Exploratory genome-wide analyses of cortical inhibition, facilitation, and plasticity in late-life depression

Late-life depression (LLD) is a heterogenous mood disorder influenced by genetic factors. Cortical physiological processes such as cortical inhibition, facilitation, and plasticity may be markers of illness that are more strongly associated with genetic factors than the clinical phenotype. Thus, exploring the relationship between genetic factors and these physiological processes may help to characterize the biological mechanisms underlying LLD and improve diagnosis and treatment selection. Transcranial magnetic stimulation (TMS) combined with electromyography was used to measure short interval intracortical inhibition (SICI), cortical silent period (CSP), intracortical facilitation (ICF), and paired associative stimulation (PAS) in 79 participants with LLD. We used exploratory genome-wide association and gene-based analyses to assess for genetic correlations of these TMS measures. MARK4 (which encodes microtubule affinity-regulating kinase 4) and PPP1R37 (which encodes protein phosphatase 1 regulatory subunit 37) showed genome-wide significant association with SICI. EGFLAM (which encodes EGF-like fibronectin type III and laminin G domain) showed genome-wide significant association with CSP. No genes met genome-wide significant association with ICF or PAS. We observed genetic influences on cortical inhibition in older adults with LLD. Replication with larger sample sizes, exploration of clinical phenotype subgroups, and functional analysis of relevant genotypes is warranted to better characterize genetic influences on cortical physiology in LLD. This work is needed to determine whether cortical inhibition may serve as a biomarker to improve diagnostic precision and guide treatment selection in LLD

The Complex Relationship between Antipsychotic-Induced Weight Gain and Therapeutic Benefits: A Systematic Review and Implications for Treatment

Background: Antipsychotic-induced weight gain (AIWG) and other adverse metabolic effects represent serious side effects faced by many patients with psychosis that can lead to numerous comorbidities and which reduce the lifespan. While the pathophysiology of AIWG remains poorly understood, numerous studies have reported a positive association between AIWG and the therapeutic benefit of antipsychotic medications.Objectives: To review the literature to (1) determine if AIWG is consistently associated with therapeutic benefit and (2) investigate which variables may mediate such an association.Data Sources: MEDLINE, Google Scholar, Cochrane Database and PsycINFO databases were searched for articles containing all the following exploded MESH terms: schizophrenia [AND] antipsychotic agents/neuroleptics [AND] (weight gain [OR] lipids [OR] insulin [OR] leptin) [AND] treatment outcome. Results were limited to full-text, English journal articles.Results: Our literature search uncovered 31 independent studies which investigated an AIWG-therapeutic benefit association with a total of 6063 enrolled individuals diagnosed with schizophrenia or another serious mental illness receiving antipsychotic medications. Twenty-two studies found a positive association while, 10 studies found no association and one study reported a negative association. Study variables including medication compliance, sex, ethnicity, or prior antipsychotic exposure did not appear to consistently affect the AIWG-therapeutic benefit relationship. In contrast, there was some evidence that controlling for baseline BMI/psychopathology, duration of treatment and specific agent studied [i.e., olanzapine (OLZ) or clozapine (CLZ)] strengthened the relationship between AIWG and therapeutic benefit.Limitations: There were limitations of the reviewed studies in that many had small sample sizes, and/or were retrospective. The heterogeneity of the studies also made comparisons difficult and publication bias was not controlled for.Conclusions: An AIWG-therapeutic benefit association may exist and is most likely to be observed in OLZ and CLZ-treated patients. The clinical meaningfulness of this association remains unclear and weight gain and other metabolic comorbidities should be identified and treated to the same targets as the general population. Further research should continue to explore the links between therapeutic benefit and metabolic health with emphasis on both pre-clinical work and well-designed prospective clinical trials examining metabolic parameters associated, but also occurring independently to AIWG

Signal from Noise: Using Machine Learning to Distil Knowledge from Data in Biological Psychiatry

Applications of machine learning (ML) in biomedical science are growing rapidly, spurred by interdisciplinary collaborations, aggregation of large datasets, accessibility of analytic routines, and availability of powerful computers. With this increased usage comes a responsibility for education, borne equally by data scientists plying their wares in medical research and biomedical scientists harnessing such methods to glean knowledge from data. This article provides a critical review of ML, covering common ML methods and historical trends of their use in psychiatry, and identifying areas of opportunity for future applications of ML in biological psychiatry. We also establish the ML in Psychiatry (MLPsych) Consortium, enumerate its objectives, and provide a set of standards (Guidelines for REporting ML Investigations in Neuropsychiatry [GREMLIN]) for designing and reporting studies that use ML. This review serves as a cautiously optimistic primer on ML for those on the precipice as they prepare to dive into the field, either as dedicated methodological practitioners or, at the very least, well-informed consumers

