Correlates of suicidal behaviors and genetic risk among United States veterans with schizophrenia or bipolar I disorder.

Persons diagnosed with schizophrenia (SCZ) or bipolar I disorder (BPI) are at high risk for self-injurious behavior, suicidal ideation, and suicidal behaviors (SB). Characterizing associations between diagnosed health problems, prior pharmacological treatments, and polygenic scores (PGS) has potential to inform risk stratification. We examined self-reported SB and ideation using the Columbia Suicide Severity Rating Scale (C-SSRS) among 3,942 SCZ and 5,414 BPI patients receiving care within the Veterans Health Administration (VHA). These cross-sectional data were integrated with electronic health records (EHRs), and compared across lifetime diagnoses, treatment histories, follow-up screenings, and mortality data. PGS were constructed using available genomic data for related traits. Genome-wide association studies were performed to identify and prioritize specific loci. Only 20% of the veterans who reported SB had a corroborating ICD-9/10 EHR code. Among those without prior SB, more than 20% reported new-onset SB at follow-up. SB were associated with a range of additional clinical diagnoses, and with treatment with specific classes of psychotropic medications (e.g., antidepressants, antipsychotics, etc.). PGS for externalizing behaviors, smoking initiation, suicide attempt, and major depressive disorder were associated with SB. The GWAS for SB yielded no significant loci. Among individuals with a diagnosed mental illness, self-reported SB were strongly associated with clinical variables across several EHR domains. Analyses point to sequelae of substance-related and psychiatric comorbidities as strong correlates of prior and subsequent SB. Nonetheless, past SB was frequently not documented in health records, underscoring the value of regular screening with direct, in-person assessments, especially among high-risk individuals.

Correlates of suicidal behaviors and genetic risk among United States veterans with schizophrenia or bipolar I disorder.

Objective: Persons diagnosed with schizophrenia (SCZ) or bipolar I disorder (BPI) are at high risk for self-injurious behavior, suicidal ideation, and suicidal behaviors (SB). Characterizing associations between diagnosed mental and physical health problems, prior pharmacological treatments, and aggregate genetic factors has potential to inform risk stratification and mitigation strategies. Methods: In this study of 3,942 SCZ and 5,414 BPI patients receiving VA care, self-reported SB and ideation were assessed using the Columbia Suicide Severity Rating Scale (C-SSRS). These cross-sectional data were integrated with electronic health records (EHR), and compared by lifetime diagnoses, treatment histories, follow-up screenings, and mortality data. Polygenic scores (PGS) for traits related to psychiatric disorders, substance use, and cognition were constructed using available genomic data, and exploratory genome-wide association studies were performed to identify and prioritize specific loci. Results: Only 20% of veterans who self-reported SB had a corroborating ICD-9/10 code in their EHR; and among those who denied prior behaviors, more than 20% reported new-onset SB at follow-up. SB were associated with a range of psychiatric and non-psychiatric diagnoses, and with treatment with specific classes of psychotropic medications (e.g., antidepressants, antipsychotics, etc.). PGS for externalizing behaviors, smoking, suicide attempt, and major depressive disorder were also associated with attempt and ideation. Conclusions: Among individuals with a diagnosed mental illness, a GWAS for SB did not yield any significant loci. Self-reported SB were strongly associated with clinical variables across several EHR domains. Overall, clinical and polygenic analyses point to sequelae of substance-use related behaviors and other psychiatric comorbidities as strong correlates of prior and subsequent SB. Nonetheless, past SB was frequently not documented in clinical settings, underscoring the value of regular screening based on direct, in-person assessments, especially among high-risk individuals.

A MUC5B Gene Polymorphism, rs35705950-T, Confers Protective Effects Against COVID-19 Hospitalization but Not Severe Disease or Mortality.

Rationale: A common MUC5B gene polymorphism, rs35705950-T, is associated with idiopathic pulmonary fibrosis (IPF), but its role in severe acute respiratory syndrome coronavirus 2 infection and disease severity is unclear. Objectives: To assess whether rs35705950-T confers differential risk for clinical outcomes associated with coronavirus disease (COVID-19) infection among participants in the Million Veteran Program (MVP). Methods: The MUC5B rs35705950-T allele was directly genotyped among MVP participants; clinical events and comorbidities were extracted from the electronic health records. Associations between the incidence or severity of COVID-19 and rs35705950-T were analyzed within each ancestry group in the MVP followed by transancestry meta-analysis. Replication and joint meta-analysis were conducted using summary statistics from the COVID-19 Host Genetics Initiative (HGI). Sensitivity analyses with adjustment for additional covariates (body mass index, Charlson comorbidity index, smoking, asbestosis, rheumatoid arthritis with interstitial lung disease, and IPF) and associations with post-COVID-19 pneumonia were performed in MVP subjects. Measurements and Main Results: The rs35705950-T allele was associated with fewer COVID-19 hospitalizations in transancestry meta-analyses within the MVP (Ncases = 4,325; Ncontrols = 507,640; OR = 0.89 [0.82-0.97]; P = 6.86 × 10-3) and joint meta-analyses with the HGI (Ncases = 13,320; Ncontrols = 1,508,841; OR, 0.90 [0.86-0.95]; P = 8.99 × 10-5). The rs35705950-T allele was not associated with reduced COVID-19 positivity in transancestry meta-analysis within the MVP (Ncases = 19,168/Ncontrols = 492,854; OR, 0.98 [0.95-1.01]; P = 0.06) but was nominally significant (P < 0.05) in the joint meta-analysis with the HGI (Ncases = 44,820; Ncontrols = 1,775,827; OR, 0.97 [0.95-1.00]; P = 0.03). Associations were not observed with severe outcomes or mortality. Among individuals of European ancestry in the MVP, rs35705950-T was associated with fewer post-COVID-19 pneumonia events (OR, 0.82 [0.72-0.93]; P = 0.001). Conclusions: The MUC5B variant rs35705950-T may confer protection in COVID-19 hospitalizations.

