Leaders in Pharmaceutical Business Intelligence (LPBI) Group

Reporter and Curator: Dr. Sudipta Saha, Ph.D.

Metabolic analysis has been widely used in laboratory research applications. One of the main uses in this field was the metabolic phenotyping of mouse models of cardiovascular diseases; this approach was pioneered by the group of Julian Griffin mainly using models of Duchenne muscular distrophy where they were able to show different metabolic profiles associated with the expression of dystrophin and utrophin in heart muscle. In a later work the same group applied the FANCY approach (Functional Analysis by Co-responses in Yeast) to mouse models of cardiac diseases and showed that although the background strain of mice was an important source of metabolic variation, multivariate statistics were able to separate each disease model from the control strain.

Since the beginning of the 21st century the term ‘personalized medicine’ has continuously gained popularity and is now considered an essential trait of present and future medicine. For personalized medicine to be successful, it is necessary to properly identify subjects at increased risk of developing a disease, which patients will respond to a given therapy or how a disease will evolve in each case. In other words it is important to genotype and or phenotype the individual patient so that its individual response to disease and treatment can be predicted.

Sabatine et al. in 2005 showed that it was possible to apply metabolomic analysis in a carefully characterized cohort of patients undergoing exercise stress testing and to differentiate between patients that developed inducible ischemia from the ones that did not. This work was done by analyzing serum samples obtained before, during and after stress testing by high performance liquid chromatography coupled to mass spectroscopy; ischaemic patients had higher circulating levels of metabolites belonging to the citric acid pathway. Ischemic patients had relatively higher lactate levels than non ischemic suggesting an underlying ischemic process although it could not be directly related to myocardial ischemia.

It has been known for a time that patients with heart failure (HF) have an altered heart metabolism and that metabolic modulation (shifting the main substrate from free fatty acids to glucose) improved VO2max, left ventricular ejection fraction, symptoms, resting and peak stress myocardial function, and skeletal muscle energetics. Metabolic modulation as a tool to treat patients with heart failure has attracted interest but the metabolomic analysis has not followed suit until recently when Kang et al. 2011 showed by profiling urine by NMR spectroscopy that it was possible to detect changes between HF patients and controls. It could be interesting to evaluate possible changes in the urine metabolic profile of patients treated with drugs targeting heart metabolism for example, perhexiline or trimetazidine. In conclusion, the future of metabolomics is now. It is clear that metabolomics can be applied to various cardiovascular related diseases, although its clinical value in different settings remains to be determined; this is the next big challenge in the field.

Source References:

http://cdn.intechopen.com/pdfs/32774/InTech-Metabolomics_in_cardiovascular_disease_towards_clinical_application.pdf

http://www.biospec.net/pubs/pdfs/Dunn-Bioanalysis2011.pdf

http://www.dovepress.com/metabolic-biomarkers-for-predicting-cardiovascular-disease-peer-reviewed-article-VHRM

http://circgenetics.ahajournals.org/content/3/2/119.full

http://www.nibr.com/research/disease/cardiometabolism.shtml

http://www.ncbi.nlm.nih.gov/pubmed/23157406