Abstract
Introduction
Metformin has known mechanistic benefits on COVID-19 infection due to its anti-inflammatory effects and its action on the ACE2 receptor. However, some physicians are reluctant to use it in hypoxemic patients due to potential lactic acidosis. The primary purpose of the study was to determine whether metformin use is associated with survival. We also wanted to determine whether there is a difference in outcomes in subcategories of metformin use, whether at home, in-hospital, or mixed home/in-hospital use.
Objectives
This study aimed to determine an association between metformin use and mortality among patients with type 2 diabetes mellitus hospitalized for COVID-19 infection.
Methodology
This was a cross-sectional analysis of data acquired from the COVID-19 database of two tertiary hospitals in Cebu from March 1, 2020, to September 30, 2020. Hospitalized adult Filipino patients with type 2 diabetes mellitus who tested positive for COVID-19 via RT-PCR were included and categorized as either metformin users or metformin non-users.
Results
We included 355 patients with type 2 diabetes mellitus in the study, 186 (52.4%) were metformin users. They were further categorized into home metformin users (n=109, 30.7%), in-hospital metformin users (n=40, 11.3%), and mixed home/in-hospital metformin users (n=37, 10.4%). Metformin use was associated with a lower risk for mortality compared to non-users (p=0.001; OR=0.424). In-hospital and mixed home/in-hospital metformin users were associated with lower mortality odds than non-users (p=0.002; OR=0.103 and p=0.005; OR 0.173, respectively). The lower risk for mortality was noted in metformin, regardless of dosage, from 500 mg to 2 g daily (p=0.002). Daily dose between ≥1000 mg to <2000 mg was associated with the greatest benefit on mortality (p≤0.001; OR=0.252). The survival distributions between metformin users and non-users were statistically different, showing inequality in survival (χ2=5.67, p=0.017).
Conclusion
Metformin was associated with a lower risk for mortality in persons with type 2 diabetes mellitus hospitalized for COVID-19 disease compared to non-users. Use of metformin in-hospital, and mixed home/in-hospital metformin use, was also associated with decreased risk for mortality. The greatest benefit seen was in those taking a daily dose of ≥1000 mg to <2000 mg.
Keywords: metformin, diabetes mellitus, COVID-19, mortality
INTRODUCTION
In December 2019, the SARS-CoV-2 infection, which initially started in China, spread internationally and was declared a pandemic.1 In over a year since its discovery, cases have reached more than 200 million globally, with more than four million deaths worldwide.2 The Philippines has more than two million cases confirmed, with nearly forty thousand deaths attributed to the virus.3
The lungs are the primary target due to the high expression of the ACE2 receptor, which serves as its entry point.4-6
The virus induces a cytokine storm causing alveolar epithelial damage, and in severe cases, may lead to acute respiratory disease syndrome and death.7
The most prevalent comorbid conditions noted with COVID-19 infection are hypertension, diabetes mellitus, cardiovascular disease, and obesity.8,9 Studies also show that type 2 diabetes mellitus (T2DM) is a risk factor for more severe disease and is associated with an increased mortality rate.10-13 Persons with diabetes have a greater risk for viral infection, adverse clinical outcomes, and mortality, as noted in previous coronavirus epidemics, namely the Severe Acute Respiratory Syndrome Coronavirus (SARS-CoV) and the Middle East Respiratory Syndrome Coronavirus (MERS-CoV).14-16
The cure for COVID-19 remains elusive. Continual shifts in the therapeutic recommendations occur as clinical trials evaluate the effectiveness of potential agents. Investigators are looking into possible risks and added benefits of current antihyperglycemic medications to ascertain their impact on the course and prognosis of COVID-19 patients with diabetes.
Metformin is an established antidiabetic agent. Despite the introduction of new drugs in the treatment of type 2 diabetes mellitus, metformin is commonly used and is considered a mainstay treatment.17 Aside from its glucose-lowering action, other potential underlying mechanisms explaining the favorable impact of metformin on the COVID-19 patients with diabetes have been explored, including its effect on reducing cytokine storm.18,19
A study by Cheng et al., supported the findings of a higher incidence of lactic acidosis in metformin users, especially with severe COVID-19 disease. Acidosis occurred in patients with higher (<3 g per day) metformin doses compared to when given lower (<1 g per day) or moderate (<2 g per day) doses of the drug.20 However, metformin rarely causes lactic acidosis on its own. Clinical conditions associated with increased circulating lactate levels include hypoxia, sepsis, chronic kidney disease, or decreased renal function and heart failure.21 Due to the risk of lactic acidosis with infections, clinicians discontinue metformin in most patients with severe illness, including COVID-19 infection.22,23 Despite the risk, the degree of mortality was comparable between the metformin and non-metformin groups in COVID-19 infected patients,20 which supports its continued use in COVID-19 patients with diabetes, despite physician reluctance.
Because metformin is a cornerstone of management in patients with type 2 diabetes mellitus due to its therapeutic effects on glucose control and low cost, it is important to determine the outcome with its use in COVID-19 patients.
METHODOLOGY
Our study was a cross-sectional analysis done in two tertiary hospitals in Cebu, Philippines. The study population included patients admitted and subsequently included in the COVID-19 database of Chong Hua Hospital – Fuente and Chong Hua Hospital – Mandaue from March 1, 2020, to September 30, 2020.
All hospitalized Filipino patients who tested positive for COVID-19 via RT-PCR, are 18 years or older, and have type 2 diabetes mellitus, either preexisting or newly diagnosed using the American Diabetes Association criteria, were included in the analysis. We excluded patients if they had type 1 diabetes mellitus, were pregnant, of a different race or ethnicity, had unknown final disposition and those who were transferred to another institution or discharged against medical advice.
We categorized patients as to use or non-use of metformin, subdividing the metformin group into home metformin users, in-hospital metformin users, and mixed home/in-hospital metformin users. We retrospectively reviewed the characteristics and medications of these patients using an electronic medical record system.
The primary outcome was in-hospital mortality defined by a recorded final disposition of either discharged improved or death. Survival function between both groups was also an additional outcome measured.
Sample size
In the study by Lalau et al.,24 which included 2449 people with type 2 diabetes mellitus, mortality on day 28 was 16% among metformin users, and 28.6% among non-users. To show a similar difference in mortality, we estimated that the sample size would have to be at least 311 persons with type 2 diabetes mellitus, similar to the population of Lalau. The Chong Hua Hospital database of COVID-19 patients from March 2020 to September 2020 revealed 952 patients, 355 of which had type 2 diabetes mellitus. It is these persons with diabetes who were included in our present study.
