Evidence map›Paper›PMID 40483414›Full record

Observational studyBMC pediatrics2025

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

Arjan Henryk Jonathan Huizing, Marieke Welten, Sylvia van der Pal, Yvonne Schönbeck, Pepijn van Empelen, Romy Gaillard, Vincent W V Jaddoe, Stef van Buuren

Abstract readObservational StudyValidation Study
PubMed Full text at PMC Publisher
In one paragraph

Observational study in BMC pediatrics, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.

0numbers the graph read from it
0cells of the map it votes in
3citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

3 citing papers in PubMed.

  1. Review
  2. Article
  3. Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

8 authors.

Arjan Henryk Jonathan Huizing *TNO (Netherlands Organisation for Applied Scientific Research), Expertise group Child Health, Leiden, The Netherlands.
Marieke Welten *Department of Pediatrics, Erasmus MC, University Medical Center Rotterdam, Rotterdam, The Netherlands.
Sylvia van der PalTNO (Netherlands Organisation for Applied Scientific Research), Expertise group Child Health, Leiden, The Netherlands.
Yvonne SchönbeckTNO (Netherlands Organisation for Applied Scientific Research), Expertise group Child Health, Leiden, The Netherlands.
Pepijn van EmpelenTNO (Netherlands Organisation for Applied Scientific Research), Expertise group Child Health, Leiden, The Netherlands.
Romy GaillardDepartment of Pediatrics, Erasmus MC, University Medical Center Rotterdam, Rotterdam, The Netherlands.
Vincent W V JaddoeDepartment of Pediatrics, Erasmus MC, University Medical Center Rotterdam, Rotterdam, The Netherlands.
Stef van BuurenTNO (Netherlands Organisation for Applied Scientific Research), Expertise group Child Health, Leiden, The Netherlands. stef.vanbuuren@tno.nl.

Funding

Consolidator Grant from the European Research Council ERC-2014-CoG-648916Netherlands Organization for Health Research and Development ZonMw VIDI 09150172110034
6 · The paper itself

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.

Indexed as

Pediatric ObesityAdolescentBody Mass IndexChildChild, PreschoolFemaleHumansInfantInfant, NewbornMaleModels, StatisticalNetherlandsProspective StudiesRisk AssessmentRisk FactorsEarly ageGeneration R studyObesityPrediction model

Identifiers

PMID40483414
PMCPMC12144741

What Socratic holds

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Registered trials

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the Socratic graph.