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Vitamin B9 for COVID-19: real-time meta analysis of 13 studies (9 treatment studies and 4 sufficiency studies)
Covid Analysis, June 2023
https://c19early.org/b9meta.html
 
0 0.5 1 1.5+ All studies -11% 9 35,148 Improvement, Studies, Patients Relative Risk Mortality -62% 5 24,871 Hospitalization 28% 1 2,148 Cases 9% 4 8,129 RCTs 88% 1 363 Peer-reviewed -11% 8 19,180 Sufficiency 12% 4 481 Prophylaxis -11% 9 35,148 Vitamin B9 for COVID-19 c19early.org/b9 Jun 2023 Favorsvitamin B9 Favorscontrol
Meta analysis using the most serious outcome reported shows 11% [-19‑52%] higher risk, without reaching statistical significance.
Sufficiency studies, analyzing outcomes based on serum levels, show 12% [2‑21%] improvement for patients with higher vitamin B9 levels (4 studies).
0 0.5 1 1.5+ All studies -11% 9 35,148 Improvement, Studies, Patients Relative Risk Mortality -62% 5 24,871 Hospitalization 28% 1 2,148 Cases 9% 4 8,129 RCTs 88% 1 363 Peer-reviewed -11% 8 19,180 Sufficiency 12% 4 481 Prophylaxis -11% 9 35,148 Vitamin B9 for COVID-19 c19early.org/b9 Jun 2023 Favorsvitamin B9 Favorscontrol
Results to date are contradictory. Several studies show higher mortality, however counfounding by indication may be significant — patients prescribed folic acid may have significantly higher risk on average. Studies independent of prescriptions based on patient condition show positive results [Deschasaux-Tanguy, Farag], as do sufficiency studies. Folic acid may not be the most effective or safest form for supplementation [Scaglione]. Studies show that a significant fraction of people have genetic variations limiting the ability to convert folic acid to the active form.
All data to reproduce this paper and sources are in the appendix.
Evolution of COVID-19 clinical evidence Vitamin B9 p=0.52 Vitamin D p<0.0000000001 2020 2021 2022 2023 Effective Harmful c19early.org June 2023 meta analysis results (pooled effects) 100% 50% 0% -50%
Percentage improvement with vitamin B9 (more)
All studies Prophylaxis Early treatment Studies Patients Authors
All studies-11% [-52‑19%]-11% [-52‑19%]- 9 35,148 172
Randomized Controlled TrialsRCTs88% [64‑96%]
***
88% [64‑96%]
***
- 1 363 9
Mortality-62% [-161‑-1%]
*
-62% [-161‑-1%]
*
- 5 24,871 55
Highlights
Vitamin B9 reduces risk for COVID-19 with very low confidence for hospitalization, however increased risk is seen with high confidence for mortality and very low confidence for pooled analysis. Results to date are contradictory. Several studies show higher mortality, however counfounding by indication may be significant — patients prescribed folic acid may have significantly higher risk on average. Folic acid may not be the best form for supplementation.
We show traditional outcome specific analyses and combined evidence from all studies, incorporating treatment delay, a primary confounding factor in COVID-19 studies.
Real-time updates and corrections, transparent analysis with all results in the same format, consistent protocol for 51 treatments.
