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<p>This study aims to systematically evaluate the associations between various exposure factors and hyperuricemia. A total of 22,765 participants derived from the Henan rural cohort, and 9 categories containing 60 different exposures. The exposome-wide association study (ExWAS) approach was used to estimate the associations between various exposure factors and hyperuricemia. An adaptive elastic net (AENET) model was implemented to select significant exposure factors, followed by the application of a gradient boosting machine (GBM) model to establish the prediction model of these variables to hyperuricemia. The importance of the indicators was assessed through Shapley Additive Explanations (SHAP). Additionally, the weighted quantile sum (WQS) method was employed to validate the selection of these variables. In ExWAS analysis, 40 exposures were significantly associated with the risk of hyperuricemia. The AENET model selected ten exposures as predictors. GBM model and SHAP result showed that the top three exposures were Creatinine, Triglycerides, and PM<sub>2.5</sub>, which interpreted the model as 0.460, 0.331, and 0.314, respectively. Furthermore, the area under characteristics (AUC) of the model was 0.815 (95% <i>CI</i>: 0.802–0.820). WQS shows the same ranking results. The systematic evaluation of this study provides new insights into the complex environment-related factors of hyperuricemia.</p> Graphical Abstract <p></p>

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An exposome-wide association study of hyperuricemia through a rural cohort study and to predict risk factors

  • 期刊论文

  • 发表日期：

    2026年1月12日

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  • Jian Zhang,
  • Huanxiang Zhang,
  • Han Pang,
  • Jia Qiu,
  • Xiuli Tang,
  • Zhaohui Zheng,
  • Jian Hou,
  • Xiaotian Liu,
  • Wenqian Huo,
  • Zhenxing Mao,
  • Chongjian Wang,
  • Yuqian Li

摘要

This study aims to systematically evaluate the associations between various exposure factors and hyperuricemia. A total of 22,765 participants derived from the Henan rural cohort, and 9 categories containing 60 different exposures. The exposome-wide association study (ExWAS) approach was used to estimate the associations between various exposure factors and hyperuricemia. An adaptive elastic net (AENET) model was implemented to select significant exposure factors, followed by the application of a gradient boosting machine (GBM) model to establish the prediction model of these variables to hyperuricemia. The importance of the indicators was assessed through Shapley Additive Explanations (SHAP). Additionally, the weighted quantile sum (WQS) method was employed to validate the selection of these variables. In ExWAS analysis, 40 exposures were significantly associated with the risk of hyperuricemia. The AENET model selected ten exposures as predictors. GBM model and SHAP result showed that the top three exposures were Creatinine, Triglycerides, and PM2.5, which interpreted the model as 0.460, 0.331, and 0.314, respectively. Furthermore, the area under characteristics (AUC) of the model was 0.815 (95% CI: 0.802–0.820). WQS shows the same ranking results. The systematic evaluation of this study provides new insights into the complex environment-related factors of hyperuricemia.

Graphical Abstract