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Metformin for COVID-19: real-time meta analysis of 65 studies

Covid Analysis, June 2023
https://c19early.org/mfmeta.html
 
0 0.5 1 1.5+ All studies 28% 65 256,116 Improvement, Studies, Patients Relative Risk Mortality 32% 49 193,729 Ventilation 29% 9 54,173 ICU admission 16% 7 82,382 Hospitalization 18% 16 82,578 Cases 4% 6 69,086 Viral clearance 1% 1 418 RCTs 24% 2 1,222 RCT mortality 24% 2 1,222 Peer-reviewed 27% 59 208,827 Prophylaxis 26% 60 228,067 Early 58% 3 27,730 Late 86% 2 319 Metformin for COVID-19 c19early.org/mf Jun 2023 Favorsmetformin Favorscontrol after exclusions
Statistically significant improvements are seen for mortality, ventilation, ICU admission, hospitalization, and progression. 42 studies from 39 independent teams in 11 different countries show statistically significant improvements in isolation (38 for the most serious outcome).
Meta analysis using the most serious outcome reported shows 28% [23‑32%] improvement. Results are similar for Randomized Controlled Trials, similar after exclusions, and similar for peer-reviewed studies. Clinical outcomes suggest benefit while viral and case outcomes do not, consistent with an intervention that aids recovery but is not antiviral.
Results are robust — in exclusion sensitivity analysis 48 of 65 studies must be excluded to avoid finding statistically significant efficacy in pooled analysis.
0 0.5 1 1.5+ All studies 28% 65 256,116 Improvement, Studies, Patients Relative Risk Mortality 32% 49 193,729 Ventilation 29% 9 54,173 ICU admission 16% 7 82,382 Hospitalization 18% 16 82,578 Cases 4% 6 69,086 Viral clearance 1% 1 418 RCTs 24% 2 1,222 RCT mortality 24% 2 1,222 Peer-reviewed 27% 59 208,827 Prophylaxis 26% 60 228,067 Early 58% 3 27,730 Late 86% 2 319 Metformin for COVID-19 c19early.org/mf Jun 2023 Favorsmetformin Favorscontrol after exclusions
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. Prophylaxis results typically include continuing use after infection and hospitalization, and greater benefit is seen for more serious outcomes. The beneficial effect of metformin may be more related to later stages of COVID-19. The TOGETHER RCT shows 27% lower mortality. While not statistically significant, p = 0.53, this is consistent with the mortality results from all studies, 32% [27‑37%].
No treatment, vaccine, or intervention is 100% effective and available. All practical, effective, and safe means should be used based on risk/benefit analysis. Multiple treatments are typically used in combination, and other treatments are more effective. None of the metformin studies show zero events with treatment.
All data to reproduce this paper and sources are in the appendix. Other meta analyses for metformin can be found in [Hariyanto, Kow, Li, Lukito, Ma, Parveen, Schlesinger, Yang], showing significant improvements for mortality, hospitalization, progression, and severity.
Evolution of COVID-19 clinical evidence Metformin p<0.0000000001 Acetaminophen p=0.0000018 2020 2021 2022 2023 Effective Harmful c19early.org June 2023 meta analysis results (pooled effects) 100% 50% 0% -50%
Percentage improvement with metformin (more)
All studies Prophylaxis Early treatment Studies Patients Authors
All studies28% [23‑32%]
****
26% [22‑30%]
****
58% [23‑77%]
**
65 256,116 783
Randomized Controlled TrialsRCTs24% [-89‑70%]-24% [-89‑70%] 2 1,222 26
Mortality32% [27‑37%]
****
30% [25‑34%]
****
58% [23‑77%]
**
49 193,729 623
RCT mortality24% [-89‑70%]-24% [-89‑70%] 2 1,222 26
Highlights
Metformin reduces risk for COVID-19 with very high confidence for mortality, ventilation, ICU admission, hospitalization, and in pooled analysis, high confidence for progression, and low confidence for recovery.
We show traditional outcome specific analyses and combined evidence from all studies, incorporating treatment delay, a primary confounding factor in COVID-19 studies.
Real-time updates and corrections, transparent analysis with all results in the same format, consistent protocol for 51 treatments.