Polygenic Breast Cancer Risk for Women Veterans in the Million Veteran Program.

Accurate breast cancer (BC) risk assessment allows personalized screening and prevention. Prospective validation of prediction models is required before clinical application. Here, we evaluate clinical- and genetic-based BC prediction models in a prospective cohort of women from the Million Veteran Program. MATERIALS AND METHODS: Clinical BC risk prediction models were validated in combination with a genetic polygenic risk score of 313 (PRS313) single-nucleotide polymorphisms in genetic females without prior BC diagnosis (n = 35,130, mean age 49 years) with 30% non-Hispanic African ancestry (AA). Clinical risk models tested were Breast and Prostate Cancer Cohort Consortium, literature review, and Breast Cancer Risk Assessment Tool, and implemented with or without PRS313. Prediction accuracy and association with incident breast cancer was evaluated with area under the receiver operating characteristic curve (AUC), hazard ratios, and proportion with high absolute lifetime risk. RESULTS: Three hundred thirty-eight participants developed incident breast cancers with a median follow-up of 3.9 years (2.5 cases/1,000 person-years), with 196 incident cases in women of European ancestry and 112 incident cases in AA women. Individualized Coherent Absolute Risk Estimator-literature review in combination with PRS313 had an AUC of 0.708 (95% CI, 0.659 to 0.758) in women with European or non-African ancestries and 0.625 (0.539 to 0.711) in AA women. Breast Cancer Risk Assessment Tool with PRS313 had an AUC of 0.695 (0.62 to 0.729) in European or non-AA and 0.675 (0.626 to 0.723) in AA women. Incorporation of PRS313 with clinical models improved prediction in European but not in AA women. Models estimated up to 9% of European and 18% of AA women with absolute lifetime risk > 20%. CONCLUSION: Clinical and genetic BC risk models predict incident BC in a large prospective multiracial cohort; however, more work is needed to improve genetic risk estimation in AA women.

Cooperative Studies Program (CSP) #572: A Study of Serious Mental Illness in Veterans as a Pathway to personalized medicine in Schizophrenia and Bipolar Illness.

BACKGROUND: Personalization of psychiatric treatment includes treatment of symptoms, cognition and functional deficits, suicide, and medical co-morbidities. VA Collaborative Study 572 examined a large sample of male and female veterans with schizophrenia (n=3,942) and with bipolar disorder (n=5,414) with phenotyping and genomic analyses. We present the results to date and future directions. METHODS: All veterans received a structured diagnostic interview and assessments of suicidal ideation and behavior, PTSD, and health. Veterans with schizophrenia were assessed for negative symptoms and lifetime depression. All were assessed with a cognitive and functional capacity assessment. Data for genome wide association studies were collected. Controls came from the VA Million Veteran Program. RESULTS: Suicidal ideation or behavior was present in 66%. Cognitive and functional deficits were consistent with previous studies. 40% of the veterans with schizophrenia had a lifetime major depressive episode and PTSD was present in over 30%. Polygenic risk score (PRS) analyses indicated that cognitive and functional deficits overlapped with PRS for cognition, education, and intelligence in the general population and PRS for suicidal ideation and behavior correlated with previous PRS for depression and suicidal ideation and behavior, as did the PRS for PTSD. DISCUSSION: Results to date provide directions for personalization of treatment in SMI, veterans with SMI, and veterans in general. The results of the genomic analyses suggest that cognitive deficits in SMI may be associated with general population features. Upcoming genomic analyses will reexamine the issues above, as well as genomic factors associated with smoking, substance abuse, negative symptoms, and treatment response.

Genome-wide Association Study of Maximum Habitual Alcohol Intake in >140,000 U.S. European and African American Veterans Yields Novel Risk Loci.

