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Risk of Post-Acute Sequelae of SARS-CoV-2 Infection (PASC) Among Patients with Type 2 Diabetes Mellitus on Anti-Hyperglycemic Medications
Authors Olawore O , Turner LE, Evans MD , Johnson SG, Huling JD, Bramante CT, Buse JB, Stürmer T
Received 22 February 2024
Accepted for publication 17 May 2024
Published 31 May 2024 Volume 2024:16 Pages 379—393
DOI https://doi.org/10.2147/CLEP.S458901
Checked for plagiarism Yes
Review by Single anonymous peer review
Peer reviewer comments 2
Editor who approved publication: Professor Henrik Sørensen
Oluwasolape Olawore,1 Lindsey E Turner,2,3 Michael D Evans,3 Steven G Johnson,4 Jared D Huling,2 Carolyn T Bramante,5 John B Buse,6 Til Stürmer1 On behalf of the N3C Consortium
1Department of Epidemiology, Gillings School of Global Public Health, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA; 2Division of Biostatistics and Health Data Science, University of Minnesota School of Public Health, Minneapolis, MN, USA; 3Clinical and Translational Science Institute, University of Minnesota, Minneapolis, MN, USA; 4Institute for Health Informatics, University of Minnesota, Minneapolis, MN, USA; 5Division of General Internal Medicine, Department of Medicine, University of Minnesota Medical School, Minneapolis, MN, USA; 6Division of Endocrinology, Department of Medicine, University of North Carolina at Chapel Hill School of Medicine, Chapel Hill, NC, USA
Correspondence: Oluwasolape Olawore, Department of Epidemiology, Gillings School of Global Public Health, University of North Carolina at Chapel Hill, McGavran-Greenberg Hall, 135 Dauer Drive, Chapel Hill, NC, 27599, USA, Email [email protected]
Background: Observed activity of metformin in reducing the risk of severe COVID-19 suggests a potential use of the anti-hyperglycemic in the prevention of post-acute sequelae of SARS-CoV-2 infection (PASC). We assessed the 3-month and 6-month risk of PASC among patients with type 2 diabetes mellitus (T2DM) comparing metformin users to sulfonylureas (SU) or dipeptidyl peptidase-4 inhibitors (DPP4i) users.
Methods: We used de-identified patient level electronic health record data from the National Covid Cohort Collaborative (N3C) between October 2021 and April 2023. Participants were adults ≥ 18 years with T2DM who had at least one outpatient healthcare encounter in health institutions in the United States prior to COVID-19 diagnosis. The outcome of PASC was defined based on the presence of a diagnosis code for the illness or using a predicted probability based on a machine learning algorithm. We estimated the 3-month and 6-month risk of PASC and calculated crude and weighted risk ratios (RR), risk differences (RD), and differences in mean predicted probability.
Results: We identified 5596 (mean age: 61.1 years; SD: 12.6) and 1451 (mean age: 64.9 years; SD 12.5) eligible prevalent users of metformin and SU/DPP4i respectively. We did not find a significant difference in risk of PASC at 3 months (RR = 0.86 [0.56; 1.32], RD = − 3.06 per 1000 [− 12.14; 6.01]), or at 6 months (RR = 0.81 [0.55; 1.20], RD = − 4.91 per 1000 [− 14.75, 4.93]) comparing prevalent users of metformin to prevalent users of SU/ DPP4i. Similar observations were made for the outcome definition using the ML algorithm.
Conclusion: The observed estimates in our study are consistent with a reduced risk of PASC among prevalent users of metformin, however the uncertainty of our confidence intervals warrants cautious interpretations of the results. A standardized clinical definition of PASC is warranted for thorough evaluation of the effectiveness of therapies under assessment for the prevention of PASC.
