Clinical course in schizophrenia – predicting who, what and whether

Variability in the clinical course of schizophrenia is problematic and a barrier to the personalization of treatment. Until it is known who is likely to develop psychosis, whether it is an affective or schizophrenic type, whether it will respond to antipsychotic medication and whether metabolic syndrome is likely, clinicians are hindered in offering prompt and effective management of psychotic symptoms. However, progress is being made to overcome these hurdles.

Predicting conversion to psychosis

Professor Tyrone Cannon, Yale University, CT, US, investigated individuals at clinical high risk (CHR) of psychosis seeking to identify those likely to convert to full psychosis.

A total of 565 CHR subjects and 99 controls were recruited to the North American Prodrome Longitudinal Study 3 (NAPLS3). These were followed longitudinally at a high frequency of follow-up (baseline, 2, 4, 6 and 8 months) and underwent a battery of investigations at each visit, including logging of sociodemographic, and clinical features, imaging (MRI, DTI, rs-fcMRI), blood work, saliva/cortisol analysis and neurocognition testing to determine whether different trajectories of disease course could be seen prior to conversion to psychosis.

The study design and analysis methods were suited to discovering the shortest possible interval in which differential change between those at CHR and controls could be seen.

Finding the shortest interval in which differential change between individuals at clinical high risk and controls has been investigated

 

Cortical thinning – a biomarker of progression to psychosis?

Professor Cannon reported NAPLS’s preliminary findings which appear to show that, in general, clinical measures show differences at baseline that are predictive of clinical outcome and are stable over time. Furthermore, cognitive and neuroimaging measures show differences in trajectory by clinical outcome; structural imaging (cortical thinning), in particular shows promise as an early biodynamic marker to identify those likely to progress to full psychosis.

Clinical measures show differences at baseline that are predictive of clinical outcome and are stable over time; cortical thinning was a promising early biodynamic marker

Once full psychosis is evident, there is the outstanding challenge of distinguishing those most likely to respond to first-line antipsychotic medication from those who are treatment resistant. 

 

Predicting treatment resistance

Professor Oliver Howes, King’s College Hospital, London, UK, reviewed the work of his group over past years and in which they report finding two ‘groups’ of schizophrenia. From positron emission tomography (PET) scans that facilitated the measurement of dopamine synthesis in the striatum, they determined that one groups was responsive to first-line antipsychotic medication because, fundamentally, this group was hyperdopaminergic; the other group was treatment resistant and appears normodopaminergic.1 In this latter group, dopaminergic antagonists would never resolve their symptoms and alternative therapies should be offered first-line instead.

One group was responsive to first-line antipsychotic medication and hyperdopaminergic; the other group treatment resistant and appeared normodopaminergic

What does this mean clinically? Current guidelines suggest that it should take a matter of weeks to identify treatment-resistant patients. In reality, it takes between 4 and almost 10 years for patients to be offered an alternative to a dopamine antagonist.

 

PET imaging - a biomarker for dopamine antagonist treatment-resistance

Professor Howes suggested that [18F] FDOPA PET imaging could be used to distinguish treatment resistant patients earlier in the course of their illness. His group has now devised a simpler alternative to the usual 90 minute procedure; a static 10 minute scan.  This shorter test has been validated and shows proven high agreement with the more intensive process, has high test-retest reliability and, importantly, is cost-effective.2

Thus, striatal dopamine imaging appears to be an effective biomarker for dopamine antagonist treatment resistance.

Striatal dopamine imaging appears to be an effective biomarker for dopamine antagonist treatment resistance

 

B-SNIP – a route to personalized medicine in schizophrenia?

Professor Carol Tamminga, University of Texas, Dallas, TX, US, outlines a forthcoming study to be undertaken utilizing the Bipolar and Schizophrenia Network for Intermediate Phenotypes (B-SNIP) database of over 2,000 psychosis probands. This very large database contains an enormous amount of clinical and biomarker data which have been used computationally to categorize patients into three different biotypes of psychosis based on cognition measures.3

Defects in the kynurenine pathway are detected in the pathophysiology of schizophrenia and a specific gene polymorphism has been associated with cognitive function in this condition.4 What Professor Tamminga now is seeking is to use this information to predict differential therapeutic responses to a kynurenine aminotransferase II inhibitor within the psychosis patient pool. It is hoped that by defining a biomarker fingerprint within this group, patients who will best respond to therapy will be identified.

 

 

Early intervention applies both to psychiatric and cardiometabolic health

The Psychosis Metabolic Risk Calculator (PsyMetRiC) tool was showcased by its developer, Dr Benjamin Perry, University of Cambridge, UK. The need for such a tool became evident following a meta-analysis of the existing algorithms for predicting cardiovascular metabolic (CVM) risk.  These showed consistent underprediction of CVM risk in young people – those most likely to have psychosis.5

PsyMetRiC was developed and externally validated using data from clinically high risk of psychosis patients from three psychosis early intervention centers. Although not yet available, it is hoped that the tool will prove of great value to healthcare professionals in their management of the cardiometabolic health of patients with psychosis.

 

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Our correspondent’s highlights from the symposium are meant as a fair representation of the scientific content presented. The views and opinions expressed on this page do not necessarily reflect those of Lundbeck.

References
  1. Potkin SG, et al. npj Schizophrenia 2020;6:1 ; https://doi.org/10.1038/s41537-019-0090-z
  2. Veronese M, et al.  Neuropsychopharm 2021;46:1122-1132
  3. Reininghaus U, et al. World Psychiatry 2019;18:67-76
  4. Wonodi I, et al. Schizophr Res 2014;160:80-87
  5. Perry B, et al. Acta Psychiatr Scand 2020;142:215-232
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