h-hydrogen

Glial fibrillary acidic protein (GFAP) is a biomarker candidate indicative of intracerebral hemorrhage (ICH) in patients with symptoms of acute stroke. GFAP is released rapidly in the presence of expanding intracerebral bleeding, whereas a more gradual release occurs in ischemic stroke. In this study the diagnostic accuracy of plasma GFAP was determined in a prospective multicenter approach.

Within a 1-year recruitment period, patients suspected of having acute (symptom onset <4.5 h before admission) hemispheric stroke were prospectively included into the study in 14 stroke centers in Germany and Switzerland. A blood sample was collected at admission, and plasma GFAP was measured by use of an electrochemiluminometric immunoassay. The final diagnosis, established at hospital discharge, was classified as ICH, ischemic stroke, or stroke mimic.

The study included 205 patients (39 ICH, 163 ischemic stroke, 3 stroke mimic). GFAP concentrations were increased in patients with ICH compared with patients with ischemic stroke [median (interquartile range) 1.91 μg/L (0.41–17.66) vs 0.08 μg/L (0.02–0.14), P < 0.001]. Diagnostic accuracy of GFAP for differentiating ICH from ischemic stroke and stroke mimic was high [area under the curve 0.915 (95% CI 0.847–0.982), P < 0.001]. A GFAP cutoff of 0.29 μg/L provided diagnostic sensitivity of 84.2% and diagnostic specificity of 96.3% for differentiating ICH from ischemic stroke and stroke mimic.

Plasma GFAP analysis performed within 4.5 h of symptom onset can differentiate ICH and ischemic stroke. Studies are needed to evaluate a GFAP point-of-care system that may help optimize the prehospital triage and management of patients with symptoms of acute stroke.

Plaque erosion and plaque rupture occur early in the pathophysiology of acute myocardial infarction (AMI). We hypothesized that markers of plaque instability might be useful in the early diagnosis and risk stratification of AMI.

In this multicenter study, we examined 4 markers of plaque instability, myeloperoxidase (MPO), myeloid-related protein 8/14 (MRP-8/14), pregnancy-associated plasma protein-A (PAPP-A), and C-reactive protein (CRP) in 398 consecutive patients presenting to the emergency department with acute chest pain and compared them to normal and high-sensitivity cardiac troponin T (cTnT and hs-cTnT). The final diagnosis was adjudicated by 2 independent cardiologists. Primary prognostic end point was death during a median follow-up of 27 months.

The adjudicated final diagnosis was AMI in 76 patients (19%). At emergency department presentation, concentrations of all 4 biomarkers of plaque instability were significantly higher in patients with AMI than in patients with other diagnoses. However, their diagnostic accuracy as quantified by the area under the ROC curve (AUC) was low (MPO 0.63, MRP-8/14 0.65, PAPP-A 0.62, CRP 0.59) and inferior to both normal and high-sensitivity cardiac troponin T (cTnT 0.88, hs-cTnT 0.96; P < 0.001 for all comparisons). Thirty-nine patients (10%) died during follow-up. Concentrations of MPO, MRP-8/14, and CRP were higher in nonsurvivors than in survivors and predicted all-cause mortality with moderate accuracy.

Biomarkers of plaque instability do not seem helpful in the early diagnosis of AMI but may provide some incremental value in the risk stratification of patients with acute chest pain.

We investigated the prognostic performance of ST2 with respect to cardiovascular death (CVD) and heart failure (HF) in patients with non–ST-elevation acute coronary syndrome (NSTE-ACS) in a large multinational trial.

Myocytes that are subjected to mechanical stress secrete ST2, a soluble interleukin-1 receptor family member that is associated with HF after STE-ACS.

We measured ST2 with a high-sensitivity assay in all available baseline samples (N = 4426) in patients enrolled in the Metabolic Efficiency With Ranolazine for Less Ischemia in the Non–ST-Elevation Acute Coronary Syndrome Thrombolysis in Myocardial Infarction 36 (MERLIN-TIMI 36), a placebo-controlled trial of ranolazine in NSTE-ACS. All events, including cardiovascular death and new or worsening HF, were adjudicated by an independent events committee.

