarly, obesity has been linked to an increased risk of AUD[ 19 ]. In contrast, other studies have suggested an inverse association between AUD and BMI. For instance, obese individuals have been reported to have a significantly lower lifetime risk of substance use disorders, including AUD[ 20 ]. Underweight status is frequently observed among individuals with alcohol dependence. A study in Paris found that 8.8% of inpatients with alcohol dependency were underweight[ 21 ]. Conversely, some studies found no significant associations. For example, Agarwal et al. reported no difference in BMI distribution between individuals with and without AUD[ 22 ]. Sunwoo et al. found that being underweight was initially associated with a lower risk of AUD; however, this association was not significant after adjusting for age and gender[ 23 ]. Gender further complicates this relationship, as the strength and direction of the association appear to differ. In men, class III obesity was associated with higher odds of AUD than lower BMI categories, whereas in women, the odds were the highest for normal BMI and lowest for class III obesity under similar levels of alcohol use[ 17 ]. Furthermore, evidence from East Asian populations is limited. Therefore, this study investigated the relationship between BMI and AUD according to gender. Specifically, it examined gender-specific associations between BMI and AUD, comparing the risk of AUD across BMI categories (underweight, normal weight, overweight, and obese) in both men and women in a large, nationally representative survey. Methods Participants and procedure This study utilised data from the 2021 National Mental Health Survey of Korea[24] conducted between 19 June and 31 August 2021. This nationwide epidemiological survey investigated the prevalence, correlations, and comorbidities of major psychiatric disorders among community-dwelling adults. The study was supervised by the National Center for Mental Health and the Ministry of Health and Welfare, and was conducted in collaboration with Seoul National University and Gallup Korea. Seoul National University was responsible for planning and managing the survey, while Gallup Korea was involved with data collection, field supervision, and quality control. The target population consisted of adults aged 18–79 years, based on the Population and Housing Census. Individuals who were institutionalised (e.g. hospitalised or incarcerated) and foreign nationals residing in Korea were excluded. A complex multistage probability sampling design was employed to ensure national representativeness with stratification by administrative region (si/do), district (dong/eup/myeon), and household type. Primary sampling units and enumeration areas were selected using probability proportional-to-size sampling, followed by systematic random sampling of households. One adult per household was invited to participate, identified using the "last birthday" method (the member with the most recent birthday). Trained interviewers conducted face-to-face interviews during home visits. Assessment AUD was diagnosed using version 2.1 of the Composite International Diagnostic Interview (CIDI)[25], a fully standardised instrument that enables trained lay interviewers to assess and diagnose psychiatric disorders. The Korean version of the CIDI, developed in accordance with World Health Organization (WHO) guidelines, demonstrated acceptable validity and reliability[26]. Participants were asked whether they had experienced symptoms consistent with the Diagnostic and Statistical Manual of Mental Disorders, fourth edition (DSM-IV) diagnostic criteria for AUD, including alcohol dependence and alcohol abuse, as well as the timing of symptom onset and when they first and most recently met these criteria. Based on their responses, AUD in the past year was confirmed. Participants also provided sociodemographic information, including age, gender, educational attainment, income, and employment status. We assessed weekly drinking frequency and average daily alcohol intake during the past year. Daily intake was measured in units of soju glasses, with one glass corresponding to approximately 7 g of pure alcohol, which is equivalent to 70% of a standard drink. Body Mass Index (BMI) BMI was calculated from self-reported height and weight using the standard formula: weight in kilograms divided by the square of height in meters (kg/m²). The WHO Asia-Pacific classification was used to define BMI categories: <18.5 as underweight, 18.5–23 as normal, 23–25 as overweight, and ≥25 as obesity[27]. Although the WHO classification further subdivides obesity into classes I, II, and III, the number of participants in each subgroup was insufficient to obtain stable regression estimates. Accordingly, all individuals with BMI ≥25 were classified as a single “obesity” group. Therefore, the participants were categorised into four groups: underweight, normal weight, overweight, and obese. Statistical analysis Analyses were conducted separately for men and women, because the association between AUD and BMI was expected to differ by gender. Sociodemographic variables were compared between participants with and without AUD using a chi-square test. We then conducted gender-stratified multivariate logistic regression analyses to identify the associations between BMI and AUD. The covariates included sociodemographic variables (age, marital status, employment, education, and income) and drinking patterns (average alcohol consumption and frequency). The odds ratios (ORs) and 95% confidence intervals (CIs) for each variable were calculated. Statistical significance was set at p < 0.05. To address the issue of multiple comparisons across BMI groups, a Bonferroni correction was applied (adjusted α = 0.0167). Supplementary post-hoc analyses were performed to explore the potential nonlinearity of the association between BMI and AUD. A quadratic term for BMI was added to the regression models, and a generalised additive model (GAM) with a smoothing spline for BMI was applied, adjusting for the same covariates as in the logistic regression. All analyses were performed using IBM SPSS Statistics for Windows version 24 (IBM Corporation, Armonk, New York, USA) and R: A Language and Environment for Statistical Computing (R Foundation for Statistical Computing, Vienna, Austria). Results The prevalence of AUD Of the 5,511 individuals who provided informed consent, 2,043 reported no significant alcohol consumption in the past year and were excluded from the analysis. Among the remaining 3,468 participants who had consumed alcohol in the past year, 144 (4.2%) met the AUD diagnostic criteria. The prevalence of AUD according to the sociodemographic characteristics is presented separately for men (Table 1 ) and women (Table 2 ). AUD was more prevalent in men (4.4%) than in women (3.7%). Table 1 Prevalence of alcohol use disorder by socio-demographic factors among male drinkers No AUD AUD p -value Total (N, %) 2058 (95.6) 95 (4.4) Body mass index d (N, %) 0.024 * Underweight 9 (90.0) 1 (10.0) Normal 622 (93.8) 41 (6.2) Overweight 753 (96.2) 30 (3.8) Obesity 589 (97.0) 18 (3.0) Age (years; N, %) 0.101 18–39 745 (94.8) 41 (5.2) 40–59 871 (95.5) 41 (4.5) 60–79 441 (97.4) 12 (2.6) Marital status (N, %) 0.105 Married 1280 (96.2) 51 (3.8) Single a 778 (94.6) 44 (5.4) Employment status b (N, %) 0.104 Employed 1752 (95.9) 74 (4.1) Unemployed 306 (93.9) 20 (6.1) Education (N, %) 1.000 High school or less 1009 (95.5) 47 (4.5) Graduate 1049 (95.6) 48 (4.4) Income level c (N, %) 0.056 5 million won 814 (94.3) 49 (5.7) Drinking frequency (N, %) 0.000 *** > 3 times a week 153 (90.5) 16 (9.5) 1–3 times a week 1050 (94.5) 61 (5.5) > 1 times a week 694 (97.6) 17 (2.4) Average drinking amount (Average, SD) 7.10 (4.356) 11.28 (6.460) 0.000 *** a, Single: Not married, divorced, bereft, living independently; b, Employed: full time or part time, Unemployed: student, housewife, unemployed; c, 2 million won: approximately 1370 dollars, 5 million won: approximately 3430 dollars; d, normal BMI: BMI of 18.5 or higher but less than 23 *, p < 0.05; **, p < 0.01; ***, p < 0.001. Table 2 Prevalence of alcohol use disorder by socio-demographic factors among female drinkers No AUD AUD p -value Total 1266 (96.3) 49 (3.7) Body mass index d 0.024 * Underweight 72 (92.3) 6 (7.7) Normal 895 (97.0) 28 (3.0) Overweight 333 (97.9) 7 (2.1) Obesity 263 (98.5) 4 (1.5) Age (years) 0.014 * 18–39 522 (94.6) 30 (5.4) 40–59 543 (97.1) 16 (2.9) 60–79 201 (98.5) 3 (1.5) Marital status 0.052 Married 810 (97.0) 25 (3.0) Single a 457 (94.8) 25 (5.2) Employment status b 0.033 * Employed 847 (97.0) 26 (3.0) Unemployed 419 (94.6) 24 (5.4) Education 0.772 High school or less 639 (96.1) 26 (3.9) Graduate 628 (96.5) 23 (3.5) Income level c 0.203 5 million won 575 (95.5) 27 (4.5) Drinking frequency 0.000 *** > 3 times a week 26 (83.9) 5 (16.1) 1–3 times a week 378 (92.4) 31 (7.6) > 1 times a week 648 (98.2) 12 (1.8) Average drinking amount (Average, SD) 4.46 (3.338) 8.70 (6.127) 0.000 *** a, Single: Not married, divorced, bereft, living independently; b, Employed: full time or part time, Unemployed: student, housewife, unemployed; c, 2 million won: approximately 1370 dollars, 5 million won: approximately 3430 dollars; d, normal BMI: BMI of 18.5 or higher but less than 23 *, p < 0.05; **, p < 0.01; ***, p < 0.001. Underweight participants exhibited the highest prevalence of AUD, with a decreasing trend as BMI increased in both genders. Among men, the prevalence of AUD decreased from 10.0% in the underweight group to 3.0% in the obese group. Among women, the corresponding rates were 7.7%, 3.0%, 2.1%, and 1.5% in the underweight, normal-BMI, overweight, and obese groups, respectively. Among men, the prevalence of AUD did not significantly differ according to sociodemographic factors. In contrast, among women, AUD was significantly more prevalent in those who were younger and unemployed. In both genders, a higher drinking frequency and greater average daily alcohol consumption were associated with a higher prevalence of AUD (Tables 1 and 2 ). Logistic regression analyses A gender-stratified binary logistic regression analysis was conducted. Among men, overweight and obese individuals had significantly lower odds of having AUD than those with a normal BMI (Table 3 ). This association remained significant after adjusting for sociodemographic covariates (Model 2) and strengthened further with additional adjustments for drinking quantity and frequency (Model 3). For the overweight group, ORs were 0.581 (95% CI 0.363–0.930, p = 0.025) in Model 1, 0.608 (0.378–0.979, p = 0.041) in Model 2, and 0.537 (0.327–0.882, p = 0.014) in Model 3. For the obesity category, ORs were 0.488 (0.281–0.848, p = 0.011), 0.492 (0.283–0.855, p = 0.012), and 0.353 (0.196–0.634, p < 0.001) in Models 1–3, respectively. After Bonferroni correction for three BMI category comparisons (adjusted α = 0.0167), both associations remained statistically significant. In contrast, being underweight was not significantly associated with AUD in any model. Unemployment was significant only after including drinking patterns (Model 3: OR 2.246, 1.205–4.184, p = 0.011). The lower odds observed for the 2–5 million KRW income group in Model 2 were attenuated to a non-significant level in Model 3. The odds of AUD increased 1.147 times for each additional cup of soju consumed per day (95% CI 1.103–1.193, p 3/week: OR 4.523, p < 0.001) also showed a strong positive association. Table 3 Risk factors of alcohol use disorder among male drinkers Model 1 Model 2 Model 3 OR (CI) p -value OR (CI) p -value OR (CI) p -value Body mass index d Normal 1 1 1 Underweight 1.692 (0.269–10.643) 0.575 1.273 (0.194–8.363) 0.801 1.167 (0.166–8.217) 0.877 Overweight 0.581 (0.362–0.933) 0.025 *† 0.608 (0.377–0.981) 0.041 *† 0.537 (0.327–0.882) 0.014 *† Obesity 0.488 (0.282–0.847) 0.011 *† 0.492 (0.283–0.856) 0.012 *† 0.353 (0.196–0.634) 0.000 ***† Age (years) 18–39 1.809 (0.789–4.146) 0.161 1.799 (0.772–4.190) 0.173 40–59 1.910 (0.930–3.922) 0.078 1.765 (0.844–3.691) 0.131 60–79 1 1 Marital status Married 1 1 Single a 1.151 (0.669–1.980) 0.612 1.046 (0.602–1.817) 0.873 Employment status b Employed 1 1 Unemployed 1.485 (0.815–2.706) 0.196 2.246 (1.205–4.184) 0.011 * Education High school or less 1.173 (0.732–1.882) 0.507 0.945 (0.577–1.549) 0.823 Graduate 1 1 Income level c 5 million won 1 1 Average drinking amount - - 1.147 (1.103–1.193) 0.000 *** Drinking frequency - - >3 times a week - - 4.523 (2.089–9.790) 0.000 *** 1–3 times a week - - 2.116 (1.202–3.724) 0.009 ** > 1 times a week - - 1 OR, odds ratio; CI, confidence interval *, p < 0.05; **, p < 0.01; ***, p < 0.001. † Bonferroni correction applied for BMI comparisons (adjusted α = 0.0167) a, Single: Not married, divorced, bereft, living independently; b, Employed: full time or part time, Unemployed: student, housewife, unemployed; c, 2 million won: approximately 1370 dollars, 5 million won: approximately 3430 dollars; d, normal BMI: BMI of 18.5 or higher but less than 23 In women, BMI was not significantly associated with AUD in the fully adjusted model. Being underweight was positively associated with AUD in Model 1 (OR 2.986, 95% CI, 1.299–6.866; p = 0.010) and remained significant in Model 2 (OR 2.605, 1.078–6.296, p = 0.033), but was not significant in Model 3 (OR 2.032, 0.749–5.516, p = 0.162). Overweight and obesity did not show a significant association in any model. Those who were unemployed (OR 3.870, 95% CI 1.902–7.877, p < 0.001), consumed more alcohol daily (OR 1.179, 95% CI 1.098–1.264, p < 0.001), and drank more frequently were more likely to meet the AUD criteria (Table 4 ). Table 4 Risk factors of alcohol use disorder among female drinkers Model 1 Model 2 Model 3 OR (CI) p -value OR (CI) p -value OR (CI) p -value Body mass index d Normal 1 1 1 Underweight 2.986 (1.292-6.900) 0.010 * 2.605 (1.080–6.284) 0.033 * 2.032 (0.753–5.486) 0.162 Overweight 0.711 (0.313–1.614) 0.415 0.825 (0.353–1.924) 0.656 0.927 (0.377–2.281) 0.869 Obesity 0.552 (0.188–1.618) 0.279 0.200 (0.032–1.239) 0.084 0.267 (0.042–1.707) 0.163 Age (years) 18–39 2.985 (0.691–12.895) 0.143 1.977 (0.277–14.133) 0.497 40–59 2.346 (0.574–9.588) 0.235 2.164 (0.326–14.347) 0.424 60–79 1 1 Marital status Married 1 1 Single a 1.464 (0.663–3.234) 0.346 1.258 (0.531–2.980) 0.603 Employment status b Employed 1 1 Unemployed 2.861 (1.492–5.487) 0.002 ** 3.870 (1.902–7.877) 0.000 *** Education High school or less 1.456 (0.750–2.824) 0.267 1.234 (0.611–2.491) 0.558 Graduate 1 1 Income level c 5 million won 1 1 Average drinking amount 1.179 (1.098–1.264) 0.000 *** Drinking frequency > 3 times a week 8.904 (2.622–30.233.) 