Microbiome and metabolome features of the cardiometabolic disease spectrum.
Journal
Nature medicine
ISSN: 1546-170X
Titre abrégé: Nat Med
Pays: United States
ID NLM: 9502015
Informations de publication
Date de publication:
02 2022
02 2022
Historique:
received:
01
03
2021
accepted:
07
01
2022
pubmed:
19
2
2022
medline:
20
4
2022
entrez:
18
2
2022
Statut:
ppublish
Résumé
Previous microbiome and metabolome analyses exploring non-communicable diseases have paid scant attention to major confounders of study outcomes, such as common, pre-morbid and co-morbid conditions, or polypharmacy. Here, in the context of ischemic heart disease (IHD), we used a study design that recapitulates disease initiation, escalation and response to treatment over time, mirroring a longitudinal study that would otherwise be difficult to perform given the protracted nature of IHD pathogenesis. We recruited 1,241 middle-aged Europeans, including healthy individuals, individuals with dysmetabolic morbidities (obesity and type 2 diabetes) but lacking overt IHD diagnosis and individuals with IHD at three distinct clinical stages-acute coronary syndrome, chronic IHD and IHD with heart failure-and characterized their phenome, gut metagenome and serum and urine metabolome. We found that about 75% of microbiome and metabolome features that distinguish individuals with IHD from healthy individuals after adjustment for effects of medication and lifestyle are present in individuals exhibiting dysmetabolism, suggesting that major alterations of the gut microbiome and metabolome might begin long before clinical onset of IHD. We further categorized microbiome and metabolome signatures related to prodromal dysmetabolism, specific to IHD in general or to each of its three subtypes or related to escalation or de-escalation of IHD. Discriminant analysis based on specific IHD microbiome and metabolome features could better differentiate individuals with IHD from healthy individuals or metabolically matched individuals as compared to the conventional risk markers, pointing to a pathophysiological relevance of these features.
Identifiants
pubmed: 35177860
doi: 10.1038/s41591-022-01688-4
pii: 10.1038/s41591-022-01688-4
pmc: PMC8863577
doi:
Types de publication
Journal Article
Research Support, Non-U.S. Gov't
Langues
eng
Sous-ensembles de citation
IM
Pagination
303-314Subventions
Organisme : Wellcome Trust
Pays : United Kingdom
Organisme : Medical Research Council
ID : MR/L01632X/1
Pays : United Kingdom
Organisme : Medical Research Council
ID : MR/S020039/1
Pays : United Kingdom
Organisme : Wellcome Trust
ID : 204834/Z/16/Z
Pays : United Kingdom
Organisme : Department of Health
Pays : United Kingdom
Commentaires et corrections
Type : CommentIn
Informations de copyright
© 2022. The Author(s).
Références
Heymsfield, S. B. & Wadden, T. A. Mechanisms, pathophysiology, and management of obesity. N. Engl. J. Med. 376, 254–266 (2017).
pubmed: 28099824
doi: 10.1056/NEJMra1514009
Rothschild, D. et al. Environment dominates over host genetics in shaping human gut microbiota. Nature 555, 210–215 (2018).
pubmed: 29489753
doi: 10.1038/nature25973
Cotillard, A. et al. Dietary intervention impact on gut microbial gene richness. Nature 500, 585–588 (2013).
pubmed: 23985875
doi: 10.1038/nature12480
Karlsson, F. H. et al. Gut metagenome in European women with normal, impaired and diabetic glucose control. Nature 498, 99–103 (2013).
pubmed: 23719380
doi: 10.1038/nature12198
Le Chatelier, E. et al. Richness of human gut microbiome correlates with metabolic markers. Nature 500, 541–546 (2013).
pubmed: 23985870
doi: 10.1038/nature12506
Forslund, K. et al. Disentangling type 2 diabetes and metformin treatment signatures in the human gut microbiota. Nature 528, 262–266 (2015).
pubmed: 26633628
pmcid: 4681099
doi: 10.1038/nature15766
Maier, L. et al. Extensive impact of non-antibiotic drugs on human gut bacteria. Nature 555, 623–628 (2018).
pubmed: 29555994
pmcid: 6108420
doi: 10.1038/nature25979
Forslund, S. K. et al. Combinatorial, additive and dose-dependent drug–microbiome associations. Nature 600, 500–505 (2021).
