Schizophrenia comprises various symptom domains, including positive and negative symptoms. Machine learning showed that a) negative symptoms are significantly interrelated with PHEM (psychosis, hostility, excitation, and mannerism) symptoms, formal thought disorders (FTD) and psychomotor retardation (PMR); and b) stable phase schizophrenia comprises two distinct classes, namely Major Neuro-Cognitive Psychosis (MNP, largely overlapping with deficit schizophrenia) and Simple NP (SNP). In this study, we recruited 120 MNP patients and 54 healthy subjects and measured the above-mentioned symptom domains. In MNP, there were significant associations between negative and PHEM symptoms, FTD and PMR. A single latent trait, which is essentially unidimensional, underlies these key domains of schizophrenia and MNP and additionally shows excellent internal consistency reliability, convergent validity, and predictive relevance. Confirmatory Tedrad Analysis indicates that this latent vector fits a reflective model. The lack of discriminant validity shows that PHEM and negative symptoms greatly overlap and probably measure the same construct. Soft Independent Modeling of Class Analogy (SIMCA) shows that MNP (diagnosis based on negative symptoms) is better modeled using PHEM symptoms, FTD, and PMR than negative symptoms. In conclusion, in stable phase MNP, a restricted sample of the schizophrenia population, negative and PHEM symptoms, FTD and PMR belong to one underlying latent vector reflecting overall severity of schizophrenia (OSOS). The bi-dimensional concept of “positive” and “negative” symptoms cannot be validated and, therefore, future research in stable phase schizophrenia should consider that the latent phenomenon OSOS as well as its 8 reflective manifestations are the key factors of schizophrenia phenomenology.

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.

Background. The aim of this study is to examine whether biomarkers of the immune-inflammatory response (IRS) and endogenous opioid (EOS) systems are associated with affective symptoms in schizophrenia. Methods. We recruited 115 schizophrenia patients and 43 healthy controls and assessed the Hamilton Depression (HDRS) and Anxiety (HAM-A) rating Scale scores as well as serum levels of interleukin (IL)-6, IL-10, eotaxin (CCL11), high mobility group box 1 (HMGB1), Dickkopf-related protein 1 (DKK1), and mu (MOR) and kappa (KOR) opioid receptors.Results. The HDRS and HAM-A scores are significantly and positively correlated with a) psychosis, hostility, excitation, mannerism, negative symptoms, psychomotor retardation, and formal thought disorders; and b) lowered scores on semantic and episodic memory, executive functions, and attention tests as measured with the Brief Assessment of Cognition in Psychiatry. Both HDRS and HAM-A are significantly increased in non-responders to treatment as compared with partial responders. Both affective scores are strongly associated with latent vectors extracted from all symptoms, reflecting overall severity of psychosis (OSOS), and neurocognitive test scores, reflecting a generalized cognitive decline (G-CoDe). The HDRS score was strongly and positively associated with IL-6, HMGB1, KOR, and MOR levels, and the HAM-A score with IL-6, IL-10, CCL11, HMGB1, KOR, and MOR levels. A single latent trait may be extracted from OSOS, G-CoDe, and the HDRS and HAMA scores, and this latent vector score is strongly predicted by HMGB1, MOR, and DKK1.Conclusion. Immune-inflammatory and EOS pathways contribute to the phenome of schizophrenia, which comprises OSOS, affective, and physiosomatic symptoms, and G-CoDe.

A subset of patients with schizophrenia experience physio-somatic symptoms reminiscent of chronic fatigue and fibromyalgia. In schizophrenia, these symptoms contribute to impaired quality of life, and are strongly related to neuro-cognitive deficits, and increased IgA responses to tryptophan catabolites. Negative and PHEM (psychosis, hostility, excitation, mannerism) symptoms, psychomotor retardation (PMR) and formal thought disorders, appear to be manifestations of a single trait reflecting overall severity of schizophrenia (OSOS). In this study, 120 patients with deficit schizophrenia (DEFSCZ) and 54 healthy subjects were assessed with the FibroFatigue (FF) rating scale, and the above-mentioned symptom domains as well as neuro-cognitive tests and biomarkers were measured. In DEFSCZ, there were robust associations between the FF score and all above-mentioned symptom domains, and impairments in semantic and episodic memory and executive functions. Furthermore, the FF score loaded highly on an OSOS latent vector (LV), which showed adequate convergent validity, internal consistency reliability and predictive relevance and fitted a reflective model. Soft Independent Modelling of Class Analogy (SIMCA) showed that the FF items discriminated DEFSCZ from controls with an overall accuracy of 100%. Interleukin IL-1β, IL-1 receptor antagonist (sIL-1RA), tumour necrosis factor (TNF)-α and CCL-11 (eotaxin) explained 66.8% of the variance in the FF score and 59.4% of the variance in OSOS. In conclusion, these data show that physio-somatic symptoms are a core component of the phenomenology of DEFSCZ and are largely mediated by neurotoxic effects of activated immune pathways, including aberrations in CCL-11, IL-1β and TNF-α signalling.