Background

Major depressive disorder (MDD) is among the most common mental illnesses, and it severely limits psychosocial functioning and negatively affects the quality of life [1]. MDD affects approximately 6% of the adult population worldwide each year [2], and patients with MDD are nearly 20-fold more likely to die by suicide than individuals without MDD [3]. According to the World Health Organization, MDD will be the leading disease burden worldwide by 2030 [4].

Evidence indicates that socioeconomic status, medical conditions, and family history play a major role in the development of mental health disorders and that environmental factors may also influence the development of such disorders through neuroinflammatory pathways and oxidative stress [5, 6]. Although air pollution is the most common environmental risk to human health, results on the correlation between air pollution and health are sparse and inconsistent. For example, a recent systematic review reported that long-term exposure to air pollution was associated with an increased risk of depression, but the association was not significant in more than half of the studies included in the review [7]. Furthermore, the size and quality of these studies varied considerably. Therefore, additional large population-based cohort studies are necessary to test the potential association between long-term exposure to air pollution and MDD.

Evidence suggests that genetic factors play a critical role in the development of MDD [8, 9]. A genome-wide association study (GWAS) identified some genetic variants associated with MDD risk [10]. Analyzing the cumulative genetic burden of these genetic variants by using polygenic risk scores (PRSs) could provide quantitative measures of genetic susceptibility and could help effectively identify individuals at high risk of MDD [11]. Recent studies have suggested that genetic susceptibility may influence the environment–disease relationship [24]. In the present analysis, 17 single-nucleotide polymorphisms (SNPs) were selected based on their association with MDD in previous GWAS to create a weighted PRS for MDD (selected SNPs are provided in Additional file 1: Table S1) [10]. Details regarding PRS calculation have been described in a previous study [46]. However, determining which pathway offers the most critical link is difficult because of the existing scarcity of particle-specific translocation kinetics and exposure levels [47]. In addition, vascular disease is an essential intermediate factor in the association between air pollution exposure and an increased risk of subsequent MDD. Increasing bodies of evidence demonstrate that exposure to air pollution leads to cerebrovascular disease, which may affect the central nervous system and the brain, contributing to an increased risk of depression and other related conditions [48]. Previous studies have also indicated that vascular disease is associated with inflammatory pathway activation, leading to MDD or dysthymia [49]. Additional studies are necessary to determine the precise mechanisms underlying the air pollution–induced pathogenesis of MDD.

Previous research demonstrated that the etiology of MDD is multifactorial and that its heritability is approximately 35% [9]. Research also demonstrated that PRS may serve as an early indicator of clinically significant levels of depression and be associated with the risk of depression [50]. Our results are consistent with these findings. Additionally, we investigated the contribution of genetic susceptibility to the association between air pollution and MDD and found that air pollution may increase the risk of MDD, particularly among individuals with high genetic susceptibility. Li et al. explored how PM2.5 exposure interacts with polygenic risk in the development of MDD across multiple levels of brain network function [51]. They observed that a combination of exposure to high levels of air pollution and a relatively high polygenic risk for MDD disproportionately augmented stress-related effects on the brain circuitry. Working memory and stress-related information transfer across cortical and subcortical brain networks were influenced by PM2.5 exposures to differing extents depending on the polygenic risk for MDD in gene-by-environment interactions [51]. However, other explanations for these mechanisms can be applied when they are separated into particular variants. Previous studies have revealed that patients with psychiatric disorders had a higher mRNA expression level of vaccinia-related kinase 2 (VRK2) than did healthy individuals [52, 53]. In addition, a randomized crossover study suggested that higher PM2.5 exposure was positively associated with the mRNA expression of cytokine [54]. Therefore, air pollution may interact with rs1518395 located in VRK2 to jointly affect the onset of MDD. In addition, some SNPs from an MDD GWAS, such as rs10514299, could be enriched in genes expressed in the central nervous system and function in transcriptional regulation related to neurodevelopment [10]. Because of their toxicity to the central nervous system, air pollutants may also contribute to the development of mental diseases [55]. Therefore, by affecting the central nervous system, SNPs and air pollution may contribute to the onset of depression. Accordingly, elucidating the pathophysiology of MDD is imperative.

In addition, we also confirmed that unhealthy lifestyles were associated with higher risks of MDD. Considering the complexity of health behaviors and that most health behaviors are interconnected, a comprehensive analysis of healthy lifestyles may better capture the impact of lifestyle than an analysis based on a single factor. Our findings are in concert with the previous studies. Adjibade and colleagues formulated a healthy lifestyle index that incorporates multiple lifestyle factors and discovered that combined healthy lifestyles were associated with a lower risk of depressive symptoms [56]. We also observed that the deleterious associations between PM2.5 and MDD were stronger among individuals who led unhealthy lifestyles. Indeed, besides long-term air pollution exposure may reach the brain through the lung–brain axis and induce systemic inflammation [57], unhealthy lifestyle factors have also been associated with elevated inflammation levels [58, 59]. Conversely, higher levels of systemic inflammation marker may contribute to the development of different neuropsychiatric disorders including depression [60]. Therefore, when air pollution and unhealthy lifestyle are employed together for MDD, it is reasonable to appear enhanced effect. These findings emphasize the importance of lifestyle changes. The benefit of air pollution exposure reduction in lowering the risk of MDD is expected to be greatest among individuals with healthy lifestyles; this finding can inform the establishment of personalized preventive strategies for reducing the risk of MDD.

