Abstract
Background:
Post-transplant diabetes mellitus (PTDM) is an important complication for solid organ transplant recipients (SOTR). COVID-19 has been associated with increased risk of incident diabetes in the general population. However, the association between COVID-19 and new-onset PTDM has not been explored.
Methods:
Using the N3C Enclave, we conducted a cohort study of non-diabetic adults receiving a solid organ transplant (heart, lung, kidney, or liver) in the U.S. between April 1, 2020, and March 31, 2023, with and without a first diagnosis of COVID-19 (COVID+ versus COVID−) within 180 days of SOT. We propensity score matched a single COVID+ SOTR with a COVID− SOTR who was diabetes-free at the same point post-transplant. Within this matched cohort, we used multivariable Cox proportional hazards models to examine the adjusted risk of PTDM associated with COVID+.
Results:
Amongst 1342 COVID+ SOTR matched to 1342 COVID− SOTR, the crude rate of newly diagnosed PTDM in the 2 years post-COVID was 17% in those with versus 13% in those without COVID-19, p-value 0.007. COVID-19 was significantly associated with new PTDM (aHR 1.37, 95% CI 1.12–1.68 at 2 years).
Conclusion:
Similar to other viral infections, COVID-19 is associated with an increased risk of PTDM in SOTR.
Keywords: Transplantation, COVID-19, SARS-CoV-2, infection, diabetes, PTDM, NODAT
Introduction:
While organ transplantation is considered the optimal treatment strategy for eligible patients with end-organ failure, transplantation also confers important risks, including the potential development of post-transplant diabetes mellitus (PTDM). PTMD reportedly occurs in 10–40% of solid organ transplant recipients (SOTR) in the first 3 years after transplant, dependent on organ type and pre-existing risk factors,1 and has been associated with increased healthcare expenditure, premature cardiovascular disease, infectious complications, graft loss, and death.2,3
Known risk factors for PTDM include central obesity, advanced age, family history, immunosuppression, elevated pre-transplant C-peptide levels, rejection episodes, and hypomagnesemia.4–6 Viral infections, including hepatitis C virus (HCV)7 and cytomegalovirus (CMV)8,9 have also been associated with risk of PTDM. More recently, in the general population, a statistically increased risk of incident diabetes has been demonstrated in the post-acute stage of severe acute respiratory coronavirus 2 (SARS-CoV-2) infection, with particular risk after severe infection, and with male sex.10–12 At a population level, a cohort study conducted in British Columbia, Canada, indicated that SARS-CoV-2 infection (the virus responsible for the Coronavirus Disease of 2019 [COVID-19] pandemic) may contribute to a 3–5% excess risk of incident diabetes.12 In a study conducted using the United States (U.S.) Department of Veterans Affairs, there was a 40% increased hazard of new-onset diabetes in 30-day survivors of COVID-19.11
Whether SARS-CoV-2 infection similarly increases the risk of PTDM is unknown. However, given the greater predisposition for new-onset diabetes in SOTR compared with the general population and the known risk of PTDM with other viral infections, we hypothesize that COVID-19 will be associated with an increased risk of new PTDM. Therefore, we used the National COVID Cohort Collaborative (N3C) to examine the risk of incident diabetes in SOTR with and without post-transplant COVID-19.
Methods
The N3C is the largest data repository for patients with SARS-CoV-2 in the U.S. and has been used to study COVID-19 outcomes in SOTR.13–17 The N3C contains information from 83 US medical centers representing 8.3 million COVID-positive patients. Data are routinely transmitted to N3C and harmonized into the OMOP 5.3.1 data model within a secure enclave. Inclusion criteria include any patients with suspected COVID-19 inpatient or outpatient diagnosis based on laboratory testing or diagnostic codes. Details of the N3C rationale, design, infrastructure, and data harmonization have been previously published.18 This retrospective cohort study received Institutional Review Board (IRB) approval from the University of Nebraska Medical Center (0853–21-EP) and Johns Hopkins University (IRB00309495). The N3C Data Access Committee approved this study, which operates under the authority of the National Institutes of Health IRB, with Johns Hopkins University School of Medicine serving as the central IRB. No informed consent was obtained because the study used a limited data set. This study followed the Enhancing the Quality and Transparency of Health Research (EQUATOR) reporting guidelines: Reporting of Studies Conducted Using Observational Routinely Collected Health Data (RECORD).19 Data extraction was performed using PySpark and SQL, and statistical work utilized R version 4.1.3. within the N3C Enclave per N3C privacy18 and download review policies.
