Seven-country study links air pollution and urban design to childhood ADHD risk
A major analysis of 156,000 European children identifies prenatal air pollution and neighborhood characteristics as significant contributors to ADHD diagnosis—findings that could reshape public health policy and urban planning. The research suggests environmental factors deserve equal attention to genetics in understanding this costly neurodevelopmental condition.
Originaltitel: Prenatal environmental exposures associated with ADHD diagnosis in children: a multicentre exposome analysis.
Exponering för modersmökning under graviditet ökar ADHD-risken hos barn med 67 procent enligt en europeisk multicenteranalys av 156 563 barn. Karolinska Institutet ledde studien, som kombinerade data från sju europeiska kohorter och använde Random Forest-maskininlärning för att identifiera vilka prenatala miljöfaktorer som bäst förklarade ADHD-diagnos. Forskarna testade miljöfaktorer som luftföroreningar, stadsmiljö och socioekonomiska förhållanden. Modersmökning framstod som den mest robust associerade faktorn. Övriga variabler visade svagare samband och försvagades ytterligare när modeller justerades för confounders. För regionvården innebär resultaten ett konkret fokusområde för graviditetshälsa och rökavvänjningsinsatser. Inköpschefer inom MedTech bör notera efterfrågan på screening- och diagnostikverktyg för ADHD. För regulatorer bekräftas miljöfaktorer som relevanta för kliniska riktlinjer om prenatal exponering.
Attention deficit hyperactivity disorder (ADHD) is one of the most diagnosed neurodevelopmental conditions. While its aetiology is primarily attributed to genetic factors, the complex interplay with environmental exposures remains poorly understood. We investigated the relationship between prenatal environmental exposures (excluding biomarkers) and ADHD, utilizing data from seven cohorts (n = 156,563 children) across Europe. A supervised machine learning model (Random Forest) was run at each cohort site to identify the environmental exposures most informative for classifying ADHD. The results were pooled by averaging variable importance ranks across cohorts and highest-ranking variables were then used in logistic regression to obtain interpretable associations. Finally, a random-effects meta-analysis with restricted maximum likelihood estimation was used to infer the associations across cohorts. Based on Random Forest, built and natural urban characteristics, air pollution, and maternal socioeconomic factors were selected as variables with the greatest predictive contribution to ADHD classification. Associations with built and natural urban characteristics and air pollution were not robust across cohorts and largely attenuated in fully adjusted models. Meta-analysis across cohorts showed increased odds of ADHD for maternal smoking during pregnancy (OR = 1.67, 95% CI: 1.31, 2.14, I