Propolis for COVID-19: real-time meta analysis of 3 studies
Abstract
Statistically significant lower risk is seen for hospitalization and recovery. 3 studies from 2 independent teams in 2 countries show significant
improvements.
Meta analysis using the most serious outcome reported shows
41% [-13‑69%] lower risk, without reaching statistical significance. Currently all studies are RCTs.
Currently there is limited data, with only 410 patients and only 23 control events for the most serious outcome in trials to date. Studies to date are from only 2 different groups.
No treatment or intervention
is 100% effective. 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.
There has been no early treatment studies to date.
All data to reproduce this paper and
sources are in the appendix.
Propolis for COVID-19 — Highlights
Propolis reduces
risk with low confidence for hospitalization, recovery, and in pooled analysis, and very low confidence for mortality and ventilation.
Real-time updates and corrections,
transparent analysis with all results in the same format, consistent protocol
for 109
treatments, outcome specific analyses and combined evidence from all studies.
Figure 1.
A. Random effects
meta-analysis. This plot shows pooled effects,
see the specific outcome analyses for individual outcomes.
Analysis validating pooled outcomes for
COVID-19 can be found below.
Effect extraction is pre-specified, using the most serious outcome reported.
For details see the appendix.
B. Timeline of results in propolis studies.
SARS-CoV-2 infection primarily begins in the upper respiratory
tract and may progress to the lower respiratory tract, other tissues, and the
nervous and cardiovascular systems, which may lead to cytokine storm,
pneumonia, ARDS, neurological injury1-11 and
cognitive deficits3,8, cardiovascular
complications12-14, organ failure, and death.
Minimizing replication as early as possible is recommended.
SARS-CoV-2 infection and replication involves the complex interplay of 50+
host and viral proteins and other factorsA,15-20, providing many
therapeutic targets for which many existing compounds have known activity.
Scientists have predicted that over 8,000 compounds may
reduce COVID-19 risk21, either by
directly minimizing infection or replication, by supporting immune system
function, or by minimizing secondary complications.
We analyze all significant
controlled studies of
propolis
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, 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.
Currently all propolis studies use late treatment.
Figure 2. Treatment stages.
An In Silico study supports the efficacy of propolis22.
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, for Randomized Controlled Trials, 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, ventilation, ICU admission, hospitalization, and recovery.
| Improvement | Studies | Patients | Authors | |
|---|---|---|---|---|
| All studies | 41% [-13‑69%] | 3 | 410 | 64 |
| Randomized Controlled TrialsRCTs | 41% [-13‑69%] | 3 | 410 | 64 |
| VentilationVent. | 37% [-23‑68%] | 2 | 270 | 49 |
| ICU admissionICU | 28% [-39‑63%] | 3 | 372 | 64 |
| HospitalizationHosp. | 26% [5‑42%] * | 2 | 272 | 49 |
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Figure 3. Random effects meta-analysis for all studies.
This plot shows pooled effects,
see the specific outcome analyses for individual outcomes.
Analysis validating pooled outcomes for
COVID-19 can be found below.
Effect extraction is pre-specified, using the most serious outcome reported.
For details see the appendix.
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Figure 4. Random effects meta-analysis for mortality results.
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Figure 5. Random effects meta-analysis for ventilation.
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Figure 6. Random effects meta-analysis for ICU admission.
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Figure 7. Random effects meta-analysis for hospitalization.
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Figure 8. Random effects meta-analysis for recovery.
Currently all studies are RCTs.
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 hours25,26.
Baloxavir marboxil studies for influenza also show that treatment delay is critical
— Ikematsu et al. report an 86% reduction in cases for post-exposure
prophylaxis, Hayden et al. 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 et al. report only 2.5
hours improvement for inpatient treatment.
| Treatment delay | Result |
| Post-exposure prophylaxis | 86% fewer cases27 |
| <24 hours | -33 hours symptoms28 |
| 24-48 hours | -13 hours symptoms28 |
| Inpatients | -2.5 hours to improvement29 |
Figure 9 shows a mixed-effects meta-regression for efficacy
as a function of treatment delay in COVID-19 studies from 109 treatments, showing
that efficacy declines rapidly with treatment delay. Early treatment is
critical for COVID-19.
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Figure 9. Early treatment is more effective. Meta-regression showing efficacy as a function of treatment delay in COVID-19 studies from 109 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, for example as in
López-Medina et al.
