In schizophrenia, non-remitters and partial remitters to treatment with antipsychotics are qualitatively distinct classes with respect to neurocognitive deficits and neuro-immune biomarkers: results of soft independent modeling of class analogy

Importance of the dysregulation of the kynurenine pathway on cognition in schizophrenia: a systematic review of clinical studies

Schizophrenia is a chronic psychotic disease burdened by cognitive deficits which hamper daily functioning causing disability and costs for society. Biological determinants underlying cognitive impairment are only partially understood and there are no convincing pharmacological targets able to improve cognitive outcome. Mounting evidence has shown the involvement of the kynurenine pathway in the pathophysiology of schizophrenia, also concerning cognitive symptoms. Therefore, the action of specific metabolites of kynurenine could affects cognition in schizophrenia. To evaluate the impact of the metabolites of kynurenine pathway on cognitive functions in schizophrenia spectrum disorders, with a focus on the modulating role of gender, to identify predictors of cognitive functioning and hypothetical pharmacological targets able to resize disability by improving cognition, thus functioning and quality of life. A systematic review was performed in PubMed/MEDLINE and Embase according to Preferred Reporting Items for Systematic Reviews and Meta-Analyses. All studies measuring the direct impact of kynurenine metabolites on cognitive performances in living individuals with schizophrenia spectrum disorders were included in the review. Six studies were included. The activation of the kynurenine pathway resulted associated with greater cognitive deficits in patients with schizophrenia and both elevations and reduction of metabolites seemed able to affect cognitive outcome. No modulating role of sex emerged. This systematic review provides evidence that the activation of the kynurenine pathway affects cognition in patients with schizophrenia and highlights this pathway as a possible future target for developing novel drugs toward this still unmet clinical need. However, evidence is still limited and future studies are needed to further clarify the relationship between kynurenine pathway and cognition in schizophrenia.

Advanced Biomarkers of Hepatotoxicity in Psychiatry: A Narrative Review and Recommendations for New Psychoactive Substances

One of the factors that increase the effectiveness of the pharmacotherapy used in patients abusing various types of new psychoactive substances (NPSs) is the proper functioning of the liver. However, the articles published to date on NPS hepatotoxicity only address non-specific hepatic parameters. The aim of this manuscript was to review three advanced markers of hepatotoxicity in psychiatry, namely, osteopontin (OPN), high-mobility group box 1 protein (HMGB1) and glutathione dehydrogenase (GDH, GLDH), and, on this basis, to identify recommendations that should be included in future studies in patients abusing NPSs. This will make it possible to determine whether NPSs do indeed have a hepatotoxic effect or whether other factors, such as additional substances taken or hepatitis C virus (HCV) infection, are responsible. NPS abusers are at particular risk of HCV infection, and for this reason, it is all the more important to determine what factors actually show a hepatotoxic effect in them.

The interleukin-6/interleukin-23/T helper 17-axis as a driver of neuro-immune toxicity in the major neurocognitive psychosis or deficit schizophrenia: A precision nomothetic psychiatry analysis

BACKGROUND

Schizophrenia and especially deficit schizophrenia (DSCZ) are characterized by increased activity of neuroimmunotoxic pathways and a generalized cognitive decline (G-CoDe). There is no data on whether the interleukin (IL)-6/IL-23/T helper 17 (IL-6/IL-23/Th17)-axis is more associated with DSCZ than with non-deficit schizophrenia (NDSCZ) and whether changes in this axis are associated with the G-CoDe and the phenome (a factor extracted from all symptom domains) of schizophrenia.

METHODS

This study included 45 DSCZ and 45 NDSCZ patients and 40 controls and delineated whether the IL-6/IL-23/Th17 axis, trace elements (copper, zinc) and ions (magnesium, calcium) are associated with DSCZ, the G-CoDe and the schizophrenia phenome.

RESULTS

Increased plasma IL-23 and IL-6 levels were associated with Th17 upregulation, assessed as a latent vector (LV) extracted from IL-17, IL-21, IL-22, and TNF-α. The IL-6/IL-23/Th17-axis score, as assessed by an LV extracted from IL-23, IL-6, and the Th17 LV, was significantly higher in DSCZ than in NDSCZ and controls. We discovered that 70.7% of the variance in the phenome was explained by the IL-6/IL-23/Th17-axis (positively) and the G-CoDe and IL-10 (both inversely); and that 54.6% of the variance in the G-CoDe was explained by the IL-6/IL-23/Th17 scores (inversely) and magnesium, copper, calcium, and zinc (all positively).

