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Metformin for COVID-19: real-time meta analysis of 25 studies
Covid Analysis, January 16, 2022, DRAFT
https://c19early.com/mfmeta.html
0 0.25 0.5 0.75 1 1.25 1.5 1.75 2+ All studies 17% 25 104,505 Improvement, Studies, Patients Relative Risk With exclusions 17% 23 88,912 Mortality 21% 20 60,020 Ventilation 6% 2 1,221 ICU admission 10% 3 29,616 Hospitalization 6% 5 24,200 Progression 46% 3 3,374 Cases 11% 3 27,119 Viral clearance 1% 1 418 RCTs 6% 1 418 Peer-reviewed 16% 24 82,381 Prophylaxis 17% 24 104,087 Early 6% 1 418 Metformin for COVID-19 c19early.com/mf Jan 16, 2022 Favors metformin Favors control
Statistically significant improvements are seen for mortality and hospitalization. 16 studies from 16 independent teams in 6 different countries show statistically significant improvements in isolation (13 for the most serious outcome).
Meta analysis using the most serious outcome reported shows 17% [11‑22%] improvement. Results are worse for Randomized Controlled Trials, similar after exclusions, and similar for peer-reviewed studies.
Results are robust — in exclusion sensitivity analysis 17 of 25 studies must be excluded to avoid finding statistically significant efficacy in pooled analysis.
0 0.25 0.5 0.75 1 1.25 1.5 1.75 2+ All studies 17% 25 104,505 Improvement, Studies, Patients Relative Risk With exclusions 17% 23 88,912 Mortality 21% 20 60,020 Ventilation 6% 2 1,221 ICU admission 10% 3 29,616 Hospitalization 6% 5 24,200 Progression 46% 3 3,374 Cases 11% 3 27,119 Viral clearance 1% 1 418 RCTs 6% 1 418 Peer-reviewed 16% 24 82,381 Prophylaxis 17% 24 104,087 Early 6% 1 418 Metformin for COVID-19 c19early.com/mf Jan 16, 2022 Favors metformin Favors control
Most studies analyze existing use with diabetic patients. Many results are subject to confounding by indication — metformin is typically used early in the progression of type 2 diabetes.
While many treatments have some level of efficacy, they do not replace vaccines and other measures to avoid infection. None of the metformin studies show zero events in the treatment arm.
Multiple treatments are typically used in combination, and other treatments are significantly more effective.
Elimination of COVID-19 is a race against viral evolution. No treatment, vaccine, or intervention is 100% available and effective for all variants. All practical, effective, and safe means should be used, including treatments, as supported by Pfizer [Pfizer]. Denying the efficacy of treatments increases mortality, morbidity, collateral damage, and endemic risk.
All data to reproduce this paper and sources are in the appendix.
Studies Early treatment Prophylaxis PatientsAuthors
All studies 256% [-61‑45%]17% [11‑23%] 104,505 348
With exclusions 236% [-61‑45%]18% [12‑23%] 88,912 300
Peer-reviewed 246% [-61‑45%]16% [10‑22%] 82,381 337
Randomized Controlled TrialsRCTs 16% [-61‑45%] 418 23
Percentage improvement with metformin treatment
A
