Effect of SARS-CoV-2 Infection on Incident Diabetes by Viral Variant: Findings From the National COVID Cohort Collaborative (N3C)

Diabetes is a leading cause of morbidity and mortality, and the increasing prevalence of disease represents a significant public health problem worldwide (1,2). Multiple studies and several meta-analyses have shown a higher incidence of diabetes after coronavirus 2019 (COVID-19), with an increased risk of ∼60% compared with those without infection (3–7). Many mechanisms for the increased risk of diabetes have been proposed, including the direct effect of viral infection on β-cells, inflammation causing reduced insulin secretion, and cytokine-mediated insulin resistance in the liver and skeletal muscle (8–10). Given the scope of the pandemic (11), the burden of morbidity associated with diabetes, and the need for clinical providers to identify and treat incident diabetes, it is critical to understand the impact of COVID-19 on the epidemiology of diabetes. Studies comparing the risk of incident diabetes after COVID-19 have been conducted in many different populations, with both COVID-19–negative control individuals (12–17) and control individuals with respiratory infection (15,18–21). However, most of the incident diabetes data are from early in the pandemic. The COVID-19 pandemic has evolved significantly over time, with differences in severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) variant, severity of acute illness (22–24), use of medications to treat both mild and severe disease (25), and vaccination to prevent severe infection (26). There is significant variability in acute COVID-19 outcomes by viral variant (22,23), but outcomes related to the development of chronic diseases such as incident diabetes have not been well studied for later variants, when vaccination and treatments were widely available. Given the end of the Federal COVID-19 Public Health Emergency Declaration in May 2023 (27) and decreased vigilance around COVID-19 transmission, it is important to understand whether the risk differs by viral variant and to define the current risk with the contemporary Omicron variant.

We conducted a retrospective cohort study using National COVID Cohort Collaborative (N3C) data to evaluate the risk of incident diabetes in adult patients with COVID-19 compared with control patients with negative COVID testing or acute respiratory illness (ARI) across viral variant time periods.

We conducted a retrospective cohort study using data from N3C, which offers the largest publicly available Health Insurance Portability and Accountability Act–limited research data set in the United States (28). Our study population included adults age ≥18 years with at least two visits in the 2 years before the index date without prior evidence of diabetes, defined as no prior HbA_1c ≥6.5%, glucose ≥200 mg/dL (fasting or nonfasting), and diabetes diagnosis code. We compared the cumulative incidence and risk of incident diabetes for each viral variant period with two control groups: 1) patients with a negative COVID test and no evidence of COVID-19 within or before the variant period and 2) patients with ARI, defined as a positive influenza test or diagnosis code for either bacterial or viral bronchitis or pneumonia, acute upper respiratory infection, or influenza and no diagnosis of COVID-19 by either SARS-CoV-2 PCR test or diagnosis code. We defined the index date as the date of first COVID-19 diagnosis by either positive SARS-CoV-2 laboratory test or diagnosis code for positive patients or the date of the first negative SARS-CoV-2 test or ARI diagnosis for the two control groups for each viral variant period. Variant time periods were defined as follows: ancestral (1 March 2020 to 30 September 2020), Alpha (1 October 2020 to 30 June 2021), Delta (1 July 2021 to 30 November 2021) and Omicron (1 December 2021 to 1 April 2023) (29). Patients were analyzed as control individuals in each viral variant period in which they had a COVID-negative test or ARI event, with COVID-negative patients censored for all future time periods if they developed COVID positivity or ARI and control patients with ARI censored if they developed COVID positivity. We excluded data partner sites where we suspected systematic missingness, including sites with the last data submission before 6 June 2022, those without ≥90% of patients with data on birthdate, those missing >25% of serum creatinine or white blood cell laboratory data for COVID-19 hospitalizations, and those with >10% of COVID-19 hospitalizations >200 days, which likely indicate long-term care facilities. An additional two sites were excluded for unusual age distribution of COVID-negative tests. We also excluded individuals who had visit days >2 SD above the mean (141 days) to exclude those in long-term care or erroneous data. Of the 17,548,831 individuals in N3C without preexisting diabetes, 7,010,922 (40%) were included in the final cohort (Supplementary Fig. 1).

