Ohno‐Machado, Lucila

Introduction: Coagulation factor XI (FXI) inhibitors have shown a better safety profile than other anticoagulants, offering similar reductions in ischemic stroke (IS) risk with a lower risk of intracerebral hemorrhage (ICH). The APOE ε4 genotype is associated with increased risks of both IS and ICH, likely due to its effects on lipid levels, intracranial atherosclerosis, and cerebral amyloid angiopathy. We hypothesized that APOE ε4 carriers would gain greater therapeutic benefit from genetically reduced FXI levels compared to non-carriers. Methods: We conducted a two-stage Mendelian randomization (MR) analysis using the random-effects, inverse-variance weighted method. First, we estimated the effect of genetically proxied FXI inhibition on IS and ICH risk in the general population using summary data from genome-wide association studies of FXI levels (16,169 participants), IS (1,503,898 controls and 110,182 cases), and ICH (3,745 controls and 3,226 cases). Second, we performed stratified analyses to estimate the effect of genetically determined FXI levels on IS (10,343 cases and 476,838 controls) and ICH (2,236 cases and 484,945 controls) risk across APOE ε4 carriership strata (0, 1, or 2 alleles) using UK Biobank data. Results: In the general population, random-effects, inverse-variance weighted MR analyses showed that every 1 unit decrease in natural log-transformed FXI levels was associated with a 37% reduction in IS risk (OR:0.63; 95%CI:0.52–0.77; p <0.001) without a significant increase in ICH risk (OR:0.86; 95%CI:0.37–2.00; p =0.73). Stratified MR analyses indicated that genetically determined reductions in FXI levels had varying protective effects on IS risk based on APOE ε4 status: 31% in non-carriers (OR:0.69, 95%CI:0.53–0.91; p =0.008), 43% in carriers of 1 allele (OR:0.57, 95%CI:0.36–0.91; p =0.02), and 75% in carriers of 2 alleles (OR:0.25, 95%CI:0.06–1.06; p =0.06). Similar MR analyses showed no significant increase in ICH risk with genetically determined reductions in FXI levels in any APOE ε4 strata (all p >0.38, Figure 1). Conclusion: This two-stage genetic study provides causal evidence that lower FXI levels reduce IS risk in the general population, with an even stronger effect among APOE ε4 carriers, while not increasing ICH risk. These results indicate that FXI inhibitors might be a safer and more effective alternative to other anticoagulants among APOE ε4 carriers, who are known to be at higher risk for both ischemic and hemorrhagic events.

Background: Underrepresented persons are often not included in biomedical research. It is unknown if the general Asian American population is being represented in All of Us . The purpose of this study was to compare the Asian demographic data in the All of Us cohort with the Asian nationally representative data from the American Community Survey. Method: Demographic characteristics and health literacy of Asians in All of Us were examined. Findings were qualitatively compared with the Asian data in the 2019 American Community Survey 1-year estimate. Results: Compared with the national composition of Asians, less All of Us participants were born outside the United States (64% vs 79%), were younger, and had higher levels of education (76% vs 52%). Over 60% of All of Us participants reported high levels of health literacy. Conclusion: This study had implications for the development of strategies that ensure diverse populations are represented in biomedical research.

What is the agreement of commercial SARS-CoV-2 immunoglobulin G (IgG) assays during a time of low coronavirus disease 2019 (COVID-19) prevalence and no vaccine availability? Serological tests produced concordant results in a time of low SARS-CoV-2 prevalence and no vaccine availability, driven largely by the proportion of samples that were negative by two immunoassays. The CDC recommends two sequential tests for positivity for future pandemic preparedness.

Abstract Objective The Genomic Medicine Working Group of the National Advisory Council for Human Genome Research virtually hosted its 13th genomic medicine meeting titled “Developing a Clinical Genomic Informatics Research Agenda”. The meeting’s goal was to articulate a research strategy to develop Genomics-based Clinical Informatics Tools and Resources (GCIT) to improve the detection, treatment, and reporting of genetic disorders in clinical settings. Materials and Methods Experts from government agencies, the private sector, and academia in genomic medicine and clinical informatics were invited to address the meeting's goals. Invitees were also asked to complete a survey to assess important considerations needed to develop a genomic-based clinical informatics research strategy. Results Outcomes from the meeting included identifying short-term research needs, such as designing and implementing standards-based interfaces between laboratory information systems and electronic health records, as well as long-term projects, such as identifying and addressing barriers related to the establishment and implementation of genomic data exchange systems that, in turn, the research community could help address. Discussion Discussions centered on identifying gaps and barriers that impede the use of GCIT in genomic medicine. Emergent themes from the meeting included developing an implementation science framework, defining a value proposition for all stakeholders, fostering engagement with patients and partners to develop applications under patient control, promoting the use of relevant clinical workflows in research, and lowering related barriers to regulatory processes. Another key theme was recognizing pervasive biases in data and information systems, algorithms, access, value, and knowledge repositories and identifying ways to resolve them.

