Identifying metabotypes from complex biological data using PARAFAC

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Skantze, Viktor,1992Chalmers tekniska högskola,Chalmers University of Technology (author)

Identifying metabotypes from complex biological data using PARAFAC

Article/chapterEnglish2021

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Elsevier BV,2021

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LIBRIS-ID:oai:research.chalmers.se:12490052-b53c-48c2-8db4-0b1012b5563d
https://doi.org/10.1093/cdn/nzab048_017DOI
https://research.chalmers.se/publication/531673URI

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Language:English
Summary in:English

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Subject category:vet swepub-contenttype

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Research have identified large individual variation in physiological response to diet, which has led to more focused investigations in precision nutrition. One approach towards personalized nutrition is to identify groups of differential responders, so called metabotypes (i.e., clusters of individuals with similar metabolic profiles and/or regulation). Metabotyping has previously been addressed using matrix decomposition tools like principal component analysis (PCA) on data organized in matrix form. However, metabotyping using data from more complex experimental designs, involving e.g., repeated measures over time or multiple treatments (tensor data), requires new methods. We developed a workflow for detecting metabotypes from experimental tensor data. The workflow is based on tensor decomposition, specifically PARAFAC which is conceptually similar to PCA but extended to multidimensional data. Metabotypes, based on metabolomics data were identified from PARAFAC scores using k-means clustering and validated by their association to anthropometric and clinical baseline data. Additionally, we evaluated the robustness of the metabotypes using bootstrapping. Furthermore, we applied the workflow to identify metabotypes using data from a crossover acute post-prandial dietary intervention study on 17 overweight males (BMI 25–30 kg/m2, 41–67 y of age) undergoing three dietary interventions (pickled herring, baked herring and baked beef), measuring 80 metabolites (from GC-MS metabolomics) at 8 time points (0–7h). We identified two metabotypes characterized by differences in amino acid levels, predominantly in the beef diet, that were also associated with creatinine (p = 0.007). The metabotype with higher postprandial amino acid levels was also associated with higher fasting creatinine compared to the other metabotype. Conclusions: The results stress the potential of PARAFAC to discover metabotypes from complex study designs. The workflow is not restricted to our data structure and can be applied to any type of tensor data. However, PARAFAC is sensitive to data pre-processing and further studies where differential metabotypes are related to clinical endpoints are highly warranted. Funding Sources: This work has been supported by the Swedish Foundation for Strategic Research and Formas, which is gratefully acknowledged.

Subject headings and genre

NATURVETENSKAP Data- och informationsvetenskap Annan data- och informationsvetenskap hsv//swe
NATURAL SCIENCES Computer and Information Sciences Other Computer and Information Science hsv//eng
MEDICIN OCH HÄLSOVETENSKAP Medicinsk bioteknologi Biomedicinsk laboratorievetenskap/teknologi hsv//swe
MEDICAL AND HEALTH SCIENCES Medical Biotechnology Biomedical Laboratory Science/Technology hsv//eng
NATURVETENSKAP Biologi Bioinformatik och systembiologi hsv//swe
NATURAL SCIENCES Biological Sciences Bioinformatics and Systems Biology hsv//eng
PARAFAC
Tensor decomposition
Metabolomics
Personalized Nutrition
Metabotyping

Added entries (persons, corporate bodies, meetings, titles ...)

Wallman, Mikael,1979Stiftelsen Fraunhofer-Chalmers Centrum för Industrimatematik (FCC),Fraunhofer-Chalmers Research Centre for Industrial Mathematics (FCC)(Swepub:cth)mikwal (author)
Sandberg, Ann-Sofie,1951Chalmers tekniska högskola,Chalmers University of Technology(Swepub:cth)annsandb (author)
Landberg, Rikard,1981Chalmers tekniska högskola,Chalmers University of Technology(Swepub:cth)riklan (author)
Jirstrand, Mats,1968Stiftelsen Fraunhofer-Chalmers Centrum för Industrimatematik (FCC),Fraunhofer-Chalmers Research Centre for Industrial Mathematics (FCC)(Swepub:cth)jirstran (author)
Brunius, Carl,1974Chalmers tekniska högskola,Chalmers University of Technology(Swepub:cth)carlbru (author)
Chalmers tekniska högskolaStiftelsen Fraunhofer-Chalmers Centrum för Industrimatematik (FCC) (creator_code:org_t)

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In:Current Developments in Nutrition, Volume 5: Elsevier BV5:2, s. 882-2475-2991

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