To the best of our knowledge, our study is the first to evaluate the modifying effect of genetic susceptibility and lifestyles on the association between air pollution exposure and the risk of MDD. The main strengths of our study are its inclusion of a large sample size, prospective design, and consistent results in several sensitivity analyses. Nevertheless, we also acknowledge several limitations of our study. First, an exposure assessment based on a single address does not eliminate the possibility of exposure misclassification caused by outside activities. Further studies with more accurate estimates are needed to confirm the present findings. In addition, we had to admit that the effect of the collinearity cannot be ruled out, single-pollutant associations may be not independent, and the results should be interpreted with caution. Second, common to most previous environmental epidemiology studies [

Availability of data and materials

The data used in this current study are available from the UK Biobank data resources. Permissions are required in order to gain access to the UK Biobank data resources, subject to successful registration and application process. Further information can be found on the UK Biobank website (https://www.ukbiobank.ac.uk/).

Abbreviations

CI:

Confidence interval

DAG:

Directed acyclic graph

FCS:

Fully conditional specification

GWAS:

Genome-wide association study

HR:

Hazards ratio

ICD-10:

International Classification of Diseases, Tenth Revision

LUR:

Land Use Regression

MDD:

Major depressive disorder

NO2 :

Nitrogen dioxide

NOx :

Nitrogen oxides

PCA:

Principal component analysis

PM10 :

Particulate matter with aerodynamic diameter ≤ 10μm

PM2.5 :

Particulate matter with aerodynamic diameter ≤ 2.5μm

PRSs:

Polygenic risk scores

SD:

Standard deviation

SNPs:

Single-nucleotide polymorphisms

VRK2:

Vaccinia-related kinase 2

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Acknowledgements

We thank the investigators of all the cohort studies included in this analysis for their hard work and dedication in collecting the underlying data. We also thank the study participants, whose time and commitment have transformed our understanding of health and disease.

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Authors and Affiliations

  1. Ministry of Education Key Laboratory of Environment and Health, and State Key Laboratory of Environmental Health (Incubating), School of Public Health, Tongji Medical College, Huazhong University of Science and Technology, No.13 Hangkong Road, Wuhan, 430030, China

    Dankang Li, Lulin Wang & Yaohua Tian

  2. Department of Maternal and Child Health, School of Public Health, Tongji Medical College, Huazhong University of Science and Technology, No.13 Hangkong Road, Wuhan, 430030, China

    Dankang Li, Lulin Wang & Yaohua Tian

  3. Center for Statistics in Medicine, NDORMS, University of Oxford, The Botnar Research Centre, Oxford, UK

    Junqing **e

  4. Department of Otorhinolaryngology, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, 1277 Jiefang Avenue, Wuhan, 430022, China

    Yu Sun

  5. Department of Epidemiology and Biostatistics, School of Public Health, Peking University, No.38 Xueyuan Road, Bei**g, 100191, China

    Yonghua Hu

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Contributions

DKL and YHT designed the study. DKL, JQX, LLW, and YHT conducted the data analysis. DKL drafted the manuscript. YS, YHH, and YHT critically revised the manuscript for important intellectual content. All authors approved the final version of the manuscript. All authors read and approved the final manuscript. The corresponding author attests that all the listed authors meet authorship criteria and that no others meeting the criteria have been omitted.

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Correspondence to Yaohua Tian.

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UK Biobank received ethical approval from the North West Multi-centre Research Ethics Committee (REC reference: 16/NW/0274). All participants provided informed consent to participate. The present analyses were conducted under UK Biobank application number 69741.

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Supplementary Information

Additional file 1: Table S1.

Summary results of SNPs. Table S2. Variables used to create lifestyle score. Table S3. ICD-10 codes to assist in identifying MDD. Table S4. Summary statistics of air pollution data. Table S5. Sensitivity analysis by excluding MDD occurred in the first 2 years of follow-up. Table S6. Sensitivity analysis by excluding participants who live in the current address for less than 5 years. Table S7. Sensitivity analysis by excluding anxiety cases. Table S8. Sensitivity analysis by excluding dementia cases. Table S9. Sensitivity analysis after additional adjustment for other covariates. Table S10. Sensitivity analysis restricted to participants with complete covariates. Table S11. Sensitivity analysis was further linked primary care records. Table S12. Time-varying air pollution exposure and MDD. Table S13. Major principal components and MDD. Table S14. Stratified analysis by age and gender. Table S15. Genetic risk and MDD. Table S16. Lifestyle category and MDD. Table S17. MDD risk according to lifestyle score. Table S18. Stratified analysis by lifestyle factors. Figure S1. Flow chat. Figure S2. The description of time line. Figure S3. Directed Acyclic Graph. Figure S4. Schoenfeld residuals test for PM2.5. Figure S5. Schoenfeld residuals test for PM10. Figure S6. Schoenfeld residuals test for NO2. Figure S7. Schoenfeld residuals test for NOx. Figure S8. Pearson correlations between air pollution. Figure S9. Distribution of MDD genetic risk score.

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Li, D., **e, J., Wang, L. et al. Genetic susceptibility and lifestyle modify the association of long-term air pollution exposure on major depressive disorder: a prospective study in UK Biobank. BMC Med 21, 67 (2023). https://doi.org/10.1186/s12916-023-02783-0

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