CONCLUSION

The pathogenic IL-6/IL-23/Th17-axis contributes to the generalized neurocognitive deficit and the phenome of schizophrenia, especially that of DSCZ, due to its key role in peripheral inflammation and neuroinflammation and its consequent immunotoxic effects on neuronal circuits. These clinical impairments are more prominent in subjects with lowered IL-10, magnesium, calcium, and zinc.

Recent advancements in biomarker research in schizophrenia: mapping the road from bench to bedside

Schizophrenia (SZ) is a severe progressive neurodegenerative as well as disruptive behavior disorder affecting innumerable people throughout the world. The discovery of potential biomarkers in the clinical scenario would lead to the development of effective methods of diagnosis and would provide an understanding of the prognosis of the disease. Moreover, breakthrough inventions for the treatment and prevention of this mysterious disease could evolve as a result of a thorough understanding of the clinical biomarkers. In this review, we have discussed about specific biomarkers of SZ an emphasis has been laid to delineate (1) diagnostic biomarkers like neuroimmune biomarkers, metabolic biomarkers, oligodendrocyte biomarkers and biomarkers of negative and cognitive symptoms, (2) therapeutic biomarkers like various neurotransmitter systems and (3) prognostic biomarkers. All the biomarkers were evaluated in drug-naïve (at least for 4 weeks) patients in order to achieve a clear comparison between schizophrenic patients and healthy controls. Also, an attempt has been made to elucidate the potential genes which serve as predictors and tools for the determination of biomarkers and would ultimately help in the prevention and treatment of this deadly illness.

False dogmas in mood disorders research: Towards a nomothetic network approach

The current understanding of major depressive disorder (MDD) and bipolar disorder (BD) is plagued by a cacophony of controversies as evidenced by competing schools to understand MDD/BD. The DSM/ICD taxonomies have cemented their status as the gold standard for diagnosing MDD/BD. The aim of this review is to discuss the false dogmas that reign in current MDD/BD research with respect to the new, data-driven, machine learning method to model psychiatric illness, namely nomothetic network psychiatry (NNP). This review discusses many false dogmas including: MDD/BD are mind-brain disorders that are best conceptualized using a bio-psycho-social model or mind-brain interactions; mood disorders due to medical disease are attributable to psychosocial stress or chemical imbalances; DSM/ICD are the gold standards to make the MDD/BD diagnosis; severity of illness should be measured using rating scales; clinical remission should be defined using threshold values on rating scale scores; existing diagnostic BD boundaries are too restrictive; and mood disorder spectra are the rule. In contrast, our NNP models show that MDD/BD are not mind-brain or psycho-social but systemic medical disorders; the DSM/ICD taxonomies are counterproductive; a shared core, namely the reoccurrence of illness (ROI), underpins the intertwined recurrence of depressive and manic episodes and suicidal behaviors; mood disorders should be ROI-defined; ROI mediates the effects of nitro-oxidative stress pathways and early lifetime trauma on the phenome of mood disorders; severity of illness and treatment response should be delineated using the NNP-derived causome, pathway, ROI and integrated phenome scores; and MDD and BD are the same illness.

Precision Nomothetic Medicine in Depression Research: A New Depression Model, and New Endophenotype Classes and Pathway Phenotypes, and A Digital Self

Machine learning approaches, such as soft independent modeling of class analogy (SIMCA) and pathway analysis, were introduced in depression research in the 1990s (Maes et al.) to construct neuroimmune endophenotype classes. The goal of this paper is to examine the promise of precision psychiatry to use information about a depressed person’s own pan-omics, environmental, and lifestyle data, or to tailor preventative measures and medical treatments to endophenotype subgroups of depressed patients in order to achieve the best clinical outcome for each individual. Three steps are emerging in precision medicine: (1) the optimization and refining of classical models and constructing digital twins; (2) the use of precision medicine to construct endophenotype classes and pathway phenotypes, and (3) constructing a digital self of each patient. The root cause of why precision psychiatry cannot develop into true sciences is that there is no correct (cross-validated and reliable) model of clinical depression as a serious medical disorder discriminating it from a normal emotional distress response including sadness, grief and demoralization. Here, we explain how we used (un)supervised machine learning such as partial least squares path analysis, SIMCA and factor analysis to construct (a) a new precision depression model; (b) a new endophenotype class, namely major dysmood disorder (MDMD), which is a nosological class defined by severe symptoms and neuro-oxidative toxicity; and a new pathway phenotype, namely the reoccurrence of illness (ROI) index, which is a latent vector extracted from staging characteristics (number of depression and manic episodes and suicide attempts), and (c) an ideocratic profile with personalized scores based on all MDMD features.