Insulin increases COVID-19 risk: real-time meta-analysis of 7 studies

@CovidAnalysis, November 2026, Version 1V1
Abstract
Meta-analysis using the most serious outcome reported shows 70% [7‑172%] higher risk.
All data and sources to reproduce this analysis are in the appendix.
Evolution of COVID-19 clinical evidence Meta-analysis results over time Insulin p=0.025 Vitamin D p<0.0000000001 2020 2021 2022 2023 2024 2025 2026 Lowerrisk Higherrisk c19early.org November 2026 50% 0% -50%

Insulin for COVID-19

Highlights
  • Real-time updates and corrections with a consistent protocol for 227 treatments. Outcome specific analysis and combined evidence from all studies including treatment delay, a primary confounding factor.
A

Insulin COVID-19 studies

c19early.org
November 2026
StudyImprovementRR · 95% CIOutcomeTreatmentControlRelative Risk
He−235%3.35 · 3.08–3.64death53,030 (all patients) Dinoi−55%1.55 · 1.06–2.26deathcase-control study
Late treatment−133%2.33 · 1.10–4.96133% higher risk
Tau² = 0.28, I² = 93.5%, p = 0.028
Luk−186%2.86 · 1.09–7.48death385 (n)679 (n) Pazoki9%0.91 · 0.50–1.65death53 (n)340 (n) Matviichuk−43%1.43 · 1.14–1.66PASC53/8371/152 Nowak−84%1.84 · 1.22–2.79death/hosp.1,021 (n)4,803 (n) Soff−28%1.28 · 1.10–1.48PASC1,218 (n)6,212 (n)
Prophylaxis−40%1.40 · 1.15–1.7053/2,76071/12,18640% higher risk
Tau² = 0.02, I² = 43.1%, p = 0.00096
All studies−70%1.70 · 1.07–2.7253/2,76071/12,18670% higher risk
Tau² = 0.35, I² = 96.4%, p = 0.02500.511.52+
Rotate screen for more detailsIncrease width for more details
← Insulin
reduces riskInsulin
increases risk →
B
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Fig. 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 insulin studies.
Introduction
Fig. 2. SARS-CoV-2 spike protein fibrin binding leads to thromboinflammation and neuropathology, from1.
Immediate treatment recommended
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 injury2-18 and cognitive deficits5,10, cardiovascular complications19-25, DNA damage26-29, organ failure, and death. Even mild untreated infections may result in persistent cognitive deficits30—the spike protein binds to fibrin leading to fibrinolysis-resistant blood clots, thromboinflammation, and neuropathology. Minimizing replication as early as possible is recommended.
Many treatments are expected to modulate infection
SARS-CoV-2 infection and replication involves the complex interplay of 500+ host and viral proteins and other factorsA,31-38, providing many therapeutic targets for which many existing compounds have known activity. Scientists have predicted that over 12,000 compounds may reduce COVID-19 risk39, either by directly minimizing infection or replication, by supporting immune system function, or by minimizing secondary complications.
Analysis
We analyze all significant controlled studies of insulin 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, studies within each treatment stage, and individual outcomes.
Treatment timing
Fig. 3 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.
Prophylaxis pre-exposure post-exposure Early treatment Late treatment Treatment delay viral load disease severity exposed symptom onset hospitalized ICU Expected benefit of antiviral therapy −2 0 2 4 6 8 10 Time relative to symptom onset (days) trajectories vary widely immediately or soon after symptoms after disease progression
Fig. 3. Treatment stages.
Preclinical Research
An in vitro study supports the efficacy of insulin40.
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
Table 1 summarizes the results for all studies, for specific outcomes, and by treatment stage. Fig. 4 plots individual results by treatment stage. Fig. 5, 6, 7, 8, and 9 show forest plots for random-effects meta-analysis of all studies with pooled effects, mortality results, ventilation, ICU admission, and long COVID.

