Dysbiosis signatures of gut microbiota in coronary artery disease

Gut microbiota dysbiosis has been considered to be an important risk factor that contributes to coronary artery disease (CAD), but limited evidence exists about the involvement of gut microbiota in the disease. Our study aimed to characterize the dysbiosis signatures of gut microbiota in coronary artery disease. The gut microbiota represented in stool samples were collected from 70 patients with coronary artery disease and 98 healthy controls. 16S rRNA sequencing was applied, and bioinformatics methods were used to decipher taxon signatures and function alteration, as well as the microbial network and diagnostic model of gut microbiota in coronary artery disease. Gut microbiota showed decreased diversity and richness in patients with coronary artery disease. The composition of the microbial community changed; Escherichia-Shigella [false discovery rate (FDR = 7.5*10−5] and Enterococcus (FDR = 2.08*10−7) were significant enriched, while Faecalibacterium (FDR = 6.19*10−10), Subdoligranulum (FDR = 1.63*10−6), Roseburia (FDR = 1.95*10−9), and Eubacterium rectale (FDR = 2.35*10−4) were significant depleted in the CAD group. Consistent with the taxon changes, functions such as amino acid metabolism, phosphotransferase system, propanoate metabolism, lipopolysaccharide biosynthesis, and protein and tryptophan metabolism were found to be enhanced in CAD patients. The microbial network revealed that Faecalibacterium and Escherichia-Shigella were the microbiotas that dominated in the healthy control and CAD groups, respectively. The microbial diagnostic model based on random forest also showed probability in identifying those who suffered from CAD. Our study successfully identifies the dysbiosis signature, dysfunctions, and comprehensive networks of gut microbiota in CAD patients. Thus, modulation targeting the gut microbiota may be a novel strategy for CAD treatment.

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Artificial neural network models for coronary artery disease

Current Bioinformatics ◽

10.2174/1574893615666200214102837 ◽

2020 ◽

Vol 15 ◽

Author(s):

Elham Shamsara ◽

Sara Saffar Soflaei ◽

Mohammad Tajfard ◽

Ivan Yamshchikov ◽

Habibollah Esmaili ◽

...

Keyword(s):

Neural Network ◽

Machine Learning ◽

Coronary Artery Disease ◽

Pattern Recognition ◽

Artificial Neural Network ◽

Coronary Artery ◽

Diagnostic Model ◽

Early Prediction ◽

Artificial Neural ◽

Artery Disease

Background: Coronary artery disease (CAD) is an important cause of mortality and morbidity globally. Objective : The early prediction of the CAD would be valuable in identifying individuals at risk, and in focusing resources on its prevention. In this paper, we aimed to establish a diagnostic model to predict CAD by using three approaches of ANN (pattern recognition-ANN, LVQ-ANN, and competitive ANN). Methods: One promising method for early prediction of disease based on risk factors is machine learning. Among diﬀerent machine learning algorithms, the artificial neural network (ANN) algo-rithms have been applied widely in medicine and a variety of real-world classifications. ANN is a non-linear computational model, that is inspired by the human brain to analyze and process complex datasets. Results: Diﬀerent methods of ANN that are investigated in this paper indicates in both pattern recognition ANN and LVQ-ANN methods, the predictions of Angiography+ class have high accuracy. Moreover, in CNN the correlations between the individuals in cluster ”c” with the class of Angiography+ is strongly high. This accuracy indicates the significant diﬀerence among some of the input features in Angiography+ class and the other two output classes. A comparison among the chosen weights in these three methods in separating control class and Angiography+ shows that hs-CRP, FSG, and WBC are the most substantial excitatory weights in recognizing the Angiography+ individuals although, HDL-C and MCH are determined as inhibitory weights. Furthermore, the effect of decomposition of a multi-class problem to a set of binary classes and random sampling on the accuracy of the diagnostic model is investigated. Conclusion : This study confirms that pattern recognition-ANN had the most accuracy of performance among diﬀerent methods of ANN. That’s due to the back-propagation procedure of the process in which the network classify input variables based on labeled classes. The results of binarization show that decomposition of the multi-class set to binary sets could achieve higher accuracy.

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The gut microbiota is associated with clinical response to statin treatment in patients with coronary artery disease

Atherosclerosis ◽

10.1016/j.atherosclerosis.2021.03.007 ◽

2021 ◽

Vol 325 ◽

pp. 16-23

Author(s):

Lijun Wang ◽

Weiwei Zhou ◽

Manyun Guo ◽

Yiming Hua ◽

Baihua Zhou ◽

...

