Publications [#355826] of Aaron Reuben

Journal Articles

1. Latham, RM; Kieling, C; Arseneault, L; Botter-Maio Rocha, T; Beddows, A; Beevers, SD; Danese, A; De Oliveira, K; Kohrt, BA; Moffitt, TE; Mondelli, V; Newbury, JB; Reuben, A; Fisher, HL (2021). Childhood exposure to ambient air pollution and predicting individual risk of depression onset in UK adolescents.. J Psychiatr Res, 138, 60-67. [doi]
   (last updated on 2024/04/19)

   Abstract:
   Knowledge about early risk factors for major depressive disorder (MDD) is critical to identify those who are at high risk. A multivariable model to predict adolescents' individual risk of future MDD has recently been developed however its performance in a UK sample was far from perfect. Given the potential role of air pollution in the aetiology of depression, we investigate whether including childhood exposure to air pollution as an additional predictor in the risk prediction model improves the identification of UK adolescents who are at greatest risk for developing MDD. We used data from the Environmental Risk (E-Risk) Longitudinal Twin Study, a nationally representative UK birth cohort of 2232 children followed to age 18 with 93% retention. Annual exposure to four pollutants - nitrogen dioxide (NO2), nitrogen oxides (NOX), particulate matter <2.5 μm (PM2.5) and <10 μm (PM10) - were estimated at address-level when children were aged 10. MDD was assessed via interviews at age 18. The risk of developing MDD was elevated most for participants with the highest (top quartile) level of annual exposure to NOX (adjusted OR = 1.43, 95% CI = 0.96-2.13) and PM2.5 (adjusted OR = 1.35, 95% CI = 0.95-1.92). The separate inclusion of these ambient pollution estimates into the risk prediction model improved model specificity but reduced model sensitivity - resulting in minimal net improvement in model performance. Findings indicate a potential role for childhood ambient air pollution exposure in the development of adolescent MDD but suggest that inclusion of risk factors other than this may be important for improving the performance of the risk prediction model.