Biological and Clinical Determinants of Treatment Resistant Schizophrenia

Up to one third of patients with schizophrenia show only limited response todopamine blocking antipsychotic medication. This could be due to distinctneurobiological abnormalities in this subgroup of patients. While there is robustevidence to suggest that the neurobiology of schizophrenia involves increasedpresynaptic striatal dopaminergic elevation, little is known as to whether thisabnormality is present in treatment resistance, and consequently therelationship between this dopamine abnormality and the lack of response totreatment remains unknown. Furthermore, it remains unclear whethertreatment resistance manifests at the outset of illness, and perhaps has aneurodevelopmental origin, or whether it evolves over time, possibly as a resultof a neurodegenerative process.The first study in this thesis investigated striatal presynaptic dopamine synthesisin twelve treatment resistant schizophrenic patients, twelve patients withschizophrenia who had responded to antipsychotics, and twelve healthyvolunteers, using [18F]-DOPA Positron Emission Tomography (PET). Thus, itwas possible to test the hypothesis that the response to treatment is determinedby differences in presynaptic dopamine function. The results demonstrated thatthere were no significant differences in striatal dopamine synthesis capacitybetween treatment resistant patients and healthy volunteers, whilst dopaminesynthesis capacity was significantly increased in responders relative totreatment resistant patients. The difference was most marked in the associativeand the limbic striatal subdivisions.A second, large follow-up study of first episode psychosis (FEP) patients,examined the course of treatment resistance over the 10 year follow up. It wasfound that over 80% of treatment resistant patients were persistently resistantfrom the initiation of antipsychotic treatment. My PET study, due to its crosssectional design, could not determine whether the normal dopamine levelspredate the antipsychotic exposure in treatment resistant patients. However, bydemonstrating that a great majority of treatment resistant patients are resistantto dopamine blocking antipsychotics at first ever initiation of treatment, mysecond study raises the possibility that these patients may have had normaldopamine levels even at the outset of their psychotic illness. In the same FEPcohort it was possible to investigate neurodevelopmental predictors of treatmentresistance. The finding that the negative symptom dimension and younger ageof onset were significant predictors of treatment resistance is compatible withthe view that TRS may be of neurodevelopmental origin.Overall, my observations in this thesis indicate that TRS may be a distinct andenduring subtype of schizophrenic illness of a possible neurodevelopmentalorigin whose pathophysiology is not marked by alterations in dopaminesynthesis capacity. Findings emerging from this thesis provide a platform forfuture studies, which may lead to the discovery of much needed new treatmentsfor this disabling and intractable condition.<br/

How Genes and Environmental Factors Determine the Different Neurodevelopmental Trajectories of Schizophrenia and Bipolar Disorder

The debate endures as to whether schizophrenia and bipolar disorder are separate entities or different manifestations of a single underlying pathological process. Here, we argue that this sterile argument obscures the fact that the truth lies somewhere in between. Thus, recent studies support a model whereby, on a background of some shared genetic liability for both disorders, patients with schizophrenia have been subject to additional genetic and/or environmental factors that impair neurodevelopment; for example, copy number variants and obstetric complications are associated with schizophrenia but not with bipolar disorder. As a result, children destined to develop schizophrenia show an excess of neuromotor delays and cognitive difficulties while those who later develop bipolar disorder perform at least as well as the general population. In keeping with this model, cognitive impairments and brain structural abnormalities are present at first onset of schizophrenia but not in the early stages of bipolar disorder. However, with repeated episodes of illness, cognitive and brain structural abnormalities accumulate in both schizophrenia and bipolar disorder, thus clouding the picture

Auditory hallucinations in non-psychotic disorders – an analytical psychological perspective

Although hallucinations are a feature of psychosis, they can present in non-psychotic disorders and may occur in non-pathological states. Jung argues that unconscious complexes underpin hallucinations and further observes that some of the symptoms of ‘hysteric' patients – including hallucinations – were also common amongst patients with schizophrenia. However, the outward presentation of symptoms was markedly different for each patient group. Jung mobilises his complex theory to explain this difference. We argue that Jung’s understanding of hallucinations applies to contemporary healthcare; it frames how hallucinations may manifest in multiple conditions, not just psychosis. This brief report discusses Jung’s theories and their continued veracity in contemporary contexts

Glyceryl trinitrate in first-episode psychosis unmedicated with antipsychotics: A randomised controlled pilot study

