Citations

Abstract Background Acamprosate is an FDA-approved medication for the treatment of alcohol use disorder (AUD), however, only about 50% of patients will respond to acamprosate. Previously, we conducted an acamprosate trial (n=442), which is one of the largest acamprosate clinical studies ever conducted and currently the only study with multi-omics data. To date, no biological measures are utilized to predict response to acamprosate treatment. Aims & Objectives We set out to apply our established pharmaco-omics informed genomics strategy to identify potential biomarkers associated with acamprosate treatment response. Method Our open-label acamprosate clinical trial recruited 442 patients with AUD, all of whom were treated with acamprosate for three months. The primary outcomes were 1) relapse to alcohol use and 2) relapse to heavy drinking. Alcohol consumption was measured using the self-reported timeline follow back method. Plasma protein levels were measured using Olink proximity extension immunoassays. Results Our established “Pharmaco-Omics-informed genome-wide association study (GWAS)” research strategy identified 12 proteins, including interleukin-17 receptor B (IL17RB), that were associated with acamprosate treatment response. A GWAS for IL17RB concentrations identified several genome-wide significant signals. Specifically, the top hit single nucleotide polymorphism (SNP) rs6801605 with a minor allele frequency of 38% in the European American population mapped 4 kilobase (Kb) upstream of IL17RB, and intron 1 of the choline dehydrogenase (CHDH) gene on chromosome 3 (p: 4.8E-20). The variant genotype (AA) for the SNP rs6801605 was associated with lower IL17RB protein expression. In addition, we identified a series of genetic variants in IL17RB that were associated with acamprosate treatment outcomes. Furthermore, the variant genotypes for all of those IL17RB SNPs were protective against alcohol relapse. Finally, we demonstrated that the basal level of mRNA expression of IL17RB was inversely correlated with those of nuclear factor-κB (NF-κB) subunits, and a significantly higher expression of NF-κB subunits was observed in AUD patients who relapsed to alcohol use. Discussion & Conclusions This study illustrates that IL17RB genetic variants might contribute to acamprosate treatment outcomes. This series of studies represents an important step toward generating functional hypotheses that could be tested to gain insight into mechanisms underlying variation in acamprosate treatment response phenotypes.

Abstract Background: Responsiveness to mood-stabilizing pharmacotherapy varies in bipolar disorder (BD). We investigated clinical correlates of second-generation antipsychotic (SGA) treatment response and conducted the first genome-wide association study (GWAS), including exploratory polygenic scores (PGS), of SGA pharmacogenomic treatment response in BD. Methods: Treatment response was quantified using the Alda scale, and GWAS was performed using Alda-A score, controlling for sex, genotyping batch, and the genomic principal components. Results: The cohort included 2,159 adults with BD (1,416 BD-I, 691 BD-II, 51 schizoaffective BD), mean age 41.8 years, 62% female, 84% white, and 14% Hispanic. Nearly half (48%) were treated with SGAs. Current SGA users were younger (41.2±14.7 vs. 42.5±15.3 years, p =0.040), more likely to be Hispanic (14% vs. 11%, p =0.047), had a higher body mass index (BMI; 30.4±7.6 vs. 29.5±7.1 kg/m 2 , p =0.005). Lifetime comorbidity patterns for current SGA users include higher rates of manic psychosis (29% vs. 17%, p <0.001) and eating disorders — Anorexia Nervosa (7% vs. 4%, p =0.003), Bulimia Nervosa (7% vs. 4%, p =0.003), and Binge Eating Disorder (14% vs. 11%, p =0.030). We detected a genome-wide significant association between SGA Alda-A scores and GAS7 variants (top variant: rs202127418, β =-2.998, p =4.96E-08). However, SGA response was not significantly associated with PGS for schizophrenia, BD, and major depression (FDR>0.05). Conclusions: SGAs are frequently utilized as mood stabilizers in patients with BD and are associated with manic psychosis and eating disorders. GAS7 variants may predict SGA response, but larger, more diverse cohorts are needed for validation.

