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Quercetin for COVID-19: real-time meta analysis of 9 studies
Covid Analysis, December 2022
https://c19early.org/qmeta.html
 
0 0.5 1 1.5+ All studies 63% 9 1,279 Improvement, Studies, Patients Relative Risk Mortality 59% 4 683 ICU admission 75% 4 683 Hospitalization 68% 2 194 Recovery 34% 4 739 Cases 93% 3 346 Viral clearance 61% 2 92 RCTs 57% 8 1,166 RCT mortality 59% 4 683 Peer-reviewed 53% 8 1,166 Exc. combined 68% 5 574 Prophylaxis 93% 3 346 Early 38% 3 244 Late 31% 3 689 Quercetin for COVID-19 c19early.org/q Dec 2022 Favorsquercetin Favorscontrol after exclusions
Statistically significant improvements are seen for ICU admission, hospitalization, recovery, cases, and viral clearance. 8 studies from 7 independent teams in 6 different countries show statistically significant improvements in isolation (3 for the most serious outcome).
Meta analysis using the most serious outcome reported shows 63% [27‑81%] improvement. Results are similar for Randomized Controlled Trials, similar after exclusions, similar for peer-reviewed studies, and similar after excluding studies using combined treatment.
0 0.5 1 1.5+ All studies 63% 9 1,279 Improvement, Studies, Patients Relative Risk Mortality 59% 4 683 ICU admission 75% 4 683 Hospitalization 68% 2 194 Recovery 34% 4 739 Cases 93% 3 346 Viral clearance 61% 2 92 RCTs 57% 8 1,166 RCT mortality 59% 4 683 Peer-reviewed 53% 8 1,166 Exc. combined 68% 5 574 Prophylaxis 93% 3 346 Early 38% 3 244 Late 31% 3 689 Quercetin for COVID-19 c19early.org/q Dec 2022 Favorsquercetin Favorscontrol after exclusions
Studies typically use advanced formulations for greatly improved bioavailability.
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 may be more effective. Only 44% of quercetin studies show zero events with treatment. The quality of non-prescription supplements can vary widely [Crawford, Crighton].
All data to reproduce this paper and sources are in the appendix.
Highlights
Quercetin reduces risk for COVID-19 with very high confidence for recovery, cases, and in pooled analysis, high confidence for ICU admission, low confidence for hospitalization and viral clearance, and very low confidence for mortality. Studies typically use advanced formulations for greatly improved bioavailability.
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+ Di Pierro (RCT) 86% 0.14 [0.01-2.72] death 0/76 3/76 Improvement, RR [CI] Treatment Control Di Pierro (RCT) 67% 0.33 [0.01-7.74] death 0/21 1/21 Khan (RCT) 33% 0.67 [0.37-1.19] no recov. 10/25 15/25 CT​1 Tau​2 = 0.00, I​2 = 0.0%, p = 0.09 Early treatment 38% 0.62 [0.35-1.08] 10/122 19/122 38% improvement Onal (RCT) -29% 1.29 [0.16-10.5] death 1/49 6/380 CT​1 Improvement, RR [CI] Treatment Control Zupanets (RCT) 29% 0.71 [0.32-1.58] no recov. 9/99 13/101 Shohan (RCT) 86% 0.14 [0.01-2.65] death 0/30 3/30 Tau​2 = 0.00, I​2 = 0.0%, p = 0.32 Late treatment 31% 0.69 [0.33-1.42] 10/178 22/511 31% improvement Arslan (RCT) 92% 0.08 [0.01-0.79] cases 1/71 9/42 CT​1 Improvement, RR [CI] Treatment Control Margolin 94% 0.06 [0.00-0.93] cases 0/53 9/60 CT​1 Rondanelli (DB RCT) 93% 0.07 [0.01-0.91] symp. case 1/60 4/60 Tau​2 = 0.00, I​2 = 0.0%, p < 0.0001 Prophylaxis 93% 0.07 [0.02-0.27] 2/184 22/162 93% improvement All studies 63% 0.37 [0.19-0.73] 22/484 63/795 63% improvement 9 quercetin COVID-19 studies c19early.org/q Dec 2022 Tau​2 = 0.31, I​2 = 35.3%, p = 0.0042 Effect extraction pre-specified(most serious outcome, see appendix) 1 CT: study uses combined treatment Favors quercetin Favors control
0 0.25 0.5 0.75 1 1.25 1.5 1.75 2+ Di Pierro (RCT) 86% death Relative Risk [CI] Di Pierro (RCT) 67% death Khan (RCT) 33% recovery CT​1 Tau​2 = 0.00, I​2 = 0.0%, p = 0.09 Early treatment 38% 38% improvement Onal (RCT) -29% death CT​1 Zupanets (RCT) 29% recovery Shohan (RCT) 86% death Tau​2 = 0.00, I​2 = 0.0%, p = 0.32 Late treatment 31% 31% improvement Arslan (RCT) 92% case CT​1 Margolin 94% case CT​1 Rondane.. (DB RCT) 93% symp. case Tau​2 = 0.00, I​2 = 0.0%, p < 0.0001 Prophylaxis 93% 93% improvement All studies 63% 63% improvement 9 quercetin COVID-19 studies c19early.org/q Dec 2022 Tau​2 = 0.31, I​2 = 35.3%, p = 0.0042 Protocol pre-specified/rotate for details1 CT: study uses combined treatment Favors quercetin 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. D. Timeline of results in quercetin studies.
