Search

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. [ABSTRACT FROM AUTHOR]

Machine learning can be used to define subtypes of psychiatric conditions based on shared biological foundations of mental disorders. Here we analyzed cross-sectional brain images from 4,222 individuals with schizophrenia and 7038 healthy subjects pooled across 41 international cohorts from the ENIGMA, non-ENIGMA cohorts and public datasets. Using the Subtype and Stage Inference (SuStaIn) algorithm, we identify two distinct neurostructural subgroups by mapping the spatial and temporal 'trajectory' of gray matter change in schizophrenia. Subgroup 1 was characterized by an early cortical-predominant loss with enlarged striatum, whereas subgroup 2 displayed an early subcortical-predominant loss in the hippocampus, striatum and other subcortical regions. We confirmed the reproducibility of the two neurostructural subtypes across various sample sites, including Europe, North America and East Asia. This imaging-based taxonomy holds the potential to identify individuals with shared neurobiological attributes, thereby suggesting the viability of redefining existing disorder constructs based on biological factors., (© 2024. The Author(s).)

Dementia with Lewy Bodies (DLB) is a common neurodegenerative disorder with poor prognosis and mainly unknown pathophysiology. Heritability estimates exceed 30% but few genetic risk variants have been identified. Here we investigated common genetic variants associated with DLB in a large European multisite sample. We performed a genome wide association study in Norwegian and European cohorts of 720 DLB cases and 6490 controls and included 19 top-associated single-nucleotide polymorphisms in an additional cohort of 108 DLB cases and 75545 controls from Iceland. Overall the study included 828 DLB cases and 82035 controls. Variants in the ASH1L/GBA (Chr1q22) and APOE ε4 (Chr19) loci were associated with DLB surpassing the genome-wide significance threshold (p < 5 × 10 -8 ). One additional genetic locus previously linked to psychosis in Alzheimer's disease, ZFPM1 (Chr16q24.2), showed suggestive association with DLB at p-value < 1 × 10 -6 . We report two susceptibility loci for DLB at genome-wide significance, providing insight into etiological factors. These findings highlight the complex relationship between the genetic architecture of DLB and other neurodegenerative disorders.

Despite great interest in using magnetic resonance imaging (MRI) for studying the effects of genes on brain structure in humans, current approaches have focused almost entirely on predefined regions of interest and had limited success. Here, we used multivariate methods to define a single neuroanatomical score of how William's Syndrome (WS) brains deviate structurally from controls. The score is trained and validated on measures of T1 structural brain imaging in two WS cohorts (training, n = 38; validating, n = 60). We then associated this score with single nucleotide polymorphisms (SNPs) in the WS hemi-deleted region in five cohorts of neurologically and psychiatrically typical individuals (healthy European descendants, n = 1863). Among 110 SNPs within the 7q11.23 WS chromosomal region, we found one associated locus (p = 5e-5) located at GTF2IRD1, which has been implicated in animal models of WS. Furthermore, the genetic signals of neuroanatomical scores are highly enriched locally in the 7q11.23 compared with summary statistics based on regions of interest, such as hippocampal volumes (n = 12,596), and also globally (SNP-heritability = 0.82, se = 0.25, p = 5e-4). The role of genetic variability in GTF2IRD1 during neurodevelopment extends to healthy subjects. Our approach of learning MRI-derived phenotypes from clinical populations with well-established brain abnormalities characterized by known genetic lesions may be a powerful alternative to traditional region of interest-based studies for identifying genetic variants regulating typical brain development.