Background: There is a pressing need for new classes of treatment for psychosis. A key therapeutic target for novel compounds is the NMDA receptor, which may be modulated by nitric oxide donors such as sodium nitroprusside (SNP). Recent studies of SNP in patients with psychosis have mixed results, and the drug has to be administered intravenously. Glyceryl trinitrate (GTN) is a well-established cardiovascular medicine that is also a nitric oxide donor, and can be given orally. Aims: We explored the safety and potential effects of GTN in unmedicated patients with a first episode of psychosis. Methods: This was a single-centre, randomised, double-blind, placebo-controlled trial from December 2016 to April 2019 (ClinicalTrials.gov identifier: NCT02906553). Patients received 3 × sprays of GTN or placebo for three consecutive days, and were re-assessed on Days 1, 2, 3 and 7. The primary outcome was cognition (Jumping to Conclusions task), secondary outcomes were symptoms (Positive and Negative Syndrome Scale (PANSS)), verbal memory (Hopkins Verbal Learning task), and mood (Bond–Lader Visual Analogue Scales). Results: Nineteen patients were randomised, and 13 participants were included in the analyses. Compared with placebo, GTN was well tolerated, but was not associated with significant effects on cognition, symptoms, or mood. Bayesian statistics indicate that our results were 2× more likely under the null hypothesis than the alternative hypothesis, providing anecdotal evidence that GTN does not improve psychotic symptoms. Conclusions: We found no indication of an effect of GTN on symptoms of psychosis or cognition

Natural Language Processing markers in first episode psychosis and people at clinical high-risk.

Funder: MQ: Transforming Mental Health; Grant(s): MQF17_24Recent work has suggested that disorganised speech might be a powerful predictor of later psychotic illness in clinical high risk subjects. To that end, several automated measures to quantify disorganisation of transcribed speech have been proposed. However, it remains unclear which measures are most strongly associated with psychosis, how different measures are related to each other and what the best strategies are to collect speech data from participants. Here, we assessed whether twelve automated Natural Language Processing markers could differentiate transcribed speech excerpts from subjects at clinical high risk for psychosis, first episode psychosis patients and healthy control subjects (total N = 54). In-line with previous work, several measures showed significant differences between groups, including semantic coherence, speech graph connectivity and a measure of whether speech was on-topic, the latter of which outperformed the related measure of tangentiality. Most NLP measures examined were only weakly related to each other, suggesting they provide complementary information. Finally, we compared the ability of transcribed speech generated using different tasks to differentiate the groups. Speech generated from picture descriptions of the Thematic Apperception Test and a story re-telling task outperformed free speech, suggesting that choice of speech generation method may be an important consideration. Overall, quantitative speech markers represent a promising direction for future clinical applications.SEM was supported by the Accelerate Programme for Scientific Discovery, funded by Schmidt Futures, a Fellowship from The Alan Turing Institute, London, and a Henslow Fellowship at Lucy Cavendish College, University of Cambridge, funded by the Cambridge Philosophical Society. PEV is supported by a fellowship from MQ: Transforming Mental Health (MQF17_24). This work was supported by The Alan Turing Institute under the EPSRC grant EP/N510129/1, the NIHR Cambridge Biomedical Research Centre (BRC-1215-20014), the UK Medical Research Council (MRC) and the National Institute for Health Research (NIHR) Mental Health Biomedical Research Centre at South London and Maudsley NHS Foundation Trust and King's College London

Natural Language Processing markers in first episode psychosis and people at clinical high-risk.

Funder: MQ: Transforming Mental Health; Grant(s): MQF17_24Recent work has suggested that disorganised speech might be a powerful predictor of later psychotic illness in clinical high risk subjects. To that end, several automated measures to quantify disorganisation of transcribed speech have been proposed. However, it remains unclear which measures are most strongly associated with psychosis, how different measures are related to each other and what the best strategies are to collect speech data from participants. Here, we assessed whether twelve automated Natural Language Processing markers could differentiate transcribed speech excerpts from subjects at clinical high risk for psychosis, first episode psychosis patients and healthy control subjects (total N = 54). In-line with previous work, several measures showed significant differences between groups, including semantic coherence, speech graph connectivity and a measure of whether speech was on-topic, the latter of which outperformed the related measure of tangentiality. Most NLP measures examined were only weakly related to each other, suggesting they provide complementary information. Finally, we compared the ability of transcribed speech generated using different tasks to differentiate the groups. Speech generated from picture descriptions of the Thematic Apperception Test and a story re-telling task outperformed free speech, suggesting that choice of speech generation method may be an important consideration. Overall, quantitative speech markers represent a promising direction for future clinical applications.SEM was supported by the Accelerate Programme for Scientific Discovery, funded by Schmidt Futures, a Fellowship from The Alan Turing Institute, London, and a Henslow Fellowship at Lucy Cavendish College, University of Cambridge, funded by the Cambridge Philosophical Society. PEV is supported by a fellowship from MQ: Transforming Mental Health (MQF17_24). This work was supported by The Alan Turing Institute under the EPSRC grant EP/N510129/1, the NIHR Cambridge Biomedical Research Centre (BRC-1215-20014), the UK Medical Research Council (MRC) and the National Institute for Health Research (NIHR) Mental Health Biomedical Research Centre at South London and Maudsley NHS Foundation Trust and King's College London