The primary outcome of the study was incident diabetes, as defined by HbA_1c ≥6.5% or diagnosis code after the index date. Diabetes was defined as a composite of all types, which were specified by concept sets of codes that explicitly attributed the diabetes diagnosis as type 1 or 2 diabetes or by additional concept sets for unspecified diabetes or diabetes resulting from other causes. Concepts for diabetes, comorbid conditions, and medications were defined using either concept sets from the N3C Knowledge Store or those developed by subject matter experts on the N3C Diabetes and Obesity Domain Team (30,31). Hospitalization was defined as an inpatient visit with a start date up to 15 days before or 15 days after the index date. Corticosteroid use was defined by record of a systemic corticosteroid medication within 30 days of the index date.

Study populations were balanced using normalized inverse propensity score weighting on age, sex, race and ethnicity, data partner site, Charlson comorbidity index score, BMI, and comorbidities, including hypertension, heart failure, and chronic obstructive pulmonary disease, using a logistic regression model in SparkR 3.4.1. To assess balance between study and both control populations, we calculated standardized mean differences (SMDs) using the method of Yang and Dalton (32), which combines all pairwise SMDs using an inverse variance-weighted average to ensure that comparisons with small sample sizes do not have an outsized influence on the average SMD. All analyses incorporated the inverse propensity score weights. For the primary analysis, we estimated the cumulative incidence function of incident diabetes for COVID-positive and COVID-negative patients and patients with ARI in each variant time period. To account for death, development of COVID-19, or ARI as a competing risk, the cumulative incidence function was based on the Aalen-Johansen estimator using prodlim competing risk model (33). The distributions were then compared using a log-rank test with the survey package (34). The cumulative incidence ratio (CIR) of incident diabetes was calculated at 180, 365, 548, and 730 days from the index date.

Data access and analysis were conducted under Data Use Request RP-5E0130 using Python 3.6 and R 3.5.1 and data release version 112 (2023-02-23) using Palantir’s Foundry platform (2021; Denver, CO), a secure analytics enclave housing the N3C data. The Stony Brook University Office of Research Compliance determined that the study did not constitute human subjects research.

The final number of COVID-positive and COVID-negative patients and patients with ARI included in the analysis for each viral variant era are shown in the flow diagram in Supplementary Fig. 1. The final cohort included patient data from 53 sites, after exclusion of 22 sites because of systemic data issues and two sites because of unusual COVID testing distributions.

Unweighted and weighted demographic and clinical characteristics of each cohort by viral variant are shown in the Supplementary Material. Overall, the groups were balanced with respect to baseline characteristics and comorbidities after weighting. The weighted rate of hospitalization was higher for COVID-negative (20.3%, 18.8%, 21.9%, and 23.8% for the ancestral, Alpha, Delta, and Omicron variant eras, respectively) than COVID-positive patients (13.2%, 10.2%, 10.3%, and 7.1%, respectively) and patients with ARI (7.1%, 5.6%, 5.2%, and 5.7%, respectively). The overall median length of hospitalization was 3 days (SD 9). The weighted rate of corticosteroid use was also higher for COVID-negative (19.1%, 19.6%, 21.1%, and 21.8%, respectively) than COVID-positive patients (9.8%, 13.7%, 18.5%, and 12.4%, respectively) and patients with ARI (11.9%, 13.3%, 16.5%, and 17.1%, respectively). Hospitalization rates for COVID-19 were highest in the ancestral (13.2%) and lowest in the Omicron (7.1%) variant eras. Remdesivir use was highest in the COVID-positive group in the Alpha (3.8%) and Delta (4.0%) variant eras, and nirmatrelvir-ritonavir use was 8.6% in the Omicron variant era.