Design
Using the N3C Enclave, we conducted a cohort study of non-diabetic adult (18+ years) SOTR (heart, lung, kidney, or liver transplant) in the U.S. between April 1, 2020, and March 31, 2023, based on COVID-19 exposure post-transplant. Patients with multiple, simultaneous, or pancreas transplant were excluded. Data were extracted on December 14, 2023 (N3C release 153), allowing a minimum follow-up time of 6 months based on the latest data deposited by the participating site. Procedure codes were used to define SOTR status to avoid misclassifying the earliest diagnostic codes with the transplant procedure. Patients without at least one visit before their transplant procedure were excluded to avoid further misclassification.
Exposure
The exposure of interest was having a first COVID-19 diagnosis (COVID+ versus COVID−) in the first 180 days after transplantation, when PTDM risk is highest.20 As for our earlier analyses, COVID-19 diagnosis was based on a positive test result from real-time polymerase chain reaction, antigen testing, or International Classification of Diseases diagnostic codes as previously reported.16–18,21
Outcomes
The outcome of interest was PTDM, defined based on diagnostic codes in the observation window starting 7 days post-transplant. Patients with pre-transplant diabetes were excluded from this analysis.
Data Collection
We collected information on potential confounders including age, sex, race/ethnicity, time since transplant, type of organ transplant (kidney, liver, heart, lung), comorbidities (chronic kidney disease [CKD], hypertension, diabetes, chronic obstructive pulmonary disease [COPD]/asthma, cancer, coronary artery disease, congestive heart failure, peripheral vascular disease, liver disease, and obesity [body mass index >30 kg/m2 or provider diagnosis]), immunosuppression (anti-thymocyte globulin [ATG] induction, basiliximab induction, and maintenance therapy with prednisone, tacrolimus, cyclosporine, mycophenolic acid, everolimus, sirolimus, belatacept or azathioprine), prior COVID vaccination status, and SARS-CoV-2 epoch based on US vaccine availability.22 Complete case analysis was performed for all primary and secondary analyses.
Analysis
To create a COVID− non-diabetic SOTR control population, we propensity score matched 1:1 using an iterative approach, matching a single COVID+ SOTR with a COVID− SOTR who was diabetes-free at the same point post-transplant. For example, if patient X was diagnosed with COVID-19 37 days after transplantation, they were matched with patient Y, who did not have COVID-19 at the same time point post-transplant based on age, sex, transplant type, race/ethnicity, quarter of transplant, and contributing center. Nearest neighbor matching was used for age, sex, race/ethnicity, and quarter of transplant, and exact matching was used for transplant type and data-contributing site. For practical purposes, we created a cohort of patients from the same site with the same organ type, matched with patients with similar demographic characteristics. Despite this restrictive approach, fewer than 20 COVID+ patients were lost due to being unmatched. Details regarding the matching approach are shown in the Supplemental Methods.
Baseline characteristics were reported for the matched cohort of SOTR with and without COVID-19 over the study period, stratified by COVID+ versus COVID− status. Significant differences were determined using chi-square testing or Wilcoxon rank sum as appropriate.
Within this matched cohort, cumulative incidence curves were used to depict the incidence of PTDM in COVID+ versus COVID− patients. Multivariable Cox proportional hazards models were used to examine the adjusted risk of developing PTDM within 2 years of COVID-19, adjusting for the possible confounders listed above.
Sensitivity Analysis
We performed a number of sensitivity analyses by repeating our primary analysis with small modifications, including:
Conducting an organ-specific analysis stratifying by organ type (heart, lung, kidney, liver).
Examining COVID severity as a three tiered exposure variable: i. No COVID; ii. mild COVID (outpatient management); iii. severe COVID (hospitalization within 45d of diagnosis).
After stratifying by pre-vaccine era (prior to January 1, 2021) and post-vaccine era (after January 1, 2021).
After excluding any patients who died, or experienced graft loss in the first 6 months post-transplant.
