Department of Computer Science and Applied Mathematics, Weizmann Institute of Science, Rehovot, Israel; Department of Molecular Cell Biology, Weizmann Institute of Science, Rehovot, Israel.
Department of Computer Science and Applied Mathematics, Weizmann Institute of Science, Rehovot, Israel; Department of Molecular Cell Biology, Weizmann Institute of Science, Rehovot, Israel; Pediatric Diabetes Unit, Ruth Rappaport Children's Hospital of Haifa, Rambam Healthcare Campus, Haifa, Israel.
J Pediatr. 2021 Jun;233:132-140.e1. doi: 10.1016/j.jpeds.2021.02.010. Epub 2021 Feb 11.
To evaluate body mass index (BMI) acceleration patterns in children and to develop a prediction model targeted to identify children at high risk for obesity before the critical time window in which the largest increase in BMI percentile occurs.
We analyzed electronic health records of children from Israel's largest healthcare provider from 2002 to 2018. Data included demographics, anthropometric measurements, medications, diagnoses, and laboratory tests of children and their families. Obesity was defined as BMI ≥95th percentile for age and sex. To identify the time window in which the largest annual increases in BMI z score occurs during early childhood, we first analyzed childhood BMI acceleration patterns among 417 915 adolescents. Next, we devised a model targeted to identify children at high risk before this time window, predicting obesity at 5-6 years of age based on data from the first 2 years of life of 132 262 children.
Retrospective BMI analysis revealed that among adolescents with obesity, the greatest acceleration in BMI z score occurred between 2 and 4 years of age. Our model, validated temporally and geographically, accurately predicted obesity at 5-6 years old (area under the receiver operating characteristic curve of 0.803). Discrimination results on subpopulations demonstrated its robustness across the pediatric population. The model's most influential predictors included anthropometric measurements of the child and family. Other impactful predictors included ancestry and pregnancy glucose.
Rapid rise in the prevalence of childhood obesity warrant the development of better prevention strategies. Our model may allow an accurate identification of children at high risk of obesity.
评估儿童体重指数（BMI）的加速模式，并建立一个预测模型，旨在在 BMI 百分位发生最大增长的关键窗口期之前，识别出肥胖风险较高的儿童。
我们分析了以色列最大医疗保健提供商 2002 年至 2018 年期间的儿童电子健康记录。数据包括儿童及其家庭的人口统计学、人体测量学测量值、药物、诊断和实验室检测。肥胖定义为 BMI 年龄和性别≥第 95 百分位。为了确定儿童早期 BMI z 分数每年最大增长的时间窗口，我们首先分析了 417915 名青少年的儿童 BMI 加速模式。接下来，我们设计了一个针对该时间窗口之前的高危儿童的模型，根据 132262 名儿童生命最初 2 年的数据，预测 5-6 岁时的肥胖。
回顾性 BMI 分析显示，在肥胖青少年中，BMI z 分数的最大加速发生在 2 至 4 岁之间。我们的模型经过时间和地域验证，准确预测了 5-6 岁时的肥胖（接受者操作特征曲线下面积为 0.803）。对亚人群的判别结果表明，该模型在儿科人群中具有稳健性。该模型最具影响力的预测因素包括儿童和家庭的人体测量测量值。其他有影响力的预测因素包括祖先和妊娠血糖。
儿童肥胖患病率的迅速上升，需要制定更好的预防策略。我们的模型可以准确识别肥胖风险较高的儿童。
