Long-term exposure to common air pollutants may significantly raise the risk of developing substance use disorders, while living surrounded by greenery appears to offer a measure of protection, according to a large new population-based study drawing on nearly a quarter of a million participants from the UK Biobank.

The research, published in BMC Psychiatry, analyzed data from 247,138 UK Biobank participants who were free of substance use disorders at baseline, tracking them over a median follow-up period of 10.9 years. During that time, 4,982 participants went on to develop a substance use disorder, providing the researchers with a robust dataset to examine the relationship between environmental exposures and mental health outcomes.

The study, led by Huan Liu and Bolun Cheng of Xi’an Jiaotong University in China, along with colleagues including Feng Zhang, represents one of the most comprehensive investigations to date into how the air people breathe and the greenness of their neighborhoods may influence vulnerability to addiction. The findings arrive at a moment when urban planners and public health officials are increasingly recognizing that the built and natural environments shape health in ways that extend well beyond respiratory and cardiovascular disease.

To assess air pollution exposure, the team employed Land Use Regression modeling to estimate concentrations of five pollutants at each participant’s residential address: fine particulate matter known as PM2.5, coarse particulate matter classified as PM2.5−10, the larger particulate fraction PM10, nitrogen dioxide (NO2), and nitrogen oxides (NOX). These pollutants, produced primarily by traffic, industry, and combustion sources, have long been associated with a range of physical health problems, but their role in psychiatric and behavioral conditions has remained far less certain.

For residential greenness, the researchers turned to the 2005–2007 Land Cover Map, measuring three distinct aspects of the environment within a 300-meter buffer around each home: the proportion of green space, the presence of a domestic garden, and the extent of natural environment. This tripartite approach allowed the investigators to distinguish between different types of vegetation and their potentially different effects on health.

Using Cox proportional hazards models, the team calculated hazard ratios with 95 percent confidence intervals to quantify the association between each exposure and the risk of developing a substance use disorder. The results were striking in their consistency. All five air pollutants were positively associated with incident SUD in fully adjusted models that controlled for a wide range of potential confounders.

Fine particulate matter, or PM2.5, showed the strongest effect, with a hazard ratio of 1.111 (95 percent CI: 1.081–1.142), followed by PM10 with a hazard ratio of 1.070 (95 percent CI: 1.054–1.086). These figures indicate that higher exposure to particulate pollution was associated with a measurable increase in the likelihood of developing a substance use disorder over the follow-up period, even after accounting for demographic, socioeconomic, and behavioral factors.

On the protective side of the ledger, two of the three greenness measures emerged as significant. Green space was inversely associated with SUD risk, showing a hazard ratio of 0.997 (95 percent CI: 0.996–0.998), and the natural environment measure produced an identical finding, with a hazard ratio of 0.997 (95 percent CI: 0.995–0.998). While these effect sizes appear modest on a per-unit basis, they represent population-level signals across a very large cohort, suggesting that even incremental increases in neighborhood greenness may contribute to reduced vulnerability.

Perhaps the most intriguing finding of the study concerns the interaction between pollution and greenery. The researchers tested whether residential greenness modified the association between air pollutants and SUD risk by introducing cross-product terms into their models and evaluating them with likelihood-ratio tests. The results pointed to a buffering role for domestic gardens specifically. Among participants in the high-exposure subgroup, the presence of a domestic garden attenuated the positive associations between pollutants and SUD, with cross-product terms of 0.979 (95 percent CI: 0.963–0.996) for PM2.5 and 0.987 (95 percent CI: 0.980–0.995) for PM10.

In practical terms, this suggests that having access to a private garden may partially offset some of the harm associated with living in areas of elevated particulate pollution, offering a form of environmental resilience that public health advocates have long hypothesized but rarely demonstrated with prospective cohort data.

