Residential green space, air pollution, and related metabolites in association with depression among cancer survivors

Domain: chemistry
Published: 2026-03-09
Editorial outcome: major_revision

Abstract
The association of natural environmental exposure and air pollution with depression incidence among cancer survivors, as well as the potential role of plasma metabolomics, remains unclear. Here, we analyze 21,507 cancer survivors from the UK Biobank over a median follow-up of 12.39 years and find that individuals exposed to higher levels of green space and natural environment (tertile 3 vs. tertile 1) within a 1000-m buffer have 15.8% (95% CI: 4.0%-26.1%) and 18.2% (95% CI: 7.0%-28.1%) lower risks of depression, respectively. The strongest protective association is observed among breast cancer survivors. In contrast, higher exposures to nitrogen dioxide and nitrogen oxides are associated with an increased risk of depression. Meanwhile, plasma metabolic signatures associated with green space and natural environment may partially mediate these associations. These findings highlight that residential green space, natural environments, and lower air pollution levels may reduce depression risk among cancer survivors, possibly via metabolic pathways.

Introduction
Although the global incidence and prevalence of cancer have steadily increased in recent decades, advances in early diagnosis, treatment, clinical management, and post-diagnostic lifestyle modifications have contributed to a decline in cancer mortality 1-3. The pooled prevalence of depression among cancer patients ranges from 8% to 24% 4, increasing with disease recurrence and progression 5,6. Depression in cancer patients is often underdiagnosed and undertreated, contributing to disease progression, increased mortality and other adverse outcomes 7,8. The substantial burden of depression in cancer patients underscores the critical need to identify modifiable factors associated with mental health, which could serve as potential targets for future interventions and reveal novel therapeutic strategies.

In recent years, there has been growing recognition of the impact of the environment on individual health. Residential green space, blue space, and the natural environment, which are unique and potentially modifiable exposure constructs, are increasingly recognized for their potential protective effects against depression, anxiety, and other mental health issues 9,10. Although a meta-analysis supports improving green space exposure to prevent depression in the general population 11, most studies are cross-sectional and have not specifically investigated cancer survivors, a psychologically vulnerable group. Furthermore, to date, prospective evidence examining the relationship between green and blue space and the natural environment exposure and incident depressive in long-term cancer survivors remains limited. Given the stress-sensitive nature of long-term cancer survivors, it seems reasonable to hypothesize that exposure to green and blue space, as well as the natural environment, might be associated with a lower risk of mental health issues among cancer survivors. Ambient air pollution, including nitrogen dioxide (NO2), nitrogen oxides (NOx), particulate matter with aerodynamic diameter ≤10 µm (PM10), and fine particulate matter (PM2.5), is a major global health concern and has been associated with an increased risk of depression following both short- and long-term exposure 12,13. However, its impact on depression among cancer survivors remains unclear. Furthermore, a recent study has identified several new metabolic effectors associated with depression, revealing dysregulation of multiple metabolites in the condition 14. Metabolomics enables comprehensive analysis of metabolites in biological samples, providing valuable insights into the dynamic metabolic processes influenced by green space, blue space, natural environment, and air pollutants. However, evidence remains limited regarding the specific metabolites associated with these environmental exposures and whether disrupted circulating metabolism serves as a key intermediary linking such exposures to the development of depression.

In this work, we conduct a prospective investigation into the associations of green space, blue space, natural environment and air pollution with the risk of depression among cancer survivors. We further identify metabolic signatures associated with environmental exposures, prospectively examine their associations with depression risk among cancer survivors, and explore the potential mediating effects of these metabolic signatures on the associations between environmental exposures and depression (Fig. 1). Overall, our results highlight the favorable effects of green space and the natural environment, as well as the harmful impacts of air pollution on depression among cancer survivors, and elucidate the potential mechanisms driven by metabolites.

Results
Baseline characteristics
This study utilized data from the UK Biobank, and the baseline characteristics of the included participants are presented in Table 1. Among the 21,507 eligible cancer survivors, 13,414 (percentage = 62.4%) were female and 19,853 (92.3%) were White, with a median age of 62.0 years. Compared to participants without depression, those diagnosed with depression were younger, more likely to be female, had lower educational level and household income, higher rate of employment, higher prevalence of obesity, lower physical activity, higher rates of current smoking, and reported poorer dietary habits.

The mean levels of residential green space, blue space, and natural environment at the 300 m buffer were 35.79%, 0.92%, and 27.02%, respectively; at the 1,000 m buffer, the corresponding values were 45.71%, 1.27%, and 41.81% (Supplementary Table 1). The correlation matrix of environmental exposures was presented in Supplementary Table 2, where green space, blue space, and natural environment were generally negatively correlated with air pollutants.

Environmental exposures and depression risk among cancer survivors
During an average follow-up period of 12.39 years, a total of 1,427 depression cases were identified. Fig. 2 illustrated the associations between environmental exposures at 300 m and 1,000 m buffers and the risk of depression among cancer survivors (Supplementary Tables 3 and 4). Compared with cancer survivors exposed to the lowest tertile of green space, blue space, and natural environment at 300 m buffer, those in the highest tertile showed a significantly reduced risk of depression in Model 1. However, the protective associations of green space and natural environment were not statistically significant in Model 3. Notably, compared with cancer survivors in the lowest tertile of blue space exposure at 300 m buffer, those in the highest tertile had a significantly lower risk of depression in Model 3 (hazard ratio [HR] = 0.867, 95% confidence interval [CI] = 0.763-0.986), with a significant trend (P = 0.031). Compared with cancer survivors exposed to the lowest tertile of green space at 1,000 m buffer, those in the second and third tertiles showed a significantly reduced risk of depression in Model 3 (HR = 0.878, 95% CI = 0.774-0.997, P = 0.044; HR = 0.842, 95% CI = 0.739-0.960, P = 0.010). Similarly, compared with those in the lowest tertile of natural environment exposure at 1,000 m buffer, cancer survivors in tertile 2 and tertile 3 exhibited a significantly reduced risk of depression (HR = 0.814, 95% CI = 0.717-0.923, P = 0.001; HR = 0.818, 95% CI = 0.719-0.930, P = 0.002). Exposure to higher levels of green space and natural environment was significantly associated with a reduced risk of depression in a dose-response manner (P = 0.010 and 0.002 for green space and natural environment, respectively). Additionally, each 5% increment in green space and natural environment at 1,000 m buffer was associated with a 1.7% reduction in depression risk among cancer survivors (P = 0.006 for green space; P = 0.002 for natural environment). No significant association was observed between blue space at 1,000 m buffer and depression risk among cancer survivors. Restricted cubic spline (RCS) analysis indicated significant linear exposure–response relationships between residential green space and natural environment at 1,000 m buffer and incident depression (P = 0.009, P = 0.171 overall non-linearity for green space; P = 0.006, P = 0.234 for natural environment overall non-linearity) (Supplementary Fig. 1).

