Compared with other psychiatric disorders of similar heritabilities, the progress of substance use disorders (SUD) genetics has been slow. With the growing availability of large-scale biobanks with extensive phenotypes from electronic health records (EHR) and genotypes across millions of individuals, this platform is the next tool to accelerate SUD genetics research.

Over the last decade, genome-wide association studies of psychiatric disorders have identified numerous significant loci. Whereas these studies initially depended on cohorts ascertained for specific disorders, there has been a gradual shift in the ascertainment strategy toward population-based cohorts for which both genotype and heterogeneous phenotypic information are available. One of the advantages of population-based cohorts is that, in addition to clinical diagnoses and various proxies for diagnoses ('minimal phenotyping'), many of them also provide non-clinical phenotypes, including putative endophenotypes, that can be used to study domains of normal function in addition to, or instead of, clinical diagnoses. By studying endophenotypes it is possible to both dissect psychiatric disorders ('splitting') and to combine multiple phenotypes ('clumping'), which can either reinforce or challenge traditional diagnostic categories. Such endophenotypes may also permit a deeper exploration of the neurobiology of psychiatric disorders. A coordinated effort to fully exploit the potential of endophenotypes is overdue.

Anorexia nervosa (AN) is a psychiatric disorder defined by anthropometric symptoms, such as low body weight, and cognitive-behavioral symptoms, such as restricted eating, fear of weight gain, and distorted body image. Recent studies have identified a genetic association between AN and metabolic/anthropometric factors, including body mass index (BMI). Although the reported associations may be under pleiotropic genetic influences, they may represent independent risk factors for AN. Here we examined the independent contributions of genetic predisposition to low body weight and polygenic risk (PRS) for AN in a clinical population (Vanderbilt University Medical Center biobank, BioVU). We fitted logistic and linear regression models in a retrospective case-control design (123 AN patients, 615 age-matched controls). We replicated the genetic correlations between PRS_BMI and AN (p = 1.12 × 10^-3, OR = 0.96), but this correlation disappeared when controlling for lowest BMI (p = 0.84, OR = 1.00). Additionally, we performed a phenome-wide association analysis of the PRS_AN and found that the associations with metabolic phenotypes were attenuated when controlling for PRS_BMI. These findings suggest that the genetic association between BMI and AN may be a consequence of the weight-related diagnostic criteria for AN and that genetically regulated anthropometric traits (like BMI) may be independent of AN psychopathology. If so, individuals with cognitive-behavioral symptomatology suggestive of AN, but with a higher PRS_BMI, may be under-diagnosed given current diagnostic criteria. Furthermore, PRS_BMI may serve as an independent risk factor for weight loss and weight gain during recovery.

Alcohol use disorder (AUD) is defined by several symptom criteria, which can be dissected further at the genetic level. Over the past several years, our understanding of the genetic factors influencing alcohol use and abuse has progressed tremendously; numerous loci have been implicated in different aspects of alcohol use. Previously known associations with alcohol-metabolizing enzymes (ADH1B, ALDH2) have been replicated definitively. In addition, novel associations with loci containing the genes KLB, GCKR, CRHR1, and CADM2 have been reported. Downstream analyses have leveraged these genetic findings to reveal important relationships between alcohol use behaviors and both physical and mental health. AUD and aspects of alcohol misuse have been shown to overlap strongly with psychiatric disorders, whereas aspects of alcohol consumption have shown stronger links to metabolism. These results demonstrate that the genetic architecture of alcohol consumption only partially overlaps with the genetics of clinically defined AUD. We discuss the limitations of using quantitative measures of alcohol use as proxy measures for AUD, and we outline how future studies will require careful phenotype harmonization to properly capture the genetic liability to AUD.

Excessive substance use and substance use disorders (SUDs) are common, serious and relapsing medical conditions. They frequently co-occur with other diseases that are leading contributors to disability worldwide. While heavy substance use may potentiate the course of some of these illnesses, there is accumulating evidence suggesting common genetic architectures. In this narrative review, we focus on four heritable medical conditions - cardiometabolic disease, chronic pain, depression and COVID-19, which are commonly overlapping with, but not necessarily a direct consequence of, SUDs. We find persuasive evidence of underlying genetic liability that predisposes to both SUDs and chronic pain, depression, and COVID-19. For cardiometabolic disease, there is greater support for a potential causal influence of problematic substance use. Our review encourages de-stigmatization of SUDs and the assessment of substance use in clinical settings. We assert that identifying shared pathways of risk has high translational potential, allowing tailoring of treatments for multiple medical conditions. FUNDING: SSR acknowledges T29KT0526, T32IR5226 and DP1DA054394; RLK acknowledges AA028292; AA acknowledges DA054869 & K02DA032573. The funders had no role in the conceptualization or writing of the paper.

