# Nutritional and clinical predictors of intestinal maturation and feeding tolerance in the preterm infant

> **NIH NIH R01** · UNIVERSITY OF ILLINOIS AT URBANA-CHAMPAIGN · 2023 · $722,487

## Abstract

Project Summary/Abstract:
In 2020, 10% of U.S. infants were born preterm and ~2%, or 60,000 infants, were born very preterm (VPI; <32
weeks PMA). VPI infants are at high risk for of substantial medical complications, including necrotizing
enterocolitis (NEC). In VPI, advancing and maintaining nutritional support reduces disease risk and improves
neurodevelopmental outcomes; however, up to 25% of preterm infants demonstrate feeding intolerance, which
may be benign or may progress to NEC. However, precise measures and clinical tools that reliably differentiate
benign, intestinal immaturity from life-threatening symptoms are lacking. Therefore, the overall objective of this
application is to establish intestinal host and microbial biomarkers of intestinal function from an existing,
longitudinal, prospective cohort of 400 analyzable VPI and to relate those biomarkers to the spectrum of intestinal
function, from consistent enteral nutrition tolerance to intermittent intolerance to ischemic injury. For this purpose,
we will utilize our novel non-invasive (exfoliated mucosal cell) methodology to simultaneously assess host-
microbiome interactions in the VPI gut. Our central hypothesis is that the transgenomic cross-talk between
intestinal mucosal cells and the fecal metagenome and metabolome will provide mechanistic insight into the
spectrum of clinical presentations ranging from normal gut developmental biology to abnormal pathophysiology.
Three specific aims will test our central hypothesis. Aim 1 will annotate the host exfoliated mucosal cell
transcriptome and fecal bacterial metagenome and metabolome profiles to identify biomarkers for preterm infants
who have consistent tolerance to enteral feeding or who are diagnosed with feeding intolerance. Aim 2 will
annotate the host exfoliated mucosal cell transcriptome and fecal bacterial metagenome and metabolome
profiles to identify biomarkers for preterm infants who are diagnosed with feeding intolerance compared to those
who develop ischemia. Aim 3 will utilize machine learning algorithms to construct putative diet-health outcome
driven Artificial Neural Networks (ANNs). Completion of these aims will provide the necessary data to develop
predictive algorithms to enable identification of at-risk VPI who would benefit from precision medicine/nutrition
guided interventions targeting specific risk factors.

## Key facts

- **NIH application ID:** 10717469
- **Project number:** 1R01HD112396-01
- **Recipient organization:** UNIVERSITY OF ILLINOIS AT URBANA-CHAMPAIGN
- **Principal Investigator:** Robert Stephen Chapkin
- **Activity code:** R01 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2023
- **Award amount:** $722,487
- **Award type:** 1
- **Project period:** 2023-09-12 → 2028-05-31

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10717469

## Citation

> US National Institutes of Health, RePORTER application 10717469, Nutritional and clinical predictors of intestinal maturation and feeding tolerance in the preterm infant (1R01HD112396-01). Retrieved via AI Analytics 2026-05-21 from https://api.ai-analytics.org/grant/nih/10717469. Licensed CC0.

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