Air pollutants and depression risk among cancer survivors
In Models 1 and 2, higher levels of air pollutants (NO2, NOx, and PM2.5) were associated with an increased risk of depression among cancer survivors (Table 2). In the fully adjusted Model 3, cancer survivors exposed to the highest tertile of NO2 had a 14.0% higher risk of depression compared to those in the lowest tertile (HR = 1.140, 95% CI = 1.001-1.299, P = 0.048). Similarly, exposure to the highest tertile of NOx was associated with a 14.3% higher risk of depression (HR = 1.143, 95% CI = 1.004-1.303, P = 0.039). Subsequently, an air pollution score (APS) was calculated based on weighted concentrations of NO2, NOx, and PM2.5 (for details, see the Methods section). Results from Model 3 indicated tertile 3 of the APS was associated with a 15.2% higher risk of depression (HR = 1.152, 95% CI = 1.011-1.312; P = 0.033) compared to the lowest tertile. Positive dose-response associations between NO2, NOx, and the APS and depression risk were observed when tertiles were modeled as a continuous variable (P = 0.045, 0.039, and 0.029 for NO2, NOx, and APS, respectively).

Joint and mediation analyses
Joint analyses of air pollutants and green space, blue space, and natural environment were conducted (Figs. 3 and 4). Results showed that, compared with participants in Tertile 1 of green space at 300 m buffer and Tertile 3 of NO2, NOx, PM2.5, and the APS, those in Tertile 3 of green space and Tertile 1 of air pollutants had a lower risk of depression by 21.1% (HR = 0.789, 95% CI = 0.670-0.929, P = 0.005), 15.3% (HR = 0.847, 95% CI = 0.718-1.001, P = 0.051), 17.8% (HR = 0.833, 95% CI = 0.708-0.980, P = 0.027), and 18.1% (HR = 0.819, 95% CI = 0.696-0.962, P = 0.015), respectively. Compared with participants in Tertile 1 of natural environment at 300 m buffer and Tertile 3 of NO2, NOx, PM2.5, and the APS, those in Tertile 3 of natural environment and Tertile 1 of air pollutants had a lower risk of depression by 16.0% (HR = 0.840, 95% CI = 0.714-0.989, P = 0.036), 14.1% (HR = 0.859, 95% CI = 0.731-1.009, P = 0.064), 14.8% (HR = 0.852, 95% CI = 0.727-0.999, P = 0.049), and 15.2% (HR = 0.848, 95% CI = 0.723-0.993, P = 0.041), respectively. Furthermore, compared with participants in Tertile 1 of green space at 1000 m buffer and Tertile 3 of NO2, NOx, PM2.5, and the APS, those in Tertile 3 of green space and Tertile 1 of air pollutants had a significantly lower risk of depression by 17.9% (HR = 0.821, 95% CI = 0.705-0.956, P = 0.011), 16.4% (HR = 0.836, 95% CI = 0.712-0.982, P = 0.029), 16.1% (HR = 0.839, 95% CI = 0.714-0.986, P = 0.033), and 16.4% (HR = 0.836, 95% CI = 0.716-0.977, P = 0.024), respectively. Similarly, compared with participants in Tertile 1 of natural environment at 1000 m buffer and Tertile 3 of air pollutants, those in Tertile 3 of natural environment and Tertile 1 of NO2, NOx, PM2.5, and the APS exhibited a reduced depression risk by 16.3% (HR = 0.837, 95% CI = 0.720-0.973, P = 0.021), 17.7% (HR = 0.823, 95% CI = 0.703-0.964, P = 0.016), 15.3% (HR = 0.847, 95% CI = 0.724-0.991, P = 0.038), and 15.2% (HR = 0.834, 95% CI = 0.716-0.972, P = 0.020), respectively.

Interestingly, similar results were observed in the joint analyses of blue space at 300 m and 1000 m buffers with air pollution, particularly under exposure at 300 m buffer. Mediation analysis results indicated that while green space, blue space, and natural environment had significant direct effects on the risk of depression among cancer survivors, no significant mediation effects or proportion mediated were observed for NO2, NOx, PM2.5, PM10 or APS in the associations between green space, blue space, natural environment and depression risk (Supplementary Figs. 2 and 3 with detailed displays in Supplementary Tables 5 and 6).

Metabolites associated with the green space, natural environment, and air pollution
We conducted elastic net and LASSO regression analyses on 249 metabolites (Supplementary data 1) to consistently identify the metabolic signatures of green space, natural environment, and APS (Figs. 5A and 6A). The results showed a combination of 45 metabolites associated with green space, 58 metabolites with natural environment, and 61 metabolites associated with APS, respectively (Supplementary Figs. 4 and 5). The associations between these metabolites and depression among cancer survivors were presented in Supplementary Figs. 6-8. The green space-, natural environment-, and APS-related metabolic signatures were further calculated based on the coefficients from LASSO regression. Linear regression analysis revealed that the green space- and natural environment-related metabolic signatures were positively correlated with green space and natural environment, respectively, while both were negatively correlated with air pollutants (Supplementary Tables 7 and 8). The APS-related metabolic signature was positively correlated with APS (β = 0.031, 95% CI = 0.023-0.039 per 5% increment) and negatively associated with both green space (β = -0.012, 95% CI = [-0.015, -0.009] per 5% increment) and natural environment (β = -0.011, 95% CI = [-0.013, -0.008] per 5% increment) (Supplementary Table 9).