Plain Language Summary: Previous research suggests that metformin, due to its anti-viral, anti-inflammatory, and anti-thrombotic properties may reduce the risk of severe COVID-19. Given the shared etiology of COVID-19 and the post-acute sequelae of SARS-CoV-2 (PASC), and the proposed inflammatory processes of PASC, metformin may also be a beneficial preventive option. We investigated the benefit of metformin for PASC prevention in a population of type 2 diabetes mellitus patients with a COVID-19 diagnosis who were on metformin or two other anti-hyperglycemic medications prior to infection with SARS-CoV-2. Our results were consistent with a reduction in the risk of PASC with the use of metformin, however, the imprecise confidence intervals obtained warrants further investigation of this association of the potential beneficial effect of metformin for preventing PASC in patients with medication-managed diabetes.
Keywords: COVID-19, Long COVID, Type 2 diabetes Mellitus, Metformin
Introduction
Post-acute sequelae of SARS-CoV-2 infection (PASC) is an emerging illness following mild or severe SARS-CoV-2 infection.1 Although the true burden of the condition in the population has not been well characterized, incidence ranges of 8% to 40% have been reported from electronic health record (EHR) data.1–4 The symptoms associated with PASC are diverse and can affect multiple organ systems.1,5–9 Symptoms reported have included fatigue, shortness of breath, cognitive impairment, chest pain, and gastrointestinal disturbances.5–9The non-specific presentation of PASC presents a challenge for clinical definition, diagnosis, and treatment of the condition, the underlying pathophysiology, however, suggests immune altering effects causing prolonged symptoms.1,10,11
Type 2 diabetes mellitus (T2DM) is a common comorbidity among patients infected with SARS-CoV-2 and is a risk factor for poor outcomes including hospitalization, need for ventilation and mortality following infection.12,13 Evidence also suggests that SARS-CoV-2 infection may be associated with incident diabetes and may aggravate pre-existing diabetes as a result of various inflammatory pathways leading to insulin resistance and hyperglycemia.14–16 Given the increased prevalence of COVID-19 in patients with T2DM, PASC may also be common in this population. There is growing evidence that metformin, a first line anti-hyperglycemic medication for the management of T2DM may be beneficial in the management of SARS-CoV-2 infection, likely due to both anti-viral and anti-inflammatory actions.17–21 These properties along with reported anti-thrombotic properties may impact the development of severe COVID-19.22–24 Given the shared etiology of COVID-19 and PASC and the hypothesized immunological and inflammatory pathway of PASC development, metformin may also be beneficial for PASC prevention.25,26 Several observational analyses in patients with T2DM describe an association between prevalent metformin use and less severe COVID-19 compared to prevalent use of other therapeutic equivalents for T2DM.24,27 However, few observational analyses have assessed PASC as an outcome when comparing metformin use at the time of SARS-CoV-2 infection in adults with T2DM.
In this study, we compared patients on metformin to patients on sulfonylureas (SU) or dipeptyl-peptidase-4-inhibitors (DPP4i) using a prevalent-user, active comparator design to investigate the association between these medications used for the management of T2DM and risk of incident PASC in adults with type 2 diabetes. SU and DPP4i were chosen as comparators because they are frequently used as an alternative antihyperglycemic treatment to metformin.
Methods
Data Source
This study uses de-identified patient-level data from the National Covid Cohort Collaborative (N3C) spanning October 2021 to April 2023. N3C’s Data Enclave integrates electronic health records from 74 US institutions, covering over 15 million patients.28 The enclave harmonizes data using the Observation Medical Outcomes Partnership (OMOP) common data model, ensuring standardized definitions for diagnoses, procedures, and laboratory values, including COVID-19 tests.28 The University of North Carolina at Chapel Hill’s Institutional Review Board (IRB00249128) approved this analysis, granting a waiver of informed consent as non-human subject research.
