Predicting obesity at adolescence from an early age in a Dutch observational cohort study: the development and internal validation of a multivariable prediction model.

IF 2 3区 医学 Q2 PEDIATRICS
Arjan Henryk Jonathan Huizing, Marieke Welten, Sylvia van der Pal, Yvonne Schönbeck, Pepijn van Empelen, Romy Gaillard, Vincent W V Jaddoe, Stef van Buuren
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Abstract

Background: - Identifying children with a high risk of developing future obesity could enable timely targeted prevention strategies. The study's objective was to develop prediction models that could detect if young children at very early age, from birth to age six, have an increased risk of being obese in early adolescence.

Methods: - We analyzed a subset of data (N = 4,309) from the Generation R study, a population-based prospective cohort study of pregnant women and their children from fetal life to young adulthood in the Netherlands. Parental, household, and birth/child characteristics were considered as predictors. We developed separate models for children at age zero (three months), two, four, and six years that predict obesity at age 10 to 14 years. Per age we fitted an optimal prediction model (full model) and a more practical model with less predictors (restricted model). For the development of the prediction models we used regularized regression models with a least absolute shrinkage and selection operator (LASSO) penalty to avoid overfitting.

Results: - Parental body mass index (BMI), parental education level, latest child BMI measurements, ethnicity of the child, breakfast consumption, cholesterol, and low-density lipoprotein (LDL) of the child were included as predictors in all models when considered as candidate predictor. The models for all age groups performed well (lowest area under the curve (AUC) 0.872 for the age 0 restricted model), with the highest performance for the 6-year model (AUC 0.954 and 0.949, full and restricted model). Sensitivity and specificity of models varied between ages with ranges 0.80-0.90 (full model); 0.79-0.89 (restricted model) and 0.80-0.88 (full model); 0.79-0.87 (restricted model).

Conclusions: - These obesity prediction models seem promising and could be used as valuable tools for early detection of children at increased risk of being obese at adolescence, even at an early age.

在荷兰观察队列研究中预测青少年早期肥胖:多变量预测模型的发展和内部验证。
背景:-确定未来发展为肥胖的高风险儿童可以使及时有针对性的预防策略成为可能。这项研究的目的是建立预测模型,以检测幼儿(从出生到6岁)在青春期早期是否有更高的肥胖风险。方法:我们分析了来自R世代研究的数据子集(N = 4309),这是一项基于人群的前瞻性队列研究,研究对象是荷兰孕妇及其子女从胎儿期到青年期。父母、家庭和出生/子女特征被认为是预测因素。我们分别为0岁(3个月)、2岁、4岁和6岁的儿童开发了预测10至14岁儿童肥胖的模型。每个年龄我们拟合了一个最优的预测模型(完整模型)和一个更实用的模型与较少的预测(限制模型)。对于预测模型的开发,我们使用具有最小绝对收缩和选择算子(LASSO)惩罚的正则化回归模型来避免过拟合。结果:父母的身体质量指数(BMI)、父母的教育水平、最新的儿童BMI测量值、儿童的种族、早餐摄入量、胆固醇和儿童的低密度脂蛋白(LDL)被认为是候选预测因子时,所有模型都将其作为预测因子。所有年龄组的模型均表现良好(0岁限制模型曲线下面积(AUC)最低0.872),其中6岁模型的性能最高(AUC分别为0.954和0.949,完全和限制模型)。模型的敏感性和特异性随年龄的变化而变化,范围为0.80 ~ 0.90(全模型);0.79-0.89(受限模型)和0.80-0.88(完整模型);0.79-0.87(限制模型)。结论:这些肥胖预测模型似乎很有希望,可以作为早期发现青春期肥胖风险增加的儿童的有价值的工具,甚至在很小的时候。
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来源期刊
BMC Pediatrics
BMC Pediatrics PEDIATRICS-
CiteScore
3.70
自引率
4.20%
发文量
683
审稿时长
3-8 weeks
期刊介绍: BMC Pediatrics is an open access journal publishing peer-reviewed research articles in all aspects of health care in neonates, children and adolescents, as well as related molecular genetics, pathophysiology, and epidemiology.
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