ArticlePediatric obesity2020
Dynamic prediction model to identify young children at high risk of future overweight: Development and internal validation in a cohort study.
Article in Pediatric obesity, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 12 papers.
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Who cites it
12 citing papers in PubMed.
- Artificial Intelligence for Weight Management in Children: A Narrative Review.Healthcare (Basel, Switzerland) · 2026Review
- First 1000 Days Strategies to Prevent Childhood Obesity: A Narrative Review and Recommendations From the EndObesity Consortium.Pediatric obesity · 2026Review
- Body Weight and Range of Motion as Predictors of Trunk Asymmetry in Children With Spinal Muscular Atrophy: A Prospective Functional Assessment.Medical science monitor : international medical journal of experimental and clinical research · 2025Article
- Risk factors and prediction for pediatric obesity: current status and future perspectives.Endocrine journal · 2025Review
- Predicting obesity at adolescence from an early age in a Dutch observational cohort study: the development and internal validation of a multivariable prediction model.BMC pediatrics · 2025Observational
- Predicting the onset of overweight in Chinese high school students: a machine-learning approach in a one-year prospective cohort study.Endocrine · 2024Article
- Prediction of childhood overweight and obesity at age 10-11: findings from the Studying Lifecourse Obesity PrEdictors and the Born in Bradford cohorts.International journal of obesity (2005) · 2023Article
- Trans-ethnic polygenic risk scores for body mass index: An international hundred K+ cohorts consortium study.Clinical and translational medicine · 2023Article
- Predicting the earliest deviation in weight gain in the course towards manifest overweight in offspring exposed to obesity in pregnancy: a longitudinal cohort study.BMC medicine · 2022Article
- Weight transitions and psychosocial factors: A longitudinal cohort study of Finnish primary school children with overweight.Preventive medicine reports · 2020Article
- Development of a Prediction Model to Identify Children at Risk of Future Developmental Delay at Age 4 in a Population-Based Setting.International journal of environmental research and public health · 2020Article
- Dynamic prediction model to identify young children at high risk of future overweight: Development and internal validation in a cohort study.Pediatric obesity · 2020Article
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Authors and funding
9 authors.
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No grant is acknowledged in the PubMed record.
Abstract
backgroundPrimary prevention of overweight is to be preferred above secondary prevention, which has shown moderate effectiveness.
objectiveTo develop and internally validate a dynamic prediction model to identify young children in the general population, applicable at every age between birth and age 6, at high risk of future overweight (age 8).
methodsData were used from the Prevention and Incidence of Asthma and Mite Allergy birth cohort, born in 1996 to 1997, in the Netherlands. Participants for whom data on the outcome overweight at age 8 and at least three body mass index SD scores (BMI SDS) at the age of ≥3 months and ≤6 years were available, were included (N = 2265). The outcome of the prediction model is overweight (yes/no) at age 8 (range 7.4-10.5 years), defined according to the sex- and age-specific BMI cut-offs of the International Obesity Task Force.
resultsAfter backward selection in a Generalized Estimating Equations analysis, the prediction model included the baseline predictors maternal BMI, paternal BMI, paternal education, birthweight, sex, ethnicity and indoor smoke exposure; and the longitudinal predictors BMI SDS, and the linear and quadratic terms of the growth curve describing a child's BMI SDS development over time, as well as the longitudinal predictors' interactions with age. The area under the curve of the model after internal validation was 0.845 and Nagelkerke R
conclusionsA dynamic prediction model for overweight was developed with a good predictive ability using easily obtainable predictor information. External validation is needed to confirm that the model has potential for use in practice.
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