TY - JOUR
T1 - Exploring the impact of environmental exposure changes on metabolic biomarkers
T2 - A 6-month GPS-GIS study among women with overweight or obesity
AU - Letellier, Noémie
AU - Yang, Jiue An
AU - Alismail, Sarah
AU - Nukavarapu, Nivedita
AU - Hartman, Sheri J.
AU - Rock, Cheryl L.
AU - Sears, Dorothy D.
AU - Jankowska, Marta M.
AU - Benmarhnia, Tarik
N1 - Publisher Copyright:
© 2023
PY - 2024/2/15
Y1 - 2024/2/15
N2 - Background: Little is known about the impact of environmental exposure change on metabolic biomarkers associated with cancer risk. Furthermore, this limited epidemiological evidence on metabolic biomarkers focused on residential exposure, without considering the activity space which can be done by modelling dynamic exposures. In this longitudinal study, we aimed to investigate the impact of environmental exposures change on metabolic biomarkers using GPS-GIS based measurements. Methods: Among two weight loss interventions, the Reach for Health and the MENU studies, which included ∼460 women at risk of breast cancer or breast cancer survivors residing in Southern California, three metabolic biomarkers (insulin resistance, fasting glucose, and C-reactive protein) were assessed. Dynamic GPS-GIS based exposure to green spaces, recreation, walkability, NO2, and PM2.5 were calculated at baseline and 6 months follow-up using time-weighted spatial averaging. Generalized estimating equations models were used to examine the relationship between changes in environmental exposures and biomarker levels over time. Results: Overall, six-month environmental exposure change was not associated with metabolic biomarkers change. Stratified analyses by level of environmental exposures at baseline revealed that reduced NO2 and PM2.5 exposure was associated with reduced fasting glucose concentration among women living in a healthier environment at baseline (β −0.010, 95%CI -0.025, 0.005; β −0.019, 95%CI -0.034, −0.003, respectively). Women living in poor environmental conditions at baseline and exposed to greener environments had decreased C-reactive protein concentrations (β −1.001, 95%CI -1.888, −0.131). Conclusions: The impact of environmental exposure changes on metabolic biomarkers over time may be modified by baseline exposure conditions.
AB - Background: Little is known about the impact of environmental exposure change on metabolic biomarkers associated with cancer risk. Furthermore, this limited epidemiological evidence on metabolic biomarkers focused on residential exposure, without considering the activity space which can be done by modelling dynamic exposures. In this longitudinal study, we aimed to investigate the impact of environmental exposures change on metabolic biomarkers using GPS-GIS based measurements. Methods: Among two weight loss interventions, the Reach for Health and the MENU studies, which included ∼460 women at risk of breast cancer or breast cancer survivors residing in Southern California, three metabolic biomarkers (insulin resistance, fasting glucose, and C-reactive protein) were assessed. Dynamic GPS-GIS based exposure to green spaces, recreation, walkability, NO2, and PM2.5 were calculated at baseline and 6 months follow-up using time-weighted spatial averaging. Generalized estimating equations models were used to examine the relationship between changes in environmental exposures and biomarker levels over time. Results: Overall, six-month environmental exposure change was not associated with metabolic biomarkers change. Stratified analyses by level of environmental exposures at baseline revealed that reduced NO2 and PM2.5 exposure was associated with reduced fasting glucose concentration among women living in a healthier environment at baseline (β −0.010, 95%CI -0.025, 0.005; β −0.019, 95%CI -0.034, −0.003, respectively). Women living in poor environmental conditions at baseline and exposed to greener environments had decreased C-reactive protein concentrations (β −1.001, 95%CI -1.888, −0.131). Conclusions: The impact of environmental exposure changes on metabolic biomarkers over time may be modified by baseline exposure conditions.
KW - Activity space
KW - Built environment
KW - Chronic disease
KW - Dynamic movement
KW - Neighborhood environment
KW - Pollution
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U2 - 10.1016/j.envres.2023.117881
DO - 10.1016/j.envres.2023.117881
M3 - Article
C2 - 38070847
AN - SCOPUS:85179689670
SN - 0013-9351
VL - 243
JO - Environmental Research
JF - Environmental Research
M1 - 117881
ER -