The associations were not uniform across the population. The positive relationships between air pollution and substance use disorder risk were most pronounced among males, participants aged 65 and older, and individuals with a history of smoking. These findings highlight the importance of considering demographic and behavioral modifiers when assessing the health consequences of environmental exposures, and they suggest that certain subgroups may benefit disproportionately from interventions aimed at reducing pollution or increasing green cover.

To ensure the robustness of their findings, the team conducted a series of sensitivity analyses. These included restricting the sample to long-term residents to minimize exposure misclassification from relocation, using alternative outcome definitions based on ICD-10 codes F10-F19 and F11-F19 to capture different ranges of substance-related diagnoses, and recomputing greenness measures at a wider 1000-meter buffer around each residence. The core conclusions held across these variations, strengthening confidence in the results.

The study was performed in accordance with the ethical principles of the Declaration of Helsinki and was registered with the UK Biobank under application number 46478. Ethical approval for the UK Biobank was granted by the North West Multi-centre Research Ethics Committee under approval reference 11/NW/0382. All participants provided written informed consent before any study-related procedures were initiated.

The work was supported by the China Postdoctoral Science Foundation, the Natural Science Basic Research Plan in Shaanxi Province of China, and the Fundamental Research Funds for the Central Universities. Corresponding authors are Bolun Cheng and Feng Zhang, both affiliated with Xi’an Jiaotong University, with Zhang also holding a position in the Department of Psychiatry at the First Affiliated Hospital of Xi’an Jiaotong University.

The findings come amid growing scientific interest in the neurological and psychiatric consequences of air pollution. Particulate matter, particularly PM2.5, is small enough to cross the blood-brain barrier and has been implicated in neuroinflammation, oxidative stress, and alterations in brain structure and function. These biological mechanisms could plausibly influence reward processing, impulse control, and stress reactivity, all of which are central to the development and perpetuation of substance use disorders.

Conversely, greenness has been associated with stress reduction, improved mental well-being, greater opportunities for physical activity and social interaction, and lower levels of psychological distress, each of which may buffer against the onset of addictive behaviors. The current study adds a significant piece of evidence to this emerging picture by demonstrating the relationship prospectively in one of the world’s largest and most deeply phenotyped biobanks.

The authors conclude that long-term exposure to ambient air pollutants was associated with a higher risk of incident substance use disorders and that residential greenness showed protective associations, with the most pronounced patterns observed among older males and individuals with a smoking history. They argue that the findings support integrating greenness exposure into environmental frameworks for the prevention of substance use disorders, a recommendation that carries implications for urban design, housing policy, and environmental regulation.

As cities around the world continue to grapple with the twin challenges of air quality and mental health, studies of this scale provide an evidence base for interventions that address both simultaneously. The prospect that something as tangible as a garden could offer measurable protection against the psychiatric consequences of polluted air adds a compelling dimension to ongoing debates about how the environments we build shape the minds that inhabit them.

The article is published open access under a Creative Commons Attribution 4.0 International License, permitting broad sharing and adaptation with appropriate credit to the original authors and source. It was received on 08 December 2025, accepted on 28 August 2026, and published on 09 September 2026.

In one of the most comprehensive investigations of its kind, researchers have taken a major step toward untangling the biology underlying suicide risk, moving the field beyond long-standing debates about whether suicidal behavior is simply a consequence of psychiatric illness. By integrating large-scale genetic data with cutting-edge approaches that map how genes act in specific brain cells and circuits, an international team has identified shared genetic liability that cuts across psychiatric diagnoses while also pinpointing druggable biological pathways that could one day inform prevention strategies. The study, published in Translational Psychiatry, offers a sweeping synthesis of how inherited risk factors may translate into molecular vulnerabilities, and it does so with a level of resolution that previous candidate-gene approaches could never achieve.