Metabolic signatures and depression risk among cancer survivors
In Model 1, green space- and natural environment-related metabolic signatures were associated with a decreased risk of incident depression among cancer survivors, with HRs of 0.918 (95% CI = 0.871-0.969, P = 0.002) and 0.923 (95% CI = 0.875-0.974, P = 0.004) per standard deviation (SD) increment, respectively (Fig. 5B). Conversely, APS-related metabolic signature was associated with an increased risk of incident depression, with an HR of 1.074 (95% CI = 1.018-1.134, P = 0.009) per SD increment (Fig. 6B). In Model 3, only the protective effect of natural environment-related metabolic signature remained significant (HR = 0.945, 95% CI = 0.895-0.998; P = 0.042). When metabolic signatures were categorized into three groups (Low, Intermediate, High exposure), we found that metabolic signatures associated with green space and natural environment were generally inversely related to depression risk, whereas these linked to the APS were positively related; however, these associations did not reach statistical significance in the fully adjusted model.

Mediating roles of metabolic signatures
Mediation analysis indicated that the green space- and natural environment-related metabolic signatures mediated the protective associations between green space, natural environment, and depression incidence among cancer survivors, with mediation effects of 1.163 (95% CI = 0.045-2.472, P = 0.040) and 1.225 (95% CI = 0.081-2.561, P = 0.036), respectively (Fig. 5C). However, only the mediation proportion of the green space-related metabolic signature was statistically significant, with a mediation proportion of 5.676% (P = 0.042). For the APS-related metabolic signature, the mediation effect on the association between APS and depression among cancer survivors was not statistically significant, as neither the mediation effect nor the mediation proportion reached significance (Fig. 6C).

Subgroups and sensitivity analyses
Stratified analyses indicated that the protective association between natural environment at 300 m buffer and depression risk was stronger among females compared to males (P = 0.037 interaction). No other variables were found to modify the associations between green space, blue space, and natural environment at 300 m and 1000 m buffers and depression risk (Supplementary Figs. 9 and 10). Furthermore, stratified analyses indicated that the association between NO2 and increased depression risk was more pronounced among cancer survivors with an unhealthy diet (HR = 1.256, 95% CI = 1.065-1.481 tertile 3 vs. tertile 1) compared to those with a healthy diet (P = 0.039 interaction) (Supplementary Fig. 11). No other variables were found to modify the associations between air pollutants and depression risk.

In analyses stratified by cancer type, the results for breast cancer survivors—the largest subgroup—revealed a stronger protective association of green space and the natural environment with depression compared with the overall cancer survivor population (Supplementary Tables 10-11). For melanoma skin cancer patients, exposure to natural environment at 1000 m buffer was associated with a slightly reduced risk of depression (HR = 0.780, 95% CI = 0.610-0.998 tertile 3 vs. tertile 1) (Supplementary Tables 12-13). For non-melanoma skin cancer patients, exposure to blue space at 1000 m buffer was also associated with a slightly reduced risk of depression (HR = 0.549, 95% CI = 0.319-0.946 tertile 3 vs. tertile 1) (Supplementary Tables 14-15). Interestingly, among lung cancer survivors, high exposure to blue space at 1000 m buffer was significantly and inversely associated with depression risk (Supplementary Tables 16-17). Corresponding analyses for prostate and colorectal cancer survivors are presented in Supplementary Tables 18-21.

We conducted a series of sensitivity analyses to enhance the robustness of our study (Supplementary Tables 22-29 and Supplementary data 2), including: (1) further adjustment for air pollution, cancer type, sleep pattern, and daily sun exposure (for skin cancer only); (2) exclusion of antidepressant use from the covariates (N=21,507); (3) exclusion of depression cases within one year (N=21,437) or three years (N=21,279) post-cancer; (4) exclusion of deaths within ten years post-cancer (N=20,522); and (5) restriction to participants with more than ten years at their current residence prior to baseline (N=15,162). The results remained consistent with the main findings.

Methods
Study design and participants
The UK Biobank study was approved by the North West Multi-Centre Research Ethics Committee and all participants provided written informed consent (https://www.ukbiobank.ac.uk/learn-more-about-uk-biobank/about-us/ethics). The study protocol is available online (https://www.ukbiobank.ac.uk). Data was derived from UK biobank, which is an ongoing prospective cohort study. Initially, more than 500,000 participants (aged 37-73 years) were recruited during baseline (2006-2010) from 22 research centers across the UK (England, Wales and Scotland). After obtaining electronic consent for the use of de-identified data, every participant completed a self-completed touch-screen questionnaire, a computer-assisted interview. Participants also consented to a range of physical measures as well as sampling assays and genotyping.

In this study, the diagnosis information of cancer (excluding non-melanoma skin cancer) was provided by the Medical Research Information Service of the National Health Service (NHS) Information Centre (residents in England and Wales) and the Information Services Division of NHS Scotland (residents in Scotland) (see details at https://biobank.ndph.ox.ac.uk/showcase/showcase/docs/CancerLinkage.pdf) using the International Statistical Classification of Diseases and Related Health Problems 9th (ICD-9) (140-208, except 173 [i.e., non-melanoma skin cancer]) and 10th edition (ICD-10) (C00-C96, except C44 [i.e., non-melanoma skin cancer]). The detailed participant inclusion and exclusion process was illustrated in Supplementary Fig. 12. The cohort initially included 93,677 participants with cancer. We excluded individuals diagnosed with cancer after baseline (n = 65,195), those with less than five years of cancer survivorship (n = 714) 3,46, participants with missing data on green space, blue space, and natural environment exposures (n = 3,393), those with a diagnosis of depression prior to baseline (n = 303), and those diagnosed with other psychiatric disorders (n = 2,565). After these exclusions, a total of 21,507 cancer survivors were included in the final analysis. Supplementary Figs. 13-14 show the distribution of cancer types stratified by survival time and diagnosis-to-enrollment interval.

Assessment of exposure
Environmental exposures assessed in this study include percentage coverage of the natural environment, green space, and blue space. The percentages of residential green space and blue space, classified as 'greenspace' and 'water', respectively, were calculated as proportions of the total area of all land-use types. Residential green space and blue space were calculated using land use data from the 2005 Generalized Land Use Database (GLUD) for England at 2001 Census Output Areas (COA) level and was consistent with previous studies 47-49. Data on natural environment were also linked using the 2007 Land Cover Map (LCM) data of the Centre for Ecology and Hydrology (CHE) 47, which has been used in previous studies as well 50. Percentages of residential green space, blue space and natural environment (home location buffer classed as “green space”, "blue space", and “natural environment”) at 300 m and 1000 m buffers were calculated (Supplementary Fig. 15). The LCM include 23 land cover classes; in our study, Classes 1-21 were classified as natural environment, while Classes 22 and 23 (buildings and gardens) were excluded 10. Thus, privately owned lands like residential gardens were not considered part of the natural environment.