Results
Overall, we identified 12,605 non-diabetic patients transplanted between April 1, 2020, and March 31, 2023. To adjust for important differences between patients with and without COVID-19, we performed propensity score matching as defined above, resulting in 1342 COVID+ SOTR matched to 1342 COVID− SOTR. Baseline characteristics for the matched cohort of SOTR with and without COVID-19 are shown in Table 1. There was good balance after matching with significant differences only for recipient age at transplant (54 years (Q1 42, Q3 64) for COVID− versus 52 years (Q1 39, Q3 62) for COVID+, p-value <0.001), recipient race/ethnicity (57% versus 51% non-Hispanic White for COVID− versus COVID+, p-value 0.003), and immunosuppression exposure (a small but significant increase in prednisone, tacrolimus, MMF, and sirolimus use in the COVID+ population).
Table 1:
Baseline Characteristics of Matched Solid Organ Transplant Recipients with and without a COVID-19 Diagnosis Between April 1, 2020 and March 31, 2023.
| Variable | Overall N (%) N = 2,684 | COVID Negative N (%) N = 1342 | COVID Positive N (%) N = 1342 |
P-value |
|---|---|---|---|---|
|
| ||||
| Sex | 0.13 | |||
| Female | 1,117 (42%) | 539 (40%) | 578 (43%) | |
| Male | 1,567 (58%) | 803 (60%) | 764 (57%) | |
|
| ||||
| Age | 53 (40, 63) | 54 (42, 64) | 52 (39, 62) | <0.001 |
|
| ||||
| Race/Ethnicity | 0.003 | |||
| Non-Hispanic White | 1,459 (54%) | 768 (57%) | 691 (51%) | |
| Non-Hispanic Black | 498 (19%) | 252 (19%) | 246 (18%) | |
| Hispanic or Latino | 418 (16%) | 185 (14%) | 233 (17%) | |
| Other/Unknown | 309 (12%) | 137 (10%) | 172 (13%) | |
|
| ||||
| Organ Type | >0.9 | |||
| Kidney | 1,540 (57%) | 770 (57%) | 770 (57%) | |
| Liver | 558 (21%) | 279 (21%) | 279 (21%) | |
| Lung | 358 (13%) | 179 (13%) | 179 (13%) | |
| Heart | 228 (8.5%) | 114 (8.5%) | 114 (8.5%) | |
|
| ||||
| Body Mass Index (kg/m2) | 27.0 (23.0, 31.0) | 27.0 (23.0, 31.0) | 27.0 (24.0, 31.0) | 0.4 |
|
| ||||
| Comorbidities | ||||
| CKD/Dialysis | 1,733 (65%) | 858 (64%) | 875 (65%) | 0.5 |
| Hypertension | 1,768 (66%) | 882 (66%) | 886 (66%) | 0.9 |
| COPD/Asthma | 364 (14%) | 180 (13%) | 184 (14%) | 0.8 |
| Cancer | 333 (12%) | 171 (13%) | 162 (12%) | 0.6 |
| CAD | 498 (19%) | 250 (19%) | 248 (18%) | >0.9 |
| PVD | 380 (14%) | 194 (14%) | 186 (14%) | 0.7 |
| CHF | 546 (20%) | 276 (21%) | 270 (20%) | 0.8 |
| Mild Liver Disease | 213 (7.9%) | 102 (7.6%) | 111 (8.3%) | 0.7 |
| Severe Liver Disease | 552 (21%) | 271 (20%) | 281 (21%) | 0.7 |
| Obesity | 1,096 (41%) | 544 (41%) | 552 (41%) | 0.8 |
|
| ||||
| Immunosuppression | ||||
| Prednisone | 2,285 (85%) | 1,120 (83%) | 1,165 (87%) | 0.015 |
| Tacrolimus | 2,390 (89%) | 1,178 (88%) | 1,212 (90%) | 0.036 |
| Cyclosporine | 154 (5.7%) | 75 (5.6%) | 79 (5.9%) | 0.7 |
| MMF | 2,126 (79%) | 1,042 (78%) | 1,084 (81%) | 0.046 |
| ATG induction | 896 (33%) | 446 (33%) | 450 (34%) | 0.9 |