Suicide remains one of the leading causes of death worldwide, claiming more than 700,000 lives each year according to global health estimates. Despite decades of research, clinical prediction remains stubbornly imprecise, and biological models of suicidal behavior have historically been hampered by small sample sizes, inconsistent findings, and a tendency to conflate suicide risk with the psychiatric disorders with which it so often co-occurs. Major depression, bipolar disorder, schizophrenia, and substance use disorders all carry elevated suicide risk, yet most people with these conditions never attempt suicide, while some individuals who die by suicide have no diagnosed psychiatric illness at all. This paradox has long suggested to researchers that suicidal behavior may possess partially distinct biological underpinnings, and the new study set out to test that hypothesis with unprecedented statistical power.

At the heart of the work lies genome-wide association study methodology, or GWAS, which scans hundreds of thousands to millions of genetic variants spread across the entire genome in very large groups of people, comparing individuals who have experienced suicidal thoughts or behaviors with those who have not. Each variant is tested for statistical association with the outcome, and the results can be combined into polygenic risk scores, numerical summaries of a person’s inherited liability based on the cumulative small effects of thousands of variants. Because no single common variant exerts a large effect, the field depends on aggregating data across massive biobanks and cohorts, a strategy that has only become feasible in the past decade as datasets containing hundreds of thousands of individuals became available.

The research team took particular care to address a central confound: psychiatric disorders themselves are highly heritable, and any genetic signal linked to suicide could simply reflect inherited liability to depression or schizophrenia rather than to suicide specifically. To disentangle this, the investigators applied sophisticated statistical techniques, including genetic correlation analysis and conditional modeling, which allow researchers to ask how much genetic overlap remains between suicidal behavior and psychiatric conditions after accounting for their shared heritability. Their findings revealed a complex architecture. Substantial genetic correlations confirmed that suicide risk shares a meaningful fraction of its inherited basis with major psychiatric disorders, supporting the idea of a common pool of genetic liability. Yet the analyses also uncovered signals that persisted beyond what could be explained by those disorders, pointing to biological mechanisms that may be more specific to the emergence of suicidal thoughts and actions.

A key conceptual innovation of the study is its framing of “shared genetic liability” as a bridge between diagnoses. Rather than treating suicide as an epiphenomenon of any single disorder, the researchers modeled it as a transdiagnostic phenomenon, one whose risk emerges partly from inherited vulnerabilities that cut across diagnostic categories. This perspective aligns with a growing movement in biological psychiatry that emphasizes symptom dimensions and mechanisms over traditional diagnostic boundaries. From this vantage point, the genetic variants that raise risk for depression, PTSD, and schizophrenia may do so partly because they converge on shared molecular pathways, and those same pathways may govern an individual’s threshold for suicidal crises regardless of which diagnosis they carry.

Identifying genetic variants, however, is only the first step. A GWAS signal typically points to a genomic region rather than a specific causal gene or mechanism, since many associated variants fall in non-coding stretches of DNA that regulate gene activity from a distance. To translate statistical associations into biological insight, the team deployed a battery of functional genomics approaches. Transcriptome-wide association studies, known as TWAS, integrate gene expression data to identify genes whose genetically predicted expression levels are linked to suicide risk. Tissue and cell-type enrichment analyses then ask whether the implicated genes are unusually active in particular organs or cell populations. The results converged strikingly on brain tissue, and more specifically on neuronal populations, providing support for the idea that inherited suicide risk is mediated largely through gene regulation within the central nervous system.

The study’s most clinically provocative findings concern druggable biology. Using databases that catalog the protein targets of approved and investigational medications, the researchers examined whether genes implicated in suicide risk were over-represented among known drug targets. The analysis identified several classes of candidate targets, including components of neurotransmitter signaling systems, ion channels, and intracellular signaling cascades that are already the focus of pharmacological development for other conditions. This “druggable genome” approach, which has gained traction in genetics research across many diseases, offers a pragmatic shortcut for translational science: rather than waiting decades for entirely new therapeutic classes to be invented, researchers can prioritize existing or in-development compounds whose molecular targets intersect with genetically implicated pathways.