Cognitive performance at first episode of psychosis and the relationship with future treatment resistance: Evidence from an international prospective cohort study

Background: Antipsychotic treatment resistance affects up to a third of individuals with schizophrenia, with recent research finding systematic biological differences between antipsychotic resistant and responsive patients. Our aim was to determine whether cognitive impairment at first episode significantly differs between future antipsychotic responders and resistant cases. Methods: Analysis of data from seven international cohorts of first-episode psychosis (FEP) with cognitive data at baseline (N = 683) and follow-up data on antipsychotic treatment response: 605 treatment responsive and 78 treatment resistant cases. Cognitive measures were grouped into seven cognitive domains based on the preexisting literature. We ran multiple imputation for missing data and used logistic regression to test for associations between cognitive performance at FEP and treatment resistant status at follow-up. Results: On average patients who were future classified as treatment resistant reported poorer performance across most cognitive domains at baseline. Univariate logistic regressions showed that antipsychotic treatment resistance cases had significantly poorer IQ/general cognitive functioning at FEP (OR = 0.70, p = .003). These findings remained significant after adjusting for additional variables in multivariable analyses (OR = 0.76, p = .049). Conclusions: Although replication in larger studies is required, it appears that deficits in IQ/general cognitive functioning at first episode are associated with future treatment resistance. Cognitive variables may be able to provide further insight into neurodevelopmental factors associated with treatment resistance or act as early predictors of treatment resistance, which could allow prompt identification of refractory illness and timely interventions.Funding: This work was supported by a Stratified Medicine Programme grant to J.H.M from the Medical Research Council (grant number MR/L011794/1 which funded the research and supported S.E.S., A.F.P., R.M.M., J.T.R.W. & J.H.M.) E.M’s PhD is funded by the MRC-doctoral training partnership studentship in Biomedical Sciences at King’s College London. J.H.M, E.K, R.M.M are part funded by the National Institute for Health Research (NIHR) Biomedical Research Centre at South London and Maudsley NHS Foundation Trust and King’s College London. A.P.K. is funded by the NIHR Biomedical Research Centre at South London and Maudsley NHS Foundation Trust and King’s College London. O.A. is further funded by an NIHR Post-Doctoral Fellowship (PDF2018-11-ST2-020). The views expressed are those of the authors and not necessarily those of the NHS, the MRC, the NIHR or the Department of Health. E.M.J. is supported by the UCL/UCLH Biomedical Research Centre. The AESOP (London, UK) cohort was funded by the UK Medical Research Council (Ref: G0500817). The Bologna (Italy) cohort was funded by the European Community’s Seventh Framework Program under grant agreement (agreement No. HEALTH-F2-2010–241909, Project EU-GEI). The GAP (London, UK) cohort was funded by the UK National Institute of Health Research (NIHR) Specialist Biomedical Research Centre for Mental Health, South London and Maudsley NHS Mental Health Foundation Trust (SLaM) and the Institute of Psychiatry, Psychology, and Neuroscience at King’s College London; Psychiatry Research Trust; Maudsley Charity Research Fund; and the European Community’s Seventh Framework Program grant (agreement No. HEALTH-F2-2009-241909, Project EU-GEI). The Oslo (Norway) cohort was funded by the Stiftelsen KG Jebsen, Research Council of Norway (#223273, under the Centers of Excellence funding scheme, and #300309, #283798) and the South-Eastern Norway Regional Health Authority (#2006233, #2006258, #2011085, #2014102, #2015088, #2017-112). The Paris (France) cohort was funded by European Community’s Seventh Framework Program grant (agreement No. HEALTHF2-2010–241909, Project EU-GEI). The Santander (Spain) cohort was funded by the following grants (to B.C.F): Instituto de Salud Carlos III, FIS 00/3095, PI020499, PI050427, PI060507, Plan Nacional de Drogas Research Grant 2005-Orden sco/3246/2004, and SENY Fundatio Research Grant CI 2005-0308007, Fundacion Marques de Valdecilla A/02/07 and API07/011. SAF2016-76046-R and SAF2013-46292-R (MINECO and FEDER). The West London (UK) cohort was funded The Wellcome Trust (Grant Numbers: 042025; 052247; 064607)