A
0 0.25 0.5 0.75 1 1.25 1.5 1.75 2+ Meisel 27% 0.73 [0.26-2.04] death 23 (n) 310 (n) Improvement, RR [CI] Treatment Control Bliek-Bueno -87% 1.87 [1.51-2.33] death 8,570 (all patients) CT​1 Deschasaux-Tanguy 16% 0.84 [0.72-0.98] cases 7,766 (all patients) per SD change Monserrat .. (PSM) -132% 2.32 [1.36-4.08] death n/a n/a Nimer 28% 0.72 [0.42-1.23] hosp. 16/213 203/1,935 MacFadden 0% 1.00 [0.93-1.07] cases n/a n/a Loucera 1% 0.99 [0.81-1.20] death 624 (n) 15,344 (n) Topless -164% 2.64 [2.15-3.24] death population-based cohort Farag (CLUS. RCT) 88% 0.12 [0.04-0.36] cases 4/224 20/139 Tau​2 = 0.18, I​2 = 94.3%, p = 0.52 Prophylaxis -11% 1.11 [0.81-1.52] 20/1,084 223/17,728 11% increased risk All studies -11% 1.11 [0.81-1.52] 20/1,084 223/17,728 11% increased risk 9 vitamin B9 COVID-19 studies c19early.org/b9 Jun 2023 Tau​2 = 0.18, I​2 = 94.3%, p = 0.52 Effect extraction pre-specified(most serious outcome, see appendix) 1 CT: study uses combined treatment Favors vitamin B9 Favors control
0 0.25 0.5 0.75 1 1.25 1.5 1.75 2+ Meisel 27% death Relative Risk [CI] Bliek-Bueno -87% death CT​1 Deschasaux-Tanguy 16% case per SD change Monserrat.. (PSM) -132% death Nimer 28% hospitalization MacFadden 0% case Loucera 1% death Topless -164% death Farag (CLUS. RCT) 88% case Tau​2 = 0.18, I​2 = 94.3%, p = 0.52 Prophylaxis -11% 11% increased risk All studies -11% 11% increased risk 9 vitamin B9 COVID-19 studies c19early.org/b9 Jun 2023 Tau​2 = 0.18, I​2 = 94.3%, p = 0.52 Protocol pre-specified/rotate for details1 CT: study uses combined treatment Favors vitamin B9 Favors control
B
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C
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D
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Figure 1. A. Random effects meta-analysis. This plot shows pooled effects, see the specific outcome analyses for individual outcomes, and the heterogeneity section for discussion. Effect extraction is pre-specified, using the most serious outcome reported. For details of effect extraction see the appendix. B. Scatter plot showing the most serious outcome in all studies, and for studies within each stage. Diamonds shows the results of random effects meta-analysis. C. Results within the context of multiple COVID-19 treatments. 0.9% of 3,946 proposed treatments show efficacy [c19early.org]. D. Timeline of results in vitamin B9 studies.
We analyze all significant studies concerning the use of vitamin B9 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, individual outcomes, peer-reviewed studies, and Randomized Controlled Trials (RCTs).
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.
5 In Silico studies support the efficacy of vitamin B9 [Eskandari, Hosseini, Kumar, Serseg, Ugurel].
An In Vitro study supports the efficacy of vitamin B9 [Chen].
An In Vivo animal study supports the efficacy of vitamin B9 [Zhang].
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 studies, with different exclusions, and for specific outcomes. Figure 3, 4, 5, 6, 7, and 8 show forest plots for random effects meta-analysis of all studies with pooled effects, mortality results, hospitalization, cases, sufficiency studies, and peer reviewed studies.
Table 1. Random effects meta-analysis for all studies, with different exclusions, and for specific outcomes. Results show the percentage improvement with treatment and the 95% confidence interval. * p<0.05  *** p<0.001.
Improvement Studies Patients Authors
All studies-11% [-52‑19%]9 35,148 172
Peer-reviewed studiesPeer-reviewed-11% [-60‑23%]8 19,180 164
Randomized Controlled TrialsRCTs88% [64‑96%]
***
1 363 9
Mortality-62% [-161‑-1%]
*
5 24,871 55
Cases9% [-29‑36%]4 8,129 119
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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 hospitalization.
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Figure 6. Random effects meta-analysis for cases.
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Figure 7. Random effects meta-analysis for sufficiency studies. Effect extraction is pre-specified, using the most serious outcome reported, see the appendix for details.
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Figure 8. Random effects meta-analysis for peer reviewed studies. [Zeraatkar] analyze 356 COVID-19 trials, finding no significant evidence that preprint results are inconsistent with peer-reviewed studies. They also show extremely long peer-review delays, with a median of 6 months to journal publication. A six month delay was equivalent to around 1.5 million deaths during the first two years of the pandemic. Authors recommend using preprint evidence, with appropriate checks for potential falsified data, which provides higher certainty much earlier. Effect extraction is pre-specified, using the most serious outcome reported, see the appendix for details.
Figure 9 shows a forest plot for random effects meta-analysis of all Randomized Controlled Trials. RCT results are included in Table 1 and Table 2. Currently there is only one RCT.