0 0.25 0.5 0.75 1 1.25 1.5 1.75 2+ Reis (DB RCT) 6% 0.94 [0.55-1.61] hosp. 24/215 24/203 Improvement, RR [CI] Treatment Control Tau​2 = 0.00, I​2 = 0.0%, p = 0.84 Early treatment 6% 0.94 [0.55-1.61] 24/215 24/203 6% improvement Luo 75% 0.25 [0.07-0.84] death 3/104 22/179 Improvement, RR [CI] Treatment Control Wang 58% 0.42 [0.01-1.98] death 1/9 13/49 Chen 33% 0.67 [0.20-1.78] death 4/43 15/77 Li 78% 0.22 [0.09-0.54] death 2/37 21/94 Pérez-Belmo.. (PSM) -10% 1.10 [0.84-1.40] death 79/249 79/249 Bramante 7% 0.93 [0.81-1.06] death 394/2,333 791/3,923 Lalau (PSM) 22% 0.78 [0.55-1.10] death 671 (n) 419 (n) Huh -1% 1.01 [0.75-1.37] progression 104/272 774/2,533 Crouse 61% 0.39 [0.16-0.87] death 8/76 34/144 Lally 52% 0.48 [0.28-0.84] death 16/127 144/648 Oh -26% 1.26 [0.81-1.95] death 5,946 (n) 5,946 (n) Holt -27% 1.27 [0.72-2.22] cases 12/429 434/14,798 Khunti 23% 0.77 [0.73-0.81] death population-based cohort Jiang (PSM) 46% 0.54 [0.13-2.26] death 3/74 10/74 Ghany 66% 0.34 [0.19-0.59] death 392 (n) 747 (n) Alamgir 27% 0.73 [0.63-0.84] death 11,062 (n) 11,062 (n) Ravindra 30% 0.70 [0.28-1.56] death 5/53 57/313 Boye 10% 0.90 [0.86-0.94] hosp. 2,067/4,250 3,196/5,281 Cheng (PSM) -65% 1.65 [0.71-3.86] death 678 (n) 535 (n) Wang 12% 0.88 [0.81-0.97] ICU 6,504 (n) 10,000 (n) Wander 15% 0.85 [0.80-0.90] death Saygili (PSM) 42% 0.58 [0.37-0.92] death 120 (n) 120 (n) Ong 47% 0.53 [0.31-0.87] death 33/186 57/169 Ojeda-Ferná.. (PSM) 16% 0.84 [0.79-0.89] death 1,476/6,556 1,787/6,556 Tau​2 = 0.01, I​2 = 76.9%, p < 0.0001 Prophylaxis 17% 0.83 [0.77-0.89] 4,207/40,171 7,434/63,916 17% improvement All studies 17% 0.83 [0.78-0.89] 4,231/40,386 7,458/64,119 17% improvement 25 metformin COVID-19 studies c19early.com/mf Jan 16, 2022 Tau​2 = 0.01, I​2 = 75.9%, p < 0.0001 Effect extraction pre-specified, see appendix Favors metformin Favors control
Figure 1. A. Random effects meta-analysis. This plot shows pooled effects, discussion can be found in the heterogeneity section, and results for specific outcomes can be found in the individual outcome analyses. Effect extraction is pre-specified, using the most serious outcome reported. For details of effect extraction see the appendix. B. Scatter plot showing the distribution of effects reported in studies. C. History of all reported effects (chronological within treatment stages).
Introduction
We analyze all significant studies concerning the use of metformin for COVID-19. Search methods, inclusion criteria, effect extraction criteria (more serious outcomes have priority), all individual study data, PRISMA answers, and statistical methods are detailed in Appendix 1. We present random effects meta-analysis results for all studies, for studies within each treatment stage, for individual outcomes, for peer-reviewed studies, for Randomized Controlled Trials (RCTs), and after exclusions.
Figure 2 shows stages of possible treatment for COVID-19. Prophylaxis refers to regularly taking medication before becoming sick, in order to prevent or minimize infection. Early Treatment refers to treatment immediately or soon after symptoms appear, while Late Treatment refers to more delayed treatment.
Figure 2. Treatment stages.
Results
Figure 3, 4, 5, 6, 7, 8, 9, 10, and 11 show forest plots for a random effects meta-analysis of all studies with pooled effects, mortality results, ventilation, ICU admission, hospitalization, progression, cases, viral clearance, and peer reviewed studies. Table 1 summarizes the results by treatment stage.
Treatment timeNumber of studies reporting positive effects Total number of studiesPercentage of studies reporting positive effects Random effects meta-analysis results
Early treatment 1 1 100% 6% improvement
RR 0.94 [0.55‑1.61]
p = 0.84
Prophylaxis 19 24 79.2% 17% improvement
RR 0.83 [0.77‑0.89]
p < 0.0001
All studies 20 25 80.0% 17% improvement
RR 0.83 [0.78‑0.89]
p < 0.0001
Table 1. Results by treatment stage.