0.000 *** 1–3 times a week 3.192 (1.511–6.745) 0.002 * > 1 times a week 1 OR, odds ratio; CI, confidence interval a, Single: Not married, divorced, bereft, living independently; b, Employed: full time or part time, Unemployed: student, housewife, unemployed; c, 2 million won: approximately 1370 dollars, 5 million won: approximately 3430 dollars; d, normal BMI: BMI of 18.5 or higher but less than 23 *, p < 0.05; **, p < 0.01; ***, p < 0.001 Post hoc analysis In the supplementary analyses using six BMI categories (underweight, normal weight, overweight, and class I–III obesity), we observed a U-shaped pattern in the prevalence of AUD among men. The prevalence declined from the underweight group to class I obesity and then increased again from class II onwards, peaking in class III obesity. Owing to the small number of participants with class II and III obesity, the adjusted ORs could not be estimated. Among women, no AUD cases were identified as class II or III obesity, precluding a reliable estimation (Fig. 1 ). To further examine the nonlinearity, BMI was modelled as a continuous variable with a quadratic term. In men, inclusion of BMI² significantly improved model fit (Δ − 2LL = 5.39, df = 1, p = 0.02; Nagelkerke R² 0.144→0.152). The coefficient for BMI² was positive (B = 0.015, p = 0.009), indicating a U-shaped association, with the lowest predicted probability of AUD at BMI ≈ 28.5 kg/m². In women, BMI² did not improve model fit (Δ − 2LL = 0.02, p = 0.88; R² unchanged), providing no evidence of nonlinearity (Table 5 ). Table 5. Logistic regression of alcohol use disorder on continuous BMI with and without a quadratic term Panel A. Model fit and test of nonlinearity Gender Model −2 Log Likelihood Nagelkerke R² Δ −2LL (vs. Linear) df p (LRT) Vertex BMI (kg/m²) Men Linear (BMI_c) d 667.481 0.144 - - - - Quadratic (BMI_c + BMI_c²) 662.096 0.152 5.385 1 ≈0.020 29.81 Women Linear (BMI_c) d 297.274 0.233 - - - - Quadratic (BMI_c + BMI_c²) 297.251 0.233 0.023 1 0.88 N/A Outcome coded as 1 = AUD (past year), 0=non-event; covariates identical to the fully adjusted main model (age, marital status, education, employment, income, drinking frequency, and daily drinking amount). BMI, body mass index; BMI_c, centred BMI (gender-specific mean subtracted); BMI_c², squared centred BMI; −2LL, minus twice the log-likelihood; Δ−2LL, difference in −2LL between quadratic and linear models; LRT, likelihood-ratio test for adding BMI_c²; Vertex BMI, BMI at the minimum predicted probability, computed as −β₁/(2β₂) + mean BMI; R², Nagelkerke pseudo-R². Panel B. BMI coefficients (key terms only) Gender Model Predictor B SE Wald χ² p OR (95% CI) Men Linear BMI_c -0.156 0.045 11.942 0.001 0.855 (0.783-0.935)- Quadratic BMI_c -0.173 0.041 17.987 <0.001 0.841 (0.776-0.911) Quadratic BMI_c² 0.015 0.006 6.851 0.009 1.015 (1.004-1.026) Women Linear BMI_c -0.161 0.077 4.308 0.038 0.852 (0.732-0.991) Quadratic BMI_c -0.162 0.080 4.092 0.043 0.850 (0.727-0.995) Quadratic BMI_c² -0.003 0.019 0.022 0.883 0.997 (0.961-1.034) B, log-odds coefficient; SE, standard error; Wald χ², Wald chi-square; p, two-sided p-value; OR, odds ratio; CI, confidence interval. BMI_c, centred BMI; BMI_c², squared-centred BMI. All models were adjusted for the same covariates used in the main analysis (age, marital status, education, employment, income, drinking frequency, and daily drinking amount). The GAM analysis for men also suggested a U-shaped association between BMI and the predicted probability of AUD. However, the CIs widened substantially above a BMI of 30 owing to the small sample size, indicating that the apparent increase at higher BMI should be interpreted with caution. In women, the GAM curve was essentially flat with no evidence of nonlinearity (Fig. 2 ). Discussion This study found that being underweight was associated with higher odds of AUD in women; however, this association was not statistically significant after adjusting for sociodemographic factors and drinking patterns. Given its cross-sectional design, reverse causality (AUD leading to weight loss) could not be excluded. In men, being overweight and obese was associated with lower odds ratios, and these associations strengthened after adjustment. Supplementary analyses revealed a U-shaped pattern in men, with a quadratic regression providing statistically significant evidence of nonlinearity. Previous findings regarding the association between AUD and BMI have been inconsistent. Some studies reported no significant relationship[ 22 , 23 ], while others identified either positive[ 17 ] or inverse[ 20 ] associations Simon et al. reported that obese individuals had a significantly lower lifetime risk of substance use disorders[ 20 ]. Negative genetic correlations between Alcohol Use Disorder Identification Test scores and BMI have also been observed[ 28 ]. Several mechanisms may explain why AUD is linked to lower BMI or underweight status. Alcohol competes with food for the activation of brain reward pathways[ 12 , 29 ] and is associated with decreased plasma ghrelin[ 30 ], both of which may suppress appetite and lower diet quality. Alcohol also impairs the digestion and absorption of essential nutrients in the gastrointestinal tract[ 31 ], contributing to weight loss in individuals with alcohol dependence[ 32 ]. Mild-to-moderate malnutrition was identified in 24% of the inpatients undergoing treatment for alcohol and drug use[ 29 ]. Heavy alcohol consumption is linked to reduced skeletal muscle mass[ 33 ]. Our findings also support the possible association between lower BMI and AUD. The AUD prevalence was highest among underweight individuals of both genders. Higher odds of AUD were observed among underweight women, although the adjustment attenuated the association to non-significance, suggesting partial mediation or confounding by drinking behaviour. Given the small number of underweight participants (n = 89, 2.6% of 3,468 drinkers), this association may have been underestimated or obscured by potential overadjustment. Larger studies with more underweight participants are needed to clarify this relationship. While our study found a lower risk of AUD among obese individuals (BMI ≥ 25), other studies have reported the opposite. Petry et al. reported that both obese and extremely obese individuals had increased odds of developing AUD[ 19 ], and Krahn et al. found greater weight gain in participants with alcohol dependence than in controls over a period of six months[ 34 ]. Various mechanisms may account for the positive association between AUD and being overweight. Alcohol is calorie-dense (7 kcal/g) and may contribute to weight gain by adding calories beyond those from non-alcoholic sources[ 35 , 36 ]. It may also enhance appetite after food consumption and increase food-related rewards[ 36 ]. Chronic drinking can promote fat accumulation by inhibiting fat oxidation and facilitating fat sparing[ 37 ]. In addition, AUD and binge eating may share a common vulnerabilities such as impulsivity during distress[ 38 ]. These discrepancies may reflect ethnic and cultural differences. East Asians frequently carry ALDH2 and ADH gene variants that induce adverse reactions to alcohol (e.g. flushing)[ 39 ], which reduce alcohol consumption and may limit alcohol-related calorie intake and fat accumulation. Consequently, the association between being overweight and AUD may be weaker in this population. Additionally, cultural differences in dietary patterns accompanying alcohol use may further contribute to these discrepancies. Our findings may not contradict those of previous studies if the relationship between BMI and AUD is nonlinear. A bell-shaped association was observed between frequency of alcohol consumption and obesity indicators (BMI and waist-to-hip ratio)[ 40 ]. Light-to-moderate alcohol intake appeared to be unrelated to adiposity gain, whereas heavy drinking was more consistently linked[ 41 ]. Our supplementary analyses indicated a U-shaped BMI–AUD association in men. The lowest predicted probability of AUD occurred at BMI ≈ 28.5 kg/m² —just below the 30 kg/m² threshold that marks class II obesity under the Asia–Pacific criteria and class I obesity under the standard (non-Asian) WHO criteria. This pattern suggests elevated odds of having AUD in both underweight and severely obese individuals. The lack of significant associations in some prior studies[ 22 , 23 ] may stem from such nonlinearity, where the increased risk at both BMI extremes could offset each other, obscuring the overall effect. Heterogeneity within overweight and obese populations may explain these complexities. Obese individuals showed greater impulsivity in neurocognitive tasks, whereas overweight individuals performed similarly to those of normal weight[ 42 ]. Such heightened impulsivity may predispose some individuals to both impulsive eating and AUD, promoting their co-occurrence in severe obesity, whereas this vulnerability may be less pronounced in individuals with lower obesity levels or overweight individuals without obesity. Supporting this, one study found that men with class III obesity had higher odds of having AUD than those with lower obesity classes or no obesity, even after adjusting for drinking patterns[ 17 ]. Regional differences in BMI classification should also be considered. Our primary analyses applied the Asia-Pacific criteria, which define overweight and obesity as BMI ≥ 23 and ≥ 25, respectively[ 27 ]. However, under the classification commonly used in non-Asian populations, where overweight begins at BMI ≥ 25 and obesity at BMI ≥ 30[ 43 ], our class II and III obesity groups (BMI ≥ 30) correspond to the obese category. Therefore, our finding of elevated AUD prevalence in these groups aligns with previous research linking obesity to increased AUD risk. This study has some limitations. First, BMI was based on self-reported height and weight rather than objective measurements, introducing a potential reporting bias. Given the household survey design, it was not feasible for the interviewers to use measuring equipment. However, validation studies have shown a strong correlation (r > 0.9) between self-reported and measured BMI[ 44 ]. Second, owing to its cross-sectional design, this study could not establish temporal order or causal relationships between AUD and weight status. Longitudinal studies are required to determine the direction of this association. Third, AUD diagnosis was based on self-reported symptoms. Although symptoms were assessed within the past year, and a standardised tool was used to improve reliability, recall bias and underreporting remain possible. Finally, we were unable to account for all potential confounders that could affect body weight. Owing to the small number of participants with physical illnesses, medical conditions could not be fully adjusted for, and other relevant factors, such as physical activity and sleep patterns, were not included. Despite these limitations, this study has several strengths. This study investigated the association between AUD and BMI in a large, nationally representative sample of community-dwelling adults rather than in a restricted clinical or occupational population. The use of a randomly selected cohort enhanced the external validity and generalisability of the findings. In addition, AUD was diagnosed using a well-validated structured interview rather than a simple screening tool, thereby improving the diagnostic accuracy and enabling a more reliable assessment of the relationship between BMI and AUD. Conclusions In conclusion, our results indicate a lower risk of AUD in the overweight group and suggest a potential nonlinear relationship between BMI and AUD, with an increased risk in both underweight and severely obese individuals in men. These findings may help explain the inconsistencies in previous studies and clarify their complex associations. Although this interpretation requires caution and confirmation through further studies, which include a substantial number of participants with severe obesity and who are underweight, clinicians should remain attentive to AUD risk in both underweight and severely obese patients. Abbreviations AUD, alcohol use disorder; DALYs, disability-adjusted life years; BMI, body mass index; CIDI, Composite International Diagnostic Interview; WHO, World Health Organization; DSM-IV, Diagnostic and Statistical Manual of Mental Disorders, fourth edition; ORs, odds ratios; CIs, confidence intervals; GAM, generalised additive model. Declarations Ethics approval and consent to participate This study was approved by the Institutional Review Boards of Kangdong Sacread Heart Hospital (IRB No. KANGDONG 2025-07-004). The analyses were performed using anonymized microdata obtained from a national survey. Informed consent was obtained from all participants at the time of the original survey. Consent for publication Not applicable Clinical trial number Not applicable Availability of data and materials The data that support the findings of this study are available from the National Center for Mental Health (Seoul, Korea), but restrictions apply to the availability of these data, which were used under license for the current study and are therefore not publicly available. Researchers may access the anonymized microdata upon reasonable request and after approval by the National Center for Mental Health. Competing interests The authors declare that they have no competing interests. Funding This research was supported by the National Research Foundation of Korea (NRF) grant funded by the Ministry of Science and ICT of the Korean government (RS-2024-00422599). Authors' contributions SJS and JEP conceptualized the study. SJS, BJH, JEP, HYS, SMC, BSK, HJJ, and JPH curated and managed the data. SJS, JEP, and HYS performed the formal analyses, while SJS and BJH designed the methodology. JEP, HYS, SMC, BSK, HJJ, and JPH administered the project. JYH, JYS, and KJP conducted the investigation and validation. SJS prepared the visualizations. BJH supervised the overall study. SJS drafted the original manuscript, and SJS, BJH, and JYH critically reviewed and edited the final version. All authors read and approved the final manuscript. Acknowledgements This study utilized microdata from the 2021 National Mental Health Survey of Korea conducted by the Ministry of Health and Welfare (NMHSK-85). 