Prediction Model for Insulin Resistance and Implications for MASLD in Youth: A Novel Marker, the Pediatric Insulin Resistance Assessment Score.

IF 2.8 4区 医学 Q1 MEDICINE, GENERAL & INTERNAL
Kyungchul Song, Eunju Lee, Young Hoon Youn, Su Jung Baik, Hyun Joo Shin, Ji-Won Lee, Hyun Wook Chae, Hye Sun Lee, Yu-Jin Kwon
{"title":"Prediction Model for Insulin Resistance and Implications for MASLD in Youth: A Novel Marker, the Pediatric Insulin Resistance Assessment Score.","authors":"Kyungchul Song, Eunju Lee, Young Hoon Youn, Su Jung Baik, Hyun Joo Shin, Ji-Won Lee, Hyun Wook Chae, Hye Sun Lee, Yu-Jin Kwon","doi":"10.3349/ymj.2024.0442","DOIUrl":null,"url":null,"abstract":"<p><strong>Purpose: </strong>Insulin resistance (IR) is a condition closely associated with cardiovascular risk factors and metabolic dysfunction-associated steatotic liver disease (MASLD) is emerging as a significant IR-related complication. We aimed to develop a predictive model for IR in youths and implicate this model for MASLD.</p><p><strong>Materials and methods: </strong>A total of 1588 youths from the population-based data were included in the training set. For the test sets, 121 participants were included for IR and 50 for MASLD from real-world clinic data. Logistic regression analysis, random forest, extreme gradient boosting (XGBoost), light gradient boosting machine (GBM), and deep neural network (DNN) were used to develop the models. A nomogram scoring system was constructed based on a model used to predict the probability of IR and MASLD.</p><p><strong>Results: </strong>After stepwise selection, age, body mass index (BMI) standard deviation score (SDS), waist circumference (WC), systolic blood pressure, HbA1c, high-density lipoprotein cholesterol, triglyceride, and alanine aminotransferase levels were included in the model. A nomogram scoring system was constructed based on a multivariable logistic regression model. The areas under the curves (AUCs) of the models for IR prediction in external validation were 0.75 (logistic regression), 0.78 (random forest), 0.72 (XGBoost), 0.71 (light GBM), and 0.71 (DNN). For MASLD prediction, the AUCs were 0.93 (logistic regression), 0.95 (random forest), 0.90 (XGBoost), 0.91 (light GBM), and 0.85 (DNN). BMI SDS and WC SDS were the most important contributors to IR prediction in all models.</p><p><strong>Conclusion: </strong>The Pediatric Insulin Resistance Assessment Score is a novel scoring system for predicting IR and MASLD in youths.</p>","PeriodicalId":23765,"journal":{"name":"Yonsei Medical Journal","volume":"66 8","pages":"464-472"},"PeriodicalIF":2.8000,"publicationDate":"2025-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12303674/pdf/","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Yonsei Medical Journal","FirstCategoryId":"3","ListUrlMain":"https://doi.org/10.3349/ymj.2024.0442","RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"MEDICINE, GENERAL & INTERNAL","Score":null,"Total":0}
引用次数: 0

Abstract

Purpose: Insulin resistance (IR) is a condition closely associated with cardiovascular risk factors and metabolic dysfunction-associated steatotic liver disease (MASLD) is emerging as a significant IR-related complication. We aimed to develop a predictive model for IR in youths and implicate this model for MASLD.

Materials and methods: A total of 1588 youths from the population-based data were included in the training set. For the test sets, 121 participants were included for IR and 50 for MASLD from real-world clinic data. Logistic regression analysis, random forest, extreme gradient boosting (XGBoost), light gradient boosting machine (GBM), and deep neural network (DNN) were used to develop the models. A nomogram scoring system was constructed based on a model used to predict the probability of IR and MASLD.

