Metabolic syndrome after childhood trauma: a 9-year longitudinal analysis.

BACKGROUND: Childhood trauma (CT) has been cross-sectionally associated with metabolic syndrome (MetS), a group of biological risk factors for cardiometabolic disease. Longitudinal studies, while rare, would clarify the development of cardiometabolic dysregulations over time. Therefore, we longitudinally investigated the association of CT with the 9-year course of MetS components. METHODS: Participants (N = 2958) from the Netherlands Study of Depression and Anxiety were assessed four times across 9 years. The CT interview retrospectively assessed childhood emotional neglect and physical, emotional, and sexual abuse. Metabolic outcomes encompassed continuous MetS components (waist circumference, triglycerides, high-density lipoprotein [HDL] cholesterol, blood pressure [BP], and glucose) and count of clinically elevated MetS components. Mixed-effects models estimated sociodemographic- and lifestyle-adjusted longitudinal associations of CT with metabolic outcomes over time. Time interactions evaluated change in these associations. RESULTS: CT was reported by 49% of participants. CT was consistently associated with increased waist (b = 0.32, s.e. = 0.10, p = 0.001), glucose (b = 0.02, s.e. = 0.01, p < 0.001), and count of MetS components (b = 0.04, s.e. = 0.01, p < 0.001); and decreased HDL cholesterol (b = -0.01, s.e.<0.01, p = .020) and systolic BP (b = -0.33, s.e. = 0.13, p = 0.010). These associations were mainly driven by severe CT and unaffected by lifestyle. Only systolic BP showed a CT-by-time interaction, where CT was associated with lower systolic BP initially and with higher systolic BP at the last follow-up. CONCLUSIONS: Over time, adults with CT have overall persistent poorer metabolic outcomes than their non-maltreated peers. Individuals with CT have an increased risk for cardiometabolic disease and may benefit from monitoring and early interventions targeting metabolism.

Association of CYP2D6 and CYP2C19 metabolizer status with switching and discontinuing antidepressant drugs: an exploratory study.

BACKGROUND: Tailoring antidepressant drugs (AD) to patients' genetic drug-metabolism profile is promising. However, literature regarding associations of ADs' treatment effect and/or side effects with drug metabolizing genes CYP2D6 and CYP2C19 has yielded inconsistent results. Therefore, our aim was to longitudinally investigate associations between CYP2D6 (poor, intermediate, and normal) and CYP2C19 (poor, intermediate, normal, and ultrarapid) metabolizer-status, and switching/discontinuing of ADs. Next, we investigated whether the number of perceived side effects differed between metabolizer statuses. METHODS: Data came from the multi-site naturalistic longitudinal cohort Netherlands Study of Depression and Anxiety (NESDA). We selected depression- and/or anxiety patients, who used AD at some point in the course of the 9 years follow-up period (n = 928). Medication use was followed to assess patterns of AD switching/discontinuation over time. CYP2D6 and CYP2C19 alleles were derived using genome-wide data of the NESDA samples and haplotype data from the PharmGKB database. Logistic regression analyses were conducted to investigate the association of metabolizer status with switching/discontinuing ADs. Mann-Whitney U-tests were conducted to compare the number of patient-perceived side effects between metabolizer statuses. RESULTS: No significant associations were observed of CYP metabolizer status with switching/discontinuing ADs, nor with the number of perceived side effects. CONCLUSIONS: We found no evidence for associations between CYP metabolizer statuses and switching/discontinuing AD, nor with side effects of ADs, suggesting that metabolizer status only plays a limited role in switching/discontinuing ADs. Additional studies with larger numbers of PM and UM patients are needed to further determine the potential added value of pharmacogenetics to guide pharmacotherapy.

Shaping tomorrow's support: baseline clinical characteristics predict later social functioning and quality of life in schizophrenia spectrum disorder.