We analyze all significant studies concerning the use of quercetin 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.
4 In Silico studies support the efficacy of quercetin [Chellasamy, Kandeil, Sekiou, Şimşek].
5 In Vitro studies support the efficacy of quercetin [Bahun, Goc, Kandeil, Munafò, Singh].
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, and 11 show forest plots for random effects meta-analysis of all studies with pooled effects, mortality results, ICU admission, hospitalization, recovery, cases, viral clearance, peer reviewed studies, and all studies excluding combined treatment studies.
Improvement Studies Patients Authors
All studies63% [27‑81%]9 1,279 94
After exclusions55% [11‑78%]7 1,124 75
Peer-reviewed studiesPeer-reviewed53% [13‑75%]8 1,166 87
Randomized Controlled TrialsRCTs57% [18‑77%]8 1,166 89
Mortality59% [-55‑89%]4 683 49
ICU admissionICU75% [13‑93%]4 683 49
HospitalizationHosp.68% [31‑85%]2 194 31
Cases93% [73‑98%]3 346 24
Viral61% [28‑79%]2 92 19
RCT mortality59% [-55‑89%]4 683 49
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 studies38% [-8‑65%] 331% [-42‑67%] 393% [73‑98%] 3
After exclusions38% [-15‑66%] 231% [-42‑67%] 394% [64‑99%] 2
Peer-reviewed studiesPeer-reviewed38% [-8‑65%] 331% [-42‑67%] 394% [64‑99%] 2
Randomized Controlled TrialsRCTs38% [-8‑65%] 331% [-42‑67%] 392% [66‑98%] 2
Mortality79% [-82‑98%] 245% [-350‑93%] 2-
ICU admissionICU87% [-5‑98%] 273% [-131‑97%] 2-
HospitalizationHosp.68% [31‑85%] 2--
Cases--93% [73‑98%] 3
Viral61% [28‑79%] 2--
RCT mortality79% [-82‑98%] 245% [-350‑93%] 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 ICU admission.
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Figure 6. Random effects meta-analysis for hospitalization.
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Figure 7. Random effects meta-analysis for recovery.
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Figure 8. Random effects meta-analysis for cases.
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Figure 9. Random effects meta-analysis for viral clearance.
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Figure 10. 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.
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Figure 11. Random effects meta-analysis for all studies excluding combined treatment studies. Effect extraction is pre-specified, using the most serious outcome reported, see the appendix for details.
Figure 12 shows a comparison of results for RCTs and non-RCT studies. Figure 13 and 14 show forest plots for random effects meta-analysis of all Randomized Controlled Trials and RCT mortality results.
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Figure 12. Results for RCTs and non-RCT studies.
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Figure 13. 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.
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Figure 14. Random effects meta-analysis for RCT mortality results.
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.
[Arslan], paper no longer available at the source, and the contact does not reply to queries.
[Di Pierro], randomization resulted in significant baseline differences that were not adjusted for.
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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. Non-prescription supplements may show very wide variations in quality [Crawford, Crighton].
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 quercetin, 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.
