How Do Environmental Characteristics Jointly Contribute to Cardiometabolic Health? A Quantile G-Computation Mixture Analysis

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.

Note: Clinical Trial Registration Details: MISSING

Funding Information: This work was supported by the National Cancer Institute [R01CA228147]. No financial disclosures were reported by the authors of this paper.

Declaration of Interests: None declared.

Ethical Approval Statement: Study ethics approval was obtained from the UCSD IRB (protocol #140510). Signed informed consent was obtained from all participants.

Keywords: mixture approach, quantile g-computation, Cardiovascular health, neighborhood determinants, area-level characteristics, health inequities, social determinants of health