Covid Analysis, May 27, 2022, DRAFT
https://c19melatonin.com/meta.html
•Statistically significant improvements are seen for mortality, ventilation, and recovery. 8 studies from 8 independent teams in 5 different countries show statistically significant
improvements in isolation (6 for the most serious outcome).
•Meta analysis using the most serious outcome reported shows
49% [33‑62%] improvement. Results are similar for Randomized Controlled Trials, similar after exclusions, and slightly worse for peer-reviewed studies. Early treatment is more effective than late treatment.
•While many treatments have some level
of efficacy, they do not replace vaccines and other measures to avoid
infection.
Only 12% of melatonin
studies show zero events in the treatment arm.
Multiple treatments are typically used
in combination, and other treatments
may be more effective.
•No treatment, vaccine, or intervention is 100%
available and effective for all variants. All practical, effective, and safe
means should be used.
Denying the efficacy of treatments increases mortality, morbidity, collateral
damage, and endemic risk.
Highlights
Melatonin reduces
risk for COVID-19 with very high confidence for mortality and in pooled analysis, high confidence for ventilation and recovery, low confidence for cases, and very low confidence for ICU admission.
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 42
treatments.
Figure 1. A. Random effects
meta-analysis. This plot shows pooled effects, discussion can be found in the heterogeneity section,
and results for specific outcomes can be found in the individual outcome analyses.
Effect extraction is pre-specified, using the most serious outcome reported.
For details of effect extraction see the appendix.
B. Scatter plot showing the
distribution of effects reported in studies. C. History of all reported
effects (chronological within treatment stages).
Introduction
We analyze all significant studies
concerning the use of
melatonin
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.
Mechanisms of Action
| CD147 | SARS-CoV-2 may enter host cells via the cluster of differentiation 147 (CD147) transmembrane protein. Melatonin inhibits the CD147 signalling pathway [Behl, Su, Wang]. |
| Heme oxygenase | COVID-19 risk may be related to low intracellular heme oxygenase (HO-1). Melatonin increases HO-1 and HO-1 has cytoprotective and anti-inflammatory properties [Anderson, Anderson (B), Hooper, Hooper (B), Shi]. |
| Inhibiting brain infection | Melatonin has been shown to inhibit SARS-CoV-2 brain infection in a K18-hACE2 mouse model via allosteric binding to ACE2. [Cecon]. |
| Limiting type I and III interferons | In a K18-hACE2 mouse model, melatonin improved survival which may be associated with limiting lung production of type I and type III interferons [Cecon (B)]. |
| Mucormycosis | Melatonin deficiency may increase the risk of mucormycosis by providing favorable conditions for growth [Sen]. |
| Glutathione | Melatonin increases glutathione levels, and glutathione deficiency may be associated with COVID-19 severity [Morvaridzadeh, Polonikov]. |
| Cytokine levels | Melatonin may lower pro-inflammatory cytokine levels [Zhang]. |
| Immune regulation | Melatonin has immune regulatory properties, enhancing the proliferation and maturation of natural killing cells, T and B lymphocytes, granulocytes, and monocytes [Miller, Zhang]. |
| Sleep improvement | Melatonin improves the quality of sleep which may be beneficial for COVID-19 [Lewis, Zhang]. |
| Anti‑inflammatory | Melatonin shows anti-inflammatory effects [Zhang]. |
| Anti‑oxidation | Melatonin shows anti-oxidative effects which may be beneficial for COVID-19 [Gitto, Gitto (B), Reiter, Wu, Zhang]. |
Table 1. Melatonin mechanisms of action.
Submit updates.
Preclinical Research
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.
Results
Figure 3 shows a visual overview of the results, with details in
Table 2 and Table 3.
Figure 4, 5, 6, 7, 8, 9, 10, 11, and 12
show forest plots for a random effects meta-analysis of
all studies with pooled effects, mortality results, ventilation, ICU admission, hospitalization, progression, recovery, cases, and peer reviewed studies.
Figure 3. Overview of results.
| Treatment time | Number of studies reporting positive effects | Total number of studies | Percentage of studies reporting positive effects | Random effects meta-analysis results |
| Early treatment | 2 | 2 | 100% |
78% improvement RR 0.22 [0.06‑0.75] p = 0.016 |
| Late treatment | 11 | 11 | 100% |
53% improvement RR 0.47 [0.32‑0.71] p = 0.00031 |
| Prophylaxis | 3 | 3 | 100% |
38% improvement RR 0.62 [0.36‑1.06] p = 0.081 |
| All studies | 16 | 16 | 100% |
49% improvement RR 0.51 [0.38‑0.67] p < 0.0001 |
Table 2. Results by treatment stage.
| Studies | Early treatment | Late treatment | Prophylaxis | Patients | Authors | |
| All studies | 16 | 78% [25‑94%] | 53% [29‑68%] | 38% [-6‑64%] | 14,009 | 143 |
| With exclusions | 15 | 78% [25‑94%] | 53% [26‑70%] | 38% [-6‑64%] | 13,561 | 139 |
| Peer-reviewed | 15 | 78% [25‑94%] | 36% [16‑51%] | 38% [-6‑64%] | 13,061 | 140 |
| Randomized Controlled TrialsRCTs | 7 | 73% [-5‑93%] | 55% [-21‑84%] | 7% [-1368‑94%] | 730 | 72 |
Table 3. Results by treatment stage for all studies and with different exclusions.
