TY  - JOUR
A1  - Gurke, Robert
A1  - Etyemez, Semra
A1  - Prvulovic, David
A1  - Thomas, Dominique Jeanette
A1  - Fleck, Stefanie Christina
A1  - Reif, Andreas
A1  - Geisslinger, Gerd
A1  - Lötsch, Jörn
T1  - A data science-based analysis points at distinct patterns of lipid mediator plasma concentrations in patients with dementia
T2  - Frontiers in psychiatry
N2  - 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.
KW  - dementia
KW  - data science
KW  - lipids
KW  - biomarker
KW  - machine learning
Y1  - 2019
UR  - http://publikationen.ub.uni-frankfurt.de/frontdoor/index/index/docId/49942
UR  - https://nbn-resolving.org/urn:nbn:de:hebis:30:3-499423
SN  - 1664-0640
N1  - Copyright © 2019 Gurke, Etyemez, Prvulovic, Thomas, Fleck, Reif, Geisslinger and Lötsch. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
VL  - 10
IS  - Art. 41
SP  - 1
EP  - 16
PB  - Frontiers Research Foundation
CY  - Lausanne
ER  -