0 0.25 0.5 0.75 1 1.25 1.5 1.75 2+ Reis (DB RCT) 6% 0.94 [0.55-1.61] hosp. 24/215 24/203 Improvement, RR [CI] Treatment Control Tau​2 = 0.00, I​2 = 0.0%, p = 0.84 Early treatment 6% 0.94 [0.55-1.61] 24/215 24/203 6% improvement Luo 75% 0.25 [0.07-0.84] death 3/104 22/179 Improvement, RR [CI] Treatment Control Wang 58% 0.42 [0.01-1.98] death 1/9 13/49 Chen 33% 0.67 [0.20-1.78] death 4/43 15/77 Li 78% 0.22 [0.09-0.54] death 2/37 21/94 Pérez-Belmo.. (PSM) -10% 1.10 [0.84-1.40] death 79/249 79/249 Bramante 7% 0.93 [0.81-1.06] death 394/2,333 791/3,923 Lalau (PSM) 22% 0.78 [0.55-1.10] death 671 (n) 419 (n) Huh -1% 1.01 [0.75-1.37] progression 104/272 774/2,533 Crouse 61% 0.39 [0.16-0.87] death 8/76 34/144 Lally 52% 0.48 [0.28-0.84] death 16/127 144/648 Oh -26% 1.26 [0.81-1.95] death 5,946 (n) 5,946 (n) Holt -27% 1.27 [0.72-2.22] cases 12/429 434/14,798 Khunti 23% 0.77 [0.73-0.81] death population-based cohort Jiang (PSM) 46% 0.54 [0.13-2.26] death 3/74 10/74 Ghany 66% 0.34 [0.19-0.59] death 392 (n) 747 (n) Alamgir 27% 0.73 [0.63-0.84] death 11,062 (n) 11,062 (n) Ravindra 30% 0.70 [0.28-1.56] death 5/53 57/313 Boye 10% 0.90 [0.86-0.94] hosp. 2,067/4,250 3,196/5,281 Cheng (PSM) -65% 1.65 [0.71-3.86] death 678 (n) 535 (n) Wang 12% 0.88 [0.81-0.97] ICU 6,504 (n) 10,000 (n) Wander 15% 0.85 [0.80-0.90] death Saygili (PSM) 42% 0.58 [0.37-0.92] death 120 (n) 120 (n) Ong 47% 0.53 [0.31-0.87] death 33/186 57/169 Ojeda-Ferná.. (PSM) 16% 0.84 [0.79-0.89] death 1,476/6,556 1,787/6,556 Tau​2 = 0.01, I​2 = 76.9%, p < 0.0001 Prophylaxis 17% 0.83 [0.77-0.89] 4,207/40,171 7,434/63,916 17% improvement All studies 17% 0.83 [0.78-0.89] 4,231/40,386 7,458/64,119 17% improvement 25 metformin COVID-19 studies c19early.com/mf Jan 16, 2022 Tau​2 = 0.01, I​2 = 75.9%, p < 0.0001 Effect extraction pre-specified, see appendix Favors metformin Favors control
Figure 3. Random effects meta-analysis for all studies with pooled effects. This plot shows pooled effects, discussion can be found in the heterogeneity section, and results for specific outcomes can be found in the individual outcome analyses. Effect extraction is pre-specified, using the most serious outcome reported. For details of effect extraction see the appendix.
0 0.25 0.5 0.75 1 1.25 1.5 1.75 2+ Luo 75% 0.25 [0.07-0.84] 3/104 22/179 Improvement, RR [CI] Treatment Control Wang 58% 0.42 [0.01-1.98] 1/9 13/49 Chen 33% 0.67 [0.20-1.78] 4/43 15/77 Li 78% 0.22 [0.09-0.54] 2/37 21/94 Pérez-Belmo.. (PSM) -10% 1.10 [0.84-1.40] 79/249 79/249 Bramante 7% 0.93 [0.81-1.06] 394/2,333 791/3,923 Lalau (PSM) 22% 0.78 [0.55-1.10] 671 (n) 419 (n) Crouse 61% 0.39 [0.16-0.87] 8/76 34/144 Lally 52% 0.48 [0.28-0.84] 16/127 144/648 Oh -26% 1.26 [0.81-1.95] 5,946 (n) 5,946 (n) Khunti 23% 0.77 [0.73-0.81] population-based cohort Jiang (PSM) 46% 0.54 [0.13-2.26] 3/74 10/74 Ghany 66% 0.34 [0.19-0.59] 392 (n) 747 (n) Alamgir 27% 0.73 [0.63-0.84] 11,062 (n) 11,062 (n) Ravindra 30% 0.70 [0.28-1.56] 5/53 57/313 Cheng (PSM) -65% 1.65 [0.71-3.86] 678 (n) 535 (n) Wander 15% 0.85 [0.80-0.90] Saygili (PSM) 42% 0.58 [0.37-0.92] 120 (n) 120 (n) Ong 47% 0.53 [0.31-0.87] 33/186 57/169 Ojeda-Ferná.. (PSM) 16% 0.84 [0.79-0.89] 1,476/6,556 1,787/6,556 Tau​2 = 0.01, I​2 = 70.6%, p < 0.0001 Prophylaxis 21% 0.79 [0.72-0.85] 2,024/28,716 3,030/31,304 21% improvement All studies 21% 0.79 [0.72-0.85] 2,024/28,716 3,030/31,304 21% improvement 20 metformin COVID-19 mortality results c19early.com/mf Jan 16, 2022 Tau​2 = 0.01, I​2 = 70.6%, p < 0.0001 Favors metformin Favors control
Figure 4. Random effects meta-analysis for mortality results.
