探索PA和久坐行为对高尿酸血症患者痛风风险的影响:来自机器学习和SHAP分析的见解

IF 2.4 4区 医学 Q2 RHEUMATOLOGY
Yanliang Jiao, Ziliang Cheng, Zhongjiang Lan, Shihu Kan, Yibin Du
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引用次数: 0

摘要

背景:高尿酸血症(HUA)患者被广泛认为是痛风的高危人群。本研究旨在探讨身体活动(PA)持续时间和久坐时间对HUA患者痛风风险的影响,并建立预测模型来评估他们患痛风的风险。方法回顾性收集2007-2018年国家健康与营养调查(NHANES)联盟中8057例HUA患者的临床特征。通过开发和比较四种经典机器学习算法,选择性能最佳的随机森林(RF)模型,并结合SHAP解释算法,分析PA持续时间、久坐时间和痛风风险之间的剂量-反应关系。此外,RF模型被用来确定影响痛风风险的最关键因素,并开发一个免费的在线工具来预测HUA个体的痛风风险。结果该模型在训练组和测试组的受试者工作特征(ROC)分别为0.957和0.799。在试验队列中,其准确率为0.778,Kappa为0.247,敏感性为0.701,特异性为0.785,阳性预测值为0.224,阴性预测值为0.967,F1评分为0.340。SHAP分析显示:(1)高血压、血清尿酸、年龄、性别和BMI是痛风风险的前五大因素;(2)血清尿酸、年龄、BMI、肌酐、久坐时间、低PA、高血压、男性、糖尿病等因素与痛风风险升高相关;(3)每周1-7小时的PA持续时间与较低的痛风风险相关,而每天久坐超过6小时会增加痛风风险,无论年龄、性别或合共病如何。结论:我们鼓励HUA患者每周进行1-7小时的PA,并将每天久坐时间限制在6小时以下,以降低痛风风险。开发的预测模型作为基于web的应用程序免费提供:https://sasuki.shinyapps.io/GoutRisk/。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Exploring the Impact of PA and Sedentary Behavior on Gout Risk in Hyperuricemia: Insights From Machine Learning and SHAP Analysis

Background

Individuals with hyperuricemia (HUA) are widely recognized as being at increased risk for gout. This study aimed to investigate how physical activity (PA) duration and sedentary duration impact gout risk in individuals with HUA and to develop predictive models to assess their risk of developing gout.

Methods

We retrospectively collected clinical characteristics of 8057 individuals with HUA from the National Health and Nutrition Examination Survey (NHANES) consortium for the period 2007–2018. By developing and comparing four classic machine learning algorithms, the best-performing Random Forest (RF) model was selected and combined with the SHAP interpreting algorithm to analyze the dose–response relationship between PA duration, sedentary time, and gout risk. Additionally, the RF model was used to identify the most critical factors influencing gout risk and to develop a free online tool for predicting gout risk in HUA individuals.

Results

The RF model outperformed others, achieving a Receiver Operating Characteristic (ROC) of 0.957 in the training cohort and 0.799 in the testing cohort. In the test cohort, it demonstrated an accuracy of 0.778, a Kappa of 0.247, a sensitivity of 0.701, a specificity of 0.785, a positive predictive value of 0.224, a negative predictive value of 0.967, and an F1 score of 0.340. SHAP analysis revealed the following insights: (1) hypertension, serum uric acid, age, gender, and BMI were identified as the top five factors for gout risk; (2) factors such as higher serum uric acid levels, age, BMI, creatinine, sedentary duration, lower PA, hypertension, male sex, and diabetes were associated with an elevated risk of gout; and (3) a PA duration of 1–7 h per week was linked to a lower risk of gout, while sedentary time exceeding 6 h per day increased gout risk, regardless of age, sex, or comorbidities.

Conclusion

We encourage individuals with HUA to engage in 1–7 h of PA per week and limit daily sedentary time to less than 6 h to reduce gout risk. The developed prediction model is freely available as a web-based app at: https://sasuki.shinyapps.io/GoutRisk/.

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来源期刊
CiteScore
3.70
自引率
4.00%
发文量
362
审稿时长
1 months
期刊介绍: The International Journal of Rheumatic Diseases (formerly APLAR Journal of Rheumatology) is the official journal of the Asia Pacific League of Associations for Rheumatology. The Journal accepts original articles on clinical or experimental research pertinent to the rheumatic diseases, work on connective tissue diseases and other immune and allergic disorders. The acceptance criteria for all papers are the quality and originality of the research and its significance to our readership. Except where otherwise stated, manuscripts are peer reviewed by two anonymous reviewers and the Editor.
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