Vujkovic-Cvijin, I. et al. Host variables confound gut microbiota studies of human disease. Nature 587, 448–454 (2020).
Mozaffarian, D. et al. Heart disease and stroke statistics—2015 update: a report from the American Heart Association. Circulation 131, e29–e322 (2015).
pubmed: 25520374
Jie, Z. et al. The gut microbiome in atherosclerotic cardiovascular disease. Nat. Commun. 8, 845 (2017).
pubmed: 29018189
pmcid: 5635030
doi: 10.1038/s41467-017-00900-1
Pasini, E. et al. Pathogenic gut flora in patients with chronic heart failure. JACC Heart Fail. 4, 220–227 (2016).
pubmed: 26682791
doi: 10.1016/j.jchf.2015.10.009
Karlsson, F. H. et al. Symptomatic atherosclerosis is associated with an altered gut metagenome. Nat. Commun. 3, 1245 (2012).
pubmed: 23212374
doi: 10.1038/ncomms2266
Li, J. et al. Gut microbiota dysbiosis contributes to the development of hypertension. Microbiome 5, 14 (2017).
pubmed: 28143587
pmcid: 5286796
doi: 10.1186/s40168-016-0222-x
Matey-Hernandez, M. L. et al. Genetic and microbiome influence on lipid metabolism and dyslipidemia. Physiol. Genomics 50, 117–126 (2018).
pubmed: 29341867
doi: 10.1152/physiolgenomics.00053.2017
Einarson, T. R., Acs, A., Ludwig, C. & Panton, U. H. Prevalence of cardiovascular disease in type 2 diabetes: a systematic literature review of scientific evidence from across the world in 2007–2017. Cardiovasc. Diabetol. 17, 83 (2018).
pubmed: 29884191
pmcid: 5994068
doi: 10.1186/s12933-018-0728-6
Association, A. D. Classification and diagnosis of diabetes: standards of medical care in diabetes—2019. Diabetes Care 42, S13–S28 (2019).
doi: 10.2337/dc19-S002
Kurilshikov, A. et al. Gut microbial associations to plasma metabolites linked to cardiovascular phenotypes and risk: a cross-sectional study. Circ. Res. 124, 1808–1820 (2019).
pubmed: 30971183
doi: 10.1161/CIRCRESAHA.118.314642
Vieira-Silva, S. et al. Quantitative microbiome profiling disentangles inflammation- and bile duct obstruction-associated microbiota alterations across PSC/IBD diagnoses. Nat. Microbiol. 4, 1826–1831 (2019).
pubmed: 31209308
doi: 10.1038/s41564-019-0483-9
Consortium, I. Adherence to predefined dietary patterns and incident type 2 diabetes in European populations: EPIC-InterAct Study. Diabetologia 57, 321–333 (2014).
doi: 10.1007/s00125-013-3092-9
Jeurnink, S. et al. Variety in vegetable and fruit consumption and the risk of gastric and esophageal cancer in the European Prospective Investigation into Cancer and Nutrition. Int. J. Cancer 131, E963–E973 (2012).
pubmed: 22392502
doi: 10.1002/ijc.27517
Sacks, F. M. et al. Rationale and design of the Dietary Approaches to Stop Hypertension trial (DASH): a multicenter controlled-feeding study of dietary patterns to lower blood pressure. Ann. Epidemiol. 5, 108–118 (1995).
pubmed: 7795829
doi: 10.1016/1047-2797(94)00055-X
Vandeputte, D. et al. Quantitative microbiome profiling links gut community variation to microbial load. Nature 551, 507–511 (2017).
pubmed: 29143816
doi: 10.1038/nature24460
Aron-Wisnewsky, J. et al. Major microbiota dysbiosis in severe obesity: fate after bariatric surgery. Gut 68, 70–82 (2019).
pubmed: 29899081
doi: 10.1136/gutjnl-2018-316103
Talmor-Barkan, Y. et al. Metabolomic and microbiome profiling reveals personalized risk factors for coronary artery disease. Nat. Med. https://doi.org/10.1038/s41591-022-01686-6 (2022).
