Abstract
Background
Gay and bisexual men (GBM) face elevated risks of hypertension, diabetes, and depression, but these conditions are strongly influenced by social discrimination, economic disadvantage, and recreational substance use, complicating their interpretation. This study aimed to compare the social, lifestyle, and health characteristics of GBM with those of heterosexual men in Japan, where epidemiological data remain limited.
Methods
We analyzed data from the 2022 wave of the Japan COVID-19 and Society Internet Survey (JACSIS), a large-scale, web-based nationwide survey. Participants were classified as heterosexual, gay, or bisexual according to reported sexual attraction. Inverse probability weighting was applied to estimate population-level prevalence. Multivariable logistic regression was performed to examine associations between sexual orientation and lifestyle-related diseases, recreational substance use, and depression.
Results
Among 13,271 eligible men, 830 (6.3%) identified as GBM (721 gay, 109 bisexual); the weighted prevalence was 6.6% (95% CI: 5.9–7.4%). GBM were more likely to report burden from night shift work (27.5% [228/830] vs. 15.9% [1,979/12,441], p < 0.001), workplace harassment (27.7% [185/667] vs. 12.2% [1,185/9,676], p < 0.001), fear of job loss (31.8% [212/667] vs. 22.1% [2,142/9,676], p < 0.001), receipt of public assistance (1.2% [10/830] vs. 0.3% [43/12,441], p < 0.001), and experience of recreational substance use (12.9% [107/830] vs. 4.1% [505/12,441], p < 0.001). Among participants under 50 years, GBM tended to have higher prevalence of hypertension (9.6% [49/508] vs. 5.2% [346/6,626]) and diabetes (6.7% [34/508] vs. 2.5% [168/6,626]), but these associations were not significant after adjustment (hypertension: gay aOR 1.20, 95% CI: 0.91–1.59; bisexual aOR 1.70, 95% CI: 0.89–3.25; diabetes: gay aOR 0.95, 95% CI: 0.65–1.39; bisexual aOR 1.46, 95% CI: 0.60–3.53). Depression was more common among GBM (7.2% [60/830] vs. 3.4% [417/12,441], p < 0.001), but not statistically significant among gay men (gay aOR 1.15 [0.76–1.75], bisexual aOR 2.34 [1.08–5.09]).
Conclusions
GBM in Japan showed higher self-reported prevalence of depression, hypertension, and diabetes, particularly among men aged under 50, although these differences were attenuated in multivariable analysis. These findings highlight the importance of social and occupational contexts and recreational substance use when considering health disparities among GBM.
Supplementary Information
The online version contains supplementary material available at 10.1186/s12889-026-26209-7.
Introduction
Sexual minorities often face multiple challenges, including social discrimination, economic disadvantage, substance use, unstable employment, and increased risks of various physical and mental health conditions. These factors are interrelated and may reinforce one another. For example, previous studies have suggested that gay and bisexual men (GBM), particularly younger individuals, are at increased risk of hypertension [1–3], diabetes [1, 4], and mental health problems [1, 5, 6]. However, the development of these conditions is strongly influenced by lifestyle factors (e.g., physical activity, smoking, body mass index [BMI]), socioeconomic status (e.g., income, occupation), psychosocial conditions (e.g., mental health, stress). In addition, sexual minorities are more likely to experience poor job quality, including workplace discrimination and lower job satisfaction [7]. Employment discrimination is a well-documented issue [8]. In addition, bisexual individuals in particular have been reported to be at higher risk of poverty [9]. These findings highlight that when considering the disease risks of GBM, it is essential to account for their broader social circumstances alongside individual lifestyle and health factors.
The social acceptance of sexual minorities varies substantially across countries, influencing the extent to which individuals can disclose their sexual orientation. In Japan, same-sex marriage is not legally recognized, and social and institutional barriers to sexual minority inclusion remain [10]. Despite this context, large-scale epidemiological studies on GBM remain scarce. Previous research among people living with HIV in Japan has reported higher prevalence of lifestyle-related diseases as well as increased rates of drug use [11], including substances associated with chemsex [12]. Although gay and bisexual men are overrepresented among people living with HIV in Japan, these findings cannot be assumed to represent the broader GBM population. To our knowledge, no study has comprehensively evaluated these issues specifically among GBM in Japan, nor directly compared them with the general male population.
To address these gaps, we conducted a comprehensive analysis using data from the Japan COVID-19 and Society Internet Survey (JACSIS), a nationwide online survey of approximately 30,000 respondents. We examined the prevalence of GBM in Japan and assessed their social and economic situations, lifestyle factors, substance use, and risks of chronic diseases, comparing them with heterosexual men. This study provides one of the first large-scale, population-based insights into the multifaceted health and social issues faced by GBM in Japan.
Methods
Setting and participants
Data were obtained from the 2022 wave of the JACSIS, a large-scale, web-based, self-administered questionnaire. The survey was conducted from September to October 2022. JACSIS is an annual nationwide survey designed to assess the impact of the COVID-19 pandemic on health and social factors in Japan. JACSIS participants were recruited from an online research panel operated by Rakuten Insight, Inc [13, 14]. Panel members voluntarily register through the Rakuten platform and agree to participate in online surveys. Invitations to participate in JACSIS are distributed electronically, and respondents complete a self-administered web-based questionnaire. Participants receive small incentives in the form of redeemable points upon survey completion, which are commonly used in online research in Japan. Previous studies using JACSIS data have reported that the demographic distribution of respondents differs from that of the general population, with overrepresentation of younger individuals and those with higher internet use. While absolute prevalence estimates should be interpreted with caution, relative associations between exposures and outcomes have been shown to be robust in prior JACSIS studies [13, 14].