Insulin for COVID-19: meta-analysis results

c19early.org
November 2026
All stages
Prophylaxis
Late treatment
All studies7 studies · 68K patients
All stages1.70* 1.07–2.7270% higher risk
Prophylaxis1.40*** 1.15–1.7040% higher risk
Late treatment2.33* 1.10–4.96133% higher risk
Mortality4 studies · 54K patients
All stages1.91 0.99–3.7191% higher risk
Prophylaxis1.50 0.49–4.5850% higher risk
Late treatment2.33* 1.10–4.96133% higher risk

Random-effects meta-analysis. Relative risk with 95% CI. * p < 0.05, ** p < 0.01, *** p < 0.001.

Table 1. Random-effects meta-analysis results for insulin. Results show the relative risk and 95% confidence interval.
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Fig. 4. 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.

Insulin COVID-19 studies

c19early.org
November 2026
StudyImprovementRR · 95% CIOutcomeTreatmentControlRelative Risk
He−235%3.35 · 3.08–3.64death53,030 (all patients) Dinoi−55%1.55 · 1.06–2.26deathcase-control study
Late treatment−133%2.33 · 1.10–4.96133% higher risk
Tau² = 0.28, I² = 93.5%, p = 0.028
Luk−186%2.86 · 1.09–7.48death385 (n)679 (n) Pazoki9%0.91 · 0.50–1.65death53 (n)340 (n) Matviichuk−43%1.43 · 1.14–1.66PASC53/8371/152 Nowak−84%1.84 · 1.22–2.79death/hosp.1,021 (n)4,803 (n) Soff−28%1.28 · 1.10–1.48PASC1,218 (n)6,212 (n)
Prophylaxis−40%1.40 · 1.15–1.7053/2,76071/12,18640% higher risk
Tau² = 0.02, I² = 43.1%, p = 0.00096
All studies−70%1.70 · 1.07–2.7253/2,76071/12,18670% higher risk
Tau² = 0.35, I² = 96.4%, p = 0.02500.511.52+
Rotate screen for more detailsIncrease width for more details
← Insulin
reduces riskInsulin
increases risk →
Fig. 5. 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.

Insulin COVID-19 mortality results

c19early.org
November 2026
StudyImprovementRR · 95% CITreatmentControlRelative Risk
He−235%3.35 · 3.08–3.6453,030 (all patients) Dinoi−55%1.55 · 1.06–2.26case-control study
Late treatment−133%2.33 · 1.10–4.96133% higher risk
Tau² = 0.28, I² = 93.5%, p = 0.028
Luk−186%2.86 · 1.09–7.48385 (n)679 (n) Pazoki9%0.91 · 0.50–1.6553 (n)340 (n)
Prophylaxis−50%1.50 · 0.49–4.58438 (n)1,019 (n)50% higher risk
Tau² = 0.49, I² = 75.0%, p = 0.48
All studies−91%1.91 · 0.99–3.71438 (n)1,019 (n)91% higher risk
Tau² = 0.38, I² = 91.2%, p = 0.05500.511.52+
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← Insulin
reduces riskInsulin
increases risk →
Fig. 6. Random-effects meta-analysis for mortality results.

Insulin COVID-19 mechanical ventilation results

c19early.org
November 2026
StudyImprovementRR · 95% CITreatmentControlRelative Risk
Luk−2099%21.99 · 4.85–99.60385 (n)679 (n)
Prophylaxis−2099%21.99 · 4.85–99.66385 (n)679 (n)2099% higher risk
Tau² = 0.00, I² = 0.0%, p < 0.0001
All studies−2099%21.99 · 4.85–99.66385 (n)679 (n)2099% higher risk
Tau² = 0.00, I² = 0.0%, p < 0.000100.511.52+
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← Insulin
reduces riskInsulin
increases risk →
Fig. 7. Random-effects meta-analysis for ventilation.