Velusamy, R. & Muhi, S. Melioidosis and the heart: a systematic review. Trop. Med. Infect. Dis. 5, 121 (2020).
pmcid: 7558958
doi: 10.3390/tropicalmed5030121
Tang, W. W., Bäckhed, F., Landmesser, U. & Hazen, S. L. Intestinal microbiota in cardiovascular health and disease: JACC state-of-the-art review. J. Am. Coll. Cardiol. 73, 2089–2105 (2019).
pubmed: 31023434
pmcid: 6518422
doi: 10.1016/j.jacc.2019.03.024
Pallister, T. et al. Hippurate as a metabolomic marker of gut microbiome diversity: modulation by diet and relationship to metabolic syndrome. Sci. Rep. 7, 13670 (2017).
pubmed: 29057986
pmcid: 5651863
doi: 10.1038/s41598-017-13722-4
Kaduce, T. L., Figard, P. H., Leifur, R. & Spector, A. A. Formation of 9-hydroxyoctadecadienoic acid from linoleic acid in endothelial cells. J. Biol. Chem. 264, 6823–6830 (1989).
pubmed: 2496121
doi: 10.1016/S0021-9258(18)83504-9
Jang, M. K. et al. Oxidized low-density lipoproteins may induce expression of monocyte chemotactic protein-3 in atherosclerotic plaques. Biochem. Biophys. Res. Commun. 323, 898–905 (2004).
pubmed: 15381085
doi: 10.1016/j.bbrc.2004.08.178
Lee, Y.-C. et al. Role of perivascular adipose tissue-derived methyl palmitate in vascular tone regulation and pathogenesis of hypertension. Circulation 124, 1160–1171 (2011).
pubmed: 21844078
doi: 10.1161/CIRCULATIONAHA.111.027375
Ziegler, M., Wallert, M., Lorkowski, S. & Peter, K. Cardiovascular and metabolic protection by vitamin E: a matter of treatment strategy? Antioxidants 9, 935 (2020).
pmcid: 7600583
doi: 10.3390/antiox9100935
Smith, E. et al. Ergothioneine is associated with reduced mortality and decreased risk of cardiovascular disease. Heart 106, 691–697 (2020).
pubmed: 31672783
doi: 10.1136/heartjnl-2019-315485
Nemet, I. et al. A cardiovascular disease-linked gut microbial metabolite acts via adrenergic receptors. Cell 180, 862–877 (2020).
pubmed: 32142679
pmcid: 7402401
doi: 10.1016/j.cell.2020.02.016
Patel, K. P., Luo, F. J.-G., Plummer, N. S., Hostetter, T. H. & Meyer, T. W. The production of p-cresol sulfate and indoxyl sulfate in vegetarians versus omnivores. Clin. J. Am. Soc. Nephrol. 7, 982–988 (2012).
pubmed: 22490877
pmcid: 3362314
doi: 10.2215/CJN.12491211
Andriamihaja, M. et al. The deleterious metabolic and genotoxic effects of the bacterial metabolite p-cresol on colonic epithelial cells. Free Radic. Biol. Med. 85, 219–227 (2015).
pubmed: 25881551
doi: 10.1016/j.freeradbiomed.2015.04.004
Wan, Y. et al. Effects of dietary fat on gut microbiota and faecal metabolites, and their relationship with cardiometabolic risk factors: a 6-month randomised controlled-feeding trial. Gut 68, 1417–1429 (2019).
pubmed: 30782617
doi: 10.1136/gutjnl-2018-317609
Poesen, R. et al. Cardiovascular disease relates to intestinal uptake of p-cresol in patients with chronic kidney disease. BMC Nephrol. 15, 87 (2014).
pubmed: 24912660
pmcid: 4064102
doi: 10.1186/1471-2369-15-87
Razavi, A. C. et al. Pseudouridine and N-formylmethionine associate with left ventricular mass index: metabolome-wide association analysis of cardiac remodeling. J. Mol. Cell. Cardiol. 140, 22–29 (2020).
