PreprintArticleVersion 1This version is not peer-reviewed
Elucidating the Causal Link Between Hearing and Chronic Kidney Disease Through Mendelian Randomization and Development of Predictive Machine Learning Models Based on NHANES Data
Version 1
: Received: 2 August 2024 / Approved: 2 August 2024 / Online: 2 August 2024 (10:56:30 CEST)
How to cite:
Yang, Y.; Li, Y.; Huang, Z.; Lin, R.; Chen, Z.; Zhang, N.; Zheng, N.; Xu, J.; Zhou, Y. Elucidating the Causal Link Between Hearing and Chronic Kidney Disease Through Mendelian Randomization and Development of Predictive Machine Learning Models Based on NHANES Data. Preprints2024, 2024080167. https://doi.org/10.20944/preprints202408.0167.v1
Yang, Y.; Li, Y.; Huang, Z.; Lin, R.; Chen, Z.; Zhang, N.; Zheng, N.; Xu, J.; Zhou, Y. Elucidating the Causal Link Between Hearing and Chronic Kidney Disease Through Mendelian Randomization and Development of Predictive Machine Learning Models Based on NHANES Data. Preprints 2024, 2024080167. https://doi.org/10.20944/preprints202408.0167.v1
Yang, Y.; Li, Y.; Huang, Z.; Lin, R.; Chen, Z.; Zhang, N.; Zheng, N.; Xu, J.; Zhou, Y. Elucidating the Causal Link Between Hearing and Chronic Kidney Disease Through Mendelian Randomization and Development of Predictive Machine Learning Models Based on NHANES Data. Preprints2024, 2024080167. https://doi.org/10.20944/preprints202408.0167.v1
APA Style
Yang, Y., Li, Y., Huang, Z., Lin, R., Chen, Z., Zhang, N., Zheng, N., Xu, J., & Zhou, Y. (2024). Elucidating the Causal Link Between Hearing and Chronic Kidney Disease Through Mendelian Randomization and Development of Predictive Machine Learning Models Based on NHANES Data. Preprints. https://doi.org/10.20944/preprints202408.0167.v1
Chicago/Turabian Style
Yang, Y., Jiean Xu and Yaqi Zhou. 2024 "Elucidating the Causal Link Between Hearing and Chronic Kidney Disease Through Mendelian Randomization and Development of Predictive Machine Learning Models Based on NHANES Data" Preprints. https://doi.org/10.20944/preprints202408.0167.v1
Abstract
Chronic kidney disease (CKD) is a growing public health issue with significant morbidity and mortality rates. Traditional diagnostics, with about 70% accuracy, often delay CKD detection, highlighting the need for more efficient methods. Recent studies indicate a potential link between CKD and auditory health, yet this relationship remains underexplored due to methodological challenges and the complexity of establishing causality. Our research utilizes data spanning from 2000 to 2020 from the National Health and Nutrition Examination Survey (NHANES), covering 12,392 participants, including 2,060 diagnosed with CKD. Through meticulous analysis employing logistic regression and Mendelian randomization, we have unearthed novel insights into the bi-directional associations between hearing impairment and CKD. Furthermore, we developed and validated a machine learning model that surpasses traditional diagnostic approaches in terms of accuracy and predictive power. These findings highlight the innovative integration of auditory examinations with demographic data to enhance CKD detection. Our approach demonstrates the potential of machine learning in transforming diagnostic methodologies, thus offering a significant advancement in the field of nephrology and public health.
Biology and Life Sciences, Endocrinology and Metabolism
Copyright:
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