100% of retrospective studies report a statistically significant positive effect for one or more outcomes, compared to 88% of prospective studies, consistent with a bias toward publishing positive results. The median effect size for retrospective studies is 94% improvement, compared to 76% 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. Quercetin for COVID-19 lacks this because it is an inexpensive and widely available supplement. In contrast, most COVID-19 quercetin 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 quercetin 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.
4 of 9 studies combine treatments. The results of quercetin alone may differ. 3 of 8 RCTs use combined treatment.
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.
Studies to date show that quercetin is an effective treatment for COVID-19. Statistically significant improvements are seen for ICU admission, hospitalization, recovery, cases, and viral clearance. 8 studies from 7 independent teams in 6 different countries show statistically significant improvements in isolation (3 for the most serious outcome). Meta analysis using the most serious outcome reported shows 63% [27‑81%] improvement. Results are similar for Randomized Controlled Trials, similar after exclusions, similar for peer-reviewed studies, and similar after excluding studies using combined treatment.
Studies typically use advanced formulations for greatly improved bioavailability.
0 0.5 1 1.5 2+ Case 92% Improvement Relative Risk c19early.org/q Arslan et al. NCT04377789 Quercetin RCT Prophylaxis Favors quercetin Favors control
[Arslan] Small prophylaxis RCT with 113 patients showing fewer cases with quercetin + vitamin C + bromelain prophylaxis. NCT04377789. Note that this paper disappeared from SSRN without explanation.
0 0.5 1 1.5 2+ Mortality 67% Improvement Relative Risk ICU admission 67% Hospitalization 67% Viral clearance 74% Viral clearance (b) 89% c19early.org/q Di Pierro et al. NCT04861298 Quercetin RCT EARLY TREATMENT Favors quercetin Favors control
[Di Pierro] RCT 42 outpatients in Pakistan, 21 treated with quercetin phytosome, showing faster viral clearance and lower symptom severity with treatment. Patients in the treatment group were younger (43 vs. 56).
0 0.5 1 1.5 2+ Mortality 86% Improvement Relative Risk ICU admission 94% Hospitalization 68% c19early.org/q Di Pierro et al. NCT04578158 Quercetin RCT EARLY TREATMENT Favors quercetin Favors control
[Di Pierro (B)] RCT 152 outpatients in Pakistan, 76 treated with quercetin phytosome, showing lower mortality, ICU admission, and hospitalization with treatment.
0 0.5 1 1.5 2+ Recovery 33% Improvement Relative Risk CRP reduction 39% Viral clearance 50% c19early.org/q Khan et al. NCT05130671 Quercetin RCT EARLY TREATMENT Favors quercetin Favors control
[Khan] RCT 50 COVID+ outpatients in Pakistan, 25 treated with curcumin, quercetin, and vitamin D, showing significantly faster viral clearance, significantly improved CRP, and faster resolution of acute symptoms (p=0.154). 168mg curcumin, 260mg quercetin and 360IU cholecalciferol.
0 0.5 1 1.5 2+ Case 94% Improvement Relative Risk COVID-19 or flu-like illness 81% c19early.org/q Margolin et al. Quercetin for COVID-19 Prophylaxis Favors quercetin Favors control
[Margolin] Retrospective 113 outpatients, 53 (patient choice) treated with zinc, quercetin, vitamin C/D/E, l-lysine, and quina, showing lower cases with treatment. Results are subject to selection bias and limited information on the groups is provided. See [journals.sagepub.com].
0 0.5 1 1.5 2+ Mortality -29% Improvement Relative Risk ICU admission 94% Discharge 78% c19early.org/q Onal et al. NCT04377789 Quercetin RCT LATE TREATMENT Favors quercetin Favors control
[Onal] RCT 447 moderate-to-severe hospitalized patients in Turkey, 52 treated with quercetin, bromelain, and vitamin C, showing no statistically significant difference in clinical outcomes. NCT04377789.
0 0.5 1 1.5 2+ Symptomatic case 93% Improvement Relative Risk c19early.org/q Rondanelli et al. NCT05037240 Quercetin RCT Prophylaxis Favors quercetin Favors control
[Rondanelli] RCT 120 healthcare workers, 60 treated with quercetin phytosome, showing lower risk of cases with treatment. Quercetin phytosome 250mg twice a day.
