Urinary metal mixtures and longitudinal changes in glucose homeostasis: The Study of Women's Health Across the Nation (SWAN).

backgroundEpidemiologic studies on associations between metals and insulin resistance and β-cell dysfunction have been cross-sectional and focused on individual metals.

objectiveWe assessed the association of exposure to metal mixtures, based on assessment of 15 urinary metals, with both baseline levels and longitudinal changes in homeostatic model assessments for insulin resistance (HOMA-IR) and β-cell function (HOMA-β).

methodsWe examined 1262 women, aged 45-56 years at baseline (1999-2000), who were followed through 2015-2016, from the Study of Women's Health Across the Nation. Urinary concentrations of 15 metals (arsenic, barium, cadmium, cobalt, cesium, copper, mercury, manganese, molybdenum, nickel, lead, antimony, tin, thallium, and zinc) were determined at baseline. HOMA-IR and HOMA-β were repeatedly measured over 16 years of follow-up. A two-stage modeling was used to account for correlations in dependent and independent variables: In stage-1, linear mixed effects models were used to estimate the participant-specific baseline HOMA levels from random intercepts and participant-specific rates of changes from random slopes. In stage-2, adaptive elastic-net (AENET) models were fit to identify components of metal mixtures associated with participant-specific baseline levels and rates of changes in HOMA-IR and HOMA-β, respectively. An environmental risk score (ERS) was used to integrate metal mixture effects from AENET results.

resultsIn multivariable adjusted AENET models, urinary zinc was associated with higher HOMA-IR at baseline, whereas molybdenum was associated with lower HOMA-IR at baseline. The estimated changes in baseline HOMA-IR for one standard deviation increase in log-transformed urinary metal concentrations were 5.76% (3.05%, 8.55%) for zinc and -3.25% (-5.45%, -1.00%) for molybdenum, respectively. Urinary zinc was also associated with lower HOMA- β at baseline. Arsenic was associated with a slightly faster rate of decline in HOMA-β in the AENET model evaluating associations between metals and rate of changes. Significant associations of ERS with both HOMA-IR and HOMA-β at baseline were observed. ERS for the rate of changes was not calculated and examined in relation to rates of changes in HOMA-IR and HOMA-β because only a single metal was selected by AENET.

conclusionExposure to metal mixtures may be exerting effects on insulin resistance and β-cell dysfunction, which might be mechanisms by which metal exposures lead to elevated diabetes risks.