Keyword(s):

Coronary Artery Disease ◽

Coronary Artery ◽

Gut Microbiota ◽

Clinical Response ◽

Statin Treatment ◽

Artery Disease

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16.32 Comparison of Tc-99m tetrofosmin SPECT and echocardiographic Doppler tissue imaging in the assessment of ventricular perfusion and function in patients with coronary artery disease

Journal of Nuclear Cardiology ◽

10.1016/s1071-3581(01)80819-9 ◽

2001 ◽

Vol 8 (1) ◽

pp. S104-S104

Author(s):

M KOSTKIEWICZ ◽

W PLAZAK ◽

T PRZEWLOCKI ◽

W TRACZ

Keyword(s):

Coronary Artery Disease ◽

Coronary Artery ◽

Tissue Imaging ◽

Doppler Tissue Imaging ◽

Artery Disease ◽

And Function

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Characterization of gut microbiota profiles in coronary artery disease patients using data mining analysis of terminal restriction fragment length polymorphism: gut microbiota could be a diagnostic marker of coronary artery disease

Heart and Vessels ◽

10.1007/s00380-016-0841-y ◽

2016 ◽

Vol 32 (1) ◽

pp. 39-46 ◽

Cited By ~ 48

Author(s):

Takuo Emoto ◽

Tomoya Yamashita ◽

Toshio Kobayashi ◽

Naoto Sasaki ◽

Yushi Hirota ◽

...

Keyword(s):

Data Mining ◽

Coronary Artery Disease ◽

Coronary Artery ◽

Gut Microbiota ◽

Fragment Length Polymorphism ◽

Diagnostic Marker ◽

Artery Disease ◽

Using Data ◽

Restriction Fragment Length

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Abstract 253: The Presence of Coronary Artery Disease Influences Number and Function of Stem Cells in the Bone Marrow

Circulation Research ◽

10.1161/res.115.suppl_1.253 ◽

2014 ◽

Vol 115 (suppl_1) ◽

Author(s):

Evelien Nollet ◽

Dina De Bock ◽

Inez R Rodrigus ◽

Vicky Y Hoymans ◽

Christiaan J Vrints ◽

...

Keyword(s):

Coronary Artery Disease ◽

Stem Cells ◽

Bone Marrow ◽

Renal Function ◽

Stem Cell ◽

Coronary Artery ◽

Therapeutic Benefits ◽

Migratory Capacity ◽

Artery Disease ◽

And Function

Purpose: Despite the observed therapeutic benefits of autologous bone marrow (BM)-derived stem cell transplantation in patients with ischemic heart disease, the efficacy of this approach could be hampered by BM dysfunction. We investigated whether BM cellularity and function is affected by coronary artery disease (CAD). Methods & Results: BM samples were obtained peri-operatively from 26 CAD patients, undergoing coronary artery bypass surgery (LVEF 54±16%), and 6 controls, undergoing mitral valve surgery (LVEF 50±12%; age 59±10yrs). CAD patients were stratified according to their Syntax score (mild ≤15, age 61±10yrs; and moderate CAD >15, age 63±8yrs; stratification based on median score), which is used to assess complexity of coronary lesions. In vitro functional analysis of isolated BM-derived mononuclear cells (BM-MNC) revealed a significant impairment of migratory capacity towards SDF-1α and VEGF in patients with moderate CAD (25.71±7.3%) compared to controls (33.82±8.3%; p=0.042) and patients with mild CAD (34.76±7.8%; p=0.007). Hematopoetic stem cells (HSC, CD45dimCD34+SSClow) were reduced in patients with moderate CAD (8178±5530 HSC/106 BM-MNC; p=0.014) and mild CAD (10655±5489 HSC/106 BM-MNC; p=0.054) compared to controls (16220±6126 HSC/106 BM-MNC). An inverse correlation was found between age and the number of granulocyte-macrophage colony forming units (r= −0.408; p=0.048), burst forming units erythroid (r= −0.458; p=0.028) and HSC (r=-0.356; p=0.046). Furthermore, our data revealed a relation between reduced renal function (CKD-EPI eGFR, 81.2±19 ml/min) and reduced number of HSC (r=0.480; p=0.011) and endothelial progenitor cells (EPC, CD45dimCD34+KDR+; r=0.522; p=0.008). Conclusions: Migratory capacity of BM-MNC and the number of HSC are reduced in patients with CAD, which is more pronounced in more complex CAD. In addition, age and renal function emerge as relevant determinants on BM function and stem cell populations. Therefore, these factors should be taken into account when assessing benefits of autologous stem cell therapy.

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