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INTRODUCTION: Type 2 diabetes (T2D) is a common comorbidity in patients with schizophrenia (SCZ). The underlying pathophysiologic mechanisms are yet to be fully elucidated, although it can be argued that shared genes, environmental factors or their interaction effect are involved. This study investigated the association between polygenic risk score of SCZ (PRSSCZ) and glycated haemoglobin (HbA1c) while adjusting for polygenic risk score of T2D (PRST2D), and clinical and demographic covariables. METHODS: Genotype, clinical and demographic data of 1129 patients with non-affective psychosis were extracted from Genetic Risk and Outcome of Psychosis (GROUP) cohort study. The glycated haemoglobin (HbA1c) was the outcome. PRS was calculated using standard methods. Univariable and multivariable linear regression analyses were applied to estimate associations. Additionally, sensitivity analysis based on multiple imputation was done. After correction for multiple testing, a two-sided p-value ≤.003 was considered to discover evidence for an association. RESULTS: Of 1129 patients, 75.8% were male with median age of 29 years. The mean (standard deviation) HbA1c level was 35.1 (5.9) mmol/mol. There was no evidence for an association between high HbA1c level and increased PRSSCZ (adjusted regression coefficient (aβ) = 0.69, standard error (SE) = 0.77, p-value = .37). On the other hand, there was evidence for an association between high HbA1c level and increased PRST2D (aβ = 0.93, SE = 0.32, p-value = .004), body mass index (aβ = 0.20, SE = 0.08, p-value = .01), diastolic blood pressure (aβ = 0.08, SE = 0.04, p-value = .03), late age of first psychosis onset (aβ = 0.19, SE = 0.05, p-value = .0004) and male gender (aβ = 1.58, SE = 0.81, p-value = .05). After multiple testing correction, there was evidence for an association between high HbA1c level and late age of first psychosis onset. Evidence for interaction effect between PRSscz and antipsychotics was not observed. The multiple imputation-based sensitivity analysis provided consistent results with complete case analysis. CONCLUSIONS: Glycemic dysregulation in patients with SCZ was not associated with PRSSCZ. This suggests that the mechanisms of hyperglycemia or diabetes are at least partly independent from genetic predisposition to SCZ. Our findings show that the change in HbA1c level can be caused by at least in part due to PRST2D, late age of illness onset, male gender, and increased body mass index and diastolic blood pressure. ispartof: Journal Of Psychosomatic Research vol:132 ispartof: location:England status: published

BACKGROUND: Disentangling psychopathological heterogeneity in schizophrenia is challenging, and previous results remain inconclusive. We employed advanced machine learning to identify a stable and generalizable factorization of the Positive and Negative Syndrome Scale and used it to identify psychopathological subtypes as well as their neurobiological differentiations. METHODS: Positive and Negative Syndrome Scale data from the Pharmacotherapy Monitoring and Outcome Survey cohort (1545 patients; 586 followed up after 1.35 +/- 0.70 years) were used for learning the factor structure by an orthonormal projective non-negative factorization. An international sample, pooled from 9 medical centers across Europe, the United States, and Asia (490 patients), was used for validation. Patients were clustered into psychopathological subtypes based on the identified factor structure, and the neurobiological divergence between the subtypes was assessed by classification analysis on functional magnetic resonance imaging connectivity patterns. RESULTS: A 4-factor structure representing negative, positive, affective, and cognitive symptoms was identified as the most stable and generalizable representation of psychopathology. It showed higher internal consistency than the original Positive and Negative Syndrome Scale subscales and previously proposed factor models. Based on this representation, the positive-negative dichotomy was confirmed as the (only) robust psychopathological subtypes, and these subtypes were longitudinally stable in about 80% of the repeatedly assessed patients. Finally, the individual subtype could be predicted with good accuracy from functional connectivity profiles of the ventromedial frontal cortex, temporoparietal junction, and precuneus. CONCLUSIONS: Machine learning applied to multisite data with cross-validation yielded a factorization generalizable across populations and medical systems. Together with subtyping and the demonstrated ability to predict subtype membership from neuroimaging data, this work further disentangles the heterogeneity in schizophrenia.