Bias in clinical research may be defined as something that tends to make conclusions differ systematically from the truth. RCTs help to make study groups more similar and can provide a higher level of evidence, however they are subject to many biases [Jadad], and analysis of double-blind RCTs has identified extreme levels of bias [Gøtzsche]. For COVID-19, the overhead may delay treatment, dramatically compromising efficacy; they may encourage monotherapy for simplicity at the cost of efficacy which may rely on combined or synergistic effects; the participants that sign up may not reflect real world usage or the population that benefits most in terms of age, comorbidities, severity of illness, or other factors; standard of care may be compromised and unable to evolve quickly based on emerging research for new diseases; errors may be made in randomization and medication delivery; and investigators may have hidden agendas or vested interests influencing design, operation, analysis, and the potential for fraud. All of these biases have been observed with COVID-19 RCTs. There is no guarantee that a specific RCT provides a higher level of evidence.
High quality RCTs for novel acute diseases are more challenging, with increased ethical issues due to the urgency of treatment, increased risk due to enrollment delays, and more difficult design with a rapidly evolving evidence base. For COVID-19, the most common site of initial infection is the upper respiratory tract. Immediate treatment is likely to be most successful and may prevent or slow progression to other parts of the body. For a non-prophylaxis RCT, it makes sense to provide treatment in advance and instruct patients to use it immediately on symptoms, just as some governments have done by providing medication kits in advance. Unfortunately, no RCTs have been done in this way. Every treatment RCT to date involves delayed treatment. Among the 51 treatments we have analyzed, 64% of RCTs involve very late treatment 5+ days after onset. No non-prophylaxis COVID-19 RCTs match the potential real-world use of early treatments (they may more accurately represent results for treatments that require visiting a medical facility, e.g., those requiring intravenous administration).
Currently, 36 of the treatments we analyze show statistically significant efficacy or harm, defined as ≥10% decreased risk or >0% increased risk from ≥3 studies. Of the 36 treatments with statistically significant efficacy/harm, 23 have been confirmed in RCTs, with a mean delay of 4.4 months. For the 13 unconfirmed treatments, 4 have zero RCTs to date. The point estimates for the remaining 9 are all consistent with the overall results (benefit or harm), with 8 showing >20%. The only treatment showing >10% efficacy for all studies, but <10% for RCTs is aspirin.
We need to evaluate each trial on its own merits. RCTs for a given medication and disease may be more reliable, however they may also be less reliable. For off-patent medications, very high conflict of interest trials may be more likely to be RCTs, and more likely to be large trials that dominate meta analyses.
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Figure 9. 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.
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 (B)] report only 2.5 hours improvement for inpatient treatment.
Table 3. Studies of baloxavir for influenza show that early treatment is more effective.
Treatment delayResult
Post exposure prophylaxis86% fewer cases [Ikematsu]
<24 hours-33 hours symptoms [Hayden]
24-48 hours-13 hours symptoms [Hayden]
Inpatients-2.5 hours to improvement [Kumar (B)]
Figure 10 shows a mixed-effects meta-regression for efficacy as a function of treatment delay in COVID-19 studies from 51 treatments, showing that efficacy declines rapidly with treatment delay. Early treatment is critical for COVID-19.
Figure 10. Early treatment is more effective. Meta-regression showing efficacy as a function of treatment delay in COVID-19 studies from 51 treatments.
Details of the patient population including age and comorbidities may critically affect how well a treatment works. For example, many COVID-19 studies with relatively young low-comorbidity patients show all patients recovering quickly with or without treatment. In such cases, there is little room for an effective treatment to improve results (as in [López-Medina]).
Efficacy may differ significantly depending on the effect measured, for example a treatment may be very effective at reducing mortality, but less effective at minimizing cases or hospitalization. Or a treatment may have no effect on viral clearance while still being effective at reducing mortality.
There are many different variants of SARS-CoV-2 and efficacy may depend critically on the distribution of variants encountered by the patients in a study. For example, the Gamma variant shows significantly different characteristics [Faria, Karita, Nonaka, Zavascki]. Different mechanisms of action may be more or less effective depending on variants, for example the viral entry process for the omicron variant has moved towards TMPRSS2-independent fusion, suggesting that TMPRSS2 inhibitors may be less effective [Peacock, Willett].
Effectiveness may depend strongly on the dosage and treatment regimen.
The use of other treatments may significantly affect outcomes, including anything from supplements, other medications, or other kinds of treatment such as prone positioning.
The quality of medications may vary significantly between manufacturers and production batches, which may significantly affect efficacy and safety. [Williams] analyze ivermectin from 11 different sources, showing highly variable antiparasitic efficacy across different manufacturers. [Xu] analyze a treatment from two different manufacturers, showing 9 different impurities, with significantly different concentrations for each manufacturer.
