Complex psychiatry最新文献

{"title":"Sex Differences in Health Conditions Associated with Sexual Assault in a Large Hospital Population.","authors":"Allison M Lake, Slavina B Goleva, Lauren R Samuels, Laura M Carpenter, Lea K Davis","doi":"10.1159/000527363","DOIUrl":"10.1159/000527363","url":null,"abstract":"<p><strong>Introduction: </strong>Sexual assault is an urgent public health concern with both immediate and long-lasting health consequences, affecting 44% of women and 25% of men during their lifetimes. Large studies are needed to understand the unique healthcare needs of this patient population.</p><p><strong>Methods: </strong>We mined clinical notes to identify patients with a history of sexual assault in the electronic health record (EHR) at Vanderbilt University Medical Center (VUMC), a large university hospital in the Southeastern USA, from 1989 to 2021 (<i>N</i> = 3,376,424). Using a phenome-wide case-control study, we identified diagnoses co-occurring with disclosures of sexual assault. We performed interaction tests to examine whether sex modified any of these associations. Association analyses were restricted to a subset of patients receiving regular care at VUMC (<i>N</i> = 833,185).</p><p><strong>Results: </strong>The phenotyping approach identified 14,496 individuals (0.43%) across the VUMC-EHR with documentation of sexual assault and achieved a positive predictive value of 93.0% (95% confidence interval = 85.6-97.0%), determined by manual patient chart review. Out of 1,703 clinical diagnoses tested across all subgroup analyses, 465 were associated with sexual assault. Sex-by-trauma interaction analysis revealed 55 sex-differential associations and demonstrated increased odds of psychiatric diagnoses in male survivors.</p><p><strong>Discussion: </strong>This case-control study identified associations between disclosures of sexual assault and hundreds of health conditions, many of which demonstrated sex-differential effects. The findings of this study suggest that patients who have experienced sexual assault are at risk for developing wide-ranging medical and psychiatric comorbidities and that male survivors may be particularly vulnerable to developing mental illness.</p>","PeriodicalId":72654,"journal":{"name":"Complex psychiatry","volume":"8 3-4","pages":"80-89"},"PeriodicalIF":0.0,"publicationDate":"2023-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ftp.ncbi.nlm.nih.gov/pub/pmc/oa_pdf/83/bc/cxp-0008-0080.PMC10288064.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"9758531","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Inferring the Genetic Influences on Psychological Traits Using MRI Connectivity Predictive Models: Demonstration with Cognition.","authors":"Alexander S Hatoum, Andrew E Reineberg, Philip A Kragel, Tor D Wager, Naomi P Friedman","doi":"10.1159/000527224","DOIUrl":"10.1159/000527224","url":null,"abstract":"<p><strong>Introduction: </strong>Genetic correlations between brain and behavioral phenotypes in analyses from major genetic consortia have been weak and mostly nonsignificant. fMRI models of systems-level brain patterns may help improve our ability to link genes, brains, and behavior by identifying reliable and reproducible endophenotypes. Work using connectivity-based predictive modeling has generated brain-based proxies of behavioral and neuropsychological variables. If such models capture activity in inherited brain systems, they may offer a more powerful link between genes and behavior.</p><p><strong>Method: </strong>As a proof of concept, we develop models predicting intelligence (IQ) based on fMRI connectivity and test their effectiveness as endophenotypes. We link brain and IQ in a model development dataset of <i>N</i> = 3,000 individuals and test the genetic correlations between brain models and measured IQ in a genetic validation sample of <i>N</i> = 13,092 individuals from the UK Biobank. We compare an additive connectivity-based model to multivariate LASSO and ridge models phenotypically and genetically. We also compare these approaches to single \"candidate\" brain areas.