The authors regret that the GROUP-investigators were not acknowledged for their work due to a missing affiliation in the original publication. The new authorship line is: Annemarie P.M. Stiekema a,b Md Atiqul Islam c,d,e Edith J. Liemburg c,d,f Stynke Castelein c,g Edwin R. van den Heuvel h Jaap van Weeghel i,j André Aleman f,k Richard Bruggeman c,d Lisette van der Meer a,c GROUP-investigators l a Lentis Psychiatric Institute, Department of Rehabilitation, Zuidlaren, The Netherlands b School for Mental Health and Neuroscience, Faculty of Health, Medicine and Life Sciences, Maastricht University Medical Centre, Maastricht, The Netherlands c University of Groningen, University Medical Center Groningen, University Center for Psychiatry, Groningen, The Netherlands d University of Groningen, University Medical Center Groningen, Groningen, Rob Giel Research center, The Netherlands e Department of Statistics, Shahjalal University of Science and Technology, Sylhet 3114, Bangladesh f University of Groningen, University Medical Center Groningen, Department of Neuroscience, Groningen, The Netherlands g Lentis Psychiatric Institute, Research Department, Groningen, The Netherlands h Department of Mathematics and Computer Science, Eindhoven University of Technology, Eindhoven, The Netherlands i Parnassia Group, Dijk en Duin Mental Health Center, Castricum, The Netherlands j Department of TRANZO, Tilburg School of Social and Behavioral Sciences, Tilburg University, The Netherlands k University of Groningen, Department of Clinical Psychology and Experimental Psychopathology, Groningen, The Netherlands l GROUP investigators are: Behrooz Z. Alizadeh m , Agna A. Bartels-Velthuis n , Nico J. van Beveren o,p,q , Richard Bruggeman n , Wiepke Cahn r , Lieuwe de Haan s , Philippe Delespaul t , Carin J. Meijer s , Inez Myin-Germeys u , Rene S. Kahn r , Frederike Schirmbeck s , Claudia J.P. Simons t,v , Neeltje E.M. van Haren r,w , Jim van Os t,x , Ruud van Winkel u m University of Groningen, University Medical Center Groningen, Department of Epidemiology, Groningen, The Netherlands n University of Groningen, University Medical Center Groningen, University Center for Psychiatry, Rob Giel Research center, Groningen, The Netherlands o Antes Center for Mental Health Care, Rotterdam, The Netherlands p Erasmus MC, Department of Psychiatry, Rotterdam, The Netherlands q Erasmus MC, Department of Neuroscience, Rotterdam, The Netherlands r University Medical Center Utrecht, Department of Psychiatry, Brain Centre Rudolf agnus, Utrecht, The Netherlands s Academic Medical Center, University of Amsterdam, Department of Psychiatry, Amsterdam, The Netherlands t Maastricht University Medical Center, Department of Psychiatry and Neuropsychology, School for Mental Health and Neuroscience, Maastricht, The Netherlands u KU Leuven, Department of Neuroscience, Research Group Psychiatry, Center for Clinical Psychiatry, Leuven, Belgium v GGzE, Institute for Mental Health Care Eindhoven and De Kempen, Eindhoven, The Netherlands w Department of Child and Adolescent Psychiatry/Psychology, Erasmus Medical Centre, Rotterdam, Netherlands x King's College London, King's Health Partners, Department of Psychosis Studies, Institute of Psychiatry, London, United Kingdom The authors apologise for any inconvenience caused.

The authors regret that the GROUP-investigators were not acknowledged for their work due to a missing affiliation in the original publication. The new authorship line is: Annemarie P.M. Stiekema a,b Md Atiqul Islam c,d,e Edith J. Liemburg c,d,f Stynke Castelein c,g Edwin R. van den Heuvel h Jaap van Weeghel i,j André Aleman f,k Richard Bruggeman c,d Lisette van der Meer a,c GROUP-investigators l a Lentis Psychiatric Institute, Department of Rehabilitation, Zuidlaren, The Netherlands b School for Mental Health and Neuroscience, Faculty of Health, Medicine and Life Sciences, Maastricht University Medical Centre, Maastricht, The Netherlands c University of Groningen, University Medical Center Groningen, University Center for Psychiatry, Groningen, The Netherlands d University of Groningen, University Medical Center Groningen, Groningen, Rob Giel Research center, The Netherlands e Department of Statistics, Shahjalal University of Science and Technology, Sylhet 3114, Bangladesh f University of Groningen, University Medical Center Groningen, Department of Neuroscience, Groningen, The Netherlands g Lentis Psychiatric Institute, Research Department, Groningen, The Netherlands h Department of Mathematics and Computer Science, Eindhoven University of Technology, Eindhoven, The Netherlands i Parnassia Group, Dijk en Duin Mental Health Center, Castricum, The Netherlands j Department of TRANZO, Tilburg School of Social and Behavioral Sciences, Tilburg University, The Netherlands k University of Groningen, Department of Clinical Psychology and Experimental Psychopathology, Groningen, The Netherlands l GROUP investigators are: Behrooz Z. Alizadeh m , Agna A. Bartels-Velthuis n , Nico J. van Beveren o,p,q , Richard Bruggeman n , Wiepke Cahn r , Lieuwe de Haan s , Philippe Delespaul t , Carin J. Meijer s , Inez Myin-Germeys u , Rene S. Kahn r , Frederike Schirmbeck s , Claudia J.P. Simons t,v , Neeltje E.M. van Haren r,w , Jim van Os t,x , Ruud van Winkel u m University of Groningen, University Medical Center Groningen, Department of Epidemiology, Groningen, The Netherlands n University of Groningen, University Medical Center Groningen, University Center for Psychiatry, Rob Giel Research center, Groningen, The Netherlands o Antes Center for Mental Health Care, Rotterdam, The Netherlands p Erasmus MC, Department of Psychiatry, Rotterdam, The Netherlands q Erasmus MC, Department of Neuroscience, Rotterdam, The Netherlands r University Medical Center Utrecht, Department of Psychiatry, Brain Centre Rudolf agnus, Utrecht, The Netherlands s Academic Medical Center, University of Amsterdam, Department of Psychiatry, Amsterdam, The Netherlands t Maastricht University Medical Center, Department of Psychiatry and Neuropsychology, School for Mental Health and Neuroscience, Maastricht, The Netherlands u KU Leuven, Department of Neuroscience, Research Group Psychiatry, Center for Clinical Psychiatry, Leuven, Belgium v GGzE, Institute for Mental Health Care Eindhoven and De Kempen, Eindhoven, The Netherlands w Department of Child and Adolescent Psychiatry/Psychology, Erasmus Medical Centre, Rotterdam, Netherlands x King's College London, King's Health Partners, Department of Psychosis Studies, Institute of Psychiatry, London, United Kingdom The authors apologise for any inconvenience caused.

