Diagnosis and Prediction of Cardiovascular Diseases by Genomic Technologies
Cardiovascular diseases, particularly atherosclerosis, are the major cause of death and morbidity in developed countries. Atherosclerosis can lead to an acute myocardial infarction (MI), or heart attack, with an incidence of approximately 650,000 per year in the U.S. alone. The current gold standard for diagnosing coronary artery disease (CAD) is coronary artery angiography. Surprisingly, despite some well-established clinical and diagnostic criteria, about 30-40% of the 1 million diagnostic catheterizations each year in the U.S. return a result of ‘no coronary blockage’.
Using the most advanced RNA sequencing technology, we have identified more than 200 transcripts associated with CAD (TRACs). Careful analysis and confirmatory studies strongly suggest that these TRACs are RNA markers of subset of T cells, consistent with numerous prior publications suggesting changes in the T cell ratios in CAD.
In the future, this test could be expanded toward diagnosing CAD in asymptomatic patients, which could potentially prevent unexpected MI and provide physicians the chance for early intervention, with simple, proven therapies such as aspirin, statins, and lifestyle changes.
Acute Appendicitis: Transcript Profiling of Blood Identifies Promising Biomarkers and Potential Underlying Processes
The diagnosis of acute appendicitis can be surprisingly difficult without computed tomography, which carries significant radiation exposure. Genome-wide expression profiling was applied to whole blood RNA of acute appendicitis patients versus patients with other abdominal disorders, in order to identify biomarkers of appendicitis. From a large cohort of emergency patients, a discovery set of patients with surgically confirmed appendicitis, or abdominal pain from other causes, was identified. RNA from whole blood was profiled by microarrays, and a combined fold-change (>2) and p value (<0.05) filter was applied. A separate set of patients, including patients with respiratory infections, was used to validate a partial least squares discriminant (PLSD) prediction model.
Transcript profiling identified 37 differentially expressed genes (DEG) in appendicitis versus abdominal pain patients. A predictive model was 100% sensitive and specific internally, and was 89% sensitive and 75% specific when applied to an independent validation set. The detected biomarkers are consistent with prior evidence that biofilm-forming bacteria in the appendix may be an important factor in appendicitis.