The weighted incidence rate of diabetes per 1,000 person-years for each viral variant era is shown in Table 1 and reflects a median of 543, 451, 270, and 128 days of follow-up for the ancestral, Alpha, Delta, and Omicron variants. A majority of diagnoses were type 2 diabetes, and the overall rate of incident diabetes diagnoses in 1,000 person-years increased over variant eras (ancestral 19.89; Alpha 20.56; Delta 21.77; Omicron 23.77). The cumulative incidence curves and CIRs at 180, 365, 548, and 730 days for the incident diabetes group compared with COVID-negative and ARI control groups are shown in Fig. 1. There was an increased risk of incident diabetes in patients after COVID-19 compared with COVID-negative and ARI control patients across all viral variants. The 1-year CIR in all viral variants eras was similar when compared with both COVID-negative (1.16 [95% CI 1.12–1.21], 1.14 [95% CI 1.11–1.17], 1.17 [95% CI 1.13–1.21], and 1.13 [95% CI 1.10–1.17] for the ancestral, Alpha, Delta and Omicron variant eras, respectively) and ARI control groups (1.17 [95% CI 1.11–1.22], 1.14 [95% CI 1.09–1.19], 1.18 [95% CI 1.11–1.26], and 1.20 [95% CI 1.13–1.27], respectively). In the Omicron variant era, there was evidence of no difference in risk of incident diabetes at 180 days (CIR 1.02 [95% CI 0.95–1.07]).

The cumulative incidence curves for death and incident diabetes by hospitalization status; cumulative incidence by variant at 180, 365, 548, and 730 days; and numbers at risk can be found in the Supplementary Materials.

A timeline of the studies comparing the risk of incident diabetes in adult COVID-positive patients versus COVID-negative (Fig. 2) and ARI (Fig. 3) control patients shows the time period over which the data were analyzed by each study, with respect to the different variant eras and the current N3C analysis. The reported primary outcome of incident diabetes risk is shown for each study as either a hazard ratio (HR), sub-HR, odds ratio, or incidence rate ratio. The CIR of incident diabetes at 1 year in the N3C analysis is shown for each viral variant era. The dates of availability for remdesivir (1 May 2020), Pfizer-BioNTech COVID-19 vaccine (11 December 2020), and nirmatrelvir-ritonavir (22 December 2021) are indicated based on the Emergency Use Authorization issuance by the U.S. Food and Drug Administration (35).

Overall, in our study, there was a similar increase in risk of incident diabetes after the index date for COVID-positive patients compared with both COVID-negative and ARI control patients. The risk estimate was also similar across variants, which corroborates findings from Kwan et al. (16) of no association of increased risk after the emergence of the Omicron variant (Fig. 2). These findings are surprising, because COVID-19 was more severe in the earlier variants in the pandemic (22–24); however, incident diabetes was associated with a similar level of risk during variant eras with milder disease. Most studies of incident diabetes have been conducted using data from early in the pandemic, with only a few including data from the Delta variant–predominant period (13,16,17) and one overlapping with the Omicron variant–predominant period (Fig. 2) (16). These findings are particularly important as vigilance around COVID-19 has diminished, and evidence that risk is still increased with milder viral variants highlights the need for ongoing measures to prevent or mitigate the development of COVID-19.