Insulin COVID-19 ICU results

c19early.org
November 2026
StudyImprovementRR · 95% CITreatmentControlRelative Risk
Luk−995%10.95 · 5.50–21.80385 (n)679 (n)
Prophylaxis−995%10.95 · 5.50–21.80385 (n)679 (n)995% higher risk
Tau² = 0.00, I² = 0.0%, p < 0.0001
All studies−995%10.95 · 5.50–21.80385 (n)679 (n)995% higher risk
Tau² = 0.00, I² = 0.0%, p < 0.000100.511.52+
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← Insulin
reduces riskInsulin
increases risk →
Fig. 8. Random-effects meta-analysis for ICU admission.

Insulin COVID-19 long COVID results

c19early.org
November 2026
StudyImprovementRR · 95% CIOutcomeTreatmentControlRelative Risk
Matviichuk−43%1.43 · 1.14–1.66PASC53/8371/152 Soff−28%1.28 · 1.10–1.48PASC1,218 (n)6,212 (n)
Prophylaxis−32%1.32 · 1.16–1.5053/1,30171/6,36432% higher risk
Tau² = 0.00, I² = 0.0%, p < 0.0001
All studies−32%1.32 · 1.16–1.5053/1,30171/6,36432% higher risk
Tau² = 0.00, I² = 0.0%, p < 0.000100.511.52+
Rotate screen for more detailsIncrease width for more details
← Insulin
reduces riskInsulin
increases risk →
Fig. 9. Random-effects meta-analysis for long COVID. Effect extraction is pre-specified, using the most serious outcome reported, see the appendix for details. Analysis validating pooled outcomes for COVID-19 can be found below.
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 hours41,42. 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.

Early treatment is more effective: baloxavir marboxil

c19early.org
November 2026
Treatment delayResult
Post-exposure prophylaxis86% fewer cases43
<24 hours-33 hours symptoms44
24-48 hours-13 hours symptoms44
Inpatients-2.5 hours to improvement45
Table 2. Studies of baloxavir marboxil for influenza show that early treatment is more effective.
Fig. 10 shows a mixed-effects meta-regression for efficacy as a function of treatment delay in COVID-19 studies from 227 treatments, showing that efficacy declines rapidly with treatment delay. Early treatment is critical for COVID-19.
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Fig. 10. Early treatment is more effective. Meta-regression showing efficacy as a function of treatment delay in COVID-19 studies from 227 treatments.
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, for example as in López-Medina et al.
SARS-CoV-2 variants
Efficacy may depend critically on the distribution of SARS-CoV-2 variants encountered by patients. Risk varies significantly across variants47, for example the Gamma variant shows significantly different characteristics48-51. 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 variants52,53.
Treatment regimen
Effectiveness may depend strongly on the dosage and treatment regimen.
Medication quality
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.
Other treatments
The use of other treatments may significantly affect outcomes, including supplements, other medications, or other interventions such as prone positioning. Treatments may be synergistic56-80, therefore efficacy may depend strongly on combined treatments.
Effect measured
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.
Meta-analysis
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.
Pooled Effects
Combining studies is required
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. Pooling the results of studies reporting different outcomes allows us to use more of the available information. Logically we should, and do, use additional information when evaluating treatments—for example dose-response and treatment delay-response relationships provide additional evidence of efficacy that is considered when reviewing the evidence for a treatment.
Specific outcome and pooled analyses
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.
Ethical and practical issues limit high-risk trials
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 and safer collection of evidence.
Validating pooled outcome analysis for COVID-19
