Citations

Abstract Background: Pharmacogenomic studies on antidepressant treatment outcomes could be conducted using previously collected data from electronic health record (EHR)-linked biobanks. However, absence of EHR based outcome measures is an unmet need in designing such studies We aimed to define EHR-derived antidepressant outcome measures and explore their utility in showing associations between treatment outcomes and Cytochrome P450 (CYP) metabolizer phenotypes in a proof-of-concept study. Methods: Using data from the EHR-linked cohort, Right Drug, Right Dose, Right Time: Using Genomic Data to Individualize Treatment (RIGHT 10K) Study, we collected prescription data and patient health questionnaire 9 (PHQ-9) scores to compute 3 proxy measures for antidepressant response, efficacy, and acceptability: change in PHQ-9 scores, longest treatment interval with a single antidepressant, and antidepressant non-refill. Subsequently, we tested the association of both prescription-based outcomes with DNA-predicted CYP metabolizer phenotypes in European-ancestry participants. Results: We identified 3920 RIGHT 10K participants with at least 1 antidepressant prescription and European-ancestry. Participants had a mean age of 61 years and 72% were women. Implementation of the PHQ-9 outcome was not feasible because of missingness. Of both prescription-based outcomes, antidepressant non-refill reproduced several known antidepressant-CYP interactions. However, the pilot was limited by small subgroups of participants with non-normal metabolizer phenotypes. Conclusions: Derived from structured data, antidepressant non-refill is a promising outcome measure for EHR-linked biobanks that partially reproduced antidepressant-CYP interactions. However, testing on larger datasets is necessary to understand whether it would be a useful for pharmacogenomic research.

ABSTRACTBackgroundInsomnia and substance use disorders (SUD) are common comorbidities of bipolar disorder (BD). Genome‐wide association studies (GWAS) have uncovered shared genetic contributions to insomnia and BD as well as SUDs and BD. Electronic health record (EHR) derived phenotypes (phecodes) and questionnaire data were used to examine the relationship between insomnia genetic liability and SUDs in BD.Methods40,839 participants from the Mayo Clinic Bipolar Disorder Biobank (BD Biobank; n = 774) and Mayo Clinic Biobank (n = 485 BD cases, n = 39,580 controls) were included in the analyses of diagnosis (phecode) outcomes (insomnia, SUD, alcohol use disorder [AUD] and tobacco use disorder [TUD]). Analyses of specific SUD outcomes obtained through the BD Biobank questionnaire included 1789 cases and considered BD subtype. Logistic regression was used to test for associations between insomnia polygenic risk scores (PRS) and insomnia and SUD outcomes in BD cases and controls.ResultsInsomnia PRS was associated with having an insomnia diagnosis (phecode) in the EHR in controls (OR = 1.19, p = 9.64e‐33) but not in BD cases (OR = 1, p = 0.95). Associations between insomnia PRS and SUD diagnoses were significant in BD cases and controls, with the association being stronger in BD cases (interaction p = 0.024). In the BD Biobank data, the insomnia PRS was associated with increased odds of AUD (OR = 1.19, p = 4.26e‐04), TUD (OR = 1.21, p = 1.25e‐05) and cannabis use disorder (OR = 1.16, p = 4.19e‐03).ConclusionThe effect of genetic predisposition to insomnia on SUD risk may be stronger in BD cases than in controls, which could have clinical care implications for individuals with BD and comorbid SUD.

Bipolar disorder (BD) is characterized by disrupted circadian rhythms and neuronal loss. Lithium amplifies circadian rhythms and is neuroprotective, indicating mechanistic overlap across cellular systems. We examined the role of neuroprotection in determining lithium response and how circadian clock genes regulate apoptosis. In stem‐cell‐derived neuronal progenitor cells (NPCs) from BD patients, and in immortalized mouse hippocampal neurons, lithium was neuroprotective against staurosporine (STS)‐induced apoptosis. However, neuroprotection did not distinguish lithium‐responders (Li‐Rs) from nonresponders (Li‐NRs). Disruption of selected clock genes—period circadian regulator 1 ( PER1 ), basic helix–loop–helix ARNT like 1 ( ARNTL , also known as BMAL1 ), and nuclear receptor subfamily 1 group D member 1 ( NR1D1 ; also known as REV‐ERBα )—with small interfering RNA (siRNA) had distinct effects on apoptosis in mouse neurons and human NPCs. In BD patient NPCs, reduced expression of PER1 and BMAL1 increased apoptosis more in Li‐NR NPCs vs. Li‐R NPCs. Reduced expression of REV‐ERBα caused more extensive apoptosis in BD vs. control NPCs, but did not distinguish Li‐Rs vs. Li‐NRs. The REV‐ERB agonist GSK4112 was neuroprotective in mouse neurons and control NPCs, but did not reduce apoptosis in BD NPCs. Simultaneous treatment of NPCs with STS and GSK4112 revealed BD‐ and Li‐R‐associated differences in apoptosis‐related gene expression. In summary, the pharmacological effects of lithium in promoting neuroprotection are not sufficient to distinguish between Li‐Rs and Li‐NRs. However, genetic and pharmacological manipulations of the circadian clock reveal distinct roles in apoptosis in BD patient NPCs, some of which do distinguish Li‐Rs and Li‐NRs. We conclude that genetic disruption of cellular circadian clocks may predispose neurons to apoptosis preferentially in BD and/or Li‐NR. These findings imply that differences in BD between Li‐Rs/Li‐NRs primarily arise from defining biological characteristics of patient subgroups, not from distinct pharmacodynamic factors following lithium treatment.

