Evidence map›Paper›PMID 31561037›Full record

ArticleEnvironmental pollution (Barking, Essex : 1987)2019

Long-term exposure to greenspace and metabolic syndrome: A Whitehall II study.

Carmen de Keijzer, Xavier Basagaña, Cathryn Tonne, Antònia Valentín, Jordi Alonso, Josep M Antó, Mark J Nieuwenhuijsen, Mika Kivimäki, Archana Singh-Manoux, Jordi Sunyer and 1 more

Open access · greenAbstract read
In one paragraph

Article in Environmental pollution (Barking, Essex : 1987), 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 29 papers, 4 of them syntheses that pooled it.

0numbers the graph read from it
0cells of the map it votes in
29citing papers in PubMed, 4 pooled it
7.6field-weighted citation impact, top 2% of its field
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

29 citing papers in PubMed, 4 syntheses or guidelines pooled it, 106 citations in OpenAlex.

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  16. The neighbourhood environment and profiles of the metabolic syndrome.Environmental health : a global access science source · 2022
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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

11 authors at 4 institutions in 2 countries.

Carmen de KeijzerISGlobal, Barcelona, Catalonia, Spain; Universitat Pompeu Fabra (UPF), Barcelona, Catalonia, Spain; CIBER Epidemiología y Salud Pública (CIBERESP), Madrid, Spain.
Xavier BasagañaISGlobal, Barcelona, Catalonia, Spain; Universitat Pompeu Fabra (UPF), Barcelona, Catalonia, Spain; CIBER Epidemiología y Salud Pública (CIBERESP), Madrid, Spain.
Cathryn TonneISGlobal, Barcelona, Catalonia, Spain; Universitat Pompeu Fabra (UPF), Barcelona, Catalonia, Spain; CIBER Epidemiología y Salud Pública (CIBERESP), Madrid, Spain.
Antònia ValentínISGlobal, Barcelona, Catalonia, Spain; Universitat Pompeu Fabra (UPF), Barcelona, Catalonia, Spain; CIBER Epidemiología y Salud Pública (CIBERESP), Madrid, Spain.
Jordi AlonsoUniversitat Pompeu Fabra (UPF), Barcelona, Catalonia, Spain; CIBER Epidemiología y Salud Pública (CIBERESP), Madrid, Spain; IMIM-Parc Salut Mar, Barcelona, Catalonia, Spain.
Josep M AntóISGlobal, Barcelona, Catalonia, Spain; Universitat Pompeu Fabra (UPF), Barcelona, Catalonia, Spain; CIBER Epidemiología y Salud Pública (CIBERESP), Madrid, Spain.
Mark J NieuwenhuijsenISGlobal, Barcelona, Catalonia, Spain; Universitat Pompeu Fabra (UPF), Barcelona, Catalonia, Spain; CIBER Epidemiología y Salud Pública (CIBERESP), Madrid, Spain.
Mika KivimäkiDepartment of Epidemiology and Public Health, University College of London, London, UK.
Archana Singh-ManouxDepartment of Epidemiology and Public Health, University College of London, London, UK; INSERM, U1153, Epidemiology of Ageing and Neurodegenerative Diseases, Paris, France.
Jordi SunyerISGlobal, Barcelona, Catalonia, Spain; Universitat Pompeu Fabra (UPF), Barcelona, Catalonia, Spain; CIBER Epidemiología y Salud Pública (CIBERESP), Madrid, Spain.
Payam DadvandISGlobal, Barcelona, Catalonia, Spain; Universitat Pompeu Fabra (UPF), Barcelona, Catalonia, Spain; CIBER Epidemiología y Salud Pública (CIBERESP), Madrid, Spain. Electronic address: payam.dadvand@isglobal.org.
Pompeu Fabra University · ESBarcelona Institute for Global Health · ESCentro de Investigación Biomédica en Red de Epidemiología y Salud Pública · ESInserm · FR

Funding

Education, socioeconomic status and Aging: transitions from multimorbidity to functional limitations and mortalityR01AG056477 · NIA · UNIVERSITY COLLEGE LONDON · PI KIVIMAKI, MIKA J, SINGH-MANOUX, ARCHANA · 2018 to 2022
$2.8M
Health behaviors over the adult lifecourse and cognitive agingR01AG034454 · NIA · UNIVERSITY COLLEGE LONDON · PI KIVIMAKI, MIKA J, SINGH-MANOUX, ARCHANA · 2009 to 2013
$1.5M
Role of mid- and late-life risk factors in social inequalities in Alzheimer's Disease and Related DementiasRF1AG062553 · NIA · INSERM PARIS 5 · PI KIVIMAKI, MIKA J, SINGH-MANOUX, ARCHANA · 2019 to 2019
$1.5M
British Heart Foundation RG/13/2/30098British Heart Foundation RG/16/11/32334NIA NIH HHS R01 AG034454NIA NIH HHS R01 AG056477NIA NIH HHS RF1 AG062553
6 · The paper itself

Abstract

backgroundMetabolic syndrome is an important risk factor for non-communicable diseases, particularly type 2 diabetes, coronary heart disease, and stroke. Long-term exposure to greenspace could be protective of metabolic syndrome, but evidence for such an association is lacking. Accordingly, we investigated the association between long-term exposure to greenspace and risk of metabolic syndrome.

methodsThe present longitudinal study was based on data from four clinical examinations between 1997 and 2013 in 6076 participants of the Whitehall II study, UK (aged 45-69 years at baseline). Long-term exposure to greenspace was assessed by satellite-based indices of greenspace including Normalized Difference Vegetation Index (NDVI) and Vegetation Continuous Field (VCF) averaged across buffers of 500 and 1000 m surrounding the participants' residential location at each follow-up. The ascertainment of metabolic syndrome was based on the World Health Organization (WHO) definition. Hazard ratios for metabolic syndrome were estimated using Cox proportional hazards regression models, controlling for age, sex, ethnicity, lifestyle factors, and socioeconomic status.

resultsHigher residential surrounding greenspace was associated with lower risk of metabolic syndrome. An interquartile range increase in NDVI and VCF in the 500 m buffer was associated with 13% (95% confidence interval (CI): 1%, 23%) and 14% (95% CI: 5%, 22%) lower risk of metabolic syndrome, respectively. Greater exposure to greenspace was also associated with each individual component of metabolic syndrome, including a lower risk of high levels of fasting glucose, large waist circumference, high triglyceride levels, low HDL cholesterol, and hypertension. The association between residential surrounding greenspace and metabolic syndrome may have been mediated by physical activity and exposure to air pollution.

conclusionsThe findings of the present study suggest that middle-aged and older adults living in greener neighbourhoods are at lower risk of metabolic syndrome than those living in neighbourhoods with less greenspace.

Indexed as

AgedAir PollutionDiabetes Mellitus, Type 2Environmental ExposureFemaleHumansHypertensionLongitudinal StudiesMaleMetabolic SyndromeMiddle AgedProportional Hazards ModelsRisk FactorsSocial ClassStrokeUnited KingdomBuilt environmentCardiometabolic riskGreennessLongitudinal studyMetabolic syndromeNatural environment

Identifiers

PMID31561037
PMCPMC7441228
OpenAlexW2972679940

What OpenQuestion holds

Textmetadata
LicenceTDM
Read underepoch 390

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.