Abstract
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.
Highlights
Accumulating evidence supports a high relevance of lipid molecules, including so-called lipid mediators [1], for the regulation of many different biological processes [2,3,4]
Applying the progeny algorithm [27], three clusters were identified as the most stable solution of hierarchical clustering of the lipid mediator plasma concentration pattern observed in the 94 subjects (Figure 2)
This result was based on both, the “greatest score” criterion and the “greatest gap” criterion [27]
Summary
Accumulating evidence supports a high relevance of lipid molecules, including so-called lipid mediators [1], for the regulation of many different biological processes [2,3,4]. Lipidomics has become one of the latest omicstechnologies used in the search for biomarkers [1], i.e., defined characteristics of biological systems measured as indicators of normal biological processes, pathogenic processes, or responses to an exposure or (therapeutic) intervention [5]. Lipidomics includes several thousands of different molecules [7] found in biological fluids at highly variable concentrations, assayed using untargeted approaches [2, 6, 7] which aim to quantity the whole lipidome in a single analytical run but lack sensitivity for molecules in the low concentration range and selectivity for differences among isomeric or isobaric molecules [3, 7]. On the other hand targeted approaches are used They are focused on a limited number of analytes at comparatively high sensitivity and selectivity [3]
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