In contrast to findings from early studies in the pandemic suggesting an increase in risk of ∼60% (3–7), our risk estimates were more in line with findings from a recent study of patients from British Columbia, which included patients in the Delta variant time period and reported an HR of 1.17 (95% CI 1.06–1.28) after COVID-19 infection (17). The modest risk estimates compared with COVID-negative control patients in our study may partially be explained by the higher rates of hospitalization and corticosteroid use in our COVID-negative group. As seen in the cumulative incidence curves in hospitalized and nonhospitalized patients in Supplementary Fig. 7, there were higher rates of incident diabetes in hospitalized patients. Severe illness in general is also associated with a higher incidence of developing type 2 diabetes (36), and several studies have found increased risk rates in patients with more severe COVID-19 who were admitted to the intensive care unit or hospital (13,17). Although there is limited ability to determine the indication for testing in electronic health record data, our COVID-negative cohort had higher rates of severe illness, and our analysis may underestimate the risk of incident diabetes compared with less severely ill COVID-negative patients. Despite this possible underestimation, we found that COVID-19 was associated with an increased risk of developing incident diabetes. The risk estimate in our study compared with that for ARI control patients was also lower than those in previous studies (15,18,19,21), but this could represent an overestimation of risk, given that our COVID-positive group had higher rates of hospitalization than the ARI group. It is notable that the risk estimate was consistent in the ARI control group, which had lower rates of hospitalization and more comparable corticosteroid use with the COVID-positive group. A study comparing hospitalized patients with COVID-19 and those with non-COVID pneumonia did not show any difference in risk of developing incident diabetes (37). An additional consideration is that the cumulative incidence of death after COVID-19 was higher than mortality in control groups in the ancestral, Alpha, and Delta variant eras, and the cumulative incidence of death in COVID-negative patients was higher than that in COVID-positive patients or patients with ARI in the Omicron era (Supplementary Fig. 6); we included death as a competing risk in our analyses to account for these differences in mortality among cohorts for each variant period.

We found that the rates of diabetes diagnoses increased over the variant eras, even in COVID-negative and ARI control groups. This may reflect pandemic-related factors, such as decreased health-seeking behaviors and routine care earlier in the pandemic (38,39), or an increase in diabetes prevalence resulting from sedentary lifestyle or dietary changes (40). Given the limitation in ascertainment of COVID-19 diagnoses later in the pandemic with the widespread use of home testing, it is also possible that control groups may have had COVID-19, which could potentially increase rates of incident diabetes. Given the scale of the population affected by the pandemic and the public health ramifications of an increased burden of diabetes, it is important to understand the risk that is specifically attributable to COVID-19 and pandemic-related factors.

Regarding the timing of risk, although several studies from the pre-Delta time period in the pandemic have reported a higher risk in the first month (18) or 120 days after COVID-19 (12), we found that there was no difference in risk at 180 days only during the Omicron variant–predominant era. Importantly, at 1 year, the Omicron era risk of incident diabetes (CIR 1.13 [CI 1.10–1.17] vs. COVID-negative control patients and CIR 1.20 [CI 1.13–1.27] vs. ARI control patients) was similar to those of other variant eras (Fig. 1). These unique findings for the Omicron variant suggest that there is latency in the increased risk of incident diabetes, which may be important in surveillance for incident diabetes after COVID-19. It is also unclear whether the latency in risk of incident diabetes is due to factors specific to the Omicron variant itself or whether factors such as disease severity, vaccination, and medications (e.g., nirmatrelvir-ritonavir) play a role. Further study is warranted to understand factors that promote or mitigate incident diabetes risk.

In conclusion, we found that the risk of incident diabetes was similar across different viral variants and compared with both COVID-negative and ARI control groups and that there was greater latency in the timing of diabetes onset in the Omicron variant era. Despite decreased disease severity and treatments for contemporary variants, we may continue to expect a greater incidence in diabetes in the current and future waves of the COVID-19 pandemic. Diabetes prevention interventions such as the Diabetes Prevention Program should be deployed or, at least, formal diet and activity interventions should be incorporated into the standard of care after COVID-19 exposure.

Acknowledgments. The analyses described in this article were conducted with data or tools accessed through the National Center for Advancing Translational Sciences (NCATS) N3C Data Enclave (https://covid.cd2h.org) and N3C Attribution & Publication Policy version 1.2-2020-08-25b, supported by NCATS U24 TR002306 and Axle Informatics Subcontract NCATS-P00438-B. This research was made possible by the patients whose information is included within the data and the organizations (https://ncats.nih.gov/n3c/resources/data-contribution/data-transfer-agreement-signatories) and scientists who have contributed to the ongoing development of this community resource (https://doi.org/10.1093/jamia/ocaa196). The N3C data transfer to NCATS was performed under Johns Hopkins University Reliance Protocol IRB00249128 or individual site agreements with National Institutes of Health (NIH). The N3C Data Enclave is managed under the authority of the NIH; information can be found at https://ncats.nih.gov/n3c/resources.

