•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).
•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.
•All data to reproduce this paper and
sources are in the appendix.
Other meta analyses for melatonin can be found in
[Lan, Pilia, Tan], showing significant improvements for recovery and mortality.
We show traditional outcome specific analyses and combined
evidence from all studies, incorporating treatment delay, a primary
confounding factor in COVID-19 studies.
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).
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.
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].
2 In Vivo animal studies support the efficacy of melatonin [Cecon, Cecon (B)].
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.
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 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 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.
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.
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.
