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

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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 factors in the pathogenesis.