A
0 0.25 0.5 0.75 1 1.25 1.5 1.75 2+ TOGETHER Reis (DB RCT) 27% 0.73 [0.28-1.94] death 7/215 9/203 impossible data, see notes Improvement, RR [CI] Treatment Control Hunt 67% 0.33 [0.25-0.43] death 73/3,956 1,539/22,552 COVID-OUT Bramante (DB RCT) 3% 0.97 [0.06-15.5] death 1/408 1/396 OT​1 Tau​2 = 0.12, I​2 = 33.5%, p = 0.0046 Early treatment 58% 0.42 [0.23-0.77] 81/4,579 1,549/23,151 58% improvement Tamura 97% 0.03 [0.00-0.58] death 115 (n) 73 (n) Improvement, RR [CI] Treatment Control Li 76% 0.24 [0.06-0.98] death 2/37 21/94 Tau​2 = 0.62, I​2 = 32.4%, p = 0.025 Late treatment 86% 0.14 [0.02-0.78] 2/152 21/167 86% improvement Luo 75% 0.25 [0.07-0.84] death 3/104 22/179 Improvement, RR [CI] Treatment Control Choi (PSM) -120% 2.20 [0.51-9.58] progression case 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 Kim 64% 0.36 [0.10-1.23] death 113 (n) 122 (n) Li 78% 0.22 [0.09-0.54] death 2/37 21/94 Goodall 3% 0.97 [0.75-1.25] death 74/210 280/771 Gao -225% 3.25 [1.03-7.41] progression 16/56 4/54 Pérez-Bel.. (PSM) -10% 1.10 [0.84-1.40] death 79/249 79/249 Bramante 12% 0.88 [0.78-1.00] 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 Ramos-Rincón 1% 0.99 [0.77-1.29] death 206/420 179/370 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) CORONADO Wargny 28% 0.72 [0.53-0.95] death 247/1,553 330/1,241 Bramante (PSM) 62% 0.38 [0.16-0.91] death 342 (n) 342 (n) COVIDENCE UK 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) Gálvez-Barrón -16% 1.16 [0.73-1.49] death 20 (n) 83 (n) Ravindra 30% 0.70 [0.28-1.56] death 5/53 57/313 Blanc 79% 0.21 [0.03-1.46] death 1/14 25/75 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) Ando 39% 0.61 [0.38-0.99] hosp. 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 Bliden 60% 0.40 [0.12-1.37] death 3/34 9/41 Al-Salameh 55% 0.45 [0.17-0.94] death/ICU 9/47 22/50 Wallace (PSW) 72% 0.28 [0.21-0.37] death 103/1,203 1,536/6,970 Ojeda-Fern.. (PSM) 16% 0.84 [0.79-0.89] death 1,476/6,556 1,787/6,556 Fu 72% 0.28 [0.09-0.84] no recov. 4/49 9/31 OT​1 Usman 60% 0.40 [0.12-1.37] death 3/34 9/41 Wong 51% 0.49 [0.43-0.57] death Wong (PSW) 59% 0.41 [0.22-0.80] death 786 (n) 428 (n) MacFadden 1% 0.99 [0.96-1.01] cases n/a n/a Ma (PSW) 74% 0.26 [0.07-0.89] death 3/361 40/995 Yeh 44% 0.56 [0.45-0.71] progression n/a n/a Cousins (PSM) 50% 0.50 [0.29-0.85] ventilation 2,463 (n) 2,463 (n) Shestakova 22% 0.78 [0.67-0.91] death population-based cohort Loucera 30% 0.70 [0.61-0.80] death 1,896 (n) 14,072 (n) Chan 59% 0.41 [0.12-1.44] death 400 (n) 2,736 (n) Zaccardi 34% 0.66 [0.60-0.72] death population-based cohort Yip (PSM) 7% 0.93 [0.72-1.22] death/hosp. 8,604 (n) 3,727 (n) Ouchi 10% 0.90 [0.77-1.05] death 6,168 (n) 9,875 (n) Morrison (PSM) 41% 0.59 [0.41-0.84] death 2,684 (n) 2,684 (n) Mannucci 38% 0.62 [0.41-0.93] death n/a n/a Milosavljevic 33% 0.67 [0.47-0.95] severe case 377 (n) 356 (n) Miao (PSM) 1% 0.99 [0.85-1.15] death 233/796 236/796 Servais 49% 0.51 [0.34-0.78] death n/a n/a Pinchera 15% 0.85 [0.71-0.96] severe case 5/19 14/24 OT​1 Sandhu 3% 0.97 [0.95-0.99] hosp. population-based cohort Yen (PSM) 25% 0.75 [0.63-0.89] death 232/20,894 295/20,894 Araldi 60% 0.40 [0.32-0.50] death 107/2,598 263/2,598 Tau​2 = 0.02, I​2 = 92.9%, p < 0.0001 Prophylaxis 26% 0.74 [0.70-0.78] 5,453/92,312 10,685/135,755 26% improvement All studies 28% 0.72 [0.68-0.77] 5,536/97,043 12,255/159,073 28% improvement 65 metformin COVID-19 studies c19early.org/mf Jun 2023 Tau​2 = 0.03, I​2 = 93.0%, p < 0.0001 Effect extraction pre-specified(most serious outcome, see appendix) 1 OT: comparison with other treatment Favors metformin Favors control
0 0.25 0.5 0.75 1 1.25 1.5 1.75 2+ TOGETHER Reis (DB RCT) 27% death impossible data Relative Risk [CI] Hunt 67% death COVID-OUT Bramante (DB RCT) 3% death OT​1 Tau​2 = 0.12, I​2 = 33.5%, p = 0.0046 Early treatment 58% 58% improvement Tamura 97% death Li 76% death Tau​2 = 0.62, I​2 = 32.4%, p = 0.025 Late treatment 86% 86% improvement Luo 75% death Choi (PSM) -120% progression Wang 58% death Chen 33% death Kim 64% death Li 78% death Goodall 3% death Gao -225% progression Pérez-Be.. (PSM) -10% death Bramante 12% death Lalau (PSM) 22% death Huh -1% progression Ramos-Rincón 1% death Crouse 61% death Lally 52% death Oh -26% death CORONADO Wargny 28% death Bramante (PSM) 62% death COVIDENCE UK Holt -27% case Khunti 23% death Jiang (PSM) 46% death Ghany 66% death Alamgir 27% death Gálvez-Barrón -16% death Ravindra 30% death Blanc 79% death Boye 10% hospitalization Cheng (PSM) -65% death Wang 12% ICU admission Ando 39% hospitalization Wander 15% death Saygili (PSM) 42% death Ong 47% death Bliden 60% death Al-Salameh 55% death/ICU Wallace (PSW) 72% death Ojeda-Fer.. (PSM) 16% death Fu 72% recovery OT​1 Usman 60% death Wong 51% death Wong (PSW) 59% death MacFadden 1% case Ma (PSW) 74% death Yeh 44% progression Cousins (PSM) 50% ventilation Shestakova 22% death Loucera 30% death Chan 59% death Zaccardi 34% death Yip (PSM) 7% death/hosp. Ouchi 10% death Morrison (PSM) 41% death Mannucci 38% death Milosavljevic 33% severe case Miao (PSM) 1% death Servais 49% death Pinchera 15% severe case OT​1 Sandhu 3% hospitalization Yen (PSM) 25% death Araldi 60% death Tau​2 = 0.02, I​2 = 92.9%, p < 0.0001 Prophylaxis 26% 26% improvement All studies 28% 28% improvement 65 metformin COVID-19 studies c19early.org/mf Jun 2023 Tau​2 = 0.03, I​2 = 93.0%, p < 0.0001 Protocol pre-specified/rotate for details1 OT: comparison with other treatment Favors metformin Favors control