Definitions and data preparation
Respondents were excluded from the analysis if they were deemed to have provided invalid responses according to the following criteria. First, individuals who failed to follow a dummy instruction embedded in the survey (e.g., selecting “the second option from the bottom” as explicitly instructed) were excluded. Second, respondents who reported frequent or daily use of all nine substances—alcohol, sleeping pills or anxiolytics, medical narcotics for cancer pain, medical narcotics for other pain, non-medical narcotics, organic solvents, designer drugs, marijuana, and stimulants or heroin—were also excluded, as such a pattern was considered indicative of potentially invalid responses. Third, respondents who indicated currently having all of the following nine comorbidities were excluded due to concerns about response validity: hypertension, diabetes mellitus, asthma, atopic dermatitis, angina pectoris, myocardial infarction, stroke (including cerebral infarction or hemorrhage), cancer, and chronic pain.
Only respondents whose assigned sex at birth was male and whose current gender identity was a man were included in this study, thereby restricting the analytic sample to cisgender men. Respondents whose current gender identity was a woman, both man and woman, undecided, or other were excluded, and transgender men were not included in the analytic sample. Among the included respondents, those who reported sexual attraction to women were classified as heterosexual, to men as gay, and to both sexes as bisexual. Respondents who selected “undecided” or “other” were excluded. The exact wording of the relevant questions is provided in Supplementary Table 1.
The following covariates were included in the analysis: age, presence of a partner and their assigned sex at birth, current employment, regular employment, burden from night shift (i.e., working between 10 PM and 5 AM), university graduation, experience of workplace harassment, fear of job loss, public assistance receipt, household income (categorized as < 2 million, 2–5 million, 5–10 million, 10–20 million, and ≥ 20 million JPY; approximate exchange rate in 2022: 1 USD ≈ 130 JPY), regular exercise, sleep deprivation (< 6 h), current smoking, daily alcohol consumption, and experience of recreational substance use (stimulants, cannabis/marijuana, non-medical morphine). Household income was self-reported and reflected the total income of all household members; the survey allowed respondents to include income from a cohabiting partner regardless of the partner’s sex. Additionally, BMI, obesity (BMI ≥ 25), hypertension, dyslipidemia, diabetes mellitus, hepatitis, immune-related diseases, malignancy, and depression were also included. Detailed definitions are provided in Supplementary Table 2. Missing data were not applicable as respondents with incomplete or invalid answers were excluded. Information on HIV status was not collected in the JACSIS survey and was therefore not available for analysis.
Statistical analysis
To estimate the population proportion of GBM, inverse probability weighting (IPW) was applied to adjust for sampling bias and to improve generalizability to the national population. Sampling weights were derived from the 2019 Comprehensive Survey of Living Conditions of People on Health and Welfare (CSLCPHW), a nationally representative dataset collected by the Ministry of Health, Labour and Welfare of Japan [13]. Data from the JACSIS survey and the CSLCPHW were pooled, and a multivariable logistic regression model was used to estimate the probability of being a respondent in the JACSIS survey versus the CSLCPHW. Covariates included age, area of residence, educational attainment, household income, employment status, self-rated health, and smoking status, which were available in both datasets [14]. The inverse of the predicted probability was used as the sampling weight. IPW was applied only to estimate the population prevalence of GBM, whereas other descriptive statistics and multivariable regression analyses were conducted without weighting to focus on within-sample associations. Therefore, the estimated odds ratios should be interpreted as associations among survey respondents rather than fully population-representative estimates. Extreme weights were not observed, and therefore no trimming was applied. The effective sample size after weighting remained sufficient for stable estimation.
For descriptive comparisons (Table 1), categorical variables were compared using chi-square tests and continuous variables using the Mann–Whitney U test. These comparisons were conducted between heterosexual men and GBM as a combined group, and p-values were not adjusted for multiple comparisons. Multivariable adjustment was not applied to these descriptive comparisons; instead, age-specific trends were described separately. Age-stratified analyses were conducted because both health conditions and lifestyle factors vary substantially by age, and the social and occupational circumstances of GBM may differ across life stages. Stratifying by age allowed us to examine whether health and social disparities associated with GBM status were consistent across age groups or were more pronounced at specific stages of adulthood. For age-stratified analyses, participants were categorized as younger (< 50 years) and older (≥ 50 years).
Table 1.
Participant characteristics
| Total n = 13,271 |
Heterosexual men n = 12,441 |
GBM | p value* | |||
|---|---|---|---|---|---|---|
| All GBM n = 830 |
Gay n = 721 |
Bisexual men n = 109 |
||||
| Age, Mean (IQR) |
47.0 (35.0–64.0) |
48.0 (36.0–64.0) |
42.0 (30.0–58.0) |
43.0 (30.0–59.0) |
37.0 (29.0–49.0) |
< 0.001 |
| 17–29 years | 1,873 (14.1%) | 1,667 (13.4%) | 206 (24.8%) | 175 (24.3%) | 31 (28.4%) | < 0.001 |
| 30–39 years | 2,997 (22.6%) | 2,819 (22.7%) | 178 (21.4%) | 151 (20.9%) | 27 (24.8%) | 0.42 |
