Authors: Martin Becker, Kelsey Fehr, Stephanie Goguen, Kozeta Miliku, Catherine Field, Bianca Robertson, Chloe Yonemitsu, Lars Bode, Elinor Simons, Jean Marshall, Bassel Dawod, Piushkumar Mandhane, Stuart E. Turvey, Theo J. Moraes, Padmaja Subbarao, Natalie Rodriguez, Nima Aghaeepour, Meghan B. Azad
Abstract: Links 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.
Data and code availability: Raw data and processed data will be made available with appropriate permissions from the CHILD Cohort Study: https://childstudy.ca/for-researchers/data-access/. The source code can be downloaded here.