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1","display":"","copyAsset":false,"role":"figure","size":53590,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eOne year prevalence of alcohol use disorder by body weight status and gender\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe prevalence of alcohol use disorder (AUD) by body mass index (BMI) category and gender. Among men, a U-shaped pattern was observed. Prevalence declined from the underweight group to class I obesity and then increased again from class II onwards, peaking at class III obesity. Among women, prevalence decreased steadily with higher BMI, and no AUD cases were identified in class II or III obesity.\u003c/p\u003e","description":"","filename":"1.png","url":"https://assets-eu.researchsquare.com/files/rs-7912946/v1/3d95671b8266862693beeb55.png"},{"id":96157266,"identity":"3f2b6249-4ae3-4655-ba1d-614adf763ce2","added_by":"auto","created_at":"2025-11-18 08:34:33","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":83898,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eGeneralized additive model (GAM) with a smoothing spline for BMI\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eGeneralised additive models (GAMs) with smoothing splines of BMI and the predicted probability of alcohol use disorder (AUD), stratified by gender.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003ePanel A: \u003c/strong\u003eIn men, a U-shaped association was observed. Confidence intervals widened markedly above a BMI of 30 due to small sample size, so the apparent increase at higher BMI should be interpreted with caution.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003ePanel B:\u003c/strong\u003e In women, the GAM curve was essentially flat with no evidence of nonlinearity.\u003c/p\u003e","description":"","filename":"2.png","url":"https://assets-eu.researchsquare.com/files/rs-7912946/v1/399dbedbf9b12d982ada8dde.png"},{"id":96256911,"identity":"cb0ca7cf-a8ed-467f-bcff-f9cd07765a49","added_by":"auto","created_at":"2025-11-19 07:50:54","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1349885,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-7912946/v1/4110f6fe-702c-4b5b-af7d-86f163bcde23.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Alcohol use disorder across the weight spectrum: a cross-sectional study from a Korean national survey","fulltext":[{"header":"Background","content":"\u003cp\u003eAlcohol consumption poses substantial health risks and contributes to more than 200 diseases including infectious diseases (e.g. tuberculosis and human immunodeficiency virus), non-communicable diseases (e.g. liver disease, cardiovascular disease, and cancer), and psychiatric disorders (e.g. depression and alcohol use disorder [AUD])[\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e]. Despite the harmful effects, alcohol consumption remains widespread. Nearly half (47.5%) of the American population aged 12 years and older reported alcohol consumption in the past year[\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e]. Consequently, the burden of alcohol-related disease is considerable. In 2019, alcohol use was responsible for approximately 2.6\u0026nbsp;million deaths worldwide, accounting for 4.7% of global mortality and contributing to 6.9% of global disability-adjusted life years (DALYs) among men and 2.0% among women[\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eAUD is characterised by a maladaptive pattern of alcohol consumption leading to clinically significant impairment or distress. It affects approximately 400\u0026nbsp;million individuals worldwide\u0026mdash;roughly 7% of the global population aged 15 years and above[\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e]. AUD frequently co-occurs with other psychiatric conditions and is associated with an approximately twofold increased risk of major depressive disorder and 1.5-fold increased risk of anxiety disorders[\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e]. It is also strongly associated with suicide. Approximately 25% of individuals who die by suicide have a history of AUD[\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e]. Thus, identifying the factors associated with AUD is critical for the early detection and development of targeted prevention and intervention strategies.\u003c/p\u003e\u003cp\u003eBody mass index (BMI) is another factor associated with diverse health outcomes. Being overweight and obese increase the risk of diabetes, cancer, cardiovascular disease, and all-cause mortality[\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e]. Conversely, low BMI has been linked to increased mortality from various diseases, including cancer, and cardiovascular and respiratory diseases[\u003cspan additionalcitationids=\"CR7\" citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e]\u003c/p\u003e\u003cp\u003eEvidence on the association between alcohol consumption, including both quantity and frequency, and BMI is inconsistent. Some studies have found that high alcohol intake is associated with high BMI[\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e, \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e]. However, other studies have reported inverse associations. For example, Shaikh et al. reported the lowest BMI among heavy drinkers[\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e], and Kleiner et al. observed a negative association between alcohol intake and BMI in women[\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e]. A recent meta-analysis suggests a sex-divergent pattern, indicating that the amount of alcohol consumption was associated with a higher BMI in men but a lower BMI in women[\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e], and that drinking frequency, as opposed to quantity, showed an inverse relationship with BMI[\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e, \u003cspan additionalcitationids=\"CR15\" citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eThe relationship between AUD and weight status appears to be equally complex. Several reports have indicated a positive association between being overweight and AUD. In men, class III obesity was associated with significantly higher odds of AUD than lower obesity classes or normal weight, even after adjusting for drinking patterns[\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e]. Among U.S. veterans, being overweight was more prevalent in those with probable AUD[\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e]. Similarly, obesity has been linked to an increased risk of AUD[\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eIn contrast, other studies have suggested an inverse association between AUD and BMI. For instance, obese individuals have been reported to have a significantly lower lifetime risk of substance use disorders, including AUD[\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e]. Underweight status is frequently observed among individuals with alcohol dependence. A study in Paris found that 8.8% of inpatients with alcohol dependency were underweight[\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e]. Conversely, some studies found no significant associations. For example, Agarwal et al. reported no difference in BMI distribution between individuals with and without AUD[\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e]. Sunwoo et al. found that being underweight was initially associated with a lower risk of AUD; however, this association was not significant after adjusting for age and gender[\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eGender further complicates this relationship, as the strength and direction of the association appear to differ. In men, class III obesity was associated with higher odds of AUD than lower BMI categories, whereas in women, the odds were the highest for normal BMI and lowest for class III obesity under similar levels of alcohol use[\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e]. Furthermore, evidence from East Asian populations is limited.\u003c/p\u003e\u003cp\u003eTherefore, this study investigated the relationship between BMI and AUD according to gender. Specifically, it examined gender-specific associations between BMI and AUD, comparing the risk of AUD across BMI categories (underweight, normal weight, overweight, and obese) in both men and women in a large, nationally representative survey.\u003c/p\u003e"},{"header":"Methods","content":"\u003cp\u003e\u003cstrong\u003eParticipants and procedure\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis study utilised data from the 2021 National Mental Health Survey of Korea[24] conducted between 19 June and 31 August 2021. This nationwide epidemiological survey investigated the prevalence, correlations, and comorbidities of major psychiatric disorders among community-dwelling adults. The study was supervised by the National Center for Mental Health and the Ministry of Health and Welfare, and was conducted in collaboration with Seoul National University and Gallup Korea. Seoul National University was responsible for planning and managing the survey, while Gallup Korea was involved with data collection, field supervision, and quality control.\u003c/p\u003e\n\u003cp\u003eThe target population consisted of adults aged 18\u0026ndash;79 years, based on the Population and Housing Census. Individuals who were institutionalised (e.g. hospitalised or incarcerated) and foreign nationals residing in Korea were excluded. A complex multistage probability sampling design was employed to ensure national representativeness with stratification by administrative region (si/do), district (dong/eup/myeon), and household type. Primary sampling units and enumeration areas were selected using probability proportional-to-size sampling, followed by systematic random sampling of households. One adult per household was invited to participate, identified using the \u0026quot;last birthday\u0026quot; method (the member with the most recent birthday). Trained interviewers conducted face-to-face interviews during home visits.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAssessment\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAUD was diagnosed using version 2.1 of the Composite International Diagnostic Interview (CIDI)[25], a fully standardised instrument that enables trained lay interviewers to assess and diagnose psychiatric disorders. The Korean version of the CIDI, developed in accordance with World Health Organization (WHO) guidelines, demonstrated acceptable validity and reliability[26].\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eParticipants were asked whether they had experienced symptoms consistent with the Diagnostic and Statistical Manual of Mental Disorders, fourth edition (DSM-IV) diagnostic criteria for AUD, including alcohol dependence and alcohol abuse, as well as the timing of symptom onset and when they first and most recently met these criteria. Based on their responses, AUD in the past year was confirmed. Participants also provided sociodemographic information, including age, gender, educational attainment, income, and employment status. We assessed weekly drinking frequency and average daily alcohol intake during the past year. Daily intake was measured in units of soju glasses, with one glass corresponding to approximately 7 g of pure alcohol, which is equivalent to 70% of a standard drink.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eBody Mass Index (BMI)\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eBMI was calculated from self-reported height and weight using the standard formula: weight in kilograms divided by the square of height in meters (kg/m\u0026sup2;). The WHO Asia-Pacific classification was used to define BMI categories: \u0026lt;18.5 as underweight, 18.5\u0026ndash;23 as normal, 23\u0026ndash;25 as overweight, and \u0026ge;25 as obesity[27]. Although the WHO classification further subdivides obesity into classes I, II, and III, the number of participants in each subgroup was insufficient to obtain stable regression estimates. Accordingly, all individuals with BMI \u0026ge;25 were classified as a single \u0026ldquo;obesity\u0026rdquo; group. Therefore, the participants were categorised into four groups: underweight, normal weight, overweight, and obese.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eStatistical analysis\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAnalyses were conducted separately for men and women, because the association between AUD and BMI was expected to differ by gender. Sociodemographic variables were compared between participants with and without AUD using a chi-square test. We then conducted gender-stratified multivariate logistic regression analyses to identify the associations between BMI and AUD. The covariates included sociodemographic variables (age, marital status, employment, education, and income) and drinking patterns (average alcohol consumption and frequency). The odds ratios (ORs) and 95% confidence intervals (CIs) for each variable were calculated. Statistical significance was set at p \u0026lt; 0.05. To address the issue of multiple comparisons across BMI groups, a Bonferroni correction was applied (adjusted \u0026alpha; = 0.0167).