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Investigating neonatal health risk variables through cell-type specific methylome-wide association studies
Thomas L Campbell1, Lin Y Xie1, Ralen H Johnson1
1Center for Biomarker Research and Precision Medicine, Virginia Commonwealth University, 1112 East Clay Street, P. O. Box 980533, Richmond, VA, 23298-0581, USA.
Insights
This study identifies common and unique factors in neonatal health risks using blood methylome data. These findings can improve infant health assessments and personalized care.
Area of Science:
- Neonatal Health
- Epigenetics
- Biomarker Discovery
Background:
- Adverse neonatal outcomes impact infant mortality and morbidity.
- Accurate assessment of neonatal health risks is crucial for prevention and care.
- Neonatal outcomes are often multifactorial and correlated.
Purpose of the Study:
- To enhance the assessment of neonatal health risks by identifying common and unique effects.
- To validate these effects using methylome-wide profiles from neonatal blood.
- To explore the potential of identified factors as clinical biomarkers.
Main Methods:
- Factor analysis was used to identify common and unique effects among nine neonatal risk variables.
- Methylome-wide profiles from 333 neonates' blood spots were analyzed.
- Cell-type specific methylome-wide association analyses and Gene Ontology analyses were performed for validation.
Main Results:
- Two common factors were identified: a 'size factor' and a 'disease factor'.
- Significant cell-type specific associations were found for common factors, gestational age, jaundice, and Apgar score.
- Gene Ontology analysis revealed biologically relevant terms for investigated risk variables.
Conclusions:
- Identified distinct factor effects (common and unique) in neonatal health risks.
- Biological profiles of these factors suggest their potential as clinical biomarkers.
- Findings support enhanced personalized care strategies for neonates.
Abstract:
Adverse neonatal outcomes are a prevailing risk factor for both short- and long-term mortality and morbidity in infants. Given the importance of these outcomes, refining their assessment is paramount for improving prevention and care. Here we aim to enhance the assessment of these often correlated and multifaceted neonatal outcomes. To achieve this, we employ factor analysis to identify common and unique effects and further confirm these effects using criterion-related validity testing. This validation leverages methylome-wide profiles from neonatal blood. Specifically, we investigate nine neonatal health risk variables, including gestational age, Apgar score, three indicators of body size, jaundice, birth diagnosis, maternal preeclampsia, and maternal age. The methylomic profiles used for this research capture data from nearly all 28 million methylation sites in human blood, derived from the blood spot collected from 333 neonates, within 72 h post-birth. Our factor analysis revealed two common factors, size factor, that captured the shared effects of weight, head size, height, and gestational age and disease factor capturing the orthogonal shared effects of gestational age, combined with jaundice and birth diagnosis. To minimize false positives in the validation studies, validation was limited to variables with significant cumulative association as estimated through an in-sample replication procedure. This screening resulted in that the two common factors and the unique effects for gestational age, jaundice and Apgar were further investigated with full-scale cell-type specific methylome-wide association analyses. Highly significant, cell-type specific, associations were detected for both common effect factors and for Apgar. Gene Ontology analyses revealed multiple significant biologically relevant terms for the five fully investigated neonatal health risk variables. Given the established links between adverse neonatal outcomes and both immediate and long-term health, the distinct factor effects (representing the common and unique effects of the risk variables) and their biological profiles confirmed in our work, suggest their potential role as clinical biomarkers for assessing health risks and enhancing personalized care.