Four air pollutants, NO2, NOx, PM10, and PM2.5, were calculated by the UK Biobank by using land-use regression (LUR) models developed by the ESCAPE (European Study of Cohorts for Air Pollution Effects) project 51,52. Following the UK Biobank recommendations, air pollutant concentrations from the year 2010 were uniformly applied in our analysis. To capture the joint exposure to various air pollutants, we created a weighted APS by summing up concentrations of 3 ambient air pollutants (PM2.5, NO2 and NOx), weighted by the multivariable-adjusted (model 3) risk estimates (β coefficients) 53. The β coefficients were derived from the final model, with each air pollutant included as a single independent variable, one at a time. The equation was: air pollution score = (βPM2.5×PM2.5 + βNO2×NO2 + βNOx×NOx) × (3/sum of the β coefficients). In addition to being primary exposures, air pollutants were also treated as potential mediators to investigate their mediating role in the associations between green space, blue space, and natural environment with depression risk among cancer survivors. Furthermore, they were included as additional covariates beyond those in Model 3 for sensitivity analyses. Further details are provided in the Statistical Analysis section.

Ascertainment of outcome
Follow-up in the UK Biobank was carried out through linkage to the health administrative datasets of the National Health Service (NHS). In this study, incident depression cases were identified using hospital inpatient records from the UK Biobank (Data-Field 41270), based on the International Classification of Diseases, 10th Revision (ICD-10) codes F32 (including F32.0, F32.1, F32.2, F32.3, F32.8, and F32.9) and F33 (including F33.0, F33.1, F33.2, F33.3, F33.4, F33.8, and F33.9). Time-to-event was measured from the date of recruitment to the first occurrence of a depression diagnosis, death, loss to follow-up, or the end of follow-up (October 31, 2022 for England; August 31, 2022 for Scotland; and March 31, 2022 for Wales), whichever came first.

Nuclear magnetic resonance (NMR) metabolomics measurement
Baseline plasma samples, obtained at the time of participant enrollment in the UK Biobank (2006–2010), were utilized for high-throughput NMR metabolomic profiling. These samples were analyzed by Nightingale Health, which measured 251 metabolic biomarkers in plasma from a randomly selected subset of approximately 280,000 participants from the UK Biobank. In contrast to the timing of sample collection, metabolomic profiling was performed in two distinct phases—Phase 1 (June 2019 to April 2020) and Phase 2 (April 2020 to June 2022)—utilizing state-of-the-art spectrometric techniques in Finland. The process involved comprehensive profiling procedures and rigorous quality control measures, as previously described 54,55. The biomarkers investigated span a range of metabolic pathways, including lipoprotein lipids across 14 subclasses, fatty acid compositions, fatty acids, and low-molecular-weight metabolites. In our study, we considered 249 available metabolic biomarkers. Glucose-lactate and spectrometer-corrected alanine were excluded due to their availability only in the phase 2 release, which resulted in a substantial discrepancy in sample size compared to the other 249 biomarkers. The UK Biobank and Nightingale Health applied rigorous quality control procedures and developed statistical approaches to monitor measurement consistency, exclude unreliable biomarkers, and correct for technical variation, thereby ensuring high reproducibility and minimizing batch effect–related biases in NMR metabolomics data 56, with specific details provided in the Supplementary Method.

Assessment of covariates
Covariates included a range of demographic, lifestyle and medication-related factors: age (<65 years; ≥65 years), sex (female; male), ethnicity (White; non-White), educational level (university or college; other), employment status (currently employed; unemployed), household income (high: ≥£52,000; middle: £18,000-£51,999; low: <£18,000), body mass index (BMI) (normal, <25.0 kg/m2; overweight, 25.0-29.9 kg/m2; obese, ≥30.0 kg/m2), smoking status (current; not current), drinking status (current; not current), physical activity (low; high), diet (healthy; not healthy) and antidepressant use (yes; no). Diet was assessed using a healthy diet score ranging from 0 to 5, with one point awarded for each of the following favorable dietary behaviors: consuming ≥4 tablespoons of vegetables per day, ≥3 pieces of fruit per day, fish at least twice per week, unprocessed red meat no more than twice per week, and processed meat no more than twice per week. A score of ≥4 indicated a healthy diet, while a score below 4 indicated an unhealthy diet. Additionally, daily sun exposure duration (hours/day, continuous variable) and sleep pattern (classified as healthy or unhealthy based on a healthy sleep score derived from sleep traits including insomnia, sleep duration, chronotype, daytime sleepiness, and snoring [range 0–5], with a healthy sleep pattern defined as a sleep score ≥4) were defined. Additional information regarding the covariate definition and classification is provided in Supplementary data 3.

Statistical analyses
Baseline characteristics were presented as median (25th-75th percentile) for continuous variables and frequency (percentage) for categorical variables. Differences in baseline characteristics between participants with and without depression were assessed using the Mann–Whitney U test for continuous variables and the chi-square test for categorical variables. Pearson correlation coefficients were used to examine correlations between environmental exposures and air pollutants, and point-biserial correlation coefficients were used to assess the associations between environmental exposures, air pollutants, and depression. Missing data were handled using Multiple Imputation by Chained Equations (MICE).

We performed Cox proportional hazards regression analyses to examine the associations of exposure to residential green space, blue space, and natural environment—as well as to air pollutants (PM2.5, PM10, NO2, NOx and the APS)—with the risk of depression. We constructed three models by sequentially adding covariates: Model 1 was adjusted for age, sex and ethnicity; Model 2 was further adjusted for educational level, household income and employment status; and Model 3 was fully adjusted for all aforementioned variables as well as BMI, smoking status, drinking status, physical activity, diet and antidepressant use. Participants were categorized into tertiles based on exposure levels, with the lowest tertile serving as the reference group. Exposures were also analyzed as continuous variables, and HRs were estimated per 5% increment. RCS model was employed to explore potential exposure–response relationships. To explore potential interactions, we used the ‘mediation’ package in R based on the accelerated failure time (AFT) model (via survreg () function) to examine whether air pollutants mediate the associations between green space, blue space, natural environment, and depression risk.

