To the Editor: In China, depression is a common mental illness with a lifetime prevalence of 6.8% and a 12-month prevalence of 3.6%.[1] The study aimed to determine the prevalence of depression and its relationship with associated lifestyle factors in Chinese adults aged 18 years and over using a large-scale, cross-sectional survey.

The sample was selected from a large-scale population survey of Chinese people's physiological and psychological constants conducted in 2010. This survey was conducted in Yunnan Province, southwest China using a two-stage cluster sampling method. First, two cities were sampled and then several communities and villages were randomly selected in each city. In these selected communities, all eligible individuals aged 18 years or over without a history of severe chronic disease or high fever in the preceding 15 days were surveyed. After providing written informed consent, the participants attended temporary physical examination centers voluntarily to participate in the survey. The study was approved by the Ethics Review Board of the Institute of Basic Medical Sciences at the Chinese Academy of Medical Sciences (No. 005-2008). In total, 8151 participants signed the consent form, of which 7985 participants completed all survey scales resulting in a completion rate of 97.96%.

The Composite International Diagnostic Interview Short Form for Major Depression was used to assess participants’ mental health. Each participant was asked seven questions about their feelings during the previous year. If participants responded “yes” to five or more questions, they were assessed as depressed.

All statistical analyses were performed by SAS Enterprise Guide Version 9.4 (SAS Institute Inc., Cary, NC, USA). P < 0.05 was defined as statistically significant. Continuous data were described using the mean and standard deviation. Categorical data were described using numbers and percentages and compared using the chi-squared test. A multi-level GEE model was used to determine the relationship between depression and lifestyle indicators while controlling for the cluster effect of location and the confounding effects of demographic characteristics. Odds ratios (ORs) and their 95% confidence intervals (CIs) were used to assess the strength of each relationship.

Of the 7985 participants, 269 reported five or more depressive symptoms, which indicated that the prevalence of depression was 3.37%. The prevalence of depression in men (2.80%) was lower than in women (3.73%, P < 0.001). The prevalence of depression was 4.74% (n = 175) in young participants (18–34 years old), 1.98% (n = 67) in middle-aged participants (35–59 years old), and 2.96% (n = 27) in older adult participants (60–80 years old), indicating that the prevalence of depression was lowest in middle-aged participants (P < 0.001). Of the ethnicities identified, Yi participants had a lower prevalence of depression (n = 45; 1.87%) than Han (n = 194; 3.85%) and others (n = 30; 5.57%). Blue-collar workers had a lower prevalence (n = 130; 2.60%) of depression than those who worked in offices (n = 139; 4.65%). Married participants had a lower prevalence (n = 118; 2.32%) of depression than single (n = 144; 5.43%), or widowed or divorced participants (n = 7; 2.97%). The prevalence of depression was 1.96% (n = 60) in participants who had completed primary school only, 3.95% (n = 109) in those with middle school education, and 4.64% (n = 100) in those with a college education. The prevalence of depression increased with educational levels (P < 0.001). Current smokers had a lower prevalence of depression (n = 50; 2.96%) than their non-smoking counterparts (n = 219; 3.48%), but current alcohol consumers had a higher prevalence of depression (n = 53; 3.77%) than their non-drinking counterparts (n = 216; 3.28%). Participants who normally slept for <6 h a day had a higher prevalence of depression (n = 52; 5.09%) than those who slept for ≥6 h (n = 217; 3.12%). Participants reporting poorer sleep quality had a higher prevalence of depression (n = 233; 4.61%) than those reporting good sleep quality (n = 36; 1.23%). Respondents who had experienced stress (13.28% [n = 102] vs. 2.31% [n = 167]) or recent significant life events (7.01% [n = 143] vs. 2.12% [n = 126]) had higher prevalence rates of depression than their non-stressed counterparts. Participants who habitually consumed a special diet (4.18% [n = 200]) had a higher prevalence of depression than those who consumed a routine diet (2.16% [n = 69]). Respondents who had breakfast, lunch, and dinner at regular fixed hours had a lower prevalence of depression than those who ate irregularly (2.87% [n = 192] vs. 5.96% [n = 77]). Respondents reporting sub-health status had a higher prevalence of depression than those who did not report sub-health status (268; 4.22% [n = 268] vs. 0.06% [n = 1]). Finally, participants with medical insurance had a lower prevalence of depression than those without (2.95% [n = 59] vs. 6.74% [n = 210]).

Table 1 presents the results of the univariate and multivariate multi-level GEE model constructed from the lifestyle factors associated with depression. After controlling for the cluster effect of location and the confounding effects of other covariates, no statistically significant associations were found between smoking status or exercise and depression. Middle-aged participants had a lower prevalence of depression than younger participants (OR = 0.872, 95% CI: 0.835–0.912). Obese participants had a higher prevalence of depression (OR = 1.162, 95% CI: 1.004–1.346) than normal-weight participants. Thus, a higher body mass index increased the risk of depression. The following also had a higher risk of depression: women (OR = 1.188, 95% CI: 1.104–1.279), single participants (OR = 1.317, 95% CI: 1.246–1.391), current drinkers (OR = 1.099, 95% CI: 1.036–1.165), those who slept for <6 h a day (OR = 1.141, 95% CI: 1.106–1.178), those with poor sleep quality (OR = 1.388, 95% CI: 1.213–1.587), those experiencing stress (OR = 1.533, 95% CI: 1.357–1.732), those who had experienced significant life events recently (OR = 1.196, 95% CI: 1.110–1.289), those consuming a special diet (OR = 1.176, 95% CI: 1.069–1.294), those without regular meal times (OR = 1.363, 95% CI: 1.105–1.681), and those reporting sub-health status (OR = 4.251, 95% CI: 4.030–4.484).

