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Based on accumulating evidence of a role of lipid signaling in many physiological and pathophysiological processes including psychiatric diseases, the present data driven analysis was designed to gather information needed to develop a prospective biomarker, using a targeted lipidomics approach covering different lipid mediators. Using unsupervised methods of data structure detection, implemented as hierarchal clustering, emergent self-organizing maps of neuronal networks, and principal component analysis, a cluster structure was found in the input data space comprising plasma concentrations of d = 35 different lipid-markers of various classes acquired in n = 94 subjects with the clinical diagnoses depression, bipolar disorder, ADHD, dementia, or in healthy controls. The structure separated patients with dementia from the other clinical groups, indicating that dementia is associated with a distinct lipid mediator plasma concentrations pattern possibly providing a basis for a future biomarker. This hypothesis was subsequently assessed using supervised machine-learning methods, implemented as random forests or principal component analysis followed by computed ABC analysis used for feature selection, and as random forests, k-nearest neighbors, support vector machines, multilayer perceptron, and naïve Bayesian classifiers to estimate whether the selected lipid mediators provide sufficient information that the diagnosis of dementia can be established at a higher accuracy than by guessing. This succeeded using a set of d = 7 markers comprising GluCerC16:0, Cer24:0, Cer20:0, Cer16:0, Cer24:1, C16 sphinganine, and LacCerC16:0, at an accuracy of 77%. By contrast, using random lipid markers reduced the diagnostic accuracy to values of 65% or less, whereas training the algorithms with randomly permuted data was followed by complete failure to diagnose dementia, emphasizing that the selected lipid mediators were display a particular pattern in this disease possibly qualifying as biomarkers.
Preclinical studies have demonstrated that the endocannabinoid system (ECS) plays an important role in the protection against intestinal inflammation and colorectal cancer (CRC); however, human data are scarce. We determined members of the ECS and related components of the ‘endocannabinoidome’ in patients with inflammatory bowel disease (IBD) and CRC, and compared them to control subjects. Anandamide (AEA) and oleoylethanolamide (OEA) were increased in plasma of ulcerative colitis (UC) and Crohn’s disease (CD) patients while 2-arachidonoylglycerol (2-AG) was elevated in patients with CD, but not UC. 2-AG, but not AEA, PEA and OEA, was elevated in CRC patients. Lysophosphatidylinositol (LPI) 18:0 showed higher levels in patients with IBD than in control subjects whereas LPI 20:4 was elevated in both CRC and IBD. Gene expression in intestinal mucosal biopsies revealed different profiles in CD and UC. CD, but not UC patients, showed increased gene expression for the 2-AG synthesizing enzyme diacylglycerol lipase alpha. Transcripts of CNR1 and GPR119 were predominantly decreased in CD. Our data show altered plasma levels of endocannabinoids and endocannabinoid-like lipids in IBD and CRC and distinct transcript profiles in UC and CD. We also report alterations for less known components in intestinal inflammation, such as GPR119, OEA and LPI.