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 227 treatments we cover confirms the validity of pooled outcome analysis for COVID-19. Fig. 11 shows that lower hospitalization is very strongly associated with lower mortality (p < 0.0000000001). Similarly, Fig. 12 shows that improved recovery is very strongly associated with lower mortality (p < 0.0000000001). 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. Fig. 13 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 = 0.0000000074 to p = 0.00000000015.
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Fig. 11. Lower hospitalization is associated with lower mortality, supporting pooled outcome analysis.
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Fig. 12. Improved recovery is associated with lower mortality, supporting pooled outcome analysis.
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Fig. 11. Improved viral clearance is associated with fewer serious outcomes, supporting pooled outcome analysis.
Pooled outcomes identify efficacy 5 months faster (7 months for RCTs)
Currently, 59 of the treatments we analyze show statistically significant efficacy or harm, defined as ≥10% decreased risk or >0% increased risk from ≥3 studies. 85% of these have been confirmed with one or more specific outcomes, with a mean delay of 4.6 months. When restricting to RCTs only, 53% of treatments showing statistically significant efficacy/harm with pooled effects have been confirmed with one or more specific outcomes, with a mean delay of 7.5 months. Fig. 14 shows when treatments were found effective during the pandemic. Pooled outcomes often resulted in earlier detection of efficacy.
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Fig. 14. 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.
Limitations
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 differences in treatment delay are more likely to hide efficacy.
Summary
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.
Discussion
Publication bias
Publishing is often biased towards positive results. Trials with patented drugs may have a financial conflict of interest that results in positive studies being more likely to be published, or bias towards more positive results. For example with molnupiravir, trials with negative results remain unpublished to date (CTRI/2021/05/033864 and CTRI/2021/08/0354242). For insulin, 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. Fig. 15 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 < 0.0582-89. 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.
Log Risk Ratio Standard Error 1.406 1.055 0.703 0.352 0 -3 -2 -1 0 1 2 A: Simulated perfect trials p > 0.05 Log Risk Ratio Standard Error 1.433 1.074 0.716 0.358 0 -4 -3 -2 -1 0 1 2 B: Simulated perfect trials with varying treatment delay p < 0.0001
Fig. 15. Example funnel plot analysis for simulated perfect trials.
Limitations
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 alone56-80. 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.
Perspective
Results compared with other treatments
SARS-CoV-2 infection and replication involves a complex interplay of 500+ host and viral proteins and other factors31-38, providing many therapeutic targets. Over 12,000 compounds have been predicted to reduce COVID-19 risk39, either by directly minimizing infection or replication, by supporting immune system function, or by minimizing secondary complications. Fig. 16 shows an overview of the results for insulin in the context of multiple COVID-19 treatments, and Fig. 17 shows a plot of efficacy vs. cost for COVID-19 treatments.
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Fig. 16. Scatter plot showing results within the context of multiple COVID-19 treatments. Diamonds shows the results of random-effects meta-analysis. 0.5% of 12,000+ proposed treatments show efficacy90.
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Fig. 17. Efficacy vs. cost for COVID-19 treatments.
Conclusion
Meta-analysis using the most serious outcome reported shows 70% [7‑172%] higher risk.
 
