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Discriminating Dietary Responses by Combining Transcriptomics and Metabolomics Data in Nutrition Intervention Studies
Author(s) -
BurtonPimentel Kathryn J,
Pimentel Grégory,
Hughes Maria,
Michielsen Charlotte CJR,
Fatima Attia,
Vionnet Nathalie,
Afman Lydia A,
Roche Helen M,
Brennan Lorraine,
Ibberson Mark,
Vergères Guy
Publication year - 2021
Publication title -
molecular nutrition and food research
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.495
H-Index - 131
eISSN - 1613-4133
pISSN - 1613-4125
DOI - 10.1002/mnfr.202000647
Subject(s) - omics , postprandial , metabolomics , transcriptome , sample size determination , biology , computational biology , bioinformatics , microbiology and biotechnology , statistics , mathematics , genetics , insulin , gene expression , gene
Scope Combining different “omics” data types in a single, integrated analysis may better characterize the effects of diet on human health. Methods and results The performance of two data integration tools, similarity network fusion tool (SNFtool) and Data Integration Analysis for Biomarker discovery using Latent variable approaches for “Omics” (DIABLO; MixOmics), in discriminating responses to diet and metabolic phenotypes is investigated by combining transcriptomics and metabolomics datasets from three human intervention studies: a postprandial crossover study testing dairy foods ( n = 7; study 1), a postprandial challenge study comparing obese and non‐obese subjects ( n = 13; study 2); and an 8‐week parallel intervention study that assessed three diets with variable lipid content on fasting parameters ( n = 39; study 3). In study 1, combining datasets using SNF or DIABLO significantly improve sample classification. For studies 2 and 3, the value of SNF integration depends on the dietary groups being compared, while DIABLO discriminates samples well but does not perform better than transcriptomic data alone. Conclusion The integration of associated “omics” datasets can help clarify the subtle signals observed in nutritional interventions. The performance of each integration tool is differently influenced by study design, size of the datasets, and sample size.