Ethical considerations
The Chong Hua Hospital Institutional Ethics Review Committee approved the study. Confidentiality was ascertained using a coding system. The principal investigator was responsible for the accuracy and integrity of the data presented. Data collection was compiled and stored in a personal computer system and tabulated in Microsoft Excel format.
Data analysis
The independent variable was metformin use, whether home use, in-hospital use, or mixed home/in-hospital use. Also, assessed were potential confounding comorbidities and medications that may cause protection or harm for patients with COVID-19 infection.
Age and glycemic control using admission HbA1c were expressed in mean ± standard deviation. Categorical variables, namely sex, body mass index, preexisting medical conditions, preadmission and in-hospital medications, and disease severity, were expressed in percentages. Chi-square test of independence was used to compare variables between metformin users and non-users and between the three subgroups of metformin users. Univariate logistic regression analysis was done for these variables, computing for odds ratios for mortality.
Any significant variables were then included in the multivariate logistic regression model to adjust for any imbalance noted between study groups. The stepwise backward deletion was also done. We analyzed survival function using the Kaplan-Meier survival curve, and a log-rank test was run to determine if there were differences in the survival distributions between both groups. Stata BE version 17 was used. A p-value of less than 0.05 was considered significant.
RESULTS
Our hospital COVID-19 database observed 952 individuals admitted with COVID-19 between March 1, 2020, and September 30, 2020. Of these, 381 patients had type 2 diabetes mellitus, 26 were excluded (3 being non-Filipino, 22 being transferred or discharged against medical advice, and 1 with unknown disposition). Of the 355 persons with type 2 diabetes, 186 (52.4%) were metformin users, further categorized into home metformin users (n=109, 30.7%), in-hospital metformin users (n=40, 11.3%), and mixed home/in-hospital metformin users (n=37, 10.4%).
Tables 1A and 1B show the comparison of the demographic profile, clinical characteristics, and in-hospital use of anti-COVID medications of patients with type 2 diabetes mellitus hospitalized for COVID-19 who are metformin users versus non-users, and between the three subcategories of metformin users, respectively.
Table 1A.
Comparison of demographic profile, clinical characteristics, and in-hospital anti-covid medications among patients with type 2 diabetes mellitus hospitalized for COVID-19 infection who are metformin users versus metformin non-users
| Variable | Metformin users n=186 | Metformin non-users n=169 | p-value | |
|---|---|---|---|---|
| Age (years) | Mean | 61.61 ± 11.555 | 63.91 ± 12.837 | 0.174 |
| <45 | 14 (7.5%) | 12 (7.1%) | ||
| 45-55 | 40 (21.5%) | 29 (17.2%) | ||
| 56-65 | 62 (33.3%) | 51 (30.2%) | ||
| 66-75 | 52 (28%) | 44 (26%) | ||
| 76-85 | 13 (7%) | 24 (14.2%) | ||
| >85 | 5 (2.7%) | 9 (5.3%) | ||
|
| ||||
| Sex | Male | 109 (58.6%) | 89 (52.7%) | 0.288 |
| Female | 77 (41.4%) | 80 (47.3%) | ||
|
| ||||
| Body Mass Index | Underweight (<18.5 kg/m2) | 4 (2.2%) | 1 (0.6%) | 0.763 |
| Normal (18.5-22.9 kg/m2) | 40 (21.5%) | 35 (20.7%) | ||
| Overweight (23-24.9 kg/m2) | 25 (13.4%) | 22 (13%) | ||
| Obese I (>25-29.9 kg/m2) | 64 (34.4%) | 58 (34.3%) | ||
| Obese II (>30 kg/m2) | 52 (28%) | 52 (30.8%) | ||
|
| ||||
| Pre-existing Medical Conditions | Hypertension | 136 (73.1%) | 129 (76.3%) | 0.460 |
| Bronchial asthma | 13 (7%) | 14 (8.3%) | 0.773 | |
| Acute coronary syndrome | 4 (2.2%) | 12 (7.1%) | 0.044* | |
| Coronary artery disease | 13 (7%) | 21 (12.4%) | 0.112 | |
| Heart failure | 2 (1.1%) | 8 (4.7%) | 0.076 | |
| Chronic obstructive pulmonary disease | 4 (2.2%) | 2 (1.2%) | 0.779 | |
| Liver disease | 8 (4.3%) | 5 (3%) | 0.711 | |
| Chronic kidney disease (eGFR <60 mL/min/1.73 m2) | 9 (4.8%) | 37 (21.9%) | <0.001* | |
| Cerebrovascular disease | 8 (4.3%) | 9 (5.3%) | 0.822 | |
| Cancer | 10 (5.4%) | 9 (5.3%) | 1.00 | |
|
| ||||
| Preadmission injectable antihyperglycemic agent | Insulin | 18 (9.7%) | 34 (20.1%) | 0.008* |
| GLP-1 agonist | 2 (1.1%) | 1 (0.6%) | 1.00 | |
|
| ||||
| Preadmission oral antihyperglycemic agent | DPP-4 inhibitor | 69 (37.1%) | 52 (30.8%) | 0.24 |
| Sulfonylurea | 29 (15.6%) | 21 (12.4%) | 0.508 | |
| Thiazolidinediones | 2 (1.1%) | 1 (0.6%) | 1.00 | |
| SGLT-2 inhibitors | 19 (10.2%) | 7 (4.1%) | 0.05 | |
| Glucosidase inhibitors | 0 | 1 (0.6%) | 0.957 | |
|
| ||||
| Baseline severity of disease | Mild | 32 (17.2%) | 28 (16.6%) | 0.460 |
| Moderate | 81 (43.5%) | 68 (40.2%) | ||
| Severe | 35 (18.8%) | 43 (25.4%) | ||
| Critical | 16 (8.6%) | 11 (6.5%) | ||
| Missing | 0 | 1 (0.6%) | ||
|
| ||||
| Admission HbA1c | 6.997 ± 2.351 | 7.590 ± 1.894 | 0.016* | |
|
| ||||
| In-hospital medications | ||||
| Tocilizumab | 97 (52.2%) | 88 (52.1%) | 1.000 | |
| Antimalarials | 14 (7.5%) | 17 (10.1%) | 0.492 | |
| Antivirals | 120 (64.5%) | 115 (68%) | 0.543 | |
| Systemic steroids | 109 (58.6%) | 100 (59.2%) | 0.904 | |
| Hemoperfusion | 9 (4.8%) | 11 (6.5%) | 0.634 | |
| Convalescent plasma therapy (CPT) | 8 (4.3%) | 9 (5.3%) | 0.822 | |
|
| ||||
| Injectable antihyperglycemic agent | Insulin | 47 (25.2%) | 71 (42%) | 0.001* |
| GLP-1 agonist | 1 (0.5%) | 1 (0.6%) | 1.000 | |
|
| ||||
| Oral antihyperglycemic agent | DPP-4 inhibitor | 64 (34.4%) | 56 (33.1%) | 0.861 |
| Sulfonylurea | 10 (5.4%) | 7 (4.1%) | 0.785 | |
| Thiazolidinediones | 0 | 0 | N/A | |
| SGLT-2 inhibitors | 11 (5.9%) | 4 (2.4%) | 0.115 | |
| Glucosidase inhibitors | 0 | 0 | N/A | |
Table 1B.
