Approximately 80% of deaths in Japan are attributed to lifestyle-related diseases, including obesity. According to the 2019 National Health and Nutrition Survey conducted by the Ministry of Health, Labour, and Welfare, the prevalence of obesity (BMI ≥25 kg/m²) ranges from 30.1% to 33.0% in men and 19.5% to 22.3% in women, with a notable increase in men between 2013 and 2019 [1]. Excess weight is a major risk factor for non-communicable diseases (NCDs) such as diabetes, cardiovascular diseases, and cancer.

Obesity-related behaviors, including alcohol consumption, drinking frequency, eating speed, physical inactivity, and psychological stress, are extensively studied [2-6]. While some studies suggest a positive correlation between alcohol consumption and obesity, others report no association, particularly among moderate drinkers with healthier lifestyles [7-9]. However, data on the relationship between alcohol consumption and obesity among young adults in Japan, especially in local settings like Yugawara Town, is limited.

The present study investigates the relationship between obesity and lifestyle behaviors, focusing on alcohol consumption, among working-age adults (19–39 years) in Yugawara Town. This study aims to provide evidence for the development of tailored health promotion strategies.

Study Design

This cross-sectional study was based on data from a health and dietary education survey conducted in Yugawara Town, Kanagawa Prefecture, between February and March 2015. Questionnaires were mailed to 1,355 residents aged 19–39 years, achieving a response rate of 29.0% (393 questionnaires returned). Participant recruitment was conducted using family registry information to ensure that all recipients were within the defined age range of 19–39 years. Although some respondents did not provide their date of birth, their eligibility within the specified age range was confirmed using the family registry data. Consequently, these respondents were included in the analysis.

Survey Items

The questionnaire comprised 34 items covering demographics, BMI, nutrition, exercise, mental health, smoking, alcohol consumption, and dental health. Participants with incomplete height or weight data were excluded, leaving 366 responses for analysis. Missing values were excluded from individual analyses.

Study Groups

BMI was calculated as weight (kg) divided by the square of height (m²). Participants with a BMI <18.5 (n = 46) were excluded from the analysis. The remaining participants were categorized as either normal weight (BMI 18.5–24.9, n = 267) or overweight/obese (BMI ≥25, n = 53). (Fig.1)

Statistical Analysis

Student’s t-test was used to compare continuous variables, while Fisher’s exact test was applied to categorical variables. Missing data were addressed using pairwise deletion, and only available data for the relevant variables were included in each analysis to maximize the use of information. Binary logistic regression was performed to evaluate associations between obesity and independent variables, with adjustments for age and sex. Odds ratios (ORs) and 95% confidence intervals (CIs) were calculated. Statistical significance was defined as p < 0.05. All statistical analyses were conducted using EZR version 1.61 [10].

Ethical Considerations

This study was conducted as part of the "Yuttari Yugawara 'Healthy and Happy Life Plan'," a public health initiative implemented by Yugawara Town. The purpose of the initiative is to promote health and improve nutritional education among residents. As the data collection was performed under this government-led program, individual informed consent was not required in accordance with the applicable legal framework.

To ensure privacy and confidentiality, all data were anonymized prior to analysis, making it impossible to identify individual participants. Since this study was part of routine public health activities and did not involve interventions or treatments beyond standard practices, it was not subject to institutional ethical review, in line with national guidelines.

Participant Characteristics (Table 1)

BMI into normal-weight and overweight/obese groups. Among the 267 participants in the normal-weight group, 39.7% were male, while 62.3% of the 53 participants in the overweight/obese group were male (p = 0.004). The mean body weight and height were significantly higher in the overweight/obese group compared to the normal-weight group (p < 0.001 and p = 0.010, respectively). Self-reported health status differed notably between the groups, with 72.3% of normal-weight participants rating their health as good or very good, compared to 58.5% in the overweight/obese group (p = 0.001).

Table 1: Participant Characteristics.

This study investigated the relationship between obesity and lifestyle factors, particularly alcohol consumption, among the working-age population in Yugawara Town, Japan. The findings revealed significant associations between obesity and infrequent alcohol consumption, high alcohol intake per occasion, poor self-reported health, fast eating speed, and difficulties in managing stress. These results highlight the complex interactions between behavioral and psychological factors contributing to obesity, emphasizing the need for targeted interventions.

Alcohol Consumption and Obesity

The study identified a dual association between alcohol consumption patterns and obesity. Participants who abstained from or consumed alcohol infrequently were at a higher risk of obesity compared to those who consumed alcohol ≥3 days/week. This trend was particularly significant among men, where those who abstained had an OR of 3.75 (95% CI: 1.05–13.30, p = 0.038) compared to frequent drinkers. Conversely, excessive alcohol intake per occasion (≥44.3g) was significantly associated with increased odds of obesity, especially in women (p for trend = 0.016). These results align with prior studies suggesting that excessive alcohol consumption contributes to weight gain by increasing caloric intake, stimulating appetite, and impairing lipid metabolism [11, 12]. Moreover, the findings support the Japanese Ministry of Health’s guideline recommending moderate alcohol consumption (≤20 grams/day) to maintain a balanced diet and healthy lifestyle [13].

Self-Reported Health and Obesity

Participants with poor self-reported health were more likely to be obese, with an adjusted OR of 3.76 (95% CI: 1.72–8.20, p < 0.001). This aligns with findings from the Yugawara Town Health Promotion Plan, which indicated lower health awareness among residents during adolescence and a decline in health status with age [14]. The association between obesity and poor health awareness underscores the importance of promoting health literacy and encouraging proactive health behaviors to prevent obesity and related conditions.