B
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C
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D
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Figure 1. A. Random effects meta-analysis. This plot shows pooled effects, see the specific outcome analyses for individual outcomes, and the heterogeneity section for discussion. Effect extraction is pre-specified, using the most serious outcome reported. For details of effect extraction see the appendix. B. Scatter plot showing the most serious outcome in all studies, and for studies within each stage. Diamonds shows the results of random effects meta-analysis. C. Results within the context of multiple COVID-19 treatments. 0.9% of 3,989 proposed treatments show efficacy [c19early.org]. D. Timeline of results in metformin studies. The marked dates indicate the time when efficacy was known with a statistically significant improvement of ≥10% from ≥3 studies for pooled outcomes and one or more specific outcome.
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, studies within each treatment stage, individual outcomes, peer-reviewed studies, 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.
2 In Vitro studies support the efficacy of metformin [Miguel, Parthasarathy].
An In Vivo animal study supports the efficacy of metformin [Miguel].
Preclinical research is an important part of the development of treatments, however results may be very different in clinical trials. Preclinical results are not used in this paper.
Table 1 summarizes the results for all stages combined, with different exclusions, and for specific outcomes. Table 2 shows results by treatment stage. Figure 3, 4, 5, 6, 7, 8, 9, 10, 11, and 12 show forest plots for random effects meta-analysis of all studies with pooled effects, mortality results, ventilation, ICU admission, hospitalization, progression, recovery, cases, viral clearance, and peer reviewed studies.
Table 1. Random effects meta-analysis for all stages combined, with different exclusions, and for specific outcomes. Results show the percentage improvement with treatment and the 95% confidence interval. * p<0.05  ** p<0.01  **** p<0.0001.
Improvement Studies Patients Authors
All studies28% [23‑32%]
****
65 256,116 783
After exclusions28% [24‑32%]
****
62 240,448 726
Peer-reviewed studiesPeer-reviewed27% [22‑32%]
****
59 208,827 712
Randomized Controlled TrialsRCTs24% [-89‑70%]2 1,222 26
Mortality32% [27‑37%]
****
49 193,729 623
VentilationVent.29% [10‑44%]
**
9 54,173 104
ICU admissionICU16% [4‑25%]
**
7 82,382 58
HospitalizationHosp.18% [10‑24%]
****
16 82,578 163
Recovery48% [-6‑75%]3 4,088 68
Cases4% [-6‑13%]6 69,086 79
RCT mortality24% [-89‑70%]2 1,222 26
Table 2. Random effects meta-analysis results by treatment stage. Results show the percentage improvement with treatment, the 95% confidence interval, and the number of studies for the stage.treatment and the 95% confidence interval. * p<0.05  ** p<0.01  **** p<0.0001.
Early treatment Late treatment Prophylaxis
All studies58% [23‑77%]
**
86% [22‑98%]
*
26% [22‑30%]
****
After exclusions58% [23‑77%]
**
86% [22‑98%]
*
26% [22‑31%]
****
Peer-reviewed studiesPeer-reviewed58% [23‑77%]
**
86% [22‑98%]
*
25% [20‑30%]
****
Randomized Controlled TrialsRCTs24% [-89‑70%]--
Mortality58% [23‑77%]
**
86% [22‑98%]
*
30% [25‑34%]
****
VentilationVent.--29% [10‑44%]
**
ICU admissionICU--16% [4‑25%]
**
HospitalizationHosp.6% [-61‑45%]-18% [10‑25%]
****
Recovery--48% [-6‑75%]
Cases--4% [-6‑13%]
RCT mortality24% [-89‑70%]--
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Figure 3. Random effects meta-analysis for all studies with pooled effects. This plot shows pooled effects, see the specific outcome analyses for individual outcomes, and the heterogeneity section for discussion. Effect extraction is pre-specified, using the most serious outcome reported. For details of effect extraction see the appendix.
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Figure 4. Random effects meta-analysis for mortality results.
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Figure 5. Random effects meta-analysis for ventilation.
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Figure 6. Random effects meta-analysis for ICU admission.
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Figure 7. Random effects meta-analysis for hospitalization.
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Figure 8. Random effects meta-analysis for progression.
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Figure 9. Random effects meta-analysis for recovery.
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Figure 10. Random effects meta-analysis for cases.
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Figure 11. Random effects meta-analysis for viral clearance.
