• Elevated Free Phosphatidylcholine Levels in Cerebrospinal Fluid Distinguish Bacterial from Viral CNS Infections.

      Al-Mekhlafi, Amani; Sühs, Kurt-Wolfram; Schuchardt, Sven; Kuhn, Maike; Müller-Vahl, Kirsten; Trebst, Corinna; Skripuletz, Thomas; Klawonn, Frank; Stangel, Martin; Pessler, Frank; et al. (MDPI, 2021-05-06)
      The identification of CSF biomarkers for bacterial meningitis can potentially improve diagnosis and understanding of pathogenesis, and the differentiation from viral CNS infections is of particular clinical importance. Considering that substantial changes in CSF metabolites in CNS infections have recently been demonstrated, we compared concentrations of 188 metabolites in CSF samples from patients with bacterial meningitis (n = 32), viral meningitis/encephalitis (n = 34), and noninflamed controls (n = 66). Metabolite reprogramming in bacterial meningitis was greatest among phosphatidylcholines, and concentrations of all 54 phosphatidylcholines were significantly (p = 1.2 × 10-25-1.5 × 10-4) higher than in controls. Indeed, all biomarkers for bacterial meningitis vs. viral meningitis/encephalitis with an AUC ≥ 0.86 (ROC curve analysis) were phosphatidylcholines. Four of the five most accurate (AUC ≥ 0.9) phosphatidylcholine biomarkers had higher sensitivity and negative predictive values than CSF lactate or cell count. Concentrations of the 10 most accurate phosphatidylcholine biomarkers were lower in meningitis due to opportunistic pathogens than in meningitis due to typical meningitis pathogens, and they correlated most strongly with parameters reflecting blood-CSF barrier dysfunction and CSF lactate (r = 0.73-0.82), less so with CSF cell count, and not with blood CRP. In contrast to the elevated phosphatidylcholine concentrations in CSF, serum concentrations remained relatively unchanged. Taken together, these results suggest that increased free CSF phosphatidylcholines are sensitive biomarkers for bacterial meningitis and do not merely reflect inflammation but are associated with local disease and a shift in CNS metabolism.
    • Sample size and performance estimation for biomarker combinations based on pilot studies with small sample sizes”

      Al-Mekhlafi, Amani; Becker, Tobias; Klawonn, Frank; HZI,Helmholtz-Zentrum für Infektionsforschung GmbH, Inhoffenstr. 7,38124 Braunschweig, Germany. (Taylor & Francis, 2020-01-01)
      High throughput technologies like microarrays, next generation sequencing and mass spectrometry enable the measurement of tens of thousands of biomarker candidates in pilot studies. Biological systems are often too complex to be based on simple single cause-effect associations and from the medical practice point of view, a single biomarker may not possess the desired sensitivity and/or specificity for disease classification and outcome prediction. Therefore, the efforts of researchers currently aims at combining biomarkers. The intention of biomarker pilot studies with small sample sizes is often to explore the possibility of finding good biomarker combinations and not to find and evaluate a final combination of biomarkers with high predictive value. The aim of the pilot study is to answer the question whether it is worthwhile to extend the study to a larger study and to obtain information about the required sample size. In this paper, we propose a method to judge the potential in a small biomarker pilot study without the need to explicitly identifying and confirming a specific subset of biomarkers. In addition, we provide a method for sample size estimation for an extended study when the results of the pilot study look promising.