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We recently reported that expression levels of tumor necrosis factor (TNF) receptors, TNFR1 and TNFR2, are significantly changed in the brains and cerebral spinal cerebral fluid (CSF) with Alzheimer's disease (AD). Moreover, we also found that, in an Alzheimer's mouse model, genetic deletion of TNF receptor (TNFR1) reduces amyloid plaques and amyloid beta peptides (Abeta) production through beta-secretase (BACE1) regulation. TNF-alpha converting enzyme (TACE/ADAM-17) does not only cleave pro- TNF-alpha but also TNF receptors, however, whether the TACE activity was changed in the CSF was not clear. In this study, we examined TACE in the CSF in 32 AD patients and 27 age-matched healthy controls (HCs). Interestingly, we found that TACE activity was significantly elevated in the CSF from AD patients compared with HCs. Furthermore, we also assayed the CSF levels of TACE cleaved soluble forms of TNFR1 and TNFR2 in the same patients. We found that AD patients had higher levels of both TACE cleaved soluble TNFR1 (sTNFR1) and TNFR2 (sTNFR2) in the CSF compared with aged- and gender-matched healthy controls. Levels of sTNFR1 correlated strongly with the levels of sTNFR2 (rs = 0.567-0.663, p < 0.01). The levels of both sTNFR1 and sTNFR2 significantly correlated with the TACE activity (rs = 0.491-0.557, p < 0.05). To examine if changes in TACE activity and in levels of cleaved soluble TNFRs are an early event in the course of Alzheimer's disease, we measured these molecules in the CSF from 47 patients with mild cognitive impairment (MCI) which is considered as a preclinical stage of AD. Unexpectedly, we found significantly higher levels of TACE activity and soluble TNFR in the MCI group. These results suggest that TACE activity and soluble TNF receptors may be potential diagnostic candidate biomarkers in AD and MCI.
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.