| 40–49 years | 2,264 (17.1%) | 2,140 (17.2%) | 124 (14.9%) | 100 (13.9%) | 24 (22.0%) | 0.09 |
| 50–59 years | 2,008 (15.1%) | 1,870 (15.0%) | 138 (16.6%) | 128 (17.8%) | 10 (9.2%) | 0.21 |
| 60–82 years | 4,129 (31.1%) | 3,945 (31.7%) | 184 (22.2%) | 167 (23.2%) | 17 (15.6%) | < 0.001 |
| Partner | 8,278 (62.4%) | 7,957 (64.0%) | 321 (38.7%) | 287 (39.8%) | 34 (31.2%) | < 0.001 |
| Male partner | 147 (1.1%) | 16 (0.1%) | 131 (15.8%) | 130 (18.0%) | 1 (0.9%) | < 0.001 |
| Female partner | 8,121 (61.2%) | 7,935 (63.8%) | 186 (22.4%) | 155 (21.5%) | 31 (28.4%) | < 0.001 |
| Socioeconomic Status and Work | ||||||
| Currently working | 10,343 (77.9%) | 9,676 (77.8%) | 667 (80.4%) | 581 (80.6%) | 86 (78.9%) | 0.08 |
| Regular employment (full-time) | 8,359 (63.0%) | 7,834 (63.0%) | 525 (63.3%) | 464 (64.4%) | 61 (56.0%) | 0.87 |
| Burden from night shift work | 2,207 (16.6%) | 1,979 (15.9%) | 228 (27.5%) | 210 (29.1%) | 18 (16.5%) | < 0.001 |
| University graduate | 8,324 (62.7%) | 7,857 (63.2%) | 467 (56.3%) | 405 (56.2%) | 62 (56.9%) | < 0.001 |
| Workplace harassment a | 1,370 (13.2%) | 1,185 (12.2%) | 185 (27.7%) | 172 (29.6%) | 13 (15.1%) | < 0.001 |
| Fear of job loss a | 2,354 (22.8%) | 2,142 (22.1%) | 212 (31.8%) | 190 (32.7%) | 22 (25.6%) | < 0.001 |
| Receiving public assistance | 53 (0.4%) | 43 (0.3%) | 10 (1.2%) | 8 (1.1%) | 2 (1.8%) | < 0.001 |
| Household income b | ||||||
| < 2 million JPY | 934 (7.5%) | 856 (7.3%) | 78 (10.4%) | 64 (9.8%) | 14 (14.3%) | 0.002 |
| 2–5 million JPY | 4,077 (32.7%) | 3,792 (32.4%) | 285 (37.9%) | 247 (37.8%) | 38 (38.8%) | 0.002 |
| 5–10 million JPY | 4,705 (37.8%) | 4,479 (38.3%) | 226 (30.1%) | 195 (29.8%) | 31 (31.6%) | < 0.001 |
| 10–20 million JPY | 1,610 (12.9%) | 1,535 (13.1%) | 75 (10.0%) | 65 (9.9%) | 10 (10.2%) | 0.013 |
| ≧ 20 million JPY | 1,126 (9.0%) | 1,038 (8.9%) | 88 (11.7%) | 83 (12.7%) | 5 (5.1%) | 0.009 |
| Lifestyle | ||||||
| Regular exercise c | 5,236 (40.5%) | 4,869 (40.1%) | 367 (46.2%) | 327 (47.6%) | 40 (37.4%) | |
| Sleep deprivation (< 6 h/day) d | 3,414 (26.0%) | 3,105 (25.2%) | 309 (38.4%) | 286 (41.2%) | 23 (21.1%) | < 0.001 |
| Current smoking | 3,524 (26.6%) | 3,290 (26.4%) | 234 (28.2%) | 207 (28.7%) | 27 (24.8%) | 0.27 |
| Daily Alcohol consumption | 3,691 (27.8%) | 3,504 (28.2%) | 187 (22.5%) | 172 (23.9%) | 15 (13.8%) | < 0.001 |
| Experience of recreational substance use | 612 (4.6%) | 505 (4.1%) | 107 (12.9%) | 103 (14.3%) | 4 (3.7%) | < 0.001 |
| Stimulants (e.g., methamphetamine, cocaine) or heroin use | 325 (2.4%) | 256 (2.1%) | 69 (8.3%) | 67 (9.3%) | 2 (1.8%) | < 0.001 |
| Cannabis or marijuana use | 497 (3.7%) | 411 (3.3%) | 86 (10.4%) | 82 (11.4%) | 4 (3.7%) | < 0.001 |
| Non-medical morphine use | 368 (2.8%) | 277 (2.2%) | 91 (11.0%) | 89 (12.3%) | 2 (1.8%) | < 0.001 |
| Health Indicators and Chronic Conditions | ||||||
| BMI |
22.8 (20.8–25.0) |
22.8 (20.8–25.0) |
22.5 (20.5–25.1) |
22.5 (20.5–24.8) |
23.4 (20.6–26.9) |
0.10 |
| Obesity | 3,358 (25.3%) | 3,148 (25.3%) | 210 (25.3%) | 168 (23.3%) | 42 (38.5%) | 1.00 |
| Hypertension | 2,702 (20.4%) | 2,547 (20.5%) | 155 (18.7%) | 132 (18.3%) | 23 (21.1%) | 0.21 |
| Dyslipidemia | 1,495 (11.3%) | 1,417 (11.4%) | 78 (9.4%) | 68 (9.4%) | 10 (9.2%) | 0.079 |
| Diabetes mellitus | 1,113 (8.4%) | 1,048 (8.4%) | 65 (7.8%) | 55 (7.6%) | 10 (9.2%) | 0.55 |
| Hepatic diseases | 151 (1.1%) | 125 (1.0%) | 26 (3.1%) | 23 (3.2%) | 3 (2.8%) | < 0.001 |
| Immune-related disease | 185 (1.4%) | 155 (1.2%) | 30 (3.6%) | 28 (3.9%) | 2 (1.8%) | < 0.001 |
| Malignant tumor | 261 (2.0%) | 235 (1.9%) | 26 (3.1%) | 21 (2.9%) | 5 (4.6%) | 0.012 |
| Depression | 477 (3.6%) | 417 (3.4%) | 60 (7.2%) | 45 (6.2%) | 15 (13.8%) | < 0.001 |
Age categories were defined to ensure sufficient sample size in the youngest and oldest age groups
Abbreviations: BMI body mass index, IQR interquartile range, JPY Japanese Yen
a, n = 10,343; b, n = 12,452; c, n = 12,937; d, n = 13,126
*p values were calculated for comparisons between GBM (all) and heterosexual men using chi-square test or Wilcoxon rank-sum test as appropriate
Separately, we conducted multivariable logistic regression analyses to estimate the associations between sexual orientation (gay or bisexual) and selected outcomes: hypertension, dyslipidemia, diabetes mellitus, depression, and recreational substance use. Covariates included in each multivariable model were selected based on a combination of theoretical relevance, prior literature, and statistical significance in univariate analysis (p < 0.05). Specifically, we included sexual orientation as the primary variable of interest, along with known risk factors for each outcome. For lifestyle-related diseases (i.e., hypertension, diabetes, dyslipidemia), we considered age, household income, sleep deprivation, smoking, alcohol use, recreational substance use, and BMI as established risk factors. For recreational substance use, known associated factors such as age, regular employment, university education, public assistance, household income, smoking, alcohol use, and depression were included. For depression, we included age, household income, regular employment, sleep deprivation, smoking, alcohol use, and recreational substance use, based on established associations. To partially account for differences in household composition, both household income and the presence of a partner were included simultaneously in the multivariable models. Covariates that could reflect reverse causality (e.g., immune-related disease and liver disease) were not included in models of recreational substance use. Similarly, hypertension, diabetes, and dyslipidemia were not mutually adjusted for in their respective models, given their close interrelations. Multicollinearity among covariates was assessed using variance inflation factors (VIFs). All analyses were conducted using Stata version 18.0 (StataCorp, College Station, TX, USA).
