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Machine learning model demonstrates stunting at birth and systemic inflammatory biomarkers as predictors of subsequent infant growth – a four-year prospective study

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 ai...

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Autores principales: Harrison, Elizabeth, Syed, Sana, Ehsan, Lubaina, Iqbal, Najeeha T., Sadiq, Kamran, Umrani, Fayyaz, Ahmed, Sheraz, Rahman, Najeeb, Jakhro, Sadaf, Ma, Jennie Z., Hughes, Molly, Ali, S. Asad
Formato: Online Artículo Texto
Lenguaje:English
Publicado: BioMed Central 2020
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7597024/
https://www.ncbi.nlm.nih.gov/pubmed/33126871
http://dx.doi.org/10.1186/s12887-020-02392-3
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author Harrison, Elizabeth
Syed, Sana
Ehsan, Lubaina
Iqbal, Najeeha T.
Sadiq, Kamran
Umrani, Fayyaz
Ahmed, Sheraz
Rahman, Najeeb
Jakhro, Sadaf
Ma, Jennie Z.
Hughes, Molly
Ali, S. Asad
author_facet Harrison, Elizabeth
Syed, Sana
Ehsan, Lubaina
Iqbal, Najeeha T.
Sadiq, Kamran
Umrani, Fayyaz
Ahmed, Sheraz
Rahman, Najeeb
Jakhro, Sadaf
Ma, Jennie Z.
Hughes, Molly
Ali, S. Asad
author_sort Harrison, Elizabeth
collection PubMed
description 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. CONCLUSION: We 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. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1186/s12887-020-02392-3.
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spelling pubmed-75970242020-11-02 Machine learning model demonstrates stunting at birth and systemic inflammatory biomarkers as predictors of subsequent infant growth – a four-year prospective study Harrison, Elizabeth Syed, Sana Ehsan, Lubaina Iqbal, Najeeha T. Sadiq, Kamran Umrani, Fayyaz Ahmed, Sheraz Rahman, Najeeb Jakhro, Sadaf Ma, Jennie Z. Hughes, Molly Ali, S. Asad BMC Pediatr Research Article 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. CONCLUSION: We 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. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1186/s12887-020-02392-3. BioMed Central 2020-10-30 /pmc/articles/PMC7597024/ /pubmed/33126871 http://dx.doi.org/10.1186/s12887-020-02392-3 Text en © The Author(s) 2020 Open AccessThis 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 permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated in a credit line to the data.
spellingShingle Research Article
Harrison, Elizabeth
Syed, Sana
Ehsan, Lubaina
Iqbal, Najeeha T.
Sadiq, Kamran
Umrani, Fayyaz
Ahmed, Sheraz
Rahman, Najeeb
Jakhro, Sadaf
Ma, Jennie Z.
Hughes, Molly
Ali, S. Asad
Machine learning model demonstrates stunting at birth and systemic inflammatory biomarkers as predictors of subsequent infant growth – a four-year prospective study
title Machine learning model demonstrates stunting at birth and systemic inflammatory biomarkers as predictors of subsequent infant growth – a four-year prospective study
title_full Machine learning model demonstrates stunting at birth and systemic inflammatory biomarkers as predictors of subsequent infant growth – a four-year prospective study
title_fullStr Machine learning model demonstrates stunting at birth and systemic inflammatory biomarkers as predictors of subsequent infant growth – a four-year prospective study
title_full_unstemmed Machine learning model demonstrates stunting at birth and systemic inflammatory biomarkers as predictors of subsequent infant growth – a four-year prospective study
title_short Machine learning model demonstrates stunting at birth and systemic inflammatory biomarkers as predictors of subsequent infant growth – a four-year prospective study
title_sort machine learning model demonstrates stunting at birth and systemic inflammatory biomarkers as predictors of subsequent infant growth – a four-year prospective study
topic Research Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7597024/
https://www.ncbi.nlm.nih.gov/pubmed/33126871
http://dx.doi.org/10.1186/s12887-020-02392-3
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