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eSupplementary post-hoc analyses were performed to explore the potential nonlinearity of the association between BMI and AUD. A quadratic term for BMI was added to the regression models, and a generalised additive model (GAM) with a smoothing spline for BMI was applied, adjusting for the same covariates as in the logistic regression. All analyses were performed using IBM SPSS Statistics for Windows version 24 (IBM Corporation, Armonk, New York, USA) and R: A Language and Environment for Statistical Computing (R Foundation for Statistical Computing, Vienna, Austria).\u003c/p\u003e"},{"header":"Results","content":"\u003cdiv id=\"Sec8\" class=\"Section2\"\u003e\n \u003ch2\u003eThe prevalence of AUD\u003c/h2\u003e\n \u003cp\u003eOf the 5,511 individuals who provided informed consent, 2,043 reported no significant alcohol consumption in the past year and were excluded from the analysis. Among the remaining 3,468 participants who had consumed alcohol in the past year, 144 (4.2%) met the AUD diagnostic criteria.\u003c/p\u003e\n \u003cp\u003eThe prevalence of AUD according to the sociodemographic characteristics is presented separately for men (Table \u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003e) and women (Table \u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003e). AUD was more prevalent in men (4.4%) than in women (3.7%).\u003c/p\u003e\n \u003cp\u003e\u003c/p\u003e\n \u003ctable id=\"Tab1\" border=\"1\"\u003e\n \u003ccaption language=\"En\"\u003e\n \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e\n \u003cdiv class=\"CaptionContent\"\u003e\n \u003cp\u003ePrevalence of alcohol use disorder by socio-demographic factors among male drinkers\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\u0026nbsp;\u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eNo AUD\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eAUD\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\u0026nbsp;\u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\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\n \u003cp\u003e\u003cem\u003ep\u003c/em\u003e-value\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eTotal (N, %)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e2058 (95.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e95 (4.4)\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 mass index\u003csup\u003ed\u003c/sup\u003e (N, %)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.024\u003csup\u003e*\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eUnderweight\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e9 (90.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1 (10.0)\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\u003eNormal\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e622 (93.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e41 (6.2)\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\u003eOverweight\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e753 (96.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e30 (3.8)\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\u003eObesity\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e589 (97.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e18 (3.0)\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\u003eAge (years; N, %)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.101\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e18\u0026ndash;39\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e745 (94.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e41 (5.2)\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\u003e40\u0026ndash;59\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e871 (95.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e41 (4.5)\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\u003e60\u0026ndash;79\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e441 (97.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e12 (2.6)\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\u003eMarital status (N, %)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.105\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eMarried\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1280 (96.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e51 (3.8)\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\u003eSingle\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e778 (94.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e44 (5.4)\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\u003eEmployment status\u003csup\u003eb\u003c/sup\u003e (N, %)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.104\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eEmployed\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1752 (95.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e74 (4.1)\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\u003eUnemployed\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e306 (93.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e20 (6.1)\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\u003eEducation (N, %)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.000\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eHigh school or less\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1009 (95.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e47 (4.5)\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\u003eGraduate\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1049 (95.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e48 (4.4)\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\u003eIncome level\u003csup\u003ec\u003c/sup\u003e (N, %)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.056\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;2\u0026nbsp;million won\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e162 (95.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e8 (4.7)\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\u003e2\u0026ndash;5\u0026nbsp;million won\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1068 (96.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e38 (3.4)\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\u0026gt;\u0026thinsp;5\u0026nbsp;million won\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e814 (94.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e49 (5.7)\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\u003eDrinking frequency (N, %)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.000\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026gt;\u0026thinsp;3 times a week\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e153 (90.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e16 (9.5)\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\u003e1\u0026ndash;3 times a week\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1050 (94.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e61 (5.5)\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\u0026gt;\u0026thinsp;1 times a week\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e694 (97.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e17 (2.4)\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\u003eAverage drinking amount (Average, SD)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e7.10 (4.356)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e11.28 (6.460)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.000\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003ctfoot\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"4\"\u003ea, Single: Not married, divorced, bereft, living independently; b, Employed: full time or part time, Unemployed: student, housewife, unemployed; c, 2\u0026nbsp;million won: approximately 1370 dollars, 5\u0026nbsp;million won: approximately 3430 dollars; d, normal BMI: BMI of 18.5 or higher but less than 23\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"4\"\u003e*, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05; **, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.01; ***, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001.\u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tfoot\u003e\n \u003c/table\u003e\n \u003cp\u003e\u003c/p\u003e\n \u003cp\u003e\u003cbr\u003e\u003c/p\u003e\n \u003cp\u003e\u003c/p\u003e\n \u003ctable id=\"Tab2\" border=\"1\"\u003e\n \u003ccaption language=\"En\"\u003e\n \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e\n \u003cdiv class=\"CaptionContent\"\u003e\n \u003cp\u003ePrevalence of alcohol use disorder by socio-demographic factors among female drinkers\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\u0026nbsp;\u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eNo AUD\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eAUD\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\u0026nbsp;\u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\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\n \u003cp\u003e\u003cem\u003ep\u003c/em\u003e-value\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eTotal\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1266 (96.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e49 (3.7)\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 mass index\u003csup\u003ed\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.024\u003csup\u003e*\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eUnderweight\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e72 (92.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e6 (7.7)\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\u003eNormal\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e895 (97.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e28 (3.0)\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\u003eOverweight\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e333 (97.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e7 (2.1)\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\u003eObesity\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e263 (98.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e4 (1.5)\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\u003eAge (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\n \u003cp\u003e0.014\u003csup\u003e*\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e18\u0026ndash;39\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e522 (94.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e30 (5.4)\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\u003e40\u0026ndash;59\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e543 (97.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e16 (2.9)\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\u003e60\u0026ndash;79\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e201 (98.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e3 (1.5)\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\u003eMarital 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\n \u003cp\u003e0.052\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eMarried\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e810 (97.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e25 (3.0)\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\u003eSingle\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e457 (94.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e25 (5.2)\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\u003eEmployment status\u003csup\u003eb\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.033\u003csup\u003e*\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eEmployed\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e847 (97.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e26 (3.0)\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\u003eUnemployed\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e419 (94.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e24 (5.4)\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\u003eEducation\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.772\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eHigh school or less\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e639 (96.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e26 (3.9)\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\u003eGraduate\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e628 (96.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e23 (3.5)\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\u003eIncome level\u003csup\u003ec\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.203\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;2\u0026nbsp;million won\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e96 (98.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e2 (2.0)\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\u003e2\u0026ndash;5\u0026nbsp;million won\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e589 (97.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e17 (2.8)\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\u0026gt;\u0026thinsp;5\u0026nbsp;million won\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e575 (95.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e27 (4.5)\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\u003eDrinking frequency\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.000\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026gt;\u0026thinsp;3 times a week\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e26 (83.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e5 (16.1)\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\u003e1\u0026ndash;3 times a week\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e378 (92.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e31 (7.6)\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\u0026gt;\u0026thinsp;1 times a week\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e648 (98.