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1. Characterizing Long COVID in Children and Adolescents

Author

Gross, Rachel S, Thaweethai, Tanayott, Kleinman, Lawrence C, Snowden, Jessica N, Rosenzweig, Erika B, Milner, Joshua D, Tantisira, Kelan G, Rhee, Kyung E, Jernigan, Terry L, Kinser, Patricia A, Salisbury, Amy L, Warburton, David, Mohandas, Sindhu, Wood, John C, Newburger, Jane W, Truong, Dongngan T, Flaherman, Valerie J, Metz, Torri D, Karlson, Elizabeth W, Chibnik, Lori B, Pant, Deepti B, Krishnamoorthy, Aparna, Gallagher, Richard, Lamendola-Essel, Michelle F, Hasson, Denise C, Katz, Stuart D, Yin, Shonna, Dreyer, Benard P, Carmilani, Megan, Coombs, K, Fitzgerald, Megan L, Güthe, Nick, Hornig, Mady, Letts, Rebecca J, Peddie, Aimee K, Taylor, Brittany D, Foulkes, Andrea S, Stockwell, Melissa S, Balaraman, Venkataraman, Bogie, Amanda, Bukulmez, Hulya, Dozor, Allen J, Eckrich, Daniel, Elliott, Amy J, Evans, Danielle N, Farkas, Jonathan S, Faustino, E Vincent S, Fischer, Laura, Gaur, Sunanda, Harahsheh, Ashraf S, Hasan, Uzma N, Hsia, Daniel S, Huerta-Montanez, Gredia, Hummel, Kathy D, Kadish, Matt P, Kaelber, David C, Krishnan, Sankaran, Kosut, Jessica S, Larrabee, Jerry, Lim, Peter Paul C, Michelow, Ian C, Oliveira, Carlos R, Raissy, Hengameh, Rosario-Pabon, Zaira, Ross, Judith L, Sato, Alice I, Stevenson, Michelle D, Talavera-Barber, Maria M, Teufel, Ronald J, Weakley, Kathryn E, Zimmerman, Emily, Bind, Marie-Abele C, Chan, James, Guan, Zoe, Morse, Richard E, Reeder, Harrison T, Akshoomoff, Natascha, Aschner, Judy L, Bhattacharjee, Rakesh, Cottrell, Lesley A, Cowan, Kelly, D'Sa, Viren A, Fiks, Alexander G, Gennaro, Maria L, Irby, Katherine, Khare, Manaswitha, Landeo Guttierrez, Jeremy, McCulloh, Russell J, Narang, Shalu, Ness- Cochinwala, Manette, Nolan, Sheila, Palumbo, Paul, Ryu, Julie, Salazar, Juan C, Selvarangan, Rangaraj, Stein, Cheryl R, Werzberger, Alan, Zempsky, William T, Aupperle, Robin, and Baker, Fiona C

Subjects
Biomedical and Clinical Sciences, Clinical Sciences, Health Sciences, Emerging Infectious Diseases, Neurosciences, Coronaviruses, Infectious Diseases, Pediatric, Minority Health, Pain Research, Infection, Good Health and Well Being, RECOVER-Pediatrics Consortium, RECOVER-Pediatrics Group Authors, Medical and Health Sciences, General & Internal Medicine, Biomedical and clinical sciences, Health sciences
Abstract

ImportanceMost research to understand postacute sequelae of SARS-CoV-2 infection (PASC), or long COVID, has focused on adults, with less known about this complex condition in children. Research is needed to characterize pediatric PASC to enable studies of underlying mechanisms that will guide future treatment.ObjectiveTo identify the most common prolonged symptoms experienced by children (aged 6 to 17 years) after SARS-CoV-2 infection, how these symptoms differ by age (school-age [6-11 years] vs adolescents [12-17 years]), how they cluster into distinct phenotypes, and what symptoms in combination could be used as an empirically derived index to assist researchers to study the likely presence of PASC.Design, setting, and participantsMulticenter longitudinal observational cohort study with participants recruited from more than 60 US health care and community settings between March 2022 and December 2023, including school-age children and adolescents with and without SARS-CoV-2 infection history.ExposureSARS-CoV-2 infection.Main outcomes and measuresPASC and 89 prolonged symptoms across 9 symptom domains.ResultsA total of 898 school-age children (751 with previous SARS-CoV-2 infection [referred to as infected] and 147 without [referred to as uninfected]; mean age, 8.6 years; 49% female; 11% were Black or African American, 34% were Hispanic, Latino, or Spanish, and 60% were White) and 4469 adolescents (3109 infected and 1360 uninfected; mean age, 14.8 years; 48% female; 13% were Black or African American, 21% were Hispanic, Latino, or Spanish, and 73% were White) were included. Median time between first infection and symptom survey was 506 days for school-age children and 556 days for adolescents. In models adjusted for sex and race and ethnicity, 14 symptoms in both school-age children and adolescents were more common in those with SARS-CoV-2 infection history compared with those without infection history, with 4 additional symptoms in school-age children only and 3 in adolescents only. These symptoms affected almost every organ system. Combinations of symptoms most associated with infection history were identified to form a PASC research index for each age group; these indices correlated with poorer overall health and quality of life. The index emphasizes neurocognitive, pain, and gastrointestinal symptoms in school-age children but change or loss in smell or taste, pain, and fatigue/malaise-related symptoms in adolescents. Clustering analyses identified 4 PASC symptom phenotypes in school-age children and 3 in adolescents.Conclusions and relevanceThis study developed research indices for characterizing PASC in children and adolescents. Symptom patterns were similar but distinguishable between the 2 groups, highlighting the importance of characterizing PASC separately for these age ranges.

Published
2024

2. Researching COVID to enhance recovery (RECOVER) adult study protocol: Rationale, objectives, and design

Author

Horwitz, Leora I, Thaweethai, Tanayott, Brosnahan, Shari B, Cicek, Mine S, Fitzgerald, Megan L, Goldman, Jason D, Hess, Rachel, Hodder, SL, Jacoby, Vanessa L, Jordan, Michael R, Krishnan, Jerry A, Laiyemo, Adeyinka O, Metz, Torri D, Nichols, Lauren, Patzer, Rachel E, Sekar, Anisha, Singer, Nora G, Stiles, Lauren E, Taylor, Barbara S, Ahmed, Shifa, Algren, Heather A, Anglin, Khamal, Aponte-Soto, Lisa, Ashktorab, Hassan, Bassett, Ingrid V, Bedi, Brahmchetna, Bhadelia, Nahid, Bime, Christian, Bind, Marie-Abele C, Black, Lora J, Blomkalns, Andra L, Brim, Hassan, Castro, Mario, Chan, James, Charney, Alexander W, Chen, Benjamin K, Chen, Li Qing, Chen, Peter, Chestek, David, Chibnik, Lori B, Chow, Dominic C, Chu, Helen Y, Clifton, Rebecca G, Collins, Shelby, Costantine, Maged M, Cribbs, Sushma K, Deeks, Steven G, Dickinson, John D, Donohue, Sarah E, Durstenfeld, Matthew S, Emery, Ivette F, Erlandson, Kristine M, Facelli, Julio C, Farah-Abraham, Rachael, Finn, Aloke V, Fischer, Melinda S, Flaherman, Valerie J, Fleurimont, Judes, Fonseca, Vivian, Gallagher, Emily J, Gander, Jennifer C, Gennaro, Maria Laura, Gibson, Kelly S, Go, Minjoung, Goodman, Steven N, Granger, Joey P, Greenway, Frank L, Hafner, John W, Han, Jenny E, Harkins, Michelle S, Hauser, Kristine SP, Heath, James R, Hernandez, Carla R, Ho, On, Hoffman, Matthew K, Hoover, Susan E, Horowitz, Carol R, Hsu, Harvey, Hsue, Priscilla Y, Hughes, Brenna L, Jagannathan, Prasanna, James, Judith A, John, Janice, Jolley, Sarah, Judd, SE, Juskowich, Joy J, Kanjilal, Diane G, Karlson, Elizabeth W, Katz, Stuart D, Kelly, J Daniel, Kelly, Sara W, Kim, Arthur Y, Kirwan, John P, Knox, Kenneth S, Kumar, Andre, Lamendola-Essel, Michelle F, Lanca, Margaret, Lee-lannotti, Joyce K, Lefebvre, R Craig, and Levy, Bruce D

Subjects
Biomedical and Clinical Sciences, Clinical Sciences, Prevention, Clinical Research, Emerging Infectious Diseases, Infectious Diseases, Infection, Good Health and Well Being
Abstract

Abstract: Importance: SARS-CoV-2 infection can result in ongoing, relapsing, or new symptoms or other health effects after the acute phase of infection; termed post-acute sequelae of SARS-CoV-2 infection (PASC), or long COVID. The characteristics, prevalence, trajectory and mechanisms of PASC are ill-defined. The objectives of the Researching COVID to Enhance Recovery (RECOVER) Multi-site Observational Study of PASC in Adults (RECOVER-Adult) are to: (1) characterize PASC prevalence; (2) characterize the symptoms, organ dysfunction, natural history, and distinct phenotypes of PASC; (3) identify demographic, social and clinical risk factors for PASC onset and recovery; and (4) define the biological mechanisms underlying PASC pathogenesis. Methods: RECOVER-Adult is a combined prospective/retrospective cohort currently planned to enroll 14,880 adults aged ≥18 years. Eligible participants either must meet WHO criteria for suspected, probable, or confirmed infection; or must have evidence of no prior infection. Recruitment occurs at 86 sites in 33 U.S. states, Washington, DC and Puerto Rico, via facility– and community-based outreach. Participants complete quarterly questionnaires about symptoms, social determinants, vaccination status, and interim SARS-CoV-2 infections. In addition, participants contribute biospecimens and undergo physical and laboratory examinations at approximately 0, 90 and 180 days from infection or negative test date, and yearly thereafter. Some participants undergo additional testing based on specific criteria or random sampling. Patient representatives provide input on all study processes. The primary study outcome is onset of PASC, measured by signs and symptoms. A paradigm for identifying PASC cases will be defined and updated using supervised and unsupervised learning approaches with cross– validation. Logistic regression and proportional hazards regression will be conducted to investigate associations between risk factors, onset, and resolution of PASC symptoms. Discussion: RECOVER-Adult is the first national, prospective, longitudinal cohort of PASC among US adults. Results of this study are intended to inform public health, spur clinical trials, and expand treatment options.

