Covid Analysis, December 2022
•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%].
•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.
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.
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.
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.
|All studies||27% [22‑31%]||55||206,012||659|
|After exclusions||27% [22‑32%]||52||190,344||602|
|Peer-reviewed studiesPeer-reviewed||26% [21‑31%]||50||159,714||608|
|Randomized Controlled TrialsRCTs||24% [-89‑70%]||2||1,222||26|
|ICU admissionICU||18% [3‑31%]||5||39,979||34|
|RCT mortality||24% [-89‑70%]||2||1,222||26|
|Early treatment||Late treatment||Prophylaxis|
|All studies||58% [23‑77%] 3||86% [22‑98%] 2||24% [19‑29%] 50|
|After exclusions||58% [23‑77%] 3||86% [22‑98%] 2||25% [20‑30%] 47|
|Peer-reviewed studiesPeer-reviewed||58% [23‑77%] 3||86% [22‑98%] 2||24% [18‑29%] 45|
|Randomized Controlled TrialsRCTs||24% [-89‑70%] 2||-||-|
|Mortality||58% [23‑77%] 3||86% [22‑98%] 2||29% [24‑34%] 37|
|VentilationVent.||-||-||32% [6‑50%] 8|
|ICU admissionICU||-||-||18% [3‑31%] 5|
|HospitalizationHosp.||6% [-61‑45%] 1||-||20% [8‑30%] 9|
|Recovery||-||-||38% [-31‑70%] 2|
|Cases||-||-||0% [-15‑14%] 6|
|RCT mortality||24% [-89‑70%] 2||-||-|
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.
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).
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.
Heterogeneity in COVID-19 studies arises from many factors including:
[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.
|Post exposure prophylaxis||86% 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 47 treatments, showing that efficacy declines rapidly with treatment delay. Early treatment is critical for COVID-19.
[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].
[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.
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.
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.
[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.
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.
[Hariyanto, Kow, Lukito, Yang], showing significant improvements for mortality and progression.
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%].
[Al-Salameh] Retrospective 140 diabetic patients in France, showing lower mortality for patients where metformin use was continued after hospitalization.
[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.
[Ando] Retrospective 28,093 COVID+ patients in the USA, showing lower risk of hospitalization with metformin use.
[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.
[Bliden] Retrospective 75 diabetes patients, 34 on metformin, showing lower mortality with treatment in unadjusted results with minimal group details.
[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).
[Bramante (B)] Retrospective 6,256 COVID-19+ diabetes patients in the USA, showing lower mortality with existing metformin treatment, statistically significant only for women.
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.
[Chan] Retrospective 3,136 patients with prediabetes and 282 with PCOS, showing metformin associated with reduced COVID-19 severity.
[Chen] Retrospective 120 COVID-19 diabetes patients, showing non-statistically significantly lower mortality with existing metformin treatment.
[Cheng] Retrospective 1,213 hospitalized diabetic COVID-19 patients in China, showing no significant difference in mortality with pre-existing metformin use.
[Choi] Retrospective 293 patients in South Korea, showing higher risk of progression with metformin use, without statistical significance.
[Cousins] PSM retrospective 64,349 COVID-19 patients in the USA, showing metformin associated with lower ICU admission and mechanical ventilation.
[Crouse] Retrospective 219 COVID-19+ diabetes patients in the USA, showing lower mortality with existing metformin treatment.
[Gao] Retrospective 110 hospitalized COVID-19 patients with diabetes in China, showing increased risk of severity with metformin.
[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.
[Goodall] Retrospective 981 hospitalized patients in the UK, showing no significant difference with metformin use.
[Gálvez-Barrón] Analysis of 103 elderly hospitalized COVID-19 patients in Spain, showing higher mortality with metformin, without statistical significance.
[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.
[Huh] Retrospective database analysis showing no significant differences with pre-existing metformin use.
[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.
[Jiang] Retrospective 328 COVID-19 patients with type 2 diabetes in China, showing significantly lower risk of ARDS with existing metformin use.
[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.
[Kim] Retrospective 235 hospitalized diabetes patients in South Korea, showing lower mortality and lower progression to severe disease with metformin.
[Kolin] 397,064 patient UK Biobank retrospective showing higher risk of COVID-19 with metformin use, without statistical significance.
[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).
[Lally] Retrospective 775 nursing home residents in the USA, showing lower mortality with existing metformin use.
[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.
[Li] Retrospective 131 hospitalized COVID-19 patients with type 2 diabetes, showing lower mortality with metformin treatment and acarbose treatment.
[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.
[Luo] Retrospective 283 COVID-19+ diabetes patients in China, showing lower mortality with existing metformin treatment.
[Ma] PSM/IPTW retrospective 1,356 hospitalized COVID-19 patients with type 2 diabetes in China, showing lower mortality/hospice with metformin use.
[MacFadden] Retrospective 26,121 cases and 2,369,020 controls ≥65yo in Canada, showing no significant differences in cases with chronic use of metformin.
[Milosavljevic] Retrospective 733 hospitalized COVID-19 patients with diabetes in the USA, showing lower risk of severity with metformin use.
[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.
[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.
[Ojeda-Fernández] Retrospective 31,966 COVID+ patients using anti-hyperglycemic drugs in Italy, showing lower mortality and ICU admission with metformin use.