ABSTRACT
Antidepressants exhibit a considerable variation in efficacy, and increasing evidence suggests that individual genetics contribute to antidepressant treatment response. Here, we combined data on antidepressant non-response measured using rating scales for depressive symptoms, questionnaires of treatment effect, and data from electronic health records, to increase statistical power to detect genomic loci associated with non-response to antidepressants in a total sample of 135,471 individuals prescribed antidepressants. We performed genome-wide association meta-analyses, leave-one-out polygenic prediction, and bioinformatics analyses for genetically informed drug prioritization. We identified two novel loci associated with non-response to antidepressants and showed significant polygenic prediction in independent samples. In addition, we investigated drugs that target proteins likely involved in mechanisms underlying antidepressant non-response, and shortlisted drugs that warrant further replication and validation of their potential to reduce depressive symptoms in individuals who do not respond to first-line antidepressant medications. These results suggest that meta-analyses of GWAS utilizing real-world measures of treatment outcomes can increase sample sizes to improve the discovery of variants associated with non-response to antidepressants.
ABSTRACT
Background: The question arises if there is an association of psycho-emotional stress and chronic soft tissue injuries caused by bruxism somatoform disorders with oral squamous cell carcinoma (OSCC). Methods: Patients with and without "somatoform disorders including psychogenic disturbances" (International Classification of Diseases [ICD]-10 code F45.8), and/or "unspecific behavioral syndromes" (F59), and/or "sleep related bruxism" (G47.63), and/or "other sleep disorders" (G47.8) were retrieved from the TriNetX network to gain cohort I. Cohort II was formed by patients without the aforementioned diagnoses, and by matching for age, gender, tobacco use, and alcohol abuse. After defining the primary outcome as "OSCC" (ICD-10 codes C00-C14), a Kaplan-Meier analysis was performed, and risk ratio (RR) and odds ratio (OR) were calculated. Results: After matching, each cohort accounted for 154,639 patients (59.7% females; 40.3% males; mean current age (± standard deviation) = 43.4 ± 24.5 years). Among cohorts I and II, 907 and 763 patients, respectively, were diagnosed with OSCC within 5 years (risk of OSCC = 0.6% and 0.5%), whereby the risk difference was significant (p < 0.001; Log-Rank test). RR and OR were 1.19 (95% confidence interval (CI), lower = 1.08 and upper = 1.31) and 1.19 (95% CI, 1.08-1.31). Conclusions: Psycho-emotional stress and/or chronic mucosal injuries may play a role in carcinogenesis. However, the results need to be interpreted cautiously due to limitations of the applied approach. It may thus far only be concluded that further research is necessary to investigate hypotheses regarding psychogenic carcinogenesis and tumor formation due to chronic tissue trauma.
ABSTRACT
Genome-wide association studies (GWAS) in psychiatry, once they reach sufficient sample size and power, have been enormously successful. The Psychiatric Genomics Consortium (PGC) aims for mega-analyses with sample sizes that will grow to >1 million individuals in the next 5 years. This should lead to hundreds of new findings for common genetic variants across nine psychiatric disorders studied by the PGC. The new targets discovered by GWAS have the potential to restart largely stalled psychiatric drug development pipelines, and the translation of GWAS findings into the clinic is a key aim of the recently funded phase 3 of the PGC. This is not without considerable technical challenges. These approaches complement the other main aim of GWAS studies, risk prediction approaches for improving detection, differential diagnosis, and clinical trial design. This paper outlines the motivations, technical and analytical issues, and the plans for translating PGC phase 3 findings into new therapeutics.