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Figure 12. Random effects meta-analysis for peer reviewed studies. [Zeraatkar] analyze 356 COVID-19 trials, finding no significant evidence that preprint results are inconsistent with peer-reviewed studies. They also show extremely long peer-review delays, with a median of 6 months to journal publication. A six month delay was equivalent to around 1.5 million deaths during the first two years of the pandemic. Authors recommend using preprint evidence, with appropriate checks for potential falsified data, which provides higher certainty much earlier. Effect extraction is pre-specified, using the most serious outcome reported, see the appendix for details.
Figure 13 shows a comparison of results for RCTs and non-RCT studies. Figure 14 shows a forest plot for random effects meta-analysis of all Randomized Controlled Trials. RCT results are included in Table 1 and Table 2.
Bias in clinical research may be defined as something that tends to make conclusions differ systematically from the truth. RCTs help to make study groups more similar and can provide a higher level of evidence, however they are subject to many biases [Jadad], and analysis of double-blind RCTs has identified extreme levels of bias [Gøtzsche]. For COVID-19, the overhead may delay treatment, dramatically compromising efficacy; they may encourage monotherapy for simplicity at the cost of efficacy which may rely on combined or synergistic effects; the participants that sign up may not reflect real world usage or the population that benefits most in terms of age, comorbidities, severity of illness, or other factors; standard of care may be compromised and unable to evolve quickly based on emerging research for new diseases; errors may be made in randomization and medication delivery; and investigators may have hidden agendas or vested interests influencing design, operation, analysis, and the potential for fraud. All of these biases have been observed with COVID-19 RCTs. There is no guarantee that a specific RCT provides a higher level of evidence.
High quality RCTs for novel acute diseases are more challenging, with increased ethical issues due to the urgency of treatment, increased risk due to enrollment delays, and more difficult design with a rapidly evolving evidence base. For COVID-19, the most common site of initial infection is the upper respiratory tract. Immediate treatment is likely to be most successful and may prevent or slow progression to other parts of the body. For a non-prophylaxis RCT, it makes sense to provide treatment in advance and instruct patients to use it immediately on symptoms, just as some governments have done by providing medication kits in advance. Unfortunately, no RCTs have been done in this way. Every treatment RCT to date involves delayed treatment. Among the 51 treatments we have analyzed, 64% of RCTs involve very late treatment 5+ days after onset. No non-prophylaxis COVID-19 RCTs match the potential real-world use of early treatments (they may more accurately represent results for treatments that require visiting a medical facility, e.g., those requiring intravenous administration).
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. 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, and may be greater when the risk of a serious outcome is overstated. 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].
Currently, 37 of the treatments we analyze show statistically significant efficacy or harm, defined as ≥10% decreased risk or >0% increased risk from ≥3 studies. Of the 37 treatments with statistically significant efficacy/harm, 23 have been confirmed in RCTs, with a mean delay of 4.4 months. For the 14 unconfirmed treatments, 4 have zero RCTs to date. The point estimates for the remaining 10 are all consistent with the overall results (benefit or harm), with 8 showing >20%. The only treatments showing >10% efficacy for all studies, but <10% for RCTs are sotrovimab and aspirin.
We need to evaluate each trial on its own merits. RCTs for a given medication and disease may be more reliable, however they may also be less reliable. For off-patent medications, very high conflict of interest trials may be more likely to be RCTs, and more likely to be large trials that dominate meta analyses.
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Figure 13. Results for RCTs and non-RCT studies.
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Figure 14. Random effects meta-analysis for all Randomized Controlled Trials. This plot shows pooled effects, see the specific outcome analyses for individual outcomes, and the heterogeneity section for discussion. Effect extraction is pre-specified, using the most serious outcome reported. For details of effect extraction see the appendix.
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 15 shows a forest plot for random effects meta-analysis of all studies after exclusions.
[Bliden], unadjusted results with minimal group details.
[Holt], significant unadjusted confounding possible.
[Ravindra], minimal details provided.
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Figure 15. Random effects meta-analysis for all studies after exclusions. This plot shows pooled effects, see the specific outcome analyses for individual outcomes, and the heterogeneity section for discussion. Effect extraction is pre-specified, using the most serious outcome reported. For details of effect extraction see the appendix.
Heterogeneity in COVID-19 studies arises from many factors including:
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]. Baloxavir studies for influenza also show that treatment delay is critical — [Ikematsu] report an 86% reduction in cases for post-exposure prophylaxis, [Hayden] show a 33 hour reduction in the time to alleviation of symptoms for treatment within 24 hours and a reduction of 13 hours for treatment within 24-48 hours, and [Kumar] report only 2.5 hours improvement for inpatient treatment.
Table 3. Studies of baloxavir for influenza show that early treatment is more effective.
Treatment delayResult
Post exposure prophylaxis86% fewer cases [Ikematsu]
<24 hours-33 hours symptoms [Hayden]
24-48 hours-13 hours symptoms [Hayden]
Inpatients-2.5 hours to improvement [Kumar]
Figure 16 shows a mixed-effects meta-regression for efficacy as a function of treatment delay in COVID-19 studies from 51 treatments, showing that efficacy declines rapidly with treatment delay. Early treatment is critical for COVID-19.
Figure 16. Early treatment is more effective. Meta-regression showing efficacy as a function of treatment delay in COVID-19 studies from 51 treatments.
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]).
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.
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].
Effectiveness may depend strongly on the dosage and treatment regimen.
