Gut microbiome data may be helpful in routine screening of
cardiovascular disease

Date:
September 10, 2020
Source:
American Heart Association
Summary:
Previous studies have found the human gut microbiome, bacteria
in the gastrointestinal tract, is associated with cardiovascular
disease (CVD).

This study used machine learning to analyze data from nearly 1,000
stool samples from people with and without CVD. Results show
potential for developing a convenient, new diagnostic approach
for CVD.



FULL STORY ========================================================================== Using artificial intelligence to analyze the bacteria in a person's gut microbiome shows promise as a new screening method for cardiovascular
disease (CVD), according to preliminary research to be presented
Sept. 10-13, 2020, at the virtual American Heart Association's
Hypertension 2020 Scientific Sessions.

The meeting is a premier global exchange for clinical and basic
researchers focusing on recent advances in hypertension research. The
full study published simultaneously today in Hypertension, an American
Heart Association journal.


========================================================================== Recent studies have found a link between gut microbiota, the
microorganisms in human digestive tracts, and CVD, which is the leading
cause of mortality worldwide. Gut microbiota is highly variable between individuals, and differences in gut microbial compositions between people
with and without CVD have been reported.

"Based on our previous research linking gut microbiota to CVD in animal
models, we designed this study to test whether it is possible to screen
for CVD in humans using artificial intelligence screening of stool
samples," said Bina Joe, Ph.D., FAHA, the study director, Distinguished University Professor and Chairwoman of the department of physiology and pharmacology at the University of Toledo in Toledo, Ohio. "Gut microbiota
has a profound effect on cardiovascular function, and this could be
a potential new strategy for evaluation of cardiovascular health."
Researchers used data from the American Gut Project (an open platform
for microbiome research based in the United States) to analyze microbial composition of stool samples with state-of-the-art machine learning
modeling.

Nearly 1,000 samples were analyzed, and approximately half of the samples
were from people with CVD. The model was able to identify different
clusters of gut bacteria that could potentially help identify individuals
with existing CVD and without CVD.

Among the bacteria identified: Bacteroides, Subdoligranulum, Clostridium, Megasphaera, Eubacterium, Veillonella, Acidaminococcus and Listeria were
more abundant in the CVD group.

Faecalibacterium, Ruminococcus, Proteus, Lachnospira, Brevundimonas,
Alistipes and Neisseria were more abundant in the non-CVD group.

"Despite the fact that gut microbiomes are highly variable among
individuals, we were surprised by the promising level of accuracy
obtained from these preliminary results, which indicate fecal microbiota composition could potentially serve as a convenient diagnostic screening
method for CVD," Joe said. "It is conceivable that one day, maybe without
even assessing detailed cardiovascular function, clinicians could analyze
the gut microbiome of patients' stool samples with an artificial machine learning method to screen patients for heart and vascular diseases."

========================================================================== Story Source: Materials provided by American_Heart_Association. Note:
Content may be edited for style and length.


========================================================================== Journal Reference:
1. Sachin Aryal, Ahmad Alimadadi, Ishan Manandhar, Bina Joe, Xi Cheng.

Machine Learning Strategy for Gut Microbiome-Based Diagnostic
Screening of Cardiovascular Disease. Hypertension, 2020; DOI:
10.1161/ HYPERTENSIONAHA.120.15885 ==========================================================================

Link to news story: https://www.sciencedaily.com/releases/2020/09/200910150336.htm

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