ArticleThe Journal of pediatrics2022
Development and Validation of a Prediction Model for Infant Fat Mass.
Article in The Journal of pediatrics, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.
What it found
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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.
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.
Who cites it
4 citing papers in PubMed, 6 citations in OpenAlex.
- Machine learning can predict surgical indication: new clustering model from a large adult spine deformity database.European spine journal : official publication of the European Spine Society, the European Spinal Deformity Society, and the European Section of the Cervical Spine Research Society · 2025Article
- Childhood obesity: The threatening apprentice of the adiposity empire.Reviews in endocrine & metabolic disorders · 2025Review
- Perceived Stress During Late Pregnancy and Infant Body Composition at 1 Month.Journal of the Endocrine Society · 2025Article
- Infant body composition: A comprehensive overview of assessment techniques, nutrition factors, and health outcomes.Nutrition in clinical practice : official publication of the American Society for Parenteral and Enteral Nutrition · 2023Review
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
10 authors at 3 institutions in 1 country.
Funding
Abstract
objectivesTo develop and validate a prediction model for fat mass in infants ≤12 kg using easily accessible measurements such as weight and length. STUDY
designWe used data from a pooled cohort of 359 infants age 1-24 months and weighing 3-12 kg from 3 studies across Southern California and New York City. The training data set (75% of the cohort) included 269 infants and the testing data set (25% of the cohort) included 90 infants age 1-24 months. Quantitative magnetic resonance was used as the standard measure for fat mass. We used multivariable linear regression analysis, with backwards selection of predictor variables and fractional polynomials for nonlinear relationships to predict infant fat mass (from which lean mass can be estimated by subtracting resulting estimates from total mass) in the training data set. We used 5-fold cross-validation to examine overfitting and generalizability of the model's predictive performance. Finally, we tested the adjusted model on the testing data set.
resultsThe final model included weight, length, sex, and age, and had high predictive ability for fat mass with good calibration of observed and predicted values in the training data set (optimism-adjusted R
conclusionsOur model accurately predicted infant fat mass and could be used to improve the accuracy of assessments of infant body composition for effective early identification, surveillance, prevention, and management of obesity and future chronic disease risk.
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Registered trials
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 OpenQuestion graph.