| Basiliximab induction | 555 (21%) | 267 (20%) | 288 (21%) | 0.3 |
| Belatacept | 101 (3.8%) | 46 (3.4%) | 55 (4.1%) | 0.4 |
| Everolimus | 97 (3.6%) | 47 (3.5%) | 50 (3.7%) | 0.8 |
| Sirolimus | 73 (2.7%) | 29 (2.2%) | 44 (3.3%) | 0.075 |
| Azathioprine | 142 (5.3%) | 64 (4.8%) | 78 (5.8%) | 0.2 |
|
| ||||
| Vaccination Status | 0.11 | |||
| No documented Vaccine | 2,085 (78%) | 1,063 (79%) | 1,022 (76%) | |
| 1–2 documented Vaccines | 392 (15%) | 188 (14%) | 204 (15%) | |
| 3+ documented Vaccines | 207 (7.7%) | 91 (6.8%) | 116 (8.6%) | |
|
| ||||
| Quarter of Transplantation | 0.3 | |||
| Q2 2020 | 117 (4.4%) | 63 (4.7%) | 54 (4.0%) | |
| Q3 2020 | 236 (8.8%) | 122 (9.1%) | 114 (8.5%) | |
| Q4 2020 | 308 (11%) | 158 (12%) | 150 (11%) | |
| Q1 2021 | 164 (6.1%) | 90 (6.7%) | 74 (5.5%) | |
| Q2 2021 | 190 (7.1%) | 111 (8.3%) | 79 (5.9%) | |
| Q3 2021 | 281 (10%) | 134 (10.0%) | 147 (11%) | |
| Q4 2021 | 468 (17%) | 216 (16%) | 252 (19%) | |
| Q1 2022 | 254 (9.5%) | 120 (8.9%) | 134 (10.0%) | |
| Q2 2022 | 270 (10%) | 128 (9.5%) | 142 (11%) | |
| Q3 2022 | 183 (6.8%) | 91 (6.8%) | 92 (6.9%) | |
| Q4 2022 | 132 (4.9%) | 66 (4.9%) | 66 (4.9%) | |
| Q1 2023 | 81 (3.0%) | 43 (3.2%) | 38 (2.8%) | |
Chronic kidney disease (CKD), coronary artery disease (CAD), congestive heart disease (CHF), peripheral vascular disease (PVD), mycophenolate mofetil (MMF), anti-thymocyte globulin (ATG), quarter (Q)
After matching, the cumulative incidence of new-onset PTDM was higher in COVID+ than in COVID− patients, Figure S1. The crude rate of newly diagnosed PTDM was higher in those with versus without a diagnosis of COVID-19 at 1 (12% versus 9.9%, p-value 0.037) and 2 (17% versus 13%, p-value 0.007) years, Table 2.
Table 2:
Crude Rates of New Onset Post-Transplant Diabetes by COVID Status in a Matched Cohort
| New Onset PTDM | Overall N (%) N = 2,684 | COVID Negative N (%) N = 1342 | COVID Positive N (%) N = 1342 | P-value |
|---|---|---|---|---|
|
| ||||
| Within 90 days | 88 (3.3%) | 42 (3.1%) | 46 (3.4%) | 0.7 |
| Within 180 days | 179 (6.7%) | 86 (6.4%) | 93 (6.9%) | 0.6 |
| Within 365 days | 300 (11%) | 133 (9.9%) | 167 (12%) | 0.037 |
| Within 730 days | 400 (15%) | 175 (13%) | 225 (17%) | 0.007 |
Transplant recipients with COVID-19 were at a significantly increased risk of developing new PTDM compared to SOTR without COVID (adjusted hazard ratio [aHR] 1.33, 95% confidence interval [CI] 1.06–1.67 at 1 year; aHR 1.37, 95% CI 1.12–1.68 at 2 years), Table 3. Other than COVID status, only recipient age at transplant (aHR 1.02, 95% CI 1.01–1.02 per year), non-Hispanic black versus non-Hispanic White race (aHR 1.39, 95% CI 1.07–1.82), and organ type (liver versus kidney: aHR 3.35, 95% CI 2.15–5.23) were associated with the development of PTDM. Traditional PTDM risk factors, including obesity and diabetogenic immunosuppressive agents (prednisone, tacrolimus, and sirolimus), were not associated with a higher risk of PTDM in the COVID-adjusted models.