The objective of this study was to examine longitudinal functional connectivity of resting-state networks in patients with and without complaints after uncomplicated mild traumatic brain injury (mTBI). Second, we aimed to determine the value of network connectivity in predicting persistent complaints, anxiety, depression and long-term outcome. Thirty mTBI patients with three or more post-traumatic complaints at 2 weeks post-injury, 19 without complaints, and 20 matched healthy controls were selected for this study. Resting-state functional MRI (fMRI) was performed in patients at 1 month and 3 months post-injury, and once in healthy controls. Independent component analysis (ICA) was used to investigate the default mode, executive and salience networks. Persistent post-traumatic complaints, anxiety, and depression were measured at 3 months post-injury, and outcome was determined at 1 year post-injury. Within the group with complaints, higher functional connectivity between the anterior and posterior components of the default mode network at 1 month post-injury was associated with a greater number of complaints at 3 months post-injury (p=0.59, p=0.001). Minor longitudinal changes in functional connectivity were found for patients with and without complaints after mTBI, which were limited to connectivity within the precuneus component of the default mode network. No significant results were found for the executive and salience networks. Current results suggest that the default mode network may serve as a biomarker of persistent complaints in patients with uncomplicated mTBI.

OBJECTIVES: To assess the role of brain networks in emotion regulation and post‐traumatic complaints in the sub‐acute phase after non‐complicated mild traumatic brain injury (mTBI). EXPERIMENTAL DESIGN: Fifty‐four patients with mTBI (34 with and 20 without complaints) and 20 healthy controls (group‐matched for age, sex, education, and handedness) were included. Resting‐state fMRI was performed at four weeks post‐injury. Static and dynamic functional connectivity were studied within and between the default mode, executive (frontoparietal and bilateral frontal network), and salience network. The hospital anxiety and depression scale (HADS) was used to measure anxiety (HADS‐A) and depression (HADS‐D). PRINCIPAL OBSERVATIONS: Regarding within‐network functional connectivity, none of the selected brain networks were different between groups. Regarding between‐network interactions, patients with complaints exhibited lower functional connectivity between the bilateral frontal and salience network compared to patients without complaints. In the total patient group, higher HADS‐D scores were related to lower functional connectivity between the bilateral frontal network and both the right frontoparietal and salience network, and to higher connectivity between the right frontoparietal and salience network. Furthermore, whereas higher HADS‐D scores were associated with lower connectivity within the parietal midline areas of the bilateral frontal network, higher HADS‐A scores were related to lower connectivity within medial prefrontal areas of the bilateral frontal network. CONCLUSIONS: Functional interactions of the executive and salience networks were related to emotion regulation and complaints after mTBI, with a key role for the bilateral frontal network. These findings may have implications for future studies on the effect of psychological interventions. Hum Brain Mapp 37:1645‐1654, 2016. © 2016 Wiley Periodicals, Inc.

Biological psychiatry 87(3), 282-293 (2020). doi:10.1016/j.biopsych.2019.08.031 special issue: "Heterogeneity in Schizophrenia: Mechanisms and Symptoms", Published by Elsevier Science, Amsterdam [u.a.]