0 0.5 1 1.5 2+ Mortality 86% Improvement Relative Risk ICU admission 40% Days from end of interven.. 32% c19early.org/q Shohan et al. Quercetin for COVID-19 RCT LATE TREATMENT Favors quercetin Favors control
[Shohan] Small RCT with 60 severe hospitalized patients in Iran, 30 treated with quercetin, showing shorter time until discharge. All patients received remdesivir or favipiravir, and vitamin C, vitamin D, famotidine, zinc, dexamethasone, and magnesium (depending on serum levels). Quercetin 1000mg daily for 7 days. IRCT20200419047128N2.
0 0.5 1 1.5 2+ Recovery 29% Improvement Relative Risk Recovery time 18% c19early.org/q Zupanets et al. Quercetin for COVID-19 RCT LATE TREATMENT Favors quercetin Favors control
[Zupanets] RCT 200 patients in Ukraine, 99 treated with IV quercetin/polyvinylirolidone followed by oral quercetin/pectin, showing improved recovery with treatment.
We performed ongoing searches of PubMed, medRxiv, ClinicalTrials.gov, The Cochrane Library, Google Scholar, Collabovid, Research Square, ScienceDirect, Oxford University Press, the reference lists of other studies and meta-analyses, and submissions to the site c19early.org. Search terms were quercetin, filtered for papers containing the terms COVID-19 or SARS-CoV-2. Automated searches are performed every few hours with notification of new matches. All studies regarding the use of quercetin for COVID-19 that report a comparison with a control group are included in the main analysis. Sensitivity analysis is performed, excluding studies with major issues, epidemiological studies, and studies with minimal available information. This is a living analysis and is updated regularly.
We extracted effect sizes and associated data from all studies. If studies report multiple kinds of effects then the most serious outcome is used in pooled analysis, while other outcomes are included in the outcome specific analyses. For example, if effects for mortality and cases are both reported, the effect for mortality is used, this may be different to the effect that a study focused on. If symptomatic results are reported at multiple times, we used the latest time, for example if mortality results are provided at 14 days and 28 days, the results at 28 days are used. Mortality alone is preferred over combined outcomes. Outcomes with zero events in both arms were not used (the next most serious outcome is used — no studies were excluded). For example, in low-risk populations with no mortality, a reduction in mortality with treatment is not possible, however a reduction in hospitalization, for example, is still valuable. Clinical outcome is considered more important than PCR testing status. When basically all patients recover in both treatment and control groups, preference for viral clearance and recovery is given to results mid-recovery where available (after most or all patients have recovered there is no room for an effective treatment to do better). If only individual symptom data is available, the most serious symptom has priority, for example difficulty breathing or low SpO2 is more important than cough. When results provide an odds ratio, we computed the relative risk when possible, or converted to a relative risk according to [Zhang]. Reported confidence intervals and p-values were used when available, using adjusted values when provided. If multiple types of adjustments are reported including propensity score matching (PSM), the PSM results are used. Adjusted primary outcome results have preference over unadjusted results for a more serious outcome when the adjustments significantly alter results. When needed, conversion between reported p-values and confidence intervals followed [Altman, Altman (B)], and Fisher's exact test was used to calculate p-values for event data. If continuity correction for zero values is required, we use the reciprocal of the opposite arm with the sum of the correction factors equal to 1 [Sweeting]. Results are expressed with RR < 1.0 favoring treatment, and using the risk of a negative outcome when applicable (for example, the risk of death rather than the risk of survival). If studies only report relative continuous values such as relative times, the ratio of the time for the treatment group versus the time for the control group is used. Calculations are done in Python (3.10.8) with scipy (1.9.3), pythonmeta (1.26), numpy (1.23.4), statsmodels (0.13.5), and plotly (5.11.0).
Forest plots are computed using PythonMeta [Deng] with the DerSimonian and Laird random effects model (the fixed effect assumption is not plausible in this case) and inverse variance weighting. Mixed-effects meta-regression results are computed with R (4.1.2) using the metafor (3.0-2) and rms (6.2-0) packages, and using the most serious sufficiently powered outcome.
We received no funding, this research is done in our spare time. We have no affiliations with any pharmaceutical companies or political parties.