The N3C Publication Committee confirmed that this manuscript is in accordance with N3C data use and attribution policies; however, this content is solely the responsibility of the authors and does not necessarily represent the official views of the NIH or the N3C program.

J.B.B. is an editor of Diabetes Care but was not involved in any of the decisions regarding review of the manuscript or its acceptance.

Funding. R.W., J.R., S.J., and H.-C.Y. receive funding from the NIH (3R01DK130351-02S1). C.B. is funded by the National Institute of Diabetes and Digestive and Kidney Diseases (NIDDK) (K23DK124654). T.S. receives investigator-initiated research funding and support as principal investigator (R01AG056479) from the National Institute on Aging and as coinvestigator (R01CA277756) from the National Cancer Institute, NIH; receives salary support as director of comparative effectiveness research, North Carolina Translational and Clinical Sciences Institute, University of North Carolina Clinical and Translational Science Award (UM1TR004406), and as codirector of the Human Studies Consultation Core, North Carolina Diabetes Research Center (P30DK124723); and receives support from the NIDDK and a contribution from Dr. Nancy A. Dreyer to the Department of Epidemiology, University of North Carolina at Chapel Hill, for nonrelated work. J.B. is supported by grants from the NIH (UL1TR002489, UM1TR004406).

Duality of Interest. T.S. receives salary support from the Center for Pharmacoepidemiology (current members GlaxoSmithKline, UCB BioSciences, Takeda Pharmaceuticals, AbbVie, Boehringer Ingelheim, Astellas, and Sarepta) and owns stock in Novartis, Roche, and Novo Nordisk. J.B. has received grant support from Bayer, Boehringer Ingelheim, Carmot, Corcept, Dexcom, Eli Lilly, Insulet, MannKind, Novo Nordisk, and vTv Therapeutics; consulting contracts from Alkahest, Altimmune, Anji, Aqua Medical, Inc., AstraZeneca, Boehringer Ingelheim, CeQur, Corcept Therapeutics, Dasman Diabetes Center (Kuwait), Eli Lilly, Embecta, Fortress Biotech, GentiBio, Glyscend, Insulet, Mediflix, Medscape, Mellitus Health, Metsera, Moderna, Novo Nordisk, Pendulum Therapeutics, Praetego, ReachMD, Stability Health, Tandem, Terns, Inc., and Vertex; expert witness engagement by Medtronic MiniMed; and stock options from Glyscend, Mellitus Health, Pendulum Therapeutics, Praetego, and Stability Health. No other potential conflicts of interest relevant to this article were reported.

Author Contributions. R.W., M.H., T.W., S.J., and L.E.T. cleaned and analyzed data. J.D.H. and K.W. provided statistical expertise and reviewed and edited the manuscript. H.-C.Y. and T.S. provided epidemiology expertise and reviewed and edited the manuscript. C.B. reviewed and edited the manuscript. J.B. and J.R. provided clinical expertise and reviewed and edited the manuscript. R.W. is the guarantor of this work and, as such, had full access to all the data in the study and takes responsibility for the integrity of the data and the accuracy of the data analysis.

Handling Editors. The journal editor responsible for overseeing the review of the manuscript was Frank B. Hu.

