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Metformin for COVID-19: real-time meta analysis of 55 studies
Covid Analysis, December 2022
https://c19early.org/mfmeta.html
 
0 0.5 1 1.5+ All studies 27% 55 206,012 Improvement, Studies, Patients Relative Risk Mortality 32% 42 143,679 Ventilation 32% 8 12,454 ICU admission 18% 5 39,979 Hospitalization 19% 10 29,643 Progression 27% 9 20,493 Recovery 38% 2 4,008 Cases 0% 6 27,298 Viral clearance 1% 1 418 RCTs 24% 2 1,222 RCT mortality 24% 2 1,222 Peer-reviewed 26% 50 159,714 Prophylaxis 24% 50 177,963 Early 58% 3 27,730 Late 86% 2 319 Metformin for COVID-19 c19early.org/mf Dec 2022 Favorsmetformin Favorscontrol after exclusions
Statistically significant improvements are seen for mortality, ventilation, ICU admission, and hospitalization. 33 studies from 31 independent teams in 10 different countries show statistically significant improvements in isolation (29 for the most serious outcome).
Meta analysis using the most serious outcome reported shows 27% [22‑31%] 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 34 of 55 studies must be excluded to avoid finding statistically significant efficacy in pooled analysis.
0 0.5 1 1.5+ All studies 27% 55 206,012 Improvement, Studies, Patients Relative Risk Mortality 32% 42 143,679 Ventilation 32% 8 12,454 ICU admission 18% 5 39,979 Hospitalization 19% 10 29,643 Progression 27% 9 20,493 Recovery 38% 2 4,008 Cases 0% 6 27,298 Viral clearance 1% 1 418 RCTs 24% 2 1,222 RCT mortality 24% 2 1,222 Peer-reviewed 26% 50 159,714 Prophylaxis 24% 50 177,963 Early 58% 3 27,730 Late 86% 2 319 Metformin for COVID-19 c19early.org/mf Dec 2022 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, Lukito, Yang], showing significant improvements for mortality and progression.
Highlights
Metformin reduces risk for COVID-19 with very high confidence for mortality, hospitalization, and in pooled analysis, high confidence for ventilation and ICU admission, low confidence for progression, and very 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 47 treatments.
A
0 0.25 0.5 0.75 1 1.25 1.5 1.75 2+ 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 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 Kolin -30% 1.30 [0.97-1.73] cases n/a n/a 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) Wargny 28% 0.72 [0.53-0.95] death 247/1,553 330/1,241 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 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 Cousins (PSM) 54% 0.46 [0.25-0.82] ventilation 2,498 (n) 2,497 (n) Shestakova 22% 0.78 [0.67-0.91] death 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 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) Milosavljevic 33% 0.67 [0.47-0.95] severe case 377 (n) 356 (n) Tau​2 = 0.02, I​2 = 91.6%, p < 0.0001 Prophylaxis 24% 0.76 [0.71-0.81] 4,666/67,229 9,689/110,734 24% improvement All studies 27% 0.73 [0.69-0.78] 4,749/71,960 11,259/134,052 27% improvement 55 metformin COVID-19 studies c19early.org/mf Dec 2022 Tau​2 = 0.03, I​2 = 91.7%, 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+ Reis (DB RCT) 27% death impossible data, see notes Relative Risk [CI] Hunt 67% death 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 Kolin -30% case Bramante 7% death Lalau (PSM) 22% death Huh -1% progression Crouse 61% death Lally 52% death Oh -26% death Wargny 28% death 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 Usman 60% death Wong 51% death Wong (PSW) 59% death MacFadden 1% case Ma (PSW) 74% death Cousins (PSM) 54% 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 Milosavljevic 33% severe case Tau​2 = 0.02, I​2 = 91.6%, p < 0.0001 Prophylaxis 24% 24% improvement All studies 27% 27% improvement 55 metformin COVID-19 studies c19early.org/mf Dec 2022 Tau​2 = 0.03, I​2 = 91.7%, p < 0.0001 Protocol pre-specified/rotate for details1 OT: comparison with other treatment Favors metformin Favors control
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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. D. Timeline of results in metformin studies.
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.
An In Vitro study supports the efficacy of metformin [Parthasarathy].
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.
Improvement Studies Patients Authors
All studies27% [22‑31%]55 206,012 659
After exclusions27% [22‑32%]52 190,344 602
Peer-reviewed studiesPeer-reviewed26% [21‑31%]50 159,714 608
Randomized Controlled TrialsRCTs24% [-89‑70%]2 1,222 26
Mortality32% [27‑37%]42 143,679 536
VentilationVent.32% [6‑50%]8 12,454 98
ICU admissionICU18% [3‑31%]5 39,979 34
HospitalizationHosp.19% [7‑30%]10 29,643 109
Cases0% [-15‑14%]6 27,298 79
RCT mortality24% [-89‑70%]2 1,222 26
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.
