COVID-19 Mortality in the Colorado Center for Personalized Medicine Biobank.

Over 6.37 million people have died from COVID-19 worldwide, but factors influencing COVID-19-related mortality remain understudied. We aimed to describe and identify risk factors for COVID-19 mortality in the Colorado Center for Personalized Medicine (CCPM) Biobank using integrated data sources, including Electronic Health Records (EHRs). We calculated cause-specific mortality and case-fatality rates for COVID-19 and common pre-existing health conditions defined by diagnostic phecodes and encounters in EHRs. We performed multivariable logistic regression analyses of the association between each pre-existing condition and COVID-19 mortality. Of the 155,859 Biobank participants enrolled as of July 2022, 20,797 had been diagnosed with COVID-19. Of 5334 Biobank participants who had died, 190 were attributed to COVID-19. The case-fatality rate was 0.91% and the COVID-19 mortality rate was 122 per 100,000 persons. The odds of dying from COVID-19 were significantly increased among older men, and those with 14 of the 61 pre-existing conditions tested, including hypertensive chronic kidney disease (OR: 10.14, 95% CI: 5.48, 19.16) and type 2 diabetes with renal manifestations (OR: 5.59, 95% CI: 3.42, 8.97). Male patients who are older and have pre-existing kidney diseases may be at higher risk for death from COVID-19 and may require special care.

Phylodynamics of a regional SARS-CoV-2 rapid spreading event in Colorado in late 2020.

Since the initial reported discovery of SARS-CoV-2 in late 2019, genomic surveillance has been an important tool to understand its transmission and evolution. Here, we sought to describe the underlying regional phylodynamics before and during a rapid spreading event that was documented by surveillance protocols of the United States Air Force Academy (USAFA) in late October-November of 2020. We used replicate long-read sequencing on Colorado SARS-CoV-2 genomes collected July through November 2020 at the University of Colorado Anschutz Medical campus in Aurora and the United States Air Force Academy in Colorado Springs. Replicate sequencing allowed rigorous validation of variation and placement in a phylogenetic relatedness network. We focus on describing the phylodynamics of a lineage that likely originated in the local Colorado Springs community and expanded rapidly over the course of two months in an outbreak within the well-controlled environment of the United States Air Force Academy. Divergence estimates from sampling dates indicate that the SARS-CoV-2 lineage associated with this rapid expansion event originated in late October 2020. These results are in agreement with transmission pathways inferred by the United States Air Force Academy, and provide a window into the evolutionary process and transmission dynamics of a potentially dangerous but ultimately contained variant.

COVID-19 Surveillance in the Biobank at the Colorado Center for Personalized Medicine: Observational Study.

BACKGROUND: Characterizing the experience and impact of the COVID-19 pandemic among various populations remains challenging due to the limitations inherent in common data sources, such as electronic health records (EHRs) or cross-sectional surveys. OBJECTIVE: This study aims to describe testing behaviors, symptoms, impact, vaccination status, and case ascertainment during the COVID-19 pandemic using integrated data sources. METHODS: In summer 2020 and 2021, we surveyed participants enrolled in the Biobank at the Colorado Center for Personalized Medicine (CCPM; N=180,599) about their experience with COVID-19. The prevalence of testing, symptoms, and impacts of COVID-19 on employment, family life, and physical and mental health were calculated overall and by demographic categories. Survey respondents who reported receiving a positive COVID-19 test result were considered a "confirmed case" of COVID-19. Using EHRs, we compared COVID-19 case ascertainment and characteristics in EHRs versus the survey. Positive cases were identified in EHRs using the International Statistical Classification of Diseases, 10th revision (ICD-10) diagnosis codes, health care encounter types, and encounter primary diagnoses. RESULTS: Of the 25,063 (13.9%) survey respondents, 10,661 (42.5%) had been tested for COVID-19, and of those, 1366 (12.8%) tested positive. Nearly half of those tested had symptoms or had been exposed to someone who was infected. Young adults (18-29 years) and Hispanics were more likely to have positive tests compared to older adults and persons of other racial/ethnic groups. Mental health (n=13,688, 54.6%) and family life (n=12,233, 48.8%) were most negatively affected by the pandemic and more so among younger groups and women; negative impacts on employment were more commonly reported among Black respondents. Of the 10,249 individuals who responded to vaccination questions from version 2 of the survey (summer 2021), 9770 (95.3%) had received the vaccine. After integration with EHR data up to the time of the survey completion, 1006 (4%) of the survey respondents had a discordant COVID-19 case status between EHRs and the survey. Using all longitudinal EHR and survey data, we identified 11,472 (6.4%) COVID-19-positive cases among Biobank participants. In comparison to COVID-19 cases identified through the survey, EHR-identified cases were younger and more likely to be Hispanic. CONCLUSIONS: We found that the COVID-19 pandemic has had far-reaching and varying effects among our Biobank participants. Integrated data assets, such as the Biobank at the CCPM, are key resources for population health monitoring in response to public health emergencies, such as the COVID-19 pandemic.

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.