Machine learning model demonstrates stunting at birth and systemic inflammatory biomarkers as predictors of subsequent i
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RESEARCH ARTICLE
Open Access
Machine learning model demonstrates stunting at birth and systemic inflammatory biomarkers as predictors of subsequent infant growth – a four-year prospective study Elizabeth Harrison1,2†, Sana Syed1,3*†, Lubaina Ehsan1, Najeeha T. Iqbal3, Kamran Sadiq3, Fayyaz Umrani3, Sheraz Ahmed3, Najeeb Rahman3, Sadaf Jakhro3, Jennie Z. Ma4, Molly Hughes5 and S. Asad Ali3*
Abstract Background: Stunting 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. Methods: Height-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. Results: Of 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. (Continued on next page)
* Correspondence: [email protected]; [email protected] † Elizabeth Harrison and Sana Syed contributed equally to this work. 1 School of Medicine, University of Virginia, Charlottesville, VA, USA 3 Department of Paediatrics and Child Health, Aga Khan University, Stadium Road, P. O. Box 3500, Karachi 74800, Pakistan Full list of author information is available at the end of the article © The Author(s). 2020 Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitte
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