We have classified studies as early treatment if most patients are not already at a severe stage at the time of treatment (for example based on oxygen status or lung involvement), and treatment started within 5 days of the onset of symptoms. If studies contain a mix of early treatment and late treatment patients, we consider the treatment time of patients contributing most to the events (for example, consider a study where most patients are treated early but late treatment patients are included, and all mortality events were observed with late treatment patients). We note that a shorter time may be preferable. Antivirals are typically only considered effective when used within a shorter timeframe, for example 0-36 or 0-48 hours for oseltamivir, with longer delays not being effective [McLean, Treanor].
A summary of study results is below. Please submit updates and corrections at the bottom of this page.
A summary of study results is below. Please submit updates and corrections at https://c19early.org/qmeta.html.
Effect extraction follows pre-specified rules as detailed above and gives priority to more serious outcomes. For pooled analyses, the first (most serious) outcome is used, which may differ from the effect a paper focuses on. Other outcomes are used in outcome specific analyses.
[Di Pierro], 6/24/2021, Randomized Controlled Trial, Pakistan, peer-reviewed, 12 authors, trial NCT04861298 (history), excluded in exclusion analyses: randomization resulted in significant baseline differences that were not adjusted for. risk of death, 66.7% lower, RR 0.33, p = 1.00, treatment 0 of 21 (0.0%), control 1 of 21 (4.8%), NNT 21, relative risk is not 0 because of continuity correction due to zero events (with reciprocal of the contrasting arm).
risk of ICU admission, 66.7% lower, RR 0.33, p = 1.00, treatment 0 of 21 (0.0%), control 1 of 21 (4.8%), NNT 21, relative risk is not 0 because of continuity correction due to zero events (with reciprocal of the contrasting arm).
risk of hospitalization, 66.7% lower, RR 0.33, p = 1.00, treatment 0 of 21 (0.0%), control 1 of 21 (4.8%), NNT 21, relative risk is not 0 because of continuity correction due to zero events (with reciprocal of the contrasting arm).
risk of no viral clearance, 73.7% lower, RR 0.26, p < 0.001, treatment 5 of 21 (23.8%), control 19 of 21 (90.5%), NNT 1.5, day 7.
risk of no viral clearance, 88.9% lower, RR 0.11, p = 0.11, treatment 0 of 21 (0.0%), control 4 of 21 (19.0%), NNT 5.2, relative risk is not 0 because of continuity correction due to zero events (with reciprocal of the contrasting arm), day 14.
[Di Pierro (B)], 6/8/2021, Randomized Controlled Trial, Pakistan, peer-reviewed, 19 authors, trial NCT04578158 (history). risk of death, 85.7% lower, RR 0.14, p = 0.25, treatment 0 of 76 (0.0%), control 3 of 76 (3.9%), NNT 25, relative risk is not 0 because of continuity correction due to zero events (with reciprocal of the contrasting arm).
risk of ICU admission, 94.1% lower, RR 0.06, p = 0.006, treatment 0 of 76 (0.0%), control 8 of 76 (10.5%), NNT 9.5, relative risk is not 0 because of continuity correction due to zero events (with reciprocal of the contrasting arm).
risk of hospitalization, 68.2% lower, RR 0.32, p = 0.003, treatment 7 of 76 (9.2%), control 22 of 76 (28.9%), NNT 5.1.
[Khan], 5/1/2022, Randomized Controlled Trial, Pakistan, peer-reviewed, 7 authors, study period 2 September, 2021 - 28 November, 2021, this trial uses multiple treatments in the treatment arm (combined with curcumin and vitamin D) - results of individual treatments may vary, trial NCT05130671 (history). risk of no recovery, 33.3% lower, RR 0.67, p = 0.15, treatment 10 of 25 (40.0%), control 15 of 25 (60.0%), NNT 5.0.
relative CRP reduction, 39.1% better, RR 0.61, p = 0.006, treatment 25, control 25.
risk of no viral clearance, 50.0% lower, RR 0.50, p = 0.009, treatment 10 of 25 (40.0%), control 20 of 25 (80.0%), NNT 2.5.
Effect extraction follows pre-specified rules as detailed above and gives priority to more serious outcomes. For pooled analyses, the first (most serious) outcome is used, which may differ from the effect a paper focuses on. Other outcomes are used in outcome specific analyses.