Patients with ST2 concentrations in the top quartile (>35 μg/L) were more likely to be older and male and have diabetes and renal dysfunction. ST2 was only weakly correlated with troponin and B-type natriuretic peptide. High ST2 was associated with increased risk for CVD/HF at 30 days (6.6% vs 1.6%, P < 0.0001) and 1 year (12.2% vs 5.2%, P < 0.0001). The risk associated with ST2 was significant after adjustment for clinical covariates and biomarkers (adjusted hazard ratio CVD/HF 1.90, 95% CI 1.15–3.13 at 30 days, P = 0.012; 1.51, 95% CI 1.15–1.98 at 1 year, P = 0.003), with a significant integrated discrimination improvement (P < 0.0001). No significant interaction was found between ST2 and ranolazine (P_interaction = 0.15).

ST2 correlates weakly with biomarkers of acute injury and hemodynamic stress but is strongly associated with the risk of HF after NSTE-ACS. This biomarker and related pathway merit further investigation as potential therapeutic targets for patients with ACS at risk for cardiac remodeling.

Galectin-3 is a β-galactoside–binding lectin that has been implicated in cardiac fibrosis and remodeling, is increased in models of failure-prone hearts, and has prognostic value in patients with heart failure (HF). The relationship between galectin-3 and the development of HF after acute coronary syndrome (ACS) is unknown.

In a nested case-control study among patients with ACS in PROVE IT-TIMI 22, we identified 100 cases with a hospitalization for new or worsening HF. Controls were matched (1:1) for age, sex, ACS type, and randomized treatment. Serum galectin-3 was measured at baseline (within 7 days post-ACS).

Patients who developed HF had higher baseline galectin-3 [median 16.7 μg/L (25th, 75th percentile 14.0, 20.6) vs 14.6 μg/L (12.0, 17.6), P = 0.004]. Patients with baseline galectin-3 above the median had an odds ratio of 2.1 (95% CI 1.2–3.6) for developing HF, P = 0.010. Galectin-3 showed a graded relationship with risk of HF. Cases were more likely to have hypertension, diabetes, prior MI, and prior HF; after adjustment for these factors, this graded relationship with galectin-3 quartile and HF remained significant [adjusted OR 1.4 (95% CI 1.1–1.9), P = 0.020]. When BNP was added to the model, the relationship between galectin-3 and HF was attenuated [adjusted OR 1.3 (95% CI: 0.96–1.9), P = 0.08].

The finding that galectin-3 is associated with the risk of developing HF following ACS adds to emerging evidence supporting galectin-3 as a biomarker of adverse remodeling contributing to HF as well as a potential therapeutic target.

Data to standardize and harmonize the differences between cardiac troponin assays are needed to support their universal status in diagnosis of myocardial infarction. We characterized the variation between methods, the comparability of the 99th-percentile cutoff thresholds, and the occurrence of outliers in 4 cardiac troponin assays.

Cardiac troponin was measured in duplicate in 2358 patient samples on 4 platforms: Abbott Architect i2000SR, Beckman Coulter Access2, Roche Cobas e601, and Siemens ADVIA Centaur XP.

The observed total variances between the 3 cardiac troponin I (cTnI) methods and between the cTnI and cardiac troponin T (cTnT) methods were larger than expected from the analytical imprecision (3.0%–3.7%). The between-method variations of 26% between cTnI assays and 127% between cTnI and cTnT assays were the dominant contributors to total variances. The misclassification of results according to the 99th percentile was 3%–4% between cTnI assays and 15%–17% between cTnI and cTnT. The Roche cTnT assay identified 49% more samples as positive than the Abbott cTnI. Outliers between methods were detected in 1 patient (0.06%) with Abbott, 8 (0.45%) with Beckman Coulter, 10 (0.56%) with Roche, and 3 (0.17%) with Siemens.

The universal definition of myocardial infarction should not depend on the choice of analyte or analyzer, and the between- and within-method differences described here need to be considered in the application of cardiac troponin in this respect. The variation between methods that cannot be explained by analytical imprecision and the discordant classification of results according to the respective 99th percentiles should be addressed.

Cardiac troponins I (cTnI) and T (cTnT) have received international endorsement as the standard biomarkers for detection of myocardial injury, for risk stratification in patients suspected of acute coronary syndrome, and for the diagnosis of myocardial infarction. An evidence-based clinical database is growing rapidly for high-sensitivity (hs) troponin assays. Thus, clarifications of the analytical principles for the immunoassays used in clinical practice are important.

The purpose of this mini-review is (a) to provide a background for the biochemistry of cTnT and cTnI and (b) to address the following analytical questions for both hs cTnI and cTnT assays: (i) How does an assay become designated hs? (ii) How does one realistically define healthy (normal) reference populations for determining the 99th percentile? (iii) What is the usual biological variation of these analytes? (iv) What assay imprecision characteristics are acceptable? (v) Will standardization of cardiac troponin assays be attainable?