</p><p><strong>Results: </strong>We found that predictive brain models were significantly phenotypically correlated with IQ and showed much stronger correlations than individual edges. Further, brain models were more heritable (h2 = 0.155-0.181) than single brain regions (h2 = 0.038-0.118) and captured about half of the genetic variance in IQ (rG = 0.422-0.576), while rGs with single brain measures were smaller and nonsignificant. For the different approaches, LASSO and ridge were similarly predictive, with slightly weaker performance of the additive model. LASSO model weights were highly theoretically interpretable and replicated known brain IQ associations. Finally, functional connectivity models trained in midlife showed genetic correlations with early life correlates of IQ, suggesting some stability in the prediction of fMRI models.</p><p><strong>Conclusion: </strong>Multisystem predictive models hold promise as imaging endophenotypes that offer complex and theoretically relevant conclusions for future imaging genetics research.</p>","PeriodicalId":72654,"journal":{"name":"Complex psychiatry","volume":"8 3-4","pages":"63-79"},"PeriodicalIF":0.0,"publicationDate":"2022-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10080187/pdf/cxp-0008-0063.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"9916752","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Chronic Pain and Psychiatric Conditions.","authors":"Keira J A Johnston, Laura M Huckins","doi":"10.1159/000527041","DOIUrl":"10.1159/000527041","url":null,"abstract":"<p><strong>Introduction: </strong>Chronic pain is a common condition with high socioeconomic and public health burden. A wide range of psychiatric conditions are often comorbid with chronic pain and chronic pain conditions, negatively impacting successful treatment of either condition. The psychiatric condition receiving most attention in the past with regard to chronic pain comorbidity has been major depressive disorder, despite the fact that many other psychiatric conditions also demonstrate epidemiological and genetic overlap with chronic pain. Further understanding potential mechanisms involved in psychiatric and chronic pain comorbidity could lead to new treatment strategies both for each type of disorder in isolation and in scenarios of comorbidity.</p><p><strong>Methods: </strong>This article provides an overview of relationships between DSM-5 psychiatric diagnoses and chronic pain, with particular focus on PTSD, ADHD, and BPD, disorders which are less commonly studied in conjunction with chronic pain. We also discuss potential mechanisms that may drive comorbidity, and present new findings on the genetic overlap of chronic pain and ADHD, and chronic pain and BPD using linkage disequilibrium score regression analyses.</p><p><strong>Results: </strong>Almost all psychiatric conditions listed in the DSM-5 are associated with increased rates of chronic pain. ADHD and BPD are significantly genetically correlated with chronic pain. Psychiatric conditions aside from major depression are often under-researched with respect to their relationship with chronic pain.</p><p><strong>Conclusion: </strong>Further understanding relationships between psychiatric conditions other than major depression (such as ADHD, BPD, and PTSD as exemplified here) and chronic pain can positively impact understanding of these disorders, and treatment of both psychiatric conditions and chronic pain.</p>","PeriodicalId":72654,"journal":{"name":"Complex psychiatry","volume":"9 1-4","pages":"24-43"},"PeriodicalIF":0.0,"publicationDate":"2022-09-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10080192/pdf/cxp-0009-0024.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"9283663","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Thoughtful Phenotype Definitions Empower Participants and Power Studies.","authors":"Laura M Huckins","doi":"10.1159/000527022","DOIUrl":"10.1159/000527022","url":null,"abstract":"","PeriodicalId":72654,"journal":{"name":"Complex psychiatry","volume":"8 3-4","pages":"57-62"},"PeriodicalIF":0.0,"publicationDate":"2022-09-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10080191/pdf/cxp-0008-0057.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"10312621","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Alcohol Use and Use Disorder and Cancer Risk: Perspective on Causal Inference.","authors":"Hang Zhou,&nbsp;Vasilis Vasiliou","doi":"10.1159/000526407","DOIUrl":"https://doi.org/10.1159/000526407","url":null,"abstract":"<jats:p>None</jats:p>","PeriodicalId":72654,"journal":{"name":"Complex psychiatry","volume":"8 1-2","pages":"9-12"},"PeriodicalIF":0.0,"publicationDate":"2022-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9669948/pdf/cxp-0008-0009.