Frye, Mark A.

We set out to determine whether machine learning–based algorithms that included functionally validated pharmacogenomic biomarkers joined with clinical measures could predict selective serotonin reuptake inhibitor (SSRI) remission/response in patients with major depressive disorder (MDD). We studied 1,030 white outpatients with MDD treated with citalopram/escitalopram in the Mayo Clinic Pharmacogenomics Research Network Antidepressant Medication Pharmacogenomic Study (PGRN‐AMPS; n = 398), Sequenced Treatment Alternatives to Relieve Depression (STAR*D; n = 467), and International SSRI Pharmacogenomics Consortium (ISPC; n = 165) trials. A genomewide association study for PGRN‐AMPS plasma metabolites associated with SSRI response (serotonin) and baseline MDD severity (kynurenine) identified single nucleotide polymorphisms (SNPs) in DEFB1, ERICH3, AHR, and TSPAN5 that we tested as predictors. Supervised machine‐learning methods trained using SNPs and total baseline depression scores predicted remission and response at 8 weeks with area under the receiver operating curve (AUC) > 0.7 (P < 0.04) in PGRN‐AMPS patients, with comparable prediction accuracies > 69% (P ≤ 0.07) in STAR*D and ISPC. These results demonstrate that machine learning can achieve accurate and, importantly, replicable prediction of SSRI therapy response using total baseline depression severity combined with pharmacogenomic biomarkers.

Post-traumatic stress disorder (PTSD) is an acquired psychiatric disorder with functionally impairing physiological and psychological symptoms following a traumatic exposure. Genetic, epigenetic, and environmental factors act together to determine both an individual’s susceptibility to PTSD and its clinical phenotype. In this literature review, we briefly review the candidate genes that have been implicated in the development and severity of the PTSD phenotype. We discuss the importance of the epigenetic regulation of these candidate genes. We review the general epigenetic mechanisms that are currently understood, with examples of each in the PTSD phenotype. Our focus then turns to studies that have examined PTSD in the context of comorbid psychiatric disorders or associated social and behavioral stressors. We examine the epigenetic variation in cases or models of PTSD with comorbid depressive disorders, anxiety disorders, psychotic disorders, and substance use disorders. We reviewed the literature that has explored epigenetic regulation in PTSD in adverse childhood experiences and suicide phenotypes. Finally, we review some of the information available from studies of the transgenerational transmission of epigenetic variation in maternal cases of PTSD. We discuss areas pertinent for future study to further elucidate the complex interactions between epigenetic modifications and this complex psychiatric disorder.

AbstractThe purpose of this study was to estimate the extent of potential antidepressant overprescribing in a geographically defined U.S. population, and to determine the indications and factors that account for it. We conducted a cohort study of new antidepressant prescriptions for elderly residents of Olmsted County, Minnesota, 2005‐2012, using the Rochester Epidemiology Project medical records‐linkage system. Indications for antidepressants were abstracted from health records for all cohort members. Potential antidepressant overprescribing was defined based on regulatory approval, the level of evidence identified from a standardized drug information database, and multidisciplinary expert review. Predictors of potential antidepressant overprescribing were investigated using logistic regression models, stratified by general antidepressant indication (general medical indication, specific psychiatric diagnosis, and non‐specific psychiatric symptoms). Potential antidepressant overprescribing occurred in 24% of 3199 incident antidepressant prescriptions during the study period, and involved primarily newer antidepressants that were prescribed for non‐specific psychiatric symptoms and subthreshold diagnoses. Potential antidepressant overprescribing was associated with nursing home residence, having a higher number of comorbid medical conditions and outpatient prescribers, taking more concomitant medications, having greater use of urgent or acute care services in the year preceding the index antidepressant prescription, and being prescribed antidepressants via telephone, e‐mail, or patient portal. In conclusion, potential antidepressant overprescribing occurred in elderly persons and involved mainly newer antidepressants used for non‐specific psychiatric symptoms and subthreshold diagnoses, and was associated with indicators of higher clinical complexity or severity and with prescribing without face‐to‐face patient contact.