Clinical predictors of antipsychotic treatment resistance: Development and internal validation of a prognostic prediction model by the STRATA-G consortium

Introduction Our aim was to, firstly, identify characteristics at first-episode of psychosis that are associated with later antipsychotic treatment resistance (TR) and, secondly, to develop a parsimonious prediction model for TR. Methods We combined data from ten prospective, first-episode psychosis cohorts from across Europe and categorised patients as TR or non-treatment resistant (NTR) after a mean follow up of 4.18 years (s.d. = 3.20) for secondary data analysis. We identified a list of potential predictors from clinical and demographic data recorded at first-episode. These potential predictors were entered in two models: a multivariable logistic regression to identify which were independently associated with TR and a penalised logistic regression, which performed variable selection, to produce a parsimonious prediction model. This model was internally validated using a 5-fold, 50-repeat cross-validation optimism-correction. Results Our sample consisted of N = 2216 participants of which 385 (17 %) developed TR. Younger age of psychosis onset and fewer years in education were independently associated with increased odds of developing TR. The prediction model selected 7 out of 17 variables that, when combined, could quantify the risk of being TR better than chance. These included age of onset, years in education, gender, BMI, relationship status, alcohol use, and positive symptoms. The optimism-corrected area under the curve was 0.59 (accuracy = 64 %, sensitivity = 48 %, and specificity = 76 %). Implications Our findings show that treatment resistance can be predicted, at first-episode of psychosis. Pending a model update and external validation, we demonstrate the potential value of prediction models for TR.Funding: This work was supported by a Stratified Medicine Programme grant to JHM from the Medical Research Council (grant number MR/L011794/1 which funded the research and supported S.E.S., D.A., A.F.P, L.K., R.M.M., D.S., J.T.R.W, & J.H.M.); funding from the National Institute for Health Research Biomedical Research Centre at South London and Maudsley National Health Service Foundation Trust and King's College London to D.A. and D.S; and funding from the Collaboration for Leadership in Applied Health Research and Care (CLAHRC) South London at King's College Hospital National Health Service Foundation Trust to S.E.S. The views expressed are those of the author(s) and not necessarily those of the Medical Research Council, National Health Service, the National Institute for Health Research, or the Department of Health. The AESOP (London, UK) cohort was funded by the UK Medical Research Council (Ref: G0500817). The Belfast (UK) cohort was funded by the Research and Development Office of Northern Ireland. The Bologna (Italy) cohort was funded by the European Community's Seventh Framework Program under grant agreement (agreement No.HEALTH-F2-2010–241909, Project EU-GEI). The GAP (London, UK) cohort was funded by the UK National Institute of Health Research(NIHR) Specialist Biomedical Research Centre for Mental Health, South London and Maudsley NHS Mental Health Foundation Trust (SLaM) and the Institute of Psychiatry, Psychology, and Neuroscience at King's College London; Psychiatry Research Trust; Maudsley Charity Research Fund; and the European Community's Seventh Framework Program grant (agreement No. HEALTH-F2-2009-241909, Project EU-GEI). The Lausanne (Switzerland) cohort was funded by the Swiss National Science Foundation (no. 320030_135736/1 to P.C. and K.Q.D., no 320030-120686, 324730-144064 and 320030-173211 to C.B.E and P.C., and no 171804 to LA); National Center of Competence in Research (NCCR) “SYNAPSY - The Synaptic Bases of Mental Diseases” from the Swiss National Science Foundation (no 51AU40_125759 to PC and KQD); and Fondation Alamaya (to KQD). The Oslo (Norway) cohort was funded by the Research Council of Norway (#223273/F50, under the Centers of Excellence funding scheme, #300309, #283798) and the South-Eastern Norway Regional Health Authority (#2006233, #2006258, #2011085, #2014102, #2015088 to IM, #2017-112). The Paris (France) cohort was funded by European Community's Seventh Framework Program grant (agreement No. HEALTH-F2-2010–241909, Project EU-GEI). The Prague (Czech Republic) cohort was funded by the Ministry of Health of the Czech Republic (Grant Number: NU20-04-00393). The Santander (Spain) cohort was funded by the following grants (to B.C.F): Instituto de Salud Carlos III, FIS 00/3095, PI020499, PI050427, PI060507, Plan Nacional de Drogas Research Grant 2005-Orden sco/3246/2004, and SENY Fundatio Research Grant CI 2005-0308007, Fundacion Marques de Valdecilla A/02/07 and API07/011. SAF2016-76046-R and SAF2013-46292-R (MINECO and FEDER). The West London (UK) cohort was funded The Wellcome Trust (Grant Number: 042025; 052247; 064607)