Published
2023

3. Selection, optimization and validation of ten chronic disease polygenic risk scores for clinical implementation in diverse US populations

Author

Lennon, Niall J., Kottyan, Leah C., Kachulis, Christopher, Abul-Husn, Noura S., Arias, Josh, Belbin, Gillian, Below, Jennifer E., Berndt, Sonja I., Chung, Wendy K., Cimino, James J., Clayton, Ellen Wright, Connolly, John J., Crosslin, David R., Dikilitas, Ozan, Velez Edwards, Digna R., Feng, QiPing, Fisher, Marissa, Freimuth, Robert R., Ge, Tian, Glessner, Joseph T., Gordon, Adam S., Patterson, Candace, Hakonarson, Hakon, Harden, Maegan, Harr, Margaret, Hirschhorn, Joel N., Hoggart, Clive, Hsu, Li, Irvin, Marguerite R., Jarvik, Gail P., Karlson, Elizabeth W., Khan, Atlas, Khera, Amit, Kiryluk, Krzysztof, Kullo, Iftikhar, Larkin, Katie, Limdi, Nita, Linder, Jodell E., Loos, Ruth J. F., Luo, Yuan, Malolepsza, Edyta, Manolio, Teri A., Martin, Lisa J., McCarthy, Li, McNally, Elizabeth M., Meigs, James B., Mersha, Tesfaye B., Mosley, Jonathan D., Musick, Anjene, Namjou, Bahram, Pai, Nihal, Pesce, Lorenzo L., Peters, Ulrike, Peterson, Josh F., Prows, Cynthia A., Puckelwartz, Megan J., Rehm, Heidi L., Roden, Dan M., Rosenthal, Elisabeth A., Rowley, Robb, Sawicki, Konrad Teodor, Schaid, Daniel J., Smit, Roelof A. J., Smith, Johanna L., Smoller, Jordan W., Thomas, Minta, Tiwari, Hemant, Toledo, Diana M., Vaitinadin, Nataraja Sarma, Veenstra, David, Walunas, Theresa L., Wang, Zhe, Wei, Wei-Qi, Weng, Chunhua, Wiesner, Georgia L., Yin, Xianyong, and Kenny, Eimear E.

Published
2024
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4. Concordance of SARS-CoV-2 Antibody Results during a Period of Low Prevalence

Author

Miller, Cheryl N, Althoff, Keri N, Schlueter, David J, Anton-Culver, Hoda, Chen, Qingxia, Garbett, Shawn, Ratsimbazafy, Francis, Thomsen, Isaac, Karlson, Elizabeth W, Cicek, Mine, Pinto, Ligia A, Malin, Bradley A, Ohno-Machado, Lucila, Williams, Carolyn, Goldstein, David, Kouame, Aymone, Ramirez, Andrea, Gebo, Kelly A, and Schully, Sheri D

Subjects
Biotechnology, Infectious Diseases, Pneumonia & Influenza, Vaccine Related, Prevention, Lung, Immunization, Biodefense, Pneumonia, Emerging Infectious Diseases, Good Health and Well Being, Humans, SARS-CoV-2, COVID-19, Prevalence, Seroepidemiologic Studies, Sensitivity and Specificity, Population Health, Antibodies, Viral, Immunoglobulin G, IgG antibodies, spike protein, nucleocapsid protein, low prevalence, All of Us Research Program, Immunology, Microbiology
Abstract

Accurate, highly specific immunoassays for severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) are needed to evaluate seroprevalence. This study investigated the concordance of results across four immunoassays targeting different antigens for sera collected at the beginning of the SARS-CoV-2 pandemic in the United States. Specimens from All of Us participants contributed between January and March 2020 were tested using the Abbott Architect SARS-CoV-2 IgG (immunoglobulin G) assay (Abbott) and the EuroImmun SARS-CoV-2 enzyme-linked immunosorbent assay (ELISA) (EI). Participants with discordant results, participants with concordant positive results, and a subset of concordant negative results by Abbott and EI were also tested using the Roche Elecsys anti-SARS-CoV-2 (IgG) test (Roche) and the Ortho-Clinical Diagnostics Vitros anti-SARS-CoV-2 IgG test (Ortho). The agreement and 95% confidence intervals were estimated for paired assay combinations. SARS-CoV-2 antibody concentrations were quantified for specimens with at least two positive results across four immunoassays. Among the 24,079 participants, the percent agreement for the Abbott and EI assays was 98.8% (95% confidence interval, 98.7%, 99%). Of the 490 participants who were also tested by Ortho and Roche, the probability-weighted percentage of agreement (95% confidence interval) between Ortho and Roche was 98.4% (97.9%, 98.9%), that between EI and Ortho was 98.5% (92.9%, 99.9%), that between Abbott and Roche was 98.9% (90.3%, 100.0%), that between EI and Roche was 98.9% (98.6%, 100.0%), and that between Abbott and Ortho was 98.4% (91.2%, 100.0%). Among the 32 participants who were positive by at least 2 immunoassays, 21 had quantifiable anti-SARS-CoV-2 antibody concentrations by research assays. The results across immunoassays revealed concordance during a period of low prevalence. However, the frequency of false positivity during a period of low prevalence supports the use of two sequentially performed tests for unvaccinated individuals who are seropositive by the first test. IMPORTANCE 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. In a subset analysis, quantified antinucleocapsid and antispike SARS-CoV-2 IgG antibodies do not suggest the need to specify the antigen targets of the sequential assays in the CDC's recommendation because false positivity varied as much between assays targeting the same antigen as it did between assays targeting different antigens.

Published
2022

6. The All of Us Research Program: Data quality, utility, and diversity

Author

Ramirez, Andrea H, Sulieman, Lina, Schlueter, David J, Halvorson, Alese, Qian, Jun, Ratsimbazafy, Francis, Loperena, Roxana, Mayo, Kelsey, Basford, Melissa, Deflaux, Nicole, Muthuraman, Karthik N, Natarajan, Karthik, Kho, Abel, Xu, Hua, Wilkins, Consuelo, Anton-Culver, Hoda, Boerwinkle, Eric, Cicek, Mine, Clark, Cheryl R, Cohn, Elizabeth, Ohno-Machado, Lucila, Schully, Sheri D, Ahmedani, Brian K, Argos, Maria, Cronin, Robert M, O’Donnell, Christopher, Fouad, Mona, Goldstein, David B, Greenland, Philip, Hebbring, Scott J, Karlson, Elizabeth W, Khatri, Parinda, Korf, Bruce, Smoller, Jordan W, Sodeke, Stephen, Wilbanks, John, Hentges, Justin, Mockrin, Stephen, Lunt, Christopher, Devaney, Stephanie A, Gebo, Kelly, Denny, Joshua C, Carroll, Robert J, Glazer, David, Harris, Paul A, Hripcsak, George, Philippakis, Anthony, Roden, Dan M, Program, the All of Us Research, Ahmedani, Brian, Johnson, Christine D Cole, Ahsan, Habib, Antoine-LaVigne, Donna, Singleton, Glendora, Topol, Eric, Baca-Motes, Katie, Steinhubl, Steven, Wade, James, Begale, Mark, Jain, Praduman, Sutherland, Scott, Lewis, Beth, Behringer, Melissa, Gharavi, Ali G, Bier, Louise, Brilliant, Murray H, Murali, Narayana, Hebbring, Scott Joseph, Farrar-Edwards, Dorothy, Burnside, Elizabeth, Drezner, Marc K, Taylor, Amy, Channamsetty, Veena, Montalvo, Wanda, Sharma, Yashoda, Chinea, Carmen, Jenks, Nancy, Thibodeau, Steve, Holmes, Beverly Wilson, Schlueter, Eric, Collier, Ever, Winkler, Joyce, Corcoran, John, D’Addezio, Nick, Daviglus, Martha, Winn, Robert, Roden, Dan, Denny, Joshua, Doheny, Kim, Nickerson, Debbie, Eichler, Evan, Jarvik, Gail, and Funk, Gretchen

Subjects
Networking and Information Technology R&D (NITRD), Clinical Research, Cardiovascular, Cancer, Generic health relevance, Good Health and Well Being, All of Us Research Program, cloud-based analytics, cohort study, electronic health records, precision medicine
Abstract

The All of Us Research Program seeks to engage at least one million diverse participants to advance precision medicine and improve human health. We describe here the cloud-based Researcher Workbench that uses a data passport model to democratize access to analytical tools and participant information including survey, physical measurement, and electronic health record (EHR) data. We also present validation study findings for several common complex diseases to demonstrate use of this novel platform in 315,000 participants, 78% of whom are from groups historically underrepresented in biomedical research, including 49% self-reporting non-White races. Replication findings include medication usage pattern differences by race in depression and type 2 diabetes, validation of known cancer associations with smoking, and calculation of cardiovascular risk scores by reported race effects. The cloud-based Researcher Workbench represents an important advance in enabling secure access for a broad range of researchers to this large resource and analytical tools.

Published
2022

7. Author Correction: Genetic variation in the human leukocyte antigen region confers susceptibility to Clostridioides difficile infection

Author

Ferar, Kathleen, Hall, Taryn O., Crawford, Dana C., Rowley, Robb, Satterfield, Benjamin A., Li, Rongling, Gragert, Loren, Karlson, Elizabeth W., de Andrade, Mariza, Kullo, Iftikhar J., McCarty, Catherine A., Kho, Abel, Hayes, M. Geoffrey, Ritchie, Marylyn D., Crane, Paul K., Mirel, Daniel B., Carlson, Christopher, Connolly, John J., Hakonarson, Hakon, Crenshaw, Andrew T., Carrell, David, Luo, Yuan, Dikilitas, Ozan, Denny, Joshua C., Jarvik, Gail P., and Crosslin, David R.

Published
2023
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8. Genetic variation in the human leukocyte antigen region confers susceptibility to Clostridioides difficile infection

Author

Ferar, Kathleen, Hall, Taryn O., Crawford, Dana C., Rowley, Robb, Satterfield, Benjamin A., Li, Rongling, Gragert, Loren, Karlson, Elizabeth W., de Andrade, Mariza, Kullo, Iftikhar J., McCarty, Catherine A., Kho, Abel, Hayes, M. Geoffrey, Ritchie, Marylyn D., Crane, Paul K., Mirel, Daniel B., Carlson, Christopher, Connolly, John J., Hakonarson, Hakon, Crenshaw, Andrew T., Carrell, David, Luo, Yuan, Dikilitas, Ozan, Denny, Joshua C., Jarvik, Gail P., and Crosslin, David R.