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"9162447","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Association of Predicted Expression and Multimodel Association Analysis of Substance Abuse Traits.","authors":"Darius M Bost, Chris Bizon, Jeffrey L Tilson, Dayne L Filer, Ian R Gizer, Kirk C Wilhelmsen","doi":"10.1159/000523748","DOIUrl":"10.1159/000523748","url":null,"abstract":"<p><strong>Introduction: </strong>Genome-wide association studies (GWAS) have played a critical role in identifying many thousands of loci associated with complex phenotypes and diseases. This has led to several translations of novel disease susceptibility genes into drug targets and care. This however has not been the case for analyses where sample sizes are small, which suffer from multiple comparisons testing. The present study examined the statistical impact of combining a burden test methodology, PrediXcan, with a multimodel meta-analysis, cross phenotype association (CPASSOC).</p><p><strong>Methods: </strong>The analysis was conducted on 5 addiction traits: family alcoholism, cannabis craving, alcohol, nicotine, and cannabis dependence and 10 brain tissues: anterior cingulate cortex BA24, cerebellar hemisphere, cortex, hippocampus, nucleus accumbens basal ganglia, caudate basal ganglia, cerebellum, frontal cortex BA9, hypothalamus, and putamen basal ganglia. Our sample consisted of 1,640 participants from the University of California, San Francisco (UCSF) Family Alcoholism Study. Genotypes were obtained through low pass whole genome sequencing and the use of Thunder, a linkage disequilibrium variant caller.</p><p><strong>Results: </strong>The post-PrediXcan, gene-phenotype association without aggregation resulted in 2 significant results, <i>HCG27</i> and <i>SPPL2B</i>. Aggregating across phenotypes resulted no significant findings. Aggregating across tissues resulted in 15 significant and 5 suggestive associations: <i>PPIE, RPL36AL, FOXN2, MTERF4, SEPTIN2, CIAO3, RPL36AL, ZNF304, CCDC66, SSPOP, SLC7A9, LY75, MTRF1L, COA5,</i> and <i>RRP7A</i>; <i>RPS23, GNMT, ERV3-1, APIP</i>, and <i>HLA-B,</i> respectively.</p><p><strong>Discussion: </strong>Given the relatively small size of the cohort, this multimodel approach was able to find over a dozen significant associations between predicted gene expression and addiction traits. Of our findings, 8 had prior associations with similar phenotypes through investigation of the GWAS Atlas. With the onset of improved transcriptome data, this approach should increase in efficacy.</p>","PeriodicalId":72654,"journal":{"name":"Complex psychiatry","volume":" ","pages":"35-46"},"PeriodicalIF":0.0,"publicationDate":"2022-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ftp.ncbi.nlm.nih.gov/pub/pmc/oa_pdf/bc/99/cxp-0008-0035.PMC9669989.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"40504231","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Accelerating Opioid Use Disorders Research by Integrating Multiple Data Modalities.","authors":"Sevim B Bianchi, Alvin D Jeffery, David C Samuels, Lori Schirle, Abraham A Palmer, Sandra Sanchez-Roige","doi":"10.1159/000525079","DOIUrl":"10.1159/000525079","url":null,"abstract":"","PeriodicalId":72654,"journal":{"name":"Complex psychiatry","volume":"8 1-2","pages":"1-8"},"PeriodicalIF":0.0,"publicationDate":"2022-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9669996/pdf/cxp-0008-0001.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"9998082","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"A Role for Histone Deacetylases in the Biology and Treatment of Post-Traumatic Stress Disorder: What Do We Know and Where Do We Go from Here?","authors":"Robin E Bonomi,&nbsp;Matthew Girgenti,&nbsp;John H Krystal,&nbsp;Kelly P Cosgrove","doi":"10.1159/000524079","DOIUrl":"https://doi.org/10.1159/000524079","url":null,"abstract":"<p><p>Post-traumatic stress disorder is a prevalent disorder within the USA and worldwide with a yearly diagnosis rate of 2-4% and affecting women more than men. One of the primary methods for study of this stress disorder relies on animal models as there are few noninvasive methods and few replicated peripheral biomarkers for use in humans. One area of active research in psychiatric neuroscience is the field of epigenetics - how the chemical modifications of the genetic code regulate behavior. The dynamic changes in histone acetylation and deacetylation in the brain are not fully reflected by the study of peripheral biomarker. In this review, we aim to examine the role