PURPOSE: We aimed to explore the multidimensional nature of social inclusion (mSI) among patients diagnosed with schizophrenia spectrum disorder (SSD), and to identify the predictors of 3-year mSI and the mSI prediction using traditional and data-driven approaches. METHODS: We used the baseline and 3-year follow-up data of 1119 patients from the Genetic Risk and Outcome in Psychosis (GROUP) cohort in the Netherlands. The outcome mSI was defined as clusters derived from combined analyses of thirteen subscales from the Social Functioning Scale and the brief version of World Health Organization Quality of Life questionnaires through K-means clustering. Prediction models were built through multinomial logistic regression (ModelMLR) and random forest (ModelRF), internally validated via bootstrapping and compared by accuracy and the discriminability of mSI subgroups. RESULTS: We identified five mSI subgroups: "very low (social functioning)/very low (quality of life)" (8.58%), "low/low" (12.87%), "high/low" (49.24%), "medium/high" (18.05%), and "high/high" (11.26%). The mSI was robustly predicted by a genetic predisposition for SSD, premorbid adjustment, positive, negative, and depressive symptoms, number of met needs, and baseline satisfaction with the environment and social life. The ModelRF (61.61% [54.90%, 68.01%]; P =0.013) was cautiously considered outperform the ModelMLR (59.16% [55.75%, 62.58%]; P =0.994). CONCLUSION: We introduced and distinguished meaningful subgroups of mSI, which were modestly predictable from baseline clinical characteristics. A possibility for early prediction of mSI at the clinical stage may unlock the potential for faster and more impactful social support that is specifically tailored to the unique characteristics of the mSI subgroup to which a given patient belongs.

The development and evaluation of a computerized decision aid for the treatment of psychotic disorders.

BACKGROUND: Routinely monitoring of symptoms and medical needs can improve the diagnostics and treatment of medical problems, including psychiatric. However, several studies show that few clinicians use Routine Outcome Monitoring (ROM) in their daily work. We describe the development and first evaluation of a ROM based computerized clinical decision aid, Treatment-E-Assist (TREAT) for the treatment of psychotic disorders. The goal is to generate personalized treatment recommendations, based on international guidelines combined with outcomes of mental and physical health acquired through ROM. We present a pilot study aimed to assess the feasibility of this computerized clinical decision aid in daily clinical practice by evaluating clinicians' experiences with the system. METHODS: Clinical decision algorithms were developed based on international schizophrenia treatment guidelines and the input of multidisciplinary expert panels from multiple psychiatric institutes. Yearly obtained diagnostic (ROM) information of patients was presented to treating clinicians combined with treatment suggestions generated by the algorithms of TREAT. In this pilot study 6 clinicians and 16 patients of Lentis Psychiatric Institute used the application. Clinicians were interviewed and asked to fill out self-report questionnaires evaluating their opinions about ROM and the effectiveness of TREAT. RESULTS: Six clinicians and 16 patients with psychotic disorders participated in the pilot study. The clinicians were psychiatrists, physicians and nurse-practitioners which all worked at least 8 years in mental health care of which at least 3 years treating patients with psychotic illnesses. All Clinicians found TREAT easy to use and would like to continue using the application. They reported that TREAT offered support in using diagnostic ROM information when drafting the treatment plans, by creating more awareness of current treatment options. CONCLUSION: This article presents a pilot study on the implementation of a computerized clinical decision aid linking routine outcome monitoring to clinical guidelines in order to generate personalized treatment advice. TREAT was found to be feasible for daily clinical practice and effective based on this first evaluation by clinicians. However, adjustments have to be made to the system and algorithms of the application. The ultimate goal is to provide appropriate evidence based care for patients with severe mental illnesses.

Brain network dysregulation, emotion, and complaints after mild traumatic brain injury.

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.

High persistence and low treatment rates of metabolic syndrome in patients with mood and anxiety disorders: A naturalistic follow-up study.

BACKGROUND: Patients with affective and anxiety disorders are at risk of metabolic syndrome (MetS) and, consequently, cardiovascular disease and premature death. In this study, the course and treatment of MetS was investigated using longitudinal data from a naturalistic sample of affective- and anxiety-disordered outpatients (Monitoring Outcome of psychiatric PHARmacotherapy [MOPHAR]). METHODS: Demographics, clinical characteristics, medication use, and MetS components were obtained for n = 2098 patients at baseline and, in a FU-subsample of n = 507 patients, after a median follow-up (FU) of 11 months. Furthermore, pharmacological treatment rates of MetS were investigated at baseline and FU. Finally, demographic and clinical determinants of change in MetS (component) scores were investigated. RESULTS: At baseline, 34.6 % of n = 2098 patients had MetS, 41.4 % of whom received treatment. Of patients with persisting MetS, 46.1 % received treatment for one (or more) MetS component(s) at baseline, and 56.6 % received treatment at FU. Treatment rates of solely elevated blood pressure and reduced HDL-cholesterol did significantly, but modestly, improve. Higher age, male sex, smoking behavior, low education, diabetes, and depressive versus anxiety disorder were predictors of worse outcome at FU on at least one MetS component. LIMITATIONS: We did not have data on lifestyle interventions as a form of treatment, which might partly have explained the observed low pharmacotherapeutic treatment rates. CONCLUSION: MetS (components) show high persistence rates in affective- and anxiety-disordered patients, and are, despite adequate monitoring, undertreated over time. This indicates that adherence and implementation of monitoring protocols should be crucially improved in psychiatric outpatients in secondary care.