Individual Acknowledgments for Core Contributors. The authors thank the following core contributors to N3C: Adam B. Wilcox, Adam M. Lee, Alexis Graves, Alfred (Jerrod) Anzalone, Amin Manna, Amit Saha, Amy Olex, Andrea Zhou, Andrew E. Williams, Andrew Southerland, Andrew T. Girvin, Anita Walden, Anjali A. Sharathkumar, Benjamin Amor, Benjamin Bates, Brian Hendricks, Brijesh Patel, Caleb Alexander, Carolyn Bramante, Cavin Ward-Caviness, Charisse Madlock-Brown, Christine Suver, Christopher Chute, Christopher Dillon, Chunlei Wu, Clare Schmitt, Cliff Takemoto, Dan Housman, Davera Gabriel, David A. Eichmann, Diego Mazzotti, Don Brown, Eilis Boudreau, Elaine Hill, Elizabeth Zampino, Emily Carlson Marti, Emily R. Pfaff, Evan French, Farrukh M. Koraishy, Federico Mariona, Fred Prior, George Sokos, Greg Martin, Harold Lehmann, Heidi Spratt, Hemalkumar Mehta, Hongfang Liu, Hythem Sidky, J.W. Awori Hayanga, Jami Pincavitch, Jaylyn Clark, Jeremy Richard Harper, Jessica Islam, Jin Ge, Joel Gagnier, Joel H. Saltz, Joel Saltz, Johanna Loomba, John Buse, Jomol Mathew, Joni L. Rutter, Julie A. McMurry, Justin Guinney, Justin Starren, Karen Crowley, Katie Rebecca Bradwell, Kellie M. Walters, Ken Wilkins, Kenneth R. Gersing, Kenrick Dwain Cato, Kimberly Murray, Kristin Kostka, Lavance Northington, Lee Allan Pyles, Leonie Misquitta, Lesley Cottrell, Lili Portilla, Mariam Deacy, Mark M. Bissell, Marshall Clark, Mary Emmett, Mary Morrison Saltz, Matvey B. Palchuk, Melissa A. Haendel, Meredith Adams, Meredith Temple-O’Connor, Michael G. Kurilla, Michele Morris, Nabeel Qureshi, Nasia Safdar, Nicole Garbarini, Noha Sharafeldin, Ofer Sadan, Patricia A. Francis, Penny Wung Burgoon, Peter Robinson, Philip R.O. Payne, Rafael Fuentes, Randeep Jawa, Rebecca Erwin-Cohen, Rena Patel, Richard A. Moffitt, Richard L. Zhu, Rishi Kamaleswaran, Robert Hurley, Robert T. Miller, Saiju Pyarajan, Sam G. Michael, Samuel Bozzette, Sandeep Mallipattu, Satyanarayana Vedula, Scott Chapman, Shawn T. O’Neil, Soko Setoguchi, Stephanie S. Hong, Steve Johnson, Tellen D. Bennett, Tiffany Callahan, Umit Topaloglu, Usman Sheikh, Valery Gordon, Vignesh Subbian, Warren A. Kibbe, Wenndy Hernandez, Will Beasley, Will Cooper, William Hillegass, and Xiaohan Tanner Zhang. Details of contributions are available at covid.cd2h.org/core-contributors.

