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Diabetes, Drug Treatment, and Mortality in COVID-19: A Multinational Retrospective Cohort Study

Patients with type 2 diabetes mellitus (T2DM) are at increased risk of severe coronavirus disease 2019 (COVID-19) outcomes possibly because of dysregulated inflammatory responses. Glucose-regulating medications, such as glucagon-like peptide 1 receptor (GLP-1R) agonists, dipeptidyl peptidase 4 (DPP-4) inhibitors, and pioglitazone, are known to have anti-inflammatory effects that may improve outcomes in patients with severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection. In a multinational retrospective cohort study, we used the TriNetX COVID-19 Research Network of 56 large health care organizations to examine these medications in relation to the incidence of hospital admissions, respiratory complications, and mortality within 28 days after a COVID-19 diagnosis. After matching for age, sex, race, ethnicity, BMI, and significant comorbidities, use of GLP-1R agonists and/or pioglitazone was associated with significant reductions in hospital admissions (GLP-1R: 15.7% vs. 23.5%, risk ratio [RR] 0.67 [95% CI 0.57–0.79; P < 0.001]; pioglitazone: 20.0% vs. 28.2%; RR 0.71 [95% CI 0.54–0.93; P = 0.01]). Use of GLP-1R agonists was also associated with reductions in respiratory complications (15.3% vs. 24.9%, RR 0.62 [95% CI 0.52–0.73]; P < 0.001) and incidence of mortality (1.9% vs. 3.3%, RR 0.58 [95% CI 0.35–0.97]; P = 0.04). Use of DPP-4 inhibitors was associated with a reduction in respiratory complications (24.0% vs. 29.2%, RR 0.82 [95% CI 0.74–0.90]; P < 0.001), and continued use of DPP-4 inhibitors after hospitalization was associated with a decrease in mortality compared with those who discontinued use (9% vs. 19%, RR 0.45 [95% CI 0.28–0.72]; P < 0.001). In conclusion, use of glucose-regulating medications, such as GLP-1R agonists, DPP-4 inhibitors, or pioglitazone, may improve COVID-19 outcomes for patients with T2DM; randomized clinical trials are needed to further investigate this possibility.

