Identifying Effects of Urinary Metals on Type 2 Diabetes in U.S. Adults: Cross-Sectional Analysis of National Health and Nutrition Examination Survey 2011–2016

Abstract

Growing evidence supports the associations of metal exposures with risk of type 2 diabetes (T2D), but the methodological limitations overlook the complexity of relationships within the metal mixtures. We identified and estimated the single and combined effects of urinary metals and their interactions with prevalence of T2D among 3078 participants in the NHANES 2011–2016. We analyzed 15 urinary metals and identified eight metals by elastic-net regression model for further analysis of the prevalence of T2D. Bayesian kernel machine regression and the weighted quantile sum (WQS) regression models identified four metals that had greater importance in T2D, namely cobalt (Co), tin (Sn), uranium (U) and strontium (Sr). The overall OR of T2D was 1.05 (95% CI: 1.01–1.08) for the positive effects and 1.00 (95% CI: 0.98–1.02) for the negative effect in the WQS models. We observed positive (Poverall = 0.008 and Pnon-linear = 0.100 for Co, Poverall = 0.011 and Pnon-linear = 0.138 for Sn) and inverse (Poverall = 0.001, Pnon-linear = 0.209 for Sr) linear dose–response relationships with T2D by restricted cubic spline analysis. Both additive and multiplicative interactions were found in urinary Sn and Sr. In conclusion, urinary Co, Sn, U and Sr played important roles in the development of T2D. The levels of Sn might modify the effect of Sr on T2D risk.

Joint Associations of Food Groups with All-Cause and Cause-Specific Mortality in the Mr. OS and Ms. OS Study: A Prospective Cohort

“…The elastic net model can perform selection, and enable the inclusion of collinear predictors through combining the least absolute shrinkage and the selection operator and ridge. We performed a 10-fold cross validation to acquire the minimum mean squared error (MSE) for an unbiased and robust estimate of prediction accuracy [ 18 ]. A set of elastic net coefficients (β EN ) were estimated by minimizing the MSE.…”

Single and combined associations of blood lead and essential metals with serum lipid profiles in community-dwelling adults

“…As far as we know, no studies have investigated the relationships of a metal mixture, including Pb and essential metals, with lipid profiles, although previous studies found that the metal mixture was closely linked to diabetes (11), non-alcoholic fatty liver diseases (31, 45) and all-cause mortality (46). Using the BKMR and WQS regression models, we found that the blood metal mixture was positively associated with all of the lipid profiles.…”

“…Generally, humans are exposed to a variety of metals simultaneously. Mixtures of metals were considered to have different effects on human health than a single metal, since multiple metals may interact synergistically, antagonistically, or in other ways (11,12). The association of metal exposure with serum lipids was not identical when different exposure profiles were considered (13).…”

Cross-Sectional Association of Blood Selenium with Glycemic Biomarkers among U.S. Adults with Normoglycemia in the National Health and Nutrition Examination Survey 2013–2016

“…Model 1 only included blood Se. Model 2 was further adjusted for sex, age (18–39, 40–59, ≥60 years), race (white, black, Hispanic, other), education (less than high school, high school, above college education), the ratio of income to poverty (<1, ≥1), smoking status (never, former smoker, current smoker) [ 14 ], drinking status (2 or fewer drinks per day, 3 drinks or above per day), body mass index (≤25, 25.1–29.9, ≥30 kg/m 2 ), energy (<1440, 1440–1950, 1950–2590, ≥2590 kcal), and hypertension history (yes, no). Considering that other metals may have confounding effects on the associations of blood Se with glycemic biomarkers, we further adjusted for blood Pb, Cd, Mn, and Hg concentrations in Model 3.…”