Contact.
Contact us on X at @CovidAnalysis.
Funding.
We have received no funding or compensation in any form, and do not accept donations. This is entirely volunteer work.
Conflicts of interest.
We have no conflicts of interest. We have no affiliation with any pharmaceutical companies, supplement companies, governments, political parties, or advocacy organizations.
AI.
We use AI models (Gemini, Grok, Claude, and ChatGPT) tasked with functioning as additional peer-reviewers to check for errors, suggest improvements, and review spelling and grammar. Any corrections are manually verified. Our preference for em dashes is independent of AI.
Updates.
Our COVID-19 meta-analyses involve the extraction of over 229,000 datapoints from thousands of papers for 227 treatments. We thank the thousands of scientists, physicians, and other contributors that have provided updates, suggestions, feedback, and corrections. These are all welcome and can be submitted at https://c19early.org/insmeta.html.
Dedication.
This work is dedicated to top evidence-based physicians that worked tirelessly to analyze evidence and greatly reduce mortality and morbidity during the pandemic. In alphabetical order: Dr. Thomas J. Borody, Dr. Mary Talley Bowden, Dr. Flavio Cadegiani, Dr. Shankara Chetty, Dr. Ryan Cole, Dr. George Fareed, Dr. Sabine Hazan, Dr. Pierre Kory, Dr. Tess Lawrie, Dr. Robert Malone, Dr. Paul Marik, Dr. Peter McCullough, Dr. Didier Raoult, Dr. Harvey Risch, Dr. Jackie Stone, Dr. Brian Tyson, Dr. Joseph Varon, and Dr. Vladimir Zelenko.
Public domain.
This is a public domain work distributed in accordance with the Creative Commons CC0 1.0 Universal license, which dedicates the work to the public domain by waiving all rights worldwide under copyright law. You can distribute, remix, adapt, and build upon this work in any medium or format, including for commercial purposes, without asking permission. Referenced material and third-party images retain any original copyrights or restrictions. See: https://creativecommons.org/publicdomain/zero/1.0/.
Study Notes
Retrospective 247 non-survivors and 247 matched survivors in hospitalized COVID-19 patients in Italy showing results for several treatments. Submit Corrections or Updates.
Late treatmentRetrospective · 53,030 patients · China · Dec 2019 – Aug 2021
Insulin for COVID-19
He et al. IJC Heart & Vasculature · November 2024
Outcome
Improvement
Relative Risk · 95% CI
Mortality, all
-235%3.35 · 3.08–3.64
Mortality, non-severe
-163%2.63 · 2.36–2.93
Mortality, severe
-277%3.77 · 3.31–4.29
00.511.52+
← Insulin
reduces riskInsulin
increases risk →
Higher mortality with insulin
p < 0.000001
Retrospective 53,030 COVID-19 patients from 138 hospitals in Hubei, China showing lower mortality with metformin. Submit Corrections or Updates.
ProphylaxisRetrospective · 1,064 patients · China · Jan 2020 – Feb 2021
Insulin for COVID-19
Luk et al. BMJ Open · October 2021
Outcome
Improvement
Relative Risk · 95% CI
Mortality
-186%2.86 · 1.09–7.48
Ventilation
-2099%22.0 · 4.85 to >9
ICU admission
-995%10.9 · 5.50 to >9
00.511.52+
← Insulin
reduces riskInsulin
increases risk →
Higher mortality and ventilation
p = 0.033 (mortality) and p < 0.0001 (ventilation)
Retrospective 1,220 hospitalized COVID-19 patients with type 2 diabetes in Hong Kong showing reduced risk of ICU admission, mechanical ventilation, and death with metformin and DPP-4 inhibitors, but increased risk with insulin and sulphonylureas. Submit Corrections or Updates.
Retrospective 469 patients with type 2 diabetes in Ukraine showing no significant difference in post-COVID-19 syndrome (PCS) with metformin. There was higher risk with insulin analogs, but lower risk with human insulin. Submit Corrections or Updates.
Retrospective 5,824 kidney transplant recipients in Sweden showing proton pump inhibitor use associated with higher risk of severe COVID-19. Submit Corrections or Updates.
Retrospective 393 hospitalized diabetic COVID-19 patients showing no significant difference in mortality with metformin and insulin use. Submit Corrections or Updates.
Retrospective 7,430 COVID-positive patients with type 2 diabetes showing lower risk of long COVID or death with metformin use, and higher risk with insulin use. Submit Corrections or Updates.
Appendix 1. Methods and Data
Search methods
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, which regularly receives notification of studies upon publication. Search terms are insulin 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 insulin for COVID-19 that report a comparison with a control group are included in the main analysis. Studies with major unexplained data issues, for example major outcome data that is impossible to be correct with no response from the authors, are excluded.
Fig. 18. Mid-recovery results can more accurately reflect efficacy when almost all patients recover. Mateja et al. confirm that intermediate viral load results more accurately reflect hospitalization/death.
Effect extraction
