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<p>This study examined associations between urinary metal exposure and the triglyceride glucose (TyG) index, a recognized metabolic risk marker for cardiovascular events. Analyzing urine samples from 3764 participants via inductively coupled plasma mass spectrometry (ICP‒MS), researchers addressed limitations of single-metal models and sex-specific knowledge gaps. Using least absolute shrinkage and selection operator (LASSO) regression and multi-metal generalized linear models (GLMs), findings revealed positive correlations between urinary arsenic (As), zinc (Zn), molybdenum (Mo), tellurium (Te) and the TyG index, while iron (Fe), selenium (Se) and cadmium (Cd) showed negative correlations. Weighted quantile sum (WQS) regression highlighted sex differences: Te contributed most strongly to positive associations in males, whereas Mo dominated in females; Fe contributed most to negative associations in both sexes. Bayesian kernel machine regression (BKMR) models indicated increasing cumulative effects of metal mixtures across higher exposure quartiles, suggesting a potential interaction between Zn and Cd—though generalized additive models (GAM) analysis found this statistically insignificant. The study concludes that specific urinary metal levels correlate with the TyG index, demonstrating significant sex-based variation in these associations.</p>

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Sex specific associations of urinary metals with TyG index in the Chinese elderly ​

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2025年12月19日

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  • Jinhao Jia,
  • Bing Wu,
  • Zhongyuan Zhang,
  • Siyu Duan,
  • Yuqing Dai,
  • Meiyan Li,
  • Zhuoheng Shen,
  • Pei He,
  • Rui Wang,
  • Limeng Xiong,
  • ZeYang Bai,
  • Jiaming Fu,
  • Yuhan Zhang,
  • Xiaoyu Li,
  • Yi Zhao,
  • Rui Zhang,
  • Huifang Yang,
  • Yue Sun,
  • Jian Sun

摘要

This study examined associations between urinary metal exposure and the triglyceride glucose (TyG) index, a recognized metabolic risk marker for cardiovascular events. Analyzing urine samples from 3764 participants via inductively coupled plasma mass spectrometry (ICP‒MS), researchers addressed limitations of single-metal models and sex-specific knowledge gaps. Using least absolute shrinkage and selection operator (LASSO) regression and multi-metal generalized linear models (GLMs), findings revealed positive correlations between urinary arsenic (As), zinc (Zn), molybdenum (Mo), tellurium (Te) and the TyG index, while iron (Fe), selenium (Se) and cadmium (Cd) showed negative correlations. Weighted quantile sum (WQS) regression highlighted sex differences: Te contributed most strongly to positive associations in males, whereas Mo dominated in females; Fe contributed most to negative associations in both sexes. Bayesian kernel machine regression (BKMR) models indicated increasing cumulative effects of metal mixtures across higher exposure quartiles, suggesting a potential interaction between Zn and Cd—though generalized additive models (GAM) analysis found this statistically insignificant. The study concludes that specific urinary metal levels correlate with the TyG index, demonstrating significant sex-based variation in these associations.