This review raises important points regarding cTnI and cTnT assays and their reference limits and specifically addresses hs assays used to measure low concentrations (nanograms per liter or picograms per milliliter). Recommendations are made to help clarify the nomenclature. The review also identifies further challenges for the evolving science of cardiac troponin measurement. It is hoped that with the introduction of these concepts, both laboratorians and clinicians can develop a more unified view of how these assays are used worldwide in clinical practice.

Until recently, biomarker testing in heart failure (HF) syndromes has been viewed as an elective supplement to diagnostic evaluation of patients suspected to suffer from this condition. This approach to the use of biomarker testing contrasts with other cardiovascular diagnoses such as acute myocardial infarction, for which biomarkers are integral to disease process definition, risk stratification, and in some cases treatment decision making.

In this review we consider various perspectives on the evaluation of biomarkers in HF. In addition, we examine recent advances in the understanding of established biomarkers in HF (such as the natriuretic peptides), the elucidation of novel biomarkers potentially useful for the evaluation and management of patients with HF, and the growing understanding of important and relevant comorbidities in HF. We also review candidate biomarkers from a number of classes: (a) myocyte stretch, (b) myocyte necrosis, (c) systemic inflammation, (d) oxidative stress, (e) extracellular matrix turnover, (f) neurohormones, and (g) biomarkers of extracardiac processes, such as renal function.

Novel applications of established biomarkers of HF as well as elucidation and validation of emerging assays for HF syndromes have collectively led to a growing interest in the more widespread use of such testing in patients affected by the diagnosis.

Metabolomics, the systematic analysis of low molecular weight biochemical compounds in a biological specimen, has been increasingly applied to biomarker discovery.

Because no single analytical method can accommodate the chemical diversity of the entire metabolome, various methods such as nuclear magnetic resonance spectroscopy (NMR) and mass spectrometry (MS) have been employed, with the latter coupled to an array of separation techniques including gas and liquid chromatography. Whereas NMR can provide structural information and absolute quantification for select metabolites without the use of exogenous standards, MS tends to have much higher analytical sensitivity, enabling broader surveys of the metabolome. Both NMR and MS can be used to characterize metabolite data either in a targeted manner or in a nontargeted, pattern-recognition manner. In addition to technical considerations, careful sample selection and study design are important to minimize potential confounding influences on the metabolome, including diet, medications, and comorbitidies. To this end, metabolite profiling has been applied to human biomarker discovery in small-scale interventions, in which individuals are extremely well phenotyped and able to serve as their own biological controls, as well as in larger epidemiological cohorts. Understanding how metabolites relate to each other and to established risk markers for diseases such as diabetes and renal failure will be important in evaluating the potential value of these metabolites as clinically useful biomarkers.

Applied to both experimental and epidemiological study designs, metabolite profiling has begun to highlight the breadth metabolic disturbances that accompany human disease. Experimental work in model systems and integration with other functional genomic approaches will be required to establish a causal link between select biomarkers and disease pathogenesis.

Because cerebrospinal fluid (CSF) is in close contact with diseased areas in neurological disorders, it is an important source of material in the search for molecular biomarkers. However, sample handling for CSF collected from patients in a clinical setting might not always be adequate for use in proteomics and metabolomics studies.

We left CSF for 0, 30, and 120 min at room temperature immediately after sample collection and centrifugation/removal of cells. At 2 laboratories CSF proteomes were subjected to tryptic digestion and analyzed by use of nano-liquid chromatography (LC) Orbitrap mass spectrometry (MS) and chipLC quadrupole TOF-MS. Metabolome analysis was performed at 3 laboratories by NMR, GC-MS, and LC-MS. Targeted analyses of cystatin C and albumin were performed by LC–tandem MS in the selected reaction monitoring mode.

We did not find significant changes in the measured proteome and metabolome of CSF stored at room temperature after centrifugation, except for 2 peptides and 1 metabolite, 2,3,4-trihydroxybutanoic (threonic) acid, of 5780 identified peptides and 93 identified metabolites. A sensitive protein stability marker, cystatin C, was not affected.

The measured proteome and metabolome of centrifuged human CSF is stable at room temperature for up to 2 hours. We cannot exclude, however, that changes undetectable with our current methodology, such as denaturation or proteolysis, might occur because of sample handling conditions. The stability we observed gives laboratory personnel at the collection site sufficient time to aliquot samples before freezing and storage at –80 °C.