Aim: Lack of insight into illness is frequent in psychotic disorders and seen as part of their primary pathology. The recognition of symptoms as psychotic, and beliefs about treatment alternatives, is also influenced by socio-cultural factors. Here we examined clinical insight into illness and beliefs about psychosis in immigrants in their first episode of psychosis compared with a reference group., Methods: A total of 277 first-episode psychosis participants were recruited to this cross-sectional study; 40 first- and 40 second-generation immigrants from Europe, Americas and Oceania (n = 37), Asia including Turkey (n = 28) or Africa (n = 15). The Birchwood Insight Scale was used to measure clinical insight and 'The Attitudes and Beliefs about Mental Health Problems' schizophrenia version to assess socio-cultural beliefs., Results: Immigrants did not differ from the reference sample in clinical insight. After controlling for education level, first-generation immigrants were less likely to recognize psychotic symptoms (odds ratio (OR) 2.9; Wald = 8.977, degrees of freedom (d.f.) 1, P = 0.003) and viewed hospitalization (OR 5.2; Wald = 20.388, d.f. 1, P = 0.001) and treatment by a psychiatrist (OR 4.9; Wald = 6.609, d.f. 1, P = 0.01)) as less beneficial than the reference group. Immigrants from Asia held more alternative explanations (OR 0.3; Wald = 6.567, d.f. 1, P = 0.010). There were significantly stronger associations between clinical insight and socio-cultural beliefs in the reference group., Conclusions: Socio-cultural beliefs about psychosis in immigrants in first-episode psychosis call for more tailored information to this group, and emphasize the importance of treatment interventions involving both a cultural and personal perspective of insight., (© 2015 Wiley Publishing Asia Pty Ltd.)

Across-nation differences in the mean values for complex traits are common, but the reasons for these differences are unknown. Here we find that many independent loci contribute to population genetic differences in height and body mass index (BMI) in 9,416 individuals across 14 European countries. Using discovery data on over 250,000 individuals and unbiased effect size estimates from 17,500 sibling pairs, we estimate that 24% (95% credible interval (CI) = 9%, 41%) and 8% (95% CI = 4%, 16%) of the captured additive genetic variance for height and BMI, respectively, reflect population genetic differences. Population genetic divergence differed significantly from that in a null model (height, P < 3.94 × 10(-8); BMI, P < 5.95 × 10(-4)), and we find an among-population genetic correlation for tall and slender individuals (r = -0.80, 95% CI = -0.95, -0.60), consistent with correlated selection for both phenotypes. Observed differences in height among populations reflected the predicted genetic means (r = 0.51; P < 0.001), but environmental differences across Europe masked genetic differentiation for BMI (P < 0.58).

Background: Common genetic polymorphisms at chromosome 3p21.1, including rs2251219 in polybromo 1 (PBRM1), have been implicated in susceptibility to bipolar affective disorder (BP) through genome-wide association studies. Subsequent studies have suggested that this is also a risk locus for other psychiatric phenotypes, including major depression and schizophrenia., Methods: To replicate the association, we studied 2562 cases with BP and 25,439 control subjects collected from seven cohorts with either genome-wide association or individual genotyping of rs2251219 and tagging single nucleotide polymorphisms across the PBRM1 gene. Results from the different case-control groups were combined with the inverse variance weighting method., Results: In our dataset, rs2251219 was associated with BP (odds ratio [OR] = .89, p = .003), and meta-analysis of previously published data with our nonoverlapping new data confirmed genome-wide significant association (OR = .875, p = 2.68 × 10(-9)). Genotypic data from the SGENE-plus consortium were used to examine the association of the same variant with schizophrenia in an overall sample of 8794 cases and 25,457 control subjects, but this was not statistically significant (OR = .97, p = .21)., Conclusions: There is strong evidence of association of rs2251219 with BP. However, our data do not support association of this marker with schizophrenia. Because the region of association has high linkage disequilibrium, forming a large haplotype block across many genes, it is not clear which gene is causally implicated in the disorder., (Copyright © 2012 Society of Biological Psychiatry. Published by Elsevier Inc. All rights reserved.)

Objective: An interaction between predisposing genes and environmental stressors is thought to underlie the neurodevelopmental disorder schizophrenia. In a targeted gene screening, we previously found that the minor allele of the single nucleotide polymorphism (SNP) rs6336 in the neurotrophic tyrosine kinase receptor 1 (NTRK1/TRKA) gene is associated with schizophrenia as a risk factor., Methods: We genotyped the TRKA SNP in a total of eight independent Caucasian schizophrenia case-control groups., Result: Remarkably, although in five of the groups a higher frequency of the risk allele was indeed found in the patients compared with the controls, in the three other groups the SNP acted as a protective factor., Conclusion: An intriguing possibility is that this dual character of the TRKA SNP is caused by its interaction with endophenotypic and/or epistatic factors.