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 quality of medications may vary significantly between manufacturers and production batches, which may significantly affect efficacy and safety. [Williams] analyze ivermectin from 11 different sources, showing highly variable antiparasitic efficacy across different manufacturers. [Xu] analyze a treatment from two different manufacturers, showing 9 different impurities, with significantly different concentrations for each manufacturer.
We present both pooled analyses and specific outcome analyses. Notably, pooled analysis often results in earlier detection of efficacy as shown in Figure 17. For many COVID-19 treatments, a reduction in mortality logically follows from a reduction in hospitalization, which follows from a reduction in symptomatic cases, etc. An antiviral tested with a low-risk population may report zero mortality in both arms, however a reduction in severity and improved viral clearance may translate into lower mortality among a high-risk population, and including these results in pooled analysis allows faster detection of efficacy. Trials with high-risk patients may also be restricted due to ethical concerns for treatments that are known or expected to be effective.
Pooled analysis enables using more of the available information. While there is much more information available, for example dose-response relationships, the advantage of the method used here is simplicity and transparency. Note that pooled analysis could hide efficacy, for example a treatment that is beneficial for late stage patients but has no effect on viral replication or early stage disease could show no efficacy in pooled analysis if most studies only examine viral clearance. While we present pooled results, we also present individual outcome analyses, which may be more informative for specific use cases.
Currently, 37 of the treatments we analyze show statistically significant efficacy or harm, defined as ≥10% decreased risk or >0% increased risk from ≥3 studies. 94% of treatments showing statistically significant efficacy/harm with pooled effects have been confirmed with one or more specific outcomes, with a mean delay of 3.1 months. When restricting to RCTs only, 55% of treatments showing statistically significant efficacy/harm with pooled effects have been confirmed with one or more specific outcomes, with a mean delay of 2.9 months.
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Figure 17. The time when studies showed that treatments were effective, defined as statistically significant improvement of ≥10% from ≥3 studies. Pooled results typically show efficacy earlier than specific outcome results. Results from all studies often shows efficacy much earlier than when restricting to RCTs. Results reflect conditions as used in trials to date, these depend on the population treated, treatment delay, and treatment regimen.
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 early treatment is very effective. This may have a greater effect than pooling different outcomes such as mortality and hospitalization. For example a treatment may have 50% efficacy for mortality but only 40% for hospitalization when used within 48 hours. However efficacy could be 0% when used late.
All meta analyses combine heterogeneous studies, varying in population, variants, and potentially all factors above, and therefore may obscure efficacy by including studies where treatment is less effective. Generally, we expect the estimated effect size from meta analysis to be less than that for the optimal case. 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. While we present results for all studies, we also present treatment time and individual outcome analyses, which may be more informative for specific use cases.
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.
Funnel plots have traditionally been used for analyzing publication bias. This is invalid for COVID-19 acute treatment trials — the underlying assumptions are invalid, which we can demonstrate with a simple example. Consider a set of hypothetical perfect trials with no bias. Figure 18 plot A shows a funnel plot for a simulation of 80 perfect trials, with random group sizes, and each patient's outcome randomly sampled (10% control event probability, and a 30% effect size for treatment). Analysis shows no asymmetry (p > 0.05). In plot B, we add a single typical variation in COVID-19 treatment trials — treatment delay. Consider that efficacy varies from 90% for treatment within 24 hours, reducing to 10% when treatment is delayed 3 days. In plot B, each trial's treatment delay is randomly selected. Analysis now shows highly significant asymmetry, p < 0.0001, with six variants of Egger's test all showing p < 0.05 [Egger, Harbord, Macaskill, Moreno, Peters, Rothstein, Rücker, Stanley]. Note that these tests fail even though treatment delay is uniformly distributed. In reality treatment delay is more complex — each trial has a different distribution of delays across patients, and the distribution across trials may be biased (e.g., late treatment trials may be more common). Similarly, many other variations in trials may produce asymmetry, including dose, administration, duration of treatment, differences in SOC, comorbidities, age, variants, and bias in design, implementation, analysis, and reporting.
Figure 18. Example funnel plot analysis for simulated perfect trials.
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.
Summary statistics from meta analysis necessarily lose information. As with all meta analyses, studies are heterogeneous, with differences in treatment delay, treatment regimen, patient demographics, variants, conflicts of interest, standard of care, and other factors. We provide analyses by specific outcomes and by treatment delay, and we aim to identify key characteristics in the forest plots and summaries. Results should be viewed in the context of study characteristics.
Some analyses classify treatment based on early or late administration, as done here, while others distinguish between mild, moderate, and severe cases. 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.
Details of treatment delay per patient is often not available. For example, a study may treat 90% of patients relatively early, but the events driving the outcome may come from 10% of patients treated very late. Our 5 day cutoff for early treatment may be too conservative, 5 days may be too late in many cases.
Comparison across treatments is confounded by differences in the studies performed, for example dose, variants, and conflicts of interest. Trials affiliated with special interests may use designs better suited to the preferred outcome.
In some cases, the most serious outcome has very few events, resulting in lower confidence results being used in pooled analysis, however the method is simpler and more transparent. This is less critical as the number of studies increases. Restriction to outcomes with sufficient power may be beneficial in pooled analysis and improve accuracy when there are few studies, however we maintain our pre-specified method to avoid any retrospective changes.
Studies show that combinations of treatments can be highly synergistic and may result in many times greater efficacy than individual treatments alone [Alsaidi, Andreani, Biancatelli, De Forni, Gasmi, Jeffreys, Jitobaom, Jitobaom (B), Ostrov, Thairu]. Therefore standard of care may be critical and benefits may diminish or disappear if standard of care does not include certain treatments.
