方案：国际母乳成分（IMiC）联盟——四项研究中母乳的统一二次分析。
Protocol: the International Milk Composition (IMiC) Consortium - a harmonized secondary analysis of human milk from four studies.
作者信息
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
机构信息
Department of Pediatrics and Child Health, University of Manitoba, Winnipeg, MB, Canada.
Manitoba Interdisciplinary Lactation Centre (MILC), Children's Hospital Research Institute of Manitoba, Winnipeg, MB, Canada.
出版信息
Front Nutr. 2025 Jun 10;12:1548739. doi: 10.3389/fnut.2025.1548739. eCollection 2025.
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, = 400), Tanzania (ELICIT Trial, = 200), Pakistan (VITAL-LW Trial, = 150), and Burkina Faso (MISAME-3 Trial, = 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.
引言
母乳含有多种营养和非营养生物活性化合物，有助于婴儿的生长、免疫和发育，但其复杂的成分仍未得到充分了解。国际母乳成分（IMiC）联盟整合了从营养与全球健康到数据科学等不同学科，旨在对来自低、中、高资源环境的母乳进行全面统一分析，为支持母婴营养与健康的新策略提供依据。
方法与分析
IMiC由母乳专家、数据科学家以及四项母婴健康研究合作开展，每项研究贡献一部分参与者：加拿大（儿童队列研究，n = 400）、坦桑尼亚（ELICIT试验，n = 200）、巴基斯坦（VITAL-LW试验，n = 150）和布基纳法索（MISAME-3试验，n = 290）。IMiC总共包括1946份从出生到5个月各时间点的母乳样本。我们使用经过验证的母乳检测方法，测量常量营养素、矿物质、B族维生素、脂溶性维生素、母乳低聚糖、选定的生物活性蛋白以及非靶向代谢物、蛋白质和细菌。多模态机器学习方法（带后期融合和两层交叉验证的极端梯度提升）将用于预测婴儿生长并确定母乳成分变化的决定因素。特征选择和通路富集分析将分别确定关键的母乳成分和生物学通路。虽然参与者数据（如母亲特征、健康状况、家庭特征）将在各研究中尽可能实现统一，但我们还将采用荟萃分析结构方法，在每项研究中分别估计母乳的影响，然后对各研究结果进行荟萃分析。
伦理与传播
IMiC获得了曼尼托巴大学人类研究伦理委员会的批准。参与研究的项目分别获得了各自主要机构和当地研究中心的批准，所有参与者均提供了知情同意书。为了为孕产妇、新生儿和婴儿的营养建议及干预措施提供依据，研究结果将通过开放获取平台进行传播，数据将可供二次分析使用。
临床试验注册
ClinicalTrials.gov，标识符：NCT05119166 。
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