Results: After stepwise selection, age, body mass index (BMI) standard deviation score (SDS), waist circumference (WC), systolic blood pressure, HbA1c, high-density lipoprotein cholesterol, triglyceride, and alanine aminotransferase levels were included in the model. A nomogram scoring system was constructed based on a multivariable logistic regression model. The areas under the curves (AUCs) of the models for IR prediction in external validation were 0.75 (logistic regression), 0.78 (random forest), 0.72 (XGBoost), 0.71 (light GBM), and 0.71 (DNN). For MASLD prediction, the AUCs were 0.93 (logistic regression), 0.95 (random forest), 0.90 (XGBoost), 0.91 (light GBM), and 0.85 (DNN). BMI SDS and WC SDS were the most important contributors to IR prediction in all models.

Conclusion: The Pediatric Insulin Resistance Assessment Score is a novel scoring system for predicting IR and MASLD in youths.

Abstract Image

Abstract Image

Abstract Image

青少年胰岛素抵抗的预测模型及其对MASLD的影响:一种新的标记物,儿童胰岛素抵抗评估评分。
目的:胰岛素抵抗(IR)是一种与心血管危险因素密切相关的疾病,代谢功能障碍相关的脂肪变性肝病(MASLD)正在成为一种重要的IR相关并发症。我们的目标是建立一个青少年IR的预测模型,并将该模型应用于MASLD。材料与方法:从基于人口的数据中选取1588名青少年作为训练集。对于测试集,来自真实世界临床数据的121名参与者包括IR和50名MASLD。采用Logistic回归分析、随机森林、极端梯度增强(XGBoost)、轻梯度增强机(GBM)和深度神经网络(DNN)建立模型。基于预测IR和MASLD概率的模型,构建了nomogram评分系统。结果:经逐步选择,模型纳入年龄、体重指数(BMI)标准差评分(SDS)、腰围(WC)、收缩压、糖化血红蛋白(HbA1c)、高密度脂蛋白胆固醇、甘油三酯、丙氨酸转氨酶水平。基于多变量logistic回归模型构建了nomogram评分系统。外部验证IR预测模型的曲线下面积(aus)分别为0.75 (logistic回归)、0.78(随机森林)、0.72 (XGBoost)、0.71 (light GBM)和0.71 (DNN)。对于MASLD预测,auc分别为0.93(逻辑回归)、0.95(随机森林)、0.90 (XGBoost)、0.91(轻GBM)和0.85 (DNN)。BMI SDS和WC SDS是所有模型中IR预测最重要的贡献者。结论:儿童胰岛素抵抗评估评分是一种预测青少年IR和MASLD的新评分系统。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
求助全文
约1分钟内获得全文 求助全文
来源期刊
Yonsei Medical Journal
Yonsei Medical Journal 医学-医学:内科
CiteScore
4.50
自引率
0.00%
发文量
167
审稿时长
3 months
期刊介绍: The goal of the Yonsei Medical Journal (YMJ) is to publish high quality manuscripts dedicated to clinical or basic research. Any authors affiliated with an accredited biomedical institution may submit manuscripts of original articles, review articles, case reports, brief communications, and letters to the Editor.
×
引用
GB/T 7714-2015
复制
MLA
复制
APA
复制
导出至
BibTeX EndNote RefMan NoteFirst NoteExpress
×
提示
您的信息不完整,为了账户安全,请先补充。
现在去补充
×
提示
您因"违规操作"
具体请查看互助需知
我知道了
×
提示
确定
请完成安全验证×
copy
已复制链接
快去分享给好友吧!
我知道了
右上角分享
点击右上角分享
0
联系我们:info@booksci.cn Book学术提供免费学术资源搜索服务,方便国内外学者检索中英文文献。致力于提供最便捷和优质的服务体验。 Copyright © 2023 布克学术 All rights reserved.
京ICP备2023020795号-1
ghs 京公网安备 11010802042870号
Book学术文献互助
Book学术文献互助群
群 号:604180095
Book学术官方微信