Multi-ancestry meta-analysis of tobacco use disorder prioritizes novel candidate risk genes and reveals associations with numerous health outcomes

Tobacco use disorder (TUD) is the most prevalent substance use disorder in the world. Genetic factors influence smoking behaviors, and although strides have been made using genome-wide association studies (GWAS) to identify risk variants, the majority of variants identified have been for nicotine consumption, rather than TUD. We leveraged five biobanks to perform a multi-ancestral meta-analysis of TUD (derived via electronic health records, EHR) in 898,680 individuals (739,895 European, 114,420 African American, 44,365 Latin American). We identified 88 independent risk loci; integration with functional genomic tools uncovered 461 potential risk genes, primarily expressed in the brain. TUD was genetically correlated with smoking and psychiatric traits from traditionally ascertained cohorts, externalizing behaviors in children, and hundreds of medical outcomes, including HIV infection, heart disease, and pain. This work furthers our biological understanding of TUD and establishes EHR as a source of phenotypic information for studying the genetics of TUD

Diabetes Mellitus and Rheumatoid Arthritis Predict Depression Disorder Diagnosis in Nonelderly Adults in Primary Care Settings

Background/Aims: Depressive disorders (DEP) are often comorbid with other chronic conditions. Bidirectional associations between DEP and these conditions are poorly understood. In this study, we aimed to identify chronic conditions with the greatest influence on the DEP diagnosis. Methods: A validated electronic health record-based algorithm was applied to identify DEP patients receiving primary care at Mayo Clinic between 2000 and 2013. Cases were identified on the basis of having \u3e 2 DEP-related ICD-9 diagnosis codes, \u3e 1 antidepressant prescription, and \u3e 3 mentions of in- or outpatient clinical notes for DEP. Controls were matched on birth year (± 2 years), sex and outpatient clinic visits at the same year. We ascertained 26 chronic condition categories, as defined by Chronic Conditions Data Warehouse, using the 5 years of medical records prior to the DEP diagnosis. For each age group at diagnosis (60 years), gradient boosting machine models were applied to estimate relative influence (RI) of the chronic conditions on the DEP diagnosis. Results: A total of 11,219 DEP cases were identified (median age at DEP diagnosis 44 years, 34% male and 89% white). The proportion of subjects with at least one co-occurring condition increased with older age and was higher in DEP cases compared to controls (9.5% vs. 6.9% for subjects 60 years). For subjects aged ≤ 45 years, diabetes mellitus (RI=19.6%) was identified as the most influential condition contributing to the risk of DEP, followed by asthma (RI=11.6%) and rheumatoid arthritis (RI=11.0%). For subjects aged 46–60 years, the most influential condition was rheumatoid arthritis (RI=13.2%) followed by diabetes mellitus (RI=12.9%). For subjects older than 60 years, dementia (RI=10.7%) and rheumatoid arthritis (RI=10.2%) showed similar relative contribution to the risk of DEP. Discussion: Among nonelderly adults who received primary care at Mayo Clinic (2000–2013), diabetes mellitus and rheumatoid arthritis were the strongest risk predictors for a diagnosis of depression among 26 chronic conditions considered. Our results suggest that certain chronic conditions may play a role in exacerbating, precipitating or increasing longitudinal risk of medically significant depression

Improving Quality of Life in Patients at Risk for Post–Intensive Care Syndrome

Objective: To improve quality of life (QOL) in patients at risk for post–intensive care syndrome (PICS). Patients and Methods: We conducted a mixed-method, prospective, observational, pre-post interventional study in an adult medical and mixed medical/surgical/transplant intensive care unit (ICU) at a tertiary academic hospital. Preintervention included patients admitted from October 1 through October 31, 2016, and postintervention included patients admitted from January 15 through February 14, 2017. First, a multidisciplinary team of stakeholders identified barriers associated with decreased QOL in patients at risk for PICS. Next, interventions were designed and implemented. The effect of interventions was assessed using a mixed-method analysis. The qualitative analysis used a modified grounded theory approach. The quantitative analysis included assessment of preexisting symptoms and risk factors associated with PICS. The 36-Item Short-Form Health Status Survey (SF-36), which surveys physical and mental composite scores, was used to assess QOL. Results: Barriers identified were lack of awareness and understanding of PICS. Interventions included educational videos, paper and online education and treatment materials, and online and in-person support groups for education and treatment. After interventions, the qualitative analysis found that patients who participated in the interventions after hospital discharge showed improved QOL, whereas education during hospitalization alone was not effective. The quantitative analysis did not find improvement in QOL, as defined by SF-36 physical or mental composite scores. Conclusion: Interventions targeted to patients after hospitalization may offer subjective improvement in QOL for those at risk for PICS