We present both pooled analyses and specific outcome analyses. Notably, pooled analysis often results in earlier detection of efficacy as shown in Figure 11. For many COVID-19 treatments, a reduction in mortality logically follows from a reduction in hospitalization, which follows from a reduction in symptomatic cases, etc. An antiviral tested with a low-risk population may report zero mortality in both arms, however a reduction in severity and improved viral clearance may translate into lower mortality among a high-risk population, and including these results in pooled analysis allows faster detection of efficacy. Trials with high-risk patients may also be restricted due to ethical concerns for treatments that are known or expected to be effective.
Pooled analysis enables using more of the available information. While there is much more information available, for example dose-response relationships, the advantage of the method used here is simplicity and transparency. Note that pooled analysis could hide efficacy, for example a treatment that is beneficial for late stage patients but has no effect on viral replication or early stage disease could show no efficacy in pooled analysis if most studies only examine viral clearance. While we present pooled results, we also present individual outcome analyses, which may be more informative for specific use cases.
Currently, 36 of the treatments we analyze show statistically significant efficacy or harm, defined as ≥10% decreased risk or >0% increased risk from ≥3 studies. 97% of treatments showing statistically significant efficacy/harm with pooled effects have been confirmed with one or more specific outcomes, with a mean delay of 3.1 months. When restricting to RCTs only, 55% of treatments showing statistically significant efficacy/harm with pooled effects have been confirmed with one or more specific outcomes, with a mean delay of 2.9 months.
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Figure 11. 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 vitamin B9, there is currently not enough data to evaluate publication bias with high confidence.
Funnel plots have traditionally been used for analyzing publication bias. This is invalid for COVID-19 acute treatment trials — the underlying assumptions are invalid, which we can demonstrate with a simple example. Consider a set of hypothetical perfect trials with no bias. Figure 12 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 12. 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. Vitamin B9 for COVID-19 lacks this because it is off-patent, has multiple manufacturers, and is very low cost. In contrast, most COVID-19 vitamin B9 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 vitamin B9 trials represent the optimal conditions for efficacy.
Summary statistics from meta analysis necessarily lose information. As with all meta analyses, studies are heterogeneous, with differences in treatment delay, treatment regimen, patient demographics, variants, conflicts of interest, standard of care, and other factors. We provide analyses by specific outcomes and by treatment delay, and we aim to identify key characteristics in the forest plots and summaries. Results should be viewed in the context of study characteristics.
Some analyses classify treatment based on early or late administration, as done here, while others distinguish between mild, moderate, and severe cases. Viral load does not indicate degree of symptoms — for example patients may have a high viral load while being asymptomatic. With regard to treatments that have antiviral properties, timing of treatment is critical — late administration may be less helpful regardless of severity.
Details of treatment delay per patient is often not available. For example, a study may treat 90% of patients relatively early, but the events driving the outcome may come from 10% of patients treated very late. Our 5 day cutoff for early treatment may be too conservative, 5 days may be too late in many cases.
Comparison across treatments is confounded by differences in the studies performed, for example dose, variants, and conflicts of interest. Trials affiliated with special interests may use designs better suited to the preferred outcome.
In some cases, the most serious outcome has very few events, resulting in lower confidence results being used in pooled analysis, however the method is simpler and more transparent. This is less critical as the number of studies increases. Restriction to outcomes with sufficient power may be beneficial in pooled analysis and improve accuracy when there are few studies, however we maintain our pre-specified method to avoid any retrospective changes.
Studies show that combinations of treatments can be highly synergistic and may result in many times greater efficacy than individual treatments alone [Alsaidi, Andreani, Biancatelli, De Forni, Gasmi, Jeffreys, Jitobaom, Jitobaom (B), Ostrov, Thairu]. Therefore standard of care may be critical and benefits may diminish or disappear if standard of care does not include certain treatments.
This real-time analysis is constantly updated based on submissions. Accuracy benefits from widespread review and submission of updates and corrections from reviewers. Less popular treatments may receive fewer reviews.
No treatment, vaccine, or intervention is 100% available and effective for all current and future variants. Efficacy may vary significantly with different variants and within different populations. All treatments have potential side effects. Propensity to experience side effects may be predicted in advance by qualified physicians. We do not provide medical advice. Before taking any medication, consult a qualified physician who can compare all options, provide personalized advice, and provide details of risks and benefits based on individual medical history and situations.