Published
2023
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9. Evaluation of the portability of computable phenotypes with natural language processing in the eMERGE network

Author

Pacheco, Jennifer A., Rasmussen, Luke V., Wiley, Jr., Ken, Person, Thomas Nate, Cronkite, David J., Sohn, Sunghwan, Murphy, Shawn, Gundelach, Justin H., Gainer, Vivian, Castro, Victor M., Liu, Cong, Mentch, Frank, Lingren, Todd, Sundaresan, Agnes S., Eickelberg, Garrett, Willis, Valerie, Furmanchuk, Al’ona, Patel, Roshan, Carrell, David S., Deng, Yu, Walton, Nephi, Satterfield, Benjamin A., Kullo, Iftikhar J., Dikilitas, Ozan, Smith, Joshua C., Peterson, Josh F., Shang, Ning, Kiryluk, Krzysztof, Ni, Yizhao, Li, Yikuan, Nadkarni, Girish N., Rosenthal, Elisabeth A., Walunas, Theresa L., Williams, Marc S., Karlson, Elizabeth W., Linder, Jodell E., Luo, Yuan, Weng, Chunhua, and Wei, WeiQi

Published
2023
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10. Author Correction: Genetic regulation of serum IgA levels and susceptibility to common immune, infectious, kidney, and cardio-metabolic traits

Author

Liu, Lili, Khan, Atlas, Sanchez-Rodriguez, Elena, Zanoni, Francesca, Li, Yifu, Steers, Nicholas, Balderes, Olivia, Zhang, Junying, Krithivasan, Priya, LeDesma, Robert A., Fischman, Clara, Hebbring, Scott J., Harley, John B., Moncrieffe, Halima, Kottyan, Leah C., Namjou-Khales, Bahram, Walunas, Theresa L., Knevel, Rachel, Raychaudhuri, Soumya, Karlson, Elizabeth W., Denny, Joshua C., Stanaway, Ian B., Crosslin, David, Rauen, Thomas, Floege, Jürgen, Eitner, Frank, Moldoveanu, Zina, Reily, Colin, Knoppova, Barbora, Hall, Stacy, Sheff, Justin T., Julian, Bruce A., Wyatt, Robert J., Suzuki, Hitoshi, Xie, Jingyuan, Chen, Nan, Zhou, Xujie, Zhang, Hong, Hammarström, Lennart, Viktorin, Alexander, Magnusson, Patrik K. E., Shang, Ning, Hripcsak, George, Weng, Chunhua, Rundek, Tatjana, Elkind, Mitchell S. V., Oelsner, Elizabeth C., Barr, R. Graham, Ionita-Laza, Iuliana, Novak, Jan, Gharavi, Ali G., and Kiryluk, Krzysztof

Published
2023
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11. Antibodies to SARS-CoV-2 in All of Us Research Program Participants, January 2-March 18, 2020

Author

Althoff, Keri N, Schlueter, David J, Anton-Culver, Hoda, Cherry, James, Denny, Joshua C, Thomsen, Isaac, Karlson, Elizabeth W, Havers, Fiona P, Cicek, Mine S, Thibodeau, Stephen N, Pinto, Ligia A, Lowy, Douglas, Malin, Bradley A, Ohno-Machado, Lucila, Williams, Carolyn, Goldstein, David, Kouame, Aymone, Ramirez, Andrea, Roman, Adrienne, Sharpless, Norman E, Gebo, Kelly A, Schully, Sheri D, and Program, on behalf of the All of Us Research

Subjects
Infectious Diseases, Emerging Infectious Diseases, Lung, Vaccine Related, Biodefense, Clinical Research, Prevention, Good Health and Well Being, Antibodies, Viral, COVID-19, Enzyme-Linked Immunosorbent Assay, Humans, Immunoglobulin G, Population Health, SARS-CoV-2, Sensitivity and Specificity, United States, Epidemic, Immunoglobulin G antibodies, All of Us Research Program, All of Us Research Program, Biological Sciences, Medical and Health Sciences, Microbiology
Abstract

BackgroundWith 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.MethodsAll 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).ResultsThe 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.ConclusionsOur findings identified SARS-CoV-2 infections weeks before the first recognized cases in 5 US states.

Published
2022

12. Geographic Variation in Obesity at the State Level in the All of Us Research Program

Author

Clark, Cheryl R, Chandler, Paulette D, Zhou, Guohai, Noel, Nyia, Achilike, Confidence, Mendez, Lizette, O’Connor, George T, Smoller, Jordan W, Weiss, Scott T, Murphy, Shawn N, Ommerborn, Mark J, Karnes, Jason H, Klimentidis, Yann C, Jordan, Christina D, Hiatt, Robert A, Ramirez, Andrea H, Loperena, Roxana, Mayo, Kelsey, Cohn, Elizabeth, Ohno-Machado, Lucila, Boerwinkle, Eric, Cicek, Mine, Schully, Sheri D, Mockrin, Stephen, Gebo, Kelly A, and Karlson, Elizabeth W

Subjects
Health Services and Systems, Public Health, Health Sciences, Nutrition, Obesity, Prevention, Networking and Information Technology R&D (NITRD), Stroke, Cancer, Cardiovascular, Oral and gastrointestinal, Metabolic and endocrine, Good Health and Well Being, Body Mass Index, Humans, Obesity, Morbid, Population Health, Prevalence, United States, Public Health and Health Services, Epidemiology, Health services and systems, Public health
Abstract

IntroductionNational obesity prevention strategies may benefit from precision health approaches involving diverse participants in population health studies. We used cohort data from the National Institutes of Health All of Us Research Program (All of Us) Researcher Workbench to estimate population-level obesity prevalence.MethodsTo estimate state-level obesity prevalence we used data from physical measurements made during All of Us enrollment visits and data from participant electronic health records (EHRs) where available. Prevalence estimates were calculated and mapped by state for 2 categories of body mass index (BMI) (kg/m2): obesity (BMI >30) and severe obesity (BMI >35). We calculated and mapped prevalence by state, excluding states with fewer than 100 All of Us participants.ResultsData on height and weight were available for 244,504 All of Us participants from 33 states, and corresponding EHR data were available for 88,840 of these participants. The median and IQR of BMI taken from physical measurements data was 28.4 (24.4- 33.7) and 28.5 (24.5-33.6) from EHR data, where available. Overall obesity prevalence based on physical measurements data was 41.5% (95% CI, 41.3%-41.7%); prevalence of severe obesity was 20.7% (95% CI, 20.6-20.9), with large geographic variations observed across states. Prevalence estimates from states with greater numbers of All of Us participants were more similar to national population-based estimates than states with fewer participants.ConclusionAll of Us participants had a high prevalence of obesity, with state-level geographic variation mirroring national trends. The diversity among All of Us participants may support future investigations on obesity prevention and treatment in diverse populations.

Published
2021

13. Hypertension prevalence in the All of Us Research Program among groups traditionally underrepresented in medical research.

Author

Chandler, Paulette D, Clark, Cheryl R, Zhou, Guohai, Noel, Nyia L, Achilike, Confidence, Mendez, Lizette, Ramirez, Andrea H, Loperena-Cortes, Roxana, Mayo, Kelsey, Cohn, Elizabeth, Ohno-Machado, Lucila, Boerwinkle, Eric, Cicek, Mine, Qian, Jun, Schully, Sheri, Ratsimbazafy, Francis, Mockrin, Stephen, Gebo, Kelly, Dedier, Julien J, Murphy, Shawn N, Smoller, Jordan W, Karlson, Elizabeth W, and All of Us Research Program Investigators

Subjects
All of Us Research Program Investigators, Hypertension, Clinical Research, Prevention, Cardiovascular, 2.4 Surveillance and distribution
Abstract

The All of Us Research Program was designed to enable broad-based precision medicine research in a cohort of unprecedented scale and diversity. Hypertension (HTN) is a major public health concern. The validity of HTN data and definition of hypertension cases in the All of Us (AoU) Research Program for use in rule-based algorithms is unknown. In this cross-sectional, population-based study, we compare HTN prevalence in the AoU Research Program to HTN prevalence in the 2015-2016 National Health and Nutrition Examination Survey (NHANES). We used AoU baseline data from patient (age ≥ 18) measurements (PM), surveys, and electronic health record (EHR) blood pressure measurements. We retrospectively examined the prevalence of HTN in the EHR cohort using Systemized Nomenclature of Medicine (SNOMED) codes and blood pressure medications recorded in the EHR. We defined HTN as the participant having at least 2 HTN diagnosis/billing codes on separate dates in the EHR data AND at least one HTN medication. We calculated an age-standardized HTN prevalence according to the age distribution of the U.S. Census, using 3 groups (18-39, 40-59, and ≥ 60). Among the 185,770 participants enrolled in the AoU Cohort (mean age at enrollment = 51.2 years) available in a Researcher Workbench as of October 2019, EHR data was available for at least one SNOMED code from 112,805 participants, medications for 104,230 participants, and 103,490 participants had both medication and SNOMED data. The total number of persons with SNOMED codes on at least two distinct dates and at least one antihypertensive medication was 33,310 for a crude prevalence of HTN of 32.2%. AoU age-adjusted HTN prevalence was 27.9% using 3 groups compared to 29.6% in NHANES. The AoU cohort is a growing source of diverse longitudinal data to study hypertension nationwide and develop precision rule-based algorithms for use in hypertension treatment and prevention research. The prevalence of hypertension in this cohort is similar to that in prior population-based surveys.

Published
2021

14. Antibodies to SARS-CoV-2 in All of Us Research Program Participants, January 2-March 18, 2020.

Author

Althoff, Keri N, Schlueter, David J, Anton-Culver, Hoda, Cherry, James, Denny, Joshua C, Thomsen, Isaac, Karlson, Elizabeth W, Havers, Fiona P, Cicek, Mine S, Thibodeau, Stephen N, Pinto, Ligia A, Lowy, Douglas, Malin, Bradley A, Ohno-Machado, Lucila, Williams, Carolyn, Goldstein, David, Kouame, Aymone, Ramirez, Andrea, Roman, Adrienne, Sharpless, Norman E, Gebo, Kelly A, and Schully, Sheri D

Subjects
All of Us Research Program, SARS-CoV-2 antibodies, United States, Emerging Infectious Diseases, Biodefense, Prevention, Vaccine Related, Clinical Research, Microbiology, Biological Sciences, Medical and Health Sciences
Abstract

BackgroundWith limited SARS-CoV-2 testing capacity in the US at the start of the epidemic (January - March), 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.MethodsAll of Us study participants in all 50 US states provided blood specimens during study visits from January 2 to March 18, 2020. A participant was considered seropositive if they tested positive for SARS-CoV-2 immunoglobulin G (IgG) antibodies on the Abbott Architect SARS-CoV-2 IgG ELISA and the EUROIMMUN SARS-CoV-2 ELISA in a sequential testing algorithm. Sensitivity and specificity of the Abbott and EUROIMMUNE ELISAs and the net sensitivity and specificity of the sequential testing algorithm were estimated with 95% confidence intervals.ResultsThe estimated sensitivity of Abbott and EUROIMMUN was 100% (107/107 [96.6%, 100%]) and 90.7% (97/107 [83.5%, 95.4%]), respectively. The estimated specificity of Abbott and EUROIMMUN was 99.5% (995/1,000 [98.8%, 99.8%]) and 99.7% (997/1,000 [99.1%, 99.9%), respectively. The net sensitivity and specificity of our sequential testing algorithm was 90.7% (97/107 [83.5%, 95.4%]) and 100.0% (1,000/1,000 [99.6%, 100%]), respectively. Of the 24,079 study participants with blood specimens from January 2 to March 18, 2020, 9 were seropositive, 7 of whom were seropositive prior to the first confirmed case in the states of Illinois, Massachusetts, Wisconsin, Pennsylvania, and Mississippi.ConclusionsOur findings indicate SARS-CoV-2 infections weeks prior to the first recognized cases in 5 US states.