Comparison of demographic profile, clinical characteristics, and in-hospital anti-COVID medications between the 3 subcategories of metformin users
| Variable | Home Metformin Use n=109 | In-hospital Metformin Use n=40 | Mixed home/ in-hospital Use n=37 | p-value | |
|---|---|---|---|---|---|
| Age (years) | <45 | 6 (5.5.%) | 5 (12.5%) | 3 (8.1%) | 0.256 |
| 45-55 | 18 (16.5%) | 13 (32.5%) | 9 (24.3%) | ||
| 56-65 | 38 (34.9%) | 11 (27.5%) | 13 (35.1%) | ||
| 66-75 | 35 (32.1%) | 7 (17.5%) | 10 (27%) | ||
| 76-85 | 10 (9.2%) | 2 (5%) | 1 (2.7%) | ||
| >85 | 2 (1.8%) | 2 (5%) | 1 (2.7%) | ||
|
| |||||
| Sex | Male | 68 (62.4%) | 26 (65%) | 15 (40.5%) | 0.098 |
| Female | 41 (37.6%) | 14 (35%) | 22 (59.5%) | ||
|
| |||||
| Body Mass Index | Underweight (<18.5 kg/m2) | 2 (1.8%) | 2 (5%) | 0 | 0.685 |
| Normal (18.5-22.9 kg/m2) | 20 (18.3%) | 9 (22.5%) | 11 (29.7%) | ||
| Overweight (23-24.9 kg/m2) | 17 (15.6%) | 4 (10%) | 4 (10.8%) | ||
| Obese I (>25-29.9 kg/m2) | 39 (35.8%) | 15 (37.5%) | 10 (27%) | ||
| Obese II (>30 kg/m2) | 30 (27.5%) | 10 (25%) | 12 (32.4%) | ||
|
| |||||
| Pre-existing Medical Conditions | Hypertension | 84 (77.1%) | 24 (60%) | 28 (75.7%) | 0.076 |
| Bronchial Asthma | 9 (8.3%) | 2 (5%) | 2 (5.4%) | 0.694 | |
| Acute Coronary Syndrome | 3 (2.8%) | 1 (2.5%) | 0 | 0.592 | |
| Coronary Artery Disease | 7 (6.4%) | 5 (12.5%) | 1 (2.7%) | 0.235 | |
| Heart Failure | 2 (1.8%) | 0 | 0 | 0.481 | |
| Chronic obstructive pulmonary disease | 1 (0.9%) | 2 (5%) | 1 (2.7%) | 0.316 | |
| Liver Disease | 3 (2.8%) | 3 (7.5%) | 2 (5.4%) | 0.440 | |
| Chronic kidney disease (eGFR <60 mL/min/1.73 m2) | 6 (5.5%) | 3 (7.5%) | 0 | 0.271 | |
| Cerebrovascular Disease | 6 (5.5%) | 1 (2.5%) | 1 (2.7%) | 0.608 | |
| Cancer | 5 (4.6%) | 3 (7.5%) | 2 (5.4%) | 0.802 | |
|
| |||||
| Preadmission injectable antihyperglycemic agent | Insulin | 14 (12.8%) | 2 (5%) | 2 (5.4%) | 0.201 |
| GLP-1 agonist | 1 (0.9%) | 0 | 1 (2.7%) | 0.509 | |
|
| |||||
| Preadmission oral antihyperglycemic agent | DPP-4 inhibitor | 50 (45.9%) | 1 (2.5%) | 18 (48.6%) | <0.001* |
| Sulfonylurea | 20 (18.3%) | 3 (7.5%) | 6 (16.2%) | 0.249 | |
| Thiazolidinediones | 0 | 0 | 2 (5.4%) | 0.019* | |
| SGLT-2 inhibitors | 14 (12.8%) | 0 | 5 (13.5%) | 0.052 | |
| Glucosidase inhibitors | 0 | 0 | 0 | N/A | |
|
| |||||
| Admission HbA1c | 6.960 ± 1.914 | 7.071 ± 2.124 | 7.026 ± 1.560 | 0.951 | |
|
| |||||
| In-hospital use of anti-COVID medications | |||||
|
| |||||
| Tocilizumab | 59 (54.1%) | 19 (47.5%) | 19 (51.4%) | 0.680 | |
| Antimalarials | 10 (9.2%) | 2 (5%) | 2 (5.4%) | 0.569 | |
| Antivirals | 70 (64.2%) | 26 (65%) | 24 (64.9%) | 0.991 | |
| Systemic steroids | 59 (54.1%) | 28 (70%) | 22 (59.5%) | 0.293 | |
| Hemoperfusion | 9 (8.3%) | 0 | 0 | 0.033* | |
| Convalescent plasma therapy (CPT) | 8 (7.3%) | 0 | 0 | 0.048* | |
|
| |||||
| Injectable antihyperglycemic agents | Insulin | 27 (24.8%) | 9 (22.5%) | 11 (29.7%) | 0.770 |
| GLP-1 agonist | 0 | 0 | 1 (2.7%) | 0.137 | |
|
| |||||
| Oral antihyperglycemic agents | DPP-4 inhibitor | 18 (16.5%) | 21 (52.5%) | 25 (67.6%) | <0.001* |
| Sulfonylurea | 4 (3.7%) | 2 (5%) | 4 (10.8%) | 0.266 | |
| Thiazolidinediones | 0 | 0 | 0 | N/A | |
| SGLT-2 inhibitors | 2 (1.8%) | 4 (10%) | 5 (13.5%) | 0.011* | |
| Glucosidase inhibitors | 0 | 0 | 0 | N/A | |
The total mean age of the population was 62.69 ± 12.21 years. The total population was composed of 198 (55.8%) males, 109 (58.6%) metformin users. Among the metformin users, 13.4% were overweight and 62.4% were obese, compared to 13% overweight and 65.1% obese in the non-metformin group. Age, sex, and body mass index between both groups were statistically similar.