This real-time analysis is constantly updated based on submissions. Accuracy benefits from widespread review and submission of updates and corrections from reviewers. Less popular treatments may receive fewer reviews.
No treatment, vaccine, or intervention is 100% available and effective for all current and future variants. Efficacy may vary significantly with different variants and within different populations. All treatments have potential side effects. Propensity to experience side effects may be predicted in advance by qualified physicians. We do not provide medical advice. Before taking any medication, consult a qualified physician who can compare all options, provide personalized advice, and provide details of risks and benefits based on individual medical history and situations.
3 of the 65 studies compare against other treatments, which may reduce the effect seen. Other meta analyses for metformin can be found in [Hariyanto, Kow, Li, Lukito, Ma, Parveen, Schlesinger, Yang], showing significant improvements for one or more of mortality, hospitalization, progression, and severity.
Statistically significant improvements are seen for mortality, ventilation, ICU admission, hospitalization, and progression. 42 studies from 39 independent teams in 11 different countries show statistically significant improvements in isolation (38 for the most serious outcome). Meta analysis using the most serious outcome reported shows 28% [23‑32%] improvement. Results are similar for Randomized Controlled Trials, similar after exclusions, and similar for peer-reviewed studies. Clinical outcomes suggest benefit while viral and case outcomes do not, consistent with an intervention that aids recovery but is not antiviral. Results are robust — in exclusion sensitivity analysis 48 of 65 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. Prophylaxis results typically include continuing use after infection and hospitalization, and greater benefit is seen for more serious outcomes. The beneficial effect of metformin may be more related to later stages of COVID-19. The TOGETHER RCT shows 27% lower mortality. While not statistically significant, p = 0.53, this is consistent with the mortality results from all studies, 32% [27‑37%].
0 0.5 1 1.5 2+ Death/ICU 55% Improvement Relative Risk Death/ICU (b) -68% c19early.org/mf Al-Salameh et al. Metformin for COVID-19 Prophylaxis Is prophylaxis with metformin beneficial for COVID-19? Retrospective 97 patients in France Lower death/ICU with metformin (p=0.04) Al-Salameh et al., Diabetes & Metabolism, doi:10.1016/j.diabet.2021.101297 Favors metformin Favors control
[Al-Salameh] Retrospective 140 diabetic patients in France, showing lower mortality for patients where metformin use was continued after hospitalization.
0 0.5 1 1.5 2+ Mortality 27% Improvement Relative Risk Mortality (b) 34% Mortality (c) 30% c19early.org/mf Alamgir et al. Metformin for COVID-19 Prophylaxis Is prophylaxis with metformin beneficial for COVID-19? Retrospective 22,124 patients in the USA Lower mortality with metformin (p=0.000022) Alamgir et al., medRxiv, doi:10.1101/2021.03.22.21254110 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.
0 0.5 1 1.5 2+ Hospitalization 39% Improvement Relative Risk c19early.org/mf Ando et al. Metformin for COVID-19 Prophylaxis Is prophylaxis with metformin beneficial for COVID-19? Retrospective 28,093 patients in the USA (January - November 2020) Lower hospitalization with metformin (p=0.044) Ando et al., Scientific Reports, doi:10.1038/s41598-021-96720-x Favors metformin Favors control
[Ando] Retrospective 28,093 COVID+ patients in the USA, showing lower risk of hospitalization with metformin use.
0 0.5 1 1.5 2+ Mortality 60% Improvement Relative Risk c19early.org/mf Araldi et al. Metformin for COVID-19 Prophylaxis Is prophylaxis with metformin beneficial for COVID-19? Retrospective 43,610 patients in the United Kingdom Lower mortality with metformin (p<0.000001) Araldi et al., medRxiv, doi:10.1101/2023.05.19.23290214 Favors metformin Favors control
[Araldi] UK Biobank retrospective including 43,610 type 2 diabetes patients, showing lower mortality with metformin use within matched type 2 diabetes patients.
0 0.5 1 1.5 2+ Mortality 79% Improvement Relative Risk Case -44% c19early.org/mf Blanc et al. Metformin for COVID-19 Prophylaxis Is prophylaxis with metformin beneficial for COVID-19? Retrospective 179 patients in France Lower mortality (p=0.058) and more cases (p=0.12), not stat. sig. Blanc et al., GeroScience, doi:10.1007/s11357-021-00397-z Favors metformin Favors control
[Blanc] Retrospective 179 patients in France exposed to COVID-19 showing, without statistical significance, a higher risk of cases, and a lower risk of mortality among cases with existing metformin treatment.
0 0.5 1 1.5 2+ Mortality 60% Improvement Relative Risk Ventilation 76% c19early.org/mf Bliden et al. Metformin for COVID-19 Prophylaxis Is prophylaxis with metformin beneficial for COVID-19? Retrospective 75 patients in the USA Lower mortality (p=0.21) and ventilation (p=0.054), not stat. sig. Bliden et al., Circulation, 144:A12228 Favors metformin Favors control
[Bliden] Retrospective 75 diabetes patients, 34 on metformin, showing lower mortality with treatment in unadjusted results with minimal group details.