Abstract Background Clozapine is effective for treatment-resistant schizophrenia and bipolar disorder but is often discontinued due to adverse effects. This study compared early clozapine discontinuation rates and reasons in patients with mood and psychotic disorders. Methods Data from all individuals with mood or psychotic disorders who initiated clozapine for the first time at the inpatient psychiatric unit of Mayo Clinic, Rochester, Minnesota, between 2014 and 2022 were retrospectively analyzed. Early clozapine discontinuation, defined as discontinuation within 90 days of initiation, was the primary outcome. Cox proportional hazards regression was used to assess factors associated with discontinuation. Results Of 83 patients (mood group n = 37, psychosis group n = 46), those in the mood group were older (P = 0.022) and more likely to be nonsmokers (P = 0.034). The overall 90-day clozapine discontinuation rate was 45.7%. Early discontinuation was significantly higher in the mood group than in the psychosis group (hazard ratio = 2.41, 95% confidence interval = 1.26–4.64, P = 0.008). Other factors associated with early discontinuation were female sex (P = 0.033), older age (P = 0.026), and nonsmoking (P = 0.001). In multivariable analysis, smoking status was the only factor significantly inversely associated with early clozapine discontinuation (hazard ratio = 0.47, 95% confidence interval = 0.22–0.99, P = 0.048), while diagnostic group, sex, and age did not show significant associations (all P > 0.05). Discontinuations were primarily due to adverse drug reactions in both groups. Conclusions Nearly half of the patients discontinued clozapine early, with higher rates in the mood group. Studies should further explore potential pharmacodynamic and pharmacokinetic factors associated with discontinuation, including the influence of smoking. Careful monitoring and personalized management of side effects are crucial for optimizing clozapine therapy and improving treatment outcomes.

In this case series, we present several examples from participants (2 patients and 1 healthy control) of a 12-week pilot feasibility study to create a digital phenotype of depression (unipolar or bipolar) through active and passive data collection from a smartphone and a wearable device combined with routine clinical care for mood disorders. The selected cases represent real clinical examples that highlight the intrinsic challenges that should be expected when conducting similar studies, including appropriate health data privacy protection, clinical standardization, and interindividual differences in levels of engagement and acceptability of active and passive data collection (ie, self-reported, behavioral, cognitive, and physiological data), particularly with patient-generated data in mobile apps, digital proficiency habituation, and consistent use of wearable devices. In the context of the rapidly growing use of digital technologies in psychiatry, anticipating challenges for the integration of personal mobile devices and smartphone mental health apps as aides to track specific aspects of depressive disorders is critical for a clinically meaningful digital transformation of mood disorders care.

