1. Integrated Metabolomics and Genomics
by Svati H. Shah, and Christopher B. Newgard
Circ Genom Precis Med
Volume 8(2):
April 21, 2015
Copyright © American Heart Association, Inc. All rights reserved.

2. Short-chain dicarboxylacylcarnitine (SCDA) levels predict cardiovascular events.
Short-chain dicarboxylacylcarnitine (SCDA) levels predict cardiovascular events. Unadjusted (A) and adjusted (B) Kaplan–Meier curves demonstrating increasing risk of death with higher baseline levels of SCDA metabolites; (C) risk reclassification analyses showing incremental risk prediction to 23-variable clinical model (net reclassification index, 8.8%). Reprinted from Shah et al15 with permission of the publisher. Copyright © 2012, Elsevier.
Svati H. Shah, and Christopher B. Newgard Circ Cardiovasc Genet. 2015;8:
Copyright © American Heart Association, Inc. All rights reserved.

3. Visual representation of omics technologies available for integrated analyses.
Visual representation of omics technologies available for integrated analyses. Molecular changes reflected in these markers, in combination with environmental influences, result in the health or disease phenotype. GC indicates gas chromatography; MS, mass spectrometry; and NMR, nuclear magnetic resonance. Reprinted from Lewis et al24 with permission of the publisher. Copyright © 2008, Elsevier.
Svati H. Shah, and Christopher B. Newgard Circ Cardiovasc Genet. 2015;8:
Copyright © American Heart Association, Inc. All rights reserved.

4. Manhattan plot of genome-wide association studies (GWAS) of metabolites from the KORA study.
Manhattan plot of genome-wide association studies (GWAS) of metabolites from the KORA study. Displayed is the strength of association with metabolite concentrations (top; P<10−7 in red) and concentration ratios (bottom; P<10−9 in red). Reprinted from Illig et al38 with permission of the publisher. Copyright © 2009, Nature Publishing Group.
Svati H. Shah, and Christopher B. Newgard Circ Cardiovasc Genet. 2015;8:
Copyright © American Heart Association, Inc. All rights reserved.

5. Genetic networks of liver metabolism revealed by integration of metabolic and transcriptional profiling.
Genetic networks of liver metabolism revealed by integration of metabolic and transcriptional profiling. Results from a study of F2 intercross between diabetes mellitus–resistant C57BL/6 leptinob/ob and diabetes mellitus–susceptible BTBR leptinob/ob mouse strains: (A) metabolic quantitative trait loci (mQTL) mapping identifying genetic hotspots for metabolite regulation and (B) causal network analysis links gene expression and metabolic changes in the context of glutamate metabolism. LOD indicates logarithm of the odds. Reprinted from Ferrara et al34.
Svati H. Shah, and Christopher B. Newgard Circ Cardiovasc Genet. 2015;8:
Copyright © American Heart Association, Inc. All rights reserved.