Working PaperArticleVersion 1This version is not peer-reviewed
Development of a Novel Neuro-Immune and Opioid-Associated Fingerprint with a Cross-Validated Ability to Identify and Authenticate Unknown Patients with Major Depression: Far Beyond Differentiation, Discrimination and Classification
Version 1
: Received: 22 March 2019 / Approved: 25 March 2019 / Online: 25 March 2019 (10:14:02 CET)
How to cite:
Al-Hakeim, H.; Al-Fadhel, S.; ALDUJAILI, A.; Carvalho, A.F.; Sriswasdi, S.; Maes, M. Development of a Novel Neuro-Immune and Opioid-Associated Fingerprint with a Cross-Validated Ability to Identify and Authenticate Unknown Patients with Major Depression: Far Beyond Differentiation, Discrimination and Classification. Preprints2019, 2019030217
Al-Hakeim, H.; Al-Fadhel, S.; ALDUJAILI, A.; Carvalho, A.F.; Sriswasdi, S.; Maes, M. Development of a Novel Neuro-Immune and Opioid-Associated Fingerprint with a Cross-Validated Ability to Identify and Authenticate Unknown Patients with Major Depression: Far Beyond Differentiation, Discrimination and Classification. Preprints 2019, 2019030217
Al-Hakeim, H.; Al-Fadhel, S.; ALDUJAILI, A.; Carvalho, A.F.; Sriswasdi, S.; Maes, M. Development of a Novel Neuro-Immune and Opioid-Associated Fingerprint with a Cross-Validated Ability to Identify and Authenticate Unknown Patients with Major Depression: Far Beyond Differentiation, Discrimination and Classification. Preprints2019, 2019030217
APA Style
Al-Hakeim, H., Al-Fadhel, S., ALDUJAILI, A., Carvalho, A.F., Sriswasdi, S., & Maes, M. (2019). Development of a Novel Neuro-Immune and Opioid-Associated Fingerprint with a Cross-Validated Ability to Identify and Authenticate Unknown Patients with Major Depression: Far Beyond Differentiation, Discrimination and Classification. Preprints. https://doi.org/
Chicago/Turabian Style
Al-Hakeim, H., Sira Sriswasdi and Michael Maes. 2019 "Development of a Novel Neuro-Immune and Opioid-Associated Fingerprint with a Cross-Validated Ability to Identify and Authenticate Unknown Patients with Major Depression: Far Beyond Differentiation, Discrimination and Classification" Preprints. https://doi.org/
Abstract
Rationale: Major depressive disorder (MDD) is characterized by signaling aberrations in interleukin (IL)-6, IL-10, beta-endorphins as well as mu (MOR) and kappa (KOR) opioid receptors. Here we examined whether these biomarkers may aid in the classification of unknown subjects into the target class MDD.Methods: The aforementioned biomarkers were assayed in 60 first-episode, drug-naïve depressed patients and 30 controls. We analyzed the data using joint principal component analysis (PCA) performed on all subjects to check whether subjects cluster by classes; support vector machine (SVM) with 10-fold validation; and linear discriminant analysis (LDA) and SIMCA performed on calibration and validation sets and we computed the figures of merit and learnt from the data. Results: PCA shows that both groups were well separated using the first three PCs, while correlation loadings show that all 5 biomarkers have discriminatory value. SVM and LDA yielded an accuracy of 100% in validation samples. Using SIMCA there was a highly significant discrimination of both groups (model-to-model distance=87.5); all biomarkers showed a significant discrimination and modeling power, while 10% of the patients were identified as outsiders and no aliens could be identified.Discussion: We have delineated that MDD is a distinct class with respect to neuro-immune and opioid biomarkers and that future unknown subjects can be authenticated as having MDD using this SIMCA fingerprint. Precision psychiatry should employ SIMCA a) to authenticate patients as belonging to the claimed target class and identify other subjects as outsiders, members of another class or aliens; and b) to acquire knowledge through learning from the data by constructing a biomarker fingerprint of the target class.
Keywords
supervised learning, major depression, cytokines, inflammation, neuro-immune, opioids
Subject
Medicine and Pharmacology, Psychiatry and Mental Health
Copyright:
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