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e12 (1.8)\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\u003eAverage drinking amount (Average, SD)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e4.46 (3.338)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e8.70 (6.127)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.000\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003ctfoot\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"4\"\u003ea, Single: Not married, divorced, bereft, living independently; b, Employed: full time or part time, Unemployed: student, housewife, unemployed; c, 2\u0026nbsp;million won: approximately 1370 dollars, 5\u0026nbsp;million won: approximately 3430 dollars; d, normal BMI: BMI of 18.5 or higher but less than 23\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"4\"\u003e*, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05; **, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.01; ***, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001.\u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tfoot\u003e\n \u003c/table\u003e\n \u003cp\u003e\u003c/p\u003e\n \u003cp\u003eUnderweight participants exhibited the highest prevalence of AUD, with a decreasing trend as BMI increased in both genders. Among men, the prevalence of AUD decreased from 10.0% in the underweight group to 3.0% in the obese group. Among women, the corresponding rates were 7.7%, 3.0%, 2.1%, and 1.5% in the underweight, normal-BMI, overweight, and obese groups, respectively. Among men, the prevalence of AUD did not significantly differ according to sociodemographic factors. In contrast, among women, AUD was significantly more prevalent in those who were younger and unemployed. In both genders, a higher drinking frequency and greater average daily alcohol consumption were associated with a higher prevalence of AUD (Tables \u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003e and \u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003e).\u003c/p\u003e\n\u003c/div\u003e\n\u003ch3\u003eLogistic regression analyses\u003c/h3\u003e\n\u003cp\u003eA gender-stratified binary logistic regression analysis was conducted. Among men, overweight and obese individuals had significantly lower odds of having AUD than those with a normal BMI (Table \u003cspan class=\"InternalRef\"\u003e3\u003c/span\u003e). This association remained significant after adjusting for sociodemographic covariates (Model 2) and strengthened further with additional adjustments for drinking quantity and frequency (Model 3). For the overweight group, ORs were 0.581 (95% CI 0.363\u0026ndash;0.930, p\u0026thinsp;=\u0026thinsp;0.025) in Model 1, 0.608 (0.378\u0026ndash;0.979, p\u0026thinsp;=\u0026thinsp;0.041) in Model 2, and 0.537 (0.327\u0026ndash;0.882, p\u0026thinsp;=\u0026thinsp;0.014) in Model 3. For the obesity category, ORs were 0.488 (0.281\u0026ndash;0.848, p\u0026thinsp;=\u0026thinsp;0.011), 0.492 (0.283\u0026ndash;0.855, p\u0026thinsp;=\u0026thinsp;0.012), and 0.353 (0.196\u0026ndash;0.634, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001) in Models 1\u0026ndash;3, respectively. After Bonferroni correction for three BMI category comparisons (adjusted \u0026alpha;\u0026thinsp;=\u0026thinsp;0.0167), both associations remained statistically significant. In contrast, being underweight was not significantly associated with AUD in any model. Unemployment was significant only after including drinking patterns (Model 3: OR 2.246, 1.205\u0026ndash;4.184, p\u0026thinsp;=\u0026thinsp;0.011). The lower odds observed for the 2\u0026ndash;5\u0026nbsp;million KRW income group in Model 2 were attenuated to a non-significant level in Model 3. The odds of AUD increased 1.147 times for each additional cup of soju consumed per day (95% CI 1.103\u0026ndash;1.193, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001). Drinking frequency (1\u0026ndash;3/week: OR 2.116, p\u0026thinsp;=\u0026thinsp;0.009; \u0026gt;3/week: OR 4.523, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001) also showed a strong positive association.\u003c/p\u003e\n\u003cp\u003e\u003c/p\u003e\n\u003ctable id=\"Tab3\" border=\"1\"\u003e\n \u003ccaption language=\"En\"\u003e\n \u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e\n \u003cdiv class=\"CaptionContent\"\u003e\n \u003cp\u003eRisk factors of alcohol use disorder among male drinkers\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\u0026nbsp;\u003c/th\u003e\n \u003cth align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003eModel 1\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\u0026nbsp;\u003c/th\u003e\n \u003cth align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003eModel 2\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\u0026nbsp;\u003c/th\u003e\n \u003cth align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003eModel 3\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\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eOR (CI)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cem\u003ep\u003c/em\u003e-value\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eOR (CI)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cem\u003ep\u003c/em\u003e-value\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eOR (CI)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cem\u003ep\u003c/em\u003e-value\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eBody mass index\u003csup\u003ed\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNormal\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1\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\u003eUnderweight\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.692 (0.269\u0026ndash;10.643)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.575\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.273 (0.194\u0026ndash;8.363)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.801\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.167 (0.166\u0026ndash;8.217)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.877\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eOverweight\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.581 (0.362\u0026ndash;0.933)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.025\u003csup\u003e*\u0026dagger;\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.608 (0.377\u0026ndash;0.981)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.041\u003csup\u003e*\u0026dagger;\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.537 (0.327\u0026ndash;0.882)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.014\u003csup\u003e*\u0026dagger;\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eObesity\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.488 (0.282\u0026ndash;0.847)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.011\u003csup\u003e*\u0026dagger;\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.492 (0.283\u0026ndash;0.856)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.012\u003csup\u003e*\u0026dagger;\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.353 (0.196\u0026ndash;0.634)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.000\u003csup\u003e***\u0026dagger;\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eAge (years)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\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\u003e18\u0026ndash;39\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\n \u003cp\u003e1.809 (0.789\u0026ndash;4.146)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.161\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.799 (0.772\u0026ndash;4.190)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.173\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e40\u0026ndash;59\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\n \u003cp\u003e1.910 (0.930\u0026ndash;3.922)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.078\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.765 (0.844\u0026ndash;3.691)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.131\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e60\u0026ndash;79\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\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1\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\u003eMarital 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=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eMarried\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\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1\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\u003eSingle\u003csup\u003ea\u003c/sup\u003e\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\n \u003cp\u003e1.151 (0.669\u0026ndash;1.980)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.612\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.046 (0.602\u0026ndash;1.817)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.873\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eEmployment status\u003csup\u003eb\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eEmployed\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\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1\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\u003eUnemployed\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\n \u003cp\u003e1.485 (0.815\u0026ndash;2.706)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.196\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2.246 (1.205\u0026ndash;4.184)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.011\u003csup\u003e*\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eEducation\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eHigh school or less\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\n \u003cp\u003e1.173 (0.732\u0026ndash;1.882)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.507\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.945 (0.577\u0026ndash;1.549)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.823\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eGraduate\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\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1\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\u003eIncome level\u003csup\u003ec\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;2\u0026nbsp;million won\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\n \u003cp\u003e0.907 (0.386\u0026ndash;2.129)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.822\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.825 (0.341\u0026ndash;1.995)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.670\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2\u0026ndash;5\u0026nbsp;million won\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\n \u003cp\u003e0.625 (0.398\u0026ndash;0.982)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.041\u003csup\u003e*\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.671 (0.422\u0026ndash;1.069)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.093\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026gt;\u0026thinsp;5\u0026nbsp;million won\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\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1\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\u003eAverage drinking amount\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\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.147 (1.103\u0026ndash;1.193)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.000\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eDrinking frequency\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\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-\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 \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026gt;3 times a week\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\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e4.523 (2.089\u0026ndash;9.790)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.000\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1\u0026ndash;3 times a week\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\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2.116 (1.202\u0026ndash;3.724)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.009\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026gt;\u0026thinsp;1 times a week\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\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1\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 \u003ctfoot\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"9\"\u003eOR, odds ratio; CI, confidence interval\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; **, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.01; ***, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001.\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"9\"\u003e\u003csup\u003e\u0026dagger;\u003c/sup\u003e Bonferroni correction applied for BMI comparisons (adjusted \u0026alpha;\u0026thinsp;=\u0026thinsp;0.0167)\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"9\"\u003ea, Single: Not married, divorced, bereft, living independently; b, Employed: full time or part time, Unemployed: student, housewife, unemployed; c, 2\u0026nbsp;million won: approximately 1370 dollars, 5\u0026nbsp;million won: approximately 3430 dollars; d, normal BMI: BMI of 18.5 or higher but less than 23\u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tfoot\u003e\n\u003c/table\u003e\n\u003cp\u003e\u003c/p\u003e\n\u003cp\u003eIn women, BMI was not significantly associated with AUD in the fully adjusted model. Being underweight was positively associated with AUD in Model 1 (OR 2.986, 95% CI, 1.299\u0026ndash;6.866; p\u0026thinsp;=\u0026thinsp;0.010) and remained significant in Model 2 (OR 2.605, 1.078\u0026ndash;6.296, p\u0026thinsp;=\u0026thinsp;0.033), but was not significant in Model 3 (OR 2.032, 0.749\u0026ndash;5.516, p\u0026thinsp;=\u0026thinsp;0.162). Overweight and obesity did not show a significant association in any model. Those who were unemployed (OR 3.870, 95% CI 1.902\u0026ndash;7.877, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001), consumed more alcohol daily (OR 1.179, 95% CI 1.098\u0026ndash;1.264, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001), and drank more frequently were more likely to meet the AUD criteria (Table \u003cspan class=\"InternalRef\"\u003e4\u003c/span\u003e).