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How do environmental characteristics jointly contribute to cardiometabolic health? A quantile g-computation mixture analysis
Accumulating evidence links cardiometabolic health with social and environmental neighborhood exposures, which may contribute to health inequities. We examined whether environmental characteristics were individually or jointly associated with insulin resistance, hypertension, obesity, type 2 diabete...
Autores principales: | , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
2022
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9562428/ https://www.ncbi.nlm.nih.gov/pubmed/36245803 http://dx.doi.org/10.1016/j.pmedr.2022.102005 |
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author | Letellier, Noémie Zamora, Steven Yang, Jiue-An Sears, Dorothy D. Jankowska, Marta M. Benmarhnia, Tarik |
author_facet | Letellier, Noémie Zamora, Steven Yang, Jiue-An Sears, Dorothy D. Jankowska, Marta M. Benmarhnia, Tarik |
author_sort | Letellier, Noémie |
collection | PubMed |
description | Accumulating evidence links cardiometabolic health with social and environmental neighborhood exposures, which may contribute to health inequities. We examined whether environmental characteristics were individually or jointly associated with insulin resistance, hypertension, obesity, type 2 diabetes, and metabolic syndrome in San Diego County, CA. As part of the Community of Mine Study, cardiometabolic outcomes of insulin resistance, hypertension, BMI, diabetes, and metabolic syndrome were collected in 570 participants. Seven census tract level characteristics of participants’ residential environment were assessed and grouped as follows: economic, education, health care access, neighborhood conditions, social environment, transportation, and clean environment. Generalized estimating equation models were performed, to take into account the clustered nature of the data and to estimate β or relative risk (RR) and 95 % confidence intervals (CIs) between each of the seven environmental characteristics and cardiometabolic outcomes. Quantile g-computation was used to examine the association between the joint effect of a simultaneous increase in all environmental characteristics and cardiometabolic outcomes. Among 570 participants (mean age 58.8 ± 11 years), environmental economic, educational and health characteristics were individually associated with insulin resistance, diabetes, obesity, and metabolic syndrome. In the mixture analyses, a joint quartile increase in all environmental characteristics (i.e., improvement) was associated with decreasing insulin resistance (β, 95 %CI: −0.09, −0.18–0.01)), risk of diabetes (RR, 95 %CI: 0.59, 0.36–0.98) and obesity (RR, 95 %CI: 0.81, 0.64–1.02). Environmental characteristics synergistically contribute to cardiometabolic health and independent analysis of these determinants may not fully capture the potential health impact of social and environmental determinants of health. |
format | Online Article Text |
id | pubmed-9562428 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2022 |
record_format | MEDLINE/PubMed |
spelling | pubmed-95624282022-10-15 How do environmental characteristics jointly contribute to cardiometabolic health? A quantile g-computation mixture analysis Letellier, Noémie Zamora, Steven Yang, Jiue-An Sears, Dorothy D. Jankowska, Marta M. Benmarhnia, Tarik Prev Med Rep Regular Article Accumulating evidence links cardiometabolic health with social and environmental neighborhood exposures, which may contribute to health inequities. We examined whether environmental characteristics were individually or jointly associated with insulin resistance, hypertension, obesity, type 2 diabetes, and metabolic syndrome in San Diego County, CA. As part of the Community of Mine Study, cardiometabolic outcomes of insulin resistance, hypertension, BMI, diabetes, and metabolic syndrome were collected in 570 participants. Seven census tract level characteristics of participants’ residential environment were assessed and grouped as follows: economic, education, health care access, neighborhood conditions, social environment, transportation, and clean environment. Generalized estimating equation models were performed, to take into account the clustered nature of the data and to estimate β or relative risk (RR) and 95 % confidence intervals (CIs) between each of the seven environmental characteristics and cardiometabolic outcomes. Quantile g-computation was used to examine the association between the joint effect of a simultaneous increase in all environmental characteristics and cardiometabolic outcomes. Among 570 participants (mean age 58.8 ± 11 years), environmental economic, educational and health characteristics were individually associated with insulin resistance, diabetes, obesity, and metabolic syndrome. In the mixture analyses, a joint quartile increase in all environmental characteristics (i.e., improvement) was associated with decreasing insulin resistance (β, 95 %CI: −0.09, −0.18–0.01)), risk of diabetes (RR, 95 %CI: 0.59, 0.36–0.98) and obesity (RR, 95 %CI: 0.81, 0.64–1.02). Environmental characteristics synergistically contribute to cardiometabolic health and independent analysis of these determinants may not fully capture the potential health impact of social and environmental determinants of health. 2022-09-26 /pmc/articles/PMC9562428/ /pubmed/36245803 http://dx.doi.org/10.1016/j.pmedr.2022.102005 Text en © 2022 The Authors https://creativecommons.org/licenses/by-nc-nd/4.0/This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/). |
spellingShingle | Regular Article Letellier, Noémie Zamora, Steven Yang, Jiue-An Sears, Dorothy D. Jankowska, Marta M. Benmarhnia, Tarik How do environmental characteristics jointly contribute to cardiometabolic health? A quantile g-computation mixture analysis |
title | How do environmental characteristics jointly contribute to cardiometabolic health? A quantile g-computation mixture analysis |
title_full | How do environmental characteristics jointly contribute to cardiometabolic health? A quantile g-computation mixture analysis |
title_fullStr | How do environmental characteristics jointly contribute to cardiometabolic health? A quantile g-computation mixture analysis |
title_full_unstemmed | How do environmental characteristics jointly contribute to cardiometabolic health? A quantile g-computation mixture analysis |
title_short | How do environmental characteristics jointly contribute to cardiometabolic health? A quantile g-computation mixture analysis |
title_sort | how do environmental characteristics jointly contribute to cardiometabolic health? a quantile g-computation mixture analysis |
topic | Regular Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9562428/ https://www.ncbi.nlm.nih.gov/pubmed/36245803 http://dx.doi.org/10.1016/j.pmedr.2022.102005 |
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