0 0.25 0.5 0.75 1 1.25 1.5 1.75 2+ Li -27% 1.27 [0.12-13.6] 1/37 2/94 Improvement, RR [CI] Treatment Control Lalau (PSM) 7% 0.93 [0.64-1.35] 671 (n) 419 (n) Tau​2 = 0.00, I​2 = 0.0%, p = 0.75 Prophylaxis 6% 0.94 [0.65-1.35] 1/708 2/513 6% improvement All studies 6% 0.94 [0.65-1.35] 1/708 2/513 6% improvement 2 metformin COVID-19 mechanical ventilation results c19early.com/mf Jan 16, 2022 Tau​2 = 0.00, I​2 = 0.0%, p = 0.75 Favors metformin Favors control
Figure 5. Random effects meta-analysis for ventilation.
0 0.25 0.5 0.75 1 1.25 1.5 1.75 2+ Wang 12% 0.88 [0.81-0.97] 6,504 (n) 10,000 (n) Improvement, RR [CI] Treatment Control Wander 2% 0.98 [0.92-1.06] Ojeda-Ferná.. (PSM) 22% 0.78 [0.64-0.95] 166/6,556 212/6,556 Tau​2 = 0.01, I​2 = 70.4%, p = 0.065 Prophylaxis 10% 0.90 [0.80-1.01] 166/13,060 212/16,556 10% improvement All studies 10% 0.90 [0.80-1.01] 166/13,060 212/16,556 10% improvement 3 metformin COVID-19 ICU results c19early.com/mf Jan 16, 2022 Tau​2 = 0.01, I​2 = 70.4%, p = 0.065 Favors metformin Favors control
Figure 6. Random effects meta-analysis for ICU admission.
0 0.25 0.5 0.75 1 1.25 1.5 1.75 2+ Reis (DB RCT) 6% 0.94 [0.55-1.61] hosp. 24/215 24/203 Improvement, RR [CI] Treatment Control Tau​2 = 0.00, I​2 = 0.0%, p = 0.84 Early treatment 6% 0.94 [0.55-1.61] 24/215 24/203 6% improvement Ghany 29% 0.71 [0.52-0.86] hosp. 392 (n) 747 (n) Improvement, RR [CI] Treatment Control Boye 10% 0.90 [0.86-0.94] hosp. 2,067/4,250 3,196/5,281 Wander 3% 0.97 [0.94-1.01] hosp. Ojeda-Ferná.. (PSM) 3% 0.97 [0.94-1.00] hosp. 3,551/6,556 3,670/6,556 Tau​2 = 0.00, I​2 = 81.5%, p = 0.017 Prophylaxis 6% 0.94 [0.89-0.99] 5,618/11,198 6,866/12,584 6% improvement All studies 6% 0.94 [0.89-0.99] 5,642/11,413 6,890/12,787 6% improvement 5 metformin COVID-19 hospitalization results c19early.com/mf Jan 16, 2022 Tau​2 = 0.00, I​2 = 75.4%, p = 0.014 Favors metformin Favors control
Figure 7. Random effects meta-analysis for hospitalization.
0 0.25 0.5 0.75 1 1.25 1.5 1.75 2+ Reis (DB RCT) 31% 0.69 [0.28-1.68] 8/216 11/205 Improvement, RR [CI] Treatment Control Tau​2 = 0.00, I​2 = 0.0%, p = 0.42 Early treatment 31% 0.69 [0.28-1.68] 8/216 11/205 31% improvement Huh -1% 1.01 [0.75-1.37] 104/272 774/2,533 Improvement, RR [CI] Treatment Control Jiang (PSM) 80% 0.20 [0.05-0.77] 8/74 17/74 Tau​2 = 1.24, I​2 = 93.8%, p = 0.36 Prophylaxis 53% 0.47 [0.10-2.30] 112/346 791/2,607 53% improvement All studies 46% 0.54 [0.20-1.49] 120/562 802/2,812 46% improvement 3 metformin COVID-19 progression results c19early.com/mf Jan 16, 2022 Tau​2 = 0.69, I​2 = 88.0%, p = 0.23 Favors metformin Favors control
Figure 8. Random effects meta-analysis for progression.