More patients suffered from acute coronary syndrome (p=0.044) and chronic kidney disease (p≤0.001) in the non-metformin group. More patients in the non-metformin group were on insulin therapy before admission (p=0.008).
Both groups were similar in other clinical profiles, including hypertension, bronchial asthma, coronary artery disease, heart failure, chronic obstructive pulmonary disease, liver disease, cerebrovascular disease, malignancy, and preadmission use of GLP-1 agonists and oral antihyperglycemic agents.
Among the three subgroups of metformin users, most patients with preadmission use of DPP4-inhibitors (p≤0.001) were on home metformin use, while patients on preadmission thiazolidinediones were in the mixed home/in-hospital metformin users (p=0.019). Other characteristics, clinical profile, and preadmission medications among the three subgroups were statistically similar.
Most metformin users (n=81, 43.5%) and metformin non-users (n=68, 40.2%) had moderate COVID-19 disease severity. There was no notable difference in the baseline severity of disease between both groups (p=0.460).
Better glycemic control was observed in patients taking metformin than non-users (p=0.016), while there was no difference in glycemic control between the three metformin groups (p= 0.951).
More metformin non-users required insulin therapy during hospitalization (p=0.001). Fewer patients on metformin at home were treated with DPP-4 inhibitors (p≤0.001) and SGLT-2 inhibitors (p=0.011) during hospitalization, but they required convalescent plasma therapy (p=0.048) and hemoperfusion (p=0.033) more frequently. The use of other in-hospital treatments, including tocilizumab, antivirals, antimalarials, and systemic steroids, were similar among the treatment groups. Among the antihyperglycemic agents, the use of GLP-1 agonists, sulfonylureas, and glucosidase inhibitors was identical between the three metformin subgroups.
In the metformin group, 33 (17.7%) died during hospitalization for COVID-19, compared to 57(33.7%) in the non-metformin group (p=0.001). More deaths occurred in those with critical COVID-19, with 31 (93.9%) deaths in the overall metformin group compared to 53 (93%) in the non-metformin group. No deaths were noted in patients with mild disease in the two groups.
Although more patients died among home metformin users (n=28, 84.8%), compared to both in-hospital (n=2, 6.1%) and mixed home/in-hospital users (n=3, 9.1%) (p=0.003), there was an overall low rate of mortality in overall metformin users compared to the metformin non-users.
Logistic regression analysis using each variable in a univariate fashion showed an increased odds ratio for mortality in patients with increased age (p≤0.001; OR=1.041), chronic kidney disease (p≤0.001; OR=3.248), and acute coronary syndrome (p≤0.001; OR=14.744).
Patients who were given tocilizumab (p=<0.001; OR=2.556), systemic steroids (p=0.048; OR=1.662), convalescent plasma therapy (p=0.042; OR=2.775), hemoperfusion (p=0.003; OR=3.960) and those started on in-hospital insulin therapy (p=0.010; OR=1.917) were also noted to have increased odds for mortality. The odds ratio for glycemic control using preadmission HbA1c and baseline severity of disease were not significant.
Metformin use was associated with lower odds for mortality (p=0.001; OR 0.424) compared to non-users. In-hospital metformin users (p=0.002; OR=0.103) and mixed home/in-hospital metformin users (p=0.005; OR=0.173) were also associated with lower odds for mortality compared to non-users.
Table 2 shows univariate logistic regression analysis using factors that may affect mortality and crude odds ratio for mortality between metformin users and non-users.
Table 2.
Univariate logistic regression analyses using factors that may affect mortality
| Variable | Survivors (n=265) | Non-survivors (n=90) | Odds Ratio (95% CI) | p-value |
|---|---|---|---|---|
| Age | 61.33 (12.20) | 67.04 (1.81) | 1.04137 (1.01961 - 1.06360) | <0.001* |
| Sex - Male | 149 (56.23%) | 49 (54.44%) | 0.93043 (0.57538 - 1.50457) | 0.769 |
| Body Mass Index | 27.81 (6.11) | 27.49 (7.16) | 0.99191 (0.95467 - 1.03062) | 0.769 |
| Hypertension | 196 (74%) | 69 (76.7%) | 1.15671 (0.66056 - 2.02551) | 0.611 |
| Bronchial asthma | 22 (8.3%) | 5 (5.6%) | 0.64973 (0.23855 - 1.76961) | 0.399 |
| Chronic obstructive pulmonary disease | 5 (1.9%) | 1 (1.1%) | 0.58427 (0.06735 - 5.06873) | 0.626 |
| Liver disease | 9 (3.4%) | 4 (4.4%) | 0.315 (0.033-2.986) | 0.314 |
| Chronic kidney disease (eGFR <60 ml/min/1.73 m2) | 24 (9.1%) | 22 (2.4%) | 3.24878 (1.71640 - 6.14922) | <0.001* |
| Heart failure | 7 (2.6%) | 3 (3.3%) | 1.27094 (0.32161 - 5.02242) | 0.732 |
| Acute coronary syndrome | 3 (1.1%) | 13 (14.4%) | 14.74458 (4.09613 - 53.07515) | <0.001* |