Study Cohort
We used an active comparator study design to compare the risk of PASC following a positive SARS-CoV-2 polymerase chain reaction result (COVID-19 diagnosis) among prevalent users of metformin vs SU or DPP4i. We included adults 18 years or older with T2DM diagnosis and at least one healthcare encounter in the two 6-month periods before incident COVID-19 diagnosis. T2DM was defined as the presence of either a hemoglobin A1C (HbA1C) level greater than 6.5% or an international Classification of Diseases, 10th Revision (ICD-10) for diabetes in the previous 12 months. Patients with a diagnosis of chronic kidney disease (CKD) stage 4, stage 5, or end stage renal disease (ESRD) and individuals over the age of 85 were excluded to reduce the potential for confounding by indication and frailty. To ensure comparisons were made among patients exposed to metformin and SU or DPP4i only and to restrict to metformin users with T2DM only, we also excluded patients with any diabetes medications, including insulin, other than metformin, SU or DPP4i in the 90 days prior to the index date of COVID-19 diagnosis and patients with indications for metformin other than T2DM, including polycystic ovarian syndrome (PCOS) and prediabetes.
Based on these inclusion and exclusion criteria, we created two study cohorts. The first cohort (“cohort one”) included patients who met inclusion criteria from 29 health institutions where the use of the diagnosis code for PASC began in October 2021 while the second cohort (“cohort two”) included data from all health institutions (37 sites) where patients met our inclusion criteria.
Exposure Assessment
We defined prevalent users as individuals on monotherapy of metformin, sulfonylureas (SU), or dipeptyl-peptidase-4-inhibitors (DPP4i). Patients with a reported use of a combination of any of the study exposures or combination of the study exposure and other antihyperglycemic medications were excluded. We used the World Health Organization’s Anatomical Therapeutic Chemical (ATC) classification and RxNorm schemas to create concept sets for drug exposures of interest. A two-physician review team manually read through each concept expression to assure appropriate inclusion of concepts to the expression list.
Outcome Definition
The outcome of interest was defined using both a clinical and a machine learning (ML) definition of PASC. The clinical definition required the presence of an ICD-10 diagnoses code for post-COVID-19 condition (U09.9). If a patient had multiple diagnoses codes for the outcome, we used the earliest occurrence.
Details of the algorithm employed for identifying the probability of PASC have been described elsewhere.29 Briefly, the ML model estimates the predicted probability of PASC for patients who had at least one healthcare visit and at least one diagnosis, or medication following COVID-19 diagnosis using their demographic, health care utilization, diagnoses, and medication information. The model calculates the probability of PASC for each patient starting 100 days after COVID-19 diagnosis based on changes in diagnoses and medications observed as of that day compared with the 100 days prior to COVID-19 diagnosis. New probabilities are estimated at 30-day intervals until 300 days after COVID-19 diagnosis (with correspondingly increasing pre-COVID-19 intervals) or June 1, 2022, whichever comes first. Data from patients in cohort two were used in the analysis assessing the difference in the predicted probability of this outcome.
Covariates
Confounders were identified based on clinical assessment of variables associated with the exposures and the outcomes and follows selections used in previous comparisons of these antidiabetic drug classes.24 Comorbidities and medications of interest were identified from patient records in the previous 12 months using ICD-10 codes and medication lists and were defined using translated OMOP concepts.
Statistical Analysis
Follow-up for the study began at a positive test for COVID-19 until PASC diagnosis, death, or administrative censoring at the end of the 6-months follow-up (Figure 1). Patients without any healthcare system use during each 3-month period were censored at the start of the period. For the ICD-10 definition of PASC, unweighted and weighted cumulative incidence of PASC were estimated using a Kaplan-Meier estimator, censoring death. The 3-and 6-months risks were compared using risk differences (RDs) and risk ratios (RRs) with corresponding 95% confidence intervals. For the ML definition of PASC, a linear regression model was used to estimate the difference in the predicted probability of PASC at approximately 3 and 6 months, using values of the predicted probability at 100 and 190 days respectively.
 |
Figure 1 Study Design with Inclusion and exclusion criteria. |
For confounding control, entropy balancing was used to compute adjustment weights. Entropy balancing adjusts for confounding by balancing means of covariates across treatment groups and is more empirically robust than inverse propensity score weighting.30 Covariate balance before and after entropy weights was assessed using standardized mean differences (SMDs).
We accounted for missingness of covariates using multiple imputation with chained equations (MICE), with predictive mean matching for continuous variables and polytomous regression for categorical variables. We completed 20 imputations with a maximum of 50 iterations. Missing data were directly imputed for race, height, weight, and creatinine, while eGFR and BMI were passively imputed based on age, gender, and race for eGFR and height and weight for BMI.