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 reported then they are both used in specific outcome analyses, while mortality is used for pooled analysis. If symptomatic results are reported at multiple times, we use 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 outcomes. 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. An IPD meta-analysis confirms that intermediate viral load reduction is more closely associated with hospitalization/death than later viral load reduction91. 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.
Statistical methods
Forest plots are computed using PythonMeta92 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. When results provide an odds ratio, we compute the relative risk when possible, or convert to a relative risk according to Zhang et al. Reported confidence intervals and p-values are used when available, and adjusted values are used 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 196. 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 (3.15.2) with scipy (1.18.1), pythonmeta (1.26), numpy (2.5.4), statsmodels (0.15.0), and plotly (6.9.0). 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 0.8.2 is used to parse PDF documents.
When evaluating potential effect modification across groups, we use an interaction test as described by Altman (C) et al. We compared the log-transformed relative risks using a z-test, deriving the standard error of the difference from the 95% confidence intervals. A two-sided interaction p-value of < 0.05 was considered a statistically significant difference in treatment effect between the groups.
Quality evaluation
Cochrane RoB 2/ROBINS-I are often used to evaluate studies, and have the advantage of providing standardized rules that can be applied with minimal understanding of the domain and study. However, the rules do not account for many real-world issues, often overemphasize or underemphasize others, and studies show low inter-rater reliability104. Certain domains are more applicable for these tools, however the time-sensitive nature of a pandemic, with significant mortality for every day of delay in evidence assessment, and the characteristics of COVID-19 make them inappropriate for this domain. This can be demonstrated with examples where expert RoB 2/ROBINS-I ratings do not match reality for COVID-19. Popp et al. use RoB 2 to classify Reis et al. as low risk of bias, however this is the opposite of reality—the trial not only has very high risk of bias, but has very high actual known bias, refusing to release data despite pledging to, reporting multiple impossible numbers, having blinding and randomization failure, and many other issues106. Axfors et al. use RoB 2 to classify Horby et al. as low risk of bias, however this is the opposite of reality—the very late treatment and excessive dosage used produces results with no relevance to recommended usage. HCQ shows poor results with late treatment and excessive dosage, and the combination shows harmB. Hempenius et al. use ROBINS-I to classify 33 studies for HCQ. The two rated as having the lowest risk of bias102,103 are far from the most informative. Both involve very late treatment, providing no information on recommended usage, and ROBINS-I does a very poor job of accounting for the impact of confounding factorsC.
Our quality evaluation focuses on known issues and bias, and the potential impact on outcomes, rather than just the risk of bias. The estimated potential impact of each confounding factor, and the direction of the impact is considered. For example, consider a study that shows significantly lower risk, the value of the study varies significantly if confounding points to an underestimate or an overestimate of efficacy. In one case, the real effect may be null, while the other case provides stronger evidence of efficacy (which may be greater than the study shows). Analysis focusing on the risk of bias, while simpler, may penalize studies for theoretical or technical issues that have no or minimal impact on outcomes. Analysis also depends on the outcome, for example certain issues are less relevant for objective outcomes such as mortality. Inaccurate penalization, and inaccurate high-quality evaluation in the face of known major issues affecting outcomes, increases in significance during a pandemic when immediate recognition of new evidence is critical, and when considering all global studies, as required during a pandemic. Investigators in other countries may have different customs for design, analysis, and reporting, and different English language skills, however they may not be less diligent or have greater bias. Investigators in lower-pharmaceutical-profit countries may have lower bias towards profitable interventions.
Treatment time
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 effective41,42.
Living analysis
This is a living analysis and is updated regularly. Submit updates or corrections with the form below. We received no funding, this research is done in our spare time. We have no affiliation with any pharmaceutical companies, supplement companies, governments, political parties, or advocacy organizations.
