Childhood exposure to ambient air pollution and predicting individual risk of depression onset in UK adolescents

Rachel Latham; Christian Kieling; Louise Arseneault; Thiago Rocha; Andrew Beddows; Sean Beevers; Andrea Danese; Kathryn De Oliveira; Brandon A Kohrt; Terrie Moffitt; Valeria Mondelli; Joanne Newbury; Aaron Reuben; Helen Fisher

doi:10.1016/j.jpsychires.2021.03.042

Childhood exposure to ambient air pollution and predicting individual risk of depression onset in UK adolescents

Rachel Latham, Christian Kieling, Louise Arseneault, Thiago Rocha, Andrew Beddows, Sean Beevers, Andrea Danese, Kathryn De Oliveira, Brandon A Kohrt, Terrie Moffitt, Valeria Mondelli, Joanne Newbury, Aaron Reuben, Helen Fisher

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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 (NO ₂), nitrogen oxides (NO _X), particulate matter <2.5 μm (PM _2.5) and <10 μm (PM ₁₀) – 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 NO _X (adjusted OR = 1.43, 95% CI = 0.96–2.13) and PM _2.5 (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.

Original language	English
Pages (from-to)	60-67
Number of pages	8
Journal	Journal of Psychiatric Research
Volume	138
Early online date	25 Mar 2021
DOIs	https://doi.org/10.1016/j.jpsychires.2021.03.042
Publication status	Published - Jun 2021

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Childhood exposure to ambient_LATHAM_Accepted19March2021_GOLD AAMAccepted author manuscript, 384 KB

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Latham, R., Kieling, C., Arseneault, L., Rocha, T., Beddows, A., Beevers, S., Danese, A., De Oliveira, K., Kohrt, B. A., Moffitt, T., Mondelli, V., Newbury, J., Reuben, A., & Fisher, H. (2021). Childhood exposure to ambient air pollution and predicting individual risk of depression onset in UK adolescents. Journal of Psychiatric Research, 138, 60-67. https://doi.org/10.1016/j.jpsychires.2021.03.042

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title = "Childhood exposure to ambient air pollution and predicting individual risk of depression onset in UK adolescents",

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{\textquoteright} 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 (NO 2), nitrogen oxides (NO X), particulate matter <2.5 μm (PM 2.5) and <10 μm (PM 10) – 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 NO X (adjusted OR = 1.43, 95% CI = 0.96–2.13) and PM 2.5 (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. ",

author = "Rachel Latham and Christian Kieling and Louise Arseneault and Thiago Rocha and Andrew Beddows and Sean Beevers and Andrea Danese and {De Oliveira}, Kathryn and Kohrt, {Brandon A} and Terrie Moffitt and Valeria Mondelli and Joanne Newbury and Aaron Reuben and Helen Fisher",

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doi = "10.1016/j.jpsychires.2021.03.042",

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Latham, R, Kieling, C, Arseneault, L, Rocha, T, Beddows, A , Beevers, S , Danese, A, De Oliveira, K, Kohrt, BA, Moffitt, T , Mondelli, V , Newbury, J, Reuben, A & Fisher, H 2021, 'Childhood exposure to ambient air pollution and predicting individual risk of depression onset in UK adolescents', Journal of Psychiatric Research, vol. 138, pp. 60-67. https://doi.org/10.1016/j.jpsychires.2021.03.042

TY - JOUR

T1 - Childhood exposure to ambient air pollution and predicting individual risk of depression onset in UK adolescents

AU - Latham, Rachel

AU - Kieling, Christian

AU - Arseneault, Louise

AU - Rocha, Thiago

AU - Beddows, Andrew

AU - Beevers, Sean

AU - Danese, Andrea

AU - De Oliveira, Kathryn

AU - Kohrt, Brandon A

AU - Moffitt, Terrie

AU - Mondelli, Valeria

AU - Newbury, Joanne

AU - Reuben, Aaron

AU - Fisher, Helen

PY - 2021/6

Y1 - 2021/6

N2 - 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 (NO 2), nitrogen oxides (NO X), particulate matter <2.5 μm (PM 2.5) and <10 μm (PM 10) – 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 NO X (adjusted OR = 1.43, 95% CI = 0.96–2.13) and PM 2.5 (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.

AB - 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 (NO 2), nitrogen oxides (NO X), particulate matter <2.5 μm (PM 2.5) and <10 μm (PM 10) – 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 NO X (adjusted OR = 1.43, 95% CI = 0.96–2.13) and PM 2.5 (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.

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U2 - 10.1016/j.jpsychires.2021.03.042

DO - 10.1016/j.jpsychires.2021.03.042

M3 - Article

SN - 0022-3956

VL - 138

SP - 60

EP - 67

JO - Journal of Psychiatric Research

JF - Journal of Psychiatric Research

ER -

Childhood exposure to ambient air pollution and predicting individual risk of depression onset in UK adolescents

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