Protocol: The International Milk Composition (IMiC) Consortium - a harmonized secondary analysis of human milk from four studies

Authors: FEHR, KELSEY - University Of Manitoba; MERTENS, ANDREW - University Of California Berkeley; SHU, CHI-HUNG - Stanford University; DAILEY-CHWALIBOG, TRENTON - Ghent University; SHENHAV, LIAT - New York University; Allen, Lindsay; BEGGS, MEGAN - University Of Toronto; BODE, LARS - University Of California, San Diego; CHOONIEDASS, RISHMA - University Of British Columbia; DEBOER, MARK - University Of Virginia; DENG, LISHI - Ghent University; ESPINOSA, CAMILO - Stanford University; HAMPEL, DANIELA - University Of California, Davis; JAHUAL, APRIL - New York University; JEHAN, FYEZAH - Aga Khan University; JAIN, MOHIT - Sapient Bioanalytics; KOLSTEREN, PATRICK - Ghent University; KAWLE, PUJA - Cytel; LAGERBORG, KIM - Sapient Bioanalytics; MANUS, MELISSA - University Of Manitoba; MATARASO, SAMSON - Stanford University; MCDERMID, JOAN - Consultant; MUHAMMAD, AMEER - Vital Pakistan Trust; PEYMANI, PAYAM - University Of Manitoba; PHAM, MARTIN - University Of Toronto; Shahab-Ferdows, Setareh; SHAFIQ, YASIR - Aga Khan University; SUBRAMONEY, VISHAK - D-Atum Vilcom Private Limited (DVPL) Internet Solutions; SUNKO, DANIEL - New York University; TOE, LAETICIA - Ghent University; TURVEY, STUART - University Of British Columbia; XUE, LEI - Stanford University; RODRIGUEZ, NATALIE - University Of Manitoba; HUBBARD, ALAN - University Of California Berkeley; AGHAEEPOUR, NIMA - Stanford University; AZAD, MEGHAN - University Of Manitoba

Submitted to: Frontiers in Nutrition
Publication Type: Peer Reviewed Journal
Publication Acceptance Date: 5/5/2025
Publication Date: 6/10/2025
Citation: Fehr, K., Mertens, A., Shu, C., Dailey-Chwalibog, T., Shenhav, L., Allen, L.H., Beggs, M.R., Bode, L., Chooniedass, R., DeBoer, M.D., Deng, L., Espinosa, C., Hampel, D., Jahual, A., Jehan, F., Jain, M., Kolsteren, P., Kawle, P., Lagerborg, K., Manus, M., Mataraso, S., McDermid, J.M., Muhammad, A., Peymani, P., Pham, M., Shahab-Ferdows, S., Shafiq, Y., Subramoney, V., Sunko, D., Toe, L.C., Turvey, S., Xue, L., Rodriguez, N., Hubbard, A., Aghaeepour, N., Azad, M.B. 2025. Protocol: The International Milk Composition (IMiC) Consortium - a harmonized secondary analysis of human milk from four studies. Frontiers in Nutrition. 12. Article 1548739. https://doi.org/10.3389/fnut.2025.1548739.
DOI: https://doi.org/10.3389/fnut.2025.1548739

Interpretive Summary: Human milk is the primary source of infant nutrition that also contains non-nutritive bioactive compounds for infant growth, immunity and development. Despite their important role in human growth and lifelong health, we only have limited knowledge of their biological functions. By using information such as nutrition, health, the analysis of a large number of milk constituents , and machine learning, the International Milk Composition (IMiC) Consortium was set up to determine what affects the composition of human milk from various low, middle and high-income countries, and infant growth. IMiC is an international collaboration of milk experts, data scientists, and samples from mother-infant pairs in Tanzania , Pakistan, Burkina Faso and Canada. Altogether there are 1946 HM samples across various time-points ranging from birth to 5 months. Milk will be analyzed for a large number of nutrients, oligosaccharides, proteins, metabolites, and bacteria.

Technical Abstract: Introduction: Human milk (HM) is the primary source of infant nutrition, and further contains a multitude of nonnutritive bioactive compounds that support infant growth, immunity and development. Despite their critical role in human development and lifelong health, the diverse components of HM and their collective biological functions and variation remain poorly understood. Integrating diverse scientific disciplines ranging from nutrition and global health to multi-omics and machine learning, the International Milk Composition (IMiC) Consortium was established to undertake a comprehensive harmonized analysis of HM from diverse low, middle and high-income settings. Methods and Analysis: IMiC is an international collaboration of HM experts, data scientists and four mother-infant health studies, each contributing a subset of mother-infant dyads from their total study populations from: Tanzania (ELICIT Trial, n=200 participating in IMiC), Pakistan (VITAL-LW Trial, n=150), Burkina Faso (MISAME-3 Trial, n=290), and Canada (CHILD Cohort, n=1039). Altogether these dyads are contributing 1946 HM samples across various time-points ranging from birth to 5 months. The following HM components will be analyzed using HM-validated assays in experienced laboratories: 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 harmonized measures of infant growth and identify determinants of HM variation. Feature selection algorithms and pathway enrichment analyses will be used to identify key HM components and biological pathways, respectively. Targeted learning and stochastic intervention approaches will be used to test HM mediation effects. While participant data (e.g. maternal age, body mass index, health, household characteristics) will be harmonized across studies to the extent possible, we will also employ a meta-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 written informed consent. IMiC results will be disseminated through Open Access platforms, and data will be available for secondary analysis. The ultimate goal of this research is to inform future maternal, newborn and infant nutritional recommendations and interventions. Registration: ClinicalTrials.gov (NCT05119166).