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Based on increasing evidence suggesting that MS pathology involves alterations in bioactive lipid metabolism, the present analysis was aimed at generating a complex serum lipid-biomarker. Using unsupervised machine-learning, implemented as emergent self-organizing maps of neuronal networks, swarm intelligence and Minimum Curvilinear Embedding, a cluster structure was found in the input data space comprising serum concentrations of d = 43 different lipid-markers of various classes. The structure coincided largely with the clinical diagnosis, indicating that the data provide a basis for the creation of a biomarker (classifier). This was subsequently assessed using supervised machine-learning, implemented as random forests and computed ABC analysis-based feature selection. Bayesian statistics-based biomarker creation was used to map the diagnostic classes of either MS patients (n = 102) or healthy subjects (n = 301). Eight lipid-markers passed the feature selection and comprised GluCerC16, LPA20:4, HETE15S, LacCerC24:1, C16Sphinganine, biopterin and the endocannabinoids PEA and OEA. A complex classifier or biomarker was developed that predicted MS at a sensitivity, specificity and accuracy of approximately 95% in training and test data sets, respectively. The present successful application of serum lipid marker concentrations to MS data is encouraging for further efforts to establish an MS biomarker based on serum lipidomics.
Background: Sphingolipids are versatile signaling molecules derived from membrane lipids of eukaryotic cells. Ceramides regulate cellular processes such as proliferation, differentiation and apoptosis and are involved in cellular stress responses. Experimental evidence suggests a pivotal role of sphingolipids in the pathogenesis of cardiovascular diseases, including ischemic stroke. A neuroprotective effect has been shown for beta-adrenergic antagonists in rodent stroke models and supported by observational clinical data. However, the exact underlying pathophysiological mechanisms are still under investigation. We aimed to examine the influence of propranolol on the ceramide metabolism in the stroke-affected brain.
Methods: Mice were subjected to 60 or 180 min transient middle cerebral artery occlusion (tMCAO) and infarct size, functional neurological deficits, glucose tolerance, and brain ceramide levels were assessed after 12, 24, and 72 h to evaluate whether the latter two processes occur in a similar time frame. Next, we assessed the effects of propranolol (10 mg/kg bw) at 0, 4 and 8 h after tMCAO and FTY720 (fingolimod; 1 mg/kg) on infarct size, functional outcome, immune cell counts and brain ceramide levels at 24 h after 60 min tMCAO.
Results: We found a temporal coincidence between stroke-associated impaired glucose tolerance and brain ceramide accumulation. Whereas propranolol reduced ischemic lesion size, improved functional outcome and reduced brain ceramide accumulation without an effect on circulating immune cells, FTY720 showed the known neuroprotective effect and strong reduction of circulating immune cells without affecting brain ceramide accumulation.
Conclusions: Propranolol ameliorates both stroke-associated impairment of glucose tolerance and brain ceramide accumulation which are temporally linked, strengthening the evidence for a role of the sympathetic nervous system in regulating post-stroke glucose metabolism and its metabolic consequences in the brain.