Early treatment Late treatment Prophylaxis
All studies58% [23‑77%] 386% [22‑98%] 224% [19‑29%] 50
After exclusions58% [23‑77%] 386% [22‑98%] 225% [20‑30%] 47
Peer-reviewed studiesPeer-reviewed58% [23‑77%] 386% [22‑98%] 224% [18‑29%] 45
Randomized Controlled TrialsRCTs24% [-89‑70%] 2--
Mortality58% [23‑77%] 386% [22‑98%] 229% [24‑34%] 37
VentilationVent.--32% [6‑50%] 8
ICU admissionICU--18% [3‑31%] 5
HospitalizationHosp.6% [-61‑45%] 1-20% [8‑30%] 9
Cases--0% [-15‑14%] 6
RCT mortality24% [-89‑70%] 2--
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.
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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 peer-reviewed studies are more trustworthy. They also show extremely slow review times during 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.
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].
In summary, 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 example, consider trials for an off-patent medication, 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.
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]
Table 3. Early treatment is more effective for baloxavir and influenza.
Figure 16 shows a mixed-effects meta-regression for efficacy as a function of treatment delay in COVID-19 studies from 47 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 47 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.
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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.
One method to evaluate bias is to compare prospective vs. retrospective studies. Prospective studies are more likely to be published regardless of the result, while retrospective studies are more likely to exhibit bias. For example, researchers may perform preliminary analysis with minimal effort and the results may influence their decision to continue. Retrospective studies also provide more opportunities for the specifics of data extraction and adjustments to influence results.
62% of retrospective studies report a statistically significant positive effect for one or more outcomes, compared to 33% of prospective studies, consistent with a bias toward publishing positive results. The median effect size for retrospective studies is 34% improvement, compared to 3% for prospective studies, suggesting a potential bias towards publishing results showing higher efficacy. Figure 18 shows a scatter plot of results for prospective and retrospective studies.
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Figure 18. Prospective vs. retrospective studies.
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 19 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 19. 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.
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.
1 of the 55 studies compare against other treatments, which may reduce the effect seen. Other meta analyses for metformin can be found in [Hariyanto, Kow, Lukito, Yang], showing significant improvements for mortality and progression.
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.
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.
Statistically significant improvements are seen for mortality, ventilation, ICU admission, and hospitalization. 33 studies from 31 independent teams in 10 different countries show statistically significant improvements in isolation (29 for the most serious outcome). Meta analysis using the most serious outcome reported shows 27% [22‑31%] 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 34 of 55 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 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 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 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 79% Improvement Relative Risk Case -44% c19early.org/mf Blanc et al. Metformin for COVID-19 Prophylaxis 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 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 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 7% Improvement Relative Risk Mortality (b) 24% Mortality (c) -3% c19early.org/mf Bramante et al. Metformin for COVID-19 Prophylaxis Favors metformin Favors control
[Bramante (B)] 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 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 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 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 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 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 54% Improvement Relative Risk ICU admission 56% c19early.org/mf Cousins et al. Metformin for COVID-19 Prophylaxis 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 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+ Progression -225% Improvement Relative Risk c19early.org/mf Gao et al. Metformin for COVID-19 Prophylaxis 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 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 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 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 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.
0 0.5 1 1.5 2+ Progression -1% Improvement Relative Risk Case 4% c19early.org/mf Huh et al. Metformin for COVID-19 Prophylaxis Favors metformin Favors control
[Huh] Retrospective database analysis showing no significant differences with pre-existing metformin use.
0 0.5 1 1.5 2+ Mortality 67% Improvement Relative Risk c19early.org/mf Hunt et al. Metformin for COVID-19 EARLY TREATMENT Favors metformin Favors control
[Hunt] Retrospective 26,508 consecutive COVID+ veterans in the USA, showing lower mortality with multiple treatments including metformin. Treatment was defined as drugs administered ≥50% of the time within 2 weeks post-COVID+, and may be a continuation of prophylactic treatment in some cases, and may be early or late treatment in other cases. Further reduction in mortality was seen with combinations of treatments.
0 0.5 1 1.5 2+ Mortality 46% Improvement Relative Risk ARDS 80% c19early.org/mf Jiang et al. Metformin for COVID-19 Prophylaxis Favors metformin Favors control
[Jiang] Retrospective 328 COVID-19 patients with type 2 diabetes in China, showing significantly lower risk of ARDS with existing metformin use.
0 0.5 1 1.5 2+ Mortality 23% Improvement Relative Risk c19early.org/mf Khunti et al. Metformin for COVID-19 Prophylaxis Favors metformin Favors control
[Khunti] Retrospective 2,851,465 people with type 2 diabetes in the UK, showing lower mortality with existing metformin use. Results are subject to confounding by indication because metformin is typically used early in the progression of type 2 diabetes.
0 0.5 1 1.5 2+ Mortality 64% Improvement Relative Risk Progression 52% c19early.org/mf Kim et al. Metformin for COVID-19 Prophylaxis Favors metformin Favors control
[Kim] Retrospective 235 hospitalized diabetes patients in South Korea, showing lower mortality and lower progression to severe disease with metformin.
0 0.5 1 1.5 2+ Case -30% Improvement Relative Risk c19early.org/mf Kolin et al. Metformin for COVID-19 Prophylaxis Favors metformin Favors control
[Kolin] 397,064 patient UK Biobank retrospective showing higher risk of COVID-19 with metformin use, without statistical significance.
