Masculine depression and its problem behaviors: use alcohol and drugs, work hard, and avoid psychiatry!

Cohort characteristics

Relative to patients with non-masculine depression, patients with masculine depression were younger (mean age: 36.4 vs. 45.7 years) and less often married (28% vs. 48%) (Table 1). Patients with masculine depression showed also more months of employment during the previous year (8.1 vs. 6.2), more hours of employment per week (25.0 vs. 19.1), and higher BDI-II scores (37.3 vs. 28.7). See Table 1 for further comparisons between the patients’ groups and the healthy controls. Table 2 reports the descriptive characteristics of the groups in terms of substance use, health services contacts, and GGT activity. The MDRS-22 sum score correlated with BDI-II scores in the group of patients with non-masculine depression (N = 81, r = 0.495, P < 0.001), in the control subjects (N = 174, r = 0.603, P < 0.001), and in the total sample (N = 335, r = 0.783, P < 0.001), but not significantly in patients with masculine depression (N = 80, r = 0.161, P = 0.153). The MDRS-22 sum score did not significantly correlate with age: patients with masculine depression: N = 81, r =  – 0.149, P = 0.184; patients with non-masculine depression: N = 82, r = 0.049, P = 0.661; control subjects: N = 176, r = 0.060, P = 0.432; and total sample: N = 339, r =  – 0.023, P = 0.670. Moreover, the MDRS-22 sum score did not significantly differ between females and males (coded “2” and “1”): patients with masculine depression: N = 81, t = 0.757, P = 0.452; patients with non-masculine depression: N = 82, t =  – 0.554, P = 0.581; control subjects: N = 176, t = 1.158, P = 0.249; and total sample: N = 339, t = 1.384, P = 0.167.

Table 1 Cohort characteristicsTable 2 Descriptive statistics of substance use parameters, health services contacts, and liver enzyme activitySubstance use parameters

Patients with masculine depression vs. patients with non-masculine depression: The group of patients with masculine depression was predicted by higher AUDIT scores (B = 0.231, P < 0.001), an AUDIT score of at least 8 (vs. less than 8; B = 2.541, P < 0.001), an AUDIT score of at least 20 (vs. less than 20; B = 3.392, P = 0.003), binge drinking for both the 2-week and the 24-month periods (yes vs. no; B = 2.917, P < 0.001 and B = 1.771, P < 0.001), higher binge drinking frequency (2-week and 24-month periods: B = 3.454, P = 0.005 and B = 9.953, P = 0.001) and severity (2-week and 24-month periods: B = 2.658, P = 0.001 and B = 1.324, P < 0.001), higher FTND scores (B = 0.198, P = 0.012), and lifetime use of cannabis (B = 1.387, P = 0.002) and hallucinogens (B = 1.934, P = 0.024) (Fig. 1A, Supplementary Table S2). Moreover, the 4-week use of sedative medication was associated with the group of patients with non-masculine depression (B =  – 0.999, P = 0.037), and there was a statistical trend for an association between smoking and the group of patients with masculine depression (B = 0.679, P = 0.072). Overall, the group of patients with masculine depression was predicted by higher BDI-II scores (B from 0.088 to 0.127, P < 0.001) and younger age (B from  – 0.055 to  – 0.030, P from < 0.001 to 0.037; except for the model with 24-month binge drinking severity).

Fig. 1figure 1

The figure shows the significant and bootstrap-validated B coefficients from binary logistic regression analyses to predict group (i.e., patients with masculine depression vs. patients with non-masculine depression vs. healthy controls; A Supplementary Tables S2, S4, S5) and linear regression analyses to predict MDRS-22 scores in the group of depressed patients (B Supplementary Table S3). MDRS-22, Male Depression Rating Scale 22; 2w, previous 2 weeks; 24 m, previous 24 months; 4w, previous 4 weeks; life, lifetime; y vs. n, yes vs. no

MDRS-22 scores: We found results similar to the above mentioned comparisons between patients with masculine depression and patients with non-masculine depression. In depressed patients, higher MDRS-22 scores were related to higher AUDIT scores (B = 0.068, P < 0.001), an AUDIT score of at least 8 (vs. less than 8; B = 1.059, P < 0.001), an AUDIT score of at least 20 (vs. less than 20; B = 1.264, P < 0.001), binge drinking behavior for both the 2-week and the 24-month periods (yes vs. no; B = 1.120, P < 0.001 and B = 0.506, P = 0.001), higher binge drinking frequency (2-week and 24-month periods: B = 0.209, P < 0.001 and B = 0.676, P < 0.001) and binge drinking severity (2-week and 24-month periods: B = 0.678, P < 0.001 and B = 0.400, P < 0.001), higher FTND scores (B = 0.073, P = 0.006), use of cannabis (4-week period, B = 0.536, P = 0.007; lifetime, B = 0.495, P = 0.002), use of stimulants (lifetime, B = 0.501, P = 0.012), and use of hallucinogens (lifetime, B = 0.697, P = 0.002) (Fig. 1B, Supplementary Table S3). Overall, higher BDI-II scores (B = from 0.034 to 0.044, P < 0.001) and younger age (B = from  – 0.018 to  – 0.008, P from < 0.001 to 0.046; except for 24-month binge drinking severity) predicted the MDRS-22 scores.

