<b><i>Background:</i></b> Suicidal thoughts and behaviors (STBs) and nonsuicidal self-injury (NSSI) behaviors are moderately heritable and may reflect an underlying predisposition to depression, impulsivity, and cognitive vulnerabilities to varying degrees. <b><i>Objectives:</i></b> We aimed to estimate the degrees of association between genetic liability to depression, impulsivity, and cognitive performance and STBs and NSSI in a high-risk sample. <b><i>Methods:</i></b> We used data on 7,482 individuals of European ancestry and 3,359 individuals of African ancestry from the Collaborative Study on the Genetics of Alcoholism to examine the links between polygenic scores (PGSs) for depression, impulsivity/risk-taking, and cognitive performance with 3 self-reported indices of STBs (suicidal ideation, persistent suicidal ideation defined as ideation occurring on at least 7 consecutive days, and suicide attempt) and with NSSI. <b><i>Results:</i></b> The PGS for depression was significantly associated with all 4 primary self-harm measures, explaining 0.6–2.5% of the variance. The PGS for risk-taking behaviors was also associated with all 4 self-harm behaviors in baseline models, but was no longer associated after controlling for a lifetime measure of DSM-IV alcohol dependence and abuse symptom counts. Polygenic predisposition for cognitive performance was negatively associated with suicide attempts (<i>q</i> = 3.8e−4) but was not significantly associated with suicidal ideation nor NSSI. We did not find any significant associations in the African ancestry subset, likely due to smaller sample sizes. <b><i>Conclusions:</i></b> Our results encourage the study of STB as transdiagnostic outcomes that show genetic overlap with a range of risk factors.

Introduction Tobacco use disorder is a complex behavior with a strong genetic component. Genome-wide association studies (GWAS) on smoking behaviors allows for the creation of polygenic risk scores (PRSs) to approximate genetic vulnerability. However, the utility of smoking-related PRSs in predicting smoking cessation in clinical trials remains unknown. Methods We evaluated the association between polygenic risk scores and bioverified smoking abstinence in a meta-analysis of two randomized, placebo-controlled smoking cessation trials. PRSs of smoking behaviors were created using the GWAS and Sequencing Consortium of Alcohol and Nicotine use (GSCAN) consortium summary statistics. We evaluated the utility of using individual PRS of specific smoking behavior vs. combined genetic risk that combines PRS of all four smoking behaviors. Study participants came from the Transdisciplinary Tobacco Use Research Centers (TTURC) Study (1,091 smokers of European descent), and the Genetically Informed Smoking Cessation Trial (GISC) Study (501 smokers of European descent). Results PRS of later age of smoking initiation (OR [95% CI]: 1.20, [1.04-1.37], p=0.0097) was significantly associated with bioverified smoking abstinence at end of treatment. In addition, the combined PRS of smoking behaviors also significantly predicted bioverified smoking abstinence (OR [95% CI] 0.71 [0.51-0.99], p=0.045). Conclusions PRS of later age at smoking initiation may be useful in predicting smoking cessation at the end of treatment. A combined PRS may be a useful predictor for smoking abstinence by capturing the genetic propensity for multiple smoking behaviors. Implications There is a potential for polygenic risk scores to inform future clinical medicine, and a great need for evidence on whether these scores predict clinically meaningful outcomes. Our meta-analysis provides early evidence for potential utility of using polygenic risk scores to predict smoking cessation amongst smokers undergoing quit attempts, informing further work to optimize use of polygenic risk scores in clinical care.

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OBJECTIVES/GOALS: Medications to treat opioid use disorder (mOUD) are available and can save lives, but are underutilized. We hypothesize that the rate of prescribing varies by treatment facility and these differences will shed light on barriers and facilitators to mOUD utilization. METHODS/STUDY POPULATION: We performed an exploratory analysis in MD Clone, a platform which generates non-identifiable synthesized data based on real patient data in the electronic health record (EHR) of St. Louis based hospitals. Our query included adults aged 18-70 with an OUD diagnosis using ICD-9 of -10 codes (opioid abuse, opioid dependence, opioid poisoning, opioid withdrawal) occurring between 2013 and 2022 along with prescriptions for buprenorphine, methadone, or naloxone within 7 days of the condition being entered in the record. We compared the rate of medication prescription within 7 days across settings and facilities where the patients were seen. We propose to replicate this analysis in actual patient records from the EHR following IRB approval. RESULTS/ANTICIPATED RESULTS: Our synthetic data comprised 24600 patient diagnoses. After filtering for patients seen in the ER or inpatient 16235 patients remained in the data set. Of these, 4376 fell into one of the categories that clearly warrant treatment with medication. Out of 4376 patients with a qualifying OUD related condition, only 815 (18.6%) received a prescription for any of the medications. Rates of prescribing within facilities varied between 67.2% of eligible patients receiving a prescription at a rural location to 0% at some urban centers. We anticipate similar findings from analysis of patient records obtained from the EHR. We will extend our analysis to explore factors which may be driving the wide difference in prescribing to better understand barriers and facilitators to mOUD utilization. DISCUSSION/SIGNIFICANCE: We identify under-utilization with differences across facilities in prescribing mOUD based on preliminary work in synthetic data. If true, this represents a gap in care and opportunity for intervention. By replicating the MD Clone results in patient data from the EHR we will confirm this finding and increase acceptability to clinicians.

BackgroundPrevious studies have shown that brain volume is negatively associated with cigarette smoking, but there is an ongoing debate whether smoking causes lowered brain volume or a lower brain volume is a risk factor for smoking. We address this debate through multiple methods that evaluate causality: Bradford Hill’s Criteria to understand a causal relationship in epidemiological studies, mediation analysis, and Mendelian Randomization.MethodsIn 28,404 participants of European descent from the UK Biobank dataset, we examined relationships between a history of daily smoking and brain imaging phenotypes as well as associations of genetic predisposition to smoking initiation with brain volume.ResultsA history of daily smoking is strongly associated with decreased brain volume, and a history of heavier smoking is associated with a greater decrease in brain volume. The strongest association was between total grey matter volume and a history of daily smoking (p-value = 8.28 × 10−33), and there was a dose response relationship with more pack years smoked associated with a greater decrease in brain volume. A polygenic risk score (PRS) for smoking initiation was strongly associated with a history of daily smoking (p-value = 4.09 ×10−72), yet only modestly associated with total grey matter volume (p-value = 0.02). Mediation analysis indicated that a history of daily smoking is a mediator between smoking initiation PRS and total grey matter volume. Mendelian Randomization showed a causal effect of daily smoking on total grey matter volume (p-value = 0.022).ConclusionsThese converging findings strongly support the hypothesis that smoking causes decreased brain volume.