Published
2021

15. Longitudinal SARS-CoV-2 Nucleocapsid Antibody Kinetics, Seroreversion, and Implications for Seroepidemiologic Studies

Author

Loesche, Michael, Karlson, Elizabeth W., Talabi, Opeyemi, Zhou, Guohai, Boutin, Natalie, Atchley, Rachel, Loevinsohn, Gideon, Chang, Jun Bai Park, Hasdianda, Mohammad A., Okenla, Adetoun, Sampson, Elizabeth, Schram, Haley, Magsipoc, Karen, Goodman, Kirsten, Donahue, Lauren, MacGowan, Maureen, Novack, Lewis A., Jarolim, Petr, Baden, Lindsey R., and Nilles, Eric J.

Subjects
Epidemiologic methods, Immune system -- Testing, Viral proteins -- Measurement -- Physiological aspects, Health
Abstract

Estimating the incidence of infections caused by SARS-CoV-2 that are frequently asymptomatic is challenging when using routine passive surveillance methods. Antibodies can provide a record of previous infection, whether symptomatic [...]

Published
2022
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17. Multi-ancestry genome-wide association analyses identify novel genetic mechanisms in rheumatoid arthritis

Author

Ishigaki, Kazuyoshi, Sakaue, Saori, Terao, Chikashi, Luo, Yang, Sonehara, Kyuto, Yamaguchi, Kensuke, Amariuta, Tiffany, Too, Chun Lai, Laufer, Vincent A., Scott, Ian C., Viatte, Sebastien, Takahashi, Meiko, Ohmura, Koichiro, Murasawa, Akira, Hashimoto, Motomu, Ito, Hiromu, Hammoudeh, Mohammed, Emadi, Samar Al, Masri, Basel K., Halabi, Hussein, Badsha, Humeira, Uthman, Imad W., Wu, Xin, Lin, Li, Li, Ting, Plant, Darren, Barton, Anne, Orozco, Gisela, Verstappen, Suzanne M. M., Bowes, John, MacGregor, Alexander J., Honda, Suguru, Koido, Masaru, Tomizuka, Kohei, Kamatani, Yoichiro, Tanaka, Hiroaki, Tanaka, Eiichi, Suzuki, Akari, Maeda, Yuichi, Yamamoto, Kenichi, Miyawaki, Satoru, Xie, Gang, Zhang, Jinyi, Amos, Christopher I., Keystone, Edward, Wolbink, Gertjan, van der Horst-Bruinsma, Irene, Cui, Jing, Liao, Katherine P., Carroll, Robert J., Lee, Hye-Soon, Bang, So-Young, Siminovitch, Katherine A., de Vries, Niek, Alfredsson, Lars, Rantapää-Dahlqvist, Solbritt, Karlson, Elizabeth W., Bae, Sang-Cheol, Kimberly, Robert P., Edberg, Jeffrey C., Mariette, Xavier, Huizinga, Tom, Dieudé, Philippe, Schneider, Matthias, Kerick, Martin, Denny, Joshua C., Matsuda, Koichi, Matsuo, Keitaro, Mimori, Tsuneyo, Matsuda, Fumihiko, Fujio, Keishi, Tanaka, Yoshiya, Kumanogoh, Atsushi, Traylor, Matthew, Lewis, Cathryn M., Eyre, Stephen, Xu, Huji, Saxena, Richa, Arayssi, Thurayya, Kochi, Yuta, Ikari, Katsunori, Harigai, Masayoshi, Gregersen, Peter K., Yamamoto, Kazuhiko, Louis Bridges, Jr, S., Padyukov, Leonid, Martin, Javier, Klareskog, Lars, Okada, Yukinori, and Raychaudhuri, Soumya

Published
2022
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18. Diagnostic TR-FRET assays for detection of antibodies in patient samples

Author

Yue, Hong, Nowak, Radosław P., Overwijn, Daan, Payne, N. Connor, Fischinger, Stephanie, Atyeo, Caroline, Lam, Evan C., St. Denis, Kerri, Brais, Lauren K., Konishi, Yoshinobu, Sklavenitis-Pistofidis, Romanos, Baden, Lindsey R., Nilles, Eric J., Karlson, Elizabeth W., Yu, Xu G., Li, Jonathan Z., Woolley, Ann E., Ghobrial, Irene M., Meyerhardt, Jeffrey A., Balazs, Alejandro B., Alter, Galit, Mazitschek, Ralph, and Fischer, Eric S.

Published
2023
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19. Returning integrated genomic risk and clinical recommendations: The eMERGE study

Author

Gordon, Adam, Sobowale, Agboade, Allworth, Aimee, Patel, Akshar, DiVietro, Alanna, Strong, Alanna, Sherafati, Alborz, Sherfati, Alborz, Bick, Alex, Miller, Alexandra, Chandel, Alka, Rosenthal, Alyssa, Khera, Amit, Kontorovich, Amy, Beck, Andrew, Beck, Andy, Espinoza, Angelica, Lewis, Anna, Prince, Anya, Khan, Atlas, Iverson, Ayuko, Khales, Bahram Namjou, Benoit, Barbara, Hernan, Becca, Kallman, Ben, Kerman, Ben, Shoemaker, Ben, Satterfield, Benjamin, Devine, Beth, Etheridge, Bethany, Goff, Blake, Freimuth, Bob, Grundmeier, Bob, Collier, Brenae, Mutai, Brenda, Harnett, Brett, Chang, Brian, Piening, Brian, Davis, Brittney, Korf, Bruce, Patterson, Candace, Demetriou, Carmen, Ta, Casey, Hammack, Catherine, Nelson, Catrina, Gascoigne, Caytie, Dorn, Chad, Moretz, Chad, Kachulis, Chris, Hoell, Christie, Cowles, Christine, Lange, Christoph, Weng, Chunhua, Prows, Cindy, Brokamp, Cole, Liu, Cong, Scherr, Courtney, Gonzalez, Crystal, Ramirez, Cynthia, Shimbo, Daichi, Roden, Dan, Schaid, Daniel, Kaufman, Dave, Crosslin, David, Kochan, David, Veenstra, David, Singh, Davinder, Karavite, Dean, Abrams, Debbie, Absher, Devin, Edwards, Digna Velez, Haverfield, Eden, Morales, Eduardo, Esplin, Edward, Malolepsza, Edyta, Alipour, Ehsan, Kenny, Eimear, Rosenthal, Elisabeth, Duvall, Eliza, McNally, Elizabeth, Bhoj, Elizabeth, Cohn, Elizabeth, Hibler, Elizabeth, Karlson, Elizabeth, Clayton, Ellen, Chesnut, Emily, DeFranco, Emily, Gallagher, Emily, Soper, Emily, Perez, Emma, Cash, Erin, Berner, Eta, Wang, Fei, Wehbe, Firas, Ricci, Francisco, Mentch, Frank, Shaibi, Gabriel, Jarvik, Gail, Hahn, George, Hripcsak, George, Wiesner, Georgia, Belbin, Gillian, Davogustto, Gio, Nadkarni, Girish, Qiu, Haijun, Hakonarson, Hakon, Bangash, Hana, Beasley, Hannah, Liu, Hao, Aungst, Heide, Tiwari, Hemant, Duckham, Hillary, Thomas, Hope, Kullo, Iftikhar, Holm, Ingrid, Allen, Isabelle, Ionita-Laza, Iuliana, Hellwege, Jacklyn, Petrzelka, Jacob, Odgis, Jacqueline, Narula, Jahnavi, Petrzelka, Jake, Patel, Jalpa, Cimino, James, Meigs, James, Snyder, James, Olson, Janet, Zahner, Janet, Pennington, Jeff, Pacheco, Jen, Pacheco, Jennifer Allen, Morse, Jennifer, Corsmo, Jeremy, Thayer, Jeritt, Cimino, Jim, Chen, Jingheng, Fournier, Jocelyn, Jackson, Jodell, Glessner, Joe, Pacyna, Joel, Smith, Johanna, Connolly, John, Lynch, John, Shelley, John, Mosley, Jonathan, Nestor, Jordan, Smoller, Jordan, Alsip, Jorge, Kannry, Joseph, Sutton, Joseph, Peterson, Josh, Smith, Joshua, Galasso, Julia, Smith, Julia, Wynn, Julia, Gundelach, Justin, Starren, Justin, Choi, Karmel, Mittendorf, Kate, Anderson, Katherine, Bonini, Katherine, Leppig, Kathleen, Muenzen, Kathleen, Larkin, Katie, Stuttgen, Kelsey, Wiley, Ken, Nguyen, Kenny, Dufendach, Kevin, Atkins, Kiley, Sawicki, Konrad, Norland, Kristjan, Kiryluk, Krzysztof, Beskow, Laura, Rasmussen-Torvik, Laura, Kottyan, Leah, Hsu, Li, Tian, Lifeng, Mahanta, Lisa, Martin, Lisa, Wang, Lisa, Gomez, Lizbeth, Thompson, Lorenzo, Orlando, Lori, Richter, Lucas, Rasmussen, Luke, Petukhova, Lynn, Seabolt, Lynn, O’Brien, Madison, Harden, Maegan, Fullerton, Malia, Harr, Margaret, Beasley, Mark, Guindo, Marta, Horike, Martha, Horike-Pyne, Martha, Abdalla, Marwah, Hamed, Marwan, Terry, Mary Beth, Maradik, Mary, Wyatt, Matt, Davis, Matthew, Lebo, Matthew, Smith, Maureen, Rosario, Maya del, Sabatello, Maya, Behr, Meckenzie, Roy-Puckelwartz, Meg, Habrat, Mel, Myers, Melanie, Yetisgen, Meliha, Iris, Merve, DaSilva, Michael, Preuss, Michael, McGowan, Michelle, Shi, Mingjian, Perera, Minoli, Thomas, Minta, Elkind, Mitch, Abbass, Mohammad, Saadatagah, Mohammad, Hess, Molly, Maradik, Molly, Vaitinadin, Nataraja “RJ”, Vaitinadin, Nataraja, Muthu, Naveen, Netherly, Neil, Lennon, Niall, Shang, Ning, Limdi, Nita, Forrest, Noah, Romero, Noheli, Robinson, Nora, Abul-Husn, Noura, Elsekaily, Omar, Dikilitas, Ozan, Kovatch, Patricia, Davis, Patrick, Appelbaum, Paul, Francaviglia, Paul, O’Reilly, Paul, Chandler, Paulette, Caraballo, Pedro, Tarczy-Hornoch, Peter, Shum, Pierre, Marathe, Priya, Murali, Priyanka, Feng, Qiping, Wells, Quinn, Atchley, Rachel, Narla, Radhika, Barton, Rene, Sterling, Rene, Chisholm, Rex, Green, Richard, Sharp, Richard, Peters, Riki, Kukafka, Rita, Rowley, Robb, Freimuth, Robert, Green, Robert, Winter, Robert, Mueller, Roger, Loos, Ruth, Irvin, Ryan, Suckiel, Sabrina, Hussain, Sajjad, Sharba, Samer, Aronson, Sandy, Jones, Sarah, Knerr, Sarah, Nigbur, Scott, Weiss, Scott, Mooney, Sean, Terek, Shannon, Aufox, Sharon, Nirenberg, Sharon, Murphy, Shawn, O’Byrne, Sheila, Wang (Sam) Choi, Shing, Aguilar, Sienna, Bland, S.T., Rodrigues, Stefanie, Ledbetter, Stephanie, Rutledge, Stephanie, Booth, Stuart James, Xian, Su, Trinidad, Susan Brown, Bakken, Suzanne, Schmidlen, Tara, Rakhra-Burris, Tejinder, Manolio, Teri, Mersha, Tesfaye, Walunas, Theresa, Chandereng, Thevaa, May, Thomas, Ge, Tian, Edwards, Todd, Kaszemacher, Tom, Hernandez, Valentina, Willis, Valerie, Desai, Vemi, Desai, Vimi, Lorenzi, Virginia, Gainer, Vivian, Wei, Wei-Qi, Chung, Wendy, Su, Wu-Chen, Chang, Xiao, Zhao, Yiqing, Luo, Yuan, Shen, Yufeng, Linder, Jodell E., Bland, Sarah T., Caraballo, Pedro J., Chisholm, Rex L., Clayton, Ellen Wright, Crosslin, David R., Esplin, Edward D., Forman, Sophie, Freimuth, Robert R., Gordon, Adam S., Harden, Maegan V., Holm, Ingrid A., Jarvik, Gail P., Karlson, Elizabeth W., Labrecque, Sofia, Lennon, Niall J., Limdi, Nita A., Mittendorf, Kathleen F., Murphy, Shawn N., Prows, Cynthia A., Rasmussen, Luke V., Sawicki, Konrad Teodor, Velez Edwards, Digna R., Abul-Husn, Noura S., Below, Jennifer E., Berner, Eta S., Booth, James, Chung, Wendy K., Cimino, James J., Fullerton, Stephanie M., Guiducci, Candace, Habrat, Melissa L., Hain, Heather, Hoell, Christin, Irvin, Marguerite R., Kachulis, Christopher, Kenny, Eimear E., Kullo, Iftikhar J., Manolio, Teri A., McNally, Elizabeth M., Mooney, Sean D., Namjou, Bahram, Perez, Emma F., Puckelwartz, Megan J., Roden, Dan M., Rosenthal, Elisabeth A., Saadatagah, Seyedmohammad, Schaid, Dan J., Schultz, Baergen, Shaibi, Gabriel Q., Sharp, Richard R., Shirts, Brian, Smith, Maureen E., Smoller, Jordan W., Suckiel, Sabrina A., Tiwari, Hemant K., Trinidad, Susan B., Wells, Quinn S., Wiesner, Georgia L., and Peterson, Josh F.