ObjectiveBipolar disorder has a wide range of clinical manifestations which may progress over time. The aim of this study was to test the applicability of a clinical staging model for bipolar disorder and to gain insight into the nature of the variables influencing progression through consecutive stages.MethodsUsing retrospectively reported longitudinal life chart data of 99 subjects from the Stanley Foundation Bipolar Network Naturalistic Follow‐up Study, the occurrence, duration and timely sequence of stages 2‐4 were determined per month. A multi‐state model was used to calculate progression rates and identify determinants of illness progression. Stages 0, 1 and several other variables were added to the multi‐state model to determine their influence on the progression rates.ResultsFive years after onset of BD (stage 2), 72% reached stage 3 (recurrent episodes) and 21% had reached stage 4 (continuous episodes), of whom 8% recovered back to stage 3. The progression from stage 2 to 3 was increased by a biphasic onset for both the depression‐mania and the mania‐depression course and by male sex.ConclusionsStaging is a useful model to determine illness progression in longitudinal life chart data. Variables influencing transition rates were successfully identified.

Objective: To derive new criteria sets for defining manic and hypomanic episodes (and thus for defining the bipolar I and II disorders), an international Task Force was assembled and termed AREDOC reflecting its role of Assessment, Revision and Evaluation of DSM and other Operational Criteria. This paper reports on the first phase of its deliberations and interim criteria recommendations. Method: The first stage of the process consisted of reviewing Diagnostic and Statistical Manual of Mental Disorders, Fifth Edition, and recent International Classification of Diseases criteria, identifying their limitations and generating modified criteria sets for further in-depth consideration. Task Force members responded to recommendations for modifying criteria and from these the most problematic issues were identified. Results: Principal issues focussed on by Task Force members were how best to differentiate mania and hypomania, how to judge ‘impairment’ (both in and of itself and allowing that functioning may sometimes improve during hypomanic episodes) and concern that rejecting some criteria (e.g. an imposed duration period) might risk false-positive diagnoses of the bipolar disorders. Conclusion: This first-stage report summarises the clinical opinions of international experts in the diagnosis and management of the bipolar disorders, allowing readers to contemplate diagnostic parameters that may influence their clinical decisions. The findings meaningfully inform subsequent Task Force stages (involving a further commentary stage followed by an empirical study) that are expected to generate improved symptom criteria for diagnosing the bipolar I and II disorders with greater precision and to clarify whether they differ dimensionally or categorically.

Objectives Bipolar disorder (BD) with early disease onset is associated with an unfavorable clinical outcome and constitutes a clinically and biologically homogenous subgroup within the heterogeneous BD spectrum. Previous studies have found an accumulation of early age at onset (AAO) in BD families and have therefore hypothesized that there is a larger genetic contribution to the early‐onset cases than to late onset BD. To investigate the genetic background of this subphenotype, we evaluated whether an increased polygenic burden of BD‐ and schizophrenia (SCZ)‐associated risk variants is associated with an earlier AAO in BD patients. Methods A total of 1995 BD type 1 patients from the Consortium of Lithium Genetics (ConLiGen), PsyCourse and Bonn‐Mannheim samples were genotyped and their BD and SCZ polygenic risk scores (PRSs) were calculated using the summary statistics of the Psychiatric Genomics Consortium as a training data set. AAO was either separated into onset groups of clinical interest (childhood and adolescence [≤18 years] vs adulthood [>18 years]) or considered as a continuous measure. The associations between BD‐ and SCZ‐PRSs and AAO were evaluated with regression models. Results BD‐ and SCZ‐PRSs were not significantly associated with age at disease onset. Results remained the same when analyses were stratified by site of recruitment. Conclusions The current study is the largest conducted so far to investigate the association between the cumulative BD and SCZ polygenic risk and AAO in BD patients. The reported negative results suggest that such a polygenic influence, if there is any, is not large, and highlight the importance of conducting further, larger scale studies to obtain more information on the genetic architecture of this clinically relevant phenotype.

