Preprint Article Version 1 This version is not peer-reviewed

The Neuro-Immune Fingerprint of Major Neuro-Cognitive Psychosis or Deficit Schizophrenia: A Supervised Machine Learning Study

Version 1 : Received: 22 May 2019 / Approved: 23 May 2019 / Online: 23 May 2019 (16:25:44 CEST)

How to cite: Al-Hakeim, H.; Almulla, A.; Maes, M. The Neuro-Immune Fingerprint of Major Neuro-Cognitive Psychosis or Deficit Schizophrenia: A Supervised Machine Learning Study. Preprints 2019, 2019050285 (doi: 10.20944/preprints201905.0285.v1). Al-Hakeim, H.; Almulla, A.; Maes, M. The Neuro-Immune Fingerprint of Major Neuro-Cognitive Psychosis or Deficit Schizophrenia: A Supervised Machine Learning Study. Preprints 2019, 2019050285 (doi: 10.20944/preprints201905.0285.v1).

Abstract

No studies have examined the immune fingerprint of major neuro-cognitive psychosis (MNP) or deficit schizophrenia using M1 macrophage cytokines in combination with chemokines such as CCL-2 and CCL-11. The present study delineated the neuro-immune fingerprint of MNP/deficit schizophrenia by analyzing plasma levels of IL-1β, sIL-1RA, TNF-α, sTNFR1, sTNFR2, CCL-2 and CCL-11 in MNP (n=120) versus healthy controls (n=54) in association with neurocognitive deficits (as assessed with the Brief Assessment of Cognition in Schizophrenia) and PHEMN (psychotic, hostility, excitation, mannerism and negative) symptoms. All immune biomarkers were significantly higher in MNP than in normal controls. MNP was best predicted by a combination of CCL-11, TNF-α, IL-1β and sIL-1RA which yielded a bootstrapped (n=2000) area under the Receiver Operating Curve of 0.985. Composite scores reflecting M1 macrophage activity and neurotoxic potential including combined effects of CCL-11 plus CCL-2 were significantly increased in MNP. Nevertheless, the effects of increased IL-1β and TNF-α in MNP were attenuated (statistically) by increased sIL-1RA and sTNFR2, two negative immune-regulatory markers. A large part of the variance in PHEM (38.4%-52.6%) and negative (65.8-7439%) symptoms was explained by combinations of immune markers whereby CCL-11 was consistently the most important. The immune markers also explained a large part of the variance in the Mini Mental State examination, list learning, digit sequencing task, category instances, controlled word association, symbol coding and Tower of London. Soft Independent Modeling of Class Analogy performed on the biomarkers showed that the inter-class distance between the models constructed around MNP and controls was 19.3 indicating a good separation. Partial Least Squares analysis showed that 72.7% of the variance in overall phenomenology was explained by the regression on IL-1β, sIL-1RA, CCL-11, TNF-α (all positively) and education (inversely). It is concluded that the combination of the above-mentioned markers defines MNP as a distinct neuro-immune disorder and that those markers in combination explain a large part of the variance in memory and executivive impairments and PHEMN symptoms.

Subject Areas

Deficit schizophrenia, machine learning, cytokine, cognition, Immunological biomarkers

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