Published
2023
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23. The All of Us Research Program: Data quality, utility, and diversity

Author

Ahmedani, Brian, Cole Johnson, Christine D., Ahsan, Habib, Antoine-LaVigne, Donna, Singleton, Glendora, Anton-Culver, Hoda, Topol, Eric, Baca-Motes, Katie, Steinhubl, Steven, Wade, James, Begale, Mark, Jain, Praduman, Sutherland, Scott, Lewis, Beth, Korf, Bruce, Behringer, Melissa, Gharavi, Ali G., Goldstein, David B., Hripcsak, George, Bier, Louise, Boerwinkle, Eric, Brilliant, Murray H., Murali, Narayana, Hebbring, Scott Joseph, Farrar-Edwards, Dorothy, Burnside, Elizabeth, Drezner, Marc K., Taylor, Amy, Channamsetty, Veena, Montalvo, Wanda, Sharma, Yashoda, Chinea, Carmen, Jenks, Nancy, Cicek, Mine, Thibodeau, Steve, Holmes, Beverly Wilson, Schlueter, Eric, Collier, Ever, Winkler, Joyce, Corcoran, John, D’Addezio, Nick, Daviglus, Martha, Winn, Robert, Wilkins, Consuelo, Roden, Dan, Denny, Joshua, Doheny, Kim, Nickerson, Debbie, Eichler, Evan, Jarvik, Gail, Funk, Gretchen, Philippakis, Anthony, Rehm, Heidi, Lennon, Niall, Kathiresan, Sekar, Gabriel, Stacey, Gibbs, Richard, Gil Rico, Edgar M., Glazer, David, Grand, Joannie, Greenland, Philip, Harris, Paul, Shenkman, Elizabeth, Hogan, William R., Igho-Pemu, Priscilla, Pollan, Cliff, Jorge, Milena, Okun, Sally, Karlson, Elizabeth W., Smoller, Jordan, Murphy, Shawn N., Ross, Margaret Elizabeth, Kaushal, Rainu, Winford, Eboni, Wallace, Febe, Khatri, Parinda, Kheterpal, Vik, Ojo, Akinlolu, Moreno, Francisco A., Kron, Irving, Peterson, Rachele, Menon, Usha, Lattimore, Patricia Watkins, Leviner, Noga, Obedin-Maliver, Juno, Lunn, Mitchell, Malik-Gagnon, Lynda, Mangravite, Lara, Marallo, Adria, Marroquin, Oscar, Visweswaran, Shyam, Reis, Steven, Marshall, Gailen, Jr., McGovern, Patrick, Mignucci, Deb, Moore, John, Munoz, Fatima, Talavera, Gregory, O'Connor, George T., O'Donnell, Christopher, Ohno-Machado, Lucila, Orr, Greg, Randal, Fornessa, Theodorou, Andreas A., Reiman, Eric, Roxas-Murray, Mercedita, Stark, Louisa, Tepp, Ronnie, Zhou, Alicia, Topper, Scott, Trousdale, Rhonda, Tsao, Phil, Weidman, Lisa, Weiss, Scott T., Wellis, David, Whittle, Jeffrey, Wilson, Amanda, Zuchner, Stephan, Zwick, Michael E., Ramirez, Andrea H., Sulieman, Lina, Schlueter, David J., Halvorson, Alese, Qian, Jun, Ratsimbazafy, Francis, Loperena, Roxana, Mayo, Kelsey, Basford, Melissa, Deflaux, Nicole, Muthuraman, Karthik N., Natarajan, Karthik, Kho, Abel, Xu, Hua, Clark, Cheryl R., Cohn, Elizabeth, Schully, Sheri D., Ahmedani, Brian K., Argos, Maria, Cronin, Robert M., O’Donnell, Christopher, Fouad, Mona, Hebbring, Scott J., Smoller, Jordan W., Sodeke, Stephen, Wilbanks, John, Hentges, Justin, Mockrin, Stephen, Lunt, Christopher, Devaney, Stephanie A., Gebo, Kelly, Denny, Joshua C., Carroll, Robert J., Harris, Paul A., and Roden, Dan M.

Published
2022
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24. Metabolomic profiling reveals extensive adrenal suppression due to inhaled corticosteroid therapy in asthma

Author

Kachroo, Priyadarshini, Stewart, Isobel D., Kelly, Rachel S., Stav, Meryl, Mendez, Kevin, Dahlin, Amber, Soeteman, Djøra I., Chu, Su H., Huang, Mengna, Cote, Margaret, Knihtilä, Hanna M., Lee-Sarwar, Kathleen, McGeachie, Michael, Wang, Alberta, Wu, Ann Chen, Virkud, Yamini, Zhang, Pei, Wareham, Nicholas J., Karlson, Elizabeth W., Wheelock, Craig E., Clish, Clary, Weiss, Scott T., Langenberg, Claudia, and Lasky-Su, Jessica A.

Published
2022
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26. Development and validation of a trans-ancestry polygenic risk score for type 2 diabetes in diverse populations

Author

Ge, Tian, Irvin, Marguerite R., Patki, Amit, Srinivasasainagendra, Vinodh, Lin, Yen-Feng, Tiwari, Hemant K., Armstrong, Nicole D., Benoit, Barbara, Chen, Chia-Yen, Choi, Karmel W., Cimino, James J., Davis, Brittney H., Dikilitas, Ozan, Etheridge, Bethany, Feng, Yen-Chen Anne, Gainer, Vivian, Huang, Hailiang, Jarvik, Gail P., Kachulis, Christopher, Kenny, Eimear E., Khan, Atlas, Kiryluk, Krzysztof, Kottyan, Leah, Kullo, Iftikhar J., Lange, Christoph, Lennon, Niall, Leong, Aaron, Malolepsza, Edyta, Miles, Ayme D., Murphy, Shawn, Namjou, Bahram, Narayan, Renuka, O’Connor, Mark J., Pacheco, Jennifer A., Perez, Emma, Rasmussen-Torvik, Laura J., Rosenthal, Elisabeth A., Schaid, Daniel, Stamou, Maria, Udler, Miriam S., Wei, Wei-Qi, Weiss, Scott T., Ng, Maggie C. Y., Smoller, Jordan W., Lebo, Matthew S., Meigs, James B., Limdi, Nita A., and Karlson, Elizabeth W.

Published
2022
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28. Genetic regulation of serum IgA levels and susceptibility to common immune, infectious, kidney, and cardio-metabolic traits

Author

Liu, Lili, Khan, Atlas, Sanchez-Rodriguez, Elena, Zanoni, Francesca, Li, Yifu, Steers, Nicholas, Balderes, Olivia, Zhang, Junying, Krithivasan, Priya, LeDesma, Robert A., Fischman, Clara, Hebbring, Scott J., Harley, John B., Moncrieffe, Halima, Kottyan, Leah C., Namjou-Khales, Bahram, Walunas, Theresa L., Knevel, Rachel, Raychaudhuri, Soumya, Karlson, Elizabeth W., Denny, Joshua C., Stanaway, Ian B., Crosslin, David, Rauen, Thomas, Floege, Jürgen, Eitner, Frank, Moldoveanu, Zina, Reily, Colin, Knoppova, Barbora, Hall, Stacy, Sheff, Justin T., Julian, Bruce A., Wyatt, Robert J., Suzuki, Hitoshi, Xie, Jingyuan, Chen, Nan, Zhou, Xujie, Zhang, Hong, Hammarström, Lennart, Viktorin, Alexander, Magnusson, Patrik K. E., Shang, Ning, Hripcsak, George, Weng, Chunhua, Rundek, Tatjana, Elkind, Mitchell S. V., Oelsner, Elizabeth C., Barr, R. Graham, Ionita-Laza, Iuliana, Novak, Jan, Gharavi, Ali G., and Kiryluk, Krzysztof

Published
2022
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30. Characterizing Long COVID in Children and Adolescents.

