Abstract
The scientific impact of translational biomedical research largely depends on the availability of high-quality biomaterials. However, evidence-based and robust quality indicators (QIs) covering the most relevant preanalytical variations are still lacking. The aim of this study was to identify and validate a QI suitable for assessing time-to-centrifugation (TTC) delays in human liquid biospecimens originating from both healthy and diseased individuals. Serum and plasma samples with varying TTCs were analyzed by liquid chromatography coupled to tandem mass spectrometry (LC-MS/MS) in a pilot cohort of healthy individuals to identify a suitable QI candidate. Taurine (TAU), as a TTC QI candidate, was validated in healthy individuals and patients with rheumatologic and cardiologic diseases, considering the (1) preanalytical handling temperature, (2) platelet count, and (3) postcentrifugation delay. For discrimination of high TTC (TTC >60 minutes) from low TTC serum specimens, a probability calculation tool was developed (Triple-T-cutoff-model). TTC-dependent changes in healthy individuals were observed for amino acids, particularly TAU. Validation of the TAU levels in an independent cohort of healthy individuals revealed a time-dependent increase in serum, but not in plasma, for a TTC delay of 30–240 minutes. TAU increases were dependent on the handling temperature and platelet count and volume. By contrast, no changes in TAU concentrations were observed for additional postcentrifugation delays. Validation of TAU and the Triple-T-cutoff-model, in rheumatologic/cardiologic patient collectives, allowed the discrimination of samples with TTC ≤60 min/>60 min with estimated AUROC (area under the receiver operating characteristic curve) values of 89% [78%–100%]/86% [71%–100%] and 91% [79%–100%]/84% [68%–100%], respectively. Considering the preanalytical handling temperature and platelet count and volume, TAU and the Triple-T-cutoff-model represent reliable QIs for TTC >60 minutes in serum samples from healthy individuals and selected rheumatologic/cardiologic patients. However, further studies in larger patient collectives with various diseases are needed to assess the robustness and potential of the QIs presented in this article as biobanking quality assurance/quality control tools to support high-quality biomedical research.
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