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Predictive value of TyG-obesity indices for hyperuricemia in community residents based on a random forest algorithm
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Ji-qi YANG1, 2, Bin YU3, 1, Shi-hong WANG3, 1, Shu-juan YANG1, 2, 4
Modern Preventive Medicine | 2026, 53(3) : 385 - 391
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Modern Preventive Medicine | 2026, 53(3): 385-391
• Epidemiology and Statistical Methods •
Predictive value of TyG-obesity indices for hyperuricemia in community residents based on a random forest algorithm
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Ji-qi YANG1, 2, Bin YU3, 1, Shi-hong WANG3, 1, Shu-juan YANG1, 2, 4
Affiliations
  • West China School of Public Health/West China Fourth Hospital of Sichuan University, Chengdu, Sichuan 610041, China
Published: 2026-02-10 doi: 10.20043/j.cnki.MPM.202510007
Outline
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Objective

To evaluate the predictive value of various TyG-obesity indices (TyG-BMI, TyG-WHR, TyG-LAP, TyG-BRI, TyG-CVAI, TyG-BAI) for hyperuricemia in community residents.

Methods

Using data from the 2020 cross-sectional survey of the Southwest China Natural Population Cohort, 960 participants were included. A base model was constructed using the random forest algorithm. The predictive performance of models incorporating different TyG-obesity indices was evaluated using the area under the ROC curve, accuracy, precision, recall, and the F1-score.

Results

Among the TyG-obesity indices, the models incorporating TyG-LAP and TyG-CVAI showed the best predictive performance, both with an AUC of 0.716. The TyG-LAP model had slightly higher accuracy and F1-score(0.667 and 0.543), while the TyG-CVAI model achieved the highest recall (0.807). Feature importance analysis indicated that TyG-obesity indices were the important predictors in models incorporating demographic characteristics and behavioral and lifestyle factors.

Conclusion

TyG-obesity indices, particularly TyG-LAP and TyG-CVAI, demonstrate strong predictive ability for hyperuricemia in community residents and may serve as useful tools for community screening and identification of high-risk groups.

Hyperuricemia  /  TyG-obesity indices  /  Prediction model  /  Random forest
杨济其, 余彬, 王仕鸿, 杨淑娟. 基于随机森林算法的TyG-肥胖指数对社区成年人高尿酸血症的预测. 现代预防医学, 2026 , 53 (3) : 385 -391 . DOI: 10.20043/j.cnki.MPM.202510007
Ji-qi YANG, Bin YU, Shi-hong WANG, Shu-juan YANG. Predictive value of TyG-obesity indices for hyperuricemia in community residents based on a random forest algorithm[J]. Modern Preventive Medicine, 2026 , 53 (3) : 385 -391 . DOI: 10.20043/j.cnki.MPM.202510007
Year 2026 volume 53 Issue 3
13
7
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Article Info
doi: 10.20043/j.cnki.MPM.202510007
  • Receive Date:2025-10-01
  • Online Date:2026-11-02
  • Published:2026-02-10
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  • Received:2025-10-01
Funding
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    West China School of Public Health/West China Fourth Hospital of Sichuan University, Chengdu, Sichuan 610041, China
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表12种不同金属材料的力学参数
科
Family
属数
Number of
genus
种数
Number of
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Percentage of
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属
Genus
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Number of
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Percentage of total
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鹅膏菌科Amanitaceae 2 11 5.26 鹅膏菌属 Amanita 10 4.78
小菇科 Mycenaceae 2 12 5.74 丝盖伞属 Inocybe 5 2.39
多孔菌科 Polyporaceae 8 14 6.70 蜡蘑属 Laccaria 5 2.39
红菇科 Russulaceae 3 23 11.00 小皮伞属 Marasmius 6 2.87
小菇属 Mycena 11 5.26
光柄菇属 Pluteus 5 2.39
红菇属 Russula 17 8.13
栓菌属 Trametes 5 2.39
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