Author

Gross, Rachel S., Thaweethai, Tanayott, Kleinman, Lawrence C., Snowden, Jessica N., Rosenzweig, Erika B., Milner, Joshua D., Tantisira, Kelan G., Rhee, Kyung E., Jernigan, Terry L., Kinser, Patricia A., Salisbury, Amy L., Warburton, David, Mohandas, Sindhu, Wood, John C., Newburger, Jane W., Truong, Dongngan T., Flaherman, Valerie J., Metz, Torri D., Karlson, Elizabeth W., and Chibnik, Lori B.

Subjects
POST-acute COVID-19 syndrome, RACE, AGE groups, QUALITY of life, AFRICAN Americans
Abstract

Key Points: Question: What prolonged symptoms experienced by youth are most associated with SARS-CoV-2 infection? Findings: Among 5367 participants in the RECOVER-Pediatrics cohort study, 14 symptoms in both school-age children (6-11 years) and adolescents (12-17 years) were more common in those with vs without SARS-CoV-2 infection history, with 4 additional symptoms in school-age children only and 3 in adolescents only. Empirically derived indices for PASC research and associated clustering patterns were developed. Meaning: This study developed research indices for characterizing pediatric PASC. Symptom patterns were similar but distinguishable between school-age children and adolescents, highlighting the importance of characterizing PASC separately in different age groups. Importance: Most research to understand postacute sequelae of SARS-CoV-2 infection (PASC), or long COVID, has focused on adults, with less known about this complex condition in children. Research is needed to characterize pediatric PASC to enable studies of underlying mechanisms that will guide future treatment. Objective: To identify the most common prolonged symptoms experienced by children (aged 6 to 17 years) after SARS-CoV-2 infection, how these symptoms differ by age (school-age [6-11 years] vs adolescents [12-17 years]), how they cluster into distinct phenotypes, and what symptoms in combination could be used as an empirically derived index to assist researchers to study the likely presence of PASC. Design, Setting, and Participants: Multicenter longitudinal observational cohort study with participants recruited from more than 60 US health care and community settings between March 2022 and December 2023, including school-age children and adolescents with and without SARS-CoV-2 infection history. Exposure: SARS-CoV-2 infection. Main Outcomes and Measures: PASC and 89 prolonged symptoms across 9 symptom domains. Results: A total of 898 school-age children (751 with previous SARS-CoV-2 infection [referred to as infected] and 147 without [referred to as uninfected]; mean age, 8.6 years; 49% female; 11% were Black or African American, 34% were Hispanic, Latino, or Spanish, and 60% were White) and 4469 adolescents (3109 infected and 1360 uninfected; mean age, 14.8 years; 48% female; 13% were Black or African American, 21% were Hispanic, Latino, or Spanish, and 73% were White) were included. Median time between first infection and symptom survey was 506 days for school-age children and 556 days for adolescents. In models adjusted for sex and race and ethnicity, 14 symptoms in both school-age children and adolescents were more common in those with SARS-CoV-2 infection history compared with those without infection history, with 4 additional symptoms in school-age children only and 3 in adolescents only. These symptoms affected almost every organ system. Combinations of symptoms most associated with infection history were identified to form a PASC research index for each age group; these indices correlated with poorer overall health and quality of life. The index emphasizes neurocognitive, pain, and gastrointestinal symptoms in school-age children but change or loss in smell or taste, pain, and fatigue/malaise–related symptoms in adolescents. Clustering analyses identified 4 PASC symptom phenotypes in school-age children and 3 in adolescents. Conclusions and Relevance: This study developed research indices for characterizing PASC in children and adolescents. Symptom patterns were similar but distinguishable between the 2 groups, highlighting the importance of characterizing PASC separately for these age ranges. This observational cohort study examines the symptoms experienced by children after SARS-CoV-2 infection and how these symptoms differ by age (6-11 years vs 12-17 years). [ABSTRACT FROM AUTHOR]

Published
2024
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31. Long COVID: A Narrative Review and Meta-Analysis of Individual Symptom Frequencies.

Author

Atchley-Challenner, Rachel, Strasser, Zachary, Krishnamoorthy, Aparna, Pant, Deepti, Chibnik, Lori B., and Karlson, Elizabeth W.

Subjects
POST-acute COVID-19 syndrome, ELECTRONIC health records, CHILD patients, PUBLISHED articles, DATA recorders & recording
Abstract

Objective: To summarize the prevalence of Long COVID symptoms among the general population reported in published articles from the pre-omicron SARS-CoV2 era. This narrative review examined 21 symptoms. Methods: A PubMed/manual search returned 114 articles on general Long COVID symptoms. Manuscripts were excluded if they were not research studies, did not report symptom prevalence, or used a pediatric population. Ninety-eight studies were selected for review and fifty-nine met the criteria for inclusion. The risk of bias was assessed with the Hoy critical appraisal tool. Results: After excluding studies with a high risk of bias, meta-analysis of prevalence for 21 symptom categories ranged from 2.6–28.7% in studies based on surveys to 0.3–7.1% in studies based on electronic health record data. Conclusions: Long COVID symptom studies are limited by the variability in study design and representation of the general population. Further research is needed to effectively cluster symptoms in meaningful ways that enable focused treatment. [ABSTRACT FROM AUTHOR]

Published
2024
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32. Differentiation of Prior SARS-CoV-2 Infection and Postacute Sequelae by Standard Clinical Laboratory Measurements in the RECOVER Cohort.

Author

Erlandson, Kristine M., Geng, Linda N., Selvaggi, Caitlin A., Thaweethai, Tanayott, Chen, Peter, Erdmann, Nathan B., Goldman, Jason D., Henrich, Timothy J., Hornig, Mady, Karlson, Elizabeth W., Katz, Stuart D., Kim, C., Cribbs, Sushma K., Laiyemo, Adeyinka O., Letts, Rebecca, Lin, Janet Y., Marathe, Jai, Parthasarathy, Sairam, Patterson, Thomas F., and Taylor, Brittany D.

Subjects
BIOMARKERS, PLATELET count, PATHOLOGICAL laboratories, SARS-CoV-2, REGRESSION analysis
Abstract

This study evaluated whether SARS-CoV-2 infection led to persistent changes in common clinical laboratory tests in people with prior infection compared with those without prior infection and whether people with postacute sequelae of SARS-CoV-2 infection (PASC) have persistent laboratory changes compared with those who are unlikely to have PASC. Visual Abstract. Differentiation of Prior SARS-CoV-2 Infection and Postacute Sequelae by Standard Clinical Laboratory Measurements in the RECOVER Cohort: This study evaluated whether SARS-CoV-2 infection led to persistent changes in common clinical laboratory tests in people with prior infection compared with those without prior infection and whether people with postacute sequelae of SARS-CoV-2 infection (PASC) have persistent laboratory changes compared with those who are unlikely to have PASC. Background: There are currently no validated clinical biomarkers of postacute sequelae of SARS-CoV-2 infection (PASC). Objective: To investigate clinical laboratory markers of SARS-CoV-2 and PASC. Design: Propensity score–weighted linear regression models were fitted to evaluate differences in mean laboratory measures by prior infection and PASC index (≥12 vs. 0). (ClinicalTrials.gov: NCT05172024) Setting: 83 enrolling sites. Participants: RECOVER-Adult cohort participants with or without SARS-CoV-2 infection with a study visit and laboratory measures 6 months after the index date (or at enrollment if >6 months after the index date). Participants were excluded if the 6-month visit occurred within 30 days of reinfection. Measurements: Participants completed questionnaires and standard clinical laboratory tests. Results: Among 10 094 participants, 8746 had prior SARS-CoV-2 infection, 1348 were uninfected, 1880 had a PASC index of 12 or higher, and 3351 had a PASC index of zero. After propensity score adjustment, participants with prior infection had a lower mean platelet count (265.9 × 10 9 cells/L [95% CI, 264.5 to 267.4 × 10 9 cells/L]) than participants without known prior infection (275.2 × 10 9 cells/L [CI, 268.5 to 282.0 × 10 9 cells/L]), as well as higher mean hemoglobin A 1c (HbA 1c) level (5.58% [CI, 5.56% to 5.60%] vs. 5.46% [CI, 5.40% to 5.51%]) and urinary albumin–creatinine ratio (81.9 mg/g [CI, 67.5 to 96.2 mg/g] vs. 43.0 mg/g [CI, 25.4 to 60.6 mg/g]), although differences were of modest clinical significance. The difference in HbA 1c levels was attenuated after participants with preexisting diabetes were excluded. Among participants with prior infection, no meaningful differences in mean laboratory values were found between those with a PASC index of 12 or higher and those with a PASC index of zero. Limitation: Whether differences in laboratory markers represent consequences of or risk factors for SARS-CoV-2 infection could not be determined. Conclusion: Overall, no evidence was found that any of the 25 routine clinical laboratory values assessed in this study could serve as a clinically useful biomarker of PASC. Primary Funding Source: National Institutes of Health. [ABSTRACT FROM AUTHOR]

Published
2024
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34. LPA Variants are Associated with Residual Cardiovascular Risk in Patients Receiving Statins

Author

Wei, Wei-Qi, Li, Xiaohui, Feng, Qiping, Kubo, Michiaki, Kullo, Iftikhar J, Peissig, Peggy L, Karlson, Elizabeth W, Jarvik, Gail P, Lee, Ming Ta Michael, Shang, Ning, Larson, Eric A, Edwards, Todd, Shaffer, Christian M, Mosley, Jonathan D, Maeda, Shiro, Horikoshi, Momoko, Ritchie, Marylyn, Williams, Marc S, Larson, Eric B, Crosslin, David R, Bland, Harris T, Pacheco, Jennifer A, Rasmussen-Torvik, Laura J, Cronkite, David, Hripcsak, George, Cox, Nancy J, Wilke, Russell A, Stein, C Michael, Rotter, Jerome I, Momozawa, Yukihide, Roden, Dan M, Krauss, Ronald M, and Denny, Joshua C

