The neighbourhood environment and profiles of the metabolic syndrome
Barnett, Anthony, Martino, Erika, Knibbs, Luke, Shaw, Jonathan, Dunstan, David Wayne, Magliano, Dianna, Donaire Gonzalez, David and Cerin, Ester. (2022). The neighbourhood environment and profiles of the metabolic syndrome. Environmental Health: A Global Access Science Source. 21(1), pp. 1-18. https://doi.org/10.1186/s12940-022-00894-4
|Authors||Barnett, Anthony, Martino, Erika, Knibbs, Luke, Shaw, Jonathan, Dunstan, David Wayne, Magliano, Dianna, Donaire Gonzalez, David and Cerin, Ester|
Background: There 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.
Methods: We 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 NO2 and PM2.5 were estimated using satellite-based land-use regression models. Latent class analysis (LCA) identified homogenous groups (latent classes) of participants based on MetS components data. Participants were then classified into five metabolic profiles according to their MetS-components latent class and MetS status. Generalised additive mixed models were used to estimate relationships of environmental attributes with MetS status and metabolic profiles.
Results: LCA 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 NO2 were associated with the odds of membership to healthier metabolic profiles without MetS, while annual average concentration of PM2.5 was associated with unhealthier metabolic profiles with MetS.
Conclusions: This 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.
|Keywords||Air pollution; Blue space; Greenspace; Metabolic health; Neighbourhood socio-economic status; Walkability|
|Year||01 Jan 2022|
|Journal||Environmental Health: A Global Access Science Source|
|Journal citation||21 (1), pp. 1-18|
|Digital Object Identifier (DOI)||https://doi.org/10.1186/s12940-022-00894-4|
|PubMed Central ID||PMC9440568|
|Web address (URL)||https://ehjournal.biomedcentral.com/articles/10.1186/s12940-022-00894-4|
|Open access||Published as ‘gold’ (paid) open access|
|Research or scholarly||Research|
File Access Level
|Online||03 Sep 2022|
|Publication process dates|
|Deposited||17 Jan 2023|
© The Author(s) 2022.
|Place of publication||United Kingdom|
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