Arrhythmic Gut Microbiome Signatures Predict Risk of Type 2 Diabetes

Journal:
Cell Host & Microbe
Published:
DOI:
10.1016/j.chom.2020.06.004
Affiliations:
19
Authors:
25

Research Highlight

Time-stamped stool reveals risk of diabetes

© KATERYNA KON/SCIENCE PHOTO LIBRARY/Getty

The relative abundances of different bacteria in the gut wax and wane predictably over the course of 24 hours. In people with obesity and type 2 diabetes, however, this regular daily pattern is perturbed — a finding that functionally links circadian rhythms in the gut microbiome with metabolic disease.

A Technical University of Munich–led team used DNA sequencing data from time-stamped stool samples, coupled with machine-learning techniques, to identify 13 taxonomic groups of oscillating gut bacteria that showed disrupted rhythmicity in obese, diabetic individuals.

This arrhythmic signature allowed the researchers to develop a predictive model that, when combined with body mass index, could identify individuals at highest risk of developing type 2 diabetes.

Microbial rhythmicity could thus serve as a potential diagnostic biomarker to improve the care and management of people with type 2 diabetes.

Supported content

References

  1. Cell Host & Microbe 28, 258–272.e6 (2020). doi: 10.1016/j.chom.2020.06.004
Institutions Authors Share
Technical University of Munich (TUM), Germany
9.500000
0.38
University College Cork (UCC), Ireland
3.000000
0.12
King's College London (KCL), United Kingdom (UK)
3.000000
0.12
Helmholtz Zentrum München - German Research Center for Environmental Health (HMGU), Germany
2.666667
0.11
German Diabetes Center - Leibniz Institute for Diabetes Research at Heinrich Heine University Düsseldorf (DDZ), Germany
2.666667
0.11
University Medical Center Schleswig-Holstein - Campus Kiel (UKSH Kiel), Germany
1.500000
0.06
German Center for Diabetes Research (DZD), Germany
1.000000
0.04
University Hospital Aachen (UKA), RWTH Aachen, Germany
0.500000
0.02
University of Kiel (CAU), Germany
0.500000
0.02
Universitary Center of Health Sciences at Klinikum Augsburg (UNIKA-T), Germany
0.333333
0.01
Ludwig Maximilians University of Munich (LMU), Germany
0.333333
0.01