[Onal], 1/19/2021, Randomized Controlled Trial, Turkey, peer-reviewed, 10 authors, this trial uses multiple treatments in the treatment arm (combined with bromelain and vitamin C) - results of individual treatments may vary, trial NCT04377789 (history). risk of death, 29.3% higher, RR 1.29, p = 0.57, treatment 1 of 49 (2.0%), control 6 of 380 (1.6%).
risk of ICU admission, 94.0% lower, RR 0.06, p = 0.39, treatment 0 of 49 (0.0%), control 14 of 380 (3.7%), NNT 27, relative risk is not 0 because of continuity correction due to zero events (with reciprocal of the contrasting arm).
risk of no hospital discharge, 77.8% lower, RR 0.22, p = 0.10, treatment 1 of 49 (2.0%), control 35 of 380 (9.2%), NNT 14.
[Shohan], 12/2/2021, Randomized Controlled Trial, Iran, peer-reviewed, mean age 50.9 (treatment) 52.7 (control), 8 authors, average treatment delay 7.8 days. risk of death, 85.7% lower, RR 0.14, p = 0.24, treatment 0 of 30 (0.0%), control 3 of 30 (10.0%), NNT 10.0, relative risk is not 0 because of continuity correction due to zero events (with reciprocal of the contrasting arm).
risk of ICU admission, 40.0% lower, RR 0.60, p = 0.71, treatment 3 of 30 (10.0%), control 5 of 30 (16.7%), NNT 15.
days from end of intervention to discharge, 32.4% lower, relative time 0.68, p = 0.04, treatment 30, control 30.
[Zupanets], 9/1/2021, Randomized Controlled Trial, Ukraine, peer-reviewed, 14 authors. risk of no recovery, 29.4% lower, RR 0.71, p = 0.50, treatment 9 of 99 (9.1%), control 13 of 101 (12.9%), NNT 26.
recovery time, 18.2% lower, relative time 0.82, p = 0.03, treatment 99, control 101.
Effect extraction follows pre-specified rules as detailed above and gives priority to more serious outcomes. For pooled analyses, the first (most serious) outcome is used, which may differ from the effect a paper focuses on. Other outcomes are used in outcome specific analyses.
[Arslan], 11/16/2020, Randomized Controlled Trial, Turkey, preprint, 7 authors, this trial uses multiple treatments in the treatment arm (combined with vitamin C and bromelain) - results of individual treatments may vary, trial NCT04377789 (history), excluded in exclusion analyses: paper no longer available at the source, and the contact does not reply to queries. risk of case, 91.7% lower, RR 0.08, p = 0.03, treatment 1 of 71 (1.4%), control 9 of 42 (21.4%), NNT 5.0, adjusted per study, inverted to make RR<1 favor treatment.
[Margolin], 7/6/2021, retrospective, USA, peer-reviewed, 5 authors, this trial uses multiple treatments in the treatment arm (combined with zinc, vitamin C/D/E, l-lysine, and quina) - results of individual treatments may vary. risk of case, 94.4% lower, RR 0.06, p = 0.003, treatment 0 of 53 (0.0%), control 9 of 60 (15.0%), NNT 6.7, relative risk is not 0 because of continuity correction due to zero events (with reciprocal of the contrasting arm).
risk of COVID-19 or flu-like illness, 81.1% lower, RR 0.19, p = 0.01, treatment 2 of 53 (3.8%), control 12 of 60 (20.0%), NNT 6.2.
[Rondanelli], 1/4/2022, Double Blind Randomized Controlled Trial, placebo-controlled, Italy, peer-reviewed, 12 authors, trial NCT05037240 (history). risk of symptomatic case, 92.9% lower, HR 0.07, p = 0.04, treatment 1 of 60 (1.7%), control 4 of 60 (6.7%), adjusted per study, inverted to make HR<1 favor treatment, Cox proportional risk.
Please send us corrections, updates, or comments. Vaccines and treatments are complementary. All practical, effective, and safe means should be used based on risk/benefit analysis. No treatment, vaccine, or intervention is 100% available and effective for all current and future variants. We do not provide medical advice. Before taking any medication, consult a qualified physician who can provide personalized advice and details of risks and benefits based on your medical history and situation. FLCCC and WCH provide treatment protocols.
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