Search

ImportancePredictors consistently associated with psychosis liability and course of illness in schizophrenia (SCZ) spectrum disorders (SSD), including the need for clozapine treatment, are lacking. Longitudinally ascertained medication use may empower studies examining associations between polygenic risk scores (PRSs) and pharmacotherapy choices.ObjectiveTo examine associations between PRS-SCZ loading and groups with different liabilities to SSD (individuals with SSD taking clozapine, individuals with SSD taking other antipsychotics, their parents and siblings, and unrelated healthy controls) and between PRS-SCZ and the likelihood of receiving a prescription of clozapine relative to other antipsychotics.Design, Setting, and ParticipantsThis genetic association study was a multicenter, observational cohort study with 6 years of follow-up. Included were individuals diagnosed with SSD who were taking clozapine or other antipsychotics, their parents and siblings, and unrelated healthy controls. Data were collected from 2004 until 2021 and analyzed between October 2021 and September 2022.ExposuresPolygenic risk scores for SCZ.Main Outcomes and MeasuresMultinomial logistic regression was used to examine possible differences between groups by computing risk ratios (RRs), ie, ratios of the probability of pertaining to a particular group divided by the probability of healthy control status. We also computed PRS-informed odd ratios (ORs) for clozapine use relative to other antipsychotics.ResultsPolygenic risk scores for SCZ were generated for 2344 participants (mean [SD] age, 36.95 years [14.38]; 994 female individuals [42.4%]) who remained after quality control screening (557 individuals with SSD taking clozapine, 350 individuals with SSD taking other antipsychotics during the 6-year follow-up, 542 parents and 574 siblings of individuals with SSD, and 321 unrelated healthy controls). All RRs were significantly different from 1; RRs were highest for individuals with SSD taking clozapine (RR, 3.24; 95% CI, 2.76-3.81; P = 2.47 × 10−46), followed by individuals with SSD taking other antipsychotics (RR, 2.30; 95% CI, 1.95-2.72; P = 3.77 × 10−22), parents (RR, 1.44; 95% CI, 1.25-1.68; P = 1.76 × 10−6), and siblings (RR, 1.40; 95% CI, 1.21-1.63; P = 8.22 × 10−6). Polygenic risk scores for SCZ were positively associated with clozapine vs other antipsychotic use (OR, 1.41; 95% CI, 1.22-1.63; P = 2.98 × 10−6), suggesting a higher likelihood of clozapine prescriptions among individuals with higher PRS-SCZ.Conclusions and RelevanceIn this study, PRS-SCZ loading differed between groups of individuals with SSD, their relatives, and unrelated healthy controls, with patients taking clozapine at the far end of PRS-SCZ loading. Additionally, PRS-SCZ was associated with a higher likelihood of clozapine prescribing. Our findings may inform early intervention and prognostic studies of the value of using PRS-SCZ to personalize antipsychotic treatment.

Additional file 2: Table S1. Amplicon sequences for technical replication of methylation profiling. Table S2. Chlorpromazine equivalent doses for the antipsychotic drugs (mg, mean±SD). Table S3. Top 20 risk-DMRs. Table S4. Top 20 RES-DMRs. Table S5. Enrichment analysis of gene ontology and biological pathways for ASM genes (Top 20 terms in each category). Table S6. Linkage disequilibrium analysis of rs11125746 from LINC01795. Table S7. Linkage disequilibrium analysis of rs12674515 from DDHD2. Table S8. Linkage disequilibrium analysis of rs28759130 from SBNO1. Table S9. Linkage disequilibrium analysis of rs498541 from KCNG2. Table S10. Linkage disequilibrium analysis of rs56370020 from RUFY1. Table S11. Linkage disequilibrium analysis of rs72728886 from SEMA7A. Table S12. Pearson correlation of methylation levels from proxy-model CpG sites between blood and brain tissues. Table S13. Evaluation of methylation proxy models. Table S14. Performance evaluation for RES-prediction models. Fig.S1. Statistical power of samples. Fig.S2. Technical replication of methylation profiling. Fig.S3. Correlations between PRSs. Fig.S4. Relationship between treatment response and PRSs. Fig.S5. Variable importance plot of PRSs. Fig.S6. Correlation of PMS to PANSS reduction rate. Fig.S7. Correlation of epigenetic clocks and PANSS reduction rate. Fig.S8. Heatmap for the distribution of CpG sites from meQTLs, risk-DMRs, RES-DMRs, and ASM genes. Fig.S9. Enrichment analysis of gene ontology and biological pathways for ASM genes. Fig.S10. Comparison between peripheral blood and brain tissues in transcription, methylation, and chromatin interaction of RUFY1. Fig.S11. Performance of the optimal proxyDNAm models.