Among the pathways receiving attention in the analysis were those involving glutamatergic and GABAergic neurotransmission, the primary excitatory and inhibitory systems of the brain, which have repeatedly surfaced in studies of mood disorders and stress response. The endocannabinoid system and inflammatory signaling pathways also appeared in the convergent analyses, consistent with a broader literature linking immune dysregulation and allostatic load to suicidal ideation. Importantly, the authors emphasize that these findings identify hypotheses rather than ready-made treatments. Genetic association with a drug target does not guarantee that modulating that target will reduce suicide risk, and any therapeutic application would require extensive preclinical validation and carefully designed clinical trials. Nevertheless, the work provides a principled, data-driven map for where to look, replacing decades of largely failed candidate-gene speculation with a genome-wide evidence base.

The methodological rigor underpinning the study deserves emphasis, because the genetics of suicidal behavior has a troubled history. Early candidate-gene studies, which typically examined a handful of variants in genes related to serotonin signaling, produced findings that largely failed to replicate and in some cases were later shown to reflect statistical artifacts, small samples, and publication bias. The field has since undergone a reckoning, and modern efforts adhere to far stricter standards: preregistered analyses, very large and ancestrally diverse cohorts, replication in independent samples, and conservative multiple-testing corrections that demand genome-wide significance thresholds on the order of five times ten to the negative eighth power. The new study reflects these lessons, applying sensitivity analyses to guard against confounding from psychiatric comorbidity and examining whether findings generalize across ancestral groups, an issue of considerable importance given that most genetic discovery to date has been conducted in populations of European ancestry.

The implications for clinical practice, while still distant, are worth considering. Polygenic risk scores for suicide-related outcomes currently explain only a small fraction of individual differences in risk, far too little to be used as stand-alone screening tools, and their predictive performance is degraded in populations not represented in the discovery data. Researchers in the field are careful to stress that genetic risk is not destiny: suicidal behavior arises from an intricate interplay of inherited vulnerability, mental illness, trauma, substance use, social isolation, access to lethal means, and the availability of support. What genetics offers is not a crystal ball but a window into mechanism, and mechanistic knowledge is the raw material from which better biomarkers, risk stratification tools, and ultimately targeted interventions are built. In the near term, the druggable pathway findings are most valuable as a guide for laboratory science, directing neurobiologists toward molecular systems whose manipulation might alter suicide-relevant phenotypes in animal models and cellular assays.

Looking forward, the study signals where the field must go next. Sample sizes for suicide-specific genetic analyses remain smaller than those for major psychiatric disorders, and expanding representation across ancestries is essential if the benefits of this research are to be shared equitably. Integrating genetic data with longitudinal measures of life stress, neural imaging, and digital phenotyping may eventually allow researchers to model how inherited liability interacts with environmental triggers across the lifespan. Functional experiments in induced pluripotent stem cell-derived neurons and brain organoids could test whether the implicated genes genuinely alter stress-response circuitry or resilience. And randomized trials in patients carrying high genetic liability, though ethically and logistically complex, represent the logical endpoint of the druggable-target approach.

What the study ultimately delivers is a reframe. Suicide risk, in this analysis, is not an amorphous clinical outcome but a measurable, partly heritable trait whose genetic architecture is now being resolved with the same tools that have transformed our understanding of heart disease, diabetes, and schizophrenia. By demonstrating that shared genetic liability links suicide risk across diagnostic boundaries and by mapping that liability onto specific, pharmacologically tractable biology, the researchers have converted a long-standing clinical puzzle into a concrete research agenda. The path from genetic correlation to effective prevention is long, and no genetic finding will soon replace the human work of crisis intervention, connection, and care. But for a problem that has defied biological explanation for generations, the identification of shared, druggable mechanisms represents a genuine and hard-won advance, one that may shape suicide research for years to come.

