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Record W2533173460 · doi:10.1016/j.ebiom.2016.10.033

Intestinal Microbiome and Atherosclerosis

2016· letter· en· W2533173460 on OpenAlexaff
J. David Spence

Bibliographic record

VenueEBioMedicine · 2016
Typeletter
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicGut microbiota and health
Canadian institutionsWestern University
Fundersnot available
KeywordsMicrobiomeGut microbiomeComputational biologyIntestinal MicrobiomeBiologyBioinformaticsMedicineGenetics

Abstract

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The understanding of the role of nutrition in atherosclerosis has been revolutionized by the recognition of effects of the intestinal microbiome. Intestinal bacteria ferment nutrients into metabolic products, with profound effects on atherosclerosis and on cardiovascular risk. Carnitine from red meat (~4 times as much as in chicken or fish) and choline, including phosphatidylcholine from egg yolk, are converted by the intestinal microbiome into trimethylamine (Wang et al., 2011Wang Z. Klipfell E. Bennett B.J. et al.Gut flora metabolism of phosphatidylcholine promotes cardiovascular disease.Nature. 2011; 472: 57-63Crossref PubMed Scopus (3451) Google Scholar, Koeth et al., 2013Koeth R.A. Wang Z. Levison B.S. et al.Intestinal microbiota metabolism of l-carnitine, a nutrient in red meat, promotes atherosclerosis.Nat. Med. 2013; 19: 576-585Crossref PubMed Scopus (2700) Google Scholar), the compound that causes the fishy odor in uremic breath. Trimethylamine is oxidized in the liver to trimethylamine N-oxide (TMAO), which causes atherosclerosis in an animal model (Wang et al., 2011Wang Z. Klipfell E. Bennett B.J. et al.Gut flora metabolism of phosphatidylcholine promotes cardiovascular disease.Nature. 2011; 472: 57-63Crossref PubMed Scopus (3451) Google Scholar), and markedly increases cardiovascular risk. That the TMAO results from fermentation by intestinal bacteria was shown in animal model and in human subjects by giving antibiotics to eliminate the intestinal bacteria. Perhaps the most interesting thing about these studies was that vegans given carnitine did not product TMAO, apparently lacking the “meat-eating” bacteria that do so (Koeth et al., 2013Koeth R.A. Wang Z. Levison B.S. et al.Intestinal microbiota metabolism of l-carnitine, a nutrient in red meat, promotes atherosclerosis.Nat. Med. 2013; 19: 576-585Crossref PubMed Scopus (2700) Google Scholar). This suggests that it may be possible to modify the intestinal microbiome, by a process similar to “repopulation” commonly used to treat clostridium difficile. Among patients referred for coronary angiogram, TMAO levels were measured after a test dose of two hard-boiled eggs. Patients with TMAO in the top quartile had 2.5 times higher three-year risk of stroke, myocardial infarction or vascular death compared to those in the lowest quartile (Tang et al., 2013Tang W.H.W. Wang Z. Levison B.S. et al.Intestinal microbial metabolism of phosphatidylcholine and cardiovascular risk.N. Engl. J. Med. 2013; 368: 1575-1584Crossref PubMed Scopus (2038) Google Scholar). The metabolic products of the intestinal microbiome are to a great extent eliminated by the kidneys; they therefore accumulate in patients with renal failure, and may be termed gut-derived uremic toxins (GDUT). Homocysteine, a leading candidate to explain high cardiovascular risk in uremia, appears to account for only ~20% of the excess carotid atherosclerosis in renal failure (Spence et al., 2016Spence J.D. Urquhart B.L. Bang H. Effect of renal impairment on atherosclerosis: only partially mediated by homocysteine.Nephrol. Dial. Transplant. 2016; 31: 937-944Crossref PubMed Scopus (50) Google Scholar). Levels of TMAO are high in renal failure, and besides increasing cardiovascular risk, also accelerate decline of renal function (Tang et al., 2015Tang W.H. Wang Z. Kennedy D.J. et al.Gut microbiota-dependent trimethylamine N-oxide (TMAO) pathway contributes to both development of renal insufficiency and mortality risk in chronic kidney disease.Circ. Res. 2015; 116: 448-455Crossref PubMed Scopus (720) Google Scholar). Besides TMAO from carnitine and phosphatidylcholine, there are a number produced from proteins/amino acids. In patients with CKD, plasma levels of indoxyl sulfate (IS) and p-cresyl sulfate (PCS) are 54 and 17 times higher, respectively, than in healthy individuals. Both IS and PCS are associated with accelerated progression to dialysis and cardiovascular risk among pre-end-stage renal disease (ESRD) patients (Lin et al., 2014Lin C.J. Pan C.F. Chuang C.K. et al.P-cresyl sulfate is a valuable predictor of clinical outcomes in pre-ESRD patients.Biomed. Res. Int. 2014; 