Prevalence and influencing factors of hyperuricemia in middle-aged and older adults in the Yao minority area of China: a cross-sectional study

Lyu, X Z; Du, Yang; Liu, Guoyu; Mai, Tingyu; Li, You; Zhang, Zhiyong; Bei, Chunhua

doi:10.1038/s41598-023-37274-y

Cited by 5 publications

(2 citation statements)

References 45 publications

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“…Considering different diagnostic criteria of HUA for different genders, a female with relatively low levels of uric acid may be diagnosed with HUA, while a male must have very high levels of uric acid that could be diagnosed with HUA, two different models designed for male and female separately might be a good solution. Several other studies conducted in different countries had demonstrated significant associations between HUA and BMI, TP, and TG levels ( Wang et al, 2022 ; Ding et al, 2023 ; Lyu et al, 2023 ). Other studies had proven smoking, drinking, sedentary lifestyle that our study did not involve could contribute to the development of HUA ( Kim et al, 2018 ; He et al, 2022 ; Teramura et al, 2023 ).…”

Section: Discussionmentioning

confidence: 87%

“…Thus far, various studies worldwide have identified different risk factors associated with the occurrence of HUA, such as age, gender, waist circumference, drinking, smoking, obesity, hypertension, dyslipidemia and triglyceride-glucose index ( Dong et al, 2022 ; Piao et al, 2022 ; Wang et al, 2022 ; Ding et al, 2023 ; Lyu et al, 2023 ; Teramura et al, 2023 ; Liu et al, 2024 ). Moreover, several prediction models for HUA have been developed using machine learning algorithms ( Lee et al, 2019 ; Zeng et al, 2020 ; Gao et al, 2021 ; Huang et al, 2022 ; Zhu et al, 2023 ).…”

Section: Introductionmentioning

confidence: 99%

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Ensemble machine learning prediction of hyperuricemia based on a prospective health checkup population

Zhang,

et al. 2024

Front. Physiol.

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Objectives: An accurate prediction model for hyperuricemia (HUA) in adults remain unavailable. This study aimed to develop a stacking ensemble prediction model for HUA to identify high-risk groups and explore risk factors.Methods: A prospective health checkup cohort of 40899 subjects was examined and randomly divided into the training and validation sets with the ratio of 7:3. LASSO regression was employed to screen out important features and then the ROSE sampling was used to handle the imbalanced classes. An ensemble model using stacking strategy was constructed based on three individual models, including support vector machine, decision tree C5.0, and eXtreme gradient boosting. Model validations were conducted using the area under the receiver operating characteristic curve (AUC) and the calibration curve, as well as metrics including accuracy, sensitivity, specificity, positive predictive value, negative predictive value, and F1 score. A model agnostic instance level variable attributions technique (iBreakdown) was used to illustrate the black-box nature of our ensemble model, and to identify contributing risk factors.Results: Fifteen important features were screened out of 23 clinical variables. Our stacking ensemble model with an AUC of 0.854, outperformed the other three models, support vector machine, decision tree C5.0, and eXtreme gradient boosting with AUCs of 0.848, 0.851 and 0.849 respectively. Calibration accuracy as well as other metrics including accuracy, specificity, negative predictive value, and F1 score were also proved our ensemble model’s superiority. The contributing risk factors were estimated using six randomly selected subjects, which showed that being female and relatively younger, together with having higher baseline uric acid, body mass index, γ-glutamyl transpeptidase, total protein, triglycerides, creatinine, and fasting blood glucose can increase the risk of HUA. To further validate our model’s applicability in the health checkup population, we used another cohort of 8559 subjects that also showed our ensemble prediction model had favorable performances with an AUC of 0.846.Conclusion: In this study, the stacking ensemble prediction model for HUA was developed, and it outperformed three individual models that compose it (support vector machine, decision tree C5.0, and eXtreme gradient boosting). The contributing risk factors were identified with insightful ideas.

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Section: Discussionmentioning

confidence: 87%

Section: Introductionmentioning

confidence: 99%

Ensemble machine learning prediction of hyperuricemia based on a prospective health checkup population

Zhang,

et al. 2024

Front. Physiol.

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Tea consumption, serum uric acid levels and hyperuricemia: a systematic review and meta-analysis

Zhu,

Wang,

et al. 2024

Clin Rheumatol

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Ensemble Machine Learning Prediction of Hyperuricemia Based on a Prospective Health Checkup Population

Zhang,

Lv,

et al. 2023

Preprint

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Objectives An accurate prediction model for hyperuricemia (HUA) is urgently needed. This study aimed to develop a stacking ensemble prediction model for the risk of hyperuricemia and to identify the contributing risk factors. Methods A prospective health checkup cohort of 40899 subjects was examined and randomly divided into the training and validation sets with the ratio of 7:3, and then the ROSE sampling technique was used to handle the imbalanced classes. LASSO regression was employed to screen out important predicting features. An ensemble model using stacking strategy was constructed based on three individual models, including Support Vector Machine (SVM), Decision Tree C5.0 (C5.0), and eXtreme Gradient Boosting (XGBoost). Model validations were conducted using the area under the receiver operating characteristic curve (AUC) and the calibration curve, as well as metrics including accuracy, sensitivity, specificity, positive predictive value (PPV), negative predictive value (NPV), and F1 score on both the validation set and the extra-validation set. The iBreakdown algorithm was used to illustrate the black-box nature of our ensemble model, and to identify contributing risk factors. Results Fifteen important features were screened out of 23 clinical variables. Our stacking ensemble model with an AUC of 0.854, outperformed the other three models, SVM, C5.0, and XGBoost with AUCs of 0.848, 0.851 and 0.849 respectively. Calibration accuracy as well as other metrics including accuracy, specificity, NPV, and F1 score were also proved our ensemble model’s superiority over the other three models. The contributing risk factors were estimated using six randomly selected subjects, which showed that being female and relatively younger, together with having higher BUA, BMI, GGT, TP, TG, Cr, and FBG values can increase the risk of HUA. To further validate our model’s applicability in the health checkup population, we used another cohort of 8559 subjects that also showed our ensemble prediction model had favorable performances with an AUC of 0.846. Conclusions In this study, the stacking ensemble prediction model for the risk of HUA was developed, which outperformed the individual machine-learning models that compose it, and the contributing risk factors were identified with insightful ideas.

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Prevalence and influencing factors of hyperuricemia in middle-aged and older adults in the Yao minority area of China: a cross-sectional study

Cited by 5 publications

References 45 publications

Ensemble machine learning prediction of hyperuricemia based on a prospective health checkup population

Ensemble machine learning prediction of hyperuricemia based on a prospective health checkup population

Tea consumption, serum uric acid levels and hyperuricemia: a systematic review and meta-analysis

Ensemble Machine Learning Prediction of Hyperuricemia Based on a Prospective Health Checkup Population

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