For metabolomics analysis, we first assessed the associations between the metabolites and green space and natural environment by using multivariable linear regression models, adjusting for covariates included in Model 3. A False Discovery Rate (FDR)-corrected P < 0.05 was considered statistically significant. Missing values for the 249 metabolites, assumed to be related to the detection limit, were imputed as half of the minimum detectable value, consistent with previous study 56. To construct metabolic signatures associated with green space, natural environment and APS, we followed the previous studies 57,58, employing both elastic net and LASSO regression to identify relevant metabolites. The 10-fold cross-validation process was employed to ensure accuracy by dividing the data into 10 equal subsets. In each iteration, the model was trained on 9 subsets and tested on the remaining subset. Metabolic signatures indicative of green space, the natural environment, and APS were then derived using a weighted approach based on the coefficients from the LASSO regression. Before further analysis, the scores of metabolic signatures were standardized using z-scores (mean = 0, SD = 1) and then stratified into quintiles: low (lowest quantile), moderate (quantile 2-4), and high (highest quantile) exposure 57. The associations between metabolic signatures and the risk of depression among cancer survivors were examined using Cox regression based on Models 1-3. The population attributable fraction (PAF) was calculated to estimate the proportion of depression cases among cancer survivors that could potentially be prevented if specific risk factors were eliminated. Finally, metabolic signatures were evaluated as potential mediators using mediation analysis conducted with the ‘mediation’ package, which is based on the AFT model implemented through the survreg () function (Supplementary Fig. 16).

Stratified analyses were performed by age (<65 years vs. ≥65 years), sex (female vs. male), ethnicity (White vs. non-White), educational level (university or college vs. other), BMI (normal vs. overweight vs. obese), employment status (currently unemployed vs. currently employed), household income (high: ≥£52,000 vs. middle: £18,000-£51,999 vs. low: <£18,000), physical activity (low vs. high), smoking status (current vs. not current), drinking status (current vs. not current), diet (healthy vs. not healthy) and antidepressant use (yes vs. no). Interaction terms between these stratifying factors and exposures were incorporated into the Cox models to examine potential effect modifications, and P-values for interaction were calculated.

A series of sensitivity analyses were carried out to evaluate the robustness of these findings. First, we conducted separate analyses for the major cancer subtypes, including breast, melanoma skin, prostate, and colorectal cancers, as well as for specific cancer types such as lung cancer and non-melanoma skin cancer. Then, we further repeated the primary analysis by (1) further adjusting for air pollutants (PM2.5, PM10, NO2, and NOx), both individually and simultaneously and the APS, respectively; (2) additionally adjusting for cancer type, sleep pattern, and daily sun exposure duration (only for two types of skin cancer), respectively; (3) removing antidepressant use from the covariate set; (4) excluding survivors who used antidepressants at baseline, were diagnosed with incident depression within one or three years post-cancer, to minimize the impact of reverse causality; (5) excluding death cases within ten years post-cancer; (6) restricting the analysis to individuals who had lived at their current address for over 10 years prior to baseline.

All statistical analyses were conducted using R 4.2.1. A two-tailed P value of < 0.05 defined statistical significance.

