ArticleAdvanced science (Weinheim, Baden-Wurttemberg, Germany)2024
Multimodal Machine Learning-Based Marker Enables Early Detection and Prognosis Prediction for Hyperuricemia.
Article in Advanced science (Weinheim, Baden-Wurttemberg, Germany), 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 19 papers.
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Who cites it
19 citing papers in PubMed.
- Multimodal Machine Learning Integrating Clinical and Proteomic Data for Early Prediction of Hypertensive Complications: A UKB Longitudinal Study.Journal of the American Heart Association · 2026Article
- A machine learning-derived polygenic risk score reveals that healthy lifestyle counteracts obesity-related mortality.NPJ digital medicine · 2026Article
- Development and internal validation of a risk model for hyperuricemia among people living with HIV in Hangzhou, China: a retrospective longitudinal cohort study.Frontiers in endocrinology · 2026Article
- Status, challenges, and prospects of artificial intelligence application in gout diagnosis and treatment, drug research and development, and disease monitoring.Frontiers in medicine · 2026Review
- The Progress of Gout Prediction Models Based on Multi-source Data.Current rheumatology reviews · 2026Review
- Quantity-effect correlation between water intake and serum uric acid in US adults: a cross-sectional study based on NHANES data.Translational andrology and urology · 2025Article
- Association of metabolic syndrome and hyperuricemia with mortality in patients with chronic kidney disease: a UK biobank study.BMC nephrology · 2025Article
- Development and Validation of Machine Learning-Based Marker for Early Detection and Prognosis Stratification of Nonalcoholic Fatty Liver Disease.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2025Article
- Identification of age-specific risk factors for hyperuricemia: a machine learning-driven stratified analysis in health examination cohorts.BMC medical informatics and decision making · 2025Article
- Identification and validation of an explainable early-stage chronic kidney disease prediction model: a multicenter retrospective study.EClinicalMedicine · 2025Article
- Tlalpan 2020 Case Study: Enhancing Uric Acid Level Prediction with Machine Learning Regression and Cross-Feature Selection.Nutrients · 2025Article
- Risk prediction of hyperuricemia based on particle swarm fusion machine learning solely dependent on routine blood tests.BMC medical informatics and decision making · 2025Article
- Purine metabolism-associated key genes depict the immune landscape in gout patients.Discover oncology · 2025Article
- Combined predictive value of uric acid and serum lipid for stroke events in non-valvular atrial fibrillation patients.Frontiers in cardiovascular medicine · 2025Article
- Multi-omics identification of a polyamine metabolism related signature for hepatocellular carcinoma and revealing tumor microenvironment characteristics.Frontiers in immunology · 2025Article
- Association between higher estimated glucose disposal rate and reduced prevalence of hyperuricemia and gout.Frontiers in nutrition · 2025Article
- Establishment and evaluation of a model for clinical feature selection and prediction in gout patients with cardiovascular diseases: a retrospective cohort study.Frontiers in endocrinology · 2025Article
- Association between prebiotic, probiotic consumption and hyperuricemia in U.S. adults: a cross-sectional study from NHANES 2011-2018.Frontiers in nutrition · 2025Article
- 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
14 authors.
Funding
Abstract
Hyperuricemia (HUA) has emerged as the second most prevalent metabolic disorder characterized by prolonged and asymptomatic period, triggering gout and metabolism-related outcomes. Early detection and prognosis prediction for HUA and gout are crucial for pre-emptive interventions. Integrating genetic and clinical data from 421287 UK Biobank and 8900 Nanfang Hospital participants, a stacked multimodal machine learning model is developed and validated to synthesize its probabilities as an in-silico quantitative marker for hyperuricemia (ISHUA). The model demonstrates satisfactory performance in detecting HUA, exhibiting area under the curves (AUCs) of 0.859, 0.836, and 0.779 within the train, internal, and external test sets, respectively. ISHUA is significantly associated with gout and metabolism-related outcomes, effectively classifying individuals into low- and high-risk groups for gout in the train (AUC, 0.815) and internal test (AUC, 0.814) sets. The high-risk group shows increased susceptibility to metabolism-related outcomes, and participants with intermediate or favorable lifestyle profiles have hazard ratios of 0.75 and 0.53 for gout compared with those with unfavorable lifestyles. Similar trends are observed for other metabolism-related outcomes. The multimodal machine learning-based ISHUA marker enables personalized risk stratification for gout and metabolism-related outcomes, and it is unveiled that lifestyle changes can ameliorate these outcomes within high-risk group, providing guidance for preventive interventions.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the Socratic graph.