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Machine learning model demonstrates stunting at birth and systemic inflammatory biomarkers as predictors of
Elizabeth Harrison1,2, Sana Syed3,4, Lubaina Ehsan1
1School of Medicine, University of Virginia, Charlottesville, VA, USA.
Insights
Childhood stunting in LMICs often persists from birth to 48 months. Early height-for-age z-scores and inflammation biomarkers like CRP predict future growth, guiding interventions.
Area of Science:
- Pediatrics
- Global Health
- Biostatistics
Background:
- Childhood stunting is prevalent in low-to-middle income countries (LMICs), impacting cognitive development and vaccine response.
- Early identification of at-risk infants is crucial for timely intervention and morbidity prevention.
Purpose of the Study:
- To investigate growth patterns in infants up to 48 months of age.
- To determine if stunting improves over time and identify predictors of growth.
Main Methods:
- Longitudinal study tracking height-for-age z-scores (HAZ) at birth, 18, and 48 months in Pakistani infants.
- Analysis of serum biomarkers (e.g., AGP, CRP, IL1) at 6 and 9 months.
- Application of random forest models to predict growth outcomes.
Main Results:
- Stunting prevalence remained high (51% at birth to 54% at 48 months).
- Stunting status at 48 months often mirrored status at 18 months.
- Height-for-age z-score at birth was the primary predictor of 18-month HAZ; AGP, CRP, and IL1 were significant biomarker predictors.
Conclusions:
- Stunting in infancy frequently persists into early childhood.
- Machine learning models effectively predict growth trajectories.
- Early interventions targeting prenatal, birth, and early infancy periods are vital for at-risk populations in resource-constrained settings.
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.
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