BACKGROUND: Habitual alcohol use can be an indicator of alcohol dependence, which is associated with a wide range of serious health problems. METHODS: We completed a genome-wide association study in 126,936 European American and 17,029 African American subjects in the Veterans Affairs Million Veteran Program for a quantitative phenotype based on maximum habitual alcohol consumption. RESULTS: ADH1B, on chromosome 4, was the lead locus for both populations: for the European American sample, rs1229984 (p = 4.9 × 10-47); for African American, rs2066702 (p = 2.3 × 10-12). In the European American sample, we identified three additional genome-wide-significant maximum habitual alcohol consumption loci: on chromosome 17, rs77804065 (p = 1.5 × 10-12), at CRHR1 (corticotropin-releasing hormone receptor 1); the protein product of this gene is involved in stress and immune responses; and on chromosomes 8 and 10. European American and African American samples were then meta-analyzed; the associated region at CRHR1 increased in significance to 1.02 × 10-13, and we identified two additional genome-wide significant loci, FGF14 (p = 9.86 × 10-9) (chromosome 13) and a locus on chromosome 11. Besides ADH1B, none of the five loci have prior genome-wide significant support. Post-genome-wide association study analysis identified genetic correlation to other alcohol-related traits, smoking-related traits, and many others. Replications were observed in UK Biobank data. Genetic correlation between maximum habitual alcohol consumption and alcohol dependence was 0.87 (p = 4.78 × 10-9). Enrichment for cell types included dopaminergic and gamma-aminobutyric acidergic neurons in midbrain, and pancreatic delta cells. CONCLUSIONS: The present study supports five novel alcohol-use risk loci, with particularly strong statistical support for CRHR1. Additionally, we provide novel insight regarding the biology of harmful alcohol use.

Effect of the Tool to Reduce Inappropriate Medications on Medication Communication and Deprescribing.

OBJECTIVES: To examine the effect of the Tool to Reduce Inappropriate Medications (TRIM), a web tool linking an electronic health record (EHR) to a clinical decision support system, on medication communication and prescribing. DESIGN: Randomized clinical trial. SETTING: Primary care clinics at a Veterans Affairs Medical Center. PARTICIPANTS: Veterans aged 65 and older prescribed seven or more medications randomized to receipt of TRIM or usual care (N = 128). INTERVENTION: TRIM extracts information on medications and chronic conditions from the EHR and contains data entry screens for information obtained from brief chart review and telephonic patient assessment. These data serve as input for automated algorithms identifying medication reconciliation discrepancies, potentially inappropriate medications (PIMs), and potentially inappropriate regimens. Clinician feedback reports summarize discrepancies and provide recommendations for deprescribing. Patient feedback reports summarize discrepancies and self-reported medication problems. MEASUREMENTS: Primary: subscales of the Patient Assessment of Care for Chronic Conditions (PACIC) related to shared decision-making; clinician and patient communication. Secondary: changes in medications. RESULTS: 29.7% of TRIM participants and 15.6% of control participants provided the highest PACIC ratings; this difference was not significant. Adjusting for covariates and clustering of patients within clinicians, TRIM was associated with significantly more-active patient communication and facilitative clinician communication and with more medication-related communication among patients and clinicians. TRIM was significantly associated with correction of medication discrepancies but had no effect on number of medications or reduction in PIMs. CONCLUSION: TRIM improved communication about medications and accuracy of documentation. Although there was no association with prescribing, the small sample size provided limited power to examine medication-related outcomes.

Development of the Tool to Reduce Inappropriate Medications (TRIM): A Clinical Decision Support System to Improve Medication Prescribing for Older Adults.

STUDY OBJECTIVE: To create a clinical decision support system (CDSS) for evaluating problems with medications among older outpatients based on a broad set of criteria. DESIGN: Web-based CDSS development. SETTING: Primary care clinics at a Veterans Affairs medical center. PARTICIPANTS: Forty veterans 65 years and older who were prescribed seven or more medications that included those for treatment of diabetes mellitus and hypertension. MEASUREMENTS AND MAIN RESULTS: The Tool to Reduce Inappropriate Medications (TRIM) uses a program to extract age, medications, and chronic conditions from the electronic health record to identify high-risk patients and as input for evaluating the medication regimen. Additional health variables obtained through chart review and direct patient assessment are entered into a Web-based program. Based on a series of algorithms, TRIM generates feedback reports for clinicians. TRIM identified medication reconciliation discrepancies in 98% (39/40) of veterans, potentially inappropriate medications in 58% (23/40), potential problems with feasibility (based on poor adherence and/or cognitive impairment) in 25% (10/40), potential overtreatment of hypertension in 50% (20/40), potential overtreatment of diabetes in 43% (17/40), inappropriate dosing of renally excreted medications in 5% (2/40), and patient-reported adverse reactions in 5% (2/40). CONCLUSION: This evaluation of TRIM demonstrated that data elements can be extracted from the electronic health record to identify older primary care patients at risk for potentially problematic medication regimens. Supplemented with chart review and direct patient assessment, these data can be processed through clinical algorithms that identify potential problems and generate patient-specific feedback reports. Additional work is necessary to assess the effects of TRIM on medication deprescribing.