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Honeycutt DC, Blom TJ, Ramsey LB, Strawn JR, Bruns KM, Welge JA, Patino LR, Singh MK, DelBello MP. Pharmacogenetic Factors Influence Escitalopram Pharmacokinetics and Adverse Events in Youth with a Family History of Bipolar Disorder: A Preliminary Study. J Child Adolesc Psychopharmacol 2024; 34:42-51. [PMID: 38377518 PMCID: PMC10880264 DOI: 10.1089/cap.2023.0073] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 02/22/2024]
Abstract
Introduction: Escitalopram is an effective and generally well-tolerated antidepressant, but children of parents with bipolar disorder (BD) may be at increased risk for adverse events associated with antidepressants, including increased irritability, restlessness, impulsivity, and manic symptoms. This risk may be influenced by polymorphisms in genes encoding cytochrome P450 enzymes (CYP2C19 or CYP2D6), the serotonin transporter (SLC6A4), and the serotonin receptor 2A subtype (HTR2A). We explored whether gene-drug interactions influence the emergence of adverse events in depressed and/or anxious youth with a family history of BD. Materials and Methods: Children and adolescents aged 12-17 years with a first-degree relative with bipolar I disorder were treated with escitalopram and monitored for adverse effects, underwent pharmacogenetic testing, and provided serum escitalopram levels. Emergence of adverse events was determined by study clinicians, and symptoms were tracked using the Treatment-Emergent Activation and Suicidality Assessment Profile (TEASAP) and Pediatric Adverse Events Rating Scale. Clinical Pharmacogenetics Implementation Consortium guidelines were used to determine CYP2C19 and CYP2D6 phenotypes. Results: Slower CYP2C19 metabolizers had greater dose-normalized 24-hour area under the curve (AUC0-24; p = 0.025), trough concentrations (Ctrough; p = 0.013), and elimination half-lives (t1/2; p < 0.001). CYP2D6 phenotype was not significantly associated with any pharmacokinetic parameter. Slower CYP2D6 metabolizers had increased TEASAP akathisia (p = 0.015) scores. HTR2A A/A and A/G genotypes were associated with increased TEASAP "self-injury, suicidality, and harm to others" subscale scores (p = 0.017). Escitalopram maximum concentration, AUC0-24, CYP2C19 phenotype, and SLC6A4 genotype were not associated with adverse events. Conclusions: CYP2C19 phenotype influences escitalopram pharmacokinetics whereas CYP2D6 phenotype does not. Slower CYP2D6 metabolism was associated with increased akathisia, and HTR2A A/A or A/G genotypes were associated with increased risk of self-harm or harm to others. Larger cohorts are needed to identify associations between genetic test results and antidepressant-associated adverse events. Trial Registration: ClinicalTrials.gov identifier: NCT02553161.
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Affiliation(s)
- Duncan C. Honeycutt
- Department of Psychiatry and Behavioral Neuroscience, University of Cincinnati College of Medicine, Cincinnati, Ohio, USA
| | - Thomas J. Blom
- Department of Psychiatry and Behavioral Neuroscience, University of Cincinnati College of Medicine, Cincinnati, Ohio, USA
| | - Laura B. Ramsey
- Department of Psychiatry and Behavioral Neuroscience, University of Cincinnati College of Medicine, Cincinnati, Ohio, USA
- Division of Clinical Pharmacology, Toxicology and Therapeutic Innovation, Children's Mercy Kansas City, Kansas City, Missouri, USA
- Department of Pediatrics, School of Medicine, University of Missouri-Kansas City, Kansas City, Missouri, USA
| | - Jeffrey R. Strawn
- Department of Psychiatry and Behavioral Neuroscience, University of Cincinnati College of Medicine, Cincinnati, Ohio, USA
- Division of Child and Adolescent Psychiatry, Cincinnati Children's Hospital Medical Center, Cincinnati, Ohio, USA
| | - Kaitlyn M. Bruns
- Department of Psychiatry and Behavioral Neuroscience, University of Cincinnati College of Medicine, Cincinnati, Ohio, USA
| | - Jeffrey A. Welge
- Department of Psychiatry and Behavioral Neuroscience, University of Cincinnati College of Medicine, Cincinnati, Ohio, USA
| | - Luis R. Patino
- Department of Psychiatry and Behavioral Neuroscience, University of Cincinnati College of Medicine, Cincinnati, Ohio, USA
| | - Manpreet K. Singh
- Department of Psychiatry and Behavioral Sciences, Stanford University School of Medicine, Stanford, California, USA
| | - Melissa P. DelBello
- Department of Psychiatry and Behavioral Neuroscience, University of Cincinnati College of Medicine, Cincinnati, Ohio, USA
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Pan N, Qin K, Patino LR, Tallman MJ, Lei D, Lu L, Li W, Blom TJ, Bruns KM, Welge JA, Strawn JR, Gong Q, Sweeney JA, Singh MK, DelBello MP. Aberrant brain network topology in youth with a familial risk for bipolar disorder: a task-based fMRI connectome study. J Child Psychol Psychiatry 2024. [PMID: 38220469 DOI: 10.1111/jcpp.13946] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Accepted: 11/26/2023] [Indexed: 01/16/2024]
