AUTHOR=Fehr Kelsey , Mertens Andrew , Shu Chi-Hung , Dailey-Chwalibóg Trenton , Shenhav Liat , Allen Lindsay H. , Beggs Megan R. , Bode Lars , Chooniedass Rishma , DeBoer Mark D. , Deng Lishi , Espinosa Camilo , Hampel Daniela , Jahual April , Jehan Fyezah , Jain Mohit , Kolsteren Patrick , Kawle Puja , Lagerborg Kim A. , Manus Melissa B. , Mataraso Samson , McDermid Joann M. , Muhammad Ameer , Peymani Payam , Pham Martin , Shahab-Ferdows Setareh , Shafiq Yasir , Subramoney Vishak , Sunko Daniel , Toe Laeticia Celine , Turvey Stuart E. , Xue Lei , Rodriguez Natalie , Hubbard Alan , Aghaeepour Nima , Azad Meghan B. 

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

JOURNAL=Frontiers in Nutrition

VOLUME=Volume 12 - 2025

YEAR=2025

URL=https://www.frontiersin.org/journals/nutrition/articles/10.3389/fnut.2025.1548739

DOI=10.3389/fnut.2025.1548739

ISSN=2296-861X

ABSTRACT=IntroductionHuman 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 analysisIMiC 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 disseminationIMiC 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 registrationClinicalTrials.gov, identifier, NCT05119166.