METHODS: We analysed genome-wide single-nucleotide polymorphism (SNP) data for the five disorders in 33,332 cases and 27,888 controls of European ancestory. To characterise allelic effects on each disorder, we applied a multinomial logistic regression procedure with model selection to identify the best-fitting model of relations between genotype and phenotype. We examined cross-disorder effects of genome-wide significant loci previously identified for bipolar disorder and schizophrenia, and used polygenic risk-score analysis to examine such effects from a broader set of common variants. We undertook pathway analyses to establish the biological associations underlying genetic overlap for the five disorders. We used enrichment analysis of expression quantitative trait loci (eQTL) data to assess whether SNPs with cross-disorder association were enriched for regulatory SNPs in post-mortem brain-tissue samples.
FINDINGS: SNPs at four loci surpassed the cutoff for genome-wide significance (p<5×10(-8)) in the primary analysis: regions on chromosomes 3p21 and 10q24, and SNPs within two L-type voltage-gated calcium channel subunits, CACNA1C and CACNB2. Model selection analysis supported effects of these loci for several disorders. Loci previously associated with bipolar disorder or schizophrenia had variable diagnostic specificity. Polygenic risk scores showed cross-disorder associations, notably between adult-onset disorders. Pathway analysis supported a role for calcium channel signalling genes for all five disorders. Finally, SNPs with evidence of cross-disorder association were enriched for brain eQTL markers.
INTERPRETATION: Our findings show that specific SNPs are associated with a range of psychiatric disorders of childhood onset or adult onset. In particular, variation in calcium-channel activity genes seems to have pleiotropic effects on psychopathology. These results provide evidence relevant to the goal of moving beyond descriptive syndromes in psychiatry, and towards a nosology informed by disease cause.
FUNDING: National Institute of Mental Health.
METHODS: In this cross-sectional study, 101 TBI patients were interviewed using the Structured Clinical Interview for DSM-IV Axis I Disorders to assess the rates of depressive and anxiety disorders after TBI. The association of socio-demographic and clinical factors with depressive and anxiety disorders were determined using Pearson's Chi-Square test.
RESULTS: A total of 25% of TBI patients (n = 25/101) were diagnosed with depressive disorders, of which 15% had major depressive disorder (n = 15/101) and 10% had minor depression (n = 10/101). Fourteen percent of TBI patients had anxiety disorders (n = 14/101), of which post-traumatic stress disorder (PTSD) was the commonest anxiety disorder (9%, n = 9/101). Seven percent of TBI patients (n = 7/101) had comorbid depressive and anxiety disorders. The only factor associated with depressive disorder was the duration of TBI (≥ 1 year) while the only factor associated with anxiety disorder was the mechanism of trauma (assault).
CONCLUSION: Major depressive disorder, minor depression and PTSD are common psychiatric complications of TBI. Clinicians should screen for depressive and anxiety disorders in TBI patients, particularly those with ≥1 year of injury and had sustained TBI from assault.
METHODS AND FINDINGS: We conducted a single-blind RCT (October 2017 -May 2019) with Chin (39.3%), Kachin (15.7%), and Rohingya (45%) refugees living in Kuala Lumpur, Malaysia. The trial included 170 participants receiving six 45-minute weekly sessions of IAT (97.6% retention, 4 lost to follow-up) and 161 receiving a multicomponent CBT also involving six 45-minute weekly sessions (96.8% retention, 5 lost to follow-up). Participants (mean age: 30.8 years, SD = 9.6) had experienced and/or witnessed an average 10.1 types (SD = 5.9, range = 1-27) of traumatic events. We applied a single-blind design in which independent assessors of pre- and posttreatment indices were masked in relation to participants' treatment allocation status. Primary outcomes were symptom scores of Post Traumatic Stress Disorder (PTSD), Complex PTSD (CPTSD), Major Depressive Disorder (MDD), the 5 scales of the Adaptive Stress Index (ASI), and a measure of resilience (the Connor-Davidson Resilience Scale [CDRS]). Compared to CBT, an intention-to-treat analysis (n = 331) at 6-week posttreatment follow-up demonstrated greater reductions in the IAT arm for all common mental disorder (CMD) symptoms and ASI domains except for ASI-3 (injustice), as well as increases in the resilience scores. Adjusted average treatment effects assessing the differences in posttreatment scores between IAT and CBT (with baseline scores as covariates) were -0.08 (95% CI: -0.14 to -0.02, p = 0.012) for PTSD, -0.07 (95% CI: -0.14 to -0.01) for CPTSD, -0.07 for MDD (95% CI: -0.13 to -0.01, p = 0.025), 0.16 for CDRS (95% CI: 0.06-0.026, p ≤ 0.001), -0.12 (95% CI: -0.20 to -0.03, p ≤ 0.001) for ASI-1 (safety/security), -0.10 for ASI-2 (traumatic losses; 95% CI: -0.18 to -0.02, p = 0.02), -0.03 for ASI-3 (injustice; (95% CI: -0.11 to 0.06, p = 0.513), -0.12 for ASI-4 (role/identity disruptions; 95% CI: -0.21 to -0.04, p ≤ 0.001), and -0.18 for ASI-5 (existential meaning; 95% CI: -0.19 to -0.05, p ≤ 0.001). Compared to CBT, the IAT group had larger effect sizes for all indices (except for resilience) including PTSD (IAT, d = 0.93 versus CBT, d = 0.87), CPTSD (d = 1.27 versus d = 1.02), MDD (d = 1.4 versus d = 1.11), ASI-1 (d = 1.1 versus d = 0.85), ASI-2 (d = 0.81 versus d = 0.66), ASI-3 (d = 0.49 versus d = 0.42), ASI-4 (d = 0.86 versus d = 0.67), and ASI-5 (d = 0.72 versus d = 0.53). No adverse events were recorded for either therapy. Limitations include a possible allegiance effect (the authors inadvertently conveying disproportionate enthusiasm for IAT in training and supervision), cross-over effects (counsellors applying elements of one therapy in delivering the other), and the brief period of follow-up.