Abstract
BACKGROUND Youth with a family history of bipolar disorder (BD) may be at increased risk for mood disorders and for developing side effects after antidepressant exposure. The neurobiological basis of these risks remains poorly understood. We aimed to identify biomarkers underlying risk by characterizing abnormalities in the brain connectome of symptomatic youth at familial risk for BD. METHODS Depressed and/or anxious youth (n = 119, age = 14.9 ± 1.6 years) with a family history of BD but no prior antidepressant exposure and typically developing controls (n = 57, age = 14.8 ± 1.7 years) received functional magnetic resonance imaging (fMRI) during an emotional continuous performance task. A generalized psychophysiological interaction (gPPI) analysis was performed to compare their brain connectome patterns, followed by machine learning of topological metrics. RESULTS High-risk youth showed weaker connectivity patterns that were mainly located in the default mode network (DMN) (network weight = 50.1%) relative to controls, and connectivity patterns derived from the visual network (VN) constituted the largest proportion of aberrant stronger pairs (network weight = 54.9%). Global local efficiency (Elocal , p = .022) and clustering coefficient (Cp , p = .029) and nodal metrics of the right superior frontal gyrus (SFG) (Elocal : p < .001; Cp : p = .001) in the high-risk group were significantly higher than those in healthy subjects, and similar patterns were also found in the left insula (degree: p = .004; betweenness: p = .005; age-by-group interaction, p = .038) and right hippocampus (degree: p = .003; betweenness: p = .003). The case-control classifier achieved a cross-validation accuracy of 78.4%. CONCLUSIONS Our findings of abnormal connectome organization in the DMN and VN may advance mechanistic understanding of risk for BD. Neuroimaging biomarkers of increased network segregation in the SFG and altered topological centrality in the insula and hippocampus in broader limbic systems may be used to target interventions tailored to mitigate the underlying risk of brain abnormalities in these at-risk youth.
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Affiliation(s)
- Nanfang Pan
- Huaxi MR Research Center (HMRRC), Department of Radiology, Research Unit of Psychoradiology, Chinese Academy of Medical Sciences, Functional & Molecular Imaging Key Laboratory of Sichuan Province, West China Hospital of Sichuan University, Chengdu, China
- Department of Radiology, Taihe Hospital, Hubei University of Medicine, Shiyan, China
- Department of Psychiatry, University of Cincinnati, Cincinnati, USA
| | - Kun Qin
- Department of Radiology, Taihe Hospital, Hubei University of Medicine, Shiyan, China
- Department of Psychiatry, University of Cincinnati, Cincinnati, USA
| | - Luis R Patino
- Department of Psychiatry, University of Cincinnati, Cincinnati, USA
| | | | - Du Lei
- College of Medical Informatics, Chongqing Medical University, Chongqing, China
| | - Lu Lu
- Huaxi MR Research Center (HMRRC), Department of Radiology, Research Unit of Psychoradiology, Chinese Academy of Medical Sciences, Functional & Molecular Imaging Key Laboratory of Sichuan Province, West China Hospital of Sichuan University, Chengdu, China
- Department of Psychiatry, University of Cincinnati, Cincinnati, USA
| | - Wenbin Li
- Huaxi MR Research Center (HMRRC), Department of Radiology, Research Unit of Psychoradiology, Chinese Academy of Medical Sciences, Functional & Molecular Imaging Key Laboratory of Sichuan Province, West China Hospital of Sichuan University, Chengdu, China
| | - Thomas J Blom
- Department of Psychiatry, University of Cincinnati, Cincinnati, USA
| | - Kaitlyn M Bruns
- Department of Psychiatry, University of Cincinnati, Cincinnati, USA
| | - Jeffrey A Welge
- Department of Psychiatry, University of Cincinnati, Cincinnati, USA
| | - Jeffrey R Strawn
- Department of Psychiatry, University of Cincinnati, Cincinnati, USA
| | - Qiyong Gong
- Huaxi MR Research Center (HMRRC), Department of Radiology, Research Unit of Psychoradiology, Chinese Academy of Medical Sciences, Functional & Molecular Imaging Key Laboratory of Sichuan Province, West China Hospital of Sichuan University, Chengdu, China
- Department of Radiology, West China Xiamen Hospital of Sichuan University, Xiamen, OH, China
| | - John A Sweeney
- Huaxi MR Research Center (HMRRC), Department of Radiology, Research Unit of Psychoradiology, Chinese Academy of Medical Sciences, Functional & Molecular Imaging Key Laboratory of Sichuan Province, West China Hospital of Sichuan University, Chengdu, China
- Department of Psychiatry, University of Cincinnati, Cincinnati, USA
| | - Manpreet K Singh
- Department of Psychiatry and Behavioral Sciences, Stanford University School of Medicine, California, USA
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