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Revisiting One-Carbon Metabolites in Human Breast Milk: Focus on S-Adenosylmethionine.

Carles LerinMaria Carmen ColladoElvira IsganaitisErland ArningBrandi WasekEllen W DemerathDavid A FieldsTeodoro Bottiglieri
Published in: Nutrients (2023)
Breastfeeding is the gold standard for early nutrition. Metabolites from the one-carbon metabolism pool are crucial for infant development. The aim of this study is to compare the breast-milk one-carbon metabolic profile to other biofluids where these metabolites are present, including cord and adult blood plasma as well as cerebrospinal fluid. Breast milk ( n = 142), cord blood plasma ( n = 23), maternal plasma ( n = 28), aging adult plasma ( n = 91), cerebrospinal fluid ( n = 92), and infant milk formula ( n = 11) samples were analyzed by LC-MS/MS to quantify choline, betaine, methionine, S-adenosylmethionine, S-adenosylhomocysteine, total homocysteine, and cystathionine. Differences between groups were visualized by principal component analysis and analyzed by Kruskal-Wallis test. Correlation analysis was performed between one-carbon metabolites in human breast milk. Principal component analysis based on these metabolites separated breast milk samples from other biofluids. The S-adenosylmethionine (SAM) concentration was significantly higher in breast milk compared to the other biofluids and was absent in infant milk formulas. Despite many significant correlations between metabolites in one-carbon metabolism, there were no significant correlations between SAM and methionine or total homocysteine. Together, our data indicate a high concentration of SAM in breast milk, which may suggest a strong demand for this metabolite during infant early growth while its absence in infant milk formulas may indicate the inadequacy of this vital metabolic nutrient.
Keyphrases
  • ms ms
  • cerebrospinal fluid
  • cord blood
  • endothelial cells
  • pregnant women
  • electronic health record
  • body mass index
  • machine learning
  • big data
  • amino acid
  • artificial intelligence
  • deep learning
  • birth weight