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Early detection and prognosis prediction for HUA and gout are crucial for pre\u2010emptive 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\u2010silico 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\u2010related outcomes, effectively classifying individuals into low\u2010 and high\u2010risk groups for gout in the train (AUC, 0.815) and internal test (AUC, 0.814) sets. The high\u2010risk group shows increased susceptibility to metabolism\u2010related 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\u2010related outcomes. The multimodal machine learning\u2010based ISHUA marker enables personalized risk stratification for gout and metabolism\u2010related outcomes, and it is unveiled that lifestyle changes can ameliorate these outcomes within high\u2010risk group, providing guidance for preventive interventions.<\/jats:p>","DOI":"10.1002\/advs.202404047","type":"journal-article","created":{"date-parts":[[2024,7,8]],"date-time":"2024-07-08T13:28:52Z","timestamp":1720445332000},"update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":35,"title":["Multimodal Machine Learning\u2010Based Marker Enables Early Detection and Prognosis Prediction for Hyperuricemia"],"prefix":"10.1002","volume":"11","author":[{"given":"Lin","family":"Zeng","sequence":"first","affiliation":[{"name":"Department of Health Management Nanfang Hospital Southern Medical University  Guangzhou 510515 China"},{"name":"Guangdong Provincial Key Laboratory of Viral Hepatitis Research Department 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