Data Partners With Released Data. The following institutions are those with data released or pending. Available: Advocate Health Care Network (UL1TR002389: Institute for Translational Medicine [ITM]); Aurora Health Care, Inc. (UL1TR002373: Wisconsin Network For Health Research); Boston University Medical Campus (UL1TR001430: Boston University Clinical and Translational Science Institute); Brown University (U54GM115677: Advance Clinical and Translational Research); Carilion Clinic (UL1TR003015: iTHRIV Integrated Translational Health Research Institute of Virginia); Case Western Reserve University (UL1TR002548: Clinical and Translational Science Collaborative of Cleveland); Charleston Area Medical Center (U54GM104942: West Virginia Clinical and Translational Science Institute); Children’s Hospital Colorado (UL1TR002535: Colorado Clinical and Translational Sciences Institute); Columbia University Irving Medical Center (UL1TR001873: Irving Institute for Clinical and Translational Research); Dartmouth College (voluntary); Duke University (UL1TR002553: Duke Clinical and Translational Science Institute); George Washington Children’s Research Institute (UL1TR001876: Clinical and Translational Science Institute at Children’s National); George Washington University (UL1TR001876: Clinical and Translational Science Institute at Children’s National); Harvard Medical School (UL1TR002541: Harvard Catalyst); Indiana University School of Medicine (UL1TR002529: Indiana Clinical and Translational Science Institute); Johns Hopkins University (UL1TR003098: Johns Hopkins Institute for Clinical and Translational Research); Louisiana Public Health Institute (voluntary); Loyola Medicine (Loyola University Medical Center); Loyola University Medical Center (UL1TR002389: Institute for Translational Medicine); Maine Medical Center (U54GM115516: Northern New England Clinical and Translational Research Network); Mary Hitchcock Memorial Hospital and Dartmouth Hitchcock Clinic (voluntary); Massachusetts General Brigham (UL1TR002541: Harvard Catalyst); Mayo Clinic Rochester (UL1TR002377: Mayo Clinic Center for Clinical and Translational Science); Medical University of South Carolina (UL1TR001450: South Carolina Clinical and Translational Research Institute); MITRE Corporation (voluntary); Montefiore Medical Center (UL1TR002556: Institute for Clinical and Translational Research at Einstein and Montefiore); Nemours (U54GM104941: Delaware CTR ACCEL Program); NorthShore University HealthSystem (UL1TR002389: Institute for Translational Medicine); Northwestern University at Chicago (UL1TR001422: Northwestern University Clinical and Translational Science Institute); OCHIN (INV-018455: Bill and Melinda Gates Foundation grant to Sage Bionetworks); Oregon Health and Science University (UL1TR002369: Oregon Clinical and Translational Research Institute); Penn State Health Milton S. Hershey Medical Center (UL1TR002014: Penn State Clinical and Translational Science Institute); Rush University Medical Center (UL1TR002389: Institute for Translational Medicine); Rutgers, The State University of New Jersey (UL1TR003017: New Jersey Alliance for Clinical and Translational Science); Stony Brook University (U24TR002306); Alliance at the University of Puerto Rico, Medical Sciences Campus (U54GM133807: Hispanic Alliance for Clinical and Translational Research); The Ohio State University (UL1TR002733: Center for Clinical and Translational Science); State University of New York at Buffalo (UL1TR001412: Clinical and Translational Science Institute); University of Chicago (UL1TR002389: Institute for Translational Medicine); University of Iowa (UL1TR002537: Institute for Clinical and Translational Science); University of Miami Leonard M. Miller School of Medicine (UL1TR002736: University of Miami Clinical and Translational Science Institute); University of Michigan at Ann Arbor (UL1TR002240: Michigan Institute for Clinical and Health Research); University of Texas Health Science Center at Houston (UL1TR003167: Center for Clinical and Translational Sciences); University of Texas Medical Branch at Galveston (UL1TR001439: Institute for Translational Sciences); University of Utah (UL1TR002538: Uhealth Center for Clinical and Translational Science); Tufts Medical Center (UL1TR002544: Tufts Clinical and Translational Science Institute); Tulane University (UL1TR003096: Center for Clinical and Translational Science); Queens Medical Center (voluntary); University Medical Center New Orleans (U54GM104940: Louisiana Clinical and Translational Science Center); University of Alabama at Birmingham (UL1TR003096: Center for Clinical and Translational Science); University of Arkansas for Medical Sciences (UL1TR003107: UAMS Translational Research Institute); University of Cincinnati (UL1TR001425: Center for Clinical and Translational Science and Training); University of Colorado Denver, Anschutz Medical Campus (UL1TR002535: Colorado Clinical and Translational Sciences Institute); University of Illinois at Chicago (UL1TR002003: UIC Center for Clinical and Translational Science); University of Kansas Medical Center (UL1TR002366: Frontiers: University of Kansas Clinical and Translational