Efficacy may
depend critically on the distribution of SARS-CoV-2 variants encountered by
patients. Risk varies significantly across variants31, for
example the Gamma variant shows significantly different characteristics32-35. Different mechanisms of action may be
more or less effective depending on variants, for example the degree to which
TMPRSS2 contributes to viral entry can differ across variants36,37.
Effectiveness may depend strongly on the dosage and treatment regimen.
The use
of other treatments may significantly affect outcomes, including supplements,
other medications, or other interventions such as prone positioning.
Treatments may be synergistic38-49, therefore
efficacy may depend strongly on combined treatments.
The
quality of medications may vary significantly between manufacturers and
production batches, which may significantly affect efficacy and safety.
Williams et al. analyze ivermectin from 11 different sources, showing
highly variable antiparasitic efficacy across different manufacturers.
Xu et al. analyze a treatment from two different manufacturers, showing 9
different impurities, with significantly different concentrations for each
manufacturer.
Across all
studies there is a strong association between different outcomes, for example
improved recovery is strongly associated with lower mortality. However,
efficacy may differ depending on the effect measured, for example a treatment
may be more effective against secondary complications and have minimal effect
on viral clearance.
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.
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.
For COVID-19, delay in clinical results translates into
additional death and morbidity, as well as additional economic and societal
damage. Combining the results of studies reporting different outcomes is
required.
There may be no mortality in a trial with low-risk patients,
however a reduction in severity or improved viral clearance may translate
into lower mortality in a high-risk population.
Different studies may report lower severity, improved recovery, and lower mortality,
and the significance may be very high when combining the results.
"The studies reported different outcomes"
is not a good reason for disregarding results.
We present both specific outcome and pooled analyses.
In order to combine the results of studies reporting different outcomes we use
the most serious outcome reported in each study, based on the thesis that
improvement in the most serious outcome provides comparable measures of
efficacy for a treatment. A critical advantage of this approach is
simplicity and transparency.
There are many other ways to combine evidence for different outcomes, along
with additional evidence such as dose-response relationships, however these
increase complexity.
Another way to view pooled analysis is that we are using more of
the available information. Logically we should, and do, use additional
information. For example dose-response and
treatment delay-response relationships provide significant additional evidence
of efficacy that is considered when reviewing the evidence for a
treatment.
Trials with high-risk patients may be restricted due to ethics for treatments
that are known or expected to be effective, and they increase difficulty for
recruiting. Using less severe outcomes as a proxy for more serious outcomes
allows faster collection of evidence.
For many COVID-19 treatments, a reduction in mortality logically
follows from a reduction in hospitalization, which follows from a reduction in
symptomatic cases, which follows from a reduction in PCR positivity. We can
directly test this for COVID-19.
Analysis of the the association between different outcomes across studies from
all 109
treatments we cover confirms the validity of pooled outcome analysis for COVID-19.
Figure 10 shows that lower hospitalization is very strongly associated
with lower mortality (p < 0.000000000001).
Similarly, Figure 11 shows that improved recovery is very strongly associated
with lower mortality (p < 0.000000000001).
Considering the extremes, Singh et al. show an association between viral clearance and
hospitalization or death, with p = 0.003 after excluding one large
outlier from a mutagenic treatment, and based on 44 RCTs including 52,384
patients.
Figure 12 shows that improved viral clearance is strongly associated
with fewer serious outcomes. The association is very similar to
Singh et al., with higher confidence due to the larger number of
studies. As with Singh et al., the confidence increases
when excluding the outlier treatment, from p = 0.00000042 to p = 0.00000002.
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Figure 10. Lower hospitalization is associated with lower mortality, supporting pooled outcome analysis.
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Figure 11. Improved recovery is associated with lower mortality, supporting pooled outcome analysis.
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Figure 10. Improved viral clearance is associated with fewer serious outcomes, supporting pooled outcome analysis.
Currently, 48 of the treatments we analyze show statistically significant efficacy or harm, defined as ≥10% decreased risk or >0% increased risk from ≥3 studies. 89% of these have been confirmed with one or more specific outcomes, with a mean delay of 5.1 months. When restricting to RCTs only, 56% of treatments showing statistically significant efficacy/harm with pooled effects have been confirmed with one or more specific outcomes, with a mean delay of 6.4 months.
Figure 13 shows when treatments were found effective during the
pandemic. Pooled outcomes often resulted in earlier detection of efficacy.
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Figure 13. 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.