Table 3:
Multivariable Cox Proportional Hazards Model for the Outcome of New Onset Post-Transplant Diabetes at 365 and 730 days Post-COVID Diagnosis (Matched Cohort)
| Variable | Diabetes Within 365 days of COVID Diagnosis HR (95% CI) | Diabetes Within 730 days of COVID Diagnosis HR (95% CI) |
|---|---|---|
|
| ||
| COVID-19 Diagnosis | 1.33 (1.06, 1.67) | 1.37 (1.12, 1.68) |
|
| ||
| Sex | ||
| Female | Reference | Reference |
| Male | 1.14 (0.89, 1.44) | 1.20 (0.98, 1.48) |
|
| ||
| Age | 1.02 (1.01, 1.03) | 1.02 (1.01, 1.02) |
|
| ||
| Race | ||
| White | Reference | Reference |
| Black | 1.35 (0.99, 1.85) | 1.39 (1.07, 1.82) |
| Hispanic or Latino | 1.26 (0.89, 1.78) | 1.33 (1.00, 1.79) |
| Other/Unknown | 1.20 (0.82, 1.78) | 1.25 (0.89, 1.74) |
|
| ||
| Organ Type | ||
| Kidney | Reference | Reference |
| Liver | 1.10 (0.54, 2.23) | 1.23 (0.66, 2.30) |
| Lung | 3.05 (1.84, 5.05) | 3.35 (2.15, 5.23) |
| Heart | 1.50 (0.86, 2.64) | 1.63 (1.00, 2.64) |
|
| ||
| Comorbidities | ||
| CKD/Dialysis | 1.09 (0.75, 1.60) | 1.12 (0.81, 1.57) |
| Hypertension | 1.06 (0.80, 1.40) | 1.03 (0.81, 1.31) |
| COPD/Asthma | 1.18 (0.87, 1.61) | 1.16 (0.88, 1.52) |
| Cancer | 1.36 (0.98, 1.90) | 1.19 (0.88, 1.60) |
| CAD | 1.19 (0.89, 1.59) | 1.12 (0.87, 1.45) |
| PVD | 0.93 (0.68, 1.28) | 0.95 (0.72, 1.26) |
| CHF | 0.93 (0.67, 1.30) | 1.00 (0.75, 1.33) |
| Mild Liver Disease | 0.83 (0.54, 1.28) | 0.86 (0.59, 1.26) |
| Severe Liver Disease | 0.86 (0.47, 1.57) | 0.73 (0.43, 1.25) |
| Obesity | 1.19 (0.94, 1.50) | 1.22 (1.00, 1.50) |
|
| ||
| Immunosuppression | ||
| Prednisone | 1.10 (0.66, 1.82) | 1.07 (0.69, 1.64) |
| Tacrolimus | 0.76 (0.39, 1.50) | 1.18 (0.65, 2.15) |
| Cyclosporine | 0.89 (0.52, 1.52) | 1.12 (0.74, 1.70) |
| MMF | 1.49 (0.94, 2.38) | 1.17 (0.81, 1.70) |
| ATG induction | 0.94 (0.69, 1.27) | 0.91 (0.70, 1.17) |
| Basiliximab induction | 0.94 (0.68, 1.30) | 0.84 (0.63, 1.12) |
| Belatacept | 0.81 (0.37, 1.77) | 1.01 (0.57, 1.80) |
| Everolimus | 0.76 (0.35, 1.63) | 0.66 (0.37, 1.19) |
| Sirolimus | 0.31 (0.08, 1.25) | 0.43 (0.19, 0.98) |
| Azathioprine | 0.90 (0.53, 1.52) | 0.70 (0.44, 1.11) |
|
| ||
| Vaccination Status | ||
| No documented Vaccine | Reference | Reference |
| 1–2 documented Vaccines | 0.69 (0.48, 1.00) | 0.77 (0.57, 1.05) |
| 3+ documented Vaccines | 1.08 (0.72, 1.61) | 1.01 (0.70, 1.47) |
Sensitivity Analyses
In an analysis stratified by transplanted organ type, results were similar with loss of statistical power on account of smaller group sizes (kidney n=770, liver n=279, lung n=179, heart n=114 for each of the COVID+ and COVID− groups). Given these limitations, it appeared as though COVID-related PTDM risk was greatest amongst liver recipients (aHR 2.29, 95% CI 1.23–4.24 at 1 year), with no signal for risk in lung recipients (aHR 0.98, 95% CI 0.64–1.50 at 1 year; aHR 1.02, 95% CI 0.69–1.51 at 2 years), Figure S2.