The Progress and Pitfalls of Pharmacogenetics-Based Precision Medicine in Schizophrenia Spectrum Disorders: A Systematic Review and Meta-Analysis.

The inadequate efficacy and adverse effects of antipsychotics severely affect the recovery of patients with schizophrenia spectrum disorders (SSD). We report the evidence for associations between pharmacogenetic (PGx) variants and antipsychotics outcomes, including antipsychotic response, antipsychotic-induced weight/BMI gain, metabolic syndrome, antipsychotic-related prolactin levels, antipsychotic-induced tardive dyskinesia (TD), clozapine-induced agranulocytosis (CLA), and drug concentration level (pharmacokinetics) in SSD patients. Through an in-depth systematic search in 2010-2022, we identified 501 records. We included 29 meta-analyses constituting pooled data from 298 original studies over 69 PGx variants across 39 genes, 4 metabolizing phenotypes of CYP2D9, and 3 of CYP2C19. We observed weak unadjusted nominal significant (p < 0.05) additive effects of PGx variants of DRD1, DRD2, DRD3, HTR1A, HTR2A, HTR3A, and COMT (10 variants) on antipsychotic response; DRD2, HTR2C, BDNF, ADRA2A, ADRB3, GNB3, INSIG2, LEP, MC4R, and SNAP25 (14 variants) on weight gain; HTR2C (one variant) on metabolic syndrome; DRD2 (one variant) on prolactin levels; COMT and BDNF (two variants) on TD; HLA-DRB1 (one variant) on CLA; CYP2D6 (four phenotypes) and CYP2C19 (two phenotypes) on antipsychotics plasma levels. In the future, well-designed longitudinal naturalistic multi-center PGx studies are needed to validate the effectiveness of PGx variants in antipsychotic outcomes before establishing any reproducible PGx passport in clinical practice.

Association of clinical symptoms and cardiometabolic dysregulations in patients with schizophrenia spectrum disorders.

BACKGROUND: Patients with schizophrenia spectrum disorders (SSD) have a shortened life expectancy related to cardiovascular diseases. We investigated the association of cognitive, positive, and negative symptoms with cardiometabolic dysregulations in SSD patients. METHODS: Overall, 1,119 patients from the Genetic Risk and Outcome in Psychosis (GROUP) study were included. Cognitive function, positive and negative symptoms were assessed at baseline, 3-year, and 6-year. Cardiometabolic biomarkers were measured at 3-year follow-up. We used linear and multinomial logistic regression models to test the association between cardiometabolic biomarkers and clinical trajectories and performed mediation analyzes, while adjusting for clinical and demographic confounders. RESULTS: Cognitive performance was inversely associated with increased body mass index (mean difference [ß], ßhigh = -1.24, 95% CI = -2.28 to 0.20, P = 0.02) and systolic blood pressure (ßmild = 2.74, 95% CI = 0.11 to 5.37, P = 0.04). The severity of positive symptoms was associated with increased glycated hemoglobin (HbA1c) levels (ßlow = -2.01, 95% CI = -3.21 to -0.82, P = 0.001). Increased diastolic blood pressure (ORhigh-decreased = 1.04, 95% CI = 1.01 to 1.08, P = 0.02; ORhigh-increased = 1.04, 95% CI = 1.00 to 1.08, P = 0.048) and decreased high-density lipoprotein (OR high-increased = 6.25, 95% CI = 1.81 to 21.59, P = 0.004) were associated with more severe negative symptoms. Increased HbA1c (ORmoderate = 1.05, 95% CI = 1.01 to 1.10, P = 0.024; ORhigh = 1.08, 95% CI = 1.02 to 1.14, P = 0.006) was associated with more severe positive symptoms. These associations were not mediated by antipsychotics. CONCLUSIONS: We showed an association between cardiometabolic dysregulations and clinical and cognitive symptoms in SSD patients. The observed associations underscore the need for early identification of patients at risk of cardiometabolic outcomes.

