Evidence map›Paper›PMID 41803118›Full record

ArticleNature communications2026

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

Jianhui Zhao, Jingyu Ye, Erxu Xue, Liying Xu, Jing Sun, Siyun Zhou, Tengfei Li, Haoze Cao, Zhongquan Sun, Weilin Wang and 3 more

Abstract read
In one paragraph

Article in Nature communications, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.

0numbers the graph read from it
0cells of the map it votes in
2citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

2 citing papers in PubMed.

  1. Article
  2. Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

13 authors.

Jianhui Zhao *Department of Big Data in Health Science, The Second Affiliated Hospital, School of Public Health, Zhejiang University School of Medicine, Hangzhou, China.ORCID http://orcid.org/0009-0008-0785-6625
Jingyu Ye *Department of Big Data in Health Science, The Second Affiliated Hospital, School of Public Health, Zhejiang University School of Medicine, Hangzhou, China.
Erxu Xue *Department of Big Data in Health Science, The Second Affiliated Hospital, School of Public Health, Zhejiang University School of Medicine, Hangzhou, China.
Liying XuDepartment of Big Data in Health Science, The Second Affiliated Hospital, School of Public Health, Zhejiang University School of Medicine, Hangzhou, China.
Jing SunDepartment of Big Data in Health Science, The Second Affiliated Hospital, School of Public Health, Zhejiang University School of Medicine, Hangzhou, China.ORCID http://orcid.org/0000-0003-0046-6663
Siyun ZhouDepartment of Big Data in Health Science, The Second Affiliated Hospital, School of Public Health, Zhejiang University School of Medicine, Hangzhou, China.
Tengfei LiDepartment of Big Data in Health Science, The Second Affiliated Hospital, School of Public Health, Zhejiang University School of Medicine, Hangzhou, China.
Haoze CaoDepartment of Big Data in Health Science, The Second Affiliated Hospital, School of Public Health, Zhejiang University School of Medicine, Hangzhou, China.
Zhongquan SunDepartment of Hepatobiliary and Pancreatic Surgery, The Second Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, China.
Weilin WangDepartment of Hepatobiliary and Pancreatic Surgery, The Second Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, China.ORCID http://orcid.org/0000-0001-9432-2649
Yazhou HeDepartment of Oncology, West China School of Public Health and West China Fourth Hospital, Sichuan University, Chengdu, China.
Yuan DingDepartment of Hepatobiliary and Pancreatic Surgery, The Second Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, China. dingyuan@zju.edu.cn.ORCID http://orcid.org/0009-0001-4948-3563
Xue LiDepartment of Big Data in Health Science, The Second Affiliated Hospital, School of Public Health, Zhejiang University School of Medicine, Hangzhou, China. xueli157@zju.edu.cn.ORCID http://orcid.org/0000-0001-6880-2577

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

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 environment, and lower air pollution levels may reduce depression risk among cancer survivors, possibly via metabolic pathways.

Indexed as

Air PollutionCancer SurvivorsDepressionEnvironmental ExposureAgedFemaleHumansMiddle AgedUnited Kingdom

Identifiers

PMID41803118
PMCPMC13100056

What OpenQuestion holds

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LicenceCC BY
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Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.