# Machine Learning and Multiomics for Predictive Models and Biomarker Discovery in Preterm Infants.

> **NIH NIH R01** · BAYLOR COLLEGE OF MEDICINE · 2024 · $656,072

## Abstract

PROJECT SUMMARY
Preterm infants born at < 32 weeks and <1500 g (very low birth weight, VLBW) suffer from increased mortality
(10-15%) and less than 70% survive without major morbidity. Microbial dysbiosis has been associated with
major preterm morbidities but the microbial metabolites or the mechanisms by which they impact
pathophysiology, survival and morbidity is not known. The purpose of this proposal is to develop holistic
prediction models integrating clinical data and multi-omic signatures, aid biomarker discovery and advance the
paradigm in Neonatal Medicine from traditional to targeted precision medicine. The overarching hypothesis is
that integrating metabolic and multi-omic signatures with clinical data will reliably predict survival and major
morbidity in preterm, VLBW infants. The long-term goal of this research is to establish causal association
between identified microbial metabolites and disease in preterm infants, contribute to the knowledgebase of
microbial metabolites and improve preterm outcomes. We will test our hypothesis using the following Specific
Aims; Aim 1) Leverage machine learning techniques to develop clinical prediction models for mortality and
specific morbidities in preterm, VLBW infants: We will test the hypothesis, that a model integrating clinical
variables in the first 2 wks. of age, will accurately predict mortality, and morbidities of late-onset sepsis, NEC,
BPD, severe ROP and severe IVH. We will employ a retrospective cohort from the Vermont Oxford Database
(VON) from Texas Children’s Hospital, (n= 3385 VLBW infants). We will validate the clinical predictive models
derived from aim 1A with the prospective clinical data from the first 2 weeks, from Aim 2 (n=300), Aim 2)
Delineate microbial metabolites and multi-omic signatures that differentiate preterm VLBW infants with
mortality and morbidity, refine predictive models and enhance biomarker discovery: We will test the hypothesis
that integrating multi-omics signatures with clinical data using machine learning techniques will refine our
predictive models (mortality and specific morbidities of late-onset sepsis, NEC, BPD, ROP and IVH/PVL) for
better accuracy and enhance biomarker discovery. We will accomplish this in a prospective study design of
enrolled preterm (< 32weeks), VLBW infants (n= 300) and collect stool, urine and blood samples, longitudinally
twice a week for 2 weeks of age. We anticipate identifying known and novel metabolites and delineating
metabolic pathways hitherto unidentified that influence preterm pathophysiology and outcomes. Holistic
prediction models using information from the first 2 weeks of life will enable us to introduce interventions early
to improve health trajectories and patient outcomes, thereby facilitating the paradigm of proactive precision
medicine in Neonatology. The impact of our results extend beyond the field of neonatology, to other patients
and diseases where microbial dysbiosis and altered metabolome are key fact...

## Key facts

- **NIH application ID:** 10917279
- **Project number:** 5R01HD112886-02
- **Recipient organization:** BAYLOR COLLEGE OF MEDICINE
- **Principal Investigator:** Mohan Pammi
- **Activity code:** R01 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2024
- **Award amount:** $656,072
- **Award type:** 5
- **Project period:** 2023-09-01 → 2028-08-31

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10917279

## Citation

> US National Institutes of Health, RePORTER application 10917279, Machine Learning and Multiomics for Predictive Models and Biomarker Discovery in Preterm Infants. (5R01HD112886-02). Retrieved via AI Analytics 2026-07-25 from https://api.ai-analytics.org/grant/nih/10917279. Licensed CC0.

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