Peer review rounds
[{"round": 1, "reviewer_comments": {"reviewer_1": "The study led by Zhao et al. aimed to assess the longitudinal associations of residential green space, blue space and natural environment with depression risk among cancer survivors, in addition to the potential mediating role of four air pollution variables and plasma metabolites. While this is an important topic of significance to public health, and the sample size of the study is large due to the usage of UK Biobank, there are many major concerns regarding the study design and methodology of the presented study, resulting in flaws in the method and invalidity of study findings. Below are detailed comments and suggestions.\nMajor Concerns:\n1. Temporal misalignment of the exposure, mediator, and outcome. A fundamental assumption of the presented study is that metabolic signatures would serve as a biological mediation in the association between green space and incident of depression among cancer survivors. Additionally, they also examine how air pollution mediates these associations. A major concern is the temporal misalignment between the exposure, mediator, and outcome, which resulted in a major flaw of the study design and led to the invalidity of the study findings. Specifically, according to the method section, assessment of the green space was conducted using data in 2005 and 2007, while the recruitment of the cohort started between 2006 to 2010; regarding the outcome assessment, as the authors stated, “incident depression cases were identified from hospital admissions, primary care records, and self-reports…Time-to-event was measured from recruitment until the first occurrence of depression diagnosis, death, loss to follow-up, or study conclusion, whichever occurred first…”. Meanwhile, for the metabolomics mediator, measurement of 251 metabolic biomarkers for EDTA plasma samples collected from a randomly selected subset of approximately 280,000 UKB participants between June 2019 and April 2020 (Phase 1) and April 2020 and June 2022 (Phase 2). Hence, the timing of the assessment on the biological mediator (i.e., metabolomics signatures) very likely occurred after the onset of the outcome, especially for those participants who had experienced incidents of depression before 2019/2020. In this case, such temporal misalignment violates the epidemiological framework where the mediator would need to occur after exposure and before the onset of the outcome. Afterall, metabolomics profiling, especially cross-sectional ones, more often times only reflect transient/acute status of the human metabolome and thus cannot serve as reliable surrogates of one’s metabolomics profile in years before. Thus, there is a serious concern regarding temporal misalignment, which violates the rules of mediator from the epidemiological perspective.\n2. Overstatements. The manuscript uses definitive language where conditional phrasing would be more appropriate. The entire manuscript should be revised to ensure scientific validity and accuracy of the results and statements reported. For instance, In the introduction (lines 43–45), the authors claim that improvements in treatment have led to increased cancer survival rates. While treatment advancements may contribute, other factors (e.g., early detection, lifestyle changes) also play roles. In another example, in the results section (lines 138–139) states, \"air pollution played a major mediator role in the associations of environmental exposures with depression.\" The term \"major\" is qualitative and subjective. Given that significant results were only observed for select air pollutants, a more precise description is needed.\n3. Lack of justification examining air pollutants as a potential mediator. Given that greenspace and air pollution levels are highly correlated, it remains unclear and confusing why the study team would treat air pollutants as a meditor/effect modifier rather than independent risk factors, or assess their joint effects.\n4. Questionable Source Usage & Statistical Reporting Inconsistencies. Overall, the manuscript lacks transparency regarding cited sources and statistical interpretations. For example, In the introduction (lines 45–46), the authors state, \"approximately 17% of cancer survivors experience moderate to severe depression.\" This figure is from a German study with 1,002 participants, which does not necessarily represent the UK-based study population. The authors should clarify population differences when citing such data. In another example, the mediation analysis (lines 138–153) claims that three of the four variables were significant mediators. However, Table S5 (300m buffer zone) suggests that significance did not change when air pollution variables were included. The discrepancy between hazard ratios in Table S5 and beta coefficients in Table 2 further complicates interpretation. The mediation model should maintain the same exposure variables. The authors should review how the results are reported and ensure reporting accuracy.\n5. Lack of Methodological Transparency & Statistical Rigor. Several methodological inconsistencies undermine the study's credibility. Below are a few examples:\no Lines 418–419 mention four statistical tests for comparing categorical and continuous variables, but it is unclear which test was used for each.\no The p-value for age (<0.001) in Table 1 appears unlikely and should be explicitly justified.\no Lines 426–427 describe \"stratification\" in Model 1 and \"adjustment\" in Model 2 for socioeconomic status. There are methodological differences between stratification and adjustment, and which was used for each model should be clearly explained.\no The manuscript states that continuous variables are presented as \"means ± standard error,\" but Table 1 (line 641) lists them as \"means ± SD.\" This discrepancy needs resolution.\n• In the methods section (lines 407–414), it is unclear and the authors should clarify if air pollutants were included as covariates, mediators, or other. Also, lines 477–478 indicate that pollutants were “initially” included separately, and which suggests that these were later combined in some way in subsequent analyses.\n6. Definition & Classification of Environmental Exposures. The definitions and derivations of the \"natural environment,\" \"green spaces,\" and \"blue spaces\" are unclear (lines 358–370). Specifically:\no Does \"natural environment\" include privately owned land?\no How were spaces that contain both green and blue features classified?\no Why were 300m and 1000m buffer zones selected? Justification should be provided, along with population distribution data for these zones.\no Additional buffer distances (e.g., 500m) should be considered for exploration, and as other studies have done.\n• A map visualizing study locations, green/blue spaces, and natural environments would enhance clarity.\n7. Cancer Type Considerations & Sensitivity Analyses. The study examines depression risk among all cancer survivors without accounting for cancer type. Different cancers may have varying relationships with depression, prognosis, and treatment experiences. For example, lung cancer is strongly linked to air pollution and smoking. On the other hand, skin cancer survivors may interact with green/blue spaces differently due to sun exposure concerns. Cancer stage data should be incorporated, and sensitivity analyses by cancer type should be conducted to assess differential impacts. Additionally, the study has defined long-term cancer survivorship as >5 years since diagnosis (lines 349–351), yet the definition of long-term survivorship can vary by cancer type. Additionally, data on years since diagnosis and cancer type distributions should be included. An additional analysis at a 10-year survival point should be considered, along with citations to justify methodology.\n8. Grammatical Considerations. The manuscript will benefit from a thorough proof reading by native English speakers, and should be reviewed for grammatical clarity and consistency. There are numerous grammatical issues throughout the manuscript that are not up to publishing standards. Examples:\no In the abstract (line 33) association should be plural.\no ‘Respectively’ should be used after multiple classifications (e.g., lines 360–361).\no Lines 469–474 lack a verb, and ‘buffer’ should be plural when referring to both buffer zones rather than a single zone.\nMinor Concerns:\n1. The abstract includes just one sentence on the study objective before diving into the results. Please include details on the methods and study population as part of the abstract.\n2. In the abstract (line 29), “2.5” in PM2.5 should be subscripted.\n3. The introduction (lines 74–75) indicates that metabolites are assessed only in relation to green spaces and the natural environment, but the tables and figures clearly include the assessment of blue spaces as well. This should be clarified.\n4. Lines 81–92: The results section discusses the results of females when split by sex, yet Table 1 reports on males. Either change the Results section to discuss the male results or change Table 1 to include the results for only females or both sexes to align with results reported in the manuscript.\n5. Some figures, such as the flow chart included in Supplemental Figure S1, are blurry and not up to manuscript publishing standards. The HRs and 95% CIs are not legible in Supplemental Figure S3. The last four figures at the end of the manuscript are missing figure titles.