Comprehensive Targeted and Quantitative Profiling of the Human Milk Metabolome: Impact of Delivery Mode, Breastfeeding Practices, and Maternal Diet

Joaquim Calvo-Lerma,Raul Cabrera-Rubio,Carles Lerin,Sonia Gonzalez,Marta Selma-Royo,Cecilia Martinez-Costa,Teodoro Bottiglieri,Maria Carmen Collado

MOLECULAR NUTRITION & FOOD RESEARCH（2024）

Natl Res Council IATA CSIC | Hosp St Joan Deu | Univ Oviedo | Univ Valencia | Baylor Scott & White Res Inst

Cited 0|Views2

Abstract

SCOPE:Human milk (HM) is rich in bioactive compounds and essential nutrients. While research has focused on lipids, minerals, immune markers, microbiota, and oligosaccharides, specific metabolites are less studied. This study uses targeted metabolomics to identify and quantify metabolites in HM and explores the impact of perinatal and dietary factors on the metabolomic profile. METHODS AND RESULTS:In a cross-sectional study of 123 healthy lactating women, HM samples were collected up to 1 month postpartum and analyzed using the Biocrates MxP Quant 500 kit. Maternal and neonatal clinical, anthropometric, and nutritional data were collected. A total of 432 metabolites were quantified and categorized into 20 groups. The metabolomic profiles formed three distinct clusters, primarily driven by triglyceride concentration differences. Docosahexaenoic acid (DHA) levels were higher in HM from mothers with vaginal delivery compared to C-section births and differences in hexoses were found between exclusive and mixed-feeding practices. Maternal diets rich in lipids and animal proteins were associated with elevated amino acids, sphingolipids, and glycosyl-ceramides. CONCLUSION:The HM metabolome was grouped into three clusters influenced by delivery mode, lactation practices, and maternal diet. This comprehensive analysis opens new avenues to explore HM composition and offers valuable insights for future dietary interventions aimed at modulating HM.

Translated text

Key words

amino acids,C-section,Docosahexaenoic acid (DHA),human milk,maternal diet,metabolites,sphingolipids

求助PDF

上传PDF

Bibtex

AI Read Science

AI Summary

AI Summary is the key point extracted automatically understanding the full text of the paper, including the background, methods, results, conclusions, icons and other key content, so that you can get the outline of the paper at a glance.

Example

Background

Key content

Introduction

Methods

Results

Related work

Fund

Key content

Pretraining has recently greatly promoted the development of natural language processing (NLP)
We show that M6 outperforms the baselines in multimodal downstream tasks, and the large M6 with 10 parameters can reach a better performance
We propose a method called M6 that is able to process information of multiple modalities and perform both single-modal and cross-modal understanding and generation
The model is scaled to large model with 10 billion parameters with sophisticated deployment, and the 10 -parameter M6-large is the largest pretrained model in Chinese
Experimental results show that our proposed M6 outperforms the baseline in a number of downstream tasks concerning both single modality and multiple modalities We will continue the pretraining of extremely large models by increasing data to explore the limit of its performance

Upload PDF to Generate Summary

Must-Reading Tree

Example

Generate MRT to find the research sequence of this paper