Ethics
This study was approved by the Research Ethics Committee of the Osaka International Cancer Institute (approval no. 20084, June 19, 2020), prior to the start of data collection, and has since been repeatedly reapproved. Additional approvals were obtained from the institutional research ethics committee of the University of Tokyo (approval no. 2020336NI) and from the Ethics Committee of Tohoku University Graduate School of Medicine (approval no. 2024-1-1035, March 26, 2025). The study was conducted in accordance with the ethical principles of the Declaration of Helsinki.
All participants provided informed consent electronically prior to beginning the survey, as part of the web-based registration process administered by the survey company.
Results
Of the 32,000 respondents, 3,370 were excluded due to invalid responses. Among 28,630 participants, 13,628 identified their sex assigned at birth as male, 14,485 as female, 190 as both, 221 as indeterminate, and 106 as other. Of the 13,628 individuals who identified their sex assigned at birth as male, 13,457 identified their gender as a man, 88 as a woman (transgender women), 35 as both, 40 as indeterminate, and 8 as other. Among the 13,457 individuals whose sex assigned at birth was male and whose gender identity was a man, we excluded 186 participants who reported their romantic partners as “unspecified” or “other,” resulting in a final analytic sample of 13,271 participants (Fig. 1).
Fig. 1.
Study flow chart. Abbreviations: GBM: gay and bisexual men
Among these 13,271 participants, 12,441 (93.7%) reported being romantically attracted to women (heterosexual men), and 830 (6.3%) reported attraction to men. Among GBM, 86.9% (721/830) identified as gay, and 13.1% (109/830) identified as bisexual. After adjustment using inverse probability weighting, the proportion of GBM was estimated to be 6.6% (95% confidence interval [CI], 5.9–7.4%).
The characteristics of the 13,271 participants are summarized in Table 1. The median age was significantly younger among GBM than among heterosexual men (42.0 years [IQR, 30.0–58.0] vs. 48.0 years [IQR, 36.0–64.0], p < 0.001). GBM were more likely to report burden from night shift work (27.5% [228/830] vs. 15.9% [1,979/12,441], p < 0.001), workplace harassment (27.7% [185/667] vs. 12.2% [1,185/9,676], p < 0.001), fear of job loss (31.8% [212/667] vs. 22.1% [2,142/9,676], p < 0.001), and receive public assistance (1.2% [10/830] vs. 0.3% [43/12,441], p < 0.001). These patterns were consistent across all age groups (Supplementary Table 3).
Household income among GBM showed a bimodal distribution, with higher proportions in both the low-income (< 2 million JPY: 10.4% [78/752] vs. 7.3% [856/11,700], p = 0.002) and high-income (≥ 20 million JPY: 11.7% [88/752] vs. 8.9% [1,038/11,700], p = 0.009) brackets. Stratified analyses indicated that bisexual men were more likely to be in the low-income group, whereas gay men were more often represented in the high-income group. Sleep deprivation (< 6 h/day) was more prevalent among GBM (30.1% [249/830] vs. 25.2% [3,105/12,441], p < 0.001), particularly among gay men. Overall, current smoking (53.1% [441/830] vs. 59.1% [7,354/12,441], p < 0.001) and daily alcohol consumption (22.5% [187/830] vs. 28.2% [3,504/12,441], p < 0.001) were less common among GBM. However, among those under 30 years, smoking was more common in GBM (37.9% [78/206] vs. 28.4% [474/1,667]). Experience of recreational substance use was significantly more prevalent among gay men (14.3% [103/721]) compared with heterosexual men (4.1% [505/12,441]), whereas bisexual men did not show an increased prevalence (3.7% [4/109]).
BMI did not differ significantly between the groups (median 22.5 [IQR, 20.5–25.1] vs. 22.8 [20.8–25.0], p = 0.10). There were no significant overall differences in the prevalence of hypertension (18.7% [155/830] vs. 20.5% [2,547/12,441], p = 0.21), dyslipidemia (9.4% [78/830] vs. 11.4% [1,417/12,441], p = 0.079), or diabetes mellitus (7.8% [65/830] vs. 8.4% [1,048/12,441], p = 0.55). However, among participants under 50 years, GBM had higher overall prevalence of hypertension (9.6% [49/508] vs. 5.2% [346/6,626]) and diabetes (6.7% [34/508] vs. 2.5% [168/6,626]) compared with heterosexual men. GBM also showed significantly higher prevalence of hepatic diseases (3.1% [26/830] vs. 1.0% [125/12,441], p < 0.001), immune-related diseases (3.6% [30/830] vs. 1.2% [155/12,441], p < 0.001), malignant tumors (3.1% [26/830] vs. 1.9% [235/12,441], p = 0.012), and depression (7.2% [60/830] vs. 3.4% [417/12,441], p < 0.001). These differences were most pronounced among younger participants (Supplementary Table 3).
Logistic regression analysis was performed to investigate factors associated with hypertension, dyslipidemia, diabetes mellitus, recreational substance use, and depression. In the multivariable analysis, odds ratios for hypertension were slightly elevated among gay (adjusted odds ratio [aOR] 1.21, 95% CI 0.91–1.60) and bisexual men (aOR 1.69, 95% CI 0.89–3.22), but neither reached statistical significance (Table 2). Similarly, no significant associations were observed for dyslipidemia (gay aOR 0.82, 95% CI 0.61–1.10; bisexual aOR 1.02, 95% CI 0.50–2.06) (Supplementary Table 4) or diabetes mellitus (gay aOR 0.96, 95% CI 0.65–1.40; bisexual aOR 1.45, 95% CI 0.60–3.50) (Supplementary Table 5).
