A key challenge in predicting a person's state of mind is that there are a wide range of contributing factors that each have a subtle, yet significant, influence on mental health. We applied data mining techniques to identify the most important risk factors for predicting current symptoms and longitudinal outcomes from the Adolescent Brain Cognitive Developmental study (n = 11,552). Our results consistently revealed that social conflicts were the strongest predictors of psychopathology, especially family fighting and reputational damage between peers. Sex-differences also emerged as a critical factor for predicting long-term mental health outcomes. Neuroimaging derived metrics were consistently the least informative. While these findings provide novel insight into the developmental origins of psychopathology, our best performing models could only explain up to 40% of the variation between individuals. Future research is needed to obtain a more complete understanding of all the factors that meaningfully contribute to mental health.

Global warming intensifies wildfires and exacerbates greenhouse-gas and pollutant emissions1. However, global projections remain incomplete, hindering effective policy interventions amid uncertain warming scenarios2. Here we developed an interpretable machine learning framework to project global burned areas and wildfire emissions. This framework accounts for the effects of future climate change on fire activity and quantifies the associated number of premature deaths and radiative forcing from fire-induced particulate matter (fine particulate matter less than 2.5 μm in diameter (PM2.5)). Here we show that from 2010-2014 to 2095-2099, fire carbon emissions are projected to increase by 23% under Shared Socio-economic Pathway 2-4.5. Increased fire-related aerosols reduce the 0.06 W m-2 cooling effect north of 60° N. Projections show a surge in the number of premature deaths from wildfire smoke, reaching 1.40 (95% confidence interval = 0.66-2.25) million people annually during 2095-2099-roughly six times higher than current levels. Africa is projected to experience the greatest rise in fire-related deaths (11-fold), driven by emission changes and an ageing population. Europe and the USA would experience a one to twofold increase under Shared Socio-economic Pathway 2-4.5, linked to rising fire occurrences in the mid-latitude Northern Hemisphere. Overall, the health burden would become more evenly distributed across nations of differing development levels than present patterns, underscoring the need for coordinated efforts.

Wildfire activity has increased in the USA and is projected to accelerate under future climate change1-3. However, our understanding of the impacts of climate change on wildfire activity, smoke and health outcomes remains highly uncertain because of the difficulty of modelling the causal chain from climate to wildfire to air pollution and health. Here we quantify the mortality burden in the USA due to wildfire smoke fine particulate matter (PM2.5) under climate change. We construct an ensemble of statistical and machine learning models that link climate to wildfire smoke PM2.5 and empirically estimate smoke PM2.5-mortality relationships using data on all recorded deaths in the USA. We project that smoke PM2.5 could result in 71,420 excess deaths (95% confidence interval: 34,930-98,430) per year by 2050 under a high-warming scenario (shared socioeconomic pathway scenario 3-7.0, SSP3-7.0)-a 73% increase relative to the estimated 2011-2020 average annual excess deaths from smoke. Cumulative excess deaths from smoke PM2.5 could reach 1.9 million between 2026 and 2055. We find evidence for mortality impacts of smoke PM2.5 that last up to 3 years after exposure. When monetized, climate-driven smoke deaths result in economic damages that exceed existing estimates of climate-driven damages from all other causes combined in the USA4,5. Our research suggests that the health impacts of climate-driven wildfire smoke could be among the most important and costly consequences of a warming climate in the USA.

Despite evidence linking prenatal acetaminophen (APAP) exposure and adverse neurodevelopment in humans and animals, over half of pregnant women in most populations use APAP. Prior studies could be biased by inaccurate self-reported APAP use, and the molecular mechanisms linking prenatal APAP with adverse neurodevelopment are unknown. We estimated associations between maternal plasma biomarkers of APAP exposure, child attention deficit hyperactivity disorder (ADHD), and placental gene expression in 307 African-American mother-child pairs. Overall, detection of APAP in 2nd trimester plasma was associated with higher odds for child ADHD diagnosis (Odds Ratio (OR)=3.15 [95%CI: 1.20-8.29]). Prenatal APAP exposure and ADHD were associated with placental upregulation of immune system pathways in females, and downregulation of oxidative phosphorylation in both sexes. In females only, prenatal APAP was associated with 5.22% higher odds (0.0456%-13.1%) of ADHD statistically mediated through increased immunoglobulin heavy constant gamma 1 (IGHG1) expression. These results highlight placental molecular mechanisms that may underlie developmental toxicity of prenatal APAP exposure.