| Literature DB >> 32619440 |
Sandra Reitmeier1, Silke Kiessling1, Thomas Clavel2, Markus List3, Eduardo L Almeida4, Tarini S Ghosh4, Klaus Neuhaus5, Harald Grallert6, Jakob Linseisen7, Thomas Skurk5, Beate Brandl5, Taylor A Breuninger6, Martina Troll6, Wolfgang Rathmann8, Birgit Linkohr6, Hans Hauner9, Matthias Laudes10, Andre Franke11, Caroline I Le Roy12, Jordana T Bell12, Tim Spector12, Jan Baumbach3, Paul W O'Toole4, Annette Peters13, Dirk Haller14.
Abstract
Lifestyle, obesity, and the gut microbiome are important risk factors for metabolic disorders. We demonstrate in 1,976 subjects of a German population cohort (KORA) that specific microbiota members show 24-h oscillations in their relative abundance and identified 13 taxa with disrupted rhythmicity in type 2 diabetes (T2D). Cross-validated prediction models based on this signature similarly classified T2D. In an independent cohort (FoCus), disruption of microbial oscillation and the model for T2D classification was confirmed in 1,363 subjects. This arrhythmic risk signature was able to predict T2D in 699 KORA subjects 5 years after initial sampling, being most effective in combination with BMI. Shotgun metagenomic analysis functionally linked 26 metabolic pathways to the diurnal oscillation of gut bacteria. Thus, a cohort-specific risk pattern of arrhythmic taxa enables classification and prediction of T2D, suggesting a functional link between circadian rhythms and the microbiome in metabolic diseases.Entities:
Keywords: amplicon sequencing; circadian rhythms; diurnal oscillations; human intestinal microbiota; machine learning; metagenomics; obesity; population-based cohorts; prediction; type 2 diabetes
Mesh:
Year: 2020 PMID: 32619440 DOI: 10.1016/j.chom.2020.06.004
Source DB: PubMed Journal: Cell Host Microbe ISSN: 1931-3128 Impact factor: 21.023