0 0.5 1 1.5 2+ Hospitalization 10% Improvement Relative Risk c19early.org/mf Boye et al. Metformin for COVID-19 Prophylaxis Is prophylaxis with metformin beneficial for COVID-19? Retrospective 9,531 patients in the USA Lower hospitalization with metformin (p=0.0000028) Boye et al., Diabetes Therapy, doi:10.1007/s13300-021-01110-1 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 0.5 1 1.5 2+ Mortality, PSM 62% Improvement Relative Risk Mortality, MV 68% ICU admission, PSM -9% ICU admission, MV 32% Hospitalization, MV 22% c19early.org/mf Bramante et al. Metformin for COVID-19 Prophylaxis Is prophylaxis with metformin beneficial for COVID-19? Retrospective 9,555 patients in the USA (March - December 2020) Lower mortality with metformin (p=0.029) Bramante et al., J. Medical Virology, doi:10.1002/jmv.26873 Favors metformin Favors control
[Bramante (B)] Retrospective 17,396 PCR+ patients in the USA, showing lower mortality with metformin use.
0 0.5 1 1.5 2+ Mortality, all 12% Improvement Relative Risk Mortality, women 21% Mortality, men 4% c19early.org/mf Bramante et al. Metformin for COVID-19 Prophylaxis Is prophylaxis with metformin beneficial for COVID-19? Retrospective 6,256 patients in the USA No significant difference in mortality Bramante et al., The Lancet Healthy Longevity, doi:10.1016/S2666-7568(20)30033-7 Favors metformin Favors control
[Bramante (C)] Retrospective 6,256 COVID-19+ diabetes patients in the USA, showing lower mortality with existing metformin treatment, statistically significant only for women.
0 0.5 1 1.5 2+ Mortality, day 28 3% Improvement Relative Risk Mortality, day 14 -197% Death/hospitalization 52% Progression 40% Progression (b) 12% primary c19early.org/mf Bramante et al. NCT04510194 COVID-OUT Metformin RCT EARLY Is early treatment with metformin beneficial for COVID-19? Double-blind RCT 1,307 patients in the USA Trial compares with fluvoxamine or ivermectin (part of the control group) Lower progression with metformin (p=0.033) Bramante et al., NEJM, doi:10.1056/NEJMoa2201662 Favors metformin Favors fluvoxamine ..
COVID-OUT remotely operated RCT, showing lower combined ER/hospitalization/death with metformin. Results for other treatments are listed separately - ivermectin, fluvoxamine.
The "control" group includes patients receiving active treatments fluvoxamine and ivermectin.
Control arm results are very different between treatments, for example considering hospitalization/death, this was 1.0% for ivermectin vs. 2.7% for overall control, however it was 1.3% for the ivermectin-specific control. 394 control patients are shared. The rate for the non-shared 261 metformin control patients is 5%, compared to 1.3% for ivermectin control patients. The metformin arm started earlier, however it is unclear why the difference in outcomes is so large.
Results were delayed for 6 months with no explanation, with followup ending Feb 14, 2022.
Adherence was very low, with 77% overall reporting 70+% adherence. Numbers for 100% adherence are not provided.
Multiple outcomes are missing, for example time to recovery (where ACTIV-6 showed superiority of ivermectin).
Treatment was 14 days for metformin and fluvoxamine, but only 3 days for ivermectin.
Trial outcomes were changed on January 20, 2022 [clinicaltrials.gov], and again on March 2, 2022 [clinicaltrials.gov (B)]. COVIDOUT.
Medication delivery varied significantly over the trial. In this presentation [vimeo.com], author indicates that delivery was initially local, later via FedEx, was much slower in August, there were delays due to team bandwidth issues, and they only realized they could use FedEx same day delivery in September.
0 0.5 1 1.5 2+ Mortality, prediabeties 59% Improvement Relative Risk Severe case, prediabeties 54% Progression, prediabeties 42% Progression, prediabe.. (b) 37% Progression, PCOS 41% Progression, PCOS (b) 34% c19early.org/mf Chan et al. Metformin for COVID-19 Prophylaxis Is prophylaxis with metformin beneficial for COVID-19? Retrospective 3,136 patients in the USA Lower severe cases (p=0.37) and progression (p=0.37), not stat. sig. Chan et al., medRxiv, doi:10.1101/2022.08.29.22279355 Favors metformin Favors control
[Chan] Retrospective 3,136 patients with prediabetes and 282 with PCOS, showing metformin associated with reduced COVID-19 severity.
0 0.5 1 1.5 2+ Mortality 33% Improvement Relative Risk c19early.org/mf Chen et al. Metformin for COVID-19 Prophylaxis Is prophylaxis with metformin beneficial for COVID-19? Retrospective 120 patients in China Lower mortality with metformin (not stat. sig., p=0.46) Chen et al., Diabetes Care, doi:10.2337/dc20-0660 Favors metformin Favors control
[Chen] Retrospective 120 COVID-19 diabetes patients, showing non-statistically significantly lower mortality with existing metformin treatment.
0 0.5 1 1.5 2+ Mortality -65% Improvement Relative Risk c19early.org/mf Cheng et al. Metformin for COVID-19 Prophylaxis Is prophylaxis with metformin beneficial for COVID-19? PSM retrospective 1,213 patients in China Higher mortality with metformin (not stat. sig., p=0.25) Cheng et al., Cell Metabolism, doi:10.1016/j.cmet.2020.08.013 Favors metformin Favors control
[Cheng] Retrospective 1,213 hospitalized diabetic COVID-19 patients in China, showing no significant difference in mortality with pre-existing metformin use.
