Human milk is a dynamic
biofluid, and its detailed composition
receives increasing attention. While most studies focus on changes
over time or differences between maternal characteristics, interindividual
variation receives little attention. Nevertheless, a comprehensive
insight into this can help interpret human milk studies and help human
milk banks provide targeted milk for recipients. This study aimed
to map interindividual variation in the human milk proteome, peptidome,
and metabolome and to investigate possible explanations for this variation.
A set of 286 milk samples was collected from 29 mothers in the third
month postpartum. Samples were pooled per mother, and proteins, peptides,
and metabolites were analyzed. A substantial coefficient of variation
(>100%) was observed for 4.6% and 36.2% of the proteins and peptides,
respectively. In addition, using weighted correlation network analysis
(WGCNA), 5 protein and 11 peptide clusters were obtained, showing
distinct characteristics. With this, several associations were found
between the different data sets and with specific sample characteristics.
This study provides insight into the dynamics of human milk protein,
peptide, and metabolite composition. In addition, it will support
future studies that evaluate the effect size of a parameter of interest
by enabling a comparison with natural variability.