Patients with masculine depression vs. healthy control subjects: The group of patients with masculine depression (vs. the group of healthy controls) was related to higher AUDIT scores (B = 0.163, P < 0.001), an AUDIT score of at least 8 (vs. less than 8; B = 1.533, P < 0.001), higher 2-week and 24-month binge drinking frequency (B = 0.503, P = 0.006 and B = 1.188, P = 0.002) and 24-month severity (B = 0.392, P = 0.022), smoking behavior (B = 2.704, P < 0.001), higher FTND scores (B = 1.076, P < 0.001), 4-week use of sedative medication (B = 2.077, P < 0.001) and cannabis (B = 2.574, P < 0.001) and lifetime use of sedative medication (B = 1.760, P < 0.001), cannabis (B = 1.769, P < 0.001), stimulants (B = 2.105, P < 0.001), opioids (B = 3.114, P = 0.004), cocaine (B = 1.740, P = 0.014), and hallucinogens (B = 2.258, P < 0.001) (Fig. 1A, Supplementary Table S4). These models were not significantly affected by sex or age.

Patients with non-masculine depression vs. healthy control subjects: The group of patients with non-masculine depression (vs. the group of healthy controls) was linked to lower AUDIT scores (B =  – 0.201, P = 0.001), risk of binge drinking behavior (yes vs. no; 2-week and 24-month periods: B =  – 2.292, P < 0.001 and B =  – 0.909, P = 0.007), a lower frequency (2-week and 24-month periods: B =  – 3.144, P = 0.001 and B =  – 5.810, P = 0.007) and a milder severity (2-week and 24-month periods: B =  – 2.235, P < 0.001 and B =  – 0.732, P = 0.004) of binge drinking, smoking (B = 1.833, P < 0.001), a higher FTND score (B = 0.699, P = 0.001), and use of sedative medication (4-week period, B = 2.237, P < 0.001; lifetime, B = 1.622, P < 0.001) (Fig. 1A, Supplementary Table S5). Overall, patients with non-masculine depression were significantly predicted by higher age (B from 0.030 to 0.044, P from < 0.001 to 0.006).

Number of health services contacts

Patients with masculine depression vs. patients with non-masculine depression: A lower number of health services contacts due to mental complaints predicted the group of patients with masculine depression (B =  – 0.025, P = 0.038), and there was a statistical trend for a higher GGT activity in patients with masculine depression (B = 0.015, P = 0.066). The models were again affected by BDI-II scores (B from 0.092 to 0.105, P < 0.001) and age (B from  – 0.055 to  – 0.051, P < 0.001) (Table 3).

Table 3 Regression analyses to predict groups of patients with masculine depression, patients with non-masculine depression, and healthy controls and Male Depression Rating Scale 22 scores

MDRS-22 scores: Similar to the above group prediction analysis, a higher MDRS-22 score was predicted by fewer previous health services contacts due to mental complaints (B =  – 0.012, P = 0.005) and there was a trend for a higher GGT activity (B = 0.005, P = 0.065) (Table 3). The models were influenced by BDI-II scores (B from 0.039 to 0.045, P < 0.001), age (B from  – 0.019 to  – 0.018, P < 0.001), and sex (B from 0.296 to 0.328, P from 0.016 to 0.032; except for GGT activity).

Patients with masculine depression and patients with non-masculine depression vs. healthy control subjects: A higher number of health service contacts for both physical and mental complaints was associated with the groups of patients with masculine depression and patients with non-masculine depression vs. healthy control subjects (physical issues: B = 0.459, P < 0.001, B = 0.259, P < 0.001; mental issues: 1.599, P < 0.001, B = 2.089, P < 0.001). Higher age predicted the patient group with non-masculine depression (B from 0.035 to 0.043, P from < 0.001 to 0.024) (Table 3) similar to the other models.

Predictors of health care services contacts due to mental complaints: We found that higher BDI-II scores indicative of more severe depression predict the group of patients with masculine vs. the group of patients with non-masculine depression and higher MDRS-22 scores in the group of depressed patients (Supplementary Tables S2 and S3). Simultaneously, fewer health services contacts due to mental complaints also predict the group of patients with masculine vs. the group of patients with non-masculine depression and lower MDRS-22 scores (Table 3). These results suggest that patients who are more severely affected by both depression symptoms and substance use request less frequently support for mental health complaints and may thus receive less intense treatment. To further explore underlying mechanisms, we analyzed how BDI-II score and substance use parameters were related to health services contacts in the group of depressed patients (Supplementary Table S6). As expected, higher BDI-II scores and use of sedative medication (4-week, lifetime) predicted more contacts (B = 0.350, P = 0.001; B = 6.543, P = 0.023; B = 6.792, P = 0.009). In contrast, alcohol, tobacco, and illicit drug use parameters were not significantly related to the number of health services contacts due to mental complaints in the prior year.

Working hours

The sociodemographic characteristics showed significant differences in working hours (months of employment during the previous year, hours of employment per week) between the groups of patients with masculine depression, patients with non-masculine depression, and healthy control subjects (Table 1). Hence, we tested these parameters as predictors in regression analyses (Table 3) and found that more working hours for both parameters were associated with patients with masculine vs. patients with non-masculine depression (B = 0.080, P = 0.030 and B = 0.027, P = 0.015) and tended to predict the MDRS-22 score (B = 0.024, P = 0.063 and B = 0.007, P = 0.053). Relative to healthy controls, more hours of employment per week were associated with patients with masculine depression (B = 0.022, P = 0.014) and fewer months of employment during the previous year with patient with non-masculine depression (B =  – 0.074, P = 0.006). Overall, higher BDI-II scores and younger age predicted the group of patients with masculine depression vs. the group of patients with non-masculine depression and MDRS-22 scores (BDI-II scores: B from 0.041 to 0.114, P < 0.001; age: B from  – 0.042 to  – 0.014, P from 0.002 to 0.004); younger age also predicted healthy controls vs. patients with non-masculine depression (B from 0.035 to 0.037, P < 0.001). Notably, months of employment during the previous year and hours of employment per week did not significantly predict the number of health services contacts due to mental complaints in the prior year (Supplementary Table S6).

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