0 0.25 0.5 0.75 1 1.25 1.5 1.75 2+ Huh 4% 0.96 [0.82-1.12] population-based cohort Improvement, RR [CI] Treatment Control Oh (PSM) 28% 0.72 [0.63-0.81] 390/5,946 541/5,946 Holt -27% 1.27 [0.72-2.22] 12/429 434/14,798 Tau​2 = 0.04, I​2 = 85.9%, p = 0.37 Prophylaxis 11% 0.89 [0.68-1.15] 402/6,375 975/20,744 11% improvement All studies 11% 0.89 [0.68-1.15] 402/6,375 975/20,744 11% improvement 3 metformin COVID-19 case results c19early.com/mf Jan 16, 2022 Tau​2 = 0.04, I​2 = 85.9%, p = 0.37 Favors metformin Favors control
Figure 9. Random effects meta-analysis for cases.
0 0.25 0.5 0.75 1 1.25 1.5 1.75 2+ Reis (DB RCT) 1% 0.99 [0.88-1.11] viral+ 215 (n) 203 (n) Improvement, RR [CI] Treatment Control Tau​2 = 0.00, I​2 = 0.0%, p = 0.85 Early treatment 1% 0.99 [0.88-1.11] 0/215 0/203 1% improvement All studies 1% 0.99 [0.90-1.09] 0/215 0/203 1% improvement 1 metformin COVID-19 viral clearance result c19early.com/mf Jan 16, 2022 Tau​2 = 0.00, I​2 = 0.0%, p = 0.85 Favors metformin Favors control
Figure 10. Random effects meta-analysis for viral clearance.
0 0.25 0.5 0.75 1 1.25 1.5 1.75 2+ Reis (DB RCT) 6% 0.94 [0.55-1.61] hosp. 24/215 24/203 Improvement, RR [CI] Treatment Control Tau​2 = 0.00, I​2 = 0.0%, p = 0.84 Early treatment 6% 0.94 [0.55-1.61] 24/215 24/203 6% improvement Luo 75% 0.25 [0.07-0.84] death 3/104 22/179 Improvement, RR [CI] Treatment Control Wang 58% 0.42 [0.01-1.98] death 1/9 13/49 Chen 33% 0.67 [0.20-1.78] death 4/43 15/77 Li 78% 0.22 [0.09-0.54] death 2/37 21/94 Pérez-Belmo.. (PSM) -10% 1.10 [0.84-1.40] death 79/249 79/249 Bramante 7% 0.93 [0.81-1.06] death 394/2,333 791/3,923 Lalau (PSM) 22% 0.78 [0.55-1.10] death 671 (n) 419 (n) Huh -1% 1.01 [0.75-1.37] progression 104/272 774/2,533 Crouse 61% 0.39 [0.16-0.87] death 8/76 34/144 Lally 52% 0.48 [0.28-0.84] death 16/127 144/648 Oh -26% 1.26 [0.81-1.95] death 5,946 (n) 5,946 (n) Holt -27% 1.27 [0.72-2.22] cases 12/429 434/14,798 Khunti 23% 0.77 [0.73-0.81] death population-based cohort Jiang (PSM) 46% 0.54 [0.13-2.26] death 3/74 10/74 Ghany 66% 0.34 [0.19-0.59] death 392 (n) 747 (n) Ravindra 30% 0.70 [0.28-1.56] death 5/53 57/313 Boye 10% 0.90 [0.86-0.94] hosp. 2,067/4,250 3,196/5,281 Cheng (PSM) -65% 1.65 [0.71-3.86] death 678 (n) 535 (n) Wang 12% 0.88 [0.81-0.97] ICU 6,504 (n) 10,000 (n) Wander 15% 0.85 [0.80-0.90] death Saygili (PSM) 42% 0.58 [0.37-0.92] death 120 (n) 120 (n) Ong 47% 0.53 [0.31-0.87] death 33/186 57/169 Ojeda-Ferná.. (PSM) 16% 0.84 [0.79-0.89] death 1,476/6,556 1,787/6,556 Tau​2 = 0.01, I​2 = 77.1%, p < 0.0001 Prophylaxis 16% 0.84 [0.78-0.90] 4,207/29,109 7,434/52,854 16% improvement All studies 16% 0.84 [0.78-0.90] 4,231/29,324 7,458/53,057 16% improvement 24 metformin COVID-19 peer reviewed trials c19early.com/mf Jan 16, 2022 Tau​2 = 0.01, I​2 = 76.1%, p < 0.0001 Effect extraction pre-specified, see appendix Favors metformin Favors control
Figure 11. Random effects meta-analysis for peer reviewed studies. Effect extraction is pre-specified, using the most serious outcome reported, see the appendix for details.