Table 2.
Logistic regression analysis of factors associated with hypertension
| Univariable analysis | Multivariable analysis | |||
|---|---|---|---|---|
| Odds ratio | p value | Adjusted odds ratio | p value | |
| Heterosexual men | ref | ref | ||
| Gay | 0.87 (0.72–1.06) | 0.16 | 1.21 (0.91–1.60) | 0.19 |
| Bisexual | 1.04 (0.65–1.65) | 0.87 | 1.69 (0.89–3.22) | 0.11 |
| Age | ||||
| < 30 | ref | ref | ||
| 30–39 | 1.11 (0.82–1.51) | 0.50 | 0.99 (0.69–1.43) | 0.95 |
| 40–49 | 2.83 (2.13–3.76) | < 0.001 | 2.47 (1.75–3.49) | < 0.001 |
| 50–59 | 7.50 (5.74–9.81) | < 0.001 | 6.57 (4.71–9.18) | < 0.001 |
| ≧ 60 | 22.8 (17.7–29.3) | < 0.001 | 18.2 (13.0–25.4) | < 0.001 |
| Partner | 2.35 (2.13–2.59) | < 0.001 | 1.00 (0.86–1.17) | 1.00 |
| Regular employment (full-time) | 0.31 (0.28–0.34) | < 0.001 | 0.84 (0.72–0.99) | 0.04 |
| Burden from night shift work | 0.46 (0.40–0.53) | < 0.001 | 0.98 (0.82–1.18) | 0.84 |
| University graduate | 0.82 (0.75–0.89) | < 0.001 | 0.87 (0.76–1.00) | 0.05 |
| Workplace harassment | 0.79 (0.67–0.94) | 0.01 | 1.08 (0.87–1.35) | 0.50 |
| Fear of job loss | 1.04 (0.92–1.18) | 0.54 | ||
| Receiving public assistance | 2.79 (1.61–4.83) | < 0.001 | 1.88 (0.50–7.10) | 0.35 |
| Household income | ||||
| < 2 million JPY | ref | ref | ||
| 2–5 million JPY | 1.19 (1.01–1.41) | 0.04 | 0.89 (0.66–1.20) | 0.45 |
| 5–10 million JPY | 0.69 (0.58–0.82) | < 0.001 | 0.99 (0.73–1.35) | 0.97 |
| 10–20 million JPY | 0.67 (0.54–0.82) | < 0.001 | 0.98 (0.70–1.37) | 0.89 |
| ≧ 20 million JPY | 1.07 (0.87–1.31) | 0.55 | 0.83 (0.59–1.18) | 0.30 |
| Regular exercise | 0.79 (0.72–0.86) | < 0.001 | 0.90 (0.79–1.03) | 0.14 |
| Sleep deprivation (< 6 h/day) | 0.96 (0.87–1.06) | 0.39 | 1.23 (1.07–1.41) | < 0.001 |
| Current smoking | 2.59 (2.35–2.85) | < 0.001 | 0.83 (0.72–0.95) | 0.01 |
| Daily Alcohol consumption | 2.38 (2.18–2.60) | < 0.001 | 1.45 (1.27–1.66) | < 0.001 |
| Experience of recreational substance use | 1.00 (0.82–1.23) | 0.97 | 1.79 (1.34–2.38) | < 0.001 |
| BMI | 1.10 (1.09–1.12) | < 0.001 | 1.09 (1.07–1.11) | < 0.001 |
| Hepatic diseases | 4.76 (3.45–6.58) | < 0.001 | 3.89 (2.32–6.52) | < 0.001 |
| Immune-related disease | 3.33 (2.48–4.46) | < 0.001 | 3.05 (1.92–4.85) | < 0.001 |
| Malignant tumor | 4.57 (3.57–5.85) | < 0.001 | 1.79 (1.33–2.41) | < 0.001 |
| Depression | 1.52 (1.24–1.87) | < 0.001 | 2.00 (1.29–3.11) | < 0.001 |
Abbreviations: BMI body mass index, JPY Japanese Yen
In contrast, gay men were significantly more likely to experience recreational substance use (aOR 2.37, 95% CI 1.78–3.16) (Table 3), whereas bisexual men were not (aOR 0.67, 95% CI 0.20–2.27). Depression was not associated with being gay (aOR 1.15, 95% CI 0.76–1.75), but bisexual men had a significantly higher risk (aOR 2.34, 95% CI 1.08–5.09) (Table 4). Associations with other covariates were generally consistent with previous reports and are detailed in Tables 2, 3 and 4, Supplementary Tables 4 and 5. In these multivariable models, several social and occupational factors—including fear of job loss, workplace harassment, and sleep deprivation—showed stronger and more consistent associations with depression and recreational substance use than sexual orientation itself.
Table 3.