Sensitivity Analyses
Sensitivity analyses were performed using two additional outcome definitions. First, using the ML algorithm for PASC, a predicted probability of PASC ≥ 0.7 was used to define a binary outcome. RDs and RRs for the risk of PASC comparing patients above and below the cut point were then estimated. Additionally, we defined PASC as the presence of an ICD10 code for PASC or a predicted probability of PASC ≥ 0.7 using the ML algorithm. The outcome was defined using the earliest occurrence of a predicted probability of PASC ≥ 0.7 over the follow-up period regardless of subsequent predicted probabilities below the cut point. A predicted probability of ≥ 0.7 was used to reduce the likelihood that only individuals with severe symptoms of PASC were being identified.
All analyses were conducted within the secure N3C computing environment using R statistical software (R Foundation for Statistical Computing, Vienna, Austria)
Results
We describe the characteristics of the patients included in cohort one in Table 1. We identified 7047 patients who met our inclusion and exclusion criteria. The mean age of our cohort at COVID-19 diagnosis was 62 years (SD: 12.7 years) and a majority of the cohort was white (65%) and women (56%). The majority of patients (5596) had a prescription for metformin while 1451 had a prescription for a SU/DPP4i. Prior to weighting (Table 1), prevalent users of metformin were slightly less likely to have several baseline comorbidities including heart failure (10 versus 16%), hypertension (80 versus 86%), any stage of chronic kidney disease (13 versus 26%), and cancer (12 versus 14%) than patients on SU/DPP4i. After weighting, patients were well-balanced on measured confounders (Table 2 and Figure 2).
 |
Table 1 Unweighted Distribution of Study Covariates at COVID-19 Diagnosis |
 |
Table 2 Weighted Distribution of Study Covariates at COVID-19 Diagnosis |
 |
Figure 2 Standardized mean differences of covariates comparing metformin users vs SU/DPP4i users before and after weighting. |
Over the 6 months follow-up of the study, we identified 116 PASC events among prevalent users of metformin and 40 PASC events among prevalent users of SU/DPP4i among patients included in cohort one. Similar estimates were obtained in the unweighted (Table 3) and weighted analysis (Table 4). In the weighted analysis using the ICD-10 definition of PASC, the combined risk of PASC at 6 months was 2.6% (95% CI: 2.0, 3.1) and the 3-month and 6-month risk of PASC among prevalent users of metformin were 2.0% and 2.3% respectively while among prevalent users of SU/DPP4i the 3-month and 6-month risks were 2.3% and 2.8% respectively (Figure 3 and Table 4). The strengths of the corresponding RD and RR increased across the two time periods and at 6 months, the RD and RR were −0.49 per 100 (95% CI: −1.48, 0.49) and 0.81 (95% CI: 0.55; 1.20), respectively (Table 4).
 |
Table 3 Unweighted Risk and Change in Mean Predicted Probability of PASC Estimates Comparing Prevalent Users of Metformin to Prevalent Users of SU/DDP4i |
 |
Table 4 Weighted Risk and Change in Mean Predicted Probability of PASC Estimates Comparing Prevalent Users of Metformin to Prevalent Users of SU/DDP4i |
 |
Figure 3 Weighted cumulative incidence curve of PASC over duration of follow-up censoring for death and lack of system use. |
In the analysis using patients included in cohort two, 10,964 patients were identified to be prevalent users of metformin, and 2860 patients were prevalent users of SU/DPP4i (Supplemental Table 1). Entropy weighting again balanced baseline characteristics (Supplemental Table 1 and Supplemental Figure 1). At approximately 3 months of follow-up, 3.20% of the patients had a predicted probability ≥ 0.7, increasing to 8.32% at approximately 6 months. The mean predicted probability at approximately 3 months of follow-up was 0.33 (SD= 0.14) and at approximately 6 months the mean predicted probability was 0.42 (SD= 0.16). At 3 months, the weighted difference in the mean predicted probability of PASC comparing metformin users to SU/DPP4i users was −0.0001 (95% CI: −0.0071, 0.0068) and at 6 months the estimate was −0.003 (95% CI: −0.011, 0.004) (Table 2). Similar imprecise results were obtained for the sensitivity analysis (Supplemental Figure 2; Supplemental Tables 2 and 3).
