Boncyk Morgan, Leroy Jef L, Brander Rebecca L, Larson Leila M, Ruel Marie T, Frongillo Edward A
Department of Health Promotion, Education, and Behavior, Arnold School of Public Health, University of South Carolina, Columbia, SC, United States.
Nutrition, Diets, and Health Unit, International Food Policy Research Institute, Washington, DC, United States.
Adv Nutr. 2025 Jul;16(7):100452. doi: 10.1016/j.advnut.2025.100452. Epub 2025 May 24.
The global increase in early childhood overweight and obesity has prompted interest in early prediction of overweight and obesity to allow timely intervention and prevent lifelong consequences. A systematic review was conducted to assess the accuracy and feasibility of predicting overweight and obesity in individual children aged 3-7 y using data available in healthcare and community settings on children aged under 24 mo. This review was registered in PROSPERO (CRD42024509603) and followed the Preferred Reporting Items for Systematic Reviews and Meta-Analyses guidelines. From 7943 unique articles identified through PubMed, CINAHL, Scopus, and Google Scholar, 14 studies met the inclusion criteria, 13 from high-income countries and 1 from a middle-income country. These studies evaluated the accuracy of predicting childhood overweight or obesity in individual children using anthropometrics-alone or multiple-predictor models. Anthropometrics-alone models yielded areas under the curve (AUCs) ≥ 0.56 with expert guidance and ≥0.77 with machine learning. Multiple-predictor models yielded AUC ≥ 0.68 with expert guidance and ≥0.76 with machine learning. The inclusion of child, parental, and community predictors improved predictive accuracy but led to greater variation in performance across models. Models were more accurate when children were older at the initial assessment, multiple assessments were made, and the time between assessment and outcome prediction was shorter. Prediction models with an AUC ≥ 0.70 used machine learning to optimize variable selection, limiting their practicality for broad-scale implementation in healthcare or community settings. There is insufficient evidence on the accuracy of overweight and obesity prediction models for children in low- and middle-income countries. Existing prediction models are not well-suited for broad-scale screening of individual children for risk of early childhood overweight or obesity.
全球幼儿超重和肥胖现象的增加引发了人们对早期预测超重和肥胖的兴趣,以便及时进行干预并预防终身后果。本研究进行了一项系统综述,以评估利用24个月以下儿童在医疗保健和社区环境中可得的数据预测3至7岁儿童个体超重和肥胖的准确性及可行性。本综述已在国际前瞻性系统评价注册库(PROSPERO,注册号:CRD42024509603)登记,并遵循系统评价和Meta分析的首选报告项目(PRISMA)指南。通过PubMed、CINAHL、Scopus和谷歌学术搜索共识别出7943篇独特文章,其中14项研究符合纳入标准,13项来自高收入国家,1项来自中等收入国家。这些研究评估了单独使用人体测量学或多预测因子模型预测儿童个体超重或肥胖的准确性。单独使用人体测量学模型在专家指导下曲线下面积(AUC)≥0.56,在机器学习辅助下≥0.77。多预测因子模型在专家指导下AUC≥0.68,在机器学习辅助下≥0.76。纳入儿童、父母和社区预测因子可提高预测准确性,但导致各模型间性能差异更大。当初次评估时儿童年龄较大、进行多次评估且评估与结局预测之间的时间较短时,模型更准确。AUC≥0.70的预测模型使用机器学习优化变量选择,限制了其在医疗保健或社区环境中广泛应用的实用性。关于低收入和中等收入国家儿童超重和肥胖预测模型的准确性,证据不足。现有的预测模型不太适合对个体儿童进行广泛筛查以确定其幼儿期超重或肥胖风险