This chapter provides an overview over the behavioral economic index of impulsivity known as delay discounting. Specifically, delay discounting refers to an individual's preference for smaller immediate rewards over a larger delayed rewards. The more precipitously an individual discounts future rewards, the more impulsive they are considered to be. First, the chapter reviews the nature of delay discounting as a psychological process and juxtaposes it with nominally similar processes, including other facets of impulsivity. Second, the chapter reviews the links between delay discounting and numerous health behaviors, including addiction, attention deficit/hyperactivity disorder, and obesity. Third, the determinants of individual variation in delay discounting are discussed, including both genetic and environmental contributions. Finally, the chapter evaluates delay discounting as a potentially modifiable risk factor and the status of clinical interventions designed to reduce delay discounting to address deficits in self-control in a variety of maladaptive behaviors.

Alcohol use disorder (AUD) is defined by several symptom criteria, which can be dissected further at the genetic level. Over the past several years, our understanding of the genetic factors influencing alcohol use and abuse has progressed tremendously; numerous loci have been implicated in different aspects of alcohol use. Previously known associations with alcohol-metabolizing enzymes (ADH1B, ALDH2) have been replicated definitively. In addition, novel associations with loci containing the genes KLB, GCKR, CRHR1, and CADM2 have been reported. Downstream analyses have leveraged these genetic findings to reveal important relationships between alcohol use behaviors and both physical and mental health. AUD and aspects of alcohol misuse have been shown to overlap strongly with psychiatric disorders, whereas aspects of alcohol consumption have shown stronger links to metabolism. These results demonstrate that the genetic architecture of alcohol consumption only partially overlaps with the genetics of clinically defined AUD. We discuss the limitations of using quantitative measures of alcohol use as proxy measures for AUD, and we outline how future studies will require careful phenotype harmonization to properly capture the genetic liability to AUD.

Delayed reward discounting (DRD) is a behavioral economic measure of impulsivity that has been consistently associated with addiction. It has also been identified as a promising addiction endophenotype, linking specific sources of genetic variation to individual risk. A challenge in the studies to date is that levels of DRD are often confounded with prior drug use, and previous studies have also had limited genomic scope. The current investigation sought to address these issues by studying DRD in healthy young adults with low levels of substance use (N = 986; 62% female, 100% European ancestry) and investigating genetic variation genome-wide. The genome-wide approach used a prioritized subset design, organizing the tests into theoretically and empirically informed categories and apportioning power accordingly. Three subsets were used: (a) a priori loci implicated by previous studies; (b) high-value addiction (HVA) markers from the recently developed SmokeScreen array; and (c) an atheoretical genome-wide scan. Among a priori loci, a nominally significant association was present between DRD and rs521674 in ADRA2A. No significant HVA loci were detected. One statistically significant genome-wide association was detected (rs13395777, p = 2.8 × 10-8), albeit in an intergenic region of unknown function. These findings are generally not supportive of the previous candidate gene studies and suggest that DRD has a complex genetic architecture that will require considerably larger samples to identify genetic associations more definitively. (PsycINFO Database Record (c) 2019 APA, all rights reserved).

Background

Although electronic health records (EHR) have significant potential for the study of opioid use disorders (OUD), detecting OUD in clinical data is challenging. Models using EHR data to predict OUD often rely on case/control classifications focused on extreme opioid use. There is a need to expand this work to characterize the spectrum of problematic opioid use.

Methods

Using a large academic medical center database, we developed 2 data-driven methods of OUD detection: (1) a Comorbidity Score developed from a Phenome-Wide Association Study of phenotypes associated with OUD and (2) a Text-based Score using natural language processing to identify OUD-related concepts in clinical notes. We evaluated the performance of both scores against a manual review with correlation coefficients, Wilcoxon rank sum tests, and area-under the receiver operating characteristic curves. Records with the highest Comorbidity and Text-based scores were re-evaluated by manual review to explore discrepancies.

Results

Both the Comorbidity and Text-based OUD risk scores were significantly elevated in the patients judged as High Evidence for OUD in the manual review compared to those with No Evidence (p = 1.3E-5 and 1.3E-6, respectively). The risk scores were positively correlated with each other (rho = 0.52, p < 0.001). AUCs for the Comorbidity and Text-based scores were high (0.79 and 0.76, respectively). Follow-up manual review of discrepant findings revealed strengths of data-driven methods over manual review, and opportunities for improvement in risk assessment.

Conclusion

Risk scores comprising comorbidities and text offer differing but synergistic insights into characterizing problematic opioid use. This pilot project establishes a foundation for more robust work in the future.