Discussion
In this EHR analysis of adults with T2DM diagnosed with COVID-19, we found results consistent with a reduced risk of incident PASC comparing prevalent users of monotherapy metformin to prevalent users of monotherapy SU/DPP4i. While we are unable to make strong inferences with these analyses due to our wide confidence intervals, to our knowledge this is the first study using real-world data to assess the prevalent metformin use and the subsequent development of PASC, and our results are at least compatible with a potential benefit. Thus, data from our study can contribute to the knowledge base of ongoing research to identify therapies for preventing this emerging chronic illness after COVID-19.
The direction of the effect estimate in our study is consistent with results from a recent randomized controlled trial reporting a 41% reduction in the hazard of PASC compared to placebo among over 9 months of follow-up.21 Similar risk reductions were not observed for two other medications used for the management of COVID-19 in the same trial.21 While there were differences between our study population and the trial participants, and our study is based on prevalent users therefore our results cannot be interpreted as causal effects, consistency of these two results suggests that metformin may be a useful candidate therapy for the prevention of PASC across at-risk groups. Unlike our study, prevalent users of metformin were excluded from the trial suggesting that metformin may have an immediate effect on PASC prevention regardless of duration of use. Results from the trial however suggest that initiation soon after COVID-19 symptom onset may provide greater benefits. Further studies will be needed to elucidate the most appropriate timing of initiation.
Metformin has previously been shown to inhibit RNA viral replication and have anti-inflammatory effects.23 These properties have been reported as potential reasons for the recent findings from both experimental and observational studies reporting a reduction in the risk of severe COVID-19 outcomes among previous users of metformin.21,24,31 While the underlying biological mechanism of PASC has not yet been well described, it is plausible that these protective properties of metformin observed in association with COVID-19 extend to longer term outcomes of the infection. Metformin is a relatively safe medication, including in children and during pregnancy and lactation, and given recent evidence of its effectiveness, it could serve as an inexpensive alternative for the treatment of COVID-19 and its associated sequelae.24,32–34 PASC has been reported to occur even among individuals who had mild symptoms of COVID-19, therefore identification of a candidate therapy for prevention of PASC is needed.1
Our study found a lower PASC incidence (2.6%) compared to the trial (8%). The trial had fewer T2DM participants (less than 2%) due to metformin use requirements.21 The differences observed may be due to underdiagnosis of PASC in T2DM patients. Conflation of PASC symptoms with the effects of T2DM and presence of other medical complexities that curb inclusion of the PASC diagnosis code in patients’ records may be contributory.
There is also a need for a standard clinical definition for what constitutes PASC. Attempts have been made to create a classification of what PASC constitutes including a clinical definition created by the World Health Organization using clinical symptoms and patient reported outcomes.35,36 However, to date, no gold standard definition or tests exists for the identification or diagnosis of PASC. In our study, we used both the ICD-10 code for PASC and use of ML algorithm developed to predict the probability of PASC in a EHR system. Both definitions have not been validated and their use has some drawbacks which may have contributed to the results observed in our study. The varying and non-specific symptomatic presentation of the condition makes selective clinical diagnosis of PASC likely. Individuals with severe presentations or individuals whose healthcare providers are more familiar with the symptoms of PASC may be more likely to receive a clinical diagnosis or ICD code for PASC. A reduced prevalence of PASC was observed in a study using the ICD definition of PASC compared to other studies where PASC was defined based on symptoms.10 Underdiagnosis or misdiagnosis may also have occurred because the ICD-10 code for PASC was introduced more than a year after the start of the COVID-19 pandemic.
