Abstract
Epidemiological studies using conventional statistical methods have reported an association between individual metal exposure and hyperuricemia (HUA). There is also evidence that diet may influence HUA development, although the available data are inconsistent. We therefore used an elastic net regression (ENR) model to screen the usefulness of various environmental and dietary factors as predictors of HUA in a large sample cohort. This study included 6217 subjects drawn from the Shenzhen Aging Related Disorder Cohort. We obtained information on the subjects’ dietary habits via face-to-face interviews and used inductively coupled plasma mass spectrometry (ICP-MS) to measure the urinary concentrations of 24 metals to which elderly persons in large urban areas may be exposed. An elastic net regression (ENR) model was generated to screen the utility of the metals and dietary factors as predictors of HUA, and we demonstrated the superiority of the ENR model by comparing it to a traditional logistic regression model. The identified predictors were used to create a clinically usable nomogram for identifying patients at risk of developing HUA. The area under curve (AUC) value of the final model was 0.692 for the training set and 0.706 for the test set. Important predictors of HUA were Zn, As, V, and Fe as well as consumption of wheat, beans, and rice; the corresponding estimated odds ratios and 95% confidence intervals were 1.091 (0.932,1.251), 1.190 (1.093,1.286), 0.924 (0.793,1.055), 0.704 (0.626,0.781), 0.998 (0.996,1.001), 0.993 (0.989,0.998), and 1.001 (0.998,1.002), respectively. In contrast to previous studies, we found that both urinary metal concentrations and dietary habits are important for predicting HUA risk. Exposure to specific metals and consumption of specific foods were identified as important predictors of HUA, indicating that the incidence of this disease could be reduced by reducing exposure to these metals and promoting improved dietary habits.
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The dataset generated during and/or analyzed during the current study are not publicly available due to ethics reason.
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Acknowledgements
We are sincerely grateful to all participants for their support of this study. We also appreciate the help of physicians and technicians from two comprehensive hospitals in Shenzhen.
Funding
This work was supported by Shenzhen Basic Research Key Project (JCYJ20200109143431341), Shenzhen Basic Research Project (JCYJ20190807103401672), Shenzhen Key Medical Discipline Construction Fund (SZXK069), and Sanming Project of Medicine in Shenzhen (SZSM201611090).
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Pengcheng Mei: conceptualization and writing—original draft and editing. Qimei Zhou: data curation and investigation. Wei Liu: formal analysis, investigation, and funding acquisition. Jia Huang: investigation and writing—review. Erwei Gao: formal analysis and investigation. Yi Luo: investigation and project administration. Xiaohu Ren: formal analysis and investigation. Haiyan Huang: project administration. Xiao Chen: formal analysis and investigation. Desheng Wu: project administration. Xinfeng Huang: project administration. Hao Yu: methodology and formal analysis. Jianjun Liu: formal analysis, methodology, and funding acquisition. All authors read and approved the final manuscript.
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This study has been approved by the Medical Ethics Research Committee of Shenzhen Center for Disease Control and Prevention. All the research subjects knew the significance of this research before participating in this project and signed an informed consent form.
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Mei, P., Zhou, Q., Liu, W. et al. Correlating metal exposures and dietary habits with hyperuricemia in a large urban elderly cohort by artificial intelligence. Environ Sci Pollut Res 30, 41570–41580 (2023). https://doi.org/10.1007/s11356-022-24824-6
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DOI: https://doi.org/10.1007/s11356-022-24824-6