Author:
Becker Martin,Fehr Kelsey,Goguen Stephanie,Miliku Kozeta,Field Catherine,Robertson Bianca,Yonemitsu Chloe,Bode Lars,Simons Elinor,Marshall Jean,Dawod Bassel,Mandhane Piushkumar,Turvey Stuart E.,Moraes Theo J.,Subbarao Padmaja,Rodriguez Natalie,Aghaeepour Nima,Azad Meghan B.
Abstract
AbstractLinks between human milk (HM) and infant development are poorly understood and often focus on individual HM components. Here we apply multi-modal predictive machine learning to study HM and head circumference (a proxy for brain development) among 1022 mother-infant dyads of the CHILD Cohort. We integrated HM data (19 oligosaccharides, 28 fatty acids, 3 hormones, 28 chemokines) with maternal and infant demographic, health, dietary and home environment data. Head circumference was significantly predictable at 3 and 12 months. Two of the most associated features were HM n3-polyunsaturated fatty acid C22:6n3 (docosahexaenoic acid, DHA; p = 9.6e−05) and maternal intake of fish (p = 4.1e−03), a key dietary source of DHA with established relationships to brain function. Thus, using a systems biology approach, we identified meaningful relationships between HM and brain development, which validates our statistical approach, gives credence to the novel associations we observed, and sets the foundation for further research with additional cohorts and HM analytes.
Funder
Bundesministerium für Bildung und Forschung
National Institutes of Health
Alfred E. Mann Foundation
Canadian Institutes of Health Research
AllerGen Network of Centers of Excellence
Research Manitoba, Children’s Hospital Research Institute of Manitoba
Canadian Respiratory Research Network
Manitoba Medical Services Foundation
Canada Research Chairs Program
Don and Debbie Morrison
SickKids Foundation
Publisher
Springer Science and Business Media LLC