of histone acetylation and deacetylation in memory formation and fear memory learning. The studies discussed here focus largely on the role of histone deacetylases (HDACs) in animal models of trauma and fear response. Many studies used HDAC inhibitors to elucidate the effects after inhibition of these enzymes after trauma or stress. These studies of memory processing and cued fear extinction in animal can often shed light on human disorders of cued fear responses and memory dysregulation after stress or trauma such as in PTSD. These results provide strong evidence for a role of these enzymes in PTSD in humans. The few clinical studies that exist with HDAC inhibitors also suggest a fundamental role of these enzymes in the neurobiology of the stress response. Further study of these enzymes in both clinical and pre-clinical settings may help elucidate the neurobiology of stress-related pathology like PTSD and provide a foundation for novel therapy to treat these disorders.</p>","PeriodicalId":72654,"journal":{"name":"Complex psychiatry","volume":"8 1-2","pages":"13-27"},"PeriodicalIF":0.0,"publicationDate":"2022-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9669946/pdf/cxp-0008-0013.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"9836160","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Identifying High-Risk Comorbidities Associated with Opioid Use Patterns Using Electronic Health Record Prescription Data.","authors":"Mariela V Jennings, Hyunjoon Lee, Daniel B Rocha, Sevim B Bianchi, Brandon J Coombes, Richard C Crist, Annika B Faucon, Yirui Hu, Rachel L Kember, Travis T Mallard, Maria Niarchou, Melissa N Poulsen, Peter Straub, Richard D Urman, Colin G Walsh, Lea K Davis, Jordan W Smoller, Vanessa Troiani, Sandra Sanchez-Roige","doi":"10.1159/000525313","DOIUrl":"10.1159/000525313","url":null,"abstract":"<p><strong>Introduction: </strong>Opioid use disorders (OUDs) constitute a major public health issue, and we urgently need alternative methods for characterizing risk for OUD. Electronic health records (EHRs) are useful tools for understanding complex medical phenotypes but have been underutilized for OUD because of challenges related to underdiagnosis, binary diagnostic frameworks, and minimally characterized reference groups. As a first step in addressing these challenges, a new paradigm is warranted that characterizes risk for opioid prescription misuse on a continuous scale of severity, i.e., as a continuum.</p><p><strong>Methods: </strong>Across sites within the PsycheMERGE network, we extracted prescription opioid data and diagnoses that co-occur with OUD (including psychiatric and substance use disorders, pain-related diagnoses, HIV, and hepatitis C) for over 2.6 million patients across three health registries (Vanderbilt University Medical Center, Mass General Brigham, Geisinger) between 2005 and 2018. We defined three groups based on levels of opioid exposure: no prescriptions, minimal exposure, and chronic exposure and then compared the comorbidity profiles of these groups to the full registries and to those with OUD diagnostic codes.</p><p><strong>Results: </strong>Our results confirm that EHR data reflects known higher prevalence of substance use disorders, psychiatric disorders, medical, and pain diagnoses in patients with OUD diagnoses and chronic opioid use. Comorbidity profiles that distinguish opioid exposure are strikingly consistent across large health systems, indicating the phenotypes described in this new quantitative framework are robust to health systems differences.</p><p><strong>Conclusion: </strong>This work indicates that EHR prescription opioid data can serve as a platform to characterize complex risk markers for OUD using existing data.</p>","PeriodicalId":72654,"journal":{"name":"Complex psychiatry","volume":"8 1-2","pages":"47-55"},"PeriodicalIF":0.0,"publicationDate":"2022-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9669950/pdf/cxp-0008-0047.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"9102124","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Front & Back Matter","authors":"Y. Milaneschi","doi":"10.1159/000527132","DOIUrl":"https://doi.org/10.1159/000527132","url":null,"abstract":"","PeriodicalId":72654,"journal":{"name":"Complex psychiatry","volume":"30 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2022-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"83300165","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}