Although additional sensitivity analyses were limited by small sample size, COVID risk was greatest amongst patients with moderate/severe COVID (Table S1), in the pre-vaccine era (Table S2), and after excluding patients with early graft loss (<6 months post SOT); Table S3.
Discussion
In this study, we show for the first time that SARS-CoV-2 is an important risk factor for developing PTDM in SOTR, with a 37% increased independent risk of PTDM above baseline in the 2 years following COVID-19 diagnosis. While other viral infections, including HCV7 and CMV,9 are established risk factors for the development of PTDM, no prior studies have examined the association between SARS-CoV-2 infection and PTDM. However, there is evidence of an increased risk of new-onset diabetes with COVID-19 in the general, non-transplant population.23 Given the known consequences of PTDM, including increased healthcare utilization and expenditure, graft compromise, premature cardiovascular events, and mortality,24 the identification of risk factors for PTDM is imperative.
The individual diabetogenic pathogenesis of viral infections varies between specific virus types. For example, while the mechanism driving HCV-mediated PTDM is not fully understood, it has been proposed to relate to hepatocyte destruction, pro-inflammatory cytokines, and altered glucose metabolism, resulting in diabetes primarily via insulin resistance and, to a lesser degree, beta cell dysfunction.25,26 Conversely, the pathogenesis of CMV-associated PTDM is hypothesized to relate primarily to beta-cell damage via several direct (e.g., cytopathic effects of viral infection of beta cells) and indirect (e.g., cytotoxic leukocyte activation, destructive pro-inflammatory cytokines) mechanisms, and less so to insulin resistance.27
The mechanism underpinning the development of incident diabetes after COVID-19 is also unclear, however is likely similarly multifactorial. SARS-CoV-2 can directly infect pancreatic beta cells that express angiotensin-converting enzyme 2 (ACE2) receptor protein and the transmembrane protease, serine 2 (TMPRSS2) enzyme protein required for cell entry, eliciting beta cell impairment and apoptosis.28,29 Further, the pro-inflammatory cytokine storm that occurs with severe COVID-19 disease may promote stress hyperglycemia,30 and has been hypothesized to promote insulin resistance, beta-cell hyperstimulation, and downstream beta-cell death.S1 In addition, murine models suggest that viral epitopes may mimic the host islet protein inducing a cross-reactivity and autoimmune T-cell response against the host islet cells, resulting in beta-cell destruction.S2 Other potential mechanisms include autonomic dysfunction, virally induced disruption of glucose disposal, and persistent low-grade inflammation resulting in further insulin resistance.11,S3
In non-immunosuppressed patients with new diabetes after COVID, frequently it is unclear whether the diagnosis represents de novo type 1 (predominantly beta cell dysfunction) or type 2 (predominantly insulin resistance) diabetes, and may in fact be some variation of the two.S3 The clinical phenotype of many patients presenting with diabetes post-COVID-19 includes hyperglycemia and diabetic ketoacidosis, a phenomenon more consistent with type 1 than type 2 diabetes, characterized more by beta cell dysfunction than insulin resistance.S4 Correspondingly, population-level studies from the U.S. and the United Kingdom have shown an increased incidence of new type 1 diabetes diagnoses since the onset of the pandemic.S5,S6
In the absence of COVID-19, the pathogenesis of PTDM differs from late-onset, typically insulin resistance mediated, diabetes in the general population. While insulin resistance is believed to contribute to the development of PTDM, the primary driver of new diabetes post-transplant is proposed to be beta cell dysfunction.S7 Therefore, given the further beta cell damage induced by SARS-CoV-2 infection, it might be anticipated that the risk of COVID-attributable PTDM would be even higher than that for incident diabetes in the general population. However, this was not what we observed. Earlier studies in the general population have shown a roughly 40% increased risk of new-onset diabetes post-COVID;11 comparable to the adjusted HR of 1.37 in our current study. Why the risk of PTDM with SARS-CoV-2 infection was not higher than in the general population is a point for future investigation. However, this may reflect mitigation of the corresponding pro-inflammatory stress hyperglycemia due to immunosuppression in SOTR with COVID-19.