Deep Clinical Phenotyping of Schizophrenia Spectrum Disorders Using Data-Driven Methods: Marching towards Precision Psychiatry.

Heterogeneity is the main challenge in the traditional classification of mental disorders, including schizophrenia spectrum disorders (SSD). This can be partly attributed to the absence of objective diagnostic criteria and the multidimensional nature of symptoms and their associated factors. This article provides an overview of findings from the Genetic Risk and Outcome of Psychosis (GROUP) cohort study on the deep clinical phenotyping of schizophrenia spectrum disorders targeting positive and negative symptoms, cognitive impairments and psychosocial functioning. Three to four latent subtypes of positive and negative symptoms were identified in patients, siblings and controls, whereas four to six latent cognitive subtypes were identified. Five latent subtypes of psychosocial function-multidimensional social inclusion and premorbid adjustment-were also identified in patients. We discovered that the identified subtypes had mixed profiles and exhibited stable, deteriorating, relapsing and ameliorating longitudinal courses over time. Baseline positive and negative symptoms, premorbid adjustment, psychotic-like experiences, health-related quality of life and PRSSCZ were found to be the strong predictors of the identified subtypes. Our findings are comprehensive, novel and of clinical interest for precisely identifying high-risk population groups, patients with good or poor disease prognosis and the selection of optimal intervention, ultimately fostering precision psychiatry by tackling diagnostic and treatment selection challenges pertaining to heterogeneity.

Pharmacogenetics of Long-Term Outcomes of Schizophrenia Spectrum Disorders: The Functional Role of CYP2D6 and CYP2C19.

Schizophrenia spectrum disorders (SSD) are complex mental disorders, and while treatment with antipsychotics is important, many patients do not respond or develop serious side effects. Genetic variation has been shown to play a considerable role in determining an individual's response to antipsychotic medication. However, previous pharmacogenetic (PGx) studies have been limited by small sample sizes, lack of consensus regarding relevant genetic variants, and cross-sectional designs. The current study aimed to investigate the association between PGx variants and long-term clinical outcomes in 691 patients of European ancestry with SSD. Using evidence from the literature on candidate genes involved in antipsychotic pharmacodynamics, we created a polygenic risk score (PRS) to investigate its association with clinical outcomes. We also created PRS using core variants of psychotropic drug metabolism enzymes CYP2D6 and CYP2C19. Furthermore, the CYP2D6 and CYP2C19 functional activity scores were calculated to determine the relationship between metabolism and clinical outcomes. We found no association for PGx PRSs and clinical outcomes; however, an association was found with CYP2D6 activity scores by the traditional method. Higher CYP2D6 metabolism was associated with high positive and high cognitive impairment groups relative to low symptom severity groups. These findings highlight the need to test PGx efficacy with different symptom domains. More evidence is needed before pharmacogenetic variation can contribute to personalized treatment plans.

Understanding Lifelong Factors and Prediction Models of Social Functioning After Psychosis Onset Using the Large-Scale GROUP Cohort Study.

BACKGROUND AND HYPOTHESIS: Current rates of poor social functioning (SF) in people with psychosis history reach 80% worldwide. We aimed to identify a core set of lifelong predictors and build prediction models of SF after psychosis onset. STUDY DESIGN: We utilized data of 1119 patients from the Genetic Risk and Outcome in Psychosis (GROUP) longitudinal Dutch cohort. First, we applied group-based trajectory modeling to identify premorbid adjustment trajectories. We further investigated the association between the premorbid adjustment trajectories, six-year-long cognitive deficits, positive, and negative symptoms trajectories, and SF at 3-year and 6-year follow-ups. Next, we checked associations between demographics, clinical, and environmental factors measured at the baseline and SF at follow-up. Finally, we built and internally validated 2 predictive models of SF. STUDY RESULTS: We found all trajectories were significantly associated with SF (P < .01), explaining up to 16% of SF variation (R2 0.15 for 3- and 0.16 for 6-year follow-up). Demographics (sex, ethnicity, age, education), clinical parameters (genetic predisposition, illness duration, psychotic episodes, cannabis use), and environment (childhood trauma, number of moves, marriage, employment, urbanicity, unmet needs of social support) were also significantly associated with SF. After validation, final prediction models explained a variance up to 27% (95% CI: 0.23, 0.30) at 3-year and 26% (95% CI: 0.22, 0.31) at 6-year follow-up. CONCLUSIONS: We found a core set of lifelong predictors of SF. Yet, the performance of our prediction models was moderate.