Logistic regression analysis of factors associated with experience of recreational substance use
| Univariable analysis | Multivariable analysis | |||
|---|---|---|---|---|
| Odds ratio | p value | Adjusted odds ratio | p value | |
| Heterosexual men | ref | ref | ||
| Gay | 3.94 (3.14–4.94) | < 0.001 | 2.37 (1.78–3.16) | < 0.001 |
| Bisexual | 0.90 (0.33–2.45) | 0.84 | 0.67 (0.20–2.27) | 0.52 |
| Age | ||||
| < 30 | ref | ref | ||
| 30–39 | 0.62 (0.50–0.77) | < 0.001 | 0.65 (0.50–0.85) | < 0.001 |
| 40–49 | 0.57 (0.44–0.72) | < 0.001 | 0.58 (0.43–0.78) | < 0.001 |
| 50–59 | 0.35 (0.26–0.46) | < 0.001 | 0.40 (0.29–0.57) | < 0.001 |
| ≧ 60 | 0.20 (0.15–0.26) | < 0.001 | 0.46 (0.31–0.67) | < 0.001 |
| Partner | 0.64 (0.55–0.76) | < 0.001 | 1.08 (0.86–1.34) | 0.51 |
| Regular employment (full-time) | 1.74 (1.45–2.10) | < 0.001 | 1.01 (0.77–1.34) | 0.92 |
| Burden from night shift work | 3.35 (2.83–3.97) | < 0.001 | 1.55 (1.25–1.91) | < 0.001 |
| University graduate | 0.86 (0.73–1.01) | 0.07 | 0.71 (0.59–0.87) | < 0.001 |
| Workplace harassment | 4.01 (3.33–4.82) | < 0.001 | 1.93 (1.52–2.46) | < 0.001 |
| Fear of losing job | 2.35 (1.97–2.81) | < 0.001 | 1.37 (1.10–1.71) | 0.01 |
| Receiving public assistance | 2.16 (0.86–5.46) | 0.10 | 7.08 (1.72–29.11) | 0.01 |
| Household income | ||||
| < 2 million JPY | ref | ref | ||
| 2–5 million JPY | 0.84 (0.61–1.17) | 0.30 | 0.96 (0.62–1.48) | 0.84 |
| 5–10 million JPY | 0.90 (0.65–1.23) | 0.51 | 0.85 (0.54–1.33) | 0.47 |
| 10–20 million JPY | 0.97 (0.67–1.39) | 0.87 | 1.00 (0.61–1.64) | 1.00 |
| ≧ 20 million JPY | 0.65 (0.42–1.00) | 0.05 | 0.75 (0.43–1.30) | 0.31 |
| Regular exercise | 2.94 (2.48–3.50) | < 0.001 | 2.47 (2.01–3.02) | < 0.001 |
| Sleep deprivation (< 6 h/day) | 2.80 (2.38–3.31) | < 0.001 | 1.90 (1.57–2.30) | < 0.001 |
| Current smoking | 2.06 (1.72–2.48) | < 0.001 | 2.11 (1.74–2.57) | < 0.001 |
| Daily Alcohol consumption | 0.61 (0.50–0.75) | < 0.001 | 0.84 (0.65–1.07) | 0.16 |
| Depression | 3.74 (2.86–4.91) | < 0.001 | 2.94 (2.12–4.08) | < 0.001 |
Abbreviations: BMI body mass index, JPY Japanese Yen
Table 4.
Logistic regression analysis of factors associated with depression
| Univariable analysis | Multivariable analysis | |||
|---|---|---|---|---|
| Odds ratio | p value | Adjusted odds ratio | p value | |
| Heterosexual men | ref | ref | ||
| Gay | 1.92 (1.40–2.64) | < 0.001 | 1.15 (0.76–1.75) | 0.50 |
| Bisexual | 4.60 (2.65–8.00) | < 0.001 | 2.34 (1.08–5.09) | 0.03 |
| Age | ||||
| < 30 | ref | ref | ||
| 30–39 | 0.78 (0.58–1.05) | 0.11 | 1.11 (0.76–1.64) | 0.59 |
| 40–49 | 1.22 (0.91–1.63) | 0.18 | 1.89 (1.29–2.77) | < 0.001 |
| 50–59 | 1.37 (1.03–1.84) | 0.03 | 2.05 (1.38–3.06) | < 0.001 |
| ≧ 60 | 0.35 (0.25–0.49) | < 0.001 | 0.70 (0.41–1.17) | 0.17 |
| Partner | 0.45 (0.37–0.54) | < 0.001 | 0.66 (0.51–0.86) | < 0.001 |
| Regular employment (full-time) | 0.79 (0.66–0.95) | 0.01 | 0.62 (0.46–0.82) | < 0.001 |
| Burden from night shift work | 1.24 (0.99–1.56) | 0.07 | 0.72 (0.54–0.96) | 0.02 |
| University graduate | 0.85 (0.71–1.03) | 0.10 | ||
| Workplace harassment | 3.09 (2.46–3.87) | < 0.001 | 2.17 (1.61–2.92) | < 0.001 |
| Fear of job loss | 2.67 (2.17–3.29) | < 0.001 | 1.82 (1.42–2.35) | < 0.001 |
| Receiving public assistance | 8.93 (4.75–16.82) | < 0.001 | 2.05 (0.49–8.55) | 0.32 |
| Household income | ||||
| < 2 million JPY | ref | ref | ||
| 2–5 million JPY | 0.40 (0.31–0.54) | < 0.001 | 0.65 (0.43–0.98) | 0.04 |
| 5–10 million JPY | 0.34 (0.25–0.45) | < 0.001 | 0.55 (0.36–0.84) | 0.01 |
| 10–20 million JPY | 0.31 (0.21–0.45) | < 0.001 | 0.53 (0.32–0.88) | 0.02 |
| ≧ 20 million JPY | 0.27 (0.18–0.42) | < 0.001 | 0.47 (0.27–0.83) | 0.01 |
| Regular exercise | 0.88 (0.72–1.06) | 0.17 | 0.79 (0.62–1.02) | 0.07 |
| Sleep deprivation (< 6 h/day) | 1.49 (1.23–1.81) | < 0.001 | 0.94 (0.73–1.20) | 0.61 |
| Current smoking | 1.23 (1.02–1.49) | 0.03 | 1.25 (0.98–1.59) | 0.07 |
| Daily Alcohol consumption | 0.51 (0.40–0.65) | < 0.001 | 0.67 (0.50–0.91) | 0.01 |
| Experience of recreational substance use | 3.74 (2.86–4.91) | < 0.001 | 1.72 (1.17–2.55) | 0.01 |
| BMI | 1.01 (1.00–1.01) | 0.01 | 1.01 (1.01–1.02) | < 0.001 |
| Hepatic diseases | 12.9 (9.00–18.48) | < 0.001 | 4.01 (2.13–7.53) | < 0.001 |
| Immune-related disease | 8.83 (6.19–12.58) | < 0.001 | 3.13 (1.74–5.63) | < 0.001 |
| Malignant tumor | 6.79 (4.91–9.41) | < 0.001 | 4.74 (2.68–8.38) | < 0.001 |
Abbreviations: BMI body mass index, JPY Japanese Yen
Discussion
This large-scale survey study investigated the social and health disparities faced by GBM in Japan by comparing them with heterosexual men. Large-scale epidemiological data on GBM in Japan have been scarce, and this study provides valuable insights into their demographics, socioeconomic conditions, and health status.
