A Multi-Omic Mosaic Model of Acetaminophen Induced Alanine Aminotransferase Elevation.

BACKGROUND: Acetaminophen (APAP) is the most common cause liver injury following alcohol in US patients. Predicting liver injury and subsequent hepatic regeneration in patients taking therapeutic doses of APAP may be possible using new 'omic methods such as metabolomics and genomics. Multi'omic techniques increase our ability to find new mechanisms of injury and regeneration. METHODS: We used metabolomic and genomic data from a randomized controlled trial of patients administered 4 g of APAP per day for 14 days or longer with blood samples obtained at 0 (baseline), 4, 7, 10, 13 and 16 days. We used the highest ALT as the clinical outcome to be predicted in our integrated analysis. We used penalized regression to model the relationship between genetic variants and day 0 metabolite level, and then performed a metabolite-wide colocalization scan to associate the genetically regulated component of metabolite expression with ALT elevation. Genome-wide association study (GWAS) analyses were conducted for ALT elevation and metabolite level using linear regression, with age, sex, and the first five principal components included as covariates. Colocalization was tested via a weighted sum test. RESULTS: Out of the 164 metabolites modeled, 120 met the criteria for predictive accuracy and were retained for genetic analyses. After genomic examination, eight metabolites were found to be under genetic control and predictive of ALT elevation due to therapeutic acetaminophen. The metabolites were: 3-oxalomalate, allantoate, diphosphate, L-carnitine, L-proline, maltose, and ornithine. These genes are important in the tricarboxylic acid cycle (TCA), urea breakdown pathway, glutathione production, mitochondrial energy production, and maltose metabolism. CONCLUSIONS: This multi'omic approach can be used to integrate metabolomic and genomic data allowing identification of genes that control downstream metabolites. These findings confirm prior work that have identified mitochondrial energy production as critical to APAP induced liver injury and have confirmed our prior work that demonstrate the importance of the urea cycle in therapeutic APAP liver injury.

Cannabis positivity rates in 17 emergency departments across the United States with varying degrees of marijuana legalization.

BACKGROUND: Many states in the United States have progressed towards legalization of marijuana including decriminalization, medicinal and/or recreational use. We studied the impact of legalization on cannabis-related emergency department visits in states with varying degrees of legalization. METHODS: Seventeen healthcare institutions in fifteen states (California, Colorado, Connecticut, Florida, Iowa, Kentucky, Maryland, Massachusetts, Missouri, New Hampshire, Oregon, South Carolina, Tennessee, Texas, Washington) participated. Cannabinoid immunoassay results and cannabis-related International Classification of Diseases (ninth and tenth versions) codes were obtained for emergency department visits over a 3- to 8-year period during various stages of legalization: no state laws, decriminalized, medical approval before dispensaries, medical dispensaries available, recreational approval before dispensaries and recreational dispensaries available. Trends and monthly rates of cannabinoid immunoassay and cannabis-related International Classification of Diseases code positivity were determined during these legalization periods. RESULTS: For most states, there was a significant increase in both cannabinoid immunoassay and International Classification of Diseases code positivity as legalization progressed; however, positivity rates differed. The availability of dispensaries may impact positivity in states with medical and/or recreational approval. In most states with no laws, there was a significant but smaller increase in cannabinoid immunoassay positivity rates. CONCLUSIONS: States may experience an increase in cannabis-related emergency department visits with progression toward marijuana legalization. The differences between states, including those in which no impact was seen, are likely multifactorial and include cultural norms, attitudes of local law enforcement, differing patient populations, legalization in surrounding states, availability of dispensaries, various ordering protocols in the emergency department, and the prevalence of non-regulated cannabis products.

Characteristics of Emergency Medicine Specimen Bank Participants Compared to the Overall Emergency Department Population.

INTRODUCTION: Biorepositories lack diversity both demographically and with regard to the clinical complaints of patients enrolled. The Emergency Medicine Specimen Bank (EMSB) seeks to enroll a diverse cohort of patients for discovery research in acute care conditions. Our objective in this study was to determine the differences in demographics and clinical complaints between participants in the EMSB and the overall emergency department (ED) population. METHODS: This was a retrospective analysis of participants of the EMSB and the entire UCHealth at University of Colorado Anschutz Medical Center (UCHealth AMC) ED population across three periods: peri-EMSB; post-EMSB; and COVID-19. We compared patients consented to the EMSB to the entire ED population to determine differences in age, gender, ethnicity, race, clinical complaints, and severity of illness. We used chi-square tests to compare categorical variables and the Elixhauser Comorbidity Index to determine differences in the severity of illness between the groups. RESULTS: Between February 5, 2018-January 29, 2022, there were 141,670 consented encounters in the EMSB, representing 40,740 unique patients and over 13,000 blood samples collected. In that same time, the ED saw approximately 188,402 unique patients for 387,590 encounters. The EMSB had significantly higher rates of participation from the following: patients 18-59 years old (80.3% vs 77.7%); White patients (52.3% vs 47.8%), and women (54.8% vs 51.1%) compared to the overall ED population. The EMSB had lower rates of participation from patients ≥70 years, Hispanic patients, Asian patients, and men. The EMSB population had higher mean comorbidity scores. During the six months after Colorado's first COVID-19 case, the rate of consented patients and samples collected increased. The odds of consent during the COVID-19 study period were 1.32 (95% CI 1.26-1.39), and the odds of sample capture were 2.19 (95% CI 2.0-2.41). CONCLUSION: The EMSB is representative of the overall ED population for most demographics and clinical complaints.

Accessing and utilizing clinical and genomic data from an electronic health record data warehouse.

