Predictive models of newborn body composition: a systematic review

Abstract

Abstract Objective: To analyze the prediction models of fat-free mass and fat mass of neonates who had air displacement plethysmography as a reference test. Data source: A systematic review of studies identified in the PubMed, Virtual Health Library (BVS), SciELO, and ScienceDirect databases was carried out. The Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) checklist was used for inclusion of studies, the Transparent Reporting of a Multivariable Prediction Model for Individual Prognosis or Diagnosis (TRIPOD) report was used to select only predictive models studies, and the Prediction Model Risk of Bias Assessment Tool (PROBAST) was used to assess the risk of bias in the models. Data synthesis: This study is registered in PROSPERO with identification CRD42020175048. Five hundred and three studies were found during the searches, and only four papers (six models) were eligible. Most studies (three) used the sum of different skinfolds to predict neonatal body fat and all presented weight as the variable with the highest contribution to predicting neonatal body composition. Two models that used skinfolds showed high coefficients of determination and explained, significantly, 81% of the body fat measured by air displacement plethysmography, while the models using bioimpedance did not find a significant correlation between the impedance index and the fat-free mass. Conclusions: The few studies found on this topic had numerous methodological differences. However, the subscapular skinfold was a strong predictor of neonatal body fat in three studies. It is noteworthy that such model validation studies should be carried out in the future, allowing them to be subsequently applied to the population. The development of these models with low-cost tools will contribute to better nutritional monitoring of children and could prevent complications in adulthood.

Keywords

• Newborn
• Regression analysis
• Body composition