Figure 4. Random effects meta-analysis for all studies with pooled effects.
This plot shows pooled effects, discussion can be found in the heterogeneity section,
and results for specific outcomes can be found in the individual outcome analyses.
Effect extraction is pre-specified, using the most serious outcome reported.
For details of effect extraction see the appendix.
Figure 5. Random effects meta-analysis for mortality results.
Figure 6. Random effects meta-analysis for ventilation.
Figure 7. Random effects meta-analysis for ICU admission.
Figure 8. Random effects meta-analysis for hospitalization.
Figure 9. Random effects meta-analysis for progression.
Figure 10. Random effects meta-analysis for recovery.
Figure 11. Random effects meta-analysis for cases.
Figure 12. Random effects meta-analysis for peer reviewed studies.
[Zeraatkar] analyze 356 COVID-19 trials, finding no
significant evidence that peer-reviewed studies are more trustworthy.
They also show extremely slow review times during a 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.
Dose Response
Melatonin trials for COVID-19 use a very wide range of dosage,
from 2mg/day to 500mg/day [Reiter (B)].
Figure 13 shows a mixed-effects meta-regression for efficacy as a
function of dose from studies to date, excluding very late stage ICU
studies.
Figure 13. Mixed-effects meta-regression
showing efficacy as a function of dose, excluding very late stage ICU studies.
Exclusions
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 14 shows a forest plot for random
effects meta-analysis of all studies after exclusions.
[Sánchez-González], immortal time bias may significantly affect results.
Figure 14. Random effects meta-analysis for all studies after exclusions.
This plot shows pooled effects, discussion can be found in the heterogeneity section,
and results for specific outcomes can be found in the individual outcome analyses.
Effect extraction is pre-specified, using the most serious outcome reported.
For details of effect extraction see the appendix.
Randomized Controlled Trials (RCTs)
Figure 15 shows the distribution of results for Randomized Controlled Trials and other studies, and
a chronological history of results.
The median effect size for
RCTs is 67% improvement,
compared to 50% for other studies.
Figure 16 and 17
show forest plots for a random effects meta-analysis of
all Randomized Controlled Trials and RCT mortality results.
Table 4 summarizes the results.
Figure 15. The distribution of results for Randomized Controlled Trials and other studies, and
a chronological history of results.
Figure 16. Random effects meta-analysis for all Randomized Controlled Trials.
This plot shows pooled effects, discussion can be found in the heterogeneity section,
and results for specific outcomes can be found in the individual outcome analyses.
Effect extraction is pre-specified, using the most serious outcome reported.
For details of effect extraction see the appendix.
Figure 17. Random effects meta-analysis for RCT mortality results.
| Treatment time | Number of studies reporting positive effects | Total number of studies | Percentage of studies reporting positive effects | Random effects meta-analysis results |
| Randomized Controlled Trials | 7 | 7 | 100% |
55% improvement RR 0.45 [0.20‑1.00] p = 0.049 |
| RCT mortality results | 3 | 3 | 100% |
64% improvement RR 0.36 [0.07‑1.87] p = 0.22 |
Table 4. Randomized Controlled Trial results.
Heterogeneity
Heterogeneity in COVID-19 studies arises from many factors including:
Treatment delay.
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].
Figure 18 shows a mixed-effects meta-regression for efficacy
as a function of treatment delay in COVID-19 studies from 42 treatments, showing
that efficacy declines rapidly with treatment delay. Early treatment is
critical for COVID-19.
Figure 18. Meta-regression
showing efficacy as a function of treatment delay in COVID-19 studies from 42 treatments. Early
treatment is critical.
Patient demographics.
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]).Effect measured.
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.Variants.
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].Regimen.
Effectiveness may depend strongly on the dosage and treatment regimen.
Treatments.
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 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 the treatment may be very effective when
used earlier.
In general, by combining heterogeneous studies, as all meta
analyses do, we run the risk of obscuring an effect by including studies where
the treatment is less effective, not effective, or harmful.
When including studies where a treatment is less effective we
expect the estimated effect size to be lower than that for the optimal case.
We do not a priori expect that pooling all studies will create a
positive result for an effective treatment. 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.
Discussion
Publication bias.
Publishing is often biased
towards positive results, however evidence suggests that there may be