ArticleBMC pediatrics2020
Machine learning model demonstrates stunting at birth and systemic inflammatory biomarkers as predictors of subsequent infant growth - a four-year prospective study.
Article in BMC pediatrics, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers.
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Who cites it
7 citing papers in PubMed, 25 citations in OpenAlex.
- Pro/synbiotics reduce systemic inflammation and improve gut health in Kenyan infants: An open label, randomized, 4-arm, phase II trial.Cell reports. Medicine · 2026Trial
- Newborn Nutritional Status at Birth and Its Association With Maternal Dietary Practices During Pregnancy in Gamo Zone, Southern Ethiopia: A Path Analysis.Food science & nutrition · 2025Article
- Development and Validation of a Predictive Model for Individual Risk Prediction of Stunting in Ethiopia: A Predictive Modeling Study.Health science reports · 2025Article
- Development and evaluation of machine learning training strategies for neonatal mortality prediction using multicountry data.Scientific reports · 2025Article
- Social and economic predictors of under-five stunting in Mexico: a comprehensive approach through the XGB model.Journal of global health · 2025Article
- Association between Pathogenic Variants of Diarrheagenic Escherichia coli and Growth in Children under 5 Years of Age in the Global Enteric Multicenter Study.The American journal of tropical medicine and hygiene · 2022Article
- Neonatal mortality prediction with routinely collected data: a machine learning approach.BMC pediatrics · 2021Article
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Authors and funding
12 authors at 3 institutions in 2 countries.
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Abstract
backgroundStunting affects up to one-third of the children in low-to-middle income countries (LMICs) and has been correlated with decline in cognitive capacity and vaccine immunogenicity. Early identification of infants at risk is critical for early intervention and prevention of morbidity. The aim of this study was to investigate patterns of growth in infants up through 48 months of age to assess whether the growth of infants with stunting eventually improved as well as the potential predictors of growth.
methodsHeight-for-age z-scores (HAZ) of children from Matiari (rural site, Pakistan) at birth, 18 months, and 48 months were obtained. Results of serum-based biomarkers collected at 6 and 9 months were recorded. A descriptive analysis of the population was followed by assessment of growth predictors via traditional machine learning random forest models.
resultsOf the 107 children who were followed up till 48 months of age, 51% were stunted (HAZ < - 2) at birth which increased to 54% by 48 months of age. Stunting status for the majority of children at 48 months was found to be the same as at 18 months. Most children with large gains started off stunted or severely stunted, while all of those with notably large losses were not stunted at birth. Random forest models identified HAZ at birth as the most important feature in predicting HAZ at 18 months. Of the biomarkers, AGP (Alpha- 1-acid Glycoprotein), CRP (C-Reactive Protein), and IL1 (interleukin-1) were identified as strong subsequent growth predictors across both the classification and regressor models.
conclusionWe demonstrated that children most children with stunting at birth remained stunted at 48 months of age. Value was added for predicting growth outcomes with the use of traditional machine learning random forest models. HAZ at birth was found to be a strong predictor of subsequent growth in infants up through 48 months of age. Biomarkers of systemic inflammation, AGP, CRP, IL1, were also strong predictors of growth outcomes. These findings provide support for continued focus on interventions prenatally, at birth, and early infancy in children at risk for stunting who live in resource-constrained regions of the world.
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