Machine-learning based lipid mediator serum concentration patterns allow identification of multiple sclerosis patients with high accuracy

Abstract

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.

Lipid Mediator Profiles Predict Response to Therapy with an Oral Frankincense Extract in Relapsing-Remitting Multiple Sclerosis

“…However, upregulation of 12-or 15-LO and COX activity in RR-MS might define an MS subtype that has a suboptimal response to SFE treatment. This matches previous observations of an involvement of the 12-or 15-LO and COX in more active RR-MS patients 18,19 . The strong positive correlation between 12/15-LO-derived LMs and the surface expression of CD86 in dendritic cells in the EDA RR-MS group is in line with previous animal studies showing that 12/15-LO regulates the maturation process of dendritic cells and its inhibition favours TH17 differentiation 20 .…”

Using Serum Metabolomics to Predict Development of Anti-drug Antibodies in Multiple Sclerosis Patients Treated With IFNβ

“…Serum metabolites are attractive candidate biomarkers in MS, and have already been shown to have diagnostic (22,23,60) and prognostic (27,61,62) potential. Furthermore, they are relatively inexpensive to measure, and a blood-draw is less invasive and time-consuming than a lumbar puncture or MRI scan.…”

“…In recent years machine learning (ML) approaches have been applied to clinical problems in MS, including computeraided diagnosis, neuroimaging analysis and prediction of disease trajectories (19)(20)(21). The majority of models have been based on clinical information, but ML-generated serum lipid signatures have also successfully been used to identify (22) and stratify (23) MS patients. Circulating lipids are dysregulated in MS, and have been associated with disease progression (24)(25)(26)(27)(28).…”

A systematic review of the applications of artificial intelligence and machine learning in autoimmune diseases

“…69 Six models from the evaluated studies returned more than one of the following measures as either 1 or 100%: AUC, accuracy, precision and recall, sensitivity and specificity. 59,67,68,[111][112][113] This perfect performance indicates that a model may not be required, as there exists data that classifies the groups without error. An alternative explanation of apparently optimal performance may reside in poor implementation of cross-validation strategies.…”