%0 Journal Article %1 becker2024multimodal %A Becker, Martin %A Fehr, Kelsey %A Goguen, Stephanie %A Miliku, Kozeta %A Field, Catherine %A Robertson, Bianca %A Yonemitsu, Chloe %A Bode, Lars %A Simons, Elinor %A Marshall, Jean %A Dawod, Bassel %A Mandhane, Piushkumar %A Turvey, Stuart E. %A Moraes, Theo J. %A Subbarao, Padmaja %A Rodriguez, Natalie %A Aghaeepour, Nima %A Azad, Meghan B. %D 2024 %J Scientific Reports %K circumference environment head milk mother multimodal multiomics myown %N 1 %P 2977 %R 10.1038/s41598-024-52323-w %T Multimodal machine learning for modeling infant head circumference, mothers' milk composition, and their shared environment %U https://doi.org/10.1038/s41598-024-52323-w %V 14 %X 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; pþinspace=þinspace9.6e−05) and maternal intake of fish (pþinspace=þinspace4.1e−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.

@article{becker2024multimodal, 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{\thinspace}={\thinspace}9.6e−05) and maternal intake of fish (p{\thinspace}={\thinspace}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.}, added-at = {2024-02-06T08:36:35.000+0100}, author = {Becker, Martin and Fehr, Kelsey and Goguen, Stephanie and Miliku, Kozeta and Field, Catherine and Robertson, Bianca and Yonemitsu, Chloe and Bode, Lars and Simons, Elinor and Marshall, Jean and Dawod, Bassel and Mandhane, Piushkumar and Turvey, Stuart E. and Moraes, Theo J. and Subbarao, Padmaja and Rodriguez, Natalie and Aghaeepour, Nima and Azad, Meghan B.}, biburl = {https://www.bibsonomy.org/bibtex/2ada216a20bfd4723fae359cde4f7bcc8/becker}, day = 05, description = {Multimodal machine learning for modeling infant head circumference, mothers’ milk composition, and their shared environment | Scientific Reports}, doi = {10.1038/s41598-024-52323-w}, interhash = {13b96998db73454d3bf62c8dca08332a}, intrahash = {ada216a20bfd4723fae359cde4f7bcc8}, issn = {2045-2322}, journal = {Scientific Reports}, keywords = {circumference environment head milk mother multimodal multiomics myown}, month = feb, number = 1, pages = 2977, timestamp = {2024-02-06T08:37:00.000+0100}, title = {Multimodal machine learning for modeling infant head circumference, mothers' milk composition, and their shared environment}, url = {https://doi.org/10.1038/s41598-024-52323-w}, volume = 14, year = 2024 }