Abstract Objective To describe and demonstrate use of pediatric data collected by the All of Us Research Program. Materials and Methods All of Us participant physical measurements and electronic health record (EHR) data were analyzed including investigation of trends in childhood obesity and correlation with adult body mass index (BMI). Results We identified 19 729 participants with legacy pediatric EHR data including diagnoses, prescriptions, visits, procedures, and measurements gathered since 1980. We found an increase in pediatric obesity diagnosis over time that correlates with BMI measurements recorded in participants’ adult EHRs and those physical measurements taken at enrollment in the research program. Discussion We highlight the availability of retrospective pediatric EHR data for nearly 20 000 All of Us participants. These data are relevant to current issues such as the rise in pediatric obesity. Conclusion All of Us contains a rich resource of retrospective pediatric EHR data to accelerate pediatric research studies.

Abstract Background With limited severe acute respiratory syndrome coronavirus (SARS-CoV-2) testing capacity in the United States at the start of the epidemic (January–March 2020), testing was focused on symptomatic patients with a travel history throughout February, obscuring the picture of SARS-CoV-2 seeding and community transmission. We sought to identify individuals with SARS-CoV-2 antibodies in the early weeks of the US epidemic. Methods All of Us study participants in all 50 US states provided blood specimens during study visits from 2 January to 18 March 2020. Participants were considered seropositive if they tested positive for SARS-CoV-2 immunoglobulin G (IgG) antibodies with the Abbott Architect SARS-CoV-2 IgG enzyme-linked immunosorbent assay (ELISA) and the EUROIMMUN SARS-CoV-2 ELISA in a sequential testing algorithm. The sensitivity and specificity of these ELISAs and the net sensitivity and specificity of the sequential testing algorithm were estimated, along with 95% confidence intervals (CIs). Results The estimated sensitivities of the Abbott and EUROIMMUN assays were 100% (107 of 107 [95% CI: 96.6%–100%]) and 90.7% (97 of 107 [83.5%–95.4%]), respectively, and the estimated specificities were 99.5% (995 of 1000 [98.8%–99.8%]) and 99.7% (997 of 1000 [99.1%–99.9%]), respectively. The net sensitivity and specificity of our sequential testing algorithm were 90.7% (97 of 107 [95% CI: 83.5%–95.4%]) and 100.0% (1000 of 1000 [99.6%–100%]), respectively. Of the 24 079 study participants with blood specimens from 2 January to 18 March 2020, 9 were seropositive, 7 before the first confirmed case in the states of Illinois, Massachusetts, Wisconsin, Pennsylvania, and Mississippi. Conclusions Our findings identified SARS-CoV-2 infections weeks before the first recognized cases in 5 US states.

AbstractLarge observational data networks that leverage routine clinical practice data in electronic health records (EHRs) are critical resources for research on coronavirus disease 2019 (COVID-19). Data normalization is a key challenge for the secondary use of EHRs for COVID-19 research across institutions. In this study, we addressed the challenge of automating the normalization of COVID-19 diagnostic tests, which are critical data elements, but for which controlled terminology terms were published after clinical implementation. We developed a simple but effective rule-based tool called COVID-19 TestNorm to automatically normalize local COVID-19 testing names to standard LOINC (Logical Observation Identifiers Names and Codes) codes. COVID-19 TestNorm was developed and evaluated using 568 test names collected from 8 healthcare systems. Our results show that it could achieve an accuracy of 97.4% on an independent test set. COVID-19 TestNorm is available as an open-source package for developers and as an online Web application for end users (https://clamp.uth.edu/covid/loinc.php). We believe that it will be a useful tool to support secondary use of EHRs for research on COVID-19.