While the ML algorithm used to define the probability of PASC our study is based on the presence of clusters of symptoms identified to be risk factors for PASC, and thus more likely to identify individuals with varying illness presentation, it is not without limitations. Presence of a diagnosis code for T2DM in a patient’s record following a positive test for COVID-19 was included in the training model as a predictor of PASC.29 Given that all the individuals in our study had a diagnosis of T2DM prior to COVID-19 diagnosis and associated ICD codes for these conditions often show up in healthcare records even after initial diagnosis, over- or underestimation of the probability of PASC using the algorithm is likely in patients with diabetes, depending on the timing of mention of diabetes in the EHR. Because the algorithm cannot capture symptoms of individuals who do not present for care in a healthcare facility, it may also miss individuals with mild symptoms or presentation of the illness.37 Such missingness is likely to depend on a variety of factors, including socioeconomic and structural reasons.38
A standardized PASC definition will minimize outcome misclassification, ensuring accurate case identification. Accurate identification PASC cases will also ensure assessment of the effectiveness of therapies for the treatment of varying presentations of the illness. In the interim, refinement and validation of algorithms and diagnosis codes to define PASC, using EHR and claims data is warranted.
The results of our study should be interpreted in light of other limitations. The patients in our study were prevalent users of metformin or SU/DPP4i.39 We did employ an active comparator design to minimize confounding. However, metformin and SU/DPP4i are not perfect active comparators and unobserved differences may exist both between users of the metformin and SU/DPP4i and between users of SU and DPP4i. This may partly explain the large differences in the observed baseline risk prior to weighting. Additionally, due to the small sample of patients on monotherapy SU or DPP4i, we combined patients who were on either SU or DPP4i. Although preliminary analysis of our data showed that individuals were similar with respect to baseline confounders, these analytical decisions make our results prone to residual confounding. Lack of longitudinal completeness is common in EHR data and this could have led to under ascertainment and misclassification of our outcome.40 To minimize these biases, we required that all study participants have evidence of healthcare use in the two 6-month period prior to the study and in each 3-month outcome ascertainment period. While this restriction reduced the sample size and likely skewed our population to include more frequent utilizers of healthcare, it minimized the likelihood of misclassifying individuals without healthcare records as not having the outcome.
Important strengths of our study include use of patient information from a large database covering multiple sites and location in the United States. Use of an active comparator to minimize the effects of confounding by indication. From a T2DM management standpoint, metformin, DPP4i, and SU have similar indications (despite the fact that metformin is recommended as first-line therapy). Thus, it is more likely that the individuals in our study were similar in terms of other unmeasured risk factors for the outcome.
Conclusion
In this cohort of T2DM adults treated with monotherapy anti-hyperglycemic medications prior to COVID-19, there was no evidence for benefit of metformin as compared to SU/DPP-4i to prevent PASC. Though directionally aligned with clinical trial results suggesting benefit, imprecise confidence intervals, the prevalent user design, and a lack of sensitivity of the PASC outcome definition limit the interpretation of our findings. Further investigation is warranted in the setting of metformin initiation following COVID-19 and with a standard clinical definition of PASC.
Abbreviations
DPP4i, Dipeptidyl Peptidase-4 Inhibitors; EHR, Electronic Health Record, ICD-10, International Classification of Diseases, 10th Revision; ML, Machine Learning; N3C, Covid Cohort Collaborative; PASC, Post-acute Sequelae of SARS-CoV-2; RD, Risk Difference; RR, Risk Ratio; SU, Sulfonylurea; T2DM, Type 2 Diabetes Mellitus.
Acknowledgments
The analyses described in this publication were conducted with data or tools accessed through the NCATS N3C Data Enclave https://covid.cd2h.org and N3C Attribution & Publication Policy v 1.2-2020-08-25b supported by NCATS U24 TR002306 and UL1TR002494 (UMN) and Axle Informatics Subcontract: NCATS-P00438-B. This research was possible because of 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 on-going development of this community resource.28 The content is solely the responsibility of the authors and does not necessarily represent the official views of the National Institutes of Health’s National Center for Advancing Translational Sciences.