In the general population it has been questioned whether COVID-19 precipitates the development of de novo diabetes, expedites progression of a pre-diabetic state, or simply unmasks undiagnosed diabetes in a population who otherwise may not have been seeking healthcare.33 While the same question remains amongst SOTR, given the more rigorous follow-up of patients post-transplant (including in many cases monitoring for the development of PTDM given the known risk), it is less likely that a COVID-19 diagnosis simply unmasked existing but undiagnosed diabetes in SOTR. This may suggest the same is true in the general population.
Whether vaccination protects against the development of COVID-related PTDM is another point of interest. A recent study in the general population demonstrated an increased risk of new-onset diabetes in the 90 days after COVID-19 diagnosis that was higher in unvaccinated (OR 1.78, 95% CI 1.35–2.37) than vaccinated (OR 1.07, 95% 0.64–1.77) patients.S8 Conversely, fulminant type 1 diabetes has been reported following COVID-19 vaccination in case reports.S9 In our current study, COVID-19 vaccination was not associated with the risk of PTDM. In addition, there is literature to suggest that pre-treatment of HCV before kidney transplant reduces the risk of subsequent PTDM.S10,S11 Whether early and aggressive treatment of COVID-19 impacts subsequent PTDM risk is an area for future investigation.
Our study is the first to examine the risk of PTDM following COVID-19 diagnosis in SOTR, with several strengths, including its novelty and our familiarity with the robust N3C Enclave data repository. However, there are also limitations. As with any retrospective analysis, this study was similarly at risk for miscoding and misclassification bias. However, we would expect any errors to be distributed at random. The risk of diabetes development has been shown to increase in a graded fashion according to COVID-19 disease severity,11 however, we examined all patients with a diagnosis of COVID-19, not restricting to those with severe disease due to diminishing numbers.
There may be important differences between patients who develop COVID-19 versus those who do not. We performed a matched analysis based on patient center and demographic data to account for this. In addition, earlier studies have suggested that Tacrolimus therapy may modify the risk of PTDM with HCV infection.S12,S13 In this study, we were unable to examine for any potential effect modification by immunosuppression regimens or COVID treatments administered. We did perform organ stratified analyses to examine for potential differential COVID-related PTDM risk by organ type, and while risk appeared greatest amongst liver recipients, relatively small sample sizes limited power to detect meaningful differences. That said, at baseline liver transplant recipients are at higher risk for developing PTDM than kidney recipients.S14 Therefore, it is plausible they are similarly at greater risk post-COVID. We did not have access to treatment strategies for patients presenting with COVID-19, therefore are unable to comment on whether early and aggressive treatment influenced subsequent PTDM risk. Finally, our new PTDM diagnoses in the 2 years post-COVID (or in the matched COVID-negative population) were lower than suggested by the literature (13% in the COVID− cohort and 17% in COVID+). Baseline PTDM risk has been cited at 10–40% in the first 3 years post-transplant,1 with the highest incidence in the first 6 months.S15 In order to be included in our study cohort, SOTR needed to be free of diabetes (including PTDM) at the time of their COVID diagnosis (or a matched date for the COVID-negative cohort).
Therefore, given the high rate of PTDM early post-transplant, many higher-risk patients developing early PTDM may have been excluded, and it is possible those surviving free of PTDM long enough to acquire SARS-CoV-2 infection may represent a lower-risk population. It is difficult to address this disease-free survival bias in the current retrospective analysis, and is an important limitation for consideration. Including patients at higher risk for PTDM (who developed early PTDM prior to COVID diagnosis and were thus excluded) may have resulted in an even higher risk of COVID-related PTDM. Due to how COVID− control patients are included in the N3C database (matching 1:2 case:control based on demographic data including age, race/ethnicity, and sex)S16 the COVID− SOT control group does not represent the uninfected population with SOT due to the partial pre-matching; therefore we are not able to obtain a comprehensive “unmatched” comparison between the COVID+ and COVID− populations. Despite this data limitation from N3C, we demonstrate the COVID-19 impact on PTDM within our matched control group. Finally, we were unable to account for potentially important factors including kidney graft function at the time of COVID diagnosis and less tangible factors such as increased risk of post-acute sequalae of COVID-19 in SOTRS17 which may have influenced health seeking behaviors, preventative health maintenance, and screening, possibly resulting in either over or under diagnosis of PTDM.