As of 14 April 2021, there were 137,811,552 confirmed cases and 2,964,835 (2.2%) deaths worldwide as a result of coronavirus disease 2019 (COVID- 19), which is caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). These figures include 31,375,111 confirmed cases and 563,873 (1.8%) deaths in the U.S. alone (1). Given the time required to distribute a vaccine, an expedient solution to reduce morbidity and mortality as a result of COVID-19 is to rapidly identify therapeutics utilizing highly scalable agents that show promise. A randomized placebo-controlled trial found that remdesivir, a broad-spectrum antiviral with activity against SARS-CoV-2, reduced median recovery time from 15 to 11 days but did not reduce mortality (2,3). An open-label trial with the synthetic glucocorticoid dexamethasone suggested a reduction in mortality by one-third in patients requiring ventilator support and by one-fifth in patients requiring oxygen (4). Together, these interventions are proving helpful; however, the number of deaths as a result of COVID-19 continues to climb in the U.S. and around the world (1). Moreover, mortality for at-risk populations, such as those with type 2 diabetes mellitus (T2DM), is much higher (5)(6)(7). In China, risk of mortality was reported to be 7.2% for patients with T2DM (8), and a recent study in England found that patients with T2DM made up 31.4% of the COVID-19-related deaths (9). One hypothesis is that patients with T2DM are more susceptible to a dysregulated inflammatory response, or cytokine storm, which has been hypothesized to lead to severe outcomes in a subset of patients with COVID-19 (10,11). Additionally, recent data indicate dysregulation of glycometabolic control in patients with COVID-19 (12).
Because SARS-CoV-2 uses ACE2 as its receptor, we selected medications that are used to treat T2DM and that have been shown to increase ACE2 and have antiinflammatory effects. The glucagon-like peptide 1 (GLP-1) agonist liraglutide has been shown to increase ACE2 expression in the lungs of rats and reduce acute lung injury caused by influenza virus in mice, which is particularly relevant to COVID-19-induced pulmonary disease (13)(14)(15)(16). Dipeptidyl peptidase 4 (DPP-4) inhibitors may have similar effects as they increase endogenous GLP-1 levels by inhibiting the breakdown of GLP-1 by the DPP-4 enzyme. The DPP-4 inhibitor linagliptin has been shown to increase ACE2 activity and has the potential to exert anti-inflammatory effects (17). Finally, thiazolidinediones (TZDs) were selected because they have been shown to increase ACE2 levels in the aorta, liver, adipose tissue, and skeletal muscle (18,19). Because GLP-1 agonists, DPP-4 inhibitors, and TZDs increase ACE2, we hypothesized that these drugs would have protective effects in patients with COVID-19 and diabetes. Increased ACE2 leads to increased production of angiotensin-(1-7), which exhibits anti-inflammatory effects and prevents endorgan damage as a result of diabetes and may also potentially prevent end-organ damage caused by COVID-19.
Several medications used to regulate blood glucose have been hypothesized to improve COVID-19-related outcomes in patients with T2DM (20-28); however, no data currently support these hypotheses or provide rationale for a randomized trial. The objective of this multinational, retrospective cohort study was to investigate whether the use of T2DM medications is associated with improved outcomes in patients with COVID-19 and T2DM. One such class of medications includes GLP-1 receptor (GLP-1R) agonists. GLP-1 is a hormone produced by L cells in the small intestine that increases insulin release, decreases glucagon release, and decreases gastric emptying (29). GLP-1R agonists are an approved and effective treatment for obesity and T2DM (30)(31)(32) and may have a protective effect for severe outcomes of COVID-19 in patients with fatty liver disease (33). In patients with T2DM, GLP-1R agonists control blood glucose, reduce elevated glycated hemoglobin (HbA 1c ), and decrease both cardiovascular risk (34) and the frequency of major adverse cardiac events (35,36). GLP-1R agonists also have positive effects on body weight, BMI, blood pressure, and cholesterol (29,37). Of particular interest, GLP-1R agonists have been shown to decrease inflammatory cytokines in animals (38) and humans (37,39). Other medications for T2DM that have anti-inflammatory effects include DPP-4 inhibitors, which block the breakdown of GLP-1 and have been shown to regulate other coronaviruses (40), and pioglitazone, which is a TDZ (23,41). We hypothesized that the use of GLP-1R agonists, DPP-4 inhibitors, or pioglitazone within 6 months before the diagnosis of COVID-19 would reduce hospital admissions, respiratory complications, and mortality within 28 days after the diagnosis of COVID-19 in patients with T2DM.