Background: Bipolar disorder (BD) involves significant mood and energy shifts reflected in speech patterns. Detecting these patterns is crucial for diagnosis and monitoring, currently assessed subjectively. Advances in natural language processing offer opportunities to objectively analyze them. Aims: To (i) correlate speech features with manic-depressive symptom severity in BD, (ii) develop predictive models for diagnostic and treatment outcomes, and (iii) determine the most relevant speech features and tasks for these analyses. Methods: This naturalistic, observational study involved longitudinal audio recordings of BD patients at euthymia, during acute manic/depressive phases, and after-response. Patients participated in clinical evaluations, cognitive tasks, standard text readings, and storytelling. After automatic diarization and transcription, speech features, including acoustics, content, formal aspects, and emotionality, will be extracted. Statistical analyses will (i) correlate speech features with clinical scales, (ii) use lasso logistic regression to develop predictive models, and (iii) identify relevant speech features. Results: Audio recordings from 76 patients (24 manic, 21 depressed, 31 euthymic) were collected. The mean age was 46.0 ± 14.4 years, with 63.2% female. The mean YMRS score for manic patients was 22.9 ± 7.1, reducing to 5.3 ± 5.3 post-response. Depressed patients had a mean HDRS-17 score of 17.1 ± 4.4, decreasing to 3.3 ± 2.8 post-response. Euthymic patients had mean YMRS and HDRS-17 scores of 0.97 ± 1.4 and 3.9 ± 2.9, respectively. Following data pre-processing, including noise reduction and feature extraction, comprehensive statistical analyses will be conducted to explore correlations and develop predictive models. Conclusions: Automated speech analysis in BD could provide objective markers for psychopathological alterations, improving diagnosis, monitoring, and response prediction. This technology could identify subtle alterations, signaling early signs of relapse. Establishing standardized protocols is crucial for creating a global speech cohort, fostering collaboration, and advancing BD understanding.

Abstract Bipolar disorder (BD) is characterized by disrupted circadian rhythms and neuronal loss. Lithium is neuroprotective and used to treat BD, but outcomes are variable. Past research identified that circadian rhythms in BD patient neurons are associated with lithium response (Li-R) or non-response (Li-NR). However, the underlying cellular mechanisms remain unknown. To study interactions among circadian clock genes and cell survival, and their role in BD and predicting lithium response, we tested selected genes (PER1, BMAL1 and REV-ERBα) and small molecule modulators of ROR/REV-ERB nuclear receptors in models of cell survival using mouse neurons and stem-cell derived neuronal progenitor cells (NPC) from BD patients and controls. In apoptosis assays using staurosporine (STS), lithium was neuroprotective. Knockdown of PER1, BMAL1 and REV-ERBα modified cell survival across models. In NPCs, reduced expression of PER1 and BMAL1 led to more extensive cell death in Li-NR vs. Li-R. Reduced REV-ERBα expression caused more extensive cell death in BD vs. control NPCs, without distinguishing Li-R and Li-NR. In IMHN, The REV-ERB agonist GSK4112 had strong effects on circadian rhythm amplitude, and was neuroprotective in mouse neurons and control NPCs, but not in BD NPCs. Expression of cell survival genes following STS and GSK4112 treatments revealed BD-associated, and Li-R associated differences in expression profiles. We conclude that the neuroprotective response to lithium is similar in NPCs from Li-R and Li-NR. However, knockdown of circadian clock genes or stimulation of REV-ERBs reveal distinct contributions to cell death in BD patient NPCs, some of which distinguish Li-R and Li-NR.

Background: Depression is prevalent in adults with cardiovascular disease; however, the association between depression and death in older adults treated with percutaneous coronary intervention (PCI) is not known. Method: We prospectively studied 596 patients 65 years of age or older undergoing PCI at Mayo Clinic (Rochester, MN and Franciscan Skemp Hospital in La Crosse, WI) from October 2005 through September 2008. Depression was assessed using the validated and reliable Center for Epidemiological Studies Depression (CES-D) scale. Patients were categorized into groups by depression symptom severity: none/mild (CES-D <16; referent), moderate (CES-D 16-21), or moderate/severe (CES-D>21). Patients were followed annually for death. Results: Mean age of patients was 75.0±6.4 years; 31% were women, 98.0% were white. There were 461 patients (77.3%) that were not depressed and 135 patients (22.7%) that were depressed; 67 patients (11.3%) had moderate depressive scores (CES-D 16-21) and 68 patients (11.4%) had moderate/severe scores (CES-D >21). Groups were similar with respect to baseline characteristics. After a median follow-up of 36 months, there were 74 deaths, and patients with moderate/severe depressive symptom scores had a higher cumulative incidence of mortality (p = 0.036 by log-rank) (Figure). In multivariable analysis, CES-D score >21 was independently predictive of all-cause mortality (HR 1.90; 95% CI 1.02-3.53; p = 0.043), along with other covariates, including age (HR 1.88; 95% CI 1.31-2.68), comorbidity (Charlson Index) (HR 1.08; 95% CI 1.02-1.15), and diabetes mellitus (HR 2.04; 95% CI 1.26-3.30). Conclusion: Over one-fifth of older adults undergoing PCI met criteria for depression and those with high depressive scores reflecting greater self-perceived depressive symptoms had worse survival. Recognition of depressive symptoms in older adults treated with PCI identifies a cohort at higher risk of adverse outcomes.