Association of trauma, post-traumatic stress disorder and non-affective psychosis across the life course: a nationwide prospective cohort study

Background We aimed to examine the temporal relationships between traumatic events (TE), post-traumatic stress disorder (PTSD) and non-affective psychotic disorders (NAPD). Methods A prospective cohort study of 1 965 214 individuals born in Sweden between 1971 and 1990 examining the independent effects of interpersonal and non-interpersonal TE on incidence of PTSD and NAPD using data from linked register data (Psychiatry-Sweden). Mediation analyses tested the hypothesis that PTSD lies on a causal pathway between interpersonal trauma and NAPD. Results Increasing doses of interpersonal and non-interpersonal TE were independently associated with increased risk of NAPD [linear-trend incidence rate ratios (IRR)adjusted = 2.17 [95% confidence interval (CI) 2.02–2.33] and IRRadjusted = 1.27 (95% CI 1.23–1.31), respectively]. These attenuated to a relatively small degree in 5-year time-lagged models. A similar pattern of results was observed for PTSD [linear-trend IRRadjusted = 3.43 (95% CI 3.21–3.66) and IRRadjusted = 1.45 (95% CI 1.39–1.50)]. PTSD was associated with increased risk of NAPD [IRRadjusted = 8.06 (95% CI 7.23–8.99)], which was substantially attenuated in 5-year time-lagged analyses [IRRadjusted = 4.62 (95% CI 3.65–5.87)]. There was little evidence that PTSD diagnosis mediated the relationship between interpersonal TE and NAPD [IRRadjusted = 0.92 (percentile CI 0.80–1.07)]. Conclusion Despite the limitations to causal inference inherent in observational designs, the large effect-sizes observed between trauma, PTSD and NAPD in this study, consistent across sensitivity analyses, suggest that trauma may be a component cause of psychotic disorders. However, PTSD diagnosis might not be a good proxy for the likely complex psychological mechanisms mediating this association

Social fragmentation, deprivation and urbanicity: relation to first-admission rates for psychoses

<i>Declaration</i> <i>of</i> <i>interest</i>: None. <i>Background</i>: Social disorganisation, fragmentation and isolation have long been posited as influencing the rate of psychoses at area level. Measuring such societal constructsis difficult. A census-based index measuring social fragmentation has been proposed. <i>Aims</i>: To investigate the association between first-admission rates for psychosis and area-based measures of social fragmentation, deprivation and urban/rural index. <i>Method</i>: We used indirect standardisation methods and logistic regression models to examine associations of social fragmentation, deprivation and urban/rural categories with first admissions for psychoses in Scotland for the 5-year period 1989–1993. <i>Results</i>: Areas characterised by high social fragmentation had higher first-ever admission rates for psychosis independent of deprivation and urban/rural status. There was a dose–response relationship between social fragmentation category and first-ever admission rates for psychosis. There was no statistically significant interaction between social fragmentation, deprivation and urban/rural index. <i>Conclusions</i>: First-admission rates are strongly associated with measures of social fragmentation, independent of material deprivation and urban/rural category

ADHD in adults with recurrent depression

BACKGROUND: Depression is highly heterogeneous in its clinical presentation. Those with attention deficit/hyperactivity disorder (ADHD) may be at risk of a more chronic and impairing depression compared to those with depression alone according to studies of young people. However, no studies to date have examined ADHD in recurrently depressed adults in mid-life. METHOD: In a sample of women in mid-life (n=148) taken from a UK based prospective cohort of adults with a history of recurrent depression, we investigated the prevalence of ADHD and the association of ADHD with clinical features of depression. RESULTS: 12.8% of the recurrently depressed women had elevated ADHD symptoms and 3.4% met DSM-5 diagnostic criteria for ADHD. None of the women reported having a diagnosis of ADHD from a medical professional. ADHD symptoms were associated with earlier age of depression onset, higher depression associated impairment, a greater recurrence of depressive episodes and increased persistence of subthreshold depression symptoms over the study period, higher levels of irritability and increased risk of self-harm or suicide attempt. ADHD symptoms were associated with increased risk of hospitalisation and receiving non-first-line antidepressant medication. LIMITATIONS: ADHD was measured using a questionnaire measure. We focussed on mothers in a longitudinal study of recurrent depression, so the findings may not apply to males or other groups. CONCLUSIONS: Higher ADHD symptoms appear to index a worse clinical presentation for depression. Clinical implications include that in women with early onset, impairing and recurrent depression, the possibility of underlying ADHD masked by depression needs to be considered