ABSTRACT Bipolar disorder’s (BD) clinical heterogeneity has an unresolved genetic basis. We meta-analyzed genome-wide association studies (GWAS) of 16 BD subphenotypes in 226,032 individuals from 57 cohorts (38,022 cases); 10 advanced to multivariate and multi-trait analyses. Four factors (compulsive, psychotic, dysregulated, internalizing) explained 82.8% of shared genetic variance. BD1 and BD2 loaded on distinct factors despite a high genetic correlation; 87.0% of common-factor loci were significant in neither subtype. Unipolar mania aligned with psychosis over internalizing, and was distinguishable from BD1, and rapid cycling showed heritable cross-domain liability. We identified 356 risk loci, 158 novel, including the first univariate-GWAS associations for psychosis, unipolar mania, rapid cycling and schizoaffective disorder—and 249 credible genes (89 high-confidence), 12 with approved-drug or clinical-phase annotations. Cell-type association showed a midbrain dopaminergic–GABAergic gradient along the psychotic factor. BD’s genetic architecture appears hierarchical—a general liability resolving into dimensions of course and comorbidity beyond diagnostic subtypes.

Abstract Background In two 12‐week, randomized, placebo‐controlled trials (RCTs) in individuals with alcohol use disorder (AUD), topiramate significantly reduced heavy drinking days (HDDs), and alcohol‐related problems. In a secondary analysis of those findings, we examined four broad measures of genetic risk—polygenic scores (PGS)—of problematic alcohol use (PAU), drinks per week (DPW), and time to relapse to any drinking (TR) and heavy drinking (THR) as moderators of topiramate's effect on HDDs and alcohol‐related problems. Methods We analyzed data from 285 individuals with AUD (65.6% male) of European‐like ancestry, who were treated with either topiramate (49.1%) or placebo (50.9%). All patients underwent genome‐wide array genotyping, and PGS were calculated using summary statistics from genome‐wide association studies of PAU, DPW, and TR and THR (two time‐to‐event outcomes among patients treated in AUD pharmacotherapy trials). We hypothesized an interaction effect in which greater genetic risk—particularly for PAU—would be associated with a greater therapeutic response to topiramate than placebo. Results As shown previously, topiramate significantly reduced both HDDs (odds ratio [OR] = 0.50, p < 0.001) and Short Index of Problems (SIP) scores ( b = −3.04, p < 0.001) more than placebo. There were nonsignificant associations of higher PGS with more HDDs (OR = 1.17, 95% CI = 0.98–1.41, p = 0.091) and a greater reduction in HDDs in the topiramate group (OR = 0.80, 95% CI = 0.62–1.03, p = 0.089). There were also significant interaction effects with treatment on SIP score by PGS for PAU ( b = −1.64, SE = 0.78, p = 0.033), TR ( b = −2.16, SE = 0.72, p = 0.003), and TRH ( b = −2.17, SE = 0.72, p = 0.003). Conclusions These findings provide proof of principle for the use of alcohol‐related PGS as moderators of the effects of topiramate for treating AUD. Larger RCTs of topiramate are needed to provide adequate statistical power to validate this pharmacogenetic approach to precision AUD treatment.

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.

Alcohol use disorder (AUD) is the most prevalent substance use disorder. Excessive alcohol consumption leads to a range of health issues. We set out to identify inflammatory markers linked to alcohol consumption, which might ultimately offer novel insight into genetic underpinnings and have implications for alcohol-associated disease. Alcohol consumption and blood-based multi-omics data were collected by The Mayo Clinic Center for Individualized Treatment of Alcohol Dependence study. Plasma samples from patients with AUD were used for proteomics analysis using the OLINK “Explore Inflammation” panel (n=410). Liver enzymes were also measured. A genome-wide association study (GWAS) was performed to explore the relationship between genetic variants and plasma TREM2 levels. Our findings show thatplasma triggering receptor expressed on myeloid cells 2 (TREM2), a key gene associated with neurodegenerative disease, was the most significant signal correlated with alcohol consumption, and has also been associated with liver enzyme levels in patients with AUD. We identified the rs7232 single nucleotide polymorphism (SNP) in MS4A6A as a key genetic variant associated with plasma TREM2 levels, with the minor allele (A) linked to higher TREM2 levels and increased alcohol consumption, particularly in men. Furthermore, MA4A6A is an ethanol-responsive gene in a SNP-dependent manner, and the variant genotype of the rs7232 SNP was associated with lower expression for MA4A6A due to proteasome-mediated protein degradation. In summary, this study provides insight into the relationship between plasma TREM2 levels, alcohol consumption, and liver function in AUD patients, shedding light on genetic factors underlying alcohol-related diseases.