RESEARCH DESIGN AND METHODS
To determine whether treatment with GLP-1R agonists, DPP-4 inhibitors, or pioglitazone in patients with COVID-19 and T2DM was associated with better outcomes, we conducted a retrospective analysis of the TriNetX COVID-19 Research Network. This global federated research network provides access to statistics in electronic medical records (EMRs) across 56 large health care organizations predominately in the U.S. TriNetX provides aggregated counts and statistical summaries of deidentified information but no protected health information. Further details of the TriNetX networks have been previously described (42,43). All analyses were conducted using the browserbased real-time analytics feature of TriNetX.
We analyzed the EMRs of 64,936,797 patients with a diagnostic code for COVID-19 or positive laboratory test results for SARS-CoV-2 between 1 January 2020 and 1 September 2020. Analyses included patients with a diagnostic code for T2DM any time within 6 months preceding the first record of COVID-19 ( Fig. 1). Because treatment for type 1 diabetes has some, but not total, overlap with T2DM treatment, patients with type 1 diabetes were excluded from analyses. There were 29,516 patients with T2DM included in the analyses. These patients were divided into cohorts based on medications listed in their EMR within 6 months preceding the first record of COVID-19 (see Supplementary Tables 1-3 for a comprehensive list of medications). These patient subsets included 1,774 patients treated with GLP-1R agonists only (dulaglutide, exenatide, liraglutide, semaglutide, or lixisenatide), 2,264 treated with DPP-4 inhibitors only (alogliptin, linagliptin, saxagliptin, or sitagliptin), and 469 patients treated with pioglitazone only. There were two control groups: The first consisted of 23,714 patients with T2DM who had none of the medications of interest (No-G/D/P cohort), and the second included 5,606 patients with T2DM who had more than one of the medications of interest (Any Med cohort) any time within 6 months preceding the first record of COVID-19.
The primary outcome was mortality and defined as the presence of the term deceased in the EMR up to 28 days after the first record of COVID-19. Hospitalization and respiratory complications were secondary outcomes. Hospitalization was defined as a direct admission to inpatient care or observation in the EMR up to 28 days after the first record of COVID-19. Respiratory complications were defined as the presence of diagnostic codes for acute respiratory distress syndrome, acute and chronic respiratory failure, dependence on mechanical ventilation, acute respiratory distress, or respiratory arrest in the EMR within 28 days after the first record of COVID-19.
Risks for hospital admissions, respiratory complications, and mortality were first assessed for each patient cohort compared with the No-G/D/P cohort before propensity matching. Subsequent analyses were propensity matched for age, sex, race, ethnicity, BMI, and the presence of diagnostic codes for the following comorbidities: hypertension, ischemic heart disease, cerebrovascular disease, heart failure, chronic kidney disease, and overweight or obesity in the EMR within 6 months of the first record of COVID-19. Sex was used for propensity matching throughout these analyses and, thus, not considered a factor for comparison. Propensity scoring was calculated using logistic regression implemented by the function LogisticRegression of the scikit-learn package in Python version 3.7. The propensity score 1:1 matching used a greedy nearest neighbor matching approach, with a caliper distance of 0.1 pooled SDs of the logit of the propensity score. All laboratory values are the most recent value in the EMR within the designated time period unless otherwise stated. After propensity matching, the risks of hospital admissions, respiratory complications, and mortality were calculated for each patient cohort compared with the No-G/D/P cohort. Additionally, hospitalized patients who remained on their respective medications were compared with those whose medications were discontinued within each cohort.

Data and Resource Availability
The data that support the findings of this study are available from TriNetX, but restrictions apply to the availability of these data, which were used under license for the current study and, therefore, are not publicly available. Data are, however, available from the authors upon reasonable request and with permission of TriNetX.

RESULTS
Of the 229,809 patients with a diagnostic code for COVID-19 or positive laboratory result for SARS-CoV-2 present in the database, 2.2% were deceased within 28 days of the first record of COVID-19 (Table 1). Patients with a diagnosis of T2DM were more than four times as likely to be deceased (6.5%) within 28 days compared with those without a diagnosis of T2DM (1.6%). This increase in mortality was observed across all age-groups, with the greatest increase (22.8-fold) in patients <30 years of age. Group characteristics were first assessed for all patients with T2DM with COVID-19 who were alive at 28 days compared with those who were deceased at 28 days after the first record of COVID-19. Those who were deceased were older, more likely to be male, and had higher blood glucose, lower oxygen saturation, and lower blood pressure ( Table 2). Additionally, patients with T2DM who were deceased within 28 days had a greater incidence of hypertension, ischemic heart disease, cerebrovascular disease, heart failure, chronic kidney disease, and overweight and obesity. Consequently, these factors were used for propensity matching in subsequent analyses (see Table 3 for baseline patient characteristics by drug cohort).

