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. 2020 Mar;18(3):642-650.
doi: 10.1111/jth.14699. Epub 2019 Dec 27.

The Intestinal Microbiome Potentially Affects Thrombin Generation in Human Subjects

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Free PMC article

The Intestinal Microbiome Potentially Affects Thrombin Generation in Human Subjects

Yassene Mohammed et al. J Thromb Haemost. .
Free PMC article

Abstract

Background: The intestinal microbiome plays a versatile role in the etiology of arterial thrombosis. In venous thrombosis, driven chiefly by plasma coagulation, no such role has yet been established. We hypothesized that the intestinal microbiome composition affects coagulation in humans.

Methods: We used healthy donor fecal microbiota transplant (FMT) to experimentally change the microbiome composition in metabolic syndrome patients. Thirty-five subjects were randomized in a blinded fashion to healthy donor FMT or autologous FMT as a control in a 2:1 ratio. We measured thrombin generation at baseline and after 6 weeks using automated calibrated thrombinography, and we determined plasma abundance of 32 coagulation related proteins using a targeted mass spectrometry-based quantitative proteomics assay with heavy labeled internal standards.

Results: Healthy donor FMT prolonged the thrombinography lag time (median delta 0.0 versus 0.25 minutes, P = .039). The other thrombinography parameters showed no significant difference. Unsupervised cluster analysis suggested overall downregulation of coagulation related plasma proteins in subject clusters containing predominantly subjects that had a metabolic response to healthy donor FMT. FMT treatment status itself showed no clear clustering pattern with up- or downregulation, however, and proteins did not cluster according to an apparent biological grouping.

Discussion: A single healthy donor FMT tends to modestly suppress the onset thrombin generation in metabolic syndrome patients, representing initial proof-of-principle that the intestinal microbiota composition might affect the coagulation system in humans. The findings merit external validation as a role for intestinal microbiota in coagulation can have clinically important implications.

Keywords: coagulation; fecal microbiota transplant; intestinal microbiome; metabolic syndrome; thrombin generation; thrombosis.

Conflict of interest statement

MN is on the Scientific Advisory Board of Caelus Pharmaceuticals, the Netherlands; CHB is the chief strategy officer of MRM Proteomics, Inc; none of these are directly relevant to the current paper. There are no patents, products in development, or marketed products to declare. The other authors declare no competing financial interests.

Figures

Figure 1
Figure 1
The change in the microbiome of healthy donor fecal microbiota transplant (FMT) and control subjects. A, The change in the microbiome within the treatment groups after 6 weeks in relation to baseline. B, The change over time in the difference of the microbiome between the subjects and their corresponding donors. The histograms in (B) are accompanied in the bottom with violin plots of the distributions and the student's t‐test P‐value for the difference between the two distributions. For all histograms, all subjects in the respective groups are aggregated by taking the mean of delta change per phylotype
Figure 2
Figure 2
Difference in lag time between baseline and 6 weeks after fecal microbiota transplant. Horizontal line indicates median
Figure 3
Figure 3
Coagulation proteome heatmaps and hierarchal clustering of subjects with different treatment and metabolic responder status. Proteins are represented as difference in abundance between baseline and 6 weeks after fecal microbiota transplant (FMT). The differential protein abundance is represented in z‐scores and displayed in continuous color levels ranging from − 4 in blue to + 4 in red. A, Healthy donor FMT versus control. The hierarchal clustering can be split with a high‐level cut into four major subtrees indicated as boxes and numbered from 1 to 4. Subtrees 2 and 3 dominate the middle part with 29 subjects. B, Healthy donor FMT metabolic responders versus control. The hierarchal clustering can be split with a high‐level cut into three major subtrees indicated as boxes and numbered from 1 to 3. Subtrees 2 and 3 dominate with 19 subjects

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