The impact of frontal lesions after mild to moderate traumatic brain injury on frontal network measures.

To investigate the impact of frontal macro-structural lesions on intrinsic network measures, we examined brain network function during resting-state fMRI in patients with frontal lesions in the subacute phase after mild to moderate traumatic brain injury. Additionally, network function was related to neuropsychological performances. 17 patients with frontal lesions, identified on admission CT after mild to moderate trauma, were compared to 30 traumatic brain injury patients without frontal lesions and 20 healthy controls. Three months post-injury, we acquired fMRI scans and neuropsychological assessments (measuring frontal executive functions and information processing speed). Using independent component analysis, the activity of and connectivity between network components (largely located in the prefrontal cortex) and relations with neuropsychological measures were examined and compared across groups. The analysis yielded five predominantly frontal components: anterior and posterior part of the default mode network, left and right frontoparietal network and salience network. No significant differences concerning fMRI measures were found across groups. However, the frontal lesions group performed significantly worse on neuropsychological tests than the other two groups. Additionally, the frontal lesions group showed a significant positive association of stronger default mode network-salience network connectivity with better executive performances. Our findings suggest that, on fMRI level, frontal network measures are not largely affected by frontal lesions following a mild to moderate traumatic brain injury. Yet, patients with damage to the frontal structures did show poorer executive abilities which might to some degree be related to altered frontal network connectivity between the default mode network and salience network.

Six-year trajectories and associated factors of positive and negative symptoms in schizophrenia patients, siblings, and controls: Genetic Risk and Outcome of Psychosis (GROUP) study.

Positive and negative symptoms are prominent but heterogeneous characteristics of schizophrenia spectrum disorder (SSD). Within the framework of the Genetic Risk and Outcome of Psychosis (GROUP) longitudinal cohort study, we aimed to distinguish and identify the genetic and non-genetics predictors of homogenous subgroups of the long-term course of positive and negative symptoms in SSD patients (n = 1119) and their unaffected siblings (n = 1059) in comparison to controls (n = 586). Data were collected at baseline, and after 3- and 6-year follow-ups. Group-based trajectory modeling was applied to identify latent subgroups using positive and negative symptoms or schizotypy scores. A multinomial random-effects logistic regression model was used to identify predictors of latent subgroups. Patients had decreasing, increasing, and relapsing symptoms course. Unaffected siblings and healthy controls had three to four subgroups characterized by stable, decreasing, or increasing schizotypy. PRSSCZ did not predict the latent subgroups. Baseline symptoms severity in patients, premorbid adjustment, depressive symptoms, and quality of life in siblings predicted long-term trajectories while were nonsignificant in controls. In conclusion, up to four homogenous latent subgroups of symptom course can be distinguished within patients, siblings, and controls, while non-genetic factors are the main factors associated with the latent subgroups.

Understanding the relationship between apathy, cognition and functional outcome in schizophrenia: The significance of an ecological assessment.

In recent years there has been an increasing interest in understanding the role apathy plays in mediating the relationship between cognitive impairment and functional outcome. In general, most studies measure cognition with traditional cognitive tests that give explicit instructions and guide the participants toward generating a response. However, given that apathy is defined by a decrease in self-initiated behavior, it is crucial to evaluate cognition with ecological tasks that do not explicitly direct the patient´s motivation to generate behaviors to assess the actual effect. This study investigated whether an ecological cognitive assessment (the Jansari Executive Function Assessment, JEF©) would uniquely contribute to the relationship between cognition, apathy, and functional outcome in schizophrenia. The Apathy Evaluation Scale (AES), neuropsychological tests and the JEF© were administered to 20 patients with schizophrenia. Hierarchical multiple regression and mediation analysis were performed to test the associations between the variables of interest. Results showed that JEF© explained a significant portion of the variance in AES (25%). In addition, apathy explained 36% of the variance in functional outcome. However, AES did not mediate between cognition and functional outcome. Our results highlight the importance of assessing cognition with tasks that require integration of cognitive functions needed for real life demands.

Interrelationships between depressive symptoms and positive and negative symptoms of recent onset schizophrenia spectrum disorders: A network analytical approach.