The N3C Publication committee confirmed that this manuscript msid: 1693.093 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 National Institutes of Health, the N3C program.
The N3C data transfer to NCATS is performed under a Johns Hopkins University Reliance Protocol # IRB00249128 or individual site agreements with NIH. The N3C Data Enclave is managed under the authority of the NIH; information can be found at https://ncats.nih.gov/n3c/resources.
Individual Acknowledgements For Core Contributors
We gratefully acknowledge 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, Xiaohan Tanner Zhang. Details of contributions available at covid.cd2h.org/core-contributors.
The following institutions whose data is released or pending:
Available: Advocate Health Care Network — UL1TR002389: The 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 Translational Research (Advance-CTR) • Carilion Clinic — UL1TR003015: iTHRIV Integrated Translational health Research Institute of Virginia • Case Western Reserve University — UL1TR002548: The Clinical & Translational Science Collaborative of Cleveland (CTSC) • Charleston Area Medical Center — U54GM104942: West Virginia Clinical and Translational Science Institute (WVCTSI) • 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 — None (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 (CTSA-CN) • George Washington University — UL1TR001876: Clinical and Translational Science Institute at Children’s National (CTSA-CN) • 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 — None (Voluntary) • Loyola Medicine — Loyola University Medical Center • Loyola University Medical Center — UL1TR002389: The Institute for Translational Medicine (ITM) • Maine Medical Center — U54GM115516: Northern New England Clinical & Translational Research (NNE-CTR) Network • Mary Hitchcock Memorial Hospital & Dartmouth Hitchcock Clinic — None (Voluntary) • Massachusetts General Brigham — UL1TR002541: Harvard Catalyst • Mayo Clinic Rochester — UL1TR002377: Mayo Clinic Center for Clinical and Translational Science (CCaTS) • Medical University of South Carolina — UL1TR001450: South Carolina Clinical & Translational Research Institute (SCTR) • MITRE Corporation — None (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: The Institute for Translational Medicine (ITM) • Northwestern University at Chicago — UL1TR001422: Northwestern University Clinical and Translational Science Institute (NUCATS) • OCHIN — INV-018455: Bill and Melinda Gates Foundation grant to Sage Bionetworks • Oregon Health & 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: The Institute for Translational Medicine (ITM) • Rutgers, The State University of New Jersey — UL1TR003017: New Jersey Alliance for Clinical and Translational Science • Stony Brook University — U24TR002306 • The Alliance at the University of Puerto Rico, Medical Sciences Campus — U54GM133807: Hispanic Alliance for Clinical and Translational Research (The Alliance) • The Ohio State University — UL1TR002733: Center for Clinical and Translational Science • The State University of New York at Buffalo — UL1TR001412: Clinical and Translational Science Institute • The University of Chicago — UL1TR002389: The Institute for Translational Medicine (ITM) • The University of Iowa — UL1TR002537: Institute for Clinical and Translational Science • The University of Miami Leonard M. 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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 • West Virginia University — U54GM104942: West Virginia Clinical and Translational Science Institute (WVCTSI) Submitted: Icahn School of Medicine at Mount Sinai — UL1TR001433: ConduITS Institute for Translational Sciences • The University of Texas Health Science Center at Tyler — UL1TR003167: Center for Clinical and Translational Sciences (CCTS) • University of California, Davis — UL1TR001860: UCDavis Health Clinical and Translational Science Center • University of California, Irvine — UL1TR001414: The UC Irvine Institute for Clinical and Translational Science (ICTS) • 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 Pending: Arkansas Children’s Hospital — UL1TR003107: UAMS Translational Research Institute • Baylor College of Medicine — None (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 — None (Voluntary) • Loyola University Chicago — UL1TR002389: The Institute for Translational Medicine (ITM) • Medical College of Wisconsin — UL1TR001436: Clinical and Translational Science Institute of Southeast Wisconsin • MedStar Health Research Institute — None (Voluntary) • Georgetown University — UL1TR001409: The Georgetown-Howard Universities Center for Clinical and Translational Science (GHUCCTS) • MetroHealth — None (Voluntary) • Montana State University — U54GM115371: American Indian/Alaska Native CTR • NYU Langone Medical Center — UL1TR001445: Langone Health’s Clinical and Translational Science Institute • Ochsner Medical Center — U54GM104940: Louisiana Clinical and Translational Science (LA CaTS) Center • Regenstrief Institute — UL1TR002529: Indiana Clinical and Translational Science Institute • Sanford Research — None (Voluntary) • Stanford University — UL1TR003142: Spectrum: The Stanford Center for Clinical and Translational Research and Education • The Rockefeller University — UL1TR001866: Center for Clinical and Translational Science • The 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 • Yale New Haven Hospital — UL1TR001863: Yale Center for Clinical Investigation
Author Contributions
All authors made a significant contribution to the work reported, whether that is in the conception, study design, execution, acquisition of data, analysis, and interpretation, or in all these areas; took part in drafting, revising or critically reviewing the article; gave final approval of the version to be published; have agreed on the journal to which the article has been submitted; and agree to be accountable for all aspects of the work.