\u003c/p\u003e\n\u003cp\u003e\u003c/p\u003e\n\u003ctable id=\"Tab4\" border=\"1\"\u003e\n \u003ccaption language=\"En\"\u003e\n \u003cdiv class=\"CaptionNumber\"\u003eTable 4\u003c/div\u003e\n \u003cdiv class=\"CaptionContent\"\u003e\n \u003cp\u003eRisk factors of alcohol use disorder among female drinkers\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\u0026nbsp;\u003c/th\u003e\n \u003cth align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003eModel 1\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\u0026nbsp;\u003c/th\u003e\n \u003cth align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003eModel 2\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\u0026nbsp;\u003c/th\u003e\n \u003cth align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003eModel 3\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\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eOR (CI)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cem\u003ep\u003c/em\u003e-value\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eOR (CI)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cem\u003ep\u003c/em\u003e-value\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eOR (CI)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cem\u003ep\u003c/em\u003e-value\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eBody mass index\u003csup\u003ed\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNormal\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1\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\u003eUnderweight\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2.986 (1.292-6.900)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.010\u003csup\u003e*\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2.605 (1.080\u0026ndash;6.284)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.033\u003csup\u003e*\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2.032 (0.753\u0026ndash;5.486)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.162\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eOverweight\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.711 (0.313\u0026ndash;1.614)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.415\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.825 (0.353\u0026ndash;1.924)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.656\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.927 (0.377\u0026ndash;2.281)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.869\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eObesity\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.552 (0.188\u0026ndash;1.618)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.279\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.200 (0.032\u0026ndash;1.239)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.084\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.267 (0.042\u0026ndash;1.707)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.163\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eAge (years)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\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\u003e18\u0026ndash;39\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\n \u003cp\u003e2.985 (0.691\u0026ndash;12.895)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.143\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.977 (0.277\u0026ndash;14.133)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.497\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e40\u0026ndash;59\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\n \u003cp\u003e2.346 (0.574\u0026ndash;9.588)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.235\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2.164 (0.326\u0026ndash;14.347)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.424\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e60\u0026ndash;79\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\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1\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\u003eMarital 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=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eMarried\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\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1\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\u003eSingle\u003csup\u003ea\u003c/sup\u003e\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\n \u003cp\u003e1.464 (0.663\u0026ndash;3.234)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.346\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.258 (0.531\u0026ndash;2.980)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.603\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eEmployment status\u003csup\u003eb\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eEmployed\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\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1\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\u003eUnemployed\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\n \u003cp\u003e2.861 (1.492\u0026ndash;5.487)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.002\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3.870 (1.902\u0026ndash;7.877)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.000\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eEducation\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eHigh school or less\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\n \u003cp\u003e1.456 (0.750\u0026ndash;2.824)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.267\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.234 (0.611\u0026ndash;2.491)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.558\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eGraduate\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\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1\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\u003eIncome level\u003csup\u003ec\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;2\u0026nbsp;million won\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\n \u003cp\u003e0.444 (0.096\u0026ndash;2.052)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.299\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.280 (0.045\u0026ndash;1.736)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.172\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2\u0026ndash;5\u0026nbsp;million won\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\n \u003cp\u003e0.523 (0.274\u0026ndash;0.998)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.049\u003csup\u003e*\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.526 (0.267\u0026ndash;1.034)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.062\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026gt;\u0026thinsp;5\u0026nbsp;million won\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\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1\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\u003eAverage drinking amount\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.179 (1.098\u0026ndash;1.264)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.000\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eDrinking frequency\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026gt;\u0026thinsp;3 times a week\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e8.904 (2.622\u0026ndash;30.233.)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.000\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1\u0026ndash;3 times a week\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3.192 (1.511\u0026ndash;6.745)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.002\u003csup\u003e*\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026gt;\u0026thinsp;1 times a week\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1\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 \u003ctfoot\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"9\"\u003eOR, odds ratio; CI, confidence interval\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"9\"\u003ea, Single: Not married, divorced, bereft, living independently; b, Employed: full time or part time, Unemployed: student, housewife, unemployed; c, 2\u0026nbsp;million won: approximately 1370 dollars, 5\u0026nbsp;million won: approximately 3430 dollars; d, normal BMI: BMI of 18.5 or higher but less than 23\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; **, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.01; ***, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001\u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tfoot\u003e\n\u003c/table\u003e\n\u003cp\u003e\u003c/p\u003e\n\u003cp\u003e\u003cbr\u003e\u003c/p\u003e\n\u003ch3\u003ePost hoc analysis\u003c/h3\u003e\n\u003cp\u003eIn the supplementary analyses using six BMI categories (underweight, normal weight, overweight, and class I\u0026ndash;III obesity), we observed a U-shaped pattern in the prevalence of AUD among men. The prevalence declined from the underweight group to class I obesity and then increased again from class II onwards, peaking in class III obesity. Owing to the small number of participants with class II and III obesity, the adjusted ORs could not be estimated. Among women, no AUD cases were identified as class II or III obesity, precluding a reliable estimation (Fig. \u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003e).\u003c/p\u003e\n\u003cp\u003eTo further examine the nonlinearity, BMI was modelled as a continuous variable with a quadratic term. In men, inclusion of BMI\u0026sup2; significantly improved model fit (\u0026Delta;\u0026thinsp;\u0026minus;\u0026thinsp;2LL\u0026thinsp;=\u0026thinsp;5.39, df\u0026thinsp;=\u0026thinsp;1, p\u0026thinsp;=\u0026thinsp;0.02; Nagelkerke R\u0026sup2; 0.144\u0026rarr;0.152). The coefficient for BMI\u0026sup2; was positive (B\u0026thinsp;=\u0026thinsp;0.015, p\u0026thinsp;=\u0026thinsp;0.009), indicating a U-shaped association, with the lowest predicted probability of AUD at BMI\u0026thinsp;\u0026asymp;\u0026thinsp;28.5 kg/m\u0026sup2;. In women, BMI\u0026sup2; did not improve model fit (\u0026Delta;\u0026thinsp;\u0026minus;\u0026thinsp;2LL\u0026thinsp;=\u0026thinsp;0.02, p\u0026thinsp;=\u0026thinsp;0.88; R\u0026sup2; unchanged), providing no evidence of nonlinearity (Table \u003cspan class=\"InternalRef\"\u003e5\u003c/span\u003e).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 5. Logistic regression of alcohol use disorder on continuous BMI with and without a quadratic term\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003ePanel A.\u003c/strong\u003e Model fit and test of nonlinearity\u003c/p\u003e\n\u003ctable border=\"0\" cellspacing=\"0\" cellpadding=\"0\" width=\"609\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 56px;\"\u003e\n \u003cp\u003eGender\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 178px;\"\u003e\n \u003cp\u003eModel\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 74px;\"\u003e\n \u003cp\u003e\u0026minus;2 Log Likelihood\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 76px;\"\u003e\n \u003cp\u003e\u003cem\u003eNagelkerke R\u0026sup2;\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 74px;\"\u003e\n \u003cp\u003e\u003cem\u003e\u0026Delta;\u003c/em\u003e\u003cem\u003e\u0026minus;2LL (vs. Linear)\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 38px;\"\u003e\n \u003cp\u003e\u003cem\u003edf\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 57px;\"\u003e\n \u003cp\u003e\u003cem\u003ep (LRT)\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 55px;\"\u003e\n \u003cp\u003e\u003cem\u003eVertex BMI (kg/m\u0026sup2;)\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 56px;\"\u003e\n \u003cp\u003eMen\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 178px;\"\u003e\n \u003cp\u003eLinear (BMI_c)\u003csup\u003ed\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 74px;\"\u003e\n \u003cp\u003e667.481\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 76px;\"\u003e\n \u003cp\u003e0.144\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 74px;\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 38px;\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 57px;\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 55px;\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 56px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 178px;\"\u003e\n \u003cp\u003eQuadratic (BMI_c + BMI_c\u0026sup2;)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 74px;\"\u003e\n \u003cp\u003e662.096\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 76px;\"\u003e\n \u003cp\u003e0.152\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 74px;\"\u003e\n \u003cp\u003e5.385\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 38px;\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 57px;\"\u003e\n \u003cp\u003e\u0026asymp;0.020\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 55px;\"\u003e\n \u003cp\u003e29.81\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 56px;\"\u003e\n \u003cp\u003eWomen\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 178px;\"\u003e\n \u003cp\u003eLinear (BMI_c)\u003csup\u003ed\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 74px;\"\u003e\n \u003cp\u003e297.274\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 76px;\"\u003e\n \u003cp\u003e0.233\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 74px;\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 38px;\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 57px;\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 55px;\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 56px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 178px;\"\u003e\n \u003cp\u003eQuadratic (BMI_c + BMI_c\u0026sup2;)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 74px;\"\u003e\n \u003cp\u003e297.251\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 76px;\"\u003e\n \u003cp\u003e0.233\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 74px;\"\u003e\n \u003cp\u003e0.023\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 38px;\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 57px;\"\u003e\n \u003cp\u003e0.88\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 55px;\"\u003e\n \u003cp\u003eN/A\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003eOutcome coded as 1 = AUD (past year), 0=non-event; covariates identical to the fully adjusted main model (age, marital status, education, employment, income, drinking frequency, and daily drinking amount).