Introduction Instead of the typical assessment of risk of illness in the offspring based on a parent with bipolar disorder, we explored the potential multigenerational conveyance across several disorders of the vulnerability to illness in the offspring of a patient with bipolar disorder. Methods A total of 968 outpatients (average age 41 years) with bipolar illness gave informed consent and filled out a detailed questionnaire about a family history in their parents, grandparents, and offspring of: depression; bipolar disorder; alcohol abuse; substance abuse; suicide attempt; or “other” illness. Of those with children, 346 were from the USA and 132 were from Europe. Amount and type of illness in progenitors in two and three previous generations were related to offspring illness. Results The type of illness seen in both prior generations was associated with the same type of illness in the offspring of a bipolar patient, including depression, bipolar disorder, alcohol and substance abuse and “other” illness, but not suicide attempts. There was an impact of multiple generations, such that depression in grandparents and/or great‐grandparents increased the risk of depression in the offspring from 12.6% to 41.4%. Conclusions A family history of illness burden in prior generations was previously related to an earlier age of onset of bipolar illness in our adult patients with bipolar disorder and is now also found to be related to the incidence of multiple psychiatric illnesses in their offspring. Genetic and epigenetic mechanisms deserve consideration for this multigenerational conveyance of illness vulnerability, and clinical and public health attempts to prevent or slow this transmission are indicated.

Abstract The relationship of personality disorder (PD) psychopathology to the course of bipolar disorder remains inadequately described. After giving informed consent, more than 782 outpatients with bipolar disorder rated themselves on the 99-item Personality Disorder Questionnaire, Version 4 (PDQ4) when depressed or euthymic. They also rated six poor prognosis factors (PPFs). The relationships of the PPFs to the total PDQ4 score were examined by a linear regression. Even after correcting for the higher PDQ4 scores observed when patients were suffering depression, the PDQ4 was significantly related to a history of child abuse, early age of onset, an anxiety disorder comorbidity, rapid cycling, and 20 or more previous episodes, but not substance abuse. The data suggest close relationships between the total burden of PD psychopathology and correlates of an adverse outcome of bipolar disorder. The nature of this of association and approaches to treatment of comorbid PD remain to be further explored.

Objectives Health disparities between individuals of African and European ancestry are well documented. The disparities in bipolar disorder may be driven by racial bias superimposed on established factors contributing to misdiagnosis, including: evolving empirically based diagnostic criteria (International Classification of Diseases [ ICD ], Research Diagnostic Criteria [ RDC ] and Diagnostic and Statistical Manual [ DSM ]), multiple symptom domains (i.e. mania, depression and psychosis), and multimodal medical and additional psychiatric comorbidity. Methods For this paper, we reviewed the phenomenological differences between bipolar individuals of African and European ancestry in the context of diagnostic criteria and clinical factors that may contribute to a potential racial bias. Results Published data show that bipolar persons of African ancestry, compared with bipolar persons of non‐African ancestry, are more often misdiagnosed with a disease other than bipolar disorder (i.e. schizophrenia). Additionally, studies show that there are disparities in recruiting patients of African ancestry to participate in important genomic studies. This gap in biological research in this underrepresented minority may represent a missed opportunity to address potential racial differences in the risk and course of bipolar illness. Conclusion A concerted effort by the research community to increase inclusion of diverse persons in studies of bipolar disorder through community engagement may facilitate fully addressing these diagnostic and treatment disparities in bipolar individuals of African ancestry.

Antidepressants have reduced the symptom burden for many Major Depressive Disorder (MDD) patients, but drug‐related side effects and treatment resistance continue to present major challenges. Pharmacogenomics represents one approach to enhance antidepressant efficacy and avoid adverse reactions, but concerns remain with regard to the overall “value equation,” and several barriers must be overcome to achieve the full potential of MDD pharmacogenomics.