Electronic health records (EHRs) and linked biobanks have tremendous potential to advance biomedical research and ultimately improve the health of future generations. Repurposing EHR data for research is not without challenges, however. In this paper, we describe the processes and considerations necessary to successfully access and utilize a data warehouse for research. Although imperfect, data warehouses are a powerful tool for harnessing a large amount of data to phenotype disease. They will have increasing relevance and applications in clinical research with growing sophistication in processes for EHR data abstraction, biobank integration, and cross-institutional linkage.

Epigenetics may characterize asymptomatic COVID-19 infection.

RT-PCR is the foremost clinical test for diagnosis of COVID-19. Unfortunately, PCR-based testing has limitations and may not result in a positive test early in the course of infection before symptoms develop. Enveloped RNA viruses, such as coronaviruses, alter peripheral blood methylation and DNA methylation signatures may characterize asymptomatic versus symptomatic infection. We used Illumina's Infinium MethylationEPIC BeadChip array to profile peripheral blood samples from 164 patients who tested positive for SARS-CoV-2 by RT-PCR, of whom 8 had no symptoms. Epigenome-wide association analysis identified 10 methylation sites associated with infection and a quantile-quantile plot showed little inflation. These preliminary results suggest that differences in methylation patterns may distinguish asymptomatic from symptomatic infection.

Immune mechanisms associated with sex-based differences in severe COVID-19 clinical outcomes.

BACKGROUND: Although biological males and females are equally likely to become infected with severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), evidence has mounted that males experience higher severity and fatality compared to females. MAIN: The objective of this review is to examine the existing literature on biological mechanisms underlying sex-based differences that could contribute to SARS-CoV-2 infection clinical outcomes. Sex-based differences in immunologic response and hormonal expression help explain the differences in coronavirus disease 2019 (COVID-19) outcomes observed in biological males and females. X inactivation facilitates a robust immune response to COVID-19 in females, who demonstrate a more profound antibody response and faster recovery when compared to males. Low testosterone levels also help explain the dysregulated inflammatory response and poor outcomes observed in some males with COVID-19. Gender differences in health expression and behaviors further compound these observed differences. CONCLUSION: Understanding the biology of sex-based differences in COVID-19 severity and mortality could help inform preventative measures, treatment decisions, and development of personalized, sex-specific therapies.

Host methylation predicts SARS-CoV-2 infection and clinical outcome.

BACKGROUND: Since the onset of the SARS-CoV-2 pandemic, most clinical testing has focused on RT-PCR1. Host epigenome manipulation post coronavirus infection2-4 suggests that DNA methylation signatures may differentiate patients with SARS-CoV-2 infection from uninfected individuals, and help predict COVID-19 disease severity, even at initial presentation. METHODS: We customized Illumina's Infinium MethylationEPIC array to enhance immune response detection and profiled peripheral blood samples from 164 COVID-19 patients with longitudinal measurements of disease severity and 296 patient controls. RESULTS: Epigenome-wide association analysis revealed 13,033 genome-wide significant methylation sites for case-vs-control status. Genes and pathways involved in interferon signaling and viral response were significantly enriched among differentially methylated sites. We observe highly significant associations at genes previously reported in genetic association studies (e.g. IRF7, OAS1). Using machine learning techniques, models built using sparse regression yielded highly predictive findings: cross-validated best fit AUC was 93.6% for case-vs-control status, and 79.1%, 80.8%, and 84.4% for hospitalization, ICU admission, and progression to death, respectively. CONCLUSIONS: In summary, the strong COVID-19-specific epigenetic signature in peripheral blood driven by key immune-related pathways related to infection status, disease severity, and clinical deterioration provides insights useful for diagnosis and prognosis of patients with viral infections.

Host methylation predicts SARS-CoV-2 infection and clinical outcome.

BACKGROUND: Since the onset of the SARS-CoV-2 pandemic, most clinical testing has focused on RT-PCR1. Host epigenome manipulation post coronavirus infection2-4 suggests that DNA methylation signatures may differentiate patients with SARS-CoV-2 infection from uninfected individuals, and help predict COVID-19 disease severity, even at initial presentation. METHODS: We customized Illumina's Infinium MethylationEPIC array to enhance immune response detection and profiled peripheral blood samples from 164 COVID-19 patients with longitudinal measurements of disease severity and 296 patient controls. RESULTS: Epigenome-wide association analysis revealed 13,033 genome-wide significant methylation sites for case-vs-control status. Genes and pathways involved in interferon signaling and viral response were significantly enriched among differentially methylated sites. We observe highly significant associations at genes previously reported in genetic association studies (e.g. IRF7, OAS1). Using machine learning techniques, models built using sparse regression yielded highly predictive findings: cross-validated best fit AUC was 93.6% for case-vs-control status, and 79.1%, 80.8%, and 84.4% for hospitalization, ICU admission, and progression to death, respectively. CONCLUSIONS: In summary, the strong COVID-19-specific epigenetic signature in peripheral blood driven by key immune-related pathways related to infection status, disease severity, and clinical deterioration provides insights useful for diagnosis and prognosis of patients with viral infections.

Viral infections affect the body in many ways, including via changes to the epigenome, the sum of chemical modifications to an individual's collection of genes that affect gene activity. Here, we analyzed the epigenome in blood samples from people with and without COVID-19 to determine whether we could find changes consistent with SARS-CoV-2 infection. Using a combination of statistical and machine learning techniques, we identify markers of SARS-CoV-2 infection as well as of severity and progression of COVID-19 disease. These signals of disease progression were present from the initial blood draw when first walking into the hospital. Together, these approaches demonstrate the potential of measuring the epigenome for monitoring SARS-CoV-2 status and severity.