2014: 526932PubMed Google Scholar, Lin et al., 2015aLin C.J. Wu V. Wu P.C. Wu C.J. Meta-analysis of the associations of p-cresyl sulfate (PCS) and indoxyl sulfate (IS) with cardiovascular events and all-cause mortality in patients with chronic renal failure.PLoS One. 2015; 10e0132589Google Scholar). Indoxyl sulfate is also associated with increased all-cause mortality, and with glycation end products (Lin et al., 2015bLin C.J. Lin J. Pan C.F. et al.Indoxyl sulfate, not p-cresyl sulfate, is associated with advanced glycation end products in patients on long-term hemodialysis.Kidney Blood Press. Res. 2015; 40: 121-129Crossref PubMed Scopus (15) Google Scholar). Cardiovascular risk is very high in patients with renal failure (Gansevoort et al., 2013Gansevoort R.T. Correa-Rotter R. Hemmelgarn B.R. et al.Chronic kidney disease and cardiovascular risk: epidemiology, mechanisms, and prevention.Lancet. 2013; 382: 339-352Summary Full Text Full Text PDF PubMed Scopus (1246) Google Scholar), so for patients with renal failure it is particularly important to avoid red meat and egg yolks. Besides the metabolic products of the intestinal microbiome, it seems that there are other non-metabolic pathways by which “gut microbes can also signal to the host to regulate innate immunity through metabolism-independent pathways, where constituents of the microbial cell wall are sensed by host cells through pattern recognition receptors to further impact CVD progression.” In this issue of the journal EBioMedicine, Chen et al., 2016Chen L. Ishigami T. Nakashima-Sasaki R. et al.Commensal microbes-specific activation of B2 cell subsets contribute to atherosclerosis development independent of lipid metabolism.EBioMedicine. 2016; 13: 237-247Summary Full Text Full Text PDF PubMed Scopus (20) Google Scholar report what appears to be a metabolism-independent mechanism by which the intestinal microbiome can affect atherosclerosis. They found that effects of the intestinal microbiome resulted in recruitment and ectopic activation of B2 cells in perivascular adipose tissue and an increase in circulating IgG, increasing development of atherosclerosis. This was prevented by antibiotic elimination of the intestinal microbiome, and also by depletion of B2 cells with antibodies. There is more to be discovered about this story, but already it is clear that the intestinal microbiome has important effects on atherosclerosis and cardiovascular risk. Learning how to mitigate these effects will be an important field of research in the coming years. The author declared no conflicts of interest. Commensal Microbe-specific Activation of B2 Cell Subsets Contributes to Atherosclerosis Development Independently of Lipid MetabolismThe relation between B2 cells and commensal microbes during atherosclerosis remains largely unexplored. Here we show that under hyperlipidemic conditions intestinal microbiota resulted in recruitment and ectopic activation of B2 cells in perivascular adipose tissue, followed by an increase in circulating IgG, promoting disease development. In contrast, disruption of the intestinal microbiota by a broad-spectrum antibiotic cocktail (AVNM) led to the attenuation of atherosclerosis by suppressing B2 cells, despite the persistence of serum lipid abnormalities. Full-Text PDF Open AccessIntestinal Microbiome and Atherosclerosis – Authors' ReplyWe highly appreciate the Commentary by Spence (Spence, 2016) on our article entitled “Commensal microbes-specific activation of B2 cell subsets contribute to atherosclerosis development independent of lipid metabolism” (Chen et al., in this issue). Atherosclerotic diseases are systemic disorders and one of the leading causes of mortality and morbidity throughout the world. Although multiple risk factors, such as hypertension, diabetes mellitus, hyperlipidemia and smoking, have been identified, atherosclerosis is considered to be initiated and sustained by various metabolic factors (Mihaylova et al., 2012; Kearney et al., 2008). Full-Text PDF Open Access

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Commentary · Consensus signal: Commentary
Teacher disagreement score0.468
Threshold uncertainty score0.933

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0010.000
Insufficient payload (model declined to judge)0.0000.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.011
GPT teacher head0.241
Teacher spread0.231 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designNot applicable
Domainnot available
GenreCommentary

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

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Citations13
Published2016
Admission routes1
Has abstractyes

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