The proportion of GBM among participants who identified as male in both sex and gender was 6.3% (6.6% after weighting). A large-scale internet survey in Japan previously reported that 9.7% of the population identified as LGBTQ+, with 1.6% identifying as gay [15], while a 2022 Gallup survey in the United States found 2.1% identifying as gay and 2.1% as bisexual [16]. In contrast, our study showed a higher proportion of gay than bisexual participants, which may reflect cultural or methodological differences.
In this study, GBM were more likely to experience workplace harassment, report burden from night shifts, and fear of job loss across all age groups, with these trends being more pronounced among younger individuals. Workplace harassment was particularly common among younger GBM and was more frequent among gay men compared to bisexual men, consistent with previous reports [17]. GBM have also been reported to be associated with precarious employment [7], and the high proportion of participants in our study who reported fear of job loss further highlights this vulnerability. In our multivariable analyses, both workplace harassment and fear of job loss were associated with depression, in line with earlier studies showing that harassment toward GBM is linked to mental health deterioration [18]. These findings underscore how stigma and discrimination in the workplace may contribute to adverse health outcomes among GBM. In the Japanese occupational context, these findings may reflect structural and cultural factors such as strong norms of conformity, limited legal protections against sexual orientation–based discrimination, and the absence of nationwide anti-discrimination legislation. Disclosure of sexual orientation in the workplace may therefore carry heightened perceived risks, contributing to harassment, job insecurity, and chronic stress among GBM. From a public health and occupational health perspective, these findings highlight the importance of workplace-based interventions, including anti-harassment policies, diversity training, and the establishment of confidential support systems for sexual minority employees.
Recreational substance use was more common, particularly among gay men. In general, previous studies have reported higher rates of recreational substance use among GBM compared with heterosexual men [19, 20]. In Japan, while studies among people living with HIV have documented elevated rates of drug use [12], data specific to GBM have been scarce. Furthermore, workplace harassment and fear of job loss were also significant risk factors for recreational substance use in our analysis, suggesting that the issue cannot be explained solely by higher prevalence of drug use reported within gay communities, but also reflects the multifaceted social and occupational determinants contributing to elevated risk among GBM. Although this study could not assess chemsex practices, previous research has reported a higher prevalence of chemsex and increased rates of blood-borne sexually transmitted infections, including HCV and HIV, among gay men [12, 19], highlighting the importance of considering sexual health implications in relation to drug use. Targeted interventions addressing social stressors and mental health disparities, including harm reduction approaches for recreational substance use, are warranted.
The age-specific pattern observed for smoking may reflect cohort and life-course effects. International studies have consistently shown higher smoking prevalence among sexual minority populations compared with heterosexual individuals, with disparities particularly pronounced in younger age groups [21, 22]. Such patterns have been interpreted in the context of minority stress, including exposure to discrimination and psychosocial stressors, which may increase vulnerability to smoking initiation among younger GBM. In contrast, lower smoking prevalence among older GBM may reflect smoking cessation following greater health awareness or medical contact.
In this cross-sectional survey, hypertension and diabetes were more commonly self-reported among younger GBM than among heterosexual men. Previous reports have also indicated that sexual minorities are at increased risk of hypertension and diabetes [2, 23, 24], and one study found that younger GBM had approximately twice the risk of hypertension compared with heterosexual men [3], which is consistent with our findings. That study reported an elevated risk even after adjustment for BMI, physical activity, alcohol use, and smoking, whereas in our multivariable analysis, hypertension, dyslipidemia, and diabetes were not significantly associated with GBM status. Instead, factors more prevalent among GBM, such as hepatic diseases, immune-related diseases, recreational substance use, and sleep deprivation, may have played an influential role. In Japan, previous studies have also reported a higher prevalence of hypertension and diabetes among people living with HIV [11]. Although HIV status and specific viral hepatitis were not assessed in this study, the higher prevalence of immune-related and liver diseases observed among GBM may partly reflect these broader health contexts. Recreational substance use has been identified as a risk factor for hypertension [25], and sleep deprivation, which increases the risk of hypertension [26], has been shown to be more common among GBM [27]. Taken together, the higher prevalence of hypertension and diabetes observed particularly among younger GBM is unlikely to be attributable to sexual orientation itself (e.g., biological or hormonal factors). Rather, it may be mediated through social disadvantages and comorbid conditions that are more common in this population, such as HIV and viral hepatitis. Chronic discrimination-related stress may further contribute to these cardiometabolic risks, potentially through behavioral and physiological pathways such as sleep disturbance, sustained activation of stress responses, and coping behaviors.
Depression was more prevalent among GBM than among heterosexual men [5, 6], consistent with previous studies reporting elevated rates of depression in sexual minorities. In our multivariable analysis, only bisexual status remained an independent risk factor, although this should be interpreted with caution given the small number of bisexual participants and the fact that comparisons between gay and bisexual men were based on descriptive analyses rather than direct adjusted models. A prior systematic review and meta-analysis has also highlighted bisexual men as being at particularly high risk [6], supporting possible differences between gay and bisexual men. Bisexual individuals may face unique stressors that differ from those experienced by gay men and heterosexual men, including bisexual invisibility and potential biphobia from both heterosexual and gay communities [28], which have been linked to elevated psychological distress in previous research. These additional minority stressors could contribute to the higher prevalence of depression observed among bisexual men in this study.
A strength of this study is the use of an anonymous web-based survey. Given the stigma surrounding sexual orientation in Japan, this format likely facilitated disclosure of sensitive information [29]. Similarly, recreational substance use is difficult to ascertain in non-anonymous settings. A previous postal survey in Japan reported a lifetime prevalence of recreational substance use of around 2–3% [30], which was lower than in our study. Although this difference may reflect selection bias toward JACSIS respondents with higher internet dependence—consistent with prior reports linking internet addiction with recreational substance use [31]—the anonymous web-based format may also have enabled more accurate disclosure of sensitive behaviors.