Identifying the Common Genetic Basis of Antidepressant Response

Background: Antidepressants are a first-line treatment for depression. However, only a third of individuals experience remission after the first treatment. Common genetic variation, in part, likely regulates antidepressant response, yet the success of previous genome-wide association studies has been limited by sample size. This study performs the largest genetic analysis of prospectively assessed antidepressant response in major depressive disorder to gain insight into the underlying biology and enable out-of-sample prediction. Methods: Genome-wide analysis of remission (nremit = 1852, nnonremit = 3299) and percentage improvement (n = 5218) was performed. Single nucleotide polymorphism–based heritability was estimated using genome-wide complex trait analysis. Genetic covariance with eight mental health phenotypes was estimated using polygenic scores/AVENGEME. Out-of-sample prediction of antidepressant response polygenic scores was assessed. Gene-level association analysis was performed using MAGMA and transcriptome-wide association study. Tissue, pathway, and drug binding enrichment were estimated using MAGMA. Results: Neither genome-wide association study identified genome-wide significant associations. Single nucleotide polymorphism–based heritability was significantly different from zero for remission (h2 = 0.132, SE = 0.056) but not for percentage improvement (h2 = −0.018, SE = 0.032). Better antidepressant response was negatively associated with genetic risk for schizophrenia and positively associated with genetic propensity for educational attainment. Leave-one-out validation of antidepressant response polygenic scores demonstrated significant evidence of out-of-sample prediction, though results varied in external cohorts. Gene-based analyses identified ETV4 and DHX8 as significantly associated with antidepressant response. Conclusions: This study demonstrates that antidepressant response is influenced by common genetic variation, has a genetic overlap schizophrenia and educational attainment, and provides a useful resource for future research. Larger sample sizes are required to attain the potential of genetics for understanding and predicting antidepressant response

Identifying the Common Genetic Basis of Antidepressant Response

Background: Antidepressants are a first-line treatment for depression. However, only a third of individuals experience remission after the first treatment. Common genetic variation, in part, likely regulates antidepressant response, yet the success of previous genome-wide association studies has been limited by sample size. This study performs the largest genetic analysis of prospectively assessed antidepressant response in major depressive disorder to gain insight into the underlying biology and enable out-of-sample prediction. ----- Methods: Genome-wide analysis of remission (n remit = 1852, n nonremit = 3299) and percentage improvement (n = 5218) was performed. Single nucleotide polymorphism-based heritability was estimated using genome-wide complex trait analysis. Genetic covariance with eight mental health phenotypes was estimated using polygenic scores/AVENGEME. Out-of-sample prediction of antidepressant response polygenic scores was assessed. Gene-level association analysis was performed using MAGMA and transcriptome-wide association study. Tissue, pathway, and drug binding enrichment were estimated using MAGMA. ----- Results: Neither genome-wide association study identified genome-wide significant associations. Single nucleotide polymorphism-based heritability was significantly different from zero for remission (h 2 = 0.132, SE = 0.056) but not for percentage improvement (h 2 = -0.018, SE = 0.032). Better antidepressant response was negatively associated with genetic risk for schizophrenia and positively associated with genetic propensity for educational attainment. Leave-one-out validation of antidepressant response polygenic scores demonstrated significant evidence of out-of-sample prediction, though results varied in external cohorts. Gene-based analyses identified ETV4 and DHX8 as significantly associated with antidepressant response. Conclusions: This study demonstrates that antidepressant response is influenced by common genetic variation, has a genetic overlap schizophrenia and educational attainment, and provides a useful resource for future research. Larger sample sizes are required to attain the potential of genetics for understanding and predicting antidepressant response

Identifying the Common Genetic Basis of Antidepressant Response

Background: Antidepressants are a first-line treatment for depression. However, only a third of individuals experience remission after the first treatment. Common genetic variation, in part, likely regulates antidepressant response, yet the success of previous genome-wide association studies has been limited by sample size. This study performs the largest genetic analysis of prospectively assessed antidepressant response in major depressive disorder to gain insight into the underlying biology and enable out-of-sample prediction. Methods: Genome-wide analysis of remission (nremit = 1852, nnonremit = 3299) and percentage improvement (n = 5218) was performed. Single nucleotide polymorphism–based heritability was estimated using genome-wide complex trait analysis. Genetic covariance with eight mental health phenotypes was estimated using polygenic scores/AVENGEME. Out-of-sample prediction of antidepressant response polygenic scores was assessed. Gene-level association analysis was performed using MAGMA and transcriptome-wide association study. Tissue, pathway, and drug binding enrichment were estimated using MAGMA. Results: Neither genome-wide association study identified genome-wide significant associations. Single nucleotide polymorphism–based heritability was significantly different from zero for remission (h2 = 0.132, SE = 0.056) but not for percentage improvement (h2 = −0.018, SE = 0.032). Better antidepressant response was negatively associated with genetic risk for schizophrenia and positively associated with genetic propensity for educational attainment. Leave-one-out validation of antidepressant response polygenic scores demonstrated significant evidence of out-of-sample prediction, though results varied in external cohorts. Gene-based analyses identified ETV4 and DHX8 as significantly associated with antidepressant response. Conclusions: This study demonstrates that antidepressant response is influenced by common genetic variation, has a genetic overlap schizophrenia and educational attainment, and provides a useful resource for future research. Larger sample sizes are required to attain the potential of genetics for understanding and predicting antidepressant response.</p