Subjects
Human Genome, Genetics, Clinical Research, Heart Disease, Atherosclerosis, Cardiovascular, Heart Disease - Coronary Heart Disease, Aetiology, 2.1 Biological and endogenous factors, Case-Control Studies, Coronary Disease, Databases, Genetic, Dyslipidemias, Electronic Health Records, Gene Frequency, Genetic Predisposition to Disease, Genome-Wide Association Study, Humans, Hydroxymethylglutaryl-CoA Reductase Inhibitors, Lipoprotein(a), Phenotype, Polymorphism, Single Nucleotide, Risk Assessment, Risk Factors, Time Factors, Treatment Outcome, cholesterol, coronary disease, electronic health records, hydroxymethylglutaryl-CoA, LDL reductase inhibitors, lysophosphatidic acid, Cardiorespiratory Medicine and Haematology, Clinical Sciences, Public Health and Health Services, Cardiovascular System & Hematology
Abstract

BackgroundCoronary heart disease (CHD) is a leading cause of death globally. Although therapy with statins decreases circulating levels of low-density lipoprotein cholesterol and the incidence of CHD, additional events occur despite statin therapy in some individuals. The genetic determinants of this residual cardiovascular risk remain unknown.MethodsWe performed a 2-stage genome-wide association study of CHD events during statin therapy. We first identified 3099 cases who experienced CHD events (defined as acute myocardial infarction or the need for coronary revascularization) during statin therapy and 7681 controls without CHD events during comparable intensity and duration of statin therapy from 4 sites in the Electronic Medical Records and Genomics Network. We then sought replication of candidate variants in another 160 cases and 1112 controls from a fifth Electronic Medical Records and Genomics site, which joined the network after the initial genome-wide association study. Finally, we performed a phenome-wide association study for other traits linked to the most significant locus.ResultsThe meta-analysis identified 7 single nucleotide polymorphisms at a genome-wide level of significance within the LPA/PLG locus associated with CHD events on statin treatment. The most significant association was for an intronic single nucleotide polymorphism within LPA/PLG (rs10455872; minor allele frequency, 0.069; odds ratio, 1.58; 95% confidence interval, 1.35-1.86; P=2.6×10-10). In the replication cohort, rs10455872 was also associated with CHD events (odds ratio, 1.71; 95% confidence interval, 1.14-2.57; P=0.009). The association of this single nucleotide polymorphism with CHD events was independent of statin-induced change in low-density lipoprotein cholesterol (odds ratio, 1.62; 95% confidence interval, 1.17-2.24; P=0.004) and persisted in individuals with low-density lipoprotein cholesterol ≤70 mg/dL (odds ratio, 2.43; 95% confidence interval, 1.18-4.75; P=0.015). A phenome-wide association study supported the effect of this region on coronary heart disease and did not identify noncardiovascular phenotypes.ConclusionsGenetic variations at the LPA locus are associated with CHD events during statin therapy independently of the extent of low-density lipoprotein cholesterol lowering. This finding provides support for exploring strategies targeting circulating concentrations of lipoprotein(a) to reduce CHD events in patients receiving statins.

Published
2018

36. Polygenic Risk for Type 2 Diabetes in African Americans

Author

Irvin, Marguerite R., primary, Ge, Tian, additional, Patki, Amit, additional, Srinivasasainagendra, Vinodh, additional, Armstrong, Nicole D., additional, Davis, Brittney, additional, Jones, Alana C, additional, Perez, Emma, additional, Stalbow, Lauren, additional, Lebo, Matthew, additional, Kenny, Eimear, additional, Loos, Ruth J.F., additional, Ng, Maggie C. Y., additional, Smoller, Jordan W., additional, Meigs, James B., additional, Lange, Leslie A., additional, Karlson, Elizabeth W., additional, Limdi, Nita A., additional, and Tiwari, Hemant K., additional

Published
2024
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37. Neptune: an environment for the delivery of genomic medicine

Author

Eric, Venner, Yi, Victoria, Murdock, David, Kalla, Sara E., Wu, Tsung-Jung, Sabo, Aniko, Li, Shoudong, Meng, Qingchang, Tian, Xia, Murugan, Mullai, Cohen, Michelle, Kovar, Christie, Wei, Wei-Qi, Chung, Wendy K., Weng, Chunhua, Wiesner, Georgia L., Jarvik, Gail P., Muzny, Donna, Gibbs, Richard A., Abrams, Debra, Adunyah, Samuel E., Albertson-Junkans, Ladia, Almoguera, Berta, Ames, Darren C., Appelbaum, Paul, Aronson, Samuel, Aufox, Sharon, Babb, Lawrence J., Balasubramanian, Adithya, Bangash, Hana, Basford, Melissa, Bastarache, Lisa, Baxter, Samantha, Behr, Meckenzie, Benoit, Barbara, Bhoj, Elizabeth, Bielinski, Suzette J., Bland, Harris T., Blout, Carrie, Borthwick, Kenneth, Bottinger, Erwin P., Bowser, Mark, Brand, Harrison, Brilliant, Murray, Brodeur, Wendy, Caraballo, Pedro, Carrell, David, Carroll, Andrew, Castillo, Lisa, Castro, Victor, Chandanavelli, Gauthami, Chiang, Theodore, Chisholm, Rex L., Christensen, Kurt D., Chung, Wendy, Chute, Christopher G., City, Brittany, Cobb, Beth L., Connolly, John J., Crane, Paul, Crew, Katherine, Crosslin, David R., Dayal, Jyoti, De Andrade, Mariza, De la Cruz, Jessica, Denny, Josh C., Denson, Shawn, DeSmet, Tim, Dikilitas, Ozan, Dinsmore, Michael J., Dodge, Sheila, Dunlea, Phil, Edwards, Todd L., Eng, Christine M., Fasel, David, Fedotov, Alex, Feng, Qiping, Fleharty, Mark, Foster, Andrea, Freimuth, Robert, Friedrich, Christopher, Fullerton, Stephanie M., Funke, Birgit, Gabriel, Stacey, Gainer, Vivian, Gharavi, Ali, Glazer, Andrew M., Glessner, Joseph T., Goehringer, Jessica, Gordon, Adam S., Graham, Chet, Green, Robert C., Gundelach, Justin H., Hain, Heather S., Hakonarson, Hakon, Harden, Maegan V., Harley, John, Harr, Margaret, Hartzler, Andrea, Hayes, M. Geoffrey, Hebbring, Scott, Henrikson, Nora, Hershey, Andrew, Hoell, Christin, Holm, Ingrid, Howell, Kayla M., Hripcsak, George, Hu, Jianhong, Hynes, Elizabeth Duffy, Jayaseelan, Joy C., Jiang, Yunyun, Joo, Yoonjung Yoonie, Jose, Sheethal, Josyula, Navya Shilpa, Justice, Anne E., Kalra, Divya, Karlson, Elizabeth W., Keating, Brendan J., Kelly, Melissa A., Kenny, Eimear E., Key, Dustin, Kiryluk, Krzysztof, Kitchner, Terrie, Klanderman, Barbara, Klee, Eric, Kochan, David C., Korchina, Viktoriya, Kottyan, Leah, Kudalkar, Emily, Rahm, Alanna Kulchak, Kullo, Iftikhar J., Lammers, Philip, Larson, Eric B., Lebo, Matthew S., Leduc, Magalie, Lee, Ming Ta (Michael), Lennon, Niall J., Leppig, Kathleen A., Leslie, Nancy D., Li, Rongling, Liang, Wayne H., Lin, Chiao-Feng, Linder, Jodell E., Lindor, Noralane M., Lingren, Todd, Linneman, James G., Liu, Cong, Liu, Wen, Liu, Xiuping, Lynch, John, Lyon, Hayley, Macbeth, Alyssa, Mahadeshwar, Harshad, Mahanta, Lisa, Malin, Bradley, Manolio, Teri, Marasa, Maddalena, Marsolo, Keith, McGowan, Michelle L., McNally, Elizabeth, Meldrim, Jim, Mentch, Frank, Rasouly, Hila Milo, Mosley, Jonathan, Mukherjee, Shubhabrata, Mullen, Thomas E., Muniz, Jesse, Murdock, David R., Murphy, Shawn, Myers, Melanie F., Namjou, Bahram, Ni, Yizhao, Onofrio, Robert C., Obeng, Aniwaa Owusu, Person, Thomas N., Peterson, Josh F., Petukhova, Lynn, Pisieczko, Cassandra J., Pratap, Siddharth, Prows, Cynthia A., Puckelwartz, Megan J., Raj, Ritika, Ralston, James D., Ramaprasan, Arvind, Ramirez, Andrea, Rasmussen, Luke, Rasmussen-Torvik, Laura, Raychaudhuri, Soumya, Rehm, Heidi L., Ritchie, Marylyn D., Rives, Catherine, Riza, Beenish, Roden, Dan M., Rosenthal, Elisabeth A., Santani, Avni, Dan, Schaid, Scherer, Steven, Scott, Stuart, Scrol, Aaron, Sengupta, Soumitra, Shang, Ning, Sharma, Himanshu, Sharp, Richard R., Singh, Rajbir, Sleiman, Patrick M.A., Slowik, Kara, Smith, Joshua C., Smith, Maureen E., Smoot, Duane T., Smoller, Jordan W., Sohn, Sunghwan, Stanaway, Ian B., Starren, Justin, Stroud, Mary, Su, Jessica, Taylor, Casey Overby, Tolwinski, Kasia, Van Driest, Sara L., Vargas, Sean M., Varugheese, Matthew, Veenstra, David, Venner, Eric, Verbitsky, Miguel, Vicente, Gina, Wagner, Michael, Walker, Kimberly, Walunas, Theresa, Wang, Liwen, Wang, Qiaoyan, Weiss, Scott T., Wells, Quinn S., White, Peter S., Wiley, Ken L., Jr, Williams, Janet L., Williams, Marc S., Wilson, Michael W., Witkowski, Leora, Woods, Laura Allison, Woolf, Betty, Wynn, Julia, Yang, Yaping, Zhang, Ge, Zhang, Lan, and Zouk, Hana

Published
2021
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38. Hematopoietic mosaic chromosomal alterations increase the risk for diverse types of infection

Author

Zekavat, Seyedeh M., Lin, Shu-Hong, Bick, Alexander G., Liu, Aoxing, Paruchuri, Kaavya, Wang, Chen, Uddin, Md Mesbah, Ye, Yixuan, Yu, Zhaolong, Liu, Xiaoxi, Kamatani, Yoichiro, Bhattacharya, Romit, Pirruccello, James P., Pampana, Akhil, Loh, Po-Ru, Kohli, Puja, McCarroll, Steven A., Kiryluk, Krzysztof, Neale, Benjamin, Ionita-Laza, Iuliana, Engels, Eric A., Brown, Derek W., Smoller, Jordan W., Green, Robert, Karlson, Elizabeth W., Lebo, Matthew, Ellinor, Patrick T., Weiss, Scott T., Daly, Mark J., Terao, Chikashi, Zhao, Hongyu, Ebert, Benjamin L., Reilly, Muredach P., Ganna, Andrea, Machiela, Mitchell J., Genovese, Giulio, and Natarajan, Pradeep

