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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.

Trypsinogen 3 is a minor trypsinogen isoform in the pancreas. In contrast with trypsin 1 and 2, trypsin 3 degrades pancreatic secretory trypsin inhibitor, which may lead to an excess of active trypsin and acute pancreatitis (AP). We developed an immunoassay for trypsinogen 3 and studied whether an assay of serum trypsinogen 3 is of clinical utility in the diagnosis of AP.

Monoclonal antibodies were generated using recombinant human trypsinogen 3 as the antigen and used to establish a sandwich-type immunoassay. We analyzed serum trypsinogen 3 concentrations in 82 patients with AP and 63 patients with upper abdominal pain (controls). The reference interval was determined using serum samples from 172 apparently healthy individuals.

The measuring range of the trypsinogen 3 assay was 1.0–250 μg/L. Intra- and interassay CVs were <11%, and cross-reactivity with other trypsinogen isoenzymes was <0.1%. The median trypsinogen 3 concentration in serum from healthy individuals was <1.0 μg/L, and the upper reference limit was 4.4 μg/L. We observed increased trypsinogen 3 concentrations in patients with mild (median 9.5 μg/L) and severe (15.0 μg/L) AP; in both groups, the concentrations were significantly higher than in controls (median <1.0 μg/L) (P < 0.0001). In ROC analysis, the area under the curve of trypsinogen 3 for separation between AP and controls was 0.90 (P < 0.0001).

We established for the first time a specific immunoassay for trypsinogen 3 using monoclonal antibodies. Patients with AP were found to have increased serum concentrations of trypsinogen 3. The availability of this assay will be useful for studies of the clinical utility of trypsinogen 3.