Latham Rachel M, Kieling Christian, Arseneault Louise, Botter-Maio Rocha Thiago, Beddows Andrew, Beevers Sean D, Danese Andrea, De Oliveira Kathryn, Kohrt Brandon A, Moffitt Terrie E, Mondelli Valeria, Newbury Joanne B, Reuben Aaron, Fisher Helen L
King's College London, Social, Genetic and Developmental Psychiatry Centre, Institute of Psychiatry, Psychology and Neuroscience, London, UK; ESRC Centre for Society and Mental Health, King's College London, London, UK.
Department of Psychiatry, Universidade Federal Do Rio Grande Do Sul, Porto Alegre, Brazil; Child and Adolescent Psychiatry Division, Hospital de Clínicas de Porto Alegre, Porto Alegre, Brazil.
J Psychiatr Res. 2021 Jun;138:60-67. doi: 10.1016/j.jpsychires.2021.03.042. Epub 2021 Mar 25.
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), nitrogen oxides (NO), particulate matter <2.5 μm (PM) and <10 μm (PM) - 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 (adjusted OR = 1.43, 95% CI = 0.96-2.13) and PM (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.
了解重度抑郁症（MDD）的早期风险因素对于识别高危人群至关重要。最近开发了一种多变量模型来预测青少年未来患MDD的个体风险，然而其在英国样本中的表现远非完美。鉴于空气污染在抑郁症病因学中的潜在作用，我们研究在风险预测模型中纳入儿童时期暴露于空气污染作为额外预测因素是否能改善对英国青少年中患MDD风险最高者的识别。我们使用了环境风险（E-Risk）纵向双胞胎研究的数据，这是一个具有全国代表性的英国出生队列，对2232名儿童进行跟踪随访至18岁，保留率为93%。在儿童10岁时，根据住址层面估计其每年接触四种污染物——二氧化氮（NO₂）、氮氧化物（NOₓ）、粒径小于2.5微米的颗粒物（PM₂.₅）和小于10微米的颗粒物（PM₁₀）的情况。在18岁时通过访谈评估MDD。对于每年接触NO₂（调整后的比值比OR = 1.43，95%置信区间CI = 0.96 - 2.13）和PM₂.₅（调整后的OR = 1.35，95% CI = 0.95 - 1.92）水平最高（四分位数最高）的参与者，患MDD的风险升高最为明显。将这些环境污染估计值单独纳入风险预测模型提高了模型的特异性，但降低了模型的敏感性——导致模型性能的净改善最小。研究结果表明儿童时期暴露于环境空气污染在青少年MDD的发生发展中可能起作用，但也表明纳入除此之外的风险因素可能对提高风险预测模型的性能很重要。