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/insmeta.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.
Dinoi, 2/20/2025, retrospective, Italy, peer-reviewed, 11 authors, study period 17 March, 2020 - 15 June, 2021.
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 risk of death, 54.9% higher, OR 1.55, p = 0.02, treatment 95 of 247 (38.5%) cases, 71 of 247 (28.7%) controls, case control OR.
He, 11/30/2024, retrospective, China, peer-reviewed, median age 59.0, 10 authors, study period 29 December, 2019 - 31 August, 2021.
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 risk of death, 235.0% higher, HR 3.35, p < 0.001, adjusted per study, all, multivariable, Cox proportional hazards.
risk of death, 163.0% higher, HR 2.63, p < 0.001, adjusted per study, non-severe, multivariable, Cox proportional hazards.
risk of death, 277.0% higher, HR 3.77, p < 0.001, adjusted per study, severe, multivariable, Cox proportional hazards.
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.
Luk, 10/20/2021, retrospective, China, peer-reviewed, median age 65.3, 7 authors, study period January 2020 - February 2021.
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 risk of death, 186.0% higher, HR 2.86, p = 0.03, treatment 385, control 679.
risk of mechanical ventilation, 2099.0% higher, HR 21.99, p < 0.001, treatment 385, control 679.
risk of ICU admission, 995.0% higher, HR 10.95, p < 0.001, treatment 385, control 679.
Matviichuk, 12/11/2024, retrospective, Ukraine, peer-reviewed, 9 authors.
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 risk of long COVID, 42.7% higher, RR 1.43, p = 0.003, treatment 53 of 83 (63.9%), control 71 of 152 (46.7%), adjusted per study, odds ratio converted to relative risk, insulin analogs.
risk of long COVID, 19.5% lower, RR 0.81, p = 0.15, treatment 36 of 94 (38.3%), control 191 of 375 (50.9%), NNT 7.9, adjusted per study, odds ratio converted to relative risk, human insulin.
Nowak, 1/29/2025, retrospective, Sweden, peer-reviewed, median age 58.0, 10 authors, study period 1 January, 2020 - 31 December, 2021.
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 risk of death/hospitalization, 84.0% higher, OR 1.84, p = 0.004, treatment 1,021, control 4,803, adjusted per study, multivariable, RR approximated with OR.
Pazoki, 11/8/2021, retrospective, Iran, peer-reviewed, mean age 59.2, 17 authors, study period 20 February, 2020 - 29 October, 2020.
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 risk of death, 9.0% lower, HR 0.91, p = 0.76, treatment 53, control 340, model 2.
risk of severe case, 7.0% higher, HR 1.07, p = 0.76, treatment 53, control 340, model 2.
Soff, 2/4/2025, retrospective, USA, peer-reviewed, mean age 62.0, 11 authors, study period 1 January, 2021 - 30 June, 2022.
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 long COVID or death, 28.0% higher, OR 1.28, p = 0.001, treatment 1,218, control 6,212, adjusted per study, multivariable, RR approximated with OR.
Footnotes
a.
Viral infection and replication involves attachment, entry, uncoating and release, genome replication and transcription, translation and protein processing, assembly and budding, and release. Each step can be disrupted by therapeutics.
b.
When administered late in infection, HCQ may enhance viral egress by further increasing lysosomal pH beyond the effect of ORF3a's water channel activity, thereby promoting lysosomal exocytosis, inactivating degradative enzymes, and facilitating the release of SARS-CoV-2 particles into the extracellular environment98,99. Research also suggests potential cardioprotective effects at lower doses, but cardiotoxicity with excessive dosage100. Bobrowski et al. also indicate negative effects if HCQ and remdesivir are combined.
c.
Peters (B) et al. is subject to confounding by calendar-time (SOC evolved rapidly early in the pandemic, the linear covariate does not reflect non-linear SOC changes and hospital specific effects), hospital type (non-treatment hospitals were tertiary university centers), confounding by indication (4/7 hospitals initiated treatment on deterioration), immortal-time bias for as-treated (exposure assigned after baseline), significant differences for other experimental treatments, potential overadjustment from collider bias (steroid use and indication bias), limited baseline severity information, differences in hospice referral propensity across hospitals, unadjusted difference in time from onset to admission, difference in PCR positivity, and other factors. Mahévas et al. is subject to confounding by hospital (treatment highly dependent on the hospital, different SOC/ICU transfer practices, not included in PS), immortal time (only partly addressed in sensitivity analysis), co-treatment differences, calendar-time (SOC evolved rapidly early in the pandemic), binary coding for age (age ≥65 despite steep age-risk gradient), residual imbalance (variables dropped from PS), a composite outcome dependent on hospital triage/capacity, and other factors.
References