Pooled analysis could hide efficacy, for example a treatment that is
beneficial for late stage patients but has no effect on viral clearance may
show no efficacy if most studies only examine viral clearance. In practice, it
is rare for a non-antiviral treatment to report viral clearance and to not
report clinical outcomes; and in practice other sources of heterogeneity such
as difference in treatment delay is more likely to hide efficacy.
Analysis validates the use of pooled effects and shows significantly faster
detection of efficacy on average.
However, as with all meta analyses, it is important to review the different
studies included. We also present 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 results53-56.
For propolis, there is currently not
enough data to evaluate publication bias with high confidence.
Pharmaceutical drug
trials often have conflicts of interest whereby sponsors or trial staff have a
financial interest in the outcome being positive. Propolis for COVID-19
lacks this because it is off-patent, has multiple manufacturers, and is very low cost.
In contrast, most COVID-19 propolis 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 propolis 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 for 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 with conflicts of interest 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 alone38-49.
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 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 3 studies
combine treatments. The results of
propolis
alone may differ.
1 of 3 RCTs use combined treatment.
Currently all studies are peer-reviewed.
SARS-CoV-2 infection and replication involves a complex interplay of 50+ host
and viral proteins and other factors15-20,
providing many therapeutic targets.
Over 8,000 compounds have been predicted to reduce COVID-19
risk21, either by directly
minimizing infection or replication, by supporting immune system function, or
by minimizing secondary complications.
Figure 14 shows an overview of the results for propolis
in the context of multiple COVID-19 treatments, and Figure 15 shows a plot
of efficacy vs. cost for COVID-19 treatments.
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Figure 14.
Scatter plot showing results within the context of multiple COVID-19 treatments.
Diamonds shows the results of random effects meta-analysis.
0.6% of 8,000+ proposed treatments show efficacy62.
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Figure 15. Efficacy vs. cost for COVID-19 treatments.
Statistically significant lower risk is seen for hospitalization and recovery. 3 studies from 2 independent teams in 2 countries show significant
improvements.
Meta analysis using the most serious outcome reported shows
41% [-13‑69%] lower risk, without reaching statistical significance. Currently all studies are RCTs.
Currently there is limited data, with only 410 patients and only 23 control events for the most serious outcome in trials to date. Studies to date are from only 2 different groups.
Kosari:
RCT 140 patients showing lower progression and improved recovery with propolis plus Hyoscyamus niger L.syrup.
Silveira:
RCT 188 patients in Brazil, showing shorter hospitalization and improved outcomes with propolis, but without statistical significance. The incidence of secondary infections was significantly lower in the treatment group.
Silveira (B):
RCT 124 hospitalized COVID-19 patients in Brazil. The treatment groups received standardized green propolis extract (EPP-AF) at doses of 400mg/day or 800mg/day for 7 days, in addition to standard care. The EPP-AF groups had significantly shorter hospital stays post-intervention. The high dose EPP-AF group also had lower rates of acute kidney injury. No significant differences were seen for other outcomes like oxygen therapy duration or need for mechanical ventilation. The propolis adjunct treatment appeared safe with no discontinuations due to side effects.
We perform ongoing searches of PubMed, medRxiv, Europe PMC,
ClinicalTrials.gov, The Cochrane Library, Google Scholar, Research
Square, ScienceDirect, Oxford University Press, the reference lists of other
studies and meta-analyses, and submissions to the site c19early.org.
Search terms are propolis and COVID-19 or SARS-CoV-2. Automated searches are performed twice daily, with all matches reviewed for inclusion.
All studies regarding the use of propolis 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 have preference. Mortality alone is preferred over combined outcomes.
Outcomes with zero events in both arms are not used, the next most serious
outcome with one or more events is used. 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 outcomes are considered more important than viral test 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 little or
no room for an effective treatment to do better, however faster recovery is
valuable.
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 compute the relative risk when
possible, or convert to a relative risk according to66.
Reported confidence intervals and p-values were used when available,
using adjusted values when provided. If multiple types of adjustments are
reported propensity score matching and multivariable regression has preference
over propensity score matching or weighting, which has preference over
multivariable regression. Adjusted 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 169.
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
(3.13.0) with
scipy (1.14.1), pythonmeta (1.26), numpy (1.26.4), statsmodels (0.14.4), and plotly (5.24.1).
Forest plots are computed using PythonMeta70
with the DerSimonian and Laird random effects model (the fixed effect
assumption is not plausible in this case) and inverse variance weighting.
