Literature DB >> 35177859

Metabolomic and microbiome profiling reveals personalized risk factors for coronary artery disease.

Yeela Talmor-Barkan1,2,3,4, Noam Bar3,4, Aviv A Shaul1,2, Nir Shahaf3,4, Anastasia Godneva3,4, Yuval Bussi3,4, Maya Lotan-Pompan3,4, Adina Weinberger3,4, Alon Shechter1,2, Chava Chezar-Azerrad1,2, Ziad Arow1,2, Yoav Hammer1,2, Kanta Chechi5,6,7, Sofia K Forslund8,9,10,11, Sebastien Fromentin12, Marc-Emmanuel Dumas5,6,13, S Dusko Ehrlich12, Oluf Pedersen14, Ran Kornowski1,2, Eran Segal15,16.   

Abstract

Complex diseases, such as coronary artery disease (CAD), are often multifactorial, caused by multiple underlying pathological mechanisms. Here, to study the multifactorial nature of CAD, we performed comprehensive clinical and multi-omic profiling, including serum metabolomics and gut microbiome data, for 199 patients with acute coronary syndrome (ACS) recruited from two major Israeli hospitals, and validated these results in a geographically distinct cohort. ACS patients had distinct serum metabolome and gut microbial signatures as compared with control individuals, and were depleted in a previously unknown bacterial species of the Clostridiaceae family. This bacterial species was associated with levels of multiple circulating metabolites in control individuals, several of which have previously been linked to an increased risk of CAD. Metabolic deviations in ACS patients were found to be person specific with respect to their potential genetic or environmental origin, and to correlate with clinical parameters and cardiovascular outcomes. Moreover, metabolic aberrations in ACS patients linked to microbiome and diet were also observed to a lesser extent in control individuals with metabolic impairment, suggesting the involvement of these aberrations in earlier dysmetabolic phases preceding clinically overt CAD. Finally, a metabolomics-based model of body mass index (BMI) trained on the non-ACS cohort predicted higher-than-actual BMI when applied to ACS patients, and the excess BMI predictions independently correlated with both diabetes mellitus (DM) and CAD severity, as defined by the number of vessels involved. These results highlight the utility of the serum metabolome in understanding the basis of risk-factor heterogeneity in CAD.
© 2022. The Author(s), under exclusive licence to Springer Nature America, Inc.

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Year:  2022        PMID: 35177859     DOI: 10.1038/s41591-022-01686-6

Source DB:  PubMed          Journal:  Nat Med        ISSN: 1078-8956            Impact factor:   87.241


  50 in total

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Journal:  Cell       Date:  2016-03-10       Impact factor: 41.582

5.  Serum indoxyl sulfate is associated with vascular disease and mortality in chronic kidney disease patients.

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Review 6.  Metaorganismal nutrient metabolism as a basis of cardiovascular disease.

Authors:  J Mark Brown; Stanley L Hazen
Journal:  Curr Opin Lipidol       Date:  2014-02       Impact factor: 4.776

7.  Lifetime risk and years lived free of total cardiovascular disease.

Authors:  John T Wilkins; Hongyan Ning; Jarett Berry; Lihui Zhao; Alan R Dyer; Donald M Lloyd-Jones
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8.  Global and regional patterns in cardiovascular mortality from 1990 to 2013.

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10.  The human serum metabolome.

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Journal:  PLoS One       Date:  2011-02-16       Impact factor: 3.240

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  5 in total

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Authors:  Rima Mohsen Chakaroun; Lisa M Olsson; Fredrik Bäckhed
Journal:  Nat Rev Cardiol       Date:  2022-10-14       Impact factor: 49.421

2.  The relationship between the number of stenotic coronary arteries and the gut microbiome in coronary heart disease patients.

Authors:  Hao Yu; Le Li; Yu Deng; Guolan Zhang; Mimi Jiang; He Huang; Cheng Li; Zhiyu Lv; Yingshun Zhou; Xing Liu
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3.  Development and the art of nutritional maintenance.

Authors:  David S Gardner; Clint Gray
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4.  Plasma metabolomics provides new insights into the relationship between metabolites and outcomes and left ventricular remodeling of coronary artery disease.

Authors:  Qian Zhu; Min Qin; Zixian Wang; Yonglin Wu; Xiaoping Chen; Chen Liu; Qilin Ma; Yibin Liu; Weihua Lai; Hui Chen; Jingjing Cai; Yemao Liu; Fang Lei; Bin Zhang; Shuyao Zhang; Guodong He; Hanping Li; Mingliang Zhang; Hui Zheng; Jiyan Chen; Min Huang; Shilong Zhong
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  5 in total

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