Computational gene expression modeling identifies salivary biomarker analysis that predict oral feeding readiness in...

To combine mathematical modeling of salivary gene expression microarray data and systems biology annotation with reverse-transcription quantitative polymerase chain reaction amplification to identify (phase I) and validate (phase II) salivary biomarker analysis for the prediction of oral feeding readiness in preterm infants.

Comparative whole-transcriptome microarray analysis from 12 preterm newborns pre- and postoral feeding success was used for computational modeling and systems biology analysis to identify potential salivary transcripts associated with oral feeding success (phase I). Selected gene expression biomarkers (15 from computational modeling; 6 evidence-based; and 3 reference) were evaluated by reverse-transcription quantitative polymerase chain reaction amplification on 400 salivary samples from successful (n = 200) and unsuccessful (n = 200) oral feeders (phase II). Genes, alone and in combination, were evaluated by a multivariate analysis controlling for sex and postconceptional age (PCA) to determine the probability that newborns achieved successful oral feeding.

Advancing PCA (P < .001) and female sex (P = .05) positively predicted an infant's ability to feed orally. A combination of 5 genes, neuropeptide Y2 receptor (hunger signaling), adneosine-monophosphate-activated protein kinase (energy homeostasis), plexin A1 (olfactory neurogenesis), nephronophthisis 4 (visual behavior), and wingless-type MMTV integration site family, member 3 (facial development), in addition to PCA and sex, demonstrated good accuracy for determining feeding success (area under the receiver operator characteristic curve = 0.78).

We have identified objective and biologically relevant salivary biomarkers that noninvasively assess a newborn's developing brain, sensory, and facial development as they relate to oral feeding success. Understanding the mechanisms that underlie the development of oral feeding readiness through translational and computational methods may improve clinical decision making while decreasing morbidities and health care costs.