Results are presented with 95% confidence intervals. Heterogeneity among studies was
assessed using the I2 statistic.
Mixed-effects meta-regression results are computed with R (4.4.0) using the metafor
(4.6-0) and rms (6.8-0) packages, and using the most serious sufficiently powered outcome.
For all statistical tests, a p-value less than 0.05 was considered statistically significant.
Grobid 0.8.0 is used to parse PDF documents.
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 effective25,26.
We received no funding, this research is done in our spare
time. We have no affiliations with any pharmaceutical companies or political
parties.
A summary of study results is below. Please submit
updates and corrections at https://c19early.org/ppmeta.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.
| Kosari, 11/22/2023, Double Blind Randomized Controlled Trial, placebo-controlled, Iran, peer-reviewed, 15 authors, this trial uses multiple treatments in the treatment arm (combined with Hyoscyamus niger L.) - results of individual treatments may vary, trial IRCT20200516047462N1. | risk of death, 85.7% lower, RR 0.14, p = 0.24, treatment 0 of 70 (0.0%), control 3 of 70 (4.3%), NNT 23, relative risk is not 0 because of continuity correction due to zero events (with reciprocal of the contrasting arm), day 6. |
| risk of ICU admission, 85.7% lower, RR 0.14, p = 0.06, treatment 1 of 70 (1.4%), control 7 of 70 (10.0%), NNT 12. | |
| risk of no recovery, 63.6% lower, RR 0.36, p = 0.007, treatment 8 of 70 (11.4%), control 22 of 70 (31.4%), NNT 5.0, day 6. | |
| Silveira, 10/27/2023, Double Blind Randomized Controlled Trial, placebo-controlled, Brazil, peer-reviewed, 24 authors, study period April 2021 - August 2021, trial NCT04800224 (history) (BeeCovid2). | risk of mechanical ventilation, 22.4% lower, RR 0.78, p = 0.54, treatment 10 of 98 (10.2%), control 12 of 90 (13.3%), NNT 32, adjusted per study, odds ratio converted to relative risk. |
| risk of ICU admission, 11.4% lower, RR 0.89, p = 0.65, treatment 23 of 97 (23.7%), control 24 of 89 (27.0%), NNT 31, adjusted per study, odds ratio converted to relative risk. | |
| hospitalization time, 16.1% lower, relative time 0.84, p = 0.19, treatment mean 6.48 (±5.99) n=98, control mean 7.72 (±7.06) n=90. | |
| secondary infection, 67.6% lower, RR 0.32, p = 0.02, treatment 6 of 98 (6.1%), control 17 of 90 (18.9%), NNT 7.8, adjusted per study, odds ratio converted to relative risk. | |
| AKI, 25.1% lower, RR 0.75, p = 0.41, treatment 13 of 98 (13.3%), control 16 of 90 (17.8%), NNT 22, adjusted per study, odds ratio converted to relative risk. | |
| Silveira (B), 6/30/2021, Randomized Controlled Trial, Brazil, peer-reviewed, 25 authors, trial NCT04480593 (history) (BeeCovid). | risk of mechanical ventilation, 62.5% lower, RR 0.37, p = 0.19, treatment 3 of 41 (7.3%), control 8 of 41 (19.5%), NNT 8.2, 800mg/day. |
| risk of mechanical ventilation, 73.0% lower, RR 0.27, p = 0.09, treatment 2 of 38 (5.3%), control 8 of 41 (19.5%), NNT 7.0, 400mg/day. | |
| risk of ICU admission, 23.6% lower, RR 0.76, p = 0.73, treatment 5 of 24 (20.8%), control 6 of 22 (27.3%), NNT 16, 800mg/day. | |
| risk of ICU admission, 93.0% lower, RR 0.07, p = 0.005, treatment 0 of 27 (0.0%), control 6 of 22 (27.3%), NNT 3.7, relative risk is not 0 because of continuity correction due to zero events (with reciprocal of the contrasting arm), 400mg/day. | |
| hospitalization time, 34.9% lower, relative time 0.65, p = 0.001, treatment mean 8.2 (±5.62) n=42, control mean 12.6 (±6.61) n=42, 800mg/day. | |
| hospitalization time, 24.6% lower, relative time 0.75, p = 0.049, treatment mean 9.5 (±7.42) n=40, control mean 12.6 (±6.61) n=42, 400mg/day. |
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