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Regulation of the immune system by biodiversity from the natural environment: An ecosystem service essential to health
Open accessAbstract
Epidemiological studies suggest that living close to the natural environment is associated with long-term health benefits including reduced death rates, reduced cardiovascular disease, and reduced psychiatric problems. This is often attributed to psychological mechanisms, boosted by exercise, social interactions, and sunlight. Compared with urban environments, exposure to green spaces does indeed trigger rapid psychological, physiological, and endocrinological effects. However, there is little evidence that these rapid transient effects cause long-term health benefits or even that they are a specific property of natural environments. Meanwhile, the illnesses that are increasing in high-income countries are associated with failing immunoregulation and poorly regulated inflammatory responses, manifested as chronically raised C-reactive protein and proinflammatory cytokines. This failure of immunoregulation is partly attributable to a lack of exposure to organisms (“Old Friends”) from mankind’s evolutionary past that needed to be tolerated and therefore evolved roles in driving immunoregulatory mechanisms. Some Old Friends (such as helminths and infections picked up at birth that established carrier states) are almost eliminated from the urban environment. This increases our dependence on Old Friends derived from our mothers, other people, animals, and the environment. It is suggested that the requirement for microbial input from the environment to drive immunoregulation is a major component of the beneficial effect of green space, and a neglected ecosystem service that is essential for our well-being. This insight will allow green spaces to be designed to optimize health benefits and will provide impetus from health systems for the preservation of ecosystem biodiversity.
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“Beyond physiological benefits, natural environments foster mental well-being and social engagement, reducing anxiety, depression, and cognitive decline risks [52,53,54].”
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Metadata
- Authors
- Rook, G. A.
- Affiliations
- National Institute for Health and Care Research (GB)
- Corresponding author
- Rook, Graham A.
- ORCID
- Rook, Graham A.
- Venue
- Proceedings of the National Academy of Sciences
- Publisher
- National Academy of Sciences
- Published
- 2013-10-23
- Volume
- 110
- Issue
- 46
- Pages
- 18360-18367
- ISSN
- 0027-8424 (ISSN-L)
- 1091-6490
- PMC
- PMC3831972
- OpenAlex
- W2051692047
- Cited by
- 760
- References
- 135
- Citations by year
- 2026: 65 · 2025: 56 · 2024: 71 · 2023: 62 · 2022: 69 · 2021: 69 · 2020: 57 · 2019: 61 · 2018: 103 · 2017: 41 · 2016: 46 · 2015: 37 · 2014: 22
- FWCI
- 4.15
- Type
- journal article
- Language
- en
- Open access
- bronze
- doi.org
- Locations
- Proceedings of the National Academy of Sciences (publishedVersion)
- Europe PMC (PubMed Central) (publishedVersion)
- PubMed Central (publishedVersion)
- PubMed (publishedVersion)
- UCL Discovery (University College London) (submittedVersion)
- Funders
- NIHR Biomedical Research Centre, Royal Marsden NHS Foundation Trust/Institute of Cancer Research
- University College London
- National Institutes of Health
- Topics
- Social Sciences › Urban Studies › Urban Green Space and Health
- Health Sciences › Medicine › Pulmonary and Respiratory Medicine › Asthma and respiratory diseases
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Gut microbiota and health
- Keywords
- immune regulation, biodiversity, ecosystem services, green spaces, old friends hypothesis, helminths, microbial exposure, inflammation, C-reactive protein, inflammatory cytokines, mental health, cardiovascular disease, green space design, biodiversity conservation
- MeSH terms
- Environment, Humans, Biodiversity, Adaptive Immunity, Hygiene Hypothesis, Microbiota
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Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
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