\u003c/p\u003e\n\u003cp\u003eBMI, body mass index; BMI_c, centred BMI (gender-specific mean subtracted); BMI_c\u0026sup2;, squared centred BMI; \u0026minus;2LL, minus twice the log-likelihood; \u0026Delta;\u0026minus;2LL, difference in \u0026minus;2LL between quadratic and linear models; LRT, likelihood-ratio test for adding BMI_c\u0026sup2;; Vertex BMI, BMI at the minimum predicted probability, computed as \u0026minus;\u0026beta;₁/(2\u0026beta;₂) + mean BMI; R\u0026sup2;, Nagelkerke pseudo-R\u0026sup2;.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003ePanel B.\u003c/strong\u003e BMI coefficients (key terms only)\u003c/p\u003e\n\u003ctable border=\"0\" cellspacing=\"0\" cellpadding=\"0\" width=\"609\" class=\"fr-table-selection-hover\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 56px;\"\u003e\n \u003cp\u003eGender\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 77px;\"\u003e\n \u003cp\u003eModel\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 77px;\"\u003e\n \u003cp\u003ePredictor\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 67px;\"\u003e\n \u003cp\u003eB\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 57px;\"\u003e\n \u003cp\u003e\u003cem\u003eSE\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 67px;\"\u003e\n \u003cp\u003e\u003cem\u003eWald \u0026chi;\u0026sup2;\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 57px;\"\u003e\n \u003cp\u003e\u003cem\u003ep\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 150px;\"\u003e\n \u003cp\u003e\u003cem\u003eOR (95% CI)\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 56px;\"\u003e\n \u003cp\u003eMen\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 77px;\"\u003e\n \u003cp\u003eLinear\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 77px;\"\u003e\n \u003cp\u003eBMI_c\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 67px;\"\u003e\n \u003cp\u003e-0.156\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 57px;\"\u003e\n \u003cp\u003e0.045\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 67px;\"\u003e\n \u003cp\u003e11.942\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 57px;\"\u003e\n \u003cp\u003e0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 150px;\"\u003e\n \u003cp\u003e0.855 (0.783-0.935)-\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 56px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 77px;\"\u003e\n \u003cp\u003eQuadratic\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 77px;\"\u003e\n \u003cp\u003eBMI_c\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 67px;\"\u003e\n \u003cp\u003e-0.173\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 57px;\"\u003e\n \u003cp\u003e0.041\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 67px;\"\u003e\n \u003cp\u003e17.987\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 57px;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 150px;\"\u003e\n \u003cp\u003e0.841 (0.776-0.911)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 56px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 77px;\"\u003e\n \u003cp\u003eQuadratic\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 77px;\"\u003e\n \u003cp\u003eBMI_c\u0026sup2;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 67px;\"\u003e\n \u003cp\u003e0.015\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 57px;\"\u003e\n \u003cp\u003e0.006\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 67px;\"\u003e\n \u003cp\u003e6.851\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 57px;\"\u003e\n \u003cp\u003e0.009\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 150px;\"\u003e\n \u003cp\u003e1.015 (1.004-1.026)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 56px;\"\u003e\n \u003cp\u003eWomen\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 77px;\"\u003e\n \u003cp\u003eLinear\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 77px;\"\u003e\n \u003cp\u003eBMI_c\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 67px;\"\u003e\n \u003cp\u003e-0.161\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 57px;\"\u003e\n \u003cp\u003e0.077\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 67px;\"\u003e\n \u003cp\u003e4.308\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 57px;\"\u003e\n \u003cp\u003e0.038\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 150px;\"\u003e\n \u003cp\u003e0.852 (0.732-0.991)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 56px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 77px;\"\u003e\n \u003cp\u003eQuadratic\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 77px;\"\u003e\n \u003cp\u003eBMI_c\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 67px;\"\u003e\n \u003cp\u003e-0.162\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 57px;\"\u003e\n \u003cp\u003e0.080\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 67px;\"\u003e\n \u003cp\u003e4.092\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 57px;\"\u003e\n \u003cp\u003e0.043\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 150px;\"\u003e\n \u003cp\u003e0.850 (0.727-0.995)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 56px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 77px;\"\u003e\n \u003cp\u003eQuadratic\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 77px;\"\u003e\n \u003cp\u003eBMI_c\u0026sup2;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 67px;\"\u003e\n \u003cp\u003e-0.003\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 57px;\"\u003e\n \u003cp\u003e0.019\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 67px;\"\u003e\n \u003cp\u003e0.022\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 57px;\"\u003e\n \u003cp\u003e0.883\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 150px;\"\u003e\n \u003cp\u003e0.997 (0.961-1.034)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003eB, log-odds coefficient; SE, standard error; Wald \u0026chi;\u0026sup2;, Wald chi-square; p, two-sided p-value; OR, odds ratio; CI, confidence interval.\u003c/p\u003e\n\u003cp\u003eBMI_c, centred BMI; BMI_c\u0026sup2;, squared-centred BMI. All models were adjusted for the same covariates used in the main analysis (age, marital status, education, employment, income, drinking frequency, and daily drinking amount).\u003c/p\u003e\n\u003cp\u003eThe GAM analysis for men also suggested a U-shaped association between BMI and the predicted probability of AUD. However, the CIs widened substantially above a BMI of 30 owing to the small sample size, indicating that the apparent increase at higher BMI should be interpreted with caution. In women, the GAM curve was essentially flat with no evidence of nonlinearity (Fig. \u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003e).\u003c/p\u003e"},{"header":"Discussion","content":"\u003cp\u003eThis study found that being underweight was associated with higher odds of AUD in women; however, this association was not statistically significant after adjusting for sociodemographic factors and drinking patterns. Given its cross-sectional design, reverse causality (AUD leading to weight loss) could not be excluded. In men, being overweight and obese was associated with lower odds ratios, and these associations strengthened after adjustment. Supplementary analyses revealed a U-shaped pattern in men, with a quadratic regression providing statistically significant evidence of nonlinearity.\u003c/p\u003e\u003cp\u003ePrevious findings regarding the association between AUD and BMI have been inconsistent. Some studies reported no significant relationship[\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e, \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e], while others identified either positive[\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e] or inverse[\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e] associations Simon et al. reported that obese individuals had a significantly lower lifetime risk of substance use disorders[\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e]. Negative genetic correlations between Alcohol Use Disorder Identification Test scores and BMI have also been observed[\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e]. Several mechanisms may explain why AUD is linked to lower BMI or underweight status. Alcohol competes with food for the activation of brain reward pathways[\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e, \u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e] and is associated with decreased plasma ghrelin[\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e], both of which may suppress appetite and lower diet quality. Alcohol also impairs the digestion and absorption of essential nutrients in the gastrointestinal tract[\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e], contributing to weight loss in individuals with alcohol dependence[\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e]. Mild-to-moderate malnutrition was identified in 24% of the inpatients undergoing treatment for alcohol and drug use[\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e]. Heavy alcohol consumption is linked to reduced skeletal muscle mass[\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e]. Our findings also support the possible association between lower BMI and AUD. The AUD prevalence was highest among underweight individuals of both genders. Higher odds of AUD were observed among underweight women, although the adjustment attenuated the association to non-significance, suggesting partial mediation or confounding by drinking behaviour. Given the small number of underweight participants (n\u0026thinsp;=\u0026thinsp;89, 2.6% of 3,468 drinkers), this association may have been underestimated or obscured by potential overadjustment. Larger studies with more underweight participants are needed to clarify this relationship.\u003c/p\u003e\u003cp\u003eWhile our study found a lower risk of AUD among obese individuals (BMI\u0026thinsp;\u0026ge;\u0026thinsp;25), other studies have reported the opposite. Petry et al. reported that both obese and extremely obese individuals had increased odds of developing AUD[\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e], and Krahn et al. found greater weight gain in participants with alcohol dependence than in controls over a period of six months[\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e]. Various mechanisms may account for the positive association between AUD and being overweight. Alcohol is calorie-dense (7 kcal/g) and may contribute to weight gain by adding calories beyond those from non-alcoholic sources[\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e, \u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e]. It may also enhance appetite after food consumption and increase food-related rewards[\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e]. Chronic drinking can promote fat accumulation by inhibiting fat oxidation and facilitating fat sparing[\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e]. In addition, AUD and binge eating may share a common vulnerabilities such as impulsivity during distress[\u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eThese discrepancies may reflect ethnic and cultural differences. East Asians frequently carry ALDH2 and ADH gene variants that induce adverse reactions to alcohol (e.g. flushing)[\u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e], which reduce alcohol consumption and may limit alcohol-related calorie intake and fat accumulation. Consequently, the association between being overweight and AUD may be weaker in this population. Additionally, cultural differences in dietary patterns accompanying alcohol use may further contribute to these discrepancies.\u003c/p\u003e\u003cp\u003eOur findings may not contradict those of previous studies if the relationship between BMI and AUD is nonlinear. A bell-shaped association was observed between frequency of alcohol consumption and obesity indicators (BMI and waist-to-hip ratio)[\u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e40\u003c/span\u003e]. Light-to-moderate alcohol intake appeared to be unrelated to adiposity gain, whereas heavy drinking was more consistently linked[\u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e41\u003c/span\u003e]. Our supplementary analyses indicated a U-shaped BMI\u0026ndash;AUD association in men. The lowest predicted probability of AUD occurred at BMI\u0026thinsp;\u0026asymp;\u0026thinsp;28.5 kg/m\u0026sup2; \u0026mdash;just below the 30 kg/m\u0026sup2; threshold that marks class II obesity under the Asia\u0026ndash;Pacific criteria and class I obesity under the standard (non-Asian) WHO criteria. This pattern suggests elevated odds of having AUD in both underweight and severely obese individuals. The lack of significant associations in some prior studies[\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e, \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e] may stem from such nonlinearity, where the increased risk at both BMI extremes could offset each other, obscuring the overall effect.\u003c/p\u003e\u003cp\u003eHeterogeneity within overweight and obese populations may explain these complexities. Obese individuals showed greater impulsivity in neurocognitive tasks, whereas overweight individuals performed similarly to those of normal weight[\u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e42\u003c/span\u003e]. Such heightened impulsivity may predispose some individuals to both impulsive eating and AUD, promoting their co-occurrence in severe obesity, whereas this vulnerability may be less pronounced in individuals with lower obesity levels or overweight individuals without obesity. Supporting this, one study found that men with class III obesity had higher odds of having AUD than those with lower obesity classes or no obesity, even after adjusting for drinking patterns[\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eRegional differences in BMI classification should also be considered. Our primary analyses applied the Asia-Pacific criteria, which define overweight and obesity as BMI\u0026thinsp;\u0026ge;\u0026thinsp;23 and \u0026ge;\u0026thinsp;25, respectively[\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e]. However, under the classification commonly used in non-Asian populations, where overweight begins at BMI\u0026thinsp;\u0026ge;\u0026thinsp;25 and obesity at BMI\u0026thinsp;\u0026ge;\u0026thinsp;30[\u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e43\u003c/span\u003e], our class II and III obesity groups (BMI\u0026thinsp;\u0026ge;\u0026thinsp;30) correspond to the obese category. Therefore, our finding of elevated AUD prevalence in these groups aligns with previous research linking obesity to increased AUD risk.