Identifying metabotypes from complex biological data using PARAFAC
Other conference contribution, 2021

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

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


Tensor decomposition


Personalized Nutrition



Viktor Skantze

Chalmers, Biology and Biological Engineering, Food and Nutrition Science

Mikael Wallman

Fraunhofer-Chalmers Centre

Ann-Sofie Sandberg

Chalmers, Biology and Biological Engineering, Food and Nutrition Science

Rikard Landberg

Chalmers, Biology and Biological Engineering, Food and Nutrition Science

Mats Jirstrand

Fraunhofer-Chalmers Centre

Carl Brunius

Chalmers, Biology and Biological Engineering, Food and Nutrition Science

Current Developments in Nutrition, Volume 5

2475-2991 (ISSN)

Vol. 5 2 882-

NUTRITION 2021 LIVE ONLINE, American Society for Nutrition
Online, ,

Subject Categories

Other Computer and Information Science

Biomedical Laboratory Science/Technology

Bioinformatics and Systems Biology

Areas of Advance

Life Science Engineering (2010-2018)



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