Following the children of depressed parents from childhood to adult life: A focus on mood and anxiety disorders

BACKGROUND: Parental depression increases risk for anxiety and depression in offspring. The transition from adolescence to adulthood is a common risk period for onset of such disorders. However, relatively few studies have considered development of these disorders from childhood to adulthood including multiple assessments during this transition period. METHOD: Offspring of depressed parents aged 9–17 years at baseline were followed prospectively for 13 years (n = 337). Average length of follow-up was 16 months between the first and second waves, 13 months between the second and third, and 8 years between the third and fourth. Current (3-month) psychopathology was assessed at each wave using diagnostic interviews. We derived estimates of 3-month prevalence, age at first diagnosis, course and comorbidity of disorders. Social functioning in adult life was assessed at the final wave and we assessed how prior and current disorder impacted adult functioning. RESULTS: A quarter of young people met criteria for a mood disorder and a third for anxiety disorder at least once. Mood and anxiety disorder prevalence increased from 4.5% and 15.8% respectively in childhood (9–11 years) to 22.3% and 20.9% respectively by age 23–28. Increased prevalence across the transition from adolescence to adulthood was particularly marked in males, while prevalence increased earlier in adolescence in females. Age at first diagnosis varied widely (mood disorder mean = 16.5 years (range 9–26); anxiety disorder mean = 14.5 years (range 9–28)). Over half (52%) reported functional impairment in early adulthood, 31% harmful alcohol use, and 10% self-harm or a suicide attempt. Both previous and current mood or anxiety disorder were associated with functional impairment in early adulthood. CONCLUSIONS: There is a prolonged risk period for mood and anxiety disorders in this group, with prevalence peaking in early adulthood. This highlights the need for prolonged vigilance and effective targeted interventions in the offspring of depressed parents

Developing and validating a prediction model of adolescent major depressive disorder in the offspring of depressed parents

Background: Parental depression is common and is a major risk factor for depression in adolescents. Early identification of adolescents at elevated risk of developing major depressive disorder (MDD) in this group could improve early access to preventive interventions. Methods: Using longitudinal data from 337 adolescents at high familial risk of depression, we developed a risk prediction model for adolescent MDD. The model was externally validated in an independent cohort of 1,384 adolescents at high familial risk. We assessed predictors at baseline and MDD at follow‐up (a median of 2–3 years later). We compared the risk prediction model to a simple comparison model based on screening for depressive symptoms. Decision curve analysis was used to identify which model‐predicted risk score thresholds were associated with the greatest clinical benefit. Results: The MDD risk prediction model discriminated between those adolescents who did and did not develop MDD in the development (C‐statistic = .783, IQR (interquartile range) = .779, .778) and the validation samples (C‐statistic = .722, IQR = −.694, .741). Calibration in the validation sample was good to excellent (calibration intercept = .011, C‐slope = .851). The MDD risk prediction model was superior to the simple comparison model where discrimination was no better than chance (C‐statistic = .544, IQR = .536, .572). Decision curve analysis found that the highest clinical utility was at the lowest risk score thresholds (0.01–0.05). Conclusions: The developed risk prediction model successfully discriminated adolescents who developed MDD from those who did not. In practice, this model could be further developed with user involvement into a tool to target individuals for low‐intensity, selective preventive intervention

Investigation of relationships between bipolar disorder phenotypes and genome-wide significant loci from PGC2 schizophrenia