Hospital Admissions
Collectively, 27.3% of all patients with T2DM with COVID-19 were admitted to the hospital within 28 days of the first record of COVID-19 (

Continuation of Medication After Hospital Admission
In a follow-up analysis, hospitalized patients within each cohort who remained on their respective medications P values indicate significance between alive and deceased groups. SMD, standardized mean difference between alive and deceased groups. *Patients with the term "deceased" in the medical record within 28 days of a diagnosis of COVID-19 or positive test results for SARS-CoV-2 were included in the deceased group, while all others were assigned to the alive group. †Age is defined as the age of the patient at the time of diagnosis of COVID-19 or positive test results for SARS-CoV-2. ‡Vitals and key laboratory findings are the most recent value recorded in the EMR within 6 months up to the time of diagnosis of COVID-19 or positive test results for SARS-CoV-2. §Comorbidities are assessed as presence of a diagnostic code for the six major comorbidities within the EMR within 6 months up to the time of diagnosis of COVID-19 or positive test results for SARS-CoV-2.    P values indicate significance compared with the No-G/D/P cohort. *Propensity matching balanced cohorts according to age, sex, race, ethnicity, BMI, and the presence of essential (primary) hypertension, ischemic heart disease, cerebrovascular disease, heart failure, chronic kidney disease, or overweight and obesity in the EMR within 6 months before COVID-19 diagnosis.
†The No-G/D/P cohort consisted of patients with T2DM with a diagnosis of COVID-19 or positive test results for SARS-CoV-2 who did not have any GLP-1R agonists, DPPwere compared with those whose medications were discontinued (

DISCUSSION
Of the 68,959,064 patients in the database, 1.6% of patients without T2DM and 6.5% of patients with T2DM died within 28 days after the first record of COVID-19. This reflects a fourfold increase in mortality for patients positive for COVID-19 with a comorbid condition of T2DM. Similar data were reported in a large study conducted in England, where 31.4% of all in-hospital COVID-19 deaths occurred in patients diagnosed with T2DM (9). In the current study, risk for mortality varied across the life span and was increased in each decade in patients with a comorbid diagnosis of T2DM. For those aged $30 years, a diagnosis of T2DM was associated with a 2.9-fold relative increase in risk for mortality. This finding is consistent with the twofold increase in mortality reported for adult (mean age 52.6 ± 17.4 years) patients positive for COVID-19 with T2DM (44). For patients <30 years of age, we found a stunning 22.8-fold relative increase in mortality for those with T2DM. With the incidence of diabetes (in particular T2DM) increasing for younger adults around the globe (45), this vulnerable population is at far greater risk of mortality. Finally, the incidence of mortality was found to peak in the ninth decade of life at 14.2% for patients without T2DM and 18.8% for patients with T2DM. This observation also is consistent with the literature showing severity of disease and death to be markedly increased in elderly patients, particularly in those with a comorbid diagnosis of T2DM (46,47). This study is the first to provide evidence that patients with T2DM treated with GLP-1R agonists, and to a lesser extent pioglitazone and DPP-4 inhibitors, within the 6 months preceding the diagnosis of COVID-19 P values indicate significance between continued and discontinued groups. SMD, standardized mean difference between continued and discontinued groups.
*Propensity matching balanced cohorts according to age, sex, race, ethnicity, BMI, and the presence of essential (primary) hypertension, ischemic heart disease, cerebrovascular disease, heart failure, chronic kidney disease, or overweight and obesity in the EMR within 6 months before COVID-19 diagnosis. †Patients with the specified medication listed in the EMR after hospitalization within 28 days of a diagnosis of COVID-19 or positive test results for SARS-CoV-2 were included in the continued group, while those without the specified medications were assigned to the discontinued group.   (15) P values indicate significance between continued and discontinued groups. *Propensity matching balanced cohorts according to age, sex, race, ethnicity, BMI, and the presence of essential (primary) hypertension, ischemic heart disease, cerebrovascular disease, heart failure, chronic kidney disease, or overweight and obesity in the EMR within 6 months before COVID-19 diagnosis. †Patients with the specified medication listed in the EMR after hospitalization within 28 days of a diagnosis of COVID-19 or positive test results for SARS-CoV-2 were included in the continued group, while those without the specified medications were assigned to the discontinued group. Among patients in the DPP-4 cohort who were hospitalized, those who continued DPP-4 treatment had reduced mortality compared with those who discontinued DPP-4 treatment during hospitalization (9% vs. 19%, respectively). This is consistent with another recent multicenter retrospective study that showed that treatment with the DPP-4 inhibitor sitagliptin during hospitalization for COVID-19 was associated with decreased mortality and improved clinical outcomes compared with standardof-care treatment in patients with T2DM (48). In addition to anti-inflammatory effects, DPP-4 inhibitors may reduce the entry of SARS-CoV-2 into cells because ACE2, the binding site for SARS-CoV-2, has high homology with DPP-4 (49).
The mean baseline HbA 1c for all patients with T2DM and a diagnosis of COVID-19 or positive SARS-CoV-2 test results was 7.7% (Table 2). This is above the target of 7% and indicates that on average, patients with T2DM and COVID-19 have impaired glycemic control. There was no significant difference in the mean baseline HbA 1c between those alive at 28 days compared with those deceased at 28 days (7.7% vs. 7.8%, respectively). This is unexpected as worse glycemic control has been associated with worse clinical outcomes in patients with diabetes who develop COVID-19. Because HbA 1c was similar in those deceased at 28 days to those alive at 28 days, this finding suggests that other factors beyond glycemic control contribute to mortality in patients with T2DM and COVID-19.
The mean baseline HbA 1c was significantly greater in the GLP-1 cohort compared with the No-G/D/P cohort (8.4% vs. 7.5%) and in the DPP-4 cohort compared with the No-G/D/P cohort (8.0% vs. 7.5%) ( Table 3). This may be a limitation of the study as differences in HbA 1c may have contributed to differences in hospitalizations, respiratory complications, and mortality between cohorts.
Considering that the mean baseline HbA 1c was higher in the GLP-1 cohort compared with the No-G/D/P cohort, the expected effect would have been worse outcomes in the GLP-1 cohort. Despite having worse baseline glycemic control, the GLP-1 cohort had significant reductions in hospitalizations, respiratory complications, and mortality compared with the No-G/D/P cohort. With regard to the DPP-4 cohort, considering that the mean baseline HbA 1c was higher in the DPP-4 cohort compared with the No-G/D/P cohort, the expected effect would have been worse outcomes in the DPP-4 cohort. Despite having worse baseline glycemic control in the DPP-4 cohort, there was no significant difference in hospitalizations in the DPP-4 cohort compared with the No-G/D/P cohort. Additionally, the DPP-4 cohort had significant reductions in respiratory complications compared with the No-G/D/P cohort suggesting that factors other than glycemic control may be responsible for the protective effects of GLP-1 analogs and DPP-4 inhibitors in patients with T2DM and COVID-19.