Conclusions
In conclusion, we demonstrate for the first time that COVID-19 is associated with an increased risk of PTDM in SOTR. Given the known complications associated with PTDM, an understanding of diabetogenic triggers and risk is imperative, particularly if preventable. Whether early and aggressive treatment of COVID in SOTR may reduce the subsequent risk of PTDM requires future study.
Supplementary Material
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 Contract No. 75N95023D00001, 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 [https://doi.org/10.1093/jamia/ocaa196].
RBM is supported in part by the Dr. Dennis Ross fund for Nephrology Research, the Nebraska Foundation. The project described was supported by the National Institute of General Medical Sciences, U54GM104942–05S2 and U54GM115458. The content is solely the responsibility of the authors and does not necessarily represent the official views of the National Institutes of Health or the N3C program.
Abbreviations:
- COVID-19
Coronavirus disease of 2019
- SOTR
Solid organ transplant recipient
- PTDM
Post-transplant diabetes mellitus
- aHR
Adjusted hazard ratio
- SARS-CoV-2
Severe acute respiratory syndrome coronavirus 2
- N3C
National COVID Cohort Collaborative
- US
United States
- COVID+
Tested positive for COVID-19
- COVID−
No diagnosis of COVID-19
Footnotes
Disclaimer: The N3C Publication Committee confirmed that this publication msid:1875.712 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 or the N3C program.
IRB
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.
Authorship was determined using ICMJE recommendations.
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, 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, Gaurav Agarwal, 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, Stephen Lee, 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
Data Partners with Released Data
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. Miller School of Medicine — UL1TR002736: University of Miami Clinical and Translational Science Institute • The University of Michigan at Ann Arbor — UL1TR002240: Michigan Institute for Clinical and Health Research • The University of Texas Health Science Center at Houston — UL1TR003167: Center for Clinical and Translational Sciences (CCTS) • The University of Texas Medical Branch at Galveston — UL1TR001439: The Institute for Translational Sciences • The 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 • The Queens Medical Center — None (Voluntary) • University Medical Center New Orleans — U54GM104940: Louisiana Clinical and Translational Science (LA CaTS) 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: The UMass Center for Clinical and Translational Science (UMCCTS) • University Medical Center of Southern Nevada — None (voluntary) • University of Minnesota — UL1TR002494: Clinical and Translational Science Institute • University of Mississippi Medical Center — U54GM115428: Mississippi Center for Clinical and Translational Research (CCTR) • University of Nebraska Medical Center — U54GM115458: Great Plains IDeA-Clinical & Translational Research • University of North Carolina at Chapel Hill — UL1TR002489: North Carolina Translational and Clinical Science Institute • University of Oklahoma Health Sciences Center — U54GM104938: Oklahoma Clinical and Translational Science Institute (OCTSI) • University of Pittsburgh — UL1TR001857: The Clinical and Translational Science Institute (CTSI) • University of Pennsylvania — UL1TR001878: Institute for Translational Medicine and Therapeutics • University of Rochester — UL1TR002001: UR Clinical & Translational Science Institute • University of Southern California — UL1TR001855: The Southern California Clinical and Translational Science Institute (SC CTSI) • University of Vermont — U54GM115516: Northern New England Clinical & Translational Research (NNE-CTR) 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 • 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 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 (RECOVER) 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
Financial Disclosures:
The authors of this manuscript have conflicts of interest to disclose as described by Transplantation. AV has done consultancy work and received funding for a fellowship project grant through Paladin Labs Inc. and consultancy work for Takeda Pharmaceuticals. RBM reports grant funding from VericiDX, honoraria from Olaris Inc and Chinook Therapeutics and personal fees from Vitaerris as member of the IMAGINE Trial Steering committee, and personal fees from the American Journal of Transplantation as Deputy Editor of the journal.
Competing Interest Statement:
The authors declare no conflict of interest pertaining to the submitted work.
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