h-hydrogen

Cardiac troponin T measured by a high-sensitivity assay (hs-cTnT) recently proved to be of prognostic value in several populations. The hs-cTnT assay may also improve risk stratification in acute dyspnea.

We prospectively studied the prognostic value of hs-cTnT in 678 consecutive patients presenting to the emergency department with acute dyspnea. On the basis of conventional cardiac troponin T assay (cTnT) and hs-cTnT assay measurements, patients were divided into 3 categories: (1) neither assay increased (cTnT <0.03 μg/L, hs-cTnT <0.016 μg/L), (2) only hs-cTnT increased ≥0.016 μg/L (cTnT <0.03 μg/L), and (3) both assays increased (cTnT ≥0.03 μg/L, hs-cTnT ≥0.016 μg/L). Moreover, the prognostic value of hs-cTnT was investigated if cTnT was not detectable (<0.01).

One hundred seventy-two patients were in the lowest, 282 patients in the middle, and 223 patients in the highest troponin category. Patients in the second and third categories had significantly higher mortality compared to those in the first category (90-day mortality rate 2%, 10%, and 26% in groups 1, 2, and 3, respectively, P < 0.001; 1-year mortality rate 9%, 21%, and 39%, P < 0.001). Importantly, in patients with undetectable cTnT (n = 347, 51%), increased hs-cTnT indicated worse outcome [90-day mortality, odds ratio 4.26 (95% CI 1.19–15.21); 1-year mortality, hazard ratio 2.27 (1.19–4.36), P = 0.013], whereas N-terminal pro–brain-type natriuretic peptide (NT-proBNP) was not predictive of short-term outcome.

hs-cTnT is associated with mortality in patients presenting with acute dyspnea. hs-cTnT concentrations provide additional prognostic information to cTnT and NT-proBNP testing in patients with cTnT concentrations below the detection limit. In particular, the hs-cTnT cutoff of 0.016 μg/L enables identification of low-risk patients.

Cardiovascular (CV) clinical trials are instrumental in understanding treatment effects and offer insights into the natural progression of CV disease. Biomarkers are a critical component of patient selection, end point definition, and safety monitoring, and clinical trials provide a platform for the discovery and validation of new biomarkers that may augment the understanding of disease mechanisms, risk stratification, and/or clinical decision-making.

We review the roles that biomarkers have played in CV clinical trials and roles that CV clinical trials have played and will continue to play in the discovery and validation of biomarkers and their implementation in clinical practice. Large biobanks containing multiple specimen types are increasingly being created from patients enrolled in clinical trials, and such biobanks, when coupled with advances in molecular techniques and bioinformatics, promise to accelerate our understanding of CV disease mechanisms and to help fuel the discovery and development of novel therapeutic targets and biomarkers of risk and treatment response.

The past, present, and future of biomarkers and clinical trials have been and will remain intertwined. Biomarkers were once the workhorses of patient selection and end point definition in clinical trials; more recently, clinical trials have been the proving ground for individual biomarkers. Attention to biobanking and the application of modern informatics and molecular techniques to samples collected within clinical trials will usher in the era of stratified and personalized medicine.

Genomic research in cardiovascular disease (CVD) has progressed rapidly over the last 5 years. In most cases, however, these groundbreaking observations have not yet been accompanied by clinically applicable tools for risk prediction, diagnosis, or therapeutic interventions.

We reviewed the scientific literature published in English for novel methods and promising genomic targets that would permit large-scale screening and follow-up of recent genomic findings for CVD. We anticipate that advances in 3 key areas will be critical for the success of these projects. First, exome-centered and whole-genome next-generation sequencing will identify rare and novel genetic variants associated with CVD and its risk factors. Improvements in methods will also greatly advance the field of epigenetics and gene expression in humans. Second, research is increasingly acknowledging that static DNA sequence variation explains only a fraction of the inherited phenotype. Therefore, we expect that multiple epigenetic and gene expression signatures will be related to CVD in experimental and clinical settings. Leveraging existing large-scale consortia and clinical biobanks in combination with electronic health records holds promise for integrating epidemiological and clinical genomics data. Finally, a systems biology approach will be needed to integrate the accumulated multidimensional data.

Novel methods in sequencing, epigenetics, and transcriptomics, plus unprecedented large-scale cooperative efforts, promise to generate insights into the complexity of CVD. The rapid accumulation and integration of knowledge will shed light on a considerable proportion of the missing heritability for CVD.