Limitations
The main limitation of this study is its retrospective nature. Causal relationships could not be inferred, and we could not control variables such as the duration of use of medications before hospital admission, dosage, and treatments received after admission. Furthermore, analyses only included patients with a diagnostic code for T2DM in the EMR within the 6 months preceding the first record of COVID-19. Therefore, patients without a visit for T2DM within that time frame or with undiagnosed diabetes or prediabetes were not captured in these analyses. While this limited the sample size, it also increased the certainty that all patients included in the analyses were diagnosed with T2DM, leading to more conservative results. This limitation does, however, prevent analysis of the duration of T2DM. The length of time each patient has had T2DM is an important variable that should be assessed in prospective analyses. Additionally, outside a randomized controlled trial, it is not possible to know whether underlying variables may have influenced the results, such as having an established relationship with a prescribing physician or access to medical care. The design of the current study may have accounted for such factors based on the wide geographical and socioeconomic inclusion of the database and propensity matching; however, a prospective trial is necessary to fully determine whether there are protective effects of GLP-1R agonists, DPP-4 inhibitors, or pioglitazone on adverse outcomes in patients with T2DM and COVID-19. Another limitation of the study is the method of collection and analysis available. To provide real-time electronic health records of patients with COVID-19, the TriNetX COVID-19 Research Network only allows access to aggregated counts and statistical summaries of deidentified information. Furthermore, all analyses are conducted using the browser-based real-time analytics feature of TriNetX as previously