We have performed a genome-wide association study (GWAS) of schizophrenia in a Norwegian discovery sample of 201 cases and 305 controls (TOP study) with a focused replication analysis in a larger European sample of 2663 cases and 13,780 control subjects (SGENE-plus study). Firstly, the discovery sample was genotyped with Affymetrix Genome-Wide Human SNP Array 6.0 and 572,888 markers were tested for schizophrenia association. No SNPs in the discovery sample attained genome-wide significance (P<8.7 x 10(-8)). Secondly, based on the GWAS data, we selected 1000 markers with the lowest P values in the discovery TOP sample, and tested these (or HapMap-based surrogates) for association in the replication sample. Sixteen loci were associated with schizophrenia (nominal P value<0.05 and concurring OR) in the replication sample. As a next step, we performed a combined analysis of the findings from these two studies, and the strongest evidence for association with schizophrenia was provided for markers rs7045881 on 9p21, rs433598 on 16p12 and rs10761482 on 10q21. The markers are located in PLAA, ACSM1 and ANK3, respectively. PLAA has not previously been described as a susceptibility gene, but 9p21 is implied as a schizophrenia linkage region. ACSM1 has been identified as a susceptibility gene in a previous schizophrenia GWAS study. The association of ANK3 with schizophrenia is intriguing in light of recent associations of ANK3 with bipolar disorder, thereby supporting the hypothesis of an overlap in genetic susceptibility between these psychopathological entities., (Copyright (c) 2010 Elsevier Ltd. All rights reserved.)

Background: Early onset of bipolar disorder (BD) is an important clinical predictor of a more severe course and poorer outcome. A higher proportion of childhood onset BD has been reported in studies from USA compared to Europe. We investigated age at onset of first affective episode in a Norwegian sample and compared it to previous European and US findings. In addition, we examined whether age at onset influenced on time to first treatment, and if patient characteristics related to illness severity influenced age at onset., Methods: Two hundred and twenty five BD patients were recruited consecutively mainly from psychiatric out-patient units at three major hospitals in Oslo, Norway, diagnosed using SCID-I and divided into four groups based on age at onset., Results: Six percent of the patients had onset in childhood, 32% in adolescence, 43% in young adulthood, and 19% as adults. Average age at onset was 22.8 years (SD 9.4). There was a significantly higher age at onset and a significantly shorter time from onset to first treatment in patients with lifetime hospitalization., Limitation: Retrospective information which could be confounded by collection bias., Conclusion: Age at onset in our sample resembled previous European studies, but not US- or Norwegian studies. The difference in age at onset seems more related to different definitions of onset, than to hospitalization history. This highlights the importance of improving the research criteria and of using similar criteria to ascertain age at onset.

Reduced fecundity, associated with severe mental disorders, places negative selection pressure on risk alleles and may explain, in part, why common variants have not been found that confer risk of disorders such as autism, schizophrenia and mental retardation. Thus, rare variants may account for a larger fraction of the overall genetic risk than previously assumed. In contrast to rare single nucleotide mutations, rare copy number variations (CNVs) can be detected using genome-wide single nucleotide polymorphism arrays. This has led to the identification of CNVs associated with mental retardation and autism. In a genome-wide search for CNVs associating with schizophrenia, we used a population-based sample to identify de novo CNVs by analysing 9,878 transmissions from parents to offspring. The 66 de novo CNVs identified were tested for association in a sample of 1,433 schizophrenia cases and 33,250 controls. Three deletions at 1q21.1, 15q11.2 and 15q13.3 showing nominal association with schizophrenia in the first sample (phase I) were followed up in a second sample of 3,285 cases and 7,951 controls (phase II). All three deletions significantly associate with schizophrenia and related psychoses in the combined sample. The identification of these rare, recurrent risk variants, having occurred independently in multiple founders and being subject to negative selection, is important in itself. CNV analysis may also point the way to the identification of additional and more prevalent risk variants in genes and pathways involved in schizophrenia.