Table 1 - Risk factors associated with depression with multi-level GEE model. Univariate Multivariate Characteristics OR 95% CI OR 95% CI Age  18–34 (years) 1.000 – 1.000 –  35–59 (years) 0.406 0.303–0.537 0.872 0.835–0.912  60–80 (years) 0.614 0.398–0.910 0.994 0.815–1.213 Gender  Male 1.000 – 1.000 –  Female 1.344 1.040–1.751 1.188 1.104–1.279 Occupation  Blue-collar 1.000 – 1.000 –  White-collar 1.827 1.432–2.332 1.066 0.896–1.268 Marital status  Married 1.000 – 1.000 –  Single 2.423 1.892–3.109 1.317 1.246–1.391  Widowed or divorced 1.29 0.540–2.600 1.005 0.854–1.183 Education level  Primary school 1.000 – 1.000 –  Middle school 2.063 1.505–2.854 1.057 0.843–1.324  College 2.438 1.768–3.390 1.009 0.816–1.246 Smoker  No 1.000 – 1.000 –  Yes 0.846 0.613–1.145 0.993 0.937–1.051 Alcohol drinker  No 1.000 – 1.000 –  Yes 1.156 0.843–1.557 1.099 1.036–1.165 Ethnicity  Han 1.000 – 1.000 –  Yi 0.474 0.338–0.652 0.966 0.849–1.099  Others 1.470 0.972–2.149 1.067 1.033–1.102 BMI  Normal 1.000 – 1.000 –  Overweight 0.834 0.604–1.130 1.079 0.997–1.167  Obesity 1.116 0.686–1.724 1.162 1.004–1.346 Sleep duration  ≥6 h 1.000 – 1.000 –  <6 h 1.669 1.212–2.256 1.141 1.106–1.178 Sleep quality  Good 1.000 – 1.000 –  Poor 3.878 2.760–5.614 1.388 1.213–1.587 Stress  No 1.000 – 1.000 –  Yes 6.465 4.979–8.360 1.533 1.357–1.732 Life event  No 1.000 – 1.000 –  Yes 3.481 2.726–4.452 1.196 1.110–1.289 Regular exercise  No 1.000 – 1.000 –  Yes 0.786 0.588–1.038 0.940 0.849–1.040 Diet choice  Routine 1.000 – 1.000 –  Unhealthy 1.975 1.505–2.623 1.176 1.069–1.294 Meal time  Regular 1.000 – 1.000 –  Irregular 2.146 1.627–2.803 1.363 1.105–1.681 Sub-health status  No 1.000 – 1.000 –  Yes 71.833 16.190 to >999.999 4.251 4.030–4.484 Medical insurance  No 1.000 – 1.000 –  Yes 0.421 0.315–0.571 0.963 0.910–1.019

BMI: Body mass index; CI: Confidence interval; GEE: Generalized estimation equation; OR: Odds ratio; –: Not available.

In this study, we found that unhealthy dietary patterns increased the risk of developing depression, in line with previous studies that had found a healthy diet was associated with a lower prevalence of depression.[2] Unhealthy dietary patterns often lead to obesity, and there is sufficient evidence that obesity is a risk factor for depression. The underlying mechanism may be two-fold. Psychologically, the negative opinions of obesity in society stigmatize obese people, and social pressure may even lead to suicidal thoughts. Physiologically, obese people are more likely to suffer from severe impairment of their body functions.[3]

This study suggested that too little sleep and poor-quality sleep are both closely associated with a higher risk of depression. Insomnia is one of the most common prodromal features of depression. A meta-analysis suggested that the risk of depression in people with insomnia is double that of those without sleep disorders.[4] Moreover, one longitudinal study indicated that mild and moderate drinkers have a lower risk of depression than non-drinkers, but that excessive drinking increases the risk of depression.[5]

This study was limited by the use of self-reported symptoms to assess mental health since these self-reported symptoms may be affected by recall bias. Another limitation is that the cross-sectional design makes it impossible to verify the causal relationship between depression and lifestyle indicators, which will require further research in the future.

As a serious public health challenge, depression in Chinese adults needs more attention. We found that unhealthy lifestyles were closely associated with depression, including current alcohol consumption, obesity, too little sleep, poor-quality sleep, unhealthy dietary choices, irregular meal times, stress, significant life events, and sub-health status. The risk of depression could be reduced by choosing healthier lifestyle.

Acknowledgement

We thank Liwen Bianji (Edanz) (www.liwenbianji.cn) for editing the English text of a draft of this manuscript.

Conflicts of interest

None.

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