Evidence map›Paper›PMID 36057588›Full record

ArticleEnvironmental health : a global access science source2022

The neighbourhood environment and profiles of the metabolic syndrome.

Anthony Barnett, Erika Martino, Luke D Knibbs, Jonathan E Shaw, David W Dunstan, Dianna J Magliano, David Donaire-Gonzalez, Ester Cerin

Open access · goldAbstract read
In one paragraph

Article in Environmental health : a global access science source, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.

0numbers the graph read from it
0cells of the map it votes in
5citing papers in PubMed
1.4field-weighted citation impact, top 21% 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

5 citing papers in PubMed, 14 citations in OpenAlex.

  1. Article
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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

8 authors at 7 institutions in 3 countries.

Anthony Barnett *Mary MacKillop Institute for Health Research, Australian Catholic University, 215 Spring St, Melbourne, VIC, Australia. Anthony.Barnett@acu.edu.au.ORCID 0000-0002-6320-4073
Erika MartinoMelbourne School of Population and Global Health, The University of Melbourne, Melbourne, VIC, 3010, Australia.
Luke D KnibbsSchool of Public Health, The University of Sydney, Sydney, NSW, 2006, Australia.
Jonathan E ShawDepartment of Diabetes and Population Health, Baker Heart and Diabetes Institute, Melbourne, Australia.
David W DunstanBaker-Deakin Department of Lifestyle and Diabetes, Deakin University, Melbourne, Australia.
Dianna J MaglianoSchool of Public Health and Preventive Medicine, Monash University, Melbourne, Australia.
David Donaire-GonzalezMary MacKillop Institute for Health Research, Australian Catholic University, 215 Spring St, Melbourne, VIC, Australia.
Ester Cerin *Mary MacKillop Institute for Health Research, Australian Catholic University, 215 Spring St, Melbourne, VIC, Australia.
Australian Catholic University · AUBaker Heart and Diabetes Institute · AUDeakin University · AUMonash University · AUThe University of Melbourne · AUThe University of Sydney · AUUiT The Arctic University of Norway · NO

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundThere is a dearth of studies on how neighbourhood environmental attributes relate to the metabolic syndrome (MetS) and profiles of MetS components. We examined the associations of interrelated aspects of the neighbourhood environment, including air pollution, with MetS status and profiles of MetS components.

methodsWe used socio-demographic and MetS-related data from 3681 urban adults who participated in the 3rd wave of the Australian Diabetes, Obesity and Lifestyle Study. Neighbourhood environmental attributes included area socio-economic status (SES), population density, street intersection density, non-commercial land use mix, percentages of commercial land, parkland and blue space. Annual average concentrations of NO

resultsLCA yielded three latent classes, one including only participants without MetS ("Lower probability of MetS components" profile). The other two classes/profiles, consisting of participants with and without MetS, were "Medium-to-high probability of high fasting blood glucose, waist circumference and blood pressure" and "Higher probability of MetS components". Area SES was the only significant predictor of MetS status: participants from high SES areas were less likely to have MetS. Area SES, percentage of commercial land and NO

conclusionsThis study supports the utility of operationalising MetS as a combination of latent classes of MetS components and MetS status in studies of environmental correlates. Higher socio-economic advantage, good access to commercial services and low air pollution levels appear to independently contribute to different facets of metabolic health. Future research needs to consider conducting longitudinal studies using fine-grained environmental measures that more accurately characterise the neighbourhood environment in relation to behaviours or other mechanisms related to MetS and its components.

Indexed as

Metabolic SyndromeAdultAustraliaHumansNitrogen DioxideParticulate MatterResidence CharacteristicsNitrogen DioxideParticulate MatterAir pollutionBlue spaceGreenspaceMetabolic healthNeighbourhood socio-economic statusWalkability

Identifiers

PMID36057588
PMCPMC9440568
OpenAlexW4294384641

What OpenQuestion holds

Textmetadata
LicenceCC BY
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.