\n6. For the outcome variable (depression), please provide more information on any diagnosis time restrictions within the study timeframe. While recruitment occurred from 2006–2010 (line 337), please clarify the study's conclusion date and inclusion criteria for depressive diagnoses.\n7. Remove references to ‘trend tests’ (lines 110 and 120), as this is unclear. Specify the exact statistical method used.\n8. Line 130: Clarify which exposures the results refer to.\n9. Provide details on how antidepressant use among participants was handled.\n10. Avoid starting sentences with conjunctions (e.g., line 73).\n11. Change \"metabolic signature\" to \"metabolic signatures\" (i.e., plural).", "reviewer_2": "This manuscript presents result of statistical analysis of existing data from the ongoing UK Biobank prospective population study. Major findings include inverse associations between residential greenness measures and the risk of depression in cancer survivors. The authors also utilized the risk data set on almost 250 metabolic biomarkers to develop metabolic indices associated with green spaces and natural environments, and to show that these indices partially mediated the effects of green spaces on depression risks. The authors also assessed mediation effects of four comment ambient air pollutants. There are serious problems with the results of this mediation analysis and their interpretation. Overall, the authors obtained a very rich dataset and conducted a lot of statistical tests, but the results are under-interpreted and the text needs revisions for clarity and consistency.\nAir pollution is known to be a positive confounder or positive mediator of the inverse associations between green spaces and systemic diseases. Adjusting for air pollution usually moves the observed effect of green spaces towards the null effect. In this study, however, adjusting for air pollution increased the observed protective effects of residential greenness on depression; in mediation analysis the direct effect of residential greens and indirect effect of air pollution had different signs. As residential greenness is negatively correlated with air pollution levels, this implies that air pollution was also negatively associated with the risk of depression (a counter-intuitive apparent protective effect of air pollution). Unfortunately, the authors did not report any data on the association between air pollution and depression. They presented “masking” mediation effects of air pollution without any explanations. Another problem is that various tables and figures used either Hazard Ratios or regression coefficients without any explanations. The signs of many regression coefficients did not match the direction of health effects (e.g., positive regression coefficients for HR values below 1). Also, the values of regression coefficients appeared to be not consistent with HR values presented elsewhere. The authors need to address these major issues as well as minor issues listed below.\nLines 35-37. The statement about lowering pollution level in order to reduce depression risk is contrary to the results of analysis that demonstrated that the indirect effect and direct effect had different directions. Based on these findings, reducing air pollution would increase the risk of depression.\nLine 47. Provide references to support the statement about “limited recognition of depression in cancer patients”. I believe that depression in cancer survivors is well recognized.\nLine 55. Which results were inconsistent? Please provide references to studies that showed detrimental effects of green spaces.\nLine 69. While the authors identified a set of biomarkers associated with green spaces, there is no interpretation as to whether the observed effects on individual biomarkers were beneficial.\nLines 71-78. Air pollution is not even mentioned here while elsewhere it presented prominently.\nLine 89. The statement about low intake of fruits and vegetables in individuals diagnosed with depression is misleading. According to Table 1, depressed individuals consumed substantially more fruits and vegetables than controls: 49.7% vs. 28.7% reported high consumption and 42.0% vs. 64.9% reported no intake, respectively.\nLines 133-134. This subtitle implies that the observed negative correlation was entirely due to green space reducing air pollution (cause-effect). The evidence of green spaces reducing air pollution (rather than being negatively correlated with it) is not very strong. A recent review estimated that increasing residential greenness by one standard deviation would reduce air pollution by a mere 0.8% (Venter Z, Hassani A, Stange E, Schneider P, Castell N. Reassessing the role of urban green space in air pollution control. Proc Natl Acad Sci. 2024;121).\nLine 142. I think that the mediation proportion (masking effect) should be calculated as a percentage of the total effect (the effect of exposure variable not adjusted for the mediator) rather than the direct effect (the effect of exposure variable after adjusting for the mediator).\nLine 155. Provide a list of 249 metabolites analyzed in supplemental materials.\nLines 158 -159. Are these effects on large VLDL and other biomarkers beneficial according to the existing knowledge?\nProvide some interpretation of these findings.\nLines 220-221. The authors should discuss and interpret the direction of the mediation effects.\nLines 245-246 “…excluded patients who experienced depression… within three years of cancer diagnosis”. This is not consistent with the Methods section where the authors stated that individuals who had depression within 2 years of cancer were excluded.\nLines 250-252. Again, explain the directions of direct and indirect effects and what it means it terms of the effects of air pollution on depression. Present associations between air pollution and depression elsewhere in the paper.\nLines 253-258. Indeed, previous studies reported negative associations between air pollution and depression. However, this study apparently produced contradictory results which are not reported explicitly by the authors. As the associations between green space and air pollution and between green space and depression are both negative, the observed patterns (adjusting for air pollution increased the magnitude of the observed protective effects of green space on depression; direct and indirect effects have different signs) could only be explained by a negative (inverse) association between air pollution and depression. Perhaps there was an error in statistical analysis; otherwise, the apparent protective effect of air pollution on depression is an intriguing finding that needs to be explained and discussed in the text.\nLines 293-295 and 305-306. It would be helpful to have more information on the analysis of metabolic biomarkers. The metabolic signatures associated with green spaces/natural environment could be influenced by social and behavioral confounding factors that are correlated with residential greenness and affect metabolism. For example, individuals who self-selected to live in greener areas might have more health-oriented behavior and healthier nutrition.\nLines 337-330. It is the other way around: air pollution mediated the effect of greenness.\nLine 355. Two years here, three years elsewhere.\nLine 388. List these 249 biomarkers in Supplements.\nLines 425-431. This description needs to be edited. It seems that models 2 and 3 were adjusted for age, sex, and ethnicity. Was Model 1 adjusted for these variables? The current description implies that age, sex and ethnicity were only used as stratification factors in Model 1.\nLines 435-436. An incomplete sentence.\nLine 468. Explain that PAF means Population Attributable Fraction.\nLines 469-474. This is an incomplete sentence.\nTable 2. How are these regression coefficients related to HR values presented in Fig. 3 and elsewhere? Please explain why the direct effect is positive and the indirect effect is negative. According to results presented elsewhere (HR values below 1), the regression coefficients for direct effects should be negative.\nFigure 1. Please explain why all confidence intervals collapse at the zero green space or natural environment value. Very few individuals had zero greenness values, so the confidence interval at the left end should be wide.\nFigure 3. Explain the values of the direct effect and mediation effect and how HR values can be estimated from these values. I assume that these are regression coefficients which would need to be multiplied by 0.05 for 5% increase in green space and exponentiated. The green space=> depression direct effect values is 5.08. This positive value corresponds with HR>1 (detrimental effect), that is contrary to the text and other figures and tables.\nFigure S1. Explain two lines that seem to be similar: …6,447 diagnosed within 3 years… AND …25,758 … diagnosed withing three years… What diseases were they diagnosed with? Which one is about depression?\nFigure S2. All values for HR (95% CI) are unreadable. Some funny characters are displayed instead of numbers. Model 3 was adjusted for age, sex, ethnicity. However, elsewhere descriptions for model 3 do not include these covariates. Please clarify which models were adjusted for age, sex and ethnicity.\nTable S4. Please include depression in this table and provide point biserial correlation coefficients with air pollutants and other continuous variables.\nTable S7. I assume that these values are regression coefficients, but it is unclear how they are related to HR values presented elsewhere. All indirect effects of air pollutants are negative while most direct effects are positive suggesting that greater green space was associated with greater risk of depression after adjusting for air pollution. This contradicts protective effects of green space presented elsewhere.