Background Schizophrenia (SZ) and Bipolar disorder (BD) show evidence for partial overlap in phenotypic and genetic influences based on family, twins, adoption and Psychiatric Genetic Consortium (PGC) studies. They have lifetime prevalence of about 1% and 2.4%, and heritability estimates of 60-80% and 40-70%, respectively. In the last decade BD has been investigated using dimensional structuring of psychoses based on symptomatic-functional checklists that provides reliable approach to phenotypic assessment. Recent research suggests moving towards developing Phenotype-based Genetic Association Studies. In this approach, patients will only be put into groups consisting of others with symptoms similar to their own. Canonical Correlation Analysis (CCA) is statistical technique designed to identify relationships (usually hidden) between two sets of variables. We use CCA to combine genotypic and phenotypic variables and measure correlation between those sets. This analysis estimates canonical correlation between psychotic symptoms measured using validated item check list (OPCRIT), and genome-wide significant (GWS) loci from PGC2 schizophrenia. Methods For our analysis we used phenotype and genetic data for 5,507 BD cases. Imputation of genetic data was performed with 1000Genomes (Phase 3, 2014) then quality control was applied (INFO>0.8, HWE>1e-6, MAF>0.01). Additional quality control was performed on phenotypic symptom coverage. CCA was employed as implemented in R, using package “CCA” with GWS loci from PGC2 SZ and OPCRIT items. SNPs were standardised and adjusted for 10 population covariates calculated from imputed data using principal component approach prior to CCA. Results Canonical correlation analysis was run on 4422 cases on 89 available GWS PGC2 SZ SNPs or their proxies (with r2>0.6). 60 phenotypic variables were taken from OPCRIT measurements including mood disturbance, biological indices, atypical depression, substance use, psychosis and social functioning. We found no significant canonical correlations indicating absence of hidden sub-clusters at individual symptom level of BD associated with SZ GWS loci. Discussion Our analysis was focused to find correlation from bipolar phenotype by using OPCRIT questionnaire and GWS SZ loci from PGC2. We have shown that there were no significant canonical correlation coefficients suggesting that there is no direct association between SZ associated genetic loci and BP at individual symptom level. CCA is canonical correlation analysis is one of potential of data-driven approaches to identify hidden genotype-phenotype relationships. It provides opportunities to generate and test different hypotheses and understand more about complex architecture of psychiatric disorders. In the next stage we plan to extend our analysis to more fine grained systematic descriptors of BD and test for correlation with genetic profiles from a number of co-morbid disorders, as well as the full range of phenotypic and genetic data that are available

Early-life inflammatory markers and subsequent psychotic and depressive episodes between 10 to 28 years of age

Inflammation is implicated in depression and psychosis, including association of childhood inflammatory markers on the subsequent risk of developing symptoms. However, it is unknown whether early-life inflammatory markers are associated with the number of depressive and psychotic symptoms from childhood to adulthood. Using the prospective Avon Longitudinal Study of Children and Parents birth cohort (N = up-to 6401), we have examined longitudinal associations of early-life inflammation [exposures: interleukin-6 (IL-6), C-reactive protein (CRP) levels at age 9y; IL-6 and CRP DNA-methylation (DNAm) scores at birth and age 7y; and IL-6 and CRP polygenic risk scores (PRSs)] with the number of depressive episodes and psychotic experiences (PEs) between ages 10–28 years. Psychiatric outcomes were assessed using the Short Mood and Feelings Questionnaire and Psychotic Like Symptoms Questionnaires, respectively. Exposure-outcome associations were tested using negative binomial models, which were adjusted for metabolic and sociodemographic factors. Serum IL-6 levels at age 9y were associated with the total number of depressive episodes between 10 and 28y in the base model (n = 4835; β = 0.066; 95%CI:0.020–0.113; pFDR = 0.041) which was weaker when adjusting for metabolic and sociodemographic factors. Weak associations were observed between inflammatory markers (serum IL-6 and CRP DNAm scores) and total number of PEs. Other inflammatory markers were not associated with depression or PEs. Early-life inflammatory markers are associated with the burden of depressive episodes and of PEs subsequently from childhood to adulthood. These findings support a potential role of early-life inflammation in the aetiology of depression and psychosis and highlight inflammation as a potential target for treatment and prevention

Associations Between Schizophrenia Polygenic Liability, Symptom Dimensions, and Cognitive Ability in Schizophrenia