Additional file 1. Supplementary methods.

Importance Approaches are needed to stratify individuals in early psychosis stages beyond positive symptom severity to investigate specificity related to affective and normative variation and to validate solutions with premorbid, longitudinal, and genetic risk measures. Objective To use machine learning techniques to cluster, compare, and combine subgroup solutions using clinical and brain structural imaging data from early psychosis and depression stages. Design, Setting, and Participants A multisite, naturalistic, longitudinal cohort study (10 sites in 5 European countries; including major follow-up intervals at 9 and 18 months) with a referred patient sample of those with clinical high risk for psychosis (CHR-P), recent-onset psychosis (ROP), recent-onset depression (ROD), and healthy controls were recruited between February 1, 2014, to July 1, 2019. Data were analyzed between January 2020 and January 2022. Main Outcomes and Measures A nonnegative matrix factorization technique separately decomposed clinical (287 variables) and parcellated brain structural volume (204 gray, white, and cerebrospinal fluid regions) data across CHR-P, ROP, ROD, and healthy controls study groups. Stability criteria determined cluster number using nested cross-validation. Validation targets were compared across subgroup solutions (premorbid, longitudinal, and schizophrenia polygenic risk scores). Multiclass supervised machine learning produced a transferable solution to the validation sample. Results There were a total of 749 individuals in the discovery group and 610 individuals in the validation group. Individuals included those with CHR-P (n = 287), ROP (n = 323), ROD (n = 285), and healthy controls (n = 464), The mean (SD) age was 25.1 (5.9) years, and 702 (51.7%) were female. A clinical 4-dimensional solution separated individuals based on positive symptoms, negative symptoms, depression, and functioning, demonstrating associations with all validation targets. Brain clustering revealed a subgroup with distributed brain volume reductions associated with negative symptoms, reduced performance IQ, and increased schizophrenia polygenic risk scores. Multilevel results distinguished between normative and illness-related brain differences. Subgroup results were largely validated in the external sample. Conclusions and Relevance The results of this longitudinal cohort study provide stratifications beyond the expression of positive symptoms that cut across illness stages and diagnoses. Clinical results suggest the importance of negative symptoms, depression, and functioning. Brain results suggest substantial overlap across illness stages and normative variation, which may highlight a vulnerability signature independent from specific presentations. Premorbid, longitudinal, and genetic risk validation suggested clinical importance of the subgroups to preventive treatments.

Background and Hypothesis: Around 30% of people with schizophrenia are refractory to antipsychotic treatment (treatment-resistant schizophrenia; TRS). While abnormal structural neuroimaging findings, in particular volume and thickness reductions, are often observed in schizophrenia, it is anticipated that biomarkers of neuronal injury like neurofilament light chain protein (NfL) can improve our understanding of the pathological basis underlying schizophrenia. The current study aimed to determine whether people with TRS demonstrate different associations between plasma NfL levels and regional cortical thickness reductions compared with controls., Study Design: Measurements of plasma NfL and cortical thickness were obtained from 39 individuals with TRS, and 43 healthy controls. T1-weighted magnetic resonance imaging sequences were obtained and processed via FreeSurfer. General linear mixed models adjusting for age and weight were estimated to determine whether the interaction between diagnostic group and plasma NfL level predicted lower cortical thickness across frontotemporal structures and the insula., Study Results: Significant (false discovery rate corrected) cortical thinning of the left ( p = 0.001, η 2 p = 0.104) and right ( p < 0.001, η 2 p = 0.167) insula was associated with higher levels of plasma NfL in TRS, but not in healthy controls., Conclusions: The association between regional thickness reduction of the insula bilaterally and plasma NfL may reflect a neurodegenerative process during the course of TRS. The findings of the present study suggest that some level of cortical degeneration localised to the bilateral insula may exist in people with TRS, which is not observed in the normal population., Competing Interests: Conflict of interest The author(s) declared the following potential conflicts of interest with respect to the research, authorship and/or publication of this article: OH has acquired research support (for the institution) from AVID Radiopharmaceuticals, Biogen, C2N Diagnostics, Eli Lilly, Eisai, Fujirebio, GE Healthcare, and Roche. In the past 2 years, he has received consultancy/speaker fees from AC Immune, Alzpath, BioArctic, Biogen, Bristol Meyer Squibb, Cerveau, Eisai, Eli Lilly, Fujirebio, Merck, Novartis, Novo Nordisk, Roche, Sanofi and Siemens. The remaining authors have nothing to disclose.