ArticleArthritis research & therapy2022
A machine learning-assisted model for renal urate underexcretion with genetic and clinical variables among Chinese men with gout.
Article in Arthritis research & therapy, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers.
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7 citing papers in PubMed, 13 citations in OpenAlex.
- Risk factor analysis and predictive model construction and evaluation of fractional uric acid excretion in Chinese inpatients with type 2 diabetes.Scientific reports · 2026Article
- Nucleotide Metabolism in Health and Disease.MedComm · 2026Review
- The Progress of Gout Prediction Models Based on Multi-source Data.Current rheumatology reviews · 2026Review
- Can some algorithms of machine learning identify osteoporosis patients after training and testing some clinical information about patients?BMC medical informatics and decision making · 2025Article
- The impact of uric acid on musculoskeletal diseases: clinical associations and underlying mechanisms.Frontiers in endocrinology · 2025Review
- Comprehensive Treatment of Gout with Traditional Chinese Medicine: A Modern Pathophysiological Perspective.International journal of general medicine · 2025Review
- Multimodal Machine Learning-Based Marker Enables Early Detection and Prognosis Prediction for Hyperuricemia.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2024Article
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Authors and funding
16 authors at 6 institutions in 3 countries.
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Abstract
objectivesThe objective of this study was to develop and validate a prediction model for renal urate underexcretion (RUE) in male gout patients.
methodsMen with gout enrolled from multicenter cohorts in China were analyzed as the development and validation data sets. The RUE phenotype was defined as fractional excretion of uric acid (FE
resultsOne thousand two hundred thirty-eight and two thousand twenty-three patients were enrolled as the development and validation cohorts, with 1220 and 754 randomly chosen patients genotyped, respectively. Rs3775948.GG of SLC2A9/GLUT9, rs504915.AA of NRXN2/URAT1, and 7 clinical features (age, hypertension, nephrolithiasis, blood glucose, serum urate, urea nitrogen, and creatinine) were generated by LASSO. Two additional SNP variants (rs2231142.GG of ABCG2 and rs11231463.GG of SLC22A9/OAT7) were selected based on their contributions to gout in the development cohort and their reported effects on renal urate handling. The optimized classifiers yielded AUCs of ~0.914 and PRCs of ~0.980 using these 11 variables. The SGD model was conducted in the validation cohort with an AUC of 0.899 and the PRC of 0.957.
conclusionsA prediction model for RUE composed of four SNPs and readily accessible clinical features was established with acceptable accuracy for men with gout.
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