Abstract
BackgroundNumerous studies have shown that specific components of breast milk, considered separately, are associated with disease status in the mother or the child using univariate analyses. However, very few studies have considered multivariate analysis approaches to evaluate the relationship between multiple breast milk components simultaneously.AimHere we aimed at visualizing breast milk component complex interactions in the context of the allergy status of the mother or the child.MethodsMilk samples were collected from lactating mothers participating in the Leipziger Forschungszentrum für Zivilisationskrankheiten (LIFE) Child cohort in Leipzig, Germany. A total of 156 breast milk samples, collected at 3 months after birth from mother/infant pairs, were analyzed for 51 breast milk components. Correlation, principal component analysis (PCA) and graphical discovery analysis were used.ResultCorrelations ranging from 0.40 to 0.96 were observed between breast milk fatty acid and breast milk phospholipids levels and correlations ranging from 0 to 0.76 between specific human milk oligosaccharides (HMO) were observed. No separation of the data based on the risk of allergy in the infants was identified using PCA. When graphical discovery analysis was used, dependencies between maternal plasma immunoglobulin E (IgE) level and the breast milk immune marker transforming growth factor-beta 2 (TGF-ß2), between TGF-ß2, breast milk immunoglobulin A (IgA) and TGF-ß1 as well as between breast milk total protein and birth weight were observed. Graphical discovery analysis also exemplifies a possible competition for the fucosyl group between 2’FL, LNFP-I and 3’FL in the HMO group. Additionally, dependencies between immune component IgA and specific HMO (6’SL and blood group A antigen tetraose type 5 or PI-HMO) were identified.ConclusionGraphical discovery analysis applied to complex matrices such as breast milk composition can aid in understanding the complexity of interactions between breast milk components and possible relations to health parameters in the mother or the infant. This approach can lead to novel discoveries in the context of health and diseases such as allergy. Our study thus represents the first attempt to visualize the complexity and the inter-dependency of breast milk components.
Highlights
Human breast milk is the gold standard for infant nutrition, providing optimum nutrition and immune protection for healthy growth and development [1]
Graphical discovery analysis applied to complex matrices such as breast milk composition can aid in understanding the complexity of interactions between breast milk components and possible relations to health parameters in the mother or the infant
Numerous studies have associated breast milk levels of transforming growth factors (TGF) such as TGF-ß1, TGF-ß2 and/or levels of immunoglobulin (Ig) A with allergy in infants or mothers [9,10,11], yet a recent review suggests that the data were not robust enough to reach a conclusion on TGF-ß1 and 2 [12]
Summary
Numerous studies have shown that specific components of breast milk, considered separately, are associated with disease status in the mother or the child using univariate analyses. Very few studies have considered multivariate analysis approaches to evaluate the relationship between multiple breast milk components simultaneously
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