| Coronary artery disease | 29 (10.9%) | 5 (5.6%) | 0.47870 (0.17950 - 1.27667) | 0.141 |
| Cerebrovascular disease | 13 (4.9%) | 4 (4.4%) | 0.90161 (0.28632 - 2.83914) | 0.860 |
| Cancer | 12 (4.5%) | 7 (7.8%) | 1.77811 (0.67773 - 4.66510) | 0.242 |
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| Severity of disease (using mild as a comparator at baseline) | ||||
|
| ||||
| Mild | 49 (18.5%) | 11 (12.2) | ||
| Moderate | 103 (39.9%) | 46 (51.1%) | 1.98941 (0.94864 - 4.172) | 0.069 |
| Severe | 61 (23%) | 17 (18.9%) | 1.24143 (0.53247 - 2.89436) | 0.617 |
| Critical | 23 (8.7%) | 4 (4.4%) | 0.77470 (0.22262 - 2.69588) | 0.688 |
| Missing | 0 | 1 (1.1%) | ||
|
| ||||
| HBA1c | 7.29987 (1.99) | 7.17014 (2.55) | 0.97148 (0.85608 - 1.10243) | 0.654 |
|
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| Preadmission medications | ||||
|
| ||||
| Insulin | 36 (9.8%) | 16 (17.8%) | 1.37538 (0.72191 - 2.62035) | 0.582 |
| GLP-1 agonists | 3 (1.1%) | 0 | 0.000 | 1.000 |
| Metformin | 113 (42.97%) | 30 (33.71) | 0.67496 (0.40826 - 1.11590) | 0.125 |
| DPP-4 inhibitors | 89 (33.58%) | 32 (35.56%) | 1.09105 (0.66092 - 1.80111) | 0.733 |
| Sulfonylureas | 41 (15.5%) | 9 (10%) | 0.60705 (0.28250 - 1.30443) | 0.201 |
| Thiazolidinediones | 2 (0.8%) | 1 (1.1%) | 1.47753 (0.13238 - 16.49124) | 0.751 |
| SGLT-2 inhibitors | 23 (8.7%) | 3 (3.3%) | 0.36282 (0.10628 - 1.23858) | 0.106 |
| Glucosidase inhibitors | 1 (0.4%) | 0 | 0.000 | 0.999 |
|
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| In-hospital medications | ||||
|
| ||||
|
| ||||
| Tocilizumab | 123 (46.42%) | 62 (68.89%) | 2.55633 (1.53909 - 4.24591) | <0.001* |
| Antimalarials | 22 (8.3%) | 9 (10%) | 1.22727 (0.54309 - 2.77339) | 0.622 |
| Antivirals | 176 (66.42%) | 59 (65.56%) | 0.96243 (0.58141 - 1.59313) | 0.882 |
| Systemic steroids | 148 (55.85%) | 61 (67.78%) | 1.66286 (1.00434 - 2.75316) | 0.048* |
| Convalescent plasma therapy | 9 (3.4%) | 8 (8.9%) | 2.77506 (1.03704 - 7.42598) | 0.042* |
| Hemoperfusion | 9 (3.4%) | 11 (12.2%) | 3.96062 (1.58417 - 9.90206) | 0.003* |
| Insulin | 78 (29.43%) | 40 (44.44%) | 1.91795 (1.17193 - 3.13886) | 0.010* |
| GLP-1 agonists | 2 (0.8%) | 0 | 0.132 | 1.000 |
| Metformin | 72 (27.1%) | 5 (5.56%) | 0.15768 (0.06149 - 0.40433) | <0.001* |
| DPP-4 inhibitors | 93 (35.09%) | 27 (30%) | 0.79263 (0.47283 - 1.32872) | 0.378 |
| Sulfonylureas | 14 (5.3%) | 3 (3.3%) | 0.61823 (0.17352 - 2.20266) | 0.458 |
| Thiazolidinediones | - | - | - | - |
| SGLT-2 inhibitors | 15 (5.7%) | 0 | 0.000 | 0.998 |
| Glucosidase inhibitors | - | - | - | - |
|
| ||||
| Crude odds ratio for mortality between metformin users versus non-users, with non-users as the reference group | ||||
|
| ||||
| Metformin use | 153 (57.7%) | 33 (36.7%) | 0.42438 (0.25881-0.69397) | 0.001* |
|
| ||||
| Crude odds ratio for mortality between 3 subgroups of metformin users, with non-users as the reference group | ||||
|
| ||||
| Metformin use | ||||
| Home | 81 (30.6%) | 28 (31.3%) | 0.67922 (0.39777-1.15984) | 0.157 |
| In-hospital | 38 (14.3%) | 2 (2.2%) | 0.10341 (0.02408-0.44407) | 0.002* |
| Mixed Home/In-hospital | 34 (12.8%) | 3 (3.3%) | 0.17337 (0.05104-0.58888) | 0.005* |
|
| ||||
| Crude odds ratio between different metformin dosages, with metformin non-use as reference group | ||||
|
| ||||
| Metformin dosage (mg/day) | ||||
| 500 - <1000 (n=85) | 66 (24.9%) | 19 (21.1%) | 0.56565 (0.30990-1.03247) | 0.063 |
| >1000 - <2000 (n=79) | 70 (26.4%) | 9 (10%) | 0.25263 (0.11769-0.54225) | <0.001* |
| >2000 (n=19) | 15 (5.7%) | 4 (4.4%) | 0.52397 (0.16622-1.65169) | 0.27 |
Metformin use, regardless of dosage from 500 mg to 2 g daily, was associated with a lower risk for mortality compared to patients not taking metformin (p=0.002). There was also a lower crude odds ratio for mortality among patients on ≥1 g to <2 g daily of metformin, compared to non-users and other dosages (p≤0.001; OR=0.252). However, analyzing metformin users only showed no association between metformin dosage and mortality noted (p=0.166) (Supplementary Table).
We did a multivariate logistic regression analysis on significant variables, namely: age, chronic kidney disease, acute coronary syndrome, tocilizumab, systemic steroid use, convalescent plasma therapy, hemoperfusion, in-hospital insulin use, and HbA1c. After controlling for these variables, metformin use was associated with reduced odds for mortality (p=0.01; OR=0.433). The stepwise deletion was also done in this model and still showed metformin use was associated with better mortality outcomes (p=0.008; OR = 0.430).
Table 3 shows multivariate logistic regression analysis for mortality controlled for significant confounders.
Table 3.