Several limitations must be acknowledged. First, as with all self-reported surveys, the accuracy of responses—particularly regarding medical conditions—may be limited. To enhance validity, we restricted our definitions to participants currently receiving medical care. However, this approach may have led to overestimation: for example, participants attending clinics for liver or immune-related diseases could also have been classified as having “treated hypertension” or “treated diabetes.” Nonetheless, this potential bias is unlikely to differ systematically between GBM and heterosexual men. In fact, relying only on self-reported “hypertension” without adjusting for other comorbidities could overstate the effect of GBM, and the ability to include these comorbid conditions in our analysis represents a strength of our study. Detailed diagnostic categories (e.g., specific hepatitis viruses or HIV status) were not available and should be addressed in future surveys.
Second, all participants were internet users, and some may have been motivated by incentives, raising concerns about representativeness. We applied weighting to mitigate demographic biases, but generalizability remains limited. However, the relative association between GBM status and recreational substance use is unlikely to have been affected. At the same time, the anonymous online format likely facilitated disclosure of sensitive information such as recreational substance use and sexual orientation, representing a unique strength of this study. In addition, selection into the Rakuten Insight survey panel may also differ by sexual orientation. GBM may be more likely to participate in anonymous online surveys, potentially introducing selection bias that cannot be fully addressed by weighting.
Third, household income may not fully capture socioeconomic position when comparing GBM and heterosexual men. Because same-sex marriage is not legally recognized in Japan, household income may reflect different living arrangements across groups. Individual income was not available in this survey, and therefore residual differences in socioeconomic position cannot be excluded.
Fourth, because this study involved a large number of survey items, the risk of Type I error due to multiple testing cannot be excluded. To mitigate this issue, we restricted our multivariable analyses to prespecified variables of primary interest, rather than testing every available item. Moreover, most of the associations that reached statistical significance were consistent with previous findings, which provides some reassurance regarding the validity of our results. In addition, although causal inferences cannot be drawn from this cross-sectional design, we considered the plausibility of causal directions when selecting covariates, and avoided including variables likely to represent reverse causality.
Finally, this study focused exclusively on cisgender men, and transgender men were not included in the analytic sample. This was a deliberate analytic decision, as transgender men are likely to experience distinct social, medical, and structural conditions that differ substantially from those of cisgender gay and bisexual men. Therefore, the findings of this study should not be generalized to transgender populations, whose health and social inequities warrant dedicated investigation in future research.
Despite these limitations, this large-scale nationwide survey provides valuable insights into the health of GBM in Japan. GBM reported higher levels of workplace harassment and fear of job loss, both of which were associated with increased risks of depression. GBM status itself was not independently associated with lifestyle-related diseases. These findings suggest the importance of addressing social stressors and mental health disparities when considering the health of GBM in Japan.
Supplementary Information
Acknowledgements
We sincerely thank the investigators and collaborators of the JACSIS study group for their efforts in designing and conducting the survey, including the development of the questionnaire. We also acknowledge Rakuten Insight, Inc. for administering the survey.
Clinical trial
Not applicable.
Abbreviations
- aOR
adjusted odds ratio
- BMI
body mass index
- CI
confidence interval
- CSLCPHW
Comprehensive Survey of Living Conditions of People on Health and Welfare
- GBM
gay and bisexual men
- IPW
inverse probability weighting
- JACSIS
Japan COVID-19 and Society Internet Survey
- VIF
variance inflation factor
Authors’ contributions
KI and Takeya Tsutsumi (TTs) conceived the study. KI performed the statistical analyses and drafted the manuscript. KO, AK, SM, TK, YA, and HY contributed to the data interpretation and critically revised the manuscript. Takahiro Tabuchi (TTa) led the JACSIS project, obtained funding, supervised the overall study administration, data collection, and contributed to data cleaning and preparation. All the authors reviewed the manuscript for important intellectual content and approved its final version.
Funding
This study (JACSIS2022) was supported by the Japan Society for the Promotion of Science (JSPS) KAKENHI Grants (grant number 21H04856, 20K10467, 20K19633, 20K13721), the JST Grant Number JPMJPF2017, the Health and Labour Sciences Research Grant 21HA2016, the grant for 2021–2022 Strategic Research Promotion (No. SK202116) of Yokohama City University, and the research program on “Using Health Metrics to Monitor and Evaluate the Impact of Health Policies” conducted at the Tokyo Foundation for Policy Research. K.I. was supported by JST PRESTO (Grant Number JPMJPR23R1), which also covered publication costs.
Data availability
The questionnaire used in this study is publicly available on the JACSIS website (https://jacsis-study.jp/). The survey responses analyzed during the current study are not publicly available due to participant confidentiality but may be provided by the corresponding author upon reasonable request.
Declarations
Ethics approval and consent to participate
This study was approved by the Research Ethics Committee of the Osaka International Cancer Institute (approval no. 20084, June 19, 2020), prior to the start of data collection, and has since been repeatedly reapproved. Additional approvals were obtained from the institutional research ethics committee of the University of Tokyo (approval no. 2020336NI) and from the Ethics Committee of Tohoku University Graduate School of Medicine (approval no. 2024-1-1035, March 26, 2025).
The study was conducted in accordance with the ethical principles of the Declaration of Helsinki. All participants provided electronic informed consent prior to participation through the web-based registration process administered by the survey company.
Consent for publication
Not applicable.
Competing interests
The authors declare no competing interests.
Footnotes
Publisher’s Note
Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
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Associated Data
This section collects any data citations, data availability statements, or supplementary materials included in this article.
Supplementary Materials
Data Availability Statement
The questionnaire used in this study is publicly available on the JACSIS website (https://jacsis-study.jp/). The survey responses analyzed during the current study are not publicly available due to participant confidentiality but may be provided by the corresponding author upon reasonable request.