Funding
This project did not receive specific funding. CB was funded by the National Institute of Digestive, Diabetes, and Kidney diseases K23DK124654. TS receives investigator-initiated research funding and support as Principal Investigator (R01AG056479) from the National Institute on Aging (NIA), and as Co-Investigator (R01CA174453, R01HL118255, R01MD011680), National Institutes of Health (NIH).
Disclosure
JBB reports contracted fees and travel support for contracted activities for consulting work paid to the University of North Carolina by Novo Nordisk; grant support by Bayer, Boehringer-Ingelheim, Carmot, Corcept, Dexcom, Eli Lilly, Insulet, MannKind, Novo Nordisk, and vTv Therapeutics; consulting fees from Alkahest, Altimmune, Anji, Aqua Medical Inc, AstraZeneca, Bayer, Biomea Fusion Inc, Boehringer-Ingelheim, CeQur, Corcept Therapeutics, Eli Lilly, embecta, Fortress Biotech, GentiBio, Glycadia, Glyscend, Janssen, MannKind, Insulet, Mediflix, Medscape, Medtronic/MiniMed, Mellitus Health, Metsera, Moderna, Pendulum Therapeutics, Praetego, ReachMD, Sanofi, Stability Health, Tandem, Terns Inc, Valo, Vertex, and Zealand Pharma; expert witness compensation from Medtronic MiniMed; and stock options from Glyscend, Mellitus Health, Pendulum Therapeutics, Praetego, and Stability Health. TS receives salary support as Director of Comparative Effectiveness Research (CER), NC TraCS Institute, UNC Clinical and Translational Science Award (UL1TR002489), the Center for Pharmacoepidemiology, investigator-initiated research funding and support as Principal Investigator (R01AG056479) from the National Institute on Aging (NIA), and as Co-Investigator (R01CA277756) from the National Cancer Institute, National Institutes of Health (NIH). He also receives salary support as Director of Comparative Effectiveness Research (CER), NC TraCS Institute, UNC Clinical and Translational Science Award (UM1TR004406), co-Director of the Human Studies Consultation Core, NC Diabetes Research Center (P30DK124723), National Institute of Diabetes and Digestive and Kidney Diseases (NIDDK), the Center for Pharmacoepidemiology (current members: GlaxoSmithKline, UCB BioSciences, Takeda, AbbVie, Boehringer Ingelheim, Astellas, and Sarepta). He owns stock in Novartis, Roche, and Novo Nordisk and from a generous contribution from Dr. Nancy A. Dreyer to the Department of Epidemiology, University of North Carolina at Chapel Hill. TS does not accept personal compensation of any kind from any pharmaceutical company. OO received funding from the Center for Pharmacoepidemiology (CPE) housed in the Department of Epidemiology at University of North Carolina Chapel Hill. AbbVie, Astellas, Boehringer Ingelheim, GlaxoSmithKline (GSK), Takeda, Sarepta, and UCB BioSciences have collaborative agreements with CPE. Other authors report no conflicts of interest in this work.
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