A multi-omics machine learning classifier for outgrowth of cow's milk allergy in children

Cow's milk protein allergy (CMA) is one of the most common food allergies in children worldwide. However, it is still not well understood why certain children outgrow their CMA and others do not. While there is increasing evidence for a link of CMA with the gut microbiome, it is still unclear how the gut microbiome and metabolome interact with the immune system. Integrating data from different omics platforms and clinical data can help to unravel these interactions. In this study, we integrate clinical, microbial, (meta)proteomics, immune and metabolomics data into machine learning (ML) classification, using multi-view learning by late integration. The aim is to group infants into those that outgrew their CMA and those that did not. The results show that integration of microbiome data with clinical, immune, (meta)proteomics and metabolomics data could considerably improve classification of infants on outgrowth of CMA, compared to only considering one type of data. Moreover, pathways previously linked to development of CMA could also be related to outgrowth of this allergy.

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Publication Details

Journal
Molecular Omics
Published
2025-05-09
DOI
https://doi.org/10.1039/d4mo00245h
Citations
1
Primary Topic
Microbial Inactivation Methods
Type
article
Field-Weighted Citation Impact
1.23

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article

A multi-omics machine learning classifier for outgrowth of cow's milk allergy in children

Harm Wopereis, Sjef Boeren, Diana M. Hendrickx, Clara Belzer et al.
1 citations
Molecular Omics
Microbial Inactivation Methods
1.23
article

A multi-omics machine learning classifier for outgrowth of cow's milk allergy in children

Harm Wopereis, Sjef Boeren, Diana M. Hendrickx, Clara Belzer, Sumanth Mutte, Amy C. Harms, Pingping Zhu, Mariyana V. Savova, Renate G. van der Molen, Ran An, Kelly Klomp, Presto Study Team
article en
1 citations

Abstract

Cow's milk protein allergy (CMA) is one of the most common food allergies in children worldwide. However, it is still not well understood why certain children outgrow their CMA and others do not. While there is increasing evidence for a link of CMA with the gut microbiome, it is still unclear how the gut microbiome and metabolome interact with the immune system. Integrating data from different omics platforms and clinical data can help to unravel these interactions. In this study, we integrate clinical, microbial, (meta)proteomics, immune and metabolomics data into machine learning (ML) classification, using multi-view learning by late integration. The aim is to group infants into those that outgrew their CMA and those that did not. The results show that integration of microbiome data with clinical, immune, (meta)proteomics and metabolomics data could considerably improve classification of infants on outgrowth of CMA, compared to only considering one type of data. Moreover, pathways previously linked to development of CMA could also be related to outgrowth of this allergy.

Molecular OmicsVol. 21(4)
Leiden University (NL), Radboud University Nijmegen (NL), Radboud University Medical Center (NL), Netherlands Metabolomics Centre (NL), Danone (Netherlands) (NL), Nutricia Research (Netherlands) (NL), Danone Nutricia Research (Netherlands), Wageningen University & Research (NL)
Texas Children's Hospital, Danone, Danone Nutricia Research, York University, Universiteit Utrecht, Chulalongkorn University, Nederlandse Organisatie voor Wetenschappelijk Onderzoek, Newcastle upon Tyne Hospitals NHS Foundation Trust, Mahidol University, Prince of Songkla University, Radboud Universitair Medisch Centrum, Wageningen University and Research, Precursory Research for Embryonic Science and Technology
Zero hunger
Openalex Percentile: Top 23%
Microbial Inactivation Methods
1.23
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