Published
2021
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40. Humoral signatures of protective and pathological SARS-CoV-2 infection in children

Author

Bartsch, Yannic C., Wang, Chuangqi, Zohar, Tomer, Fischinger, Stephanie, Atyeo, Caroline, Burke, John S., Kang, Jaewon, Edlow, Andrea G., Fasano, Alessio, Baden, Lindsey R., Nilles, Eric J., Woolley, Ann E., Karlson, Elizabeth W., Hopke, Alex R., Irimia, Daniel, Fischer, Eric S., Ryan, Edward T., Charles, Richelle C., Julg, Boris D., Lauffenburger, Douglas A., Yonker, Lael M., and Alter, Galit

Published
2021
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42. Contribution of Rare Deleterious Variants in Telomere-Related Genes to the Risk of Interstitial Lung Disease in Rheumatoid Arthritis and Idiopathic Pulmonary Fibrosis

Author

Juge, Pierre-Antoine, primary, Kawano-Dourado, Leticia, additional, Stockwell, Amy D., additional, Lee, Joyce S., additional, Ebstein, Esther, additional, Gazal, Steven, additional, McDermott, Gregory, additional, Hayashi, Keigo, additional, Cui, Jing, additional, Granger, Benjamin, additional, Kannengiesser, Caroline, additional, Borie, Raphaël, additional, Wemeau, Ludivine, additional, Debray, Marie Pierre, additional, Wolters, Paul J., additional, Marchand-Adam, Sylvain, additional, RICHEZ, Christophe, additional, Nunes, Hilario, additional, Froguel, Philippe, additional, Avouac, Jérôme, additional, Flipo, René-Marc, additional, Cottin, Vincent, additional, Boissier, Marie-Christophe, additional, Schaeverbeke, Thierry, additional, Kermanac’h, Nathalie Saidenberg, additional, Crestani, Bruno, additional, Doyle, Tracy, additional, Raychaudhuri, Soumya, additional, Karlson, Elizabeth W., additional, Yaspan, Brian L., additional, Sparks, Jeffrey A., additional, and Dieudé, Philippe, additional

Published
2024
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44. Medical records-based chronic kidney disease phenotype for clinical care and “big data” observational and genetic studies

Author

Shang, Ning, Khan, Atlas, Polubriaginof, Fernanda, Zanoni, Francesca, Mehl, Karla, Fasel, David, Drawz, Paul E., Carrol, Robert J., Denny, Joshua C., Hathcock, Matthew A., Arruda-Olson, Adelaide M., Peissig, Peggy L., Dart, Richard A., Brilliant, Murray H., Larson, Eric B., Carrell, David S., Pendergrass, Sarah, Verma, Shefali Setia, Ritchie, Marylyn D., Benoit, Barbara, Gainer, Vivian S., Karlson, Elizabeth W., Gordon, Adam S., Jarvik, Gail P., Stanaway, Ian B., Crosslin, David R., Mohan, Sumit, Ionita-Laza, Iuliana, Tatonetti, Nicholas P., Gharavi, Ali G., Hripcsak, George, Weng, Chunhua, and Kiryluk, Krzysztof

Published
2021
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45. High-throughput phenotyping with electronic medical record data using a common semi-supervised approach (PheCAP)

Author

Zhang, Yichi, Cai, Tianrun, Yu, Sheng, Cho, Kelly, Hong, Chuan, Sun, Jiehuan, Huang, Jie, Ho, Yuk-Lam, Ananthakrishnan, Ashwin N., Xia, Zongqi, Shaw, Stanley Y., Gainer, Vivian, Castro, Victor, Link, Nicholas, Honerlaw, Jacqueline, Huang, Sicong, Gagnon, David, Karlson, Elizabeth W., Plenge, Robert M., Szolovits, Peter, Savova, Guergana, Churchill, Susanne, O’Donnell, Christopher, Murphy, Shawn N., Gaziano, J. Michael, Kohane, Isaac, Cai, Tianxi, and Liao, Katherine P.

Published
2019
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47. Author Correction: Metabolomic profiling reveals extensive adrenal suppression due to inhaled corticosteroid therapy in asthma

Author

Kachroo, Priyadarshini, Stewart, Isobel D., Kelly, Rachel S., Stav, Meryl, Mendez, Kevin, Dahlin, Amber, Soeteman, Djøra I., Chu, Su H., Huang, Mengna, Cote, Margaret, Knihtilä, Hanna M., Lee-Sarwar, Kathleen, McGeachie, Michael, Wang, Alberta, Wu, Ann Chen, Virkud, Yamini, Zhang, Pei, Wareham, Nicholas J., Karlson, Elizabeth W., Wheelock, Craig E., Clish, Clary, Weiss, Scott T., Langenberg, Claudia, and Lasky-Su, Jessica A.

Published
2022
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48. Researching COVID to enhance recovery (RECOVER) pediatric study protocol: Rationale, objectives and design.

Author

Gross, Rachel S., Thaweethai, Tanayott, Rosenzweig, Erika B., Chan, James, Chibnik, Lori B., Cicek, Mine S., Elliott, Amy J., Flaherman, Valerie J., Foulkes, Andrea S., Gage Witvliet, Margot, Gallagher, Richard, Gennaro, Maria Laura, Jernigan, Terry L., Karlson, Elizabeth W., Katz, Stuart D., Kinser, Patricia A., Kleinman, Lawrence C., Lamendola-Essel, Michelle F., Milner, Joshua D., and Mohandas, Sindhu

Subjects
YOUNG adults, POST-acute COVID-19 syndrome, COVID-19, NEURAL development, RESEARCH protocols, COGNITIVE development
Abstract

Importance: The prevalence, pathophysiology, and long-term outcomes of COVID-19 (post-acute sequelae of SARS-CoV-2 [PASC] or "Long COVID") in children and young adults remain unknown. Studies must address the urgent need to define PASC, its mechanisms, and potential treatment targets in children and young adults. Observations: We describe the protocol for the Pediatric Observational Cohort Study of the NIH's REsearching COVID to Enhance Recovery (RECOVER) Initiative. RECOVER-Pediatrics is an observational meta-cohort study of caregiver-child pairs (birth through 17 years) and young adults (18 through 25 years), recruited from more than 100 sites across the US. This report focuses on two of four cohorts that comprise RECOVER-Pediatrics: 1) a de novo RECOVER prospective cohort of children and young adults with and without previous or current infection; and 2) an extant cohort derived from the Adolescent Brain Cognitive Development (ABCD) study (n = 10,000). The de novo cohort incorporates three tiers of data collection: 1) remote baseline assessments (Tier 1, n = 6000); 2) longitudinal follow-up for up to 4 years (Tier 2, n = 6000); and 3) a subset of participants, primarily the most severely affected by PASC, who will undergo deep phenotyping to explore PASC pathophysiology (Tier 3, n = 600). Youth enrolled in the ABCD study participate in Tier 1. The pediatric protocol was developed as a collaborative partnership of investigators, patients, researchers, clinicians, community partners, and federal partners, intentionally promoting inclusivity and diversity. The protocol is adaptive to facilitate responses to emerging science. Conclusions and relevance: RECOVER-Pediatrics seeks to characterize the clinical course, underlying mechanisms, and long-term effects of PASC from birth through 25 years old. RECOVER-Pediatrics is designed to elucidate the epidemiology, four-year clinical course, and sociodemographic correlates of pediatric PASC. The data and biosamples will allow examination of mechanistic hypotheses and biomarkers, thus providing insights into potential therapeutic interventions. Clinical trials.gov identifier: Clinical Trial Registration: http://www.clinicaltrials.gov. Unique identifier: NCT05172011. [ABSTRACT FROM AUTHOR]

Published
2024
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50. Modeling Disease Severity in Multiple Sclerosis Using Electronic Health Records

Author

Xia, Zongqi, Secor, Elizabeth, Chibnik, Lori B, Bove, Riley M, Cheng, Suchun, Chitnis, Tanuja, Cagan, Andrew, Gainer, Vivian S, Chen, Pei J, Liao, Katherine P, Shaw, Stanley Y, Ananthakrishnan, Ashwin N, Szolovits, Peter, Weiner, Howard L, Karlson, Elizabeth W, Murphy, Shawn N, Savova, Guergana K, Cai, Tianxi, Churchill, Susanne E, Plenge, Robert M, Kohane, Isaac S, and De Jager, Philip L

Subjects
Health Services and Systems, Health Sciences, Neurodegenerative, Clinical Research, Multiple Sclerosis, Autoimmune Disease, Neurosciences, Brain Disorders, Neurological, Algorithms, Electronic Health Records, Female, Humans, Male, Models, Biological, Severity of Illness Index, General Science & Technology
Abstract

ObjectiveTo optimally leverage the scalability and unique features of the electronic health records (EHR) for research that would ultimately improve patient care, we need to accurately identify patients and extract clinically meaningful measures. Using multiple sclerosis (MS) as a proof of principle, we showcased how to leverage routinely collected EHR data to identify patients with a complex neurological disorder and derive an important surrogate measure of disease severity heretofore only available in research settings.MethodsIn a cross-sectional observational study, 5,495 MS patients were identified from the EHR systems of two major referral hospitals using an algorithm that includes codified and narrative information extracted using natural language processing. In the subset of patients who receive neurological care at a MS Center where disease measures have been collected, we used routinely collected EHR data to extract two aggregate indicators of MS severity of clinical relevance multiple sclerosis severity score (MSSS) and brain parenchymal fraction (BPF, a measure of whole brain volume).ResultsThe EHR algorithm that identifies MS patients has an area under the curve of 0.958, 83% sensitivity, 92% positive predictive value, and 89% negative predictive value when a 95% specificity threshold is used. The correlation between EHR-derived and true MSSS has a mean R(2) = 0.38±0.05, and that between EHR-derived and true BPF has a mean R(2) = 0.22±0.08. To illustrate its clinical relevance, derived MSSS captures the expected difference in disease severity between relapsing-remitting and progressive MS patients after adjusting for sex, age of symptom onset and disease duration (p = 1.56×10(-12)).ConclusionIncorporation of sophisticated codified and narrative EHR data accurately identifies MS patients and provides estimation of a well-accepted indicator of MS severity that is widely used in research settings but not part of the routine medical records. Similar approaches could be applied to other complex neurological disorders.

Published
2013

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