Science Institute); University of Kentucky (UL1TR001998: UK Center for Clinical and Translational Science); University of Massachusetts Medical School Worcester (UL1TR001453: UMass Center for Clinical and Translational Science); University Medical Center of Southern Nevada (voluntary); University of Minnesota (UL1TR002494: Clinical and Translational Science Institute); University of Mississippi Medical Center (U54GM115428: Mississippi Center for Clinical and Translational Research); University of Nebraska Medical Center (U54GM115458: Great Plains IDeA-Clinical and Translational Research); University of North Carolina at Chapel Hill (UL1TR002489 and UM1TR004406: North Carolina Translational and Clinical Science Institute); University of Oklahoma Health Sciences Center (U54GM104938: Oklahoma Clinical and Translational Science Institute); University of Pittsburgh (UL1TR001857: Clinical and Translational Science Institute); University of Pennsylvania (UL1TR001878: Institute for Translational Medicine and Therapeutics); University of Rochester (UL1TR002001: UR Clinical and Translational Science Institute); University of Southern California (UL1TR001855: Southern California Clinical and Translational Science Institute); University of Vermont (U54GM115516: Northern New England Clinical and Translational Research Network); University of Virginia (UL1TR003015: iTHRIV Integrated Translational Health Research Institute of Virginia); University of Washington (UL1TR002319: Institute of Translational Health Sciences); University of Wisconsin-Madison (UL1TR002373: UW Institute for Clinical and Translational Research); Vanderbilt University Medical Center (UL1TR002243: Vanderbilt Institute for Clinical and Translational Research); Virginia Commonwealth University (UL1TR002649: C. Kenneth and Dianne Wright Center for Clinical and Translational Research); Wake Forest University Health Sciences (UL1TR001420: Wake Forest Clinical and Translational Science Institute); Washington University in St. Louis (UL1TR002345: Institute of Clinical and Translational Sciences); Weill Medical College of Cornell University (UL1TR002384: Weill Cornell Medicine Clinical and Translational Science Center); and West Virginia University (U54GM104942: West Virginia Clinical and Translational Science Institute). Submitted: Icahn School of Medicine at Mount Sinai (UL1TR001433: ConduITS Institute for Translational Sciences); University of Texas Health Science Center at Tyler (UL1TR003167: Center for Clinical and Translational Sciences); University of California Davis (UL1TR001860: UC Davis Health Clinical and Translational Science Center); University of California Irvine (UL1TR001414: UC Irvine Institute for Clinical and Translational Science); University of California Los Angeles (UL1TR001881: UCLA Clinical Translational Science Institute); University of California San Diego (UL1TR001442: Altman Clinical and Translational Research Institute); University of California San Francisco (UL1TR001872: UCSF Clinical and Translational Science Institute); and NYU Langone Health Clinical Science Core, Data Resource Core, and PASC Biorepository Core (OTA-21-015A: Post-Acute Sequelae of SARS-CoV-2 Infection Initiative). Pending: Arkansas Children’s Hospital (UL1TR003107: UAMS Translational Research Institute); Baylor College of Medicine (voluntary); Children’s Hospital of Philadelphia (UL1TR001878: Institute for Translational Medicine and Therapeutics); Cincinnati Children’s Hospital Medical Center (UL1TR001425: Center for Clinical and Translational Science and Training); Emory University (UL1TR002378: Georgia Clinical and Translational Science Alliance); HonorHealth (voluntary); Loyola University Chicago (UL1TR002389: Institute for Translational Medicine); Medical College of Wisconsin (UL1TR001436: Clinical and Translational Science Institute of Southeast Wisconsin); MedStar Health Research Institute (voluntary); Georgetown University (UL1TR001409: Georgetown-Howard Universities Center for Clinical and Translational Science); MetroHealth (voluntary); Montana State University (U54GM115371: American Indian/Alaska Native Clinical and Translational Research Program); NYU Langone Medical Center (UL1TR001445: Langone Health’s Clinical and Translational Science Institute); Ochsner Medical Center (U54GM104940: Louisiana Clinical and Translational Science Center); Regenstrief Institute (UL1TR002529: Indiana Clinical and Translational Science Institute); Sanford Research (voluntary); Stanford University (UL1TR003142: Spectrum: Stanford Center for Clinical and Translational Research and Education); Rockefeller University (UL1TR001866: Center for Clinical and Translational Science); Scripps Research Institute (UL1TR002550: Scripps Research Translational Institute); University of Florida (UL1TR001427: UF Clinical and Translational Science Institute); University of New Mexico Health Sciences Center (UL1TR001449: University of New Mexico Clinical and Translational Science Center); University of Texas Health Science Center at San Antonio (UL1TR002645: Institute for Integration of Medicine and Science); and Yale New Haven Hospital (UL1TR001863: Yale Center for Clinical Investigation).