1 of 9 studies combine treatments. The results of vitamin B9 alone may differ. None of the RCTs use combined treatment.
Meta analysis using the most serious outcome reported shows 11% [-19‑52%] higher risk, without reaching statistical significance. Sufficiency studies, analyzing outcomes based on serum levels, show 12% [2‑21%] improvement for patients with higher vitamin B9 levels (4 studies).
Results to date are contradictory. Several studies show higher mortality, however counfounding by indication may be significant — patients prescribed folic acid may have significantly higher risk on average. Studies independent of prescriptions based on patient condition show positive results [Deschasaux-Tanguy, Farag], as do sufficiency studies. Folic acid may not be the most effective or safest form for supplementation [Scaglione]. Studies show that a significant fraction of people have genetic variations limiting the ability to convert folic acid to the active form.
0 0.5 1 1.5 2+ Mortality -75% Improvement Relative Risk Progression, hosp./ICU/.. 45% c19early.org/b9 Abdulrahman et al. Vitamin B9 for COVID-19 Sufficiency Are vitamin B9 levels associated with COVID-19 outcomes? Retrospective 81 patients in the United Kingdom (Apr 2020 - May 2021) Lower progression with higher vitamin B9 levels (not stat. sig., p=0.42) Abdulrahman et al., The Int. J. Psychiatry in Me.., doi:10.1177/00912174231171220 Favors vitamin B9 Favors control
[Abdulrahman] Retrospective 81 pyschiatric inpatients in the UK, mean age 76, showing no significant difference in COVID-19 mortality with folate deficiency.
0 0.5 1 1.5 2+ Mortality, combined -87% Improvement Relative Risk Mortality, Campania -170% Mortality, Aragon -59% c19early.org/b9 Bliek-Bueno et al. Vitamin B9 for COVID-19 Prophylaxis Is prophylaxis with vitamin B9+Vitamin B12 beneficial for COVID-19? Retrospective study in multiple countries (March - April 2020) Higher mortality with vitamin B9+Vitamin B12 (p<0.000001) Bliek-Bueno et al., Int. J. Environmental Resear.., doi:10.3390/ijerph182211786 Favors vitamin B9 Favors control
[Bliek-Bueno] Retrospective 8,570 individuals in Spain and Italy, showing higher mortality with combined vitamin B9 and B12 supplementation. Adjustments only considered age.
0 0.5 1 1.5 2+ Case 16% per SD change Improvement Relative Risk c19early.org/b9 Deschasaux-Tanguy et al. Vitamin B9 Prophylaxis Does vitamin B9 reduce COVID-19 infections? Retrospective 7,766 patients in France Fewer cases with vitamin B9 (p=0.02) Deschasaux-Tanguy et al., BMC Medicine, doi:10.1186/s12916-021-02168-1 Favors vitamin B9 Favors control
[Deschasaux-Tanguy] Analysis of 7,766 adults in France, showing higher intakes of vitamin C, folate, vitamin K, dietary fibre, and fruit and vegetables associated with lower seropositivity.
0 0.5 1 1.5 2+ Mortality 56% Improvement Relative Risk ICU admission -11% c19early.org/b9 Doğan et al. Vitamin B9 for COVID-19 Sufficiency Are vitamin B9 levels associated with COVID-19 outcomes? Retrospective 66 patients in Turkey (January - March 2022) Lower mortality with higher vitamin B9 levels (not stat. sig., p=0.46) Doğan et al., Sağlık Akademisi Kastamonu, doi:10.25279/sak.1102076 Favors vitamin B9 Favors control
[Doğan] Retrospective 70 COVID-19 cases and 70 non-COVID-19 controls in Turkey, showing no significant differences based on folic acid levels.
0 0.5 1 1.5 2+ Case, 1000µg 88% Improvement Relative Risk Case, 500µg 66% c19early.org/b9 Farag et al. PACTR202005599385499 Vitamin B9 RCT Prophylaxis Does vitamin B9 reduce COVID-19 infections? RCT 363 patients in Egypt (May - June 2020) Fewer cases with vitamin B9 (p=0.000004) Farag et al., Microbes and Infectious Diseases, doi:10.21608/mid.2022.170328.1405 Favors vitamin B9 Favors control
[Farag] Cluster RCT 526 healthcare workers in Egypt, showing lower COVID-19 cases with folic acid supplementation, and a dose-response relationship. Each wave of health care workers was randomized within 14 day isolation periods, introducing potential confounding by time.