0 0.5 1 1.5 2+ Mortality 22% Improvement Relative Risk Death/intubation 18% primary Ventilation 7% c19early.org/mf Lalau et al. Metformin for COVID-19 Prophylaxis Favors metformin Favors control
[Lalau] Retrospective 2,449 hospitalized COVID-19 diabetes patients in France, 1,496 with existing metformin use, showing lower mortality with treatment. Statistical significance was reached in model 1 but not in models 2-4 which also adjust for HbA1c, eGFR, and diabetes duration, but have a lower number of patients. CORONADO (Coronavirus SARS-CoV-2 and Diabetes Outcomes).
0 0.5 1 1.5 2+ Mortality 52% Improvement Relative Risk c19early.org/mf Lally et al. Metformin for COVID-19 Prophylaxis Favors metformin Favors control
[Lally] Retrospective 775 nursing home residents in the USA, showing lower mortality with existing metformin use.
0 0.5 1 1.5 2+ Mortality 78% Improvement Relative Risk Ventilation -27% c19early.org/mf Li et al. Metformin for COVID-19 Prophylaxis Favors metformin Favors control
[Li (B)] Retrospective 131 type II diabetes patients with COVID pneumonia, showing lower mortality with existing metformin use. Acarbose (commonly used in China as an initial therapy for diabetes) did not have a similar association with mortality, suggesting that the result may not be explained by metformin being used early in type II diabetes.
0 0.5 1 1.5 2+ Mortality 76% Improvement Relative Risk c19early.org/mf Li et al. Metformin for COVID-19 LATE TREATMENT Favors metformin Favors control
[Li] Retrospective 131 hospitalized COVID-19 patients with type 2 diabetes, showing lower mortality with metformin treatment and acarbose treatment.
0 0.5 1 1.5 2+ Mortality 30% Improvement Relative Risk c19early.org/mf Loucera et al. Metformin for COVID-19 Prophylaxis Favors metformin Favors control
[Loucera] Retrospective 15,968 COVID-19 hospitalized patients in Spain, showing lower mortality with existing use of several medications including metformin, HCQ, aspirin, vitamin D, vitamin C, and budesonide.
0 0.5 1 1.5 2+ Mortality 75% Improvement Relative Risk c19early.org/mf Luo et al. Metformin for COVID-19 Prophylaxis Favors metformin Favors control
[Luo] Retrospective 283 COVID-19+ diabetes patients in China, showing lower mortality with existing metformin treatment.
0 0.5 1 1.5 2+ Mortality 74% Improvement Relative Risk Ventilation 25% c19early.org/mf Ma et al. Metformin for COVID-19 Prophylaxis Favors metformin Favors control
[Ma] PSM/IPTW retrospective 1,356 hospitalized COVID-19 patients with type 2 diabetes in China, showing lower mortality/hospice with metformin use.
0 0.5 1 1.5 2+ Case 1% Improvement Relative Risk c19early.org/mf MacFadden et al. Metformin for COVID-19 Prophylaxis Favors metformin Favors control
[MacFadden] Retrospective 26,121 cases and 2,369,020 controls ≥65yo in Canada, showing no significant differences in cases with chronic use of metformin.
0 0.5 1 1.5 2+ Severe case 33% Improvement Relative Risk c19early.org/mf Milosavljevic et al. Metformin for COVID-19 Prophylaxis Favors metformin Favors control
[Milosavljevic] Retrospective 733 hospitalized COVID-19 patients with diabetes in the USA, showing lower risk of severity with metformin use.
0 0.5 1 1.5 2+ Mortality 41% Improvement Relative Risk Ventilation -16% ICU admission 3% Hospitalization -4% c19early.org/mf Morrison et al. Metformin for COVID-19 Prophylaxis Favors metformin Favors control
[Morrison] Retrospective 13,585 COVID+ patients in the USA, showing lower mortality with metformin use, but no significant difference for ventilation, ICU admission, and hospitalization.
0 0.5 1 1.5 2+ Mortality -26% Improvement Relative Risk Case 28% c19early.org/mf Oh et al. Metformin for COVID-19 Prophylaxis Favors metformin Favors control
[Oh] Retrospective 27,493 type II diabetes patients in the USA, 7,204 on metformin, showing significantly lower COVID-19 cases, but no significant difference in mortality.
0 0.5 1 1.5 2+ Mortality 16% Improvement Relative Risk Mortality (b) 22% ICU admission 22% Hospitalization 3% Mortality (c) 8% Mortality (d) 16% ICU admission (b) 39% Hospitalization (b) -2% c19early.org/mf Ojeda-Fernández et al. Metformin for COVID-19 Prophylaxis Favors metformin Favors control
[Ojeda-Fernández] Retrospective 31,966 COVID+ patients using anti-hyperglycemic drugs in Italy, showing lower mortality and ICU admission with metformin use.
0 0.5 1 1.5 2+ Mortality 47% Improvement Relative Risk Mortality (b)