Citations

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.

Abstract Catechol-O-methyltransferase (COMT) regulates extracellular catecholamines. A widely studied COMT single nucleotide polymorphism (rs4680) changes the translated amino acid from valine to methionine (Val158Met); the polymorphism has been shown to influence opioid use. The aims of this study were to investigate the influence of COMT Val158Met on the likelihood and dose of opioid use in adults with chronic pain. Adults with chronic pain consecutively admitted to an outpatient pain rehabilitation program were recruited for study participation (N = 298). Individuals were genotyped for COMT Val158Met (rs4680). The polymorphism was analyzed using an additive and codominant genotype model. The distribution of genotypes was 23% (N = 70) for Val/Val, 49% (N = 146) for Val/Met, and 27% (N = 82) for Met/Met (Hardy–Weinberg, P > 0.90). No significant association was observed between opioid use and genotype under the additive model; however, a significant association was observed under the codominant model (P = 0.027). A post hoc comparison demonstrated that the Met/Met genotype was more likely to use opioids compared with the Val/Met genotype (P = 0.0089). No significant association was observed between morphine equivalent dose and genotype under the additive model; however, a significant association was observed under the codominant model (P = 0.0496). A post hoc comparison demonstrated that the Val/Met (P = 0.019) and Met/Met (P = 0.043) genotypes used greater morphine equivalent dose compared with the Val/Val genotype. This study extends key knowledge about the influence of the Met/Met genotype and Met allele on opioid use in adults with chronic pain.

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.

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.