Exclusions
To avoid bias in the selection of studies, we analyze all non-retracted studies. Here we show the results after excluding studies with major issues likely to alter results, non-standard studies, and studies where very minimal detail is currently available. Our bias evaluation is based on analysis of each study and identifying when there is a significant chance that limitations will substantially change the outcome of the study. We believe this can be more valuable than checklist-based approaches such as Cochrane GRADE, which may underemphasize serious issues not captured in the checklists, overemphasize issues unlikely to alter outcomes in specific cases (for example, lack of blinding for an objective mortality outcome, or certain specifics of randomization with a very large effect size), or be easily influenced by potential bias. However, they can also be very high quality.
The studies excluded are as below. Figure 12 shows a forest plot for random effects meta-analysis of all studies after exclusions.
[Holt], significant unadjusted confounding possible.
[Ravindra], minimal details provided.
0 0.25 0.5 0.75 1 1.25 1.5 1.75 2+ Reis (DB RCT) 6% 0.94 [0.55-1.61] hosp. 24/215 24/203 Improvement, RR [CI] Treatment Control Tau​2 = 0.00, I​2 = 0.0%, p = 0.84 Early treatment 6% 0.94 [0.55-1.61] 24/215 24/203 6% improvement Luo 75% 0.25 [0.07-0.84] death 3/104 22/179 Improvement, RR [CI] Treatment Control Wang 58% 0.42 [0.01-1.98] death 1/9 13/49 Chen 33% 0.67 [0.20-1.78] death 4/43 15/77 Li 78% 0.22 [0.09-0.54] death 2/37 21/94 Pérez-Belmo.. (PSM) -10% 1.10 [0.84-1.40] death 79/249 79/249 Bramante 7% 0.93 [0.81-1.06] death 394/2,333 791/3,923 Lalau (PSM) 22% 0.78 [0.55-1.10] death 671 (n) 419 (n) Huh -1% 1.01 [0.75-1.37] progression 104/272 774/2,533 Crouse 61% 0.39 [0.16-0.87] death 8/76 34/144 Lally 52% 0.48 [0.28-0.84] death 16/127 144/648 Oh -26% 1.26 [0.81-1.95] death 5,946 (n) 5,946 (n) Khunti 23% 0.77 [0.73-0.81] death population-based cohort Jiang (PSM) 46% 0.54 [0.13-2.26] death 3/74 10/74 Ghany 66% 0.34 [0.19-0.59] death 392 (n) 747 (n) Alamgir 27% 0.73 [0.63-0.84] death 11,062 (n) 11,062 (n) Boye 10% 0.90 [0.86-0.94] hosp. 2,067/4,250 3,196/5,281 Cheng (PSM) -65% 1.65 [0.71-3.86] death 678 (n) 535 (n) Wang 12% 0.88 [0.81-0.97] ICU 6,504 (n) 10,000 (n) Wander 15% 0.85 [0.80-0.90] death Saygili (PSM) 42% 0.58 [0.37-0.92] death 120 (n) 120 (n) Ong 47% 0.53 [0.31-0.87] death 33/186 57/169 Ojeda-Ferná.. (PSM) 16% 0.84 [0.79-0.89] death 1,476/6,556 1,787/6,556 Tau​2 = 0.01, I​2 = 78.4%, p < 0.0001 Prophylaxis 18% 0.82 [0.77-0.88] 4,190/39,689 6,943/48,805 18% improvement All studies 17% 0.83 [0.77-0.88] 4,214/39,904 6,967/49,008 17% improvement 23 metformin COVID-19 studies after exclusions c19early.com/mf Jan 16, 2022 Tau​2 = 0.01, I​2 = 77.4%, p < 0.0001 Effect extraction pre-specified, see appendix Favors metformin Favors control
Figure 12. Random effects meta-analysis for all studies after exclusions. This plot shows pooled effects, discussion can be found in the heterogeneity section, and results for specific outcomes can be found in the individual outcome analyses. Effect extraction is pre-specified, using the most serious outcome reported. For details of effect extraction see the appendix.
Randomized Controlled Trials (RCTs)
Figure 13 shows a forest plot for random effects meta-analysis of all Randomized Controlled Trials. Table 2 summarizes the results. Currently there is only one RCT.
RCTs help to make study groups more similar, however they are subject to many biases, including age bias, treatment delay bias, severity of illness bias, regulation bias, recruitment bias, trial design bias, followup time bias, selective reporting bias, fraud bias, hidden agenda bias, vested interest bias, publication bias, and publication delay bias [Jadad], all of which have been observed with COVID-19 RCTs.
RCTs have a bias against finding an effect for interventions that are widely available — patients that believe they need the intervention are more likely to decline participation and take the intervention. This is illustrated with the extreme example of an RCT showing no significant differences for use of a parachute when jumping from a plane [Yeh]. RCTs for metformin are more likely to enroll low-risk participants that do not need treatment to recover, making the results less applicable to clinical practice. This bias is likely to be greater for widely known treatments. Note that this bias does not apply to the typical pharmaceutical trial of a new drug that is otherwise unavailable.