\u003c/p\u003e\u003cp\u003eThis study has some limitations. First, BMI was based on self-reported height and weight rather than objective measurements, introducing a potential reporting bias. Given the household survey design, it was not feasible for the interviewers to use measuring equipment. However, validation studies have shown a strong correlation (r\u0026thinsp;\u0026gt;\u0026thinsp;0.9) between self-reported and measured BMI[\u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e44\u003c/span\u003e]. Second, owing to its cross-sectional design, this study could not establish temporal order or causal relationships between AUD and weight status. Longitudinal studies are required to determine the direction of this association. Third, AUD diagnosis was based on self-reported symptoms. Although symptoms were assessed within the past year, and a standardised tool was used to improve reliability, recall bias and underreporting remain possible. Finally, we were unable to account for all potential confounders that could affect body weight. Owing to the small number of participants with physical illnesses, medical conditions could not be fully adjusted for, and other relevant factors, such as physical activity and sleep patterns, were not included.\u003c/p\u003e\u003cp\u003eDespite these limitations, this study has several strengths. This study investigated the association between AUD and BMI in a large, nationally representative sample of community-dwelling adults rather than in a restricted clinical or occupational population. The use of a randomly selected cohort enhanced the external validity and generalisability of the findings. In addition, AUD was diagnosed using a well-validated structured interview rather than a simple screening tool, thereby improving the diagnostic accuracy and enabling a more reliable assessment of the relationship between BMI and AUD.\u003c/p\u003e"},{"header":"Conclusions","content":"\u003cp\u003eIn conclusion, our results indicate a lower risk of AUD in the overweight group and suggest a potential nonlinear relationship between BMI and AUD, with an increased risk in both underweight and severely obese individuals in men. These findings may help explain the inconsistencies in previous studies and clarify their complex associations. Although this interpretation requires caution and confirmation through further studies, which include a substantial number of participants with severe obesity and who are underweight, clinicians should remain attentive to AUD risk in both underweight and severely obese patients.\u003c/p\u003e"},{"header":"Abbreviations","content":"\u003cp\u003eAUD, alcohol use disorder; DALYs, disability-adjusted life years; BMI, body mass index; CIDI, Composite International Diagnostic Interview; WHO, World Health Organization; DSM-IV, Diagnostic and Statistical Manual of Mental Disorders, fourth edition; ORs, odds ratios; CIs, confidence intervals; GAM, generalised additive model.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eEthics approval and consent to participate\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis study was approved by the Institutional Review Boards of Kangdong Sacread Heart Hospital (IRB No. KANGDONG 2025-07-004). The analyses were performed using anonymized microdata obtained from a national survey. Informed consent was obtained from all participants at the time of the original survey.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent for publication\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eClinical trial number\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAvailability of data and materials\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe data that support the findings of this study are available from the National Center for Mental Health (Seoul, Korea), but restrictions apply to the availability of these data, which were used under license for the current study and are therefore not publicly available. Researchers may access the anonymized microdata upon reasonable request and after approval by the National Center for Mental Health.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting interests\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare that they have no competing interests.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis research was supported by the National Research Foundation of Korea (NRF) grant funded by the Ministry of Science and ICT of the Korean government (RS-2024-00422599).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthors\u0026apos; contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eSJS and JEP conceptualized the study. SJS, BJH, JEP, HYS, SMC, BSK, HJJ, and JPH curated and managed the data. SJS, JEP, and HYS performed the formal analyses, while SJS and BJH designed the methodology. JEP, HYS, SMC, BSK, HJJ, and JPH administered the project. JYH, JYS, and KJP conducted the investigation and validation. SJS prepared the visualizations. BJH supervised the overall study. SJS drafted the original manuscript, and SJS, BJH, and JYH critically reviewed and edited the final version. All authors read and approved the final manuscript.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAcknowledgements\u003c/strong\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis study utilized microdata from the 2021 National Mental Health Survey of Korea conducted by the Ministry of Health and Welfare (NMHSK-85). The findings and conclusions of this study are solely those of the authors and do not necessarily represent the views of the Ministry of Health and Welfare.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eThe authors thank Jinseob Kim (Zarathu Co., Ltd., Seoul, Republic of Korea) for statistical advice.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eWorld Health Organization. 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Early life stress and body-mass-index modulate brain connectivity in alcohol use disorder. Transl Psychiatry. 2024;14:1:43. doi:10.1038/s41398-024-02756-8.\u003c/li\u003e\n\u003cli\u003eSunwoo YK, Bae JN, Hahm BJ, Lee DW, Park JI, Cho SJ, et al. Relationships of mental disorders and weight status in the Korean adult population. J Korean Med Sci. 2011;26:1:108-15. doi:10.3346/jkms.2011.26.1.108.\u003c/li\u003e\n\u003cli\u003eRim SJ, Hahm BJ, Seong SJ, Park JE, Chang SM, Kim BS, et al. Prevalence of Mental Disorders and Associated Factors in Korean Adults: National Mental Health Survey of Korea 2021. Psychiatry Investig. 2023;20:3:262-72. doi:10.30773/pi.2022.0307.\u003c/li\u003e\n\u003cli\u003eKessler RC, Ust\u0026uuml;n TB. The World Mental Health (WMH) Survey Initiative Version of the World Health Organization (WHO) Composite International Diagnostic Interview (CIDI). Int J Methods Psychiatr Res. 2004;13:2:93-121. doi:10.1002/mpr.168.\u003c/li\u003e\n\u003cli\u003eM.J. Cho, B.J. 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Binge eating, problem drinking, and pathological gambling: Linking behavior to shared traits and social learning. Personality and Individual Differences. 2008;44:4:789-800. doi:https://doi.org/10.1016/j.paid.2007.10.008.\u003c/li\u003e\n\u003cli\u003eEng MY, Luczak SE, Wall TL. ALDH2, ADH1B, and ADH1C genotypes in Asians: a literature review. Alcohol Res Health. 2007;30:1:22-7. \u003c/li\u003e\n\u003cli\u003eO\u0026apos;Donovan G, Stamatakis E, Hamer M. Associations between alcohol and obesity in more than 100 000 adults in England and Scotland. Br J Nutr. 2018;119:2:222-7. doi:10.1017/s000711451700352x.\u003c/li\u003e\n\u003cli\u003eTraversy G, Chaput JP. Alcohol Consumption and Obesity: An Update. Curr Obes Rep. 2015;4:1:122-30. doi:10.1007/s13679-014-0129-4.\u003c/li\u003e\n\u003cli\u003eChamberlain SR, Derbyshire KL, Leppink E, Grant JE. Obesity and dissociable forms of impulsivity in young adults. CNS Spectr. 2015;20:5:500-7. doi:10.1017/s1092852914000625.\u003c/li\u003e\n\u003cli\u003eWorld Health Organization. Obesity and overweight. https://www.who.int/news-room/fact-sheets/detail/obesity-and-overweight (2025). Accessed July 14 2025.\u003c/li\u003e\n\u003cli\u003eDavies A, Wellard-Cole L, Rangan A, Allman-Farinelli M. Validity of self-reported weight and height for BMI classification: A cross-sectional study among young adults. Nutrition. 2020;71:110622. doi:10.1016/j.nut.2019.110622.\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"bmc-psychiatry","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"bpsy","sideBox":"Learn more about [BMC Psychiatry](http://bmcpsychiatry.biomedcentral.com/)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/bpsy/default.aspx","title":"BMC Psychiatry","twitterHandle":"@BMC_series","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"em","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"alcohol use disorder, weight status, body mass index, underweight, obesity","lastPublishedDoi":"10.21203/rs.3.rs-7912946/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-7912946/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eBackground\u003c/h2\u003e\u003cp\u003eFindings on the relationship between weight status and alcohol use disorder (AUD) have been inconsistent. Therefore, this cross-sectional study investigated the relationship between AUD and body mass index (BMI) in a nationally representative sample of South Korean adults.\u003c/p\u003e\u003ch2\u003eMethods\u003c/h2\u003e\u003cp\u003eWe analysed the data of 5,511 adults aged 18\u0026ndash;79 years who participated in the 2021 National Mental Health Survey of Korea. AUD was diagnosed using the Korean version of the Composite International Diagnostic Interview. BMI was calculated using self-reported height and weight. Participants were categorised according to the World Health Organization Asia-Pacific criteria as underweight (\u0026lt;\u0026thinsp;18.5), normal weight (18.5\u0026ndash;23), overweight (23\u0026ndash;25), and obese (\u0026ge;\u0026thinsp;25). Gender-stratified multivariate logistic regression analyses were conducted after adjusting for socio-demographic variables and drinking patterns. Potential nonlinear associations were evaluated using quadratic terms and generalised additive models.\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e\u003cp\u003eAmong 3,468 past-year drinkers, 144 (4.2%) met the AUD criteria. AUD prevalence was highest in the underweight group and decreased with increasing BMI in both genders. In men, overweight and obesity were associated with significantly lower odds of AUD (OR 0.537, 95% CI 0.327\u0026ndash;0.882; OR 0.353, 95% CI 0.196\u0026ndash;0.634, respectively). No significant association was found in women after adjustment. Supplementary analyses suggested a nonlinear U-shaped association between AUD and BMI in men.\u003c/p\u003e\u003ch2\u003eConclusions\u003c/h2\u003e\u003cp\u003eThe association between BMI and AUD differed by gender. In men, overweight and obesity were associated with lower odds of AUD, whereas a potential U-shaped pattern with increased odds at both BMI extremes. No significant associations were observed among women.\u003c/p\u003e","manuscriptTitle":"Alcohol use disorder across the weight spectrum: a cross-sectional study from a Korean national survey","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-11-18 08:34:28","doi":"10.21203/rs.3.rs-7912946/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2026-03-13T05:42:17+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2026-01-22T11:55:44+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"240964389234823840004354346713783233306","date":"2026-01-20T03:49:51+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"256707260464538138145903747201783050987","date":"2026-01-18T23:31:51+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"102054723400008809481583634644746208063","date":"2026-01-18T05:42:04+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-11-17T16:33:03+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"129229579686933914562933738940999345173","date":"2025-11-06T14:17:24+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2025-11-05T20:31:56+00:00","index":"","fulltext":""},{"type":"editorInvited","content":"","date":"2025-10-31T18:48:45+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2025-10-28T09:23:25+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2025-10-28T09:23:10+00:00","index":"","fulltext":""},{"type":"submitted","content":"BMC Psychiatry","date":"2025-10-21T09:23:36+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"bmc-psychiatry","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"bpsy","sideBox":"Learn more about [BMC Psychiatry](http://bmcpsychiatry.biomedcentral.com/)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/bpsy/default.aspx","title":"BMC Psychiatry","twitterHandle":"@BMC_series","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"em","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"3506ee42-c4a8-4557-adf1-b2632c4aa0fc","owner":[],"postedDate":"November 18th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"under-review","subjectAreas":[],"tags":[],"updatedAt":"2026-05-22T10:08:32+00:00","versionOfRecord":[],"versionCreatedAt":"2025-11-18 08:34:28","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-7912946","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-7912946","identity":"rs-7912946","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}