Eating Speed and Obesity

Fast eating speed was significantly associated with obesity (Adjusted OR = 2.18, 95% CI: 1.09–4.37, p = 0.028). This finding is consistent with previous research suggesting that rapid eating disrupts satiety signaling by reducing the time required for gastrointestinal hormone secretion, leading to overeating (15, 16). Promoting slower eating and mindful eating practices, such as thorough chewing and increased awareness of food intake, could serve as effective strategies to address obesity.

Stress and Obesity

Difficulties in managing stress were also significantly associated with obesity (Adjusted OR = 2.33, 95% CI: 1.24–4.40, p = 0.009). Stress is known to alter food choices, often increasing preference for high-fat, energy-dense foods, while simultaneously reducing physical activity(6). Stress-induced changes in eating habits and energy expenditure may exacerbate unhealthy behaviors, contributing to weight gain. Implementing stress management programs and addressing the interplay between stress and eating behaviors may help mitigate the risk of obesity. Further research is required to explore these relationships in greater detail.

Limitations

This study has several limitations. As an anonymous, self-administered survey, it may be subject to recall bias, particularly for self-reported height and weight, which are often over- or underreported depending on BMI status. The cross-sectional design precludes establishing causal relationships between obesity and associated factors. Furthermore, the study is geographically limited to adolescents in Yugawara Town, Kanagawa Prefecture, and the findings may not be generalizable to other regions or age groups. Additionally, the questionnaire lacked detailed dietary information, such as food quantity and nutritional content, which should be incorporated in future research to provide a more comprehensive analysis.

This study highlights the multifactorial nature of obesity, identifying significant associations with alcohol consumption patterns, self-reported health, eating speed, and stress management difficulties. The findings underscore the importance of tailored interventions targeting these lifestyle factors to address obesity. Promoting moderate alcohol consumption, improving health literacy, encouraging mindful eating, and implementing stress management strategies are critical for effective obesity prevention. Future studies should explore the causal pathways linking these factors to obesity and consider a broader geographic and demographic scope to validate the findings.

COI: There is no conflict of interest.

1. Ministry of Health, Labour and Welfare. The National Health and Nutrition Survey (NHNS) Japan. 2020.
2. Hurst Y, Fukuda H. Effects of changes in eating speed on obesity in patients with diabetes: a secondary analysis of longitudinal health check-up data. BMJ Open. 2018; 8: e019589.
3. Kirihara Y. MHe. Association of Clinical Laboratory Data with Drinking and Smoking Habits by Age Group (in Japanese). The 55th Annual Meeting of the Japanese Association for Rural Medicine. 2006; 55: 252.
4. Shimoshikiryo I, Ibusuki R, Shimatani K, Nishimoto D, Takezaki T, Nishida Y, et al. Association between alcohol intake pattern and metabolic syndrome components and simulated change by alcohol intake reduction: A cross-sectional study from the Japan Multi-Institutional Collaborative Cohort Study. Alcohol. 2020; 89: 129-138.
5. Morita Y, Ohta M, Inoue T, Honda T, Konno Y, Eguchi Y, et al. Sense of coherence is significantly associated with both metabolic syndrome and lifestyle in Japanese computer software office workers. Int J Occup Med Environ Health. 2014; 27: 967-979.
6. Moore CJ, Cunningham SA. Social position, psychological stress, and obesity: a systematic review. J Acad Nutr Diet. 2012; 112: 518-526.
7. Booranasuksakul U, Singhato A, Rueangsri N, Prasertsri P. Association between Alcohol Consumption and Body Mass Index in University Students. Asian Pac Isl Nurs J. 2019; 4: 57-65.
8. Kim BY, Nam H, Yoo JJ, Cho YY, Choi DH, Jung CH, et al. Association between alcohol consumption status and obesity-related comorbidities in men: data from the 2016 Korean community health survey. BMC Public Health. 2021; 21: 733.
9. Traversy G, Chaput JP. Alcohol Consumption and Obesity: An Update. Curr Obes Rep. 2015; 4: 122-130.
10. Kanda Y. Investigation of the freely available easy-to-use software 'EZR' for medical statistics. Bone Marrow Transplant. 2013; 48: 452-458.
11. Eiler WJ, 2nd, Dzemidzic M, Case KR, Soeurt CM, Armstrong CL, Mattes RD, et al. The aperitif effect: Alcohol's effects on the brain's response to food aromas in women. Obesity (Silver Spring). 2015; 23: 1386-1393.
12. Moriles KE, Azer SA. Alanine Amino Transferase. StatPearls. Treasure Island (FL): StatPearls Publishing Copyright © 2023, StatPearls Publishing LLC.; 2023.
13. Ministry of Health, and Labour and Welfare. Health Japan 21 (the second term). 2000.
14. Yugawara Town Welfare Department Health Center. Yutari Yugawara Healthy Plan (Yugawara Town Health Promotion Plan/Food Education Promotion Plan). 2023 Feb17 access,Yugawara Town.
15. Garciduenas-Fimbres TE, Paz-Graniel I, Nishi SK, Salas-Salvado J, Babio N. Eating Speed, Eating Frequency, and Their Relationships with Diet Quality, Adiposity, and Metabolic Syndrome, or Its Components. Nutrients. 2021; 13: 1687.
16. Ohkuma T, Hirakawa Y, Nakamura U, Kiyohara Y, Kitazono T, Ninomiya T. Association between eating rate and obesity: a systematic review and meta-analysis. Int J Obes (Lond). 2015; 39: 1589-1596.