Evidence shows that non-RCT trials can also provide reliable results. [Concato] find that well-designed observational studies do not systematically overestimate the magnitude of the effects of treatment compared to RCTs. [Anglemyer] summarized reviews comparing RCTs to observational studies and found little evidence for significant differences in effect estimates. [Lee] shows that only 14% of the guidelines of the Infectious Diseases Society of America were based on RCTs. Evaluation of studies relies on an understanding of the study and potential biases. Limitations in an RCT can outweigh the benefits, for example excessive dosages, excessive treatment delays, or Internet survey bias could have a greater effect on results. Ethical issues may also prevent running RCTs for known effective treatments. For more on issues with RCTs see [Deaton, Nichol].
0 0.25 0.5 0.75 1 1.25 1.5 1.75 2+ Reis (DB RCT) 6% 0.94 [0.55-1.61] hosp. 24/215 24/203 Improvement, RR [CI] Treatment Control Tau​2 = 0.00, I​2 = 0.0%, p = 0.84 Early treatment 6% 0.94 [0.55-1.61] 24/215 24/203 6% improvement All studies 6% 0.94 [0.55-1.61] 24/215 24/203 6% improvement 1 metformin COVID-19 Randomized Controlled Trials c19early.com/mf Jan 16, 2022 Tau​2 = 0.00, I​2 = 0.0%, p = 0.84 Effect extraction pre-specified, see appendix Favors metformin Favors control
Figure 13. Random effects meta-analysis for all Randomized Controlled Trials. This plot shows pooled effects, discussion can be found in the heterogeneity section, and results for specific outcomes can be found in the individual outcome analyses. Effect extraction is pre-specified, using the most serious outcome reported. For details of effect extraction see the appendix.
Treatment timeNumber of studies reporting positive effects Total number of studiesPercentage of studies reporting positive effects Random effects meta-analysis results
Randomized Controlled Trials 1 1 100% 6% improvement
RR 0.94 [0.55‑1.61]
p = 0.84
Table 2. Randomized Controlled Trial results.
Heterogeneity
Heterogeneity in COVID-19 studies arises from many factors including:
Treatment delay.
The time between infection or the onset of symptoms and treatment may critically affect how well a treatment works. For example an antiviral may be very effective when used early but may not be effective in late stage disease, and may even be harmful. Oseltamivir, for example, is generally only considered effective for influenza when used within 0-36 or 0-48 hours [McLean, Treanor]. Other medications might be beneficial for late stage complications, while early use may not be effective or may even be harmful. Figure 14 shows an example where efficacy declines as a function of treatment delay.
Figure 14. Effectiveness may depend critically on treatment delay.
Patient demographics.
Details of the patient population including age and comorbidities may critically affect how well a treatment works. For example, many COVID-19 studies with relatively young low-comorbidity patients show all patients recovering quickly with or without treatment. In such cases, there is little room for an effective treatment to improve results (as in [López-Medina]).
Effect measured.
Efficacy may differ significantly depending on the effect measured, for example a treatment may be very effective at reducing mortality, but less effective at minimizing cases or hospitalization. Or a treatment may have no effect on viral clearance while still being effective at reducing mortality.
Variants.
There are many different variants of SARS-CoV-2 and efficacy may depend critically on the distribution of variants encountered by the patients in a study. For example, the Gamma variant shows significantly different characteristics [Faria, Karita, Nonaka, Zavascki]. Different mechanisms of action may be more or less effective depending on variants, for example the viral entry process for the omicron variant has moved towards TMPRSS2-independent fusion, suggesting that TMPRSS2 inhibitors may be less effective [Peacock, Willett].
Regimen.
Effectiveness may depend strongly on the dosage and treatment regimen.
Treatments.
The use of other treatments may significantly affect outcomes, including anything from supplements, other medications, or other kinds of treatment such as prone positioning.
The distribution of studies will alter the outcome of a meta analysis. Consider a simplified example where everything is equal except for the treatment delay, and effectiveness decreases to zero or below with increasing delay. If there are many studies using very late treatment, the outcome may be negative, even though the treatment may be very effective when used earlier.
In general, by combining heterogeneous studies, as all meta analyses do, we run the risk of obscuring an effect by including studies where the treatment is less effective, not effective, or harmful.
