Predictive Analytics for Precision Nutrition in Preterm Infants

Saturday, April 29, 2023

11:00 AM – 12:30 PM ET

Location: Convention Center: 152 A

CE: Live CME: 1.50

Selected By: MOD
Co-sponsored By: AAP SoNPM

Chair(s)

JN

Josef Neu, MD

Professor
Pediatrics/Neonatology
University of Florida
Gainesville, Florida, United States
Mohan Pammi, MD, PhD, MRCPCH (he/him/his)

Professor
Baylor College of Medicine
Bellaire, Texas, United States

Disclosure(s):

Josef Neu, MD: Glycome: Consultant (Ongoing); infant bacterial therapeutics: Research Support including clinical trials & the principal or named investigator (Ongoing); Medela: Advisory Committee (Ongoing); Nestle Nutrition Institute: Advisory Committee (Ongoing); Siolta Therapeutics: Consultant (Ongoing)

Mohan Pammi, MD, PhD, MRCPCH: No financial relationships to disclose

Session

Description: Current nutritional guidelines in preterm infants are based on population statistics, although helpful may marginalize some preterm infants. Optimal nutrition not only enhances lifelong health but also influences transgenerational outcomes by epigenetic mechanisms. Once growth faltering or malnutrition is noticed using a growth curve or existing indicators (retroactive approach), it may already be too late for meaningful interventions. Optimization of nutrition via a personalized proactive approach, where malnutrition or faltering growth curve can be predicted before they occur for timely interventions is urgently needed. The NIH recently initiated the 2020-2030 Strategic Plan for NIH Nutrition Research, which emphasizes the urgent need for precision nutrition for preterm infants. The NIH defines precision medicine as ‘an innovative approach that takes into account individual differences in patients’ genes, environments, and lifestyles’.

Recent advances in high-throughput technologies have provided access to multiomics biological data (including genomics, epigenomics, transcriptomics, proteomics and metabolomics). Biological insights gained from the multi-omics should be integrated with clinical and social data and applied for the best nutritional outcomes possible. The insights gained by integration of multi-omics, clinical and social datasets, with machine learning enables development of predictive models and biomarkers that can predict short- and long-term growth outcomes. Early predictions coupled with timely appropriate interventions, personalized for each preterm infant has the best chance of achieving precision nutrition. In this workshop, we aim to summarize the most recent data on predictive models and strategies to achieve precision nutrition for preterm infants.

Learning Objectives:

Summarize current advances in multi-omics data acquisition and integration.
Discuss predictive models in perinatology developed by machine learning, clinical and multiomics data.
Strategize precision nutrition for preterm infants for better nutritional and growth outcomes

Presentations:

11:00 AM – 11:15 AM ET

Nutritional models and trajectories to identify preterm infants at risk and who will benefit from a personalized approach
Speaker: Josef Neu, MD – University of Florida
11:15 AM – 11:30 AM ET

Machine learning, multi-omics and prediction models
Speaker: Mohan Pammi, MD, PhD, MRCPCH (he/him/his) – Baylor College of Medicine
11:30 AM – 11:45 AM ET

Multiomics in Neonatal Lung disease
Speaker: Namasivayam Ambalavanan, MD (he/him/his) – University of Alabama School of Medicine
11:45 AM – 12:10 PM ET

Multi-omic investigation with machine learning in Perinatology
Speaker: Nima Aghaeepour, PhD – Stanford University School of Medicine
12:10 PM – 12:25 PM ET

Examples of a personalized nutrition approach
Speaker: Dominick J. Lemas, PhD (he/him/his) – University of Florida College of Medicine