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Major depressive disorder (MDD) is a common mood disorder, with a heritability of around 34%. Molecular genetic studies made significant progress and identified genetic markers associated with the risk of MDD; however, progress is slowed down by substantial heterogeneity as MDD is assessed differently across international cohorts. Here, we used a standardized online approach to measure MDD in multiple cohorts in the Netherlands and evaluated whether this approach can be used in epidemiological and genetic association studies of depression.
Within the Biobank Netherlands Internet Collaboration (BIONIC) project, we collected MDD data in eight cohorts involving 31 936 participants, using the online Lifetime Depression Assessment Self-report (LIDAS), and estimated the prevalence of current and lifetime MDD in 22 623 unrelated individuals. In a large Netherlands Twin Register (NTR) twin-family dataset (n ≈ 18 000), we estimated the heritability of MDD, and the prediction of MDD in a subset (n = 4782) through Polygenic Risk Score (PRS).
Estimates of current and lifetime MDD prevalence were 6.7% and 18.1%, respectively, in line with population estimates based on validated psychiatric interviews. In the NTR heritability estimates were 0.34/0.30 (s.e. = 0.02/0.02) for current/lifetime MDD, respectively, showing that the LIDAS gives similar heritability rates for MDD as reported in the literature. The PRS predicted risk of MDD (OR 1.23, 95% CI 1.15–1.32, R2 = 1.47%).
By assessing MDD status in the Netherlands using the LIDAS instrument, we were able to confirm previously reported MDD prevalence and heritability estimates, which suggests that this instrument can be used in epidemiological and genetic association studies of depression.
Studies on neighbourhood characteristics and depression show equivocal results.
This large-scale pooled analysis examines whether urbanisation, socioeconomic, physical and social neighbourhood characteristics are associated with the prevalence and severity of depression.
Cross-sectional design including data are from eight Dutch cohort studies (n= 32 487). Prevalence of depression, either DSM-IV diagnosis of depressive disorder or scoring for moderately severe depression on symptom scales, and continuous depression severity scores were analysed. Neighbourhood characteristics were linked using postal codes and included (a) urbanisation grade, (b) socioeconomic characteristics: socioeconomic status, home value, social security beneficiaries and non-Dutch ancestry, (c) physical characteristics: air pollution, traffic noise and availability of green space and water, and (d) social characteristics: social cohesion and safety. Multilevel regression analyses were adjusted for the individual's age, gender, educational level and income. Cohort-specific estimates were pooled using random-effects analysis.
The pooled analysis showed that higher urbanisation grade (odds ratio (OR) = 1.05, 95% CI 1.01–1.10), lower socioeconomic status (OR = 0.90, 95% CI 0.87–0.95), higher number of social security beneficiaries (OR = 1.12, 95% CI 1.06–1.19), higher percentage of non-Dutch residents (OR = 1.08, 95% CI 1.02–1.14), higher levels of air pollution (OR = 1.07, 95% CI 1.01–1.12), less green space (OR = 0.94, 95% CI 0.88–0.99) and less social safety (OR = 0.92, 95% CI 0.88–0.97) were associated with higher prevalence of depression. All four socioeconomic neighbourhood characteristics and social safety were also consistently associated with continuous depression severity scores.
This large-scale pooled analysis across eight Dutch cohort studies shows that urbanisation and various socioeconomic, physical and social neighbourhood characteristics are associated with depression, indicating that a wide range of environmental aspects may relate to poor mental health.
Declaration of interest
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