A Gender-, Age-, and Weight Status-Specific Analysis of the High Prevalence of Hyperuricemia Among Chinese Children and Adolescents with Obesity

Liu, Meijuan; Cao, Bingyan; Luo, Qipeng; Song, Yanning; Shi, Yuting; Cheng, Ming; Liu, Kai; Mao, Di; Wang, Xinmeng; Gong, Chunxiu

doi:10.2147/dmso.s448638

Cited by 2 publications

(1 citation statement)

References 46 publications

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“…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%

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: 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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The Effects of Parental Food Education on Children’s Food Literacy: The Mediating Role of Parent–Child Relationship and Learning Motivation

Xu,

Cai,

Zhang

et al. 2024

Nutrients

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Parental food education has been recognized among the important factors influencing children’s food literacy; however, the intrinsic mechanisms through which this influence occurs are unclear. In this study, a mediation model was constructed to explore this issue, using the parent–child relationship and learning motivation as mediating variables. In total, 204 children, aged 9–14 years old, responded to questionnaires on parental food education, children’s food literacy, the parent–child relationship, and learning motivation, which were used to measure the variables of interest. The results showed that parental food education was significantly and positively related to the parent–child relationship, learning motivation, and children’s food literacy; the parent–child relationship was significantly and positively related to learning motivation; and learning motivation was significantly and positively related to children’s food literacy. Parental food education influenced children’s food literacy in the following two main ways: the mediating role of learning motivation and the chain-mediating roles of the parent–child relationship and learning motivation. In addition, we attempt to explore the moderating role of the teaching stage between parental food education and the parent–child relationship, learning motivation, and children’s food literacy. In this paper, we discuss possible guidelines for family food education and children’s health based on the findings of the current study.

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A Gender-, Age-, and Weight Status-Specific Analysis of the High Prevalence of Hyperuricemia Among Chinese Children and Adolescents with Obesity

Cited by 2 publications

References 46 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

The Effects of Parental Food Education on Children’s Food Literacy: The Mediating Role of Parent–Child Relationship and Learning Motivation

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