How do environmental characteristics jointly contribute to cardiometabolic health? A quantile g-computation mixture analysis

Noémie Letellier; Steven Zamora; Jiue-An Yang; Dorothy D Sears; Marta M Jankowska; Tarik Benmarhnia

doi:10.1016/j.pmedr.2022.102005

How do environmental characteristics jointly contribute to cardiometabolic health? A quantile g-computation mixture analysis

Prev Med Rep. 2022 Sep 26:30:102005. doi: 10.1016/j.pmedr.2022.102005. eCollection 2022 Dec.

Authors

Noémie Letellier¹, Steven Zamora¹, Jiue-An Yang², Dorothy D Sears^{3

4

5}, Marta M Jankowska², Tarik Benmarhnia¹

Affiliations

¹ Scripps Institution of Oceanography, UC San Diego, USA.
² Population Sciences, Beckman Research Institute, City of Hope, 1500 E Duarte Rd, Duarte, CA 91010, USA.
³ College of Health Solutions, Arizona State University, Phoenix, AZ, USA.
⁴ Department of Medicine, UC San Diego, La Jolla, CA, USA.
⁵ Moores Cancer Center, UC San Diego, La Jolla, CA, USA.

Abstract

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.

Keywords: Area-level characteristics; Cardiovascular health; Health inequities; Mixture approach; Neighborhood determinants; Quantile g-computation; Social determinants of health.

Grants and funding

R01 CA228147/CA/NCI NIH HHS/United States