Figure 1 Experimental design

Slides:

Advertisements

Similar presentations

Distribution of metabolic syndrome (MS) components by age and gender Zhao D, et al. Am J Cardiol 2007;100:835– 839.

Advertisements

Date of download: 11/12/2016 Copyright © The American College of Cardiology. All rights reserved. From: 2016 ACC Expert Consensus Decision Pathway on the.

Nat. Rev. Cardiol. doi: /nrcardio

Figure 7 Mendelian randomization of overlapping exposures

Nat. Rev. Nephrol. doi: /nrneph

Nat. Rev. Cardiol. doi: /nrcardio

Figure 1 Rheumatoid arthritis development over time in relation to the level of inflammation Figure 1 | Rheumatoid arthritis development over time in relation.

Figure 1 Mendelian randomization study

Figure 5 BMI and cardiorespiratory fitness levels

Nat. Rev. Endocrinol. doi: /nrendo

Nat. Rev. Cardiol. doi: /nrcardio

Figure 4 BMI and mortality in patients with heart failure

Nat. Rev. Cardiol. doi: /nrcardio

Nat. Rev. Cardiol. doi: /nrcardio

Figure 5 Two approaches to therapeutic genome editing

Nat. Rev. Cardiol. doi: /nrcardio

Figure 3 Global rates of sodium, fruit, and vegetable intake

Nat. Rev. Rheumatol. doi: /nrrheum

Figure 2 Size categorization of airborne pollutants

Nat. Rev. Cardiol. doi: /nrcardio

Nat. Rev. Cardiol. doi: /nrcardio

Figure 2 Three-year survival rates on the basis of body composition

Fig. 3. Copy number loss as a potential resistance mechanism in an independent cohort. Copy number loss as a potential resistance mechanism in an independent.

Figure 3 Transcriptome studies performed in the target

Nat. Rev. Cardiol. doi: /nrcardio

Nat. Rev. Cardiol. doi: /nrcardio

Nat. Rev. Cardiol. doi: /nrcardio

Nat. Rev. Nephrol. doi: /nrneph

Nat. Rev. Cardiol. doi: /nrcardio

Nat. Rev. Clin. Oncol. doi: /nrclinonc

Figure 4 Prevalence of HFpEF and HFrEF by age

Nat. Rev. Cardiol. doi: /nrcardio

Nat. Rev. Cardiol. doi: /nrcardio

Nat. Rev. Cardiol. doi: /nrcardio

Figure 1 Health-care system today

Nat. Rev. Rheumatol. doi: /nrrheum

Figure 2 Endocrine dysfunction in mitochondrial disease and their associated gene defects Figure 2 | Endocrine dysfunction in mitochondrial disease and.

Figure 1 Cardiovascular risk and disease across the life-course

Nat. Rev. Cardiol. doi: /nrcardio

Figure 1 Decision tree integrating the assessment,

Nat. Rev. Cardiol. doi: /nrcardio

Nat. Rev. Cardiol. doi: /nrcardio

Figure 1 Translocations involved in multiple myeloma

Figure 3 Challenges for big data applications in cardiovascular care

Nat. Rev. Cardiol. doi: /nrcardio

Nat. Rev. Cardiol. doi: /nrcardio

Nat. Rev. Cardiol. doi: /nrcardio

Figure 5 Mendelian randomization of biomarkers on the same pathway

Nat. Rev. Cardiol. doi: /nrcardio

Figure 4 Observational studies on multiple treatment strategies

Nat. Rev. Cardiol. doi: /nrcardio

Dissemination of the mcr-1 colistin resistance gene

Figure 3 Enrichment strategies for clinical trials

Nat. Rev. Cardiol. doi: /nrcardio

Figure 1 A large number of genes are potentially associated with CIPN

Figure 2 Gut microbial gene content and development of T1DM

Figure 2 Impaired mitochondrial capacity and function in heart failure

Nat. Rev. Cardiol. doi: /nrcardio

Nat. Rev. Cardiol. doi: /nrcardio

Nat. Rev. Cardiol. doi: /nrcardio

Nat. Rev. Cardiol. doi: /nrcardio

Nat. Rev. Cardiol. doi: /nrcardio

Nat. Rev. Cardiol. doi: /nrcardio

Nat. Rev. Cardiol. doi: /nrcardio

Nat. Rev. Cardiol. doi: /nrcardio

Flow chart of the cohort selection for the study

RBP4 and glucose metabolism.

Correlation between serum visfatin concentrations and the clinical stage (A) and tumor size (B, C) of HCCs. A and B, the correlations were determined by.

The future TB clinic. The future TB clinic. Sputum, saliva, and fecal samples are collected from patients for genome sequencing, SNP typing, or metagenomic.

General overview of the bioinformatic pipelines for the 16S rRNA gene microbial profiling and shotgun metagenomics. General overview of the bioinformatic.

Presentation transcript:

Figure 1 Experimental design Figure 1 | Experimental design. Jie et al. performed whole-genome shotgun sequencing on 218 patients with atherosclerotic cardiovascular disease (ASCVD) and 187 healthy controls. Metagenomic linkage groups (MLGs) were correlated with clinical traits in a metagenome-wide association analysis. A Kyoto Encyclopaedia of Genes and Genomes (KEGG) pathway enrichment analysis was used to identify differences in functional potential between ASCVD and control cohorts. Next, the community structure and functionality of the ASCVD microbiome were compared with those of other disease-associated microbiomes including liver cirrhosis, obesity, type 2 diabetes mellitus (T2DM), and rheumatoid arthritis (RA). Greater similarities in community structure and functionality were observed between ASCVD and the three cardiometabolic disease cohorts than between ASCVD and RA. Barrington, W. T. & Lusis, A. J. (2017) Association between the gut microbiome and atherosclerosis Nat. Rev. Cardiol. doi:10.1038/nrcardio.2017.169