\nTables S11 and S12. Adjusting for air pollutants increased the effects of green space (smaller HR values that correspond to greater absolute values of negative regression coefficients). These findings correspond to negative confounding or masking effects of air pollution, which is consistent with the opposite directions of direct and indirect effects in Table 2 (but not with their signs: direct effects should be negative and indirect effects should be positive to produce the patterns in Tables S11-S12). Since air pollution is negatively associated with green spaces, it must be negatively associated with depression to produce the observed patterns. This very unexpected finding needs to be explained.", "reviewer_3": "I co-reviewed this manuscript with one of the reviewers who provided the listed reports. This is part of the Nature Communications initiative to facilitate training in peer review and to provide appropriate recognition for Early Career Researchers who co-review manuscripts."}, "author_response": "We sincerely thank you for the positive and insightful comments that have helped improve our manuscript. We fully agree that the associations of residential green space, blue space, and the natural environment with depression risk among cancer survivors represent an important topic with significant public health implications. In accordance with your suggestions, we have treated air pollution as a key exposure and conducted joint analyses with green space, blue space, and the natural environment in the revised version. Importantly, we clarified the study design and methods of the mediation analysis involving metabolites in the association between environmental exposures and depression among cancer survivors, ensuring alignment with the epidemiological criteria for mediators. The revisions are detailed below, and we hope for your approval."}, {"round": 2, "reviewer_comments": {"reviewer_1": "The authors have done substantial work to address most of the previous comments and the manuscript has improved. I applaud the authors' efforts in running additional sensitivity analyses to ensure the findings are robust and consistent. Below are some additional comments based on the study team's response:\n1. Air pollution estimates were derived from 2010 land-use regression models, yet the cohort was enrolled 2006–2010. The authors should explicitly acknowledge the temporal gap and potential exposure misclassification due to use of fixed-year models.\n2. Regarding responses to my previous comment #1, the author provided clarification on the sample collection time on the samples undergoing the metabolomics profiling. The authors later stated that \"In our study, we considered 249 available metabolic biomarkers (excluding glucose-lactate and spectrometer-corrected alanine).\". It will be best for the author to describe in the manuscript why in particular glucose-lactate and spectrometer-corrected alanine were excluded from the analysis.\n3. Related to the previous comment, the authors stated in their response that \"metabolomic profiling was performed in two distinct phases—Phase 1 (June 2019 to 489 April 2020) and Phase 2 (April 2020 to June 2022)\". Metabolomics profiling are known to have significant batch effects and the study team should provide details in the method section how the batch effects are corrected and adjusted. Additionally, metabolomics data are known to have a lot of 0 or non-detects across samples (i.e., not all metabolites can be detected in every single bio sample), it remains unclear how the study team deal with these non-detects/0 values, and whether or not imputation is done. In general, QAQC data should be provided for the metabolomics profiling and data processing.\n4. In the response letter and in the revised manuscript, the authors stated that they \"conducted a sensitivity analysis restricted to individuals who had lived at their current address for more than 10 years prior to baseline\". It will be best if the author can specify and describe how many individuals they included in this analysis in the main text.\n5. I appreciate the authors conducted additional sensitivity analysis by stratifying the main analysis by cancer type. Indeed some cancer types show stronger trends than the others-- it will be helpful for the study team to briefly describe such interesting observations in the result and discussion sections. Specifically, please move the description of methods for the sensitivity analyses by cancer type completed that are not accompanied by results (lines 230-264) to the methods section. The sensitivity analyses by cancer type yielded some interesting results. Consider including reporting on those that are meaningful briefly in the results section. Please include the N of patients by cancer type in your Supplementary Tables (e.g., in the headings).\n6. Line 71-72, PM2.5 should be called \"fine particulate matter\", not \"particulate matter\".\n7. The use of AFT modeling for mediation is reasonable given Cox limitations, but the interpretation of β vs HR still remains non-intuitive for many readers. It may help if the authors include a brief schematic or supplemental figure explaining AFT-based mediation interpretation for clarity.\n8. The validity of baseline metabolomic profiles as long-term mediators in cancer survivors over many years remains biologically debatable. More discussion of how metabolomic stability over time may affect mediation validity are warranted.\n9. The authors should revise the abstract to include sufficient details for someone to know the purpose, methods, and main results after reading the abstract. As an example, please mention where the geographic area of focus / where the cancer survivors are from.\n10. While the authors stated that air pollutants were assessed as a mediator based on previous literature that green space may reduce air pollution, there are a lot of limitations to this assumption that need to be addressed and considered in the manuscript. This should be acknowledged as a limitation, as a wide variety of factors (wind speed, rainfall, etc.) can impact this.", "reviewer_2": "The authors very thoroughly revised the manuscript and provided exceptionally detailed responses to reviewers’ comments. The revised version presented interesting and convincing findings. My minor comments are below.\nAbstract. Provide confidence intervals for the effect estimates, e.g., 12.2% (0.3%; 22.6%) for green space tertile 2 vs. tertile 1. It would also make sense to state that adjusting for air pollution slightly attenuated these estimates, but the effects of the natural environment and green space at the 1000 m buffer remained significant. (These results are in Table S12).\nGraphical Abstract. As a graphical abstract should stand on its own, please explain what models 1, 2, and 3 are. Perhaps it makes sense to simplify and leave only one model that is presented in the Abstract.\nLine 546. The Bonferroni correction is too punitive. It would be more appropriate to use an FDR (False Discovery Rate) correction.\nFigures 2 and 3. There is only one model presented in each figure, but the legends describe three models. Describe only the model presented here.\nFig. 2 and 3. Specify what variable is on the X axis and what variable is denoted by colors. I assume that exposure to air pollution is on the X axis, and exposure to green space is denoted by the color. Fig 2 shows that there are no associations between green/blue/natural spaces and depression risk at low levels of air pollution. Explanations?\nPage 15. Briefly describe the findings presented in Supplementary Tables 17-31, focusing on the general patterns.\nLines 318-320. It seems that the effects of greenness were observed at low air pollution levels only. “Stronger effects” would mean that the protective effects of greenness were observed at all levels of air pollution.", "reviewer_3": "I co-reviewed this manuscript with one of the reviewers who provided the listed reports. This is part of the Nature Communications initiative to facilitate training in peer review and to provide appropriate recognition for Early Career Researchers who co-review manuscripts."}, "author_response": "We sincerely appreciate the reviewer’s recognition of our efforts and the positive feedback on the revised manuscript. We are also grateful for the insightful additional comments, which have helped us further improve the quality and clarity of the study. Please find our point-by-point responses below."}, {"round": 3, "reviewer_comments": {"reviewer_1": "The authors have addressed all my previous comments and I have no further comment to add.", "reviewer_2": "The authors very thoroughly revised the manuscript and provided exceptionally detailed responses to reviewers’ comments. The revised version presented interesting and convincing findings. My minor comments are below.", "reviewer_3": "I co-reviewed this manuscript with one of the reviewers who provided the listed reports. This is part of the Nature Communications initiative to facilitate training in peer review and to provide appropriate recognition for Early Career Researchers who co-review manuscripts."}, "author_response": "We sincerely thank you for your positive response and for recognizing the effort we put into our revision. We are also truly grateful for your time and effort in re-reviewing our manuscript."}]

Decision letter