Prenatal metal concentrations and childhood cardio-metabolic risk using Bayesian Kernel Machine Regression to assess mixture and interaction effects.

Abstract

Background:

Trace metal concentrations may affect cardio-metabolic risk, but the role of prenatal exposure is unclear. We examined: 1) the relationship between blood metal concentrations during pregnancy and child cardio-metabolic risk factors; 2) overall effects of metals mixture (essential vs. nonessential); and 3) interactions between metals.

Methods:

We measured 11 metals in maternal 2 ^nd trimester whole blood in a prospective birth cohort in Mexico City. In children 4–6 years old, we measured body mass index (BMI), percent body fat, and blood pressure (N=609); and plasma hemoglobin A1C (HbA1c) , non-high density lipoprotein (HDL) cholesterol, triglycerides, leptin, and adiponectin (N=411). We constructed cardio-metabolic component scores using age- and sex-adjusted z-scores and averaged five scores to create a global risk score. We estimated linear associations of each metal with individual z-scores and used Bayesian Kernel Machine Regression to assess metal mixtures and interactions.

Results:

Higher total metals were associated with lower HbA1c, leptin, and systolic blood pressure, and with higher adiponectin and non-HDL cholesterol. We observed no interactions between metals. Higher selenium was associated with lower triglycerides in linear (β=−1.01 z-score units per 1 unit ln(Se), 95%CI = −1.84; −0.18) and Bayesian Kernel Machine Regression models. Manganese was associated with decreased HbA1c in linear models (β = −0.32 and 95% CI: −0.61, −0.03). Antimony and arsenic were associated with lower leptin in Bayesian Kernel Machine Regression models. Essential metals were more strongly associated with cardio-metabolic risk than were nonessential metals.

Conclusions:

Low essential metals during pregnancy were associated with increased cardio-metabolic risk factors in childhood.