Abstract Objectives Social support (SS) and social isolation (SI) are social determinants of health (SDOH) associated with psychiatric outcomes. In electronic health records (EHRs), individual-level SS/SI is typically documented in narrative clinical notes rather than as structured coded data. Natural language processing (NLP) algorithms can automate the otherwise labor-intensive process of extraction of such information. Materials and Methods Psychiatric encounter notes from Mount Sinai Health System (MSHS, n = 300) and Weill Cornell Medicine (WCM, n = 225) were annotated to create a gold-standard corpus. A rule-based system (RBS) involving lexicons and a large language model (LLM) using FLAN-T5-XL were developed to identify mentions of SS and SI and their subcategories (eg, social network, instrumental support, and loneliness). Results For extracting SS/SI, the RBS obtained higher macroaveraged F1-scores than the LLM at both MSHS (0.89 versus 0.65) and WCM (0.85 versus 0.82). For extracting the subcategories, the RBS also outperformed the LLM at both MSHS (0.90 versus 0.62) and WCM (0.82 versus 0.81). Discussion and Conclusion Unexpectedly, the RBS outperformed the LLMs across all metrics. An intensive review demonstrates that this finding is due to the divergent approach taken by the RBS and LLM. The RBS was designed and refined to follow the same specific rules as the gold-standard annotations. Conversely, the LLM was more inclusive with categorization and conformed to common English-language understanding. Both approaches offer advantages, although additional replication studies are warranted.

Abstract Background Alcohol consumption behaviors and alcohol use disorder risk and presentation differ by sex, and these complex traits are associated with blood concentrations of the steroid sex hormones, testosterone and estradiol, and their regulatory binding proteins, sex hormone binding globulin (SHBG) and albumin. Genetic variation is associated with alcohol consumption and alcohol use disorder, as well as levels of steroid sex hormones and their binding proteins. Methods To assess the contribution of genetic factors to previously described phenotypic associations between alcohol-use traits and sex-hormone levels, we estimated genetic correlations (rg) using summary statistics from prior published, large sample size genome-wide association studies (GWAS) of alcohol consumption, alcohol dependence, testosterone, estradiol, SHBG, and albumin. Results For alcohol consumption, we observed positive genetic correlation (i.e. genetic effects in the same direction) with total testosterone in males (rg = 0.084, p = 0.007) and trends toward positive genetic correlation with bioavailable testosterone (rg = 0.060, p = 0.084) and SHBG in males (rg = 0.056, p = 0.086) and with albumin in a sex-combined cohort (rg = 0.082, p = 0.015); however in females, we observed positive genetic correlation with SHBG (rg = 0.089, p = 0.004) and a trend toward negative genetic correlation (i.e. genetic effects in opposite directions) with bioavailable testosterone (rg = -0.064, p = 0.032). For alcohol dependence, we observed a trend toward negative genetic correlation with total testosterone in females (rg = -0.106, p = 0.024) and positive genetic correlation with BMI-adjusted SHBG in males (rg = 0.119, p = 0.017). Several of these genetic correlations differed between females and males and were not in the same direction as the corresponding phenotypic associations. Conclusions Findings suggest that shared genetic effects may contribute to positive associations of alcohol consumption with albumin in both sexes, as well as positive associations between alcohol consumption and bioavailable testosterone and between alcohol dependence and SHBG in males. However, relative contributions of heritable and environmental factors to associations between alcohol-use traits and sex-hormone levels may differ by sex, with genetic factors contributing more in males and environmental factors contributing more in females.