Multivariate logistic regression analyses for mortality controlled for significant confounders
| Variable | Odds Ratio (95% CI) | p-value |
|---|---|---|
| Metformin use | 0.43320 (0.22869-0.82061) | 0.010* |
| Age | 1.02510 (0.99773-1.05322) | 0.073 |
| Chronic kidney disease | 2.08117 (0.78501-5.51749) | 0.141 |
| Acute coronary syndrome | 14.80458 (2.89512-75.70515) | 0.001* |
| Tocilizumab | 2.31083 (1.11154-4.80409) | 0.025* |
| Systemic steroids | 1.35679 (0.64987-2.83268) | 0.417 |
| Convalescent plasma therapy | 2.07477 (0.62739-6.86119) | 0.232 |
| Hemoperfusion | 3.56889 (1.03360-12.32302) | 0.044* |
| In-hospital insulin use | 1.25407 (0.63349-2.48259) | 0.516 |
| HbA1c | 0.96426 (0.82677-1.12461) | 0.643 |
|
| ||
| Using stepwise deletion | ||
|
| ||
| Metformin use | 0.43064 (0.23006-0.80609) | 0.008* |
| Age | 1.02656 (0.99984-1.05399) | 0.051 |
| Chronic kidney disease | 2.18395 (0.84120-5.67001) | 0.109 |
| Acute coronary syndrome | 15.36630 (3.00181-78.66032) | 0.001* |
| Tocilizumab | 2.65869 (1.35206-5.228065) | 0.005* |
| Hemoperfusion | 3.72632 (1.07222-12.9502) | 0.038* |
| Convalescent plasma therapy | 2.14171 (0.65775-6.97367) | 0.206 |
The survival distributions between metformin users and non-users were statistically different, showing the inequality of survival (χ2=5.67, p=0.017).
Figure 1 illustrates the Kaplan-Meier survival curve between metformin users versus non-users.
Figure 1.
Kaplan-Meier survival curve between metformin users versus non-users.
DISCUSSION
Findings from several studies demonstrate the negative impact of type 2 diabetes mellitus on the morbidity and mortality of COVID-19 infected patients.10-13 Thus, the potential role of antihyperglycemic agents, especially metformin, in this viral infection should also be explored.
COVID-19 patients with diabetes have one or more accompanying comorbidities, higher levels of circulating inflammatory markers, worse lung involvement by chest imaging, and thus are associated with more severe disease, more complications, and higher mortality rate.10-13 Poor glycemic control is associated with severe COVID-19 infection and increased mortality.11,25
Aside from its effects on glucose metabolism, another potential role of metformin is immunomodulation. It inhibits the mTOR pathway, which plays a role in viral protein production, viral replication and release, and is critical for apoptosis and senescence.26 It can also cause modulation of the ACE2 receptor, which serves as the viral entry point via the AMP-activated protein kinase.27,28 This medication provides anti-inflammatory effects, reducing the cytokine storm by decreasing TNFα and IL-6 levels and increasing IL-10.18,19 A reduction in the neutrophil extracellular traps and neutrophil to lymphocyte count have also been observed.29
Patients with stage 3 to 5 chronic kidney disease or dialysis therapy were less likely to be on metformin therapy before and during admission. This was an expected finding since metformin is contraindicated in patients with end-stage renal disease, and those with an eGFR of less than 30 mL/min/1.73 m2. Initiation of metformin therapy is also contraindicated in patients with eGFR of less than 45 mL/min/1.73 m2.
Metformin has previously been reported to decrease the incidence of cardiovascular events in the landmark UK Protective Diabetes Study (UKPDS) which showed lower all-cause mortality and incidence of myocardial infarction with its use versus conventional treatment.30 The SPREAD-DIMCAD study also showed a significantly lower cardiovascular endpoint for persons with type 2 diabetes with coronary artery disease in its metformin group compared to its glipizide group.31 This may explain the higher prevalence of acute coronary syndrome in patients admitted without prior metformin use in our study population.
After computing for crude odds ratio, our study showed that metformin use was associated with a lower risk for mortality compared to the non-metformin group. More patients in the non-metformin group in our study population had chronic kidney disease and acute coronary syndrome, which can also be associated risk factors for mortality.
Tocilizumab, systemic steroids, hemoperfusion, convalescent plasma therapy, and in-hospital insulin use were also associated with mortality. The association noted between mortality and the use of these medications, especially tocilizumab, may be because of more severe diseases requiring these treatments.
After adjusting for these significant variables using multivariate logistics regression, metformin use, whether in the hospital or mixed home/in-hospital use, was still associated with a lower risk for mortality. This correlates with studies by Bramante32 and Luo33 who showed mortality benefits in patients with preadmission and in-hospital metformin use, respectively. Three other studies similarly showed the beneficial effects on overall mortality by this medication.24,34,35 The CORONADO study also noted a lower risk for death in patients on metformin therapy.36,37
Our study finding, however, differed only from the study by Cheng et al., which concluded there was no difference in outcomes of patients with and without metformin use.20 Our study showed a metformin dose from 500 mg to 2000 mg per day was associated with a lower risk for mortality. The greatest benefit was seen with a dosage between 1000 mg to <2000 mg daily. Patients taking higher metformin doses had fewer deaths, but estimates of benefit across dose categories cannot be made due to the small study population. No reports have currently surfaced recommending an optimal protective dose of metformin, and prospective studies are suggested or ongoing.
Luo et al. found no significant difference in the length of hospital stay between both groups.33 The CORONADO study noted lower death rates at day seven and higher chances of discharge among patients on metformin therapy.36-37 The study done by Lalau24 showed lower mortality rates for metformin users on day seven and day 28. Our study showed that metformin users were associated with longer survival than non-users.
Results of this retrospective observational study showed beneficial effects of metformin on mortality in patients with type 2 diabetes mellitus hospitalized for COVID-19. Randomized controlled trials are still ongoing, and their results may or may not be similar to our study findings.
CONCLUSION
Metformin was associated with a lower risk for mortality in patients with type 2 diabetes mellitus hospitalized for COVID-19 disease, especially in patients with in-hospital and mixed home/in-hospital metformin use. Metformin, regardless of dosage, was associated with a lower risk for mortality compared to its non-use. The greatest benefit was seen in those on a daily dose of ≥1000 mg to <2000 mg. Despite the results from this study, the decision whether to initiate metformin in patients hospitalized for COVID-19 infection is upon the physician’s discretion.
Limitation and Recommendation
Most patients in our study population had moderate disease. Therefore, our study results may not apply to patients with severe or critical COVID-19 infection.
We only collected data from admitted Filipinos with type 2 diabetes, and results may differ in the outpatient setting and among different ethnicities or races. The duration of metformin intake is not specified in this study. Compliance with preadmission metformin was also lacking and could not be assured. Several cells had frequencies less than 5, and significance may not be valid. Mortality prediction scoring, such as APACHE II and qSOFA, was not applied to help determine baseline risk for death between metformin users and non-users. Other confounding variables such as comorbid conditions and medications not included in this study analysis may also affect study results.