Even years after the acute phase of the COVID-19 pandemic faded from daily headlines, one of its most stubborn legacies continues to shadow millions of working adults: post-COVID conditions, the cluster of persistent symptoms now broadly known as long COVID. A new cross-sectional study from Taiwan, published in BMC Psychology, offers a sobering snapshot of what these conditions mean for mental health and the capacity to work, even among people who were highly vaccinated and infected predominantly during the milder Omicron era. The findings challenge a lingering assumption that vaccination and less virulent variants have fully neutralized the long-term occupational and psychological costs of SARS-CoV-2 infection.

The research, led by Jhe-Ping Lin of the Department of Occupational Medicine at Linkou Chang Gung Memorial Hospital in Taoyuan City, together with Chia-Cheng Chang, Chen-Chang Yang, Ting-An Yang and corresponding author Yu-Chung Tsao, set out to answer a question that has become increasingly urgent for occupational health practitioners worldwide: how do employed adults who report persistent post-COVID symptoms differ, in measurable psychological and functional terms, from infected peers who consider themselves fully recovered? Much of the existing long COVID literature emerged from populations infected with earlier viral variants, before widespread vaccination, and focused heavily on physical sequelae. Relatively less is known about the psychological and occupational footprint of post-COVID conditions under contemporary epidemiological conditions, which is precisely the gap the Taiwanese team sought to fill.

The study design was straightforward but methodologically deliberate. Employed adults with prior confirmed SARS-CoV-2 infection were recruited through clinical and occupational-health channels using non-probability sampling, a practical approach for reaching workers with documented infection histories. All participants completed a battery of validated questionnaires assessing four core domains: health-related quality of life, fear of COVID-19, insomnia severity and work ability. The investigators then divided the cohort into two groups based on whether participants reported persistent symptoms consistent with post-COVID conditions, and statistically compared the two groups on every psychological and occupational measure. This symptom-based categorization mirrors the approach increasingly favored internationally, since no single laboratory biomarker can yet reliably define long COVID.

The results were striking in their consistency. Participants reporting post-COVID conditions demonstrated significantly lower quality of life, with a mean difference of 13.9 points on the quality-of-life instrument compared with fully recovered peers. They also scored higher on the Fear of COVID-19 Scale, a validated measure of pandemic-related anxiety, and reported greater insomnia severity, suggesting that disturbed sleep is a common companion of persistent post-viral illness. Perhaps most consequential for employers and policymakers, the post-COVID group showed reduced scores on the Work Ability Index, a well-established instrument that quantifies how well a worker perceives themselves able to meet the physical and mental demands of their job. The mean difference of 3.3 points on this index may sound modest, but in occupational medicine research, shifts of this magnitude on the Work Ability Index are considered clinically meaningful and are associated with elevated risks of reduced productivity, prolonged sickness absence and early exit from the labor force.

To appreciate why these numbers matter, it helps to understand what each instrument measures. Health-related quality-of-life scales capture how illness erodes daily functioning across physical, emotional and social dimensions; a 13.9-point deficit represents a substantial perceived deterioration in wellbeing, not a marginal statistical blip. The Fear of COVID-19 Scale taps illness-related anxiety that can itself become disabling, feeding avoidance behaviors and hypervigilance. Insomnia severity indices quantify difficulties falling asleep, staying asleep and feeling restored by sleep, and chronic insomnia is strongly linked to depression, impaired concentration and workplace accidents. The Work Ability Index synthesizes a worker’s self-assessed physical and mental capacity, diagnosed illnesses and resources relative to job demands, making it one of the most predictive single measures available for future work disability. That all four domains moved in the same negative direction among the post-COVID group paints a coherent picture of a population under compounding psychological and functional strain.

Crucially, these associations emerged within a population that reflects the current reality of the pandemic rather than its early, pre-vaccine chapter. Taiwan maintained exceptionally high vaccination coverage, and the overwhelming majority of infections in the study cohort occurred during the Omicron-dominant period, when the variant itself was generally associated with less severe acute disease. The persistence of measurable psychological and occupational burden under these comparatively favorable conditions suggests that the risk of long-term impairment has been reduced but not eliminated. For the millions of workers globally who have been infected with Omicron-lineage viruses despite vaccination, the Taiwanese data imply that lingering symptoms remain a genuine occupational health concern rather than a relic of earlier pandemic waves.