When including studies where a treatment is less effective we expect the estimated effect size to be lower than that for the optimal case. We do not a priori expect that pooling all studies will create a positive result for an effective treatment. Looking at all studies is valuable for providing an overview of all research, important to avoid cherry-picking, and informative when a positive result is found despite combining less-optimal situations. However, the resulting estimate does not apply to specific cases such as early treatment in high-risk populations.
Discussion
Publication bias.
Publishing is often biased towards positive results, however evidence suggests that there may be a negative bias for inexpensive treatments for COVID-19. Both negative and positive results are very important for COVID-19, media in many countries prioritizes negative results for inexpensive treatments (inverting the typical incentive for scientists that value media recognition), and there are many reports of difficulty publishing positive results [Boulware, Meeus, Meneguesso]. For metformin, there is currently not enough data to evaluate publication bias with high confidence.
Conflicts of interest.
Pharmaceutical drug trials often have conflicts of interest whereby sponsors or trial staff have a financial interest in the outcome being positive. Metformin for COVID-19 lacks this because it is off-patent, has multiple manufacturers, and is very low cost. In contrast, most COVID-19 metformin trials have been run by physicians on the front lines with the primary goal of finding the best methods to save human lives and minimize the collateral damage caused by COVID-19. While pharmaceutical companies are careful to run trials under optimal conditions (for example, restricting patients to those most likely to benefit, only including patients that can be treated soon after onset when necessary, and ensuring accurate dosing), not all metformin trials represent the optimal conditions for efficacy.
Early/late vs. mild/moderate/severe.
Some analyses classify treatment based on early/late administration (as we do here), while others distinguish between mild/moderate/severe cases. We note that viral load does not indicate degree of symptoms — for example patients may have a high viral load while being asymptomatic. With regard to treatments that have antiviral properties, timing of treatment is critical — late administration may be less helpful regardless of severity.
Conclusion
Statistically significant improvements are seen for mortality and hospitalization. 16 studies from 16 independent teams in 6 different countries show statistically significant improvements in isolation (13 for the most serious outcome). Meta analysis using the most serious outcome reported shows 17% [11‑22%] improvement. Results are worse for Randomized Controlled Trials, similar after exclusions, and similar for peer-reviewed studies. Results are robust — in exclusion sensitivity analysis 17 of 25 studies must be excluded to avoid finding statistically significant efficacy in pooled analysis.
Most studies analyze existing use with diabetic patients. Many results are subject to confounding by indication — metformin is typically used early in the progression of type 2 diabetes.
Study Notes
0 0.5 1 1.5 2+ Mortality 27% Improvement Relative Risk Mortality (b) 34% Mortality (c) 30% c19early.com/alamgir.html Favors metformin Favors control
[Alamgir] In Silico study followed by PSM analysis of the National COVID Cohort Collaborative data in the USA, showing 27% lower mortality with metformin use.

ABSTRACTBackgroundDrug repositioning is a key component of COVID-19 pandemic response, through identification of existing drugs that can effectively disrupt COVID-19 disease processes, contributing valuable insights into disease pathways. Traditional non in silico drug repositioning approaches take substantial time and cost to discover effect and, crucially, to validate repositioned effects.MethodsUsing a novel in-silico quasi-quantum molecular simulation platform that analyzes energies and electron densities of both target proteins and candidate interruption compounds on High Performance Computing (HPC), we identified a list of FDA-approved compounds with potential to interrupt specific SARS-CoV-2 proteins. Subsequently we used 1.5M patient records from the National COVID Cohort Collaborative to create matched cohorts to refine our in-silico hits to those candidates that show statistically significant clinical effect.ResultsWe identified four drugs, Metformin, Triamcinolone, Amoxicillin and Hydrochlorothiazide, that were associated with reduced mortality by 27%, 26%, 26%, and 23%, respectively, in COVID-19 patients.ConclusionsTogether, these findings provide support to our hypothesis that in-silico simulation of active compounds against SARS-CoV-2 proteins followed by statistical analysis of electronic health data results in effective therapeutics identification.
0 0.25 0.5 0.75 1 1.25 1.5 1.75 2+ Hospitalization 10% Improvement Relative Risk c19early.com/boye.html Favors metformin Favors control
[Boye] Retrospective 9531 COVID+ diabetes patients in the USA, showing lower risk of hospitalization with existing biguanides treatment (defined as mainly metformin in the abstract and entirely metformin in the text).
0 0.25 0.5 0.75 1 1.25 1.5 1.75 2+ Mortality 7% Improvement Relative Risk Mortality (b) 24% Mortality (c) -3% c19early.com/bramante.html Favors metformin Favors control
[Bramante] Retrospective 6,256 COVID-19+ diabetes patients in the USA, showing lower mortality with existing metformin treatment, statistically significant only for women.