Findings were obtained from a retrospective observational study, and due to limitations, any results derived should be considered only hypothesis-generating. We recommend prospective studies to ensure complete data, fewer potential biases, and confounders.
A randomized prospective study can best determine the definitive effect of metformin on mortality in COVID-19 disease. Further sub-analysis on the beneficial effects of metformin on mortality outcome and survival time between different disease severities may also be investigated with a bigger study population.
Acknowledgments
The authors thank their mentors in the Section of Endocrinology, Diabetes and Metabolism, Department of Internal Medicine of Chong Hua Hospital, especially their Section Chief, research coordinator, as well as their seniors. They also thank the Chong Hua Hospital Administration and COVID-19 Research Team for the ONE-COVID Initiative.
APPENDIX
Supplementary Table 1.
Association between metformin dosage and mortality
| Metformin users with dosage from 500 mg to 2000 mg daily versus no metformin use | |||||
|---|---|---|---|---|---|
|
| |||||
| Metformin dosage (mg/d) | Mortality | p-value | |||
| 0 | 57 | 0.002* | |||
| 500-<1000 | 19 | ||||
| >1000-<2000 | 9 | ||||
| >2000 | 4 | ||||
|
| |||||
| Dosage from 500 mg to 2000 mg daily among metformin users | |||||
|
| |||||
| Metformin dosage (mg/d) | Alive | Mortality | p-value | ||
|
| |||||
| 500-<1000 | 66 | 19 (22.4%) | 0.166 | ||
| >1000-<2000 | 70 | 9 (11.4%) | |||
| >2000 | 15 | 4 (21.1%) | |||
|
| |||||
| Dosage from 500 mg to 2000 mg daily among three subcategories of metformin users | |||||
|
| |||||
| Metformin dosage (mg/d) | Home metformin use n=109 | In-hospital metformin use n=40 | Mixed home/in-hospital use n=37 | Mortality | p-value |
|
| |||||
| 500-<1000 n=85 | 56 | 15 | 14 | 19 (22.4%) | 0.166 |
|
| |||||
| >1000-<2000 n=79 | 36 | 23 | 20 | 9 (11.4%) | |
|
| |||||
| >2000 n=19 | 14 | 2 | 3 | 4 (21.1%) | |
Statement of Authorship
All authors certified fulfillment of ICMJE authorship criteria.
Author Disclosure
The authors declared no conflict of interest.
Funding Source
None.
References
- 1.World Health Organization . COVID-19 situation reports. WHO Director-General’s opening remarks at the media briefing on COVID-19. March 2020. https://www.who.int/director-general/speeches/detail/who-director-general-s-opening-remarks-at-the-media-briefing-on-covid-19---11-march-2020.
- 2.Worldometer COVID-19 data . Coronavirus cases. Worldometer. September 28, 2021. https://www.worldometers.info/coronavirus/. [Google Scholar]
- 3.Worldometer COVID-19 data . Reported cases and deaths by country or territory. Worldometer. September 28, 2021. https://www.worldometers.info/coronavirus/#countries. [Google Scholar]
- 4.Zhou P, Yang XL, Wang XG, et al. A pneumonia outbreak associated with a new coronavirus of probable bat origin. Nature. 2020; 579(7798):270–3. PMID: . PMCID: . 10.1038/s41586-020-2012-7. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 5.Zou X, Chen K, Zou JW, Han PY, Hao J, Han Z. The single-cell RNAseq data analysis on the receptor ACE2 expression reveals the potential risk of different human organs vulnerable to Wuhan 2019-nCoV infection. Front Med. 2020; 14(2):185–92. PMID: . PMCID: . 10.1007/s11684-020-0754-0. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 6.Hoffman M, Weber-Kleine H, Schroeder S, et al. SARS-CoV-2 cell entry depends on ACE2 and TMPRSS2 and is blocked by a clinically proven protease inhibitor. Cell. 2020;181(2):271-80.e8. PMID: . PMCID: . 10.1016/j.cell.2020.02.052. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 7.Hojyo S, Uchida M, Tanaka K, et al. How COVID-19 induces cytokine storm with high mortality. Inflamm Regen. 2020;40:37. PMID: . PMCID: . 10.1186/s41232-020-00146-3. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 8.Yang J, Zheng Y, Gou X, et al. Prevalence of comorbidities and its effects in patients infected with SARS-CoV-2: A systematic review and meta-analysis. Int J Infect Dis. 2020;94:91-5. PMID: . PMCID: . 10.1016/j.ijid.2020.03.017. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 9.Bajgain KT, Badal S, Bajgain BB, Santana MJ. Prevalence of comorbidities among individuals with COVID-19: A rapid review of current literature. Am J Infect Control. 2021;49(2):238-46. PMID: . PMCID: . 10.1016/j.ajic.2020.06.213. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 10.Shi Q, Zhang X, Jiang F, et al. Clinical characteristics and risk factors for mortality of COVID-19 patients with diabetes in Wuhan, China: A two-center, retrospective study. Diabetes Care. 2020;43(7):1382-91. PMID: . 10.2337/dc20-0598. [DOI] [PubMed] [Google Scholar]
- 11.Targher G, Mantovani A, Wang XB, et al. Patients with diabetes are at higher risk for severe illness from COVID-19. Diabetes Metab. 2020;46(4):335-37. PMID: . PMCID: . 10.1016/j.diabet.2020.05.001. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 12.Kumar A, Arora A, Sharma P, et al. Is diabetes mellitus associated with mortality and severity of COVID-19? A meta-analysis. Diabetes Metab Syndr. 2020;14(4):535–45. PMID: . PMCID: . 10.1016/j.dsx.2020.04.044. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 13.Shang J, Wang Q, Zhang H, et al. The relationship between diabetes mellitus and COVID-19 prognosis: A retrospective cohort study in Wuhan, China. Am J Med. 2021;134(1):e6-14. PMID: . PMCID: . 10.1016/j.amjmed.2020.05.033. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 14.Assiri A, Al-Tawfiq JA, Al-Rabeeah AA, et al. Epidemiological, demographic, and clinical characteristics of 47 cases of Middle East respiratory syndrome coronavirus disease from Saudi Arabia: A descriptive study. Lancet Infect Dis. 2013;13(9):752-61. PMID: . PMCID: . 10.1016/S1473-3099(13)70204-4. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 15.Alqahtani FY, Aleanizy FS, Ali El Hadi Mohamed R, et al. Prevalence of comorbidities in cases of Middle East respiratory syndrome coronavirus: A retrospective study. Epidemiol Infect. 