OBJECTIVE: There is a need to better understand the interrelationships between positive and negative symptoms of recent-onset schizophrenia spectrum disorders (SSD) and co-occurring depressive symptoms. Aims were to determine: (1) whether depressive symptoms are best conceptualised as distinct from, or intrinsic to, positive and negative symptoms; and (2) bridging symptoms. METHODS: Network analysis was applied to data from 198 individuals with depressive and psychotic symptoms in SSD from the Psychosis Recent Onset GRoningen Survey (PROGR-S). Measures were: Montgomery-Åsberg Depression Rating Scale and Positive and Negative Syndrome Scale. RESULTS: Positive symptoms were just as likely to be associated with depressive and negative symptoms, and had more strong associations with depressive than negative symptoms. Negative symptoms were more likely to be associated with depressive than positive symptoms, and had more strong associations with depressive than positive symptoms. Suspiciousness and stereotyped thinking bridged between positive and depressive symptoms, and apparent sadness and lassitude between negative and depressive symptoms. CONCLUSIONS: Depressive symptoms might be best conceptualised as intrinsic to positive and negative symptoms pertaining to deficits in motivation and interest in the psychotic phase of SSD. Treatments targeting bridges between depressive and positive symptoms, and depressive and such negative symptoms, might prevent or improve co-occurring depressive symptoms, or vice-versa, in the psychotic phase of SSD.

[Anxiety and mood disorders are independent risk factors for cardiovascular diseases]. / Stemmings- en angststoornissen als risicofactor voor cardiovasculaire ziekten.

OBJECTIVE: Psychiatric conditions are insufficiently highlighted as cardiovascular risk factors in the CVRM guideline. Objectives of this review are 1) to determine if anxiety and mood symptoms/disorders are independent cardiovascular risk factors; 2) to compare this risk to a population without these psychiatric conditions and 3) to ascertain the influence of psychiatric disease severity. DESIGN: Narrative systematic review METHOD: We searched for meta-analyses and systematic reviews in PubMed. Quality assessment by AMSTAR criteria. RESULTS: 10 reviews were included from 172 hits. (Sub)clinical depression and mood disorders are associated with an increased independent risk to develop cardiovascular diseases, coronary artery disease, myocardial infarction and cerebrovascular disease. Bipolar disorders increase the cerebrovascular risk, but not myocardial infarction. Anxiety disorders/symptoms heighten the cardiovascular, myocardial and cerebrovascular risk. CONCLUSION: Anxiety and mood symptoms/disorders are independent cardiovascular risk factors. Severe anxiety and mood disorders should be included as separate risk factors in the CVRM guideline.

Expressive deficits and amotivation as mediators of the associations between cognitive problems and functional outcomes: Results from two independent cohorts.

Many individuals with severe mental disorders have difficulties in vocational and social functioning, which are regarded the most important outcomes, together with clinical symptoms. To understand the underlying mechanisms, research is increasingly focused on factors influencing functional outcomes. One established association has been shown between cognition and community functioning with negative symptoms as a possible mediator. Although it has been shown that negative symptoms consist of two subdomains, thus far negative symptoms have been assessed as one unitary construct. This study considers for the first time subdomains of negative symptoms as putative mediators (expressive deficits, amotivation) of the association between cognition (neuro- and social cognition) and functional outcome (living situation, occupation, social functioning). We expected that specific subdomains of negative symptoms (e.g. amotivation) would mediate the effect of cognition on specific functional outcomes (e.g. social functioning) independently from illness duration. To assess this, we included two independent cohorts, consisting of participants with different illness duration. These two independent cohorts consisted of patients with a recent-onset psychotic disorder: PROGR-S (first time treated; N = 1129) and GROUP (illness duration preferably <5 years; N = 1200). Using linear regression, mediation analyses were performed with two cognition domains (neurocognition and social cognition) as predictors, negative symptoms (Expressive deficits and Amotivation as indexed with items from the Positive and Negative Syndrome Scale) as mediators and three measures of functional outcomes (living situation, occupation and social functioning) as outcome measures. The analyses were repeated with the same outcome measures three years later. Three main results were obtained. I) Both in the cross-sectional and longitudinal analyses, the associations of neurocognition (both cohorts) and social cognition (GROUP) with social functioning were mediated by amotivation. II) The association between cognition and living situation was mediated by Expressive deficits in one cohort (GROUP) but not in the cohort assessing first-episode psychosis (PROGR-S). III) The association between cognition and occupation was mediated by Amotivation in PROGR-S and by Expressive deficits in GROUP. CONCLUSION: The current results show a less robust mediating role for specific negative symptom domains regarding the associations of cognition with occupation and living situation that may depend on the duration of psychotic illness. However, Amotivation, mediates the association between cognition and social functioning, which holds true for patients experiencing a first-onset and patients with a longer illness duration alike. The results may have implications for the development of therapeutic approaches focusing on amotivation to improve social functioning. GENERAL SCIENTIFIC SUMMARY: This study stresses the importance of distinguishing subdomains of negative symptoms, cognition and functioning. Our results show that specific negative symptom dimensions mediate the effects of cognition on specific functional outcomes.