CONCLUSIONS: Compared to CBT, IAT showed superiority in improving mental health symptoms and adaptative stress from baseline to 6-week posttreatment. The differences in scores between IAT and CBT were modest and future studies conducted by independent research teams need to confirm the findings.
TRIAL REGISTRATION: The study is registered under Australian New Zealand Clinical Trials Registry (ANZCTR) (http://www.anzctr.org.au/). The trial registration number is: ACTRN12617001452381.
OBJECTIVE: To use an individual participant data meta-analysis to evaluate the accuracy of two PHQ-9 diagnostic algorithms for detecting major depression and compare accuracy between the algorithms and the standard PHQ-9 cutoff score of ≥10.
METHODS: Medline, Medline In-Process and Other Non-Indexed Citations, PsycINFO, Web of Science (January 1, 2000, to February 7, 2015). Eligible studies that classified current major depression status using a validated diagnostic interview.
RESULTS: Data were included for 54 of 72 identified eligible studies (n participants = 16,688, n cases = 2,091). Among studies that used a semi-structured interview, pooled sensitivity and specificity (95% confidence interval) were 0.57 (0.49, 0.64) and 0.95 (0.94, 0.97) for the original algorithm and 0.61 (0.54, 0.68) and 0.95 (0.93, 0.96) for a modified algorithm. Algorithm sensitivity was 0.22-0.24 lower compared to fully structured interviews and 0.06-0.07 lower compared to the Mini International Neuropsychiatric Interview. Specificity was similar across reference standards. For PHQ-9 cutoff of ≥10 compared to semi-structured interviews, sensitivity and specificity (95% confidence interval) were 0.88 (0.82-0.92) and 0.86 (0.82-0.88).
CONCLUSIONS: The cutoff score approach appears to be a better option than a PHQ-9 algorithm for detecting major depression.
METHOD: Genome-wide association studies (GWASs) were conducted in Australian (between 1988 and 1990 and between 2010 and 2013) and Amish (between May 2010 and December 2011) samples in whom the Seasonal Pattern Assessment Questionnaire (SPAQ) had been administered, and the results were meta-analyzed in a total sample of 4,156 individuals. Genetic risk scores based on results from prior large GWAS studies of bipolar disorder, major depressive disorder (MDD), and schizophrenia were calculated to test for overlap in risk between psychiatric disorders and seasonality.
RESULTS: The most significant association was with rs11825064 (P = 1.7 × 10⁻⁶, β = 0.64, standard error = 0.13), an intergenic single nucleotide polymorphism (SNP) found on chromosome 11. The evidence for overlap in risk factors was strongest for schizophrenia and seasonality, with the schizophrenia genetic profile scores explaining 3% of the variance in log-transformed global seasonality scores. Bipolar disorder genetic profile scores were also associated with seasonality, although at much weaker levels (minimum P value = 3.4 × 10⁻³), and no evidence for overlap in risk was detected between MDD and seasonality.
CONCLUSIONS: Common SNPs of large effect most likely do not exist for seasonality in the populations examined. As expected, there were overlapping genetic risk factors for bipolar disorder (but not MDD) with seasonality. Unexpectedly, the risk for schizophrenia and seasonality had the largest overlap, an unprecedented finding that requires replication in other populations and has potential clinical implications considering overlapping cognitive deficits in seasonal affective disorders and schizophrenia.