0 0.5 1 1.5 2+ Mortality 1% Improvement Relative Risk c19early.org/b9 Loucera et al. Vitamin B9 for COVID-19 Prophylaxis Is prophylaxis with vitamin B9 beneficial for COVID-19? Retrospective 15,968 patients in Spain (January - November 2020) No significant difference in mortality Loucera et al., medRxiv, doi:10.1101/2022.08.14.22278751 Favors vitamin B9 Favors control
[Loucera] Retrospective 15,968 COVID-19 hospitalized patients in Spain, showing no significant difference in mortality with existing use of folic acid.
0 0.5 1 1.5 2+ Case 0% Improvement Relative Risk c19early.org/b9 MacFadden et al. Vitamin B9 for COVID-19 Prophylaxis Does vitamin B9 reduce COVID-19 infections? Retrospective study in Canada (January - December 2020) No significant difference in cases MacFadden et al., Open Forum Infectious Diseases, doi:10.1093/ofid/ofac156 Favors vitamin B9 Favors control
[MacFadden] Retrospective 26,121 cases and 2,369,020 controls ≥65yo in Canada, showing no significant difference in cases with chronic use of vitamin B9.
0 0.5 1 1.5 2+ Mortality 27% Improvement Relative Risk Death/intubation 6% Mortality, 5.9ng/mL 15% levels Death/intubation, 5.9ng.. 40% levels c19early.org/b9 Meisel et al. Vitamin B9 for COVID-19 Prophylaxis Is prophylaxis with vitamin B9 beneficial for COVID-19? Retrospective 334 patients in Israel (January - November 2020) Lower mortality with vitamin B9 (not stat. sig., p=0.54) Meisel et al., Nutrients, doi:10.3390/nu13030812 Favors vitamin B9 Favors control
[Meisel] Retrospective 333 hospitalized patients in Israel, showing no significant difference in outcomes with low folate levels or with folic acid supplementation.
0 0.5 1 1.5 2+ Mortality -132% Improvement Relative Risk c19early.org/b9 Monserrat Villatoro et al. Vitamin B9 Prophylaxis Is prophylaxis with vitamin B9 beneficial for COVID-19? PSM retrospective study in Spain Higher mortality with vitamin B9 (p=0.0027) Monserrat Villatoro et al., Pharmaceuticals, doi:10.3390/ph15010078 Favors vitamin B9 Favors control
[Monserrat Villatoro] PSM retrospective 3,712 hospitalized patients in Spain, showing lower mortality with existing use of azithromycin, bemiparine, budesonide-formoterol fumarate, cefuroxime, colchicine, enoxaparin, ipratropium bromide, loratadine, mepyramine theophylline acetate, oral rehydration salts, and salbutamol sulphate, and higher mortality with acetylsalicylic acid, digoxin, folic acid, mirtazapine, linagliptin, enalapril, atorvastatin, and allopurinol.
0 0.5 1 1.5 2+ Hospitalization 28% Improvement Relative Risk Severe case 28% c19early.org/b9 Nimer et al. Vitamin B9 for COVID-19 Prophylaxis Is prophylaxis with vitamin B9 beneficial for COVID-19? Retrospective 2,148 patients in Jordan (March - July 2021) Lower hospitalization (p=0.23) and severe cases (p=0.16), not stat. sig. Nimer et al., Bosnian J. Basic Medical Sciences, doi:10.17305/bjbms.2021.7009 Favors vitamin B9 Favors control
[Nimer] Retrospective 2,148 COVID-19 recovered patients in Jordan, showing lower risk of severity and hospitalization with vitamin B9 prophylaxis, without statistical significance.
0 0.5 1 1.5 2+ Mortality -164% Improvement Relative Risk Case -51% c19early.org/b9 Topless et al. Vitamin B9 for COVID-19 Prophylaxis Is prophylaxis with vitamin B9 beneficial for COVID-19? Retrospective 376,254 patients in the United Kingdom Higher mortality (p<0.0001) and more cases (p<0.0001) Topless et al., BMJ Open, doi:10.1136/bmjopen-2022-062945 Favors vitamin B9 Favors control
[Topless] UK Biobank retrospective showing higher cases and mortality with folic acid supplementation.
