TY - JOUR
T1 - Protocol
T2 - the International Milk Composition (IMiC) Consortium - a harmonized secondary analysis of human milk from four studies
AU - Fehr, Kelsey
AU - Mertens, Andrew
AU - Shu, Chi Hung
AU - Dailey-Chwalibóg, Trenton
AU - Shenhav, Liat
AU - Allen, Lindsay H.
AU - Beggs, Megan R.
AU - Bode, Lars
AU - Chooniedass, Rishma
AU - DeBoer, Mark D.
AU - Deng, Lishi
AU - Espinosa, Camilo
AU - Hampel, Daniela
AU - Jahual, April
AU - Jehan, Fyezah
AU - Jain, Mohit
AU - Kolsteren, Patrick
AU - Kawle, Puja
AU - Lagerborg, Kim A.
AU - Manus, Melissa B.
AU - Mataraso, Samson
AU - McDermid, Joann M.
AU - Muhammad, Ameer
AU - Peymani, Payam
AU - Pham, Martin
AU - Shahab-Ferdows, Setareh
AU - Shafiq, Yasir
AU - Subramoney, Vishak
AU - Sunko, Daniel
AU - Toe, Laeticia Celine
AU - Turvey, Stuart E.
AU - Xue, Lei
AU - Rodriguez, Natalie
AU - Hubbard, Alan
AU - Aghaeepour, Nima
AU - Azad, Meghan B.
N1 - Publisher Copyright:
Copyright © 2025 Fehr, Mertens, Shu, Dailey-Chwalibóg, Shenhav, Allen, Beggs, Bode, Chooniedass, DeBoer, Deng, Espinosa, Hampel, Jahual, Jehan, Jain, Kolsteren, Kawle, Lagerborg, Manus, Mataraso, McDermid, Muhammad, Peymani, Pham, Shahab-Ferdows, Shafiq, Subramoney, Sunko, Toe, Turvey, Xue, Rodriguez, Hubbard, Aghaeepour and Azad.
PY - 2025
Y1 - 2025
N2 - Introduction: Human milk (HM) contains a multitude of nutritive and nonnutritive bioactive compounds that support infant growth, immunity and development, yet its complex composition remains poorly understood. Integrating diverse scientific disciplines from nutrition and global health to data science, the International Milk Composition (IMiC) Consortium was established to undertake a comprehensive harmonized analysis of HM from low, middle and high-resource settings to inform novel strategies for supporting maternal-child nutrition and health. Methods and analysis: IMiC is a collaboration of HM experts, data scientists and four mother-infant health studies, each contributing a subset of participants: Canada (CHILD Cohort, n = 400), Tanzania (ELICIT Trial, n = 200), Pakistan (VITAL-LW Trial, n = 150), and Burkina Faso (MISAME-3 Trial, n = 290). Altogether IMiC includes 1,946 HM samples across time-points ranging from birth to 5 months. Using HM-validated assays, we are measuring macronutrients, minerals, B-vitamins, fat-soluble vitamins, HM oligosaccharides, selected bioactive proteins, and untargeted metabolites, proteins, and bacteria. Multi-modal machine learning methods (extreme gradient boosting with late fusion and two-layered cross-validation) will be applied to predict infant growth and identify determinants of HM variation. Feature selection and pathway enrichment analyses will identify key HM components and biological pathways, respectively. While participant data (e.g., maternal characteristics, health, household characteristics) will be harmonized across studies to the extent possible, we will also employ a meta-analytic structure approach where HM effects will be estimated separately within each study, and then meta-analyzed across studies. Ethics and dissemination: IMiC was approved by the human research ethics board at the University of Manitoba. Contributing studies were approved by their respective primary institutions and local study centers, with all participants providing informed consent. Aiming to inform maternal, newborn, and infant nutritional recommendations and interventions, results will be disseminated through Open Access platforms, and data will be available for secondary analysis. Clinical trial registration: ClinicalTrials.gov, identifier, NCT05119166.
AB - Introduction: Human milk (HM) contains a multitude of nutritive and nonnutritive bioactive compounds that support infant growth, immunity and development, yet its complex composition remains poorly understood. Integrating diverse scientific disciplines from nutrition and global health to data science, the International Milk Composition (IMiC) Consortium was established to undertake a comprehensive harmonized analysis of HM from low, middle and high-resource settings to inform novel strategies for supporting maternal-child nutrition and health. Methods and analysis: IMiC is a collaboration of HM experts, data scientists and four mother-infant health studies, each contributing a subset of participants: Canada (CHILD Cohort, n = 400), Tanzania (ELICIT Trial, n = 200), Pakistan (VITAL-LW Trial, n = 150), and Burkina Faso (MISAME-3 Trial, n = 290). Altogether IMiC includes 1,946 HM samples across time-points ranging from birth to 5 months. Using HM-validated assays, we are measuring macronutrients, minerals, B-vitamins, fat-soluble vitamins, HM oligosaccharides, selected bioactive proteins, and untargeted metabolites, proteins, and bacteria. Multi-modal machine learning methods (extreme gradient boosting with late fusion and two-layered cross-validation) will be applied to predict infant growth and identify determinants of HM variation. Feature selection and pathway enrichment analyses will identify key HM components and biological pathways, respectively. While participant data (e.g., maternal characteristics, health, household characteristics) will be harmonized across studies to the extent possible, we will also employ a meta-analytic structure approach where HM effects will be estimated separately within each study, and then meta-analyzed across studies. Ethics and dissemination: IMiC was approved by the human research ethics board at the University of Manitoba. Contributing studies were approved by their respective primary institutions and local study centers, with all participants providing informed consent. Aiming to inform maternal, newborn, and infant nutritional recommendations and interventions, results will be disseminated through Open Access platforms, and data will be available for secondary analysis. Clinical trial registration: ClinicalTrials.gov, identifier, NCT05119166.
KW - breastfeeding
KW - human milk
KW - infant growth
KW - infant nutrition
KW - machine learning
UR - https://www.scopus.com/pages/publications/105009263237
U2 - 10.3389/fnut.2025.1548739
DO - 10.3389/fnut.2025.1548739
M3 - Article
AN - SCOPUS:105009263237
SN - 2296-861X
VL - 12
JO - Frontiers in Nutrition
JF - Frontiers in Nutrition
M1 - 1548739
ER -