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Introduction <p>Human breast milk is the optimal source of nutrition for newborns and contains hundreds of bioactive compounds that influence infant health and development. Characterizing its composition at scale is important for understanding how maternal diet, health status, and other factors affect milk quality. However, manual NMR profiling methods are too slow for large cohort studies. MagMet is a program designed for the rapid, automated processing and profiling of 1D <sup>1</sup>H NMR spectra from complex mixtures of small molecules. In this study, we develop a version of MagMet (called MagMet-HM) capable of rapid, quantitative automated NMR analysis of human breast milk.</p> Methods <p>A library consisting of 72 metabolites was created based on the literature and comparison with experimental NMR spectra, that are known to be abundant or consistently detectable in breast milk. NMR spectra of ultrafiltered breast milk was then used to optimize and validate the performance of MagMet-HM in the automated NMR analysis of human breast milk.</p> Results <p>Performance was benchmarked against manual profiling using Chenomx (version 8.3), with median and mean absolute percent errors of approximately 5.1% and 9.1%, respectively. MagMet-HM completes profiling in 10&#xa0;min (on a single CPU) which is 3–6 times faster than manual methods.</p> Conclusions <p>MagMet-HM offers a convenient, fast, and accurate method for the high-throughput metabolomic profiling of human breast milk. MagMet-HM is available at <a href="https://www.magmet.ca">https://www.magmet.ca</a>.</p>

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Automated, targeted, NMR spectral profiling of human breast milk

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2026年5月29日

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  • Brian L. Lee,
  • Alanne Tenório Nunes,
  • Prashanthi Kovur,
  • Morteza Gholami,
  • Amirhossein Firouzi,
  • Rupasri Mandal,
  • David S. Wishart

摘要

Introduction

Human breast milk is the optimal source of nutrition for newborns and contains hundreds of bioactive compounds that influence infant health and development. Characterizing its composition at scale is important for understanding how maternal diet, health status, and other factors affect milk quality. However, manual NMR profiling methods are too slow for large cohort studies. MagMet is a program designed for the rapid, automated processing and profiling of 1D 1H NMR spectra from complex mixtures of small molecules. In this study, we develop a version of MagMet (called MagMet-HM) capable of rapid, quantitative automated NMR analysis of human breast milk.

Methods

A library consisting of 72 metabolites was created based on the literature and comparison with experimental NMR spectra, that are known to be abundant or consistently detectable in breast milk. NMR spectra of ultrafiltered breast milk was then used to optimize and validate the performance of MagMet-HM in the automated NMR analysis of human breast milk.

Results

Performance was benchmarked against manual profiling using Chenomx (version 8.3), with median and mean absolute percent errors of approximately 5.1% and 9.1%, respectively. MagMet-HM completes profiling in 10 min (on a single CPU) which is 3–6 times faster than manual methods.

Conclusions

MagMet-HM offers a convenient, fast, and accurate method for the high-throughput metabolomic profiling of human breast milk. MagMet-HM is available at https://www.magmet.ca.