2018;147:1-5. PMID: . PMCID: . 10.1017/S0950268818002923. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 16.Chan J, Ng C, Chan Y, et al. Short-term outcome and risk factors for adverse clinical outcomes in adults with severe respiratory syndrome (SARS). Thorax. 2003;58(8):686-9. PMID: . PMCID: . 10.1136/thorax.58.8.686. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 17.Raz I. Guideline approach to therapy in patients with newly diagnosed type 2 diabetes. Diabetes Care. 2013;36(Suppl 2): S139-44. PMID: . PMCID: . 10.2337/dcS13-2035. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 18.Hyun B, Shin S, Lee A, et al. Metformin down-regulates TNF-alpha secretion via suppression of scavenger receptors in macrophages. Immune Netw. 2013;13(4):123–32. PMID: . PMCID: . 10.4110/in.2013.13.4.123. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 19.Cameron AR, Morrison VL, Levin D, et al. Anti-inflammatory effects of metformin irrespective of diabetes status. Circ Res. 2016;119(5):652–65. PMID: . PMCID: . 10.1161/CIRCRESAHA.116.308445. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 20.Cheng X, Liu YM, Li H, et al. Metformin is associated with higher incidence of acidosis, but not mortality, in individuals with COVID-19 and preexisting type 2 diabetes. Cell Metab. 2020;32(4):537-47. PMID: . PMCID: . 10.1016/j.cmet.2020.08.013. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 21.Silvestre J, Carvalho S, Mendes V, et al. Metformin-induced lactic acidosis: A case series. J Med Case Rep. 2007;1:126. PMID: . PMCID: . 10.1186/1752-1947-1-126. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 22.Gupta R, Ghosh A, Singh AK, Misra A. Clinical considerations for patients with diabetes in times of COVID-19 epidemic. Diabetes Metab Syndr. 2020;14(3):211-2. PMID: . PMCID: . 10.1016/j.dsx.2020.03.002. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 23.Bornstein SR, Rubino F, Khunti K, et al. Practical recommendations for the management of diabetes in patients with COVID-19. Lancet Diabetes Endocrinol. 2020;8(6):546-50. PMID: . PMCID: . 10.1016/S2213-8587(20)30152-2. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 24.Lalau JD, Al-Salameh A, Hadjadj S, et al. Metformin use is associated with a reduced risk of mortality in patients with diabetes hospitalized for COVID-19. Diabetes Metab. 2020;47(5):101216. PMID: . PMCID: . 10.1016/j.diabet.2020.101216 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 25.Bode B, Garrett V, Messler J, et al. Glycemic characteristics and clinical outcomes of COVID-19 patients hospitalized in the United States. J Diabetes Sci Technol. 2020;14(4):813-21. PMID: . PMCID: . 10.1177/1932296820924469. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 26.Maiese K. The mechanistic target of Rapamycin (mTOR): Novel considerations as an antiviral treatment. Curr Neurovasc Res. 2020;17(3):332-7. PMID: . PMCID: . 10.2174/1567202617666200425205122. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 27.Liu J, Li X, Lu Q, et al. AMPK: A balancer of the renin-angiotensin system. Biosci Rep. 2019;39(9): BSR20181994. PMID: . PMCID: . 10.1042/BSR20181994. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 28.Plattner F, Bibb JA. Elsevier. Serine and Threonine Phosphorylation. In Basic Neurochemistry; 2012. 10.1016/B978-0-12-374947-5.00025-0 [DOI] [Google Scholar]
- 29.Dalan R. Metformin, neutrophils, and COVID-19 infection. Diabetes Res Clin Pract. 2020;164:108230. PMID: . PMCID: . 10.1016/j.diabres.2020.108230. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 30.Effect of intensive blood-glucose control with metformin on complications in overweight patients with type 2 diabetes (UKPDS 34). UK Prospective Diabetes Study (UKPDS) Group. Lancet. 1998;352(9131):854–65. PMID: . [PubMed] [Google Scholar]
- 31.Hong J, Zhang Y, Lai S, et al. Effects of metformin versus glipizide on cardiovascular outcomes in patients with type 2 diabetes and coronary artery disease. Diabetes Care. 2013;36(5):1304–11. PMID: . PMCID: . 10.2337/dc12-0719. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 32.Bramante CT, Ingraham NE, Murray TA, et al. Metformin and risk of mortality in patients hospitalized with COVID-19: A retrospective cohort analysis. Lancet Healthy Longev. 2021;2(1): e34-41. PMID: . PMCID: . 10.1016/S2666-7568(20)30033-7. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 33.Luo P, Qiu L, Liu Y, et al. Metformin treatment was associated with decreased mortality in COVID-19 patients with diabetes in a retrospective analysis. Am J Trop Med Hyg. 2020;103(1):69-72. PMID: . PMCID: . 10.4269/ajtmh.20-0375. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 34.Crouse A, Grimes T, Li P, Might M, Ovalle F, Shalev A. Metformin use is associated with reduced mortality in a diverse population with COVID-19 and diabetes. medRxiv. 2020;2020.07.29.20164020. PMID: . PMCID: . 10.1101/2020.07.29.20164020. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 35.Lally MA, Tsoukas P, Halladay CW, O’Neill E, Gravenstein S, Rudolph JL. Metformin is associated with decreased 30-day mortality among nursing home residents infected with SARS-CoV2. J AM Med Dir Assoc. 2021;22(1):193-8. PMID: . PMCID: . 10.1016/j.jamda.2020.10.031. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 36.Cariou B, Hadjadj S, Wargny M, et al. Phenotypic characteristics and prognosis of in-patients with COVID-19 and diabetes: The CORONADO study. Diabetologia. 2020;63(8):1500-15. PMID: . PMCID: . 10.1007/s00125-020-05180-x. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 37.Wargny M, Potier L, Gourdy P, et al. Predictors of hospital discharge and mortality in patients with diabetes and COVID-19: Updated results from the nationwide CORONADO study. Diabetologia. 2021;64(4):778-94. PMID: . PMCID: . 10.1007/s00125-020-05351-w. [DOI] [PMC free article] [PubMed] [Google Scholar]