pubmed: 32057737
pmcid: 7255589
doi: 10.1016/j.yjmcc.2020.02.005
Gu, S. X., Stevens, J. W. & Lentz, S. R. Regulation of thrombosis and vascular function by protein methionine oxidation. Blood 125, 3851–3859 (2015).
pubmed: 25900980
pmcid: 4473114
doi: 10.1182/blood-2015-01-544676
Lopaschuk, G. D., Ussher, J. R., Folmes, C. D., Jaswal, J. S. & Stanley, W. C. Myocardial fatty acid metabolism in health and disease. Physiol. Rev. 90, 207–258 (2010).
pubmed: 20086077
doi: 10.1152/physrev.00015.2009
Vieira-Silva, S. et al. Statin therapy is associated with lower prevalence of gut microbiota dysbiosis. Nature 581, 310–315 (2020).
pubmed: 32433607
doi: 10.1038/s41586-020-2269-x
Pedersen, H. K. et al. Human gut microbes impact host serum metabolome and insulin sensitivity. Nature 535, 376–381 (2016).
pubmed: 27409811
doi: 10.1038/nature18646
Levey, A. S. et al. A more accurate method to estimate glomerular filtration rate from serum creatinine: a new prediction equation. Ann. Intern. Med. 130, 461–470 (1999).
pubmed: 10075613
doi: 10.7326/0003-4819-130-6-199903160-00002
Hunter, I., Rehfeld, J. F. & Goetze, J. P. Measurement of the total proANP product in mammals by processing independent analysis. J. Immunol. Methods 370, 104–110 (2011).
pubmed: 21703274
doi: 10.1016/j.jim.2011.06.005
Prest, E., Hammes, F., Kötzsch, S., van Loosdrecht, M. C. & Vrouwenvelder, J. S. Monitoring microbiological changes in drinking water systems using a fast and reproducible flow cytometric method. Water Res. 47, 7131–7142 (2013).
pubmed: 24183559
doi: 10.1016/j.watres.2013.07.051
Criscuolo, A. & Brisse, S. AlienTrimmer: a tool to quickly and accurately trim off multiple short contaminant sequences from high-throughput sequencing reads. Genomics 102, 500–506 (2013).
pubmed: 23912058
doi: 10.1016/j.ygeno.2013.07.011
Li, J. et al. An integrated catalog of reference genes in the human gut microbiome. Nat. Biotechnol. 32, 834–841 (2014).
pubmed: 24997786
doi: 10.1038/nbt.2942
Langmead, B. & Salzberg, S. L. Fast gapped-read alignment with Bowtie 2. Nat. Methods 9, 357 (2012).
pubmed: 22388286
pmcid: 3322381
doi: 10.1038/nmeth.1923
Pons, N. et al. METEOR, a platform for quantitative metagenomic profiling of complex ecosystems. Journées Ouvertes en Biologie, Informatique et Mathématiques. http://www.jobim2010.fr/sites/default/files/presentations/27Pons.pdf (2010).
Vieira-Silva, S. et al. Species–function relationships shape ecological properties of the human gut microbiome. Nat. Microbiol. 1, 16088 (2016).
pubmed: 27573110
doi: 10.1038/nmicrobiol.2016.88
Falony, G., Vieira-Silva, S. & Raes, J. Microbiology meets Big Data: the case of gut microbiota-derived trimethylamine. Annu. Rev. Microbiol. 69, 305–321 (2015).
pubmed: 26274026
doi: 10.1146/annurev-micro-091014-104422
Valles-Colomer, M. et al. The neuroactive potential of the human gut microbiota in quality of life and depression. Nat. Microbiol. 4, 623–632 (2019).
pubmed: 30718848
doi: 10.1038/s41564-018-0337-x
Darzi, Y., Falony, G., Vieira-Silva, S. & Raes, J. Towards biome-specific analysis of meta-omics data. ISME J. 10, 1025–1028 (2016).
pubmed: 26623543
doi: 10.1038/ismej.2015.188
Dona, A. C. et al. A guide to the identification of metabolites in NMR-based metabonomics/metabolomics experiments. Comput. Struct. Biotechnol. J. 14, 135–153 (2016).