Importance Schizophrenia is a clinically heterogeneous disorder. It is currently unclear how variability in symptom dimensions and cognitive ability is associated with genetic liability for schizophrenia. Objective To determine whether phenotypic dimensions within schizophrenia are associated with genetic liability to schizophrenia, other neuropsychiatric disorders, and intelligence. Design, Setting, and Participants In a genetic association study, 3 cross-sectional samples of 1220 individuals with a diagnosis of schizophrenia were recruited from community, inpatient, and voluntary sector mental health services across the UK. Confirmatory factor analysis was used to create phenotypic dimensions from lifetime ratings of the Scale for the Assessment of Positive Symptoms, Scale for the Assessment of Negative Symptoms, and the MATRICS Consensus Cognitive Battery. Analyses of polygenic risk scores (PRSs) were used to assess whether genetic liability to schizophrenia, other neuropsychiatric disorders, and intelligence were associated with these phenotypic dimensions. Data collection for the cross-sectional studies occurred between 1993 and 2016. Data analysis for this study occurred between January 2019 and March 2021. Main Outcomes and Measures Outcome measures included phenotypic dimensions defined from confirmatory factor analysis relating to positive symptoms, negative symptoms of diminished expressivity, negative symptoms of motivation and pleasure, disorganized symptoms, and current cognitive ability. Exposure measures included PRSs for schizophrenia, bipolar disorder, major depression, attention-deficit/hyperactivity disorder, autism spectrum disorder, and intelligence. Results Of the 1220 study participants, 817 were men (67.0%). Participants’ mean (SD) age at interview was 43.10 (12.74) years. Schizophrenia PRS was associated with increased disorganized symptom dimension scores in both a 5-factor model (β = 0.14; 95% CI, 0.07-0.22; P = 2.80 × 10−4) and a 3-factor model across all samples (β = 0.10; 95% CI, 0.05-0.15; P = 2.80 × 10−4). Current cognitive ability was associated with genetic liability to schizophrenia (β = −0.11; 95% CI, −0.19 to −0.04; P = 1.63 × 10−3) and intelligence (β = 0.23; 95% CI, 0.16-0.30; P = 1.52 × 10−10). After controlling for estimated premorbid IQ, current cognitive performance was associated with schizophrenia PRS (β = −0.08; 95% CI, −0.14 to −0.02; P = 8.50 × 10−3) but not intelligence PRS. Conclusions and Relevance The findings of this study suggest that genetic liability for schizophrenia is associated with higher disorganized dimension scores but not other symptom dimensions. Cognitive performance in schizophrenia appears to reflect distinct contributions from genetic liabilities to both intelligence and schizophrenia

Psychosis and the level of mood incongruence in Bipolar Disorder are related to genetic liability for Schizophrenia

Abstract Importance Bipolar disorder (BD) overlaps schizophrenia in its clinical presentation and genetic liability. Alternative approaches to patient stratification beyond current diagnostic categories are needed to understand the underlying disease processes/mechanisms. Objectives To investigate the relationship between common-variant liability for schizophrenia, indexed by polygenic risk scores (PRS) and psychotic presentations of BD, using clinical descriptions which consider both occurrence and level of mood-incongruent psychotic features. Design Case-control design: using multinomial logistic regression, to estimate differential associations of PRS across categories of cases and controls. Settings & Participants 4399 BDcases, mean [sd] age-at-interview 46[12] years, of which 2966 were woman (67%) from the BD Research Network (BDRN) were included in the final analyses, with data for 4976 schizophrenia cases and 9012 controls from the Type-1 diabetes genetics consortium and Generation Scotland included for comparison. Exposure Standardised PRS, calculated using alleles with an association p-value threshold < 0.05 in the second Psychiatric Genomics Consortium genome-wide association study of schizophrenia, adjusted for the first 10 population principal components and genotyping-platform. Main outcome measure Multinomial logit models estimated PRS associations with BD stratified by (1) Research Diagnostic Criteria (RDC) BD subtypes (2) Lifetime occurrence of psychosis.(3) Lifetime mood-incongruent psychotic features and (4) ordinal logistic regression examined PRS associations across levels of mood-incongruence. Ratings were derived from the Schedule for Clinical Assessment in Neuropsychiatry interview (SCAN) and the Bipolar Affective Disorder Dimension Scale (BADDS). Results Across clinical phenotypes, there was an exposure-response gradient with the strongest PRS association for schizophrenia (RR=1.94, (95% C.I. 1.86, 2.01)), then schizoaffective BD (RR=1.37, (95% C.I. 1.22, 1.54)), BD I (RR= 1.30, (95% C.I. 1.24, 1.36)) and BD II (RR=1.04, (95% C.I. 0.97, 1.11)). Within BD cases, there was an effect gradient, indexed by the nature of psychosis, with prominent mood-incongruent psychotic features having the strongest association (RR=1.46, (95% C.I. 1.36, 1.57)), followed by mood-congruent psychosis (RR= 1.24, (95% C.I. 1.17, 1.33)) and lastly, BD cases with no history of psychosis (RR=1.09, (95% C.I. 1.04, 1.15)). Conclusion We show for the first time a polygenic-risk gradient, across schizophrenia and bipolar disorder, indexed by the occurrence and level of mood-incongruent psychotic symptoms