The authors are careful about what their study can and cannot claim. Because the design is cross-sectional, capturing participants at a single point in time, causal relationships cannot be inferred. It remains possible, for instance, that pre-existing anxiety, poor sleep or low work ability influenced how participants recalled and reported persistent symptoms, rather than the symptoms driving the psychological burden. The non-probability recruitment strategy, while effective for reaching the target population, also limits generalizability; workers who chose to participate in an occupational health study may differ systematically from those who did not. Self-reported symptom status, the standard for long COVID research given the absence of a definitive diagnostic test, introduces additional subjectivity. The researchers openly acknowledge these constraints and call for longitudinal studies to clarify recovery trajectories and functional outcomes over time, which would allow investigators to determine whether reduced work ability precedes, follows or merely co-occurs with post-COVID symptomatology.

Even with those caveats, the study carries practical implications that extend well beyond Taiwan. The authors argue that mental health assessment deserves a central place in post-COVID follow-up care, not as an afterthought but as a routine component of clinical evaluation. This recommendation aligns with a growing body of international evidence linking long COVID to depression, anxiety and sleep disorders, and it reframes post-COVID conditions as a psychosomatic and occupational problem in equal measure with a physical one. For occupational physicians, the message is that a worker presenting with persistent fatigue or brain fog should not only be evaluated for physical sequelae but also screened for insomnia, pandemic-related fear and diminished work capacity. For employers, the findings underscore the value of graded return-to-work programs, workplace accommodations and access to psychological support, since untreated mental health burdens may prolong functional impairment even after physical symptoms begin to subside.

The policy stakes are considerable. Post-COVID conditions affect a substantial fraction of infected individuals worldwide, and because the condition disproportionately impairs the ability to sustain full-time work, its economic ripple effects touch social insurance systems, disability programs and national productivity. Studies conducted in vaccinated, Omicron-era populations are especially valuable for forecasting the forward-looking burden, since they describe the risk profile relevant to new infections rather than historical ones. By demonstrating a 13.9-point quality-of-life deficit and a 3.3-point reduction in work ability under exactly these modern conditions, the Taiwanese study provides quantitative anchors that health economists and policymakers can use to model the ongoing costs of long COVID in the workforce.

The research also highlights the importance of studying long COVID outside the Europe-North America axis, where much of the literature has concentrated. Taiwan’s pandemic trajectory, characterized by early containment followed by a large, late Omicron wave in a highly vaccinated population, offers a distinct natural experiment. The fact that measurable occupational and psychological impairment still emerges in this setting strengthens the argument that post-COVID conditions are a global phenomenon requiring coordinated surveillance, standardized assessment tools and sustained investment in rehabilitation services. The study received no external funding and was approved by the Institutional Review Board of the Chang Gung Medical Foundation, with all participants providing informed consent under protocols compliant with the Declaration of Helsinki.

For now, the study’s central contribution is to quantify, with validated instruments, the invisible toll that persistent post-viral symptoms exact on working adults: poorer perceived health, greater fear, worse sleep and a weakened sense of capacity to meet job demands. As health systems worldwide transition from emergency response to long-term management of COVID-19’s aftermath, evidence of this kind, gathered under the most favorable epidemiological conditions yet seen, serves as a reminder that the pandemic’s occupational and psychological dimensions are far from over. The authors’ call for longitudinal follow-up is the natural next step, and its findings will help determine whether the burdens documented here fade with time or harden into chronic disability for a vulnerable segment of the working population.

In a study of 2,266 workers in Finland, evening-type people, or night owls, were up to three times more likely to be treated for depression or anxiety than morning types, with poor work-life balance identified as a key contributing factor.

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