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

Jingli Yang, Kayue Chan, Cheukling Choi, Aimin Yang, Kenneth Lo

Research output: Journal article publicationJournal articleAcademic researchpeer-review

1 Citation (Scopus)

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.

Original languageEnglish
Article number1552
JournalNutrients
Volume14
Issue number8
DOIs
Publication statusPublished - 8 Apr 2022

Keywords

  • Bayesian kernel machine regression
  • cardiometabolic disease
  • diabetes
  • urinary metals

ASJC Scopus subject areas

  • Food Science
  • Nutrition and Dietetics

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