pubmed: 27087910
pmcid: 4821453
doi: 10.1016/j.csbj.2016.02.005
Rodriguez-Martinez, A. et al. J-resolved
pubmed: 28937204
doi: 10.1021/acs.analchem.7b02374
Dona, A. C. et al. Precision high-throughput proton NMR spectroscopy of human urine, serum, and plasma for large-scale metabolic phenotyping. Anal. Chem. 86, 9887–9894 (2014).
pubmed: 25180432
doi: 10.1021/ac5025039
Würtz, P. et al. Metabolite profiling and cardiovascular event risk: a prospective study of 3 population-based cohorts. Circulation 131, 774–785 (2015).
pubmed: 25573147
pmcid: 4351161
doi: 10.1161/CIRCULATIONAHA.114.013116
Long, T. et al. Whole-genome sequencing identifies common-to-rare variants associated with human blood metabolites. Nat. Genet. 49, 568–578 (2017).
pubmed: 28263315
doi: 10.1038/ng.3809
DeHaven, C. D., Evans, A. M., Dai, H. & Lawton, K. A. Organization of GC/MS and LC/MS metabolomics data into chemical libraries. J. Cheminformatics 2, 1–12 (2010).
doi: 10.1186/1758-2946-2-9
Liu, H. et al. Alterations in the gut microbiome and metabolism with coronary artery disease severity. Microbiome 7, 68 (2019).
pubmed: 31027508
pmcid: 6486680
doi: 10.1186/s40168-019-0683-9
Lanter, B. B., Sauer, K. & Davies, D. G. Bacteria present in carotid arterial plaques are found as biofilm deposits which may contribute to enhanced risk of plaque rupture. MBio 5, e01206-14 (2014).
Emoto, T. et al. 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 Vessels 32, 39–46 (2017).
pubmed: 27125213
doi: 10.1007/s00380-016-0841-y
Ott, S. J. et al. Detection of diverse bacterial signatures in atherosclerotic lesions of patients with coronary heart disease. Circulation 113, 929–937 (2006).
pubmed: 16490835
doi: 10.1161/CIRCULATIONAHA.105.579979
Yin, J. et al. Dysbiosis of gut microbiota with reduced trimethylamine‐N‐oxide level in patients with large‐artery atherosclerotic stroke or transient ischemic attack. J. Am. Heart Assoc. 4, e002699 (2015).
pubmed: 26597155
pmcid: 4845212
doi: 10.1161/JAHA.115.002699
Zhu, Q. et al. Dysbiosis signatures of gut microbiota in coronary artery disease. Physiol. Genomics 50, 893–903 (2018).
pubmed: 30192713
doi: 10.1152/physiolgenomics.00070.2018
Kelly, T. N. et al. Gut microbiome associates with lifetime cardiovascular disease risk profile among Bogalusa Heart Study participants. Circ. Res. 119, 956–964 (2016).
pubmed: 27507222
pmcid: 5045790
doi: 10.1161/CIRCRESAHA.116.309219
Zheng, Y.-Y. et al. Gut microbiome-based diagnostic model to predict coronary artery disease. J. Agric. Food Chem. 68, 3548–3557 (2020).
pubmed: 32100534
doi: 10.1021/acs.jafc.0c00225
Koren, O. et al. Human oral, gut, and plaque microbiota in patients with atherosclerosis. Proc. Natl Acad. Sci. USA 108, 4592–4598 (2011).
pubmed: 20937873
doi: 10.1073/pnas.1011383107
Feng, Q. et al. Integrated metabolomics and metagenomics analysis of plasma and urine identified microbial metabolites associated with coronary heart disease. Sci. Rep. 6, 22525 (2016).
Cui, X. et al. Metagenomic and metabolomic analyses unveil dysbiosis of gut microbiota in chronic heart failure patients. Sci. Rep. 8, 635 (2018).
Sanchez-Alcoholado, L. et al. Role of gut microbiota on cardio-metabolic parameters and immunity in coronary artery disease patients with and without type-2 diabetes mellitus. Front. Microbiol. 8, 1936 (2017).
pubmed: 29051757
pmcid: 5633746
doi: 10.3389/fmicb.2017.01936