0 0.5 1 1.5 2+ Death/ICU 12% per SD change Improvement Relative Risk Death/ICU, 7nmol/l 98% c19early.org/b9 Voelkle et al. Vitamin B9 for COVID-19 Sufficiency Are vitamin B9 levels associated with COVID-19 outcomes? Prospective study of 57 patients in Switzerland (Mar - Apr 2020) Lower death/ICU with vitamin B9 (p=0.02) Voelkle et al., Nutrients, doi:10.3390/nu14091862 Favors vitamin B9 Favors control
[Voelkle] Prospective study of 57 consecutive hospitalized COVID-19 patients in Switzerland, showing lower risk of mortality/ICU admission with vitamin B9. Adjustments only considered age.
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 vitamin B9, 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 vitamin B9 for COVID-19 that report a comparison with a control group are included in the main analysis. 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 (B)]. 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 (3.11.3) with scipy (1.10.1), pythonmeta (1.26), numpy (1.24.3), statsmodels (0.14.0), and plotly (5.14.1).
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/b9meta.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.
[Bliek-Bueno], 11/10/2021, retrospective, multiple countries, peer-reviewed, mean age 67.7, 15 authors, study period 4 March, 2020 - 17 April, 2020, this trial uses multiple treatments in the treatment arm (combined with Vitamin B12) - results of individual treatments may vary. risk of death, 87.4% higher, OR 1.87, p < 0.001, combined, RR approximated with OR.
risk of death, 170.0% higher, OR 2.70, p < 0.001, Campania, RR approximated with OR.
risk of death, 59.0% higher, OR 1.59, p < 0.001, Aragon, RR approximated with OR.
[Deschasaux-Tanguy], 11/30/2021, retrospective, France, peer-reviewed, 95 authors. risk of case, 16.0% lower, OR 0.84, p = 0.02, RR approximated with OR, per standard deviation change.
[Farag], 11/20/2022, Cluster Randomized Controlled Trial, Egypt, peer-reviewed, mean age 37.5, 9 authors, study period 17 May, 2020 - 30 June, 2020, trial PACTR202005599385499. risk of case, 87.6% lower, RR 0.12, p < 0.001, treatment 4 of 224 (1.8%), control 20 of 139 (14.4%), NNT 7.9, 1000µg.
risk of case, 65.9% lower, RR 0.34, p = 0.005, treatment 8 of 163 (4.9%), control 20 of 139 (14.4%), NNT 11, 500µg.
[Loucera], 8/16/2022, retrospective, Spain, preprint, 8 authors, study period January 2020 - November 2020. risk of death, 1.5% lower, HR 0.99, p = 0.88, treatment 624, control 15,344, Cox proportional hazards, day 30.
[MacFadden], 3/29/2022, retrospective, Canada, peer-reviewed, 9 authors, study period 15 January, 2020 - 31 December, 2020. risk of case, no change, OR 1.00, p = 1.00, RR approximated with OR.
[Meisel], 3/2/2021, retrospective, Israel, peer-reviewed, 8 authors, study period 27 January, 2020 - 23 November, 2020. risk of death, 27.0% lower, OR 0.73, p = 0.54, treatment 23, control 310, RR approximated with OR.
risk of death/intubation, 6.0% lower, OR 0.94, p = 0.88, treatment 23, control 310, RR approximated with OR.
[Monserrat Villatoro], 1/8/2022, retrospective, propensity score matching, Spain, peer-reviewed, 18 authors. risk of death, 132.0% higher, OR 2.32, p = 0.003, RR approximated with OR.
[Nimer], 2/28/2022, retrospective, Jordan, peer-reviewed, survey, 4 authors, study period March 2021 - July 2021. risk of hospitalization, 27.7% lower, RR 0.72, p = 0.23, treatment 16 of 213 (7.5%), control 203 of 1,935 (10.5%), NNT 34, adjusted per study, odds ratio converted to relative risk, multivariable.
risk of severe case, 28.2% lower, RR 0.72, p = 0.16, treatment 19 of 213 (8.9%), control 241 of 1,935 (12.5%), NNT 28, adjusted per study, odds ratio converted to relative risk, multivariable.
[Topless], 8/24/2022, retrospective, United Kingdom, peer-reviewed, 6 authors. risk of death, 164.0% higher, OR 2.64, p < 0.001, adjusted per study, multivariable, model 2, RR approximated with OR.
risk of case, 51.0% higher, OR 1.51, p < 0.001, adjusted per study, multivariable, model 2, RR approximated with OR.
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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