0 0.5 1 1.5 2+ Progression -120% Improvement Relative Risk c19early.org/mf Choi et al. Metformin for COVID-19 Prophylaxis Is prophylaxis with metformin beneficial for COVID-19? PSM retrospective 72 patients in South Korea (Mar - Mar 2020) Higher progression with metformin (not stat. sig., p=0.26) Choi et al., J. Clinical Medicine, doi:10.3390/jcm9061959 Favors metformin Favors control
[Choi] Retrospective 293 patients in South Korea, showing higher risk of progression with metformin use, without statistical significance.
0 0.5 1 1.5 2+ Ventilation 50% Improvement Relative Risk ICU admission 51% c19early.org/mf Cousins et al. Metformin for COVID-19 Prophylaxis Is prophylaxis with metformin beneficial for COVID-19? PSM retrospective 64,349 patients in the USA Lower ventilation (p=0.014) and ICU admission (p<0.0001) Cousins et al., Cell Reports Methods, doi:10.1016/j.crmeth.2023.100503 Favors metformin Favors control
[Cousins] PSM retrospective 64,349 COVID-19 patients in the USA, showing metformin associated with lower ICU admission and mechanical ventilation.
0 0.5 1 1.5 2+ Mortality 61% Improvement Relative Risk c19early.org/mf Crouse et al. Metformin for COVID-19 Prophylaxis Is prophylaxis with metformin beneficial for COVID-19? Retrospective 220 patients in the USA Lower mortality with metformin (p=0.021) Crouse et al., Front. Endocrinol., doi:10.3389/fendo.2020.600439 Favors metformin Favors control
[Crouse] Retrospective 219 COVID-19+ diabetes patients in the USA, showing lower mortality with existing metformin treatment.
0 0.5 1 1.5 2+ Unfavorable outcome 72% Improvement Relative Risk c19early.org/mf Fu et al. Metformin for COVID-19 Prophylaxis Is prophylaxis with metformin beneficial for COVID-19? Retrospective 80 patients in China (January - March 2020) Study compares with other diabetes medications Improved recovery with metformin (p=0.026) Fu et al., Int. J. Endocrinology, doi:10.1155/2022/9322332 Favors metformin Favors other diabet..
[Fu] Retrospective 108 T2D patients hospitalized with COVID-19, showing lower risk of unfavorable outcomes with metformin use vs. other diabetic medications.
0 0.5 1 1.5 2+ Progression -225% Improvement Relative Risk c19early.org/mf Gao et al. Metformin for COVID-19 Prophylaxis Is prophylaxis with metformin beneficial for COVID-19? Retrospective 110 patients in China (January - March 2020) Higher progression with metformin (p=0.045) Gao et al., Clinical and Translational Science, doi:10.1111/cts.12897 Favors metformin Favors control
[Gao] Retrospective 110 hospitalized COVID-19 patients with diabetes in China, showing increased risk of severity with metformin.
0 0.5 1 1.5 2+ Mortality 66% Improvement Relative Risk Hospitalization 29% ARDS 68% c19early.org/mf Ghany et al. Metformin for COVID-19 Prophylaxis Is prophylaxis with metformin beneficial for COVID-19? Retrospective 1,139 patients in the USA Lower mortality (p=0.00021) and hospitalization (p=0.0076) Ghany et al., Diabetes & Metabolic Syndrome: Cli.., doi:10.1016/j.dsx.2021.02.022 Favors metformin Favors control
[Ghany] Retrospective 1,139 elderly COVID+ patients in the USA, 392 with pre-existing metformin use, showing significantly lower mortality, hospitalization, and ARDS with treatment.
0 0.5 1 1.5 2+ Mortality 3% Improvement Relative Risk c19early.org/mf Goodall et al. Metformin for COVID-19 Prophylaxis Is prophylaxis with metformin beneficial for COVID-19? Retrospective 981 patients in the United Kingdom (Mar - Apr 2020) No significant difference in mortality Goodall et al., Epidemiology and Infection, doi:10.1017/S0950268820002472 Favors metformin Favors control
[Goodall] Retrospective 981 hospitalized patients in the UK, showing no significant difference with metformin use.
0 0.5 1 1.5 2+ Mortality -16% Improvement Relative Risk Severe case -16% c19early.org/mf Gálvez-Barrón et al. Metformin for COVID-19 Prophylaxis Is prophylaxis with metformin beneficial for COVID-19? Retrospective 103 patients in Spain (March - May 2020) Higher mortality (p=0.46) and severe cases (p=0.46), not stat. sig. Gálvez-Barrón et al., Gerontology, doi:10.1159/000515159 Favors metformin Favors control
[Gálvez-Barrón] Analysis of 103 elderly hospitalized COVID-19 patients in Spain, showing higher mortality with metformin, without statistical significance.
0 0.5 1 1.5 2+ Case -27% Improvement Relative Risk c19early.org/mf Holt et al. NCT04330599 COVIDENCE UK Metformin Prophylaxis Does metformin reduce COVID-19 infections? Prospective study of 15,227 patients in the United Kingdom (May 2020 - Feb 2021) More cases with metformin (not stat. sig., p=0.42) Holt et al., Thorax, doi:10.1136/thoraxjnl-2021-217487 Favors metformin Favors control
[Holt] Prospective survey-based study with 15,227 people in the UK, showing lower risk of COVID-19 cases with vitamin A, vitamin D, zinc, selenium, probiotics, and inhaled corticosteroids; and higher risk with metformin and vitamin C. Statistical significance was not reached for any of these. Except for vitamin D, the results for treatments we follow were only adjusted for age, sex, duration of participation, and test frequency. NCT04330599. COVIDENCE UK.