New research suggests that long-term exposure to air pollution may contribute to the development of several psychiatric disorders, including depression, anxiety, schizophrenia, autism spectrum disorder, and attention-related problems. The study, which used genetic methods to investigate causal links, also found that changes in brain structure and function may help explain how air pollution influences mental health. The findings were published in the Journal of Affective Disorders.
Psychiatric disorders represent a major public health concern, affecting hundreds of millions of people worldwide. These conditions are known to reduce life expectancy and quality of life, and they often require long-term care. While genetic, social, and individual lifestyle factors are known contributors, environmental influences are also thought to play an important role. Among these, air pollution has emerged as a suspected risk factor.
Over the past decade, observational studies have reported associations between air pollution and psychiatric disorders. However, such studies often struggle to clarify whether pollution is truly causing these conditions or whether other factors, like socioeconomic status or regional differences, are confounding the results. To move beyond correlation, the research team turned to a genetic method that helps isolate potential causal pathways.
The research team, based at Peking University Sixth Hospital and affiliated institutions, used Mendelian randomization to explore the potential causal effects of air pollution on mental health. This method uses genetic variations that are associated with environmental exposures—such as levels of air pollution—to estimate the impact of those exposures on health outcomes. Because these genetic variants are randomly assigned at conception, they are generally not influenced by lifestyle or environmental confounders.
The analysis focused on three key air pollutants: fine particulate matter smaller than 2.5 micrometers (PM2.5), PM2.5 absorbance (an indicator of black carbon and related components), and nitrogen dioxide (NO2), a gas commonly emitted by vehicle exhaust and industrial activity. Genetic data related to these exposures were drawn from large-scale genome-wide association studies conducted by the UK Biobank, covering more than 400,000 individuals of European ancestry.
The study then evaluated associations between these pollutants and five psychiatric conditions: major depressive disorder, anxiety disorders, autism spectrum disorder, schizophrenia, and attention-deficit/hyperactivity disorder. Genetic data for these mental health conditions were obtained from publicly available datasets maintained by the Psychiatric Genomics Consortium. These datasets included tens of thousands of individuals, allowing for robust statistical comparisons.
To explore how air pollution might influence the brain in ways that could lead to psychiatric disorders, the researchers also included imaging-derived brain phenotypes from over 8,000 individuals. These included measurements of brain volume, connectivity, and tissue contrast. The team used a two-step Mendelian randomization approach to investigate whether these brain features acted as mediators between air pollution and psychiatric outcomes.
The analysis revealed several patterns linking air pollution to psychiatric disorders:
To better understand the biological mechanisms behind these links, the researchers examined how pollution-related genetic variations were associated with specific features of the brain. One brain region stood out in relation to depression: the left CA4-body, part of the hippocampus, a region involved in memory and emotional regulation. The study found that smaller volume in this area partially mediated the association between PM2.5 exposure and major depressive disorder, accounting for about 6 percent of the total effect.
Additionally, a separate feature called grey-white contrast in the right frontal pole—an indicator of tissue boundary sharpness in a region implicated in high-level decision-making—appeared to mask the relationship between PM2.5 and depression. In this case, the contrast change in the frontal pole seemed to reduce the overall impact of pollution on depression risk.
In the case of attention-related disorders, a functional brain connectivity pattern showed a similar masking effect, potentially offering a neuroprotective counterbalance to pollution’s negative influence.
These brain features offer clues into how pollution may reshape brain development or function in ways that increase vulnerability to mental illness. For example, changes in hippocampal volume have been consistently associated with depression, while alterations in frontal brain structure and connectivity are known to affect mood regulation, attention, and cognitive control.
While the study offers evidence pointing to causal links between air pollution and mental health, several limitations remain. One major constraint is that all participants were of European ancestry, which limits the ability to generalize the findings to other populations. Air pollution levels and compositions also vary significantly by region, and the study focused only on three pollutants, leaving out others such as ozone, sulfur dioxide, and carbon monoxide.
Another challenge involves the genetic instruments used in the analysis. In order to ensure sufficient statistical power, the researchers relaxed some of their criteria for selecting genetic variants, which could introduce a degree of uncertainty. While extensive sensitivity tests were conducted to check for bias, the possibility of residual confounding cannot be completely excluded.
Although the brain imaging analyses provided potential mechanisms, they were based on a relatively small sample compared to the genetic datasets. Larger studies with more diverse populations and individual-level data are needed to confirm these findings and to better understand how changes in brain structure or function might translate into psychiatric symptoms.
The study, “Air pollution is the risk factor for psychiatric disorders: a two-step Mendelian randomization study,” was authored by Jingying Zhou, Zhe Lu, Ke Xu, Guorui Zhao, Yunqing Zhu, Rui Yuan, Yaoyao Sun, Yuyanan Zhang, and Weihua Yue.