Polygenic risk score for schizophrenia was not associated with glycemic level (HbA1c) in patients with non-affective psychosis: Genetic Risk and Outcome of Psychosis (GROUP) cohort study.

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 (aß) = 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 (aß = 0.93, SE = 0.32, p-value = .004), body mass index (aß = 0.20, SE = 0.08, p-value = .01), diastolic blood pressure (aß = 0.08, SE = 0.04, p-value = .03), late age of first psychosis onset (aß = 0.19, SE = 0.05, p-value = .0004) and male gender (aß = 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.

A systematic review and narrative synthesis of data-driven studies in schizophrenia symptoms and cognitive deficits.

To tackle the phenotypic heterogeneity of schizophrenia, data-driven methods are often applied to identify subtypes of its symptoms and cognitive deficits. However, a systematic review on this topic is lacking. The objective of this review was to summarize the evidence obtained from longitudinal and cross-sectional data-driven studies in positive and negative symptoms and cognitive deficits in patients with schizophrenia spectrum disorders, their unaffected siblings and healthy controls or individuals from general population. Additionally, we aimed to highlight methodological gaps across studies and point out future directions to optimize the translatability of evidence from data-driven studies. A systematic review was performed through searching PsycINFO, PubMed, PsycTESTS, PsycARTICLES, SCOPUS, EMBASE and Web of Science electronic databases. Both longitudinal and cross-sectional studies published from 2008 to 2019, which reported at least two statistically derived clusters or trajectories were included. Two reviewers independently screened and extracted the data. In this review, 53 studies (19 longitudinal and 34 cross-sectional) that conducted among 17,822 patients, 8729 unaffected siblings and 5520 controls or general population were included. Most longitudinal studies found four trajectories that characterized by stability, progressive deterioration, relapsing and progressive amelioration of symptoms and cognitive function. Cross-sectional studies commonly identified three clusters with low, intermediate (mixed) and high psychotic symptoms and cognitive profiles. Moreover, identified subgroups were predicted by numerous genetic, sociodemographic and clinical factors. Our findings indicate that schizophrenia symptoms and cognitive deficits are heterogeneous, although methodological limitations across studies are observed. Identified clusters and trajectories along with their predictors may be used to base the implementation of personalized treatment and develop a risk prediction model for high-risk individuals with prodromal symptoms.

Association of schizophrenia polygenic risk score with data-driven cognitive subtypes: A six-year longitudinal study in patients, siblings and controls.

Cross-sectional studies have shown that the polygenic risk score for schizophrenia (PRSSCZ) may influence heterogeneity in cognitive performance although evidence from family-based longitudinal study is limited. This study aimed to identify trajectories of cognitive function and assess whether the PRSSCZ is associated with baseline cognitive performance and predicted six-year trajectories. We included 1119 patients with a schizophrenia spectrum disorder, and 1059 unaffected siblings and 586 unrelated controls who are eligible at baseline. Genotype data were collected at baseline, whereas clinical and sociodemographic data were collected at baseline, three and six years. Group-based trajectory modeling was applied on a weighted standardized composite score of general cognition to unravel cognitive subtypes and explore trajectories over time. We followed a standard procedure to calculate the polygenic risk score. A random-effects ordinal regression model was used to investigate the association between PRSSCZ and cognitive subtypes. Five cognitive subtypes with variable trajectories were found in patients, four in siblings and controls, and six in all combined samples. PRSSCZ significantly predicted poor cognitive trajectories in patients, siblings and all samples. After Bonferroni correction and adjustment for non-genetic factors, only the results in all combined sample remained significant. Cognitive impairment in schizophrenia is heterogeneous and may be linked with high PRSSCZ. Our finding confirmed at least in all combined samples the presence of genetic overlap between schizophrenia and cognitive function and can give insight into the mechanisms of cognitive deficits.