SARS-CoV-2 is a new single-stranded RNA virus, which was first identified in December 2019, and has rapidly spread into a global pandemic of primarily respiratory illness designated as coronavirus disease 2019 (COVID-19). This disease is associated with significant mortality, particularly among elderly or overweight individuals, raising considerable concerns for public health. Until a vaccine or specifically designed therapies are available, it is urgent to identify whether existing medications have protective effects against COVID-19 complications using available real-world data. With this aim, we performed a case-control study on electronic health records (EHRs) from Clalit Health Services (CHS), the largest healthcare provider in Israel.

Through December 31, 2020, 10,295 adult patients between the ages of 18 and 95 had a recorded COVID-19 related hospitalization in the CHS database. The matching procedure was able to identify control individuals from the general population in ratio 5:1 for 6530 patients in the first cohort, and control patients in ratio 2:1 for 6953 SARS-CoV-2 positive individuals in the second cohort. The characteristics of the matched populations are shown in Table 1.

In each of the two cohorts, we counted the number of patients from each group who acquired drugs and other medical products from each ATC class and computed the OR and p-values using Fisher’s exact test. The distribution of OR for drugs for which the p-value was statistically significant (p<0.05) is shown in Figure 1. The OR for most drugs are neutral or associated with an increased risk of COVID-19 hospitalization. Only a small number of items are associated with decreased risk: 1.15% in cohort 1 and 1.75% in cohort 2.

Figure 1

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Table 2 presents the list of drugs and products that were found to be negatively associated with COVID-19 hospitalization in a statistically significant manner in cohort 1 (A) and in cohort 2 (B). We display items for which the p-value is below 0.05, and for which the FDR is less than 0.20, meaning that at least 80% of the items in the displayed list are expected to be true positives. Items are sorted in decreasing order of significance.

The top ranked medications by significance in cohort 1 were rosuvastatin (OR=0.673, 95% confidence interval [CI] 0.596 to 0.758), ezetimibe (OR=0.488, CI 0.377 to 0.622), and ubiquinone (OR=0.181, CI 0.065 to 0.403); these same three medications were also in the top five by significance of cohort 2: rosuvastatin (OR=0.732, CI 0.643 to 0.83), ezetimibe (OR=0.602; CI 0.471 to 0.764), and ubiquinone (OR=0.181, CI 0.065 to 0.403). It is remarkable that these three drugs act on the cholesterol and ubiquinone synthesis pathways, which both stem from the mevalonate pathway (Buhaescu and Izzedine, 2007); the intermediate product at the branch point is farnesyl polyphosphate (FPP) (Figure 2). Rosuvastatin and other statins specifically inhibit he enzyme HMG-CoA reductase. Ubiquinone is a food supplement available over the counter, which is often recommended to patients prone to muscular pain and receiving a statin treatment (Qu et al., 2018). Risedronate, which also acts on this pathway, and is commonly used to prevent osteoporosis, by blocking the enzyme FPP synthase, is also identified by both cohorts, and is ranked 4th by significance in cohort 2 (OR=0.567; CI 0.400 to 0.789), and 13th in cohort 1 (OR=0.705; CI 0.522 to 0.935).

Figure 2

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Other medications that fulfilled the stringent criteria of being identified by both cohorts with an FDR of 80% include the pneumococcal conjugate vaccine (OR=0.476, CI 0.288 to 0.746 in cohort 1; 0.602, CI 0.245 to 0.685 in cohort 2), magnesium citrate (OR=0.652, CI 0.433 to 0.952 in cohort 1; 0.609, CI 0.399 to 0.908 in cohort 2), vitamin D (OR=0.898, CI 0.821 to 0.980 in cohort 1; 0.869, CI 0.792 to 0.954 in cohort 2), flecainide (OR=0.301, CI 0.118 to 0.641 in cohort 1; 0.325, CI 0.123 to 0.729 in cohort 2), escitalopram (OR=0.824, CI 0.708 to 0.955 in cohort 1; 0.766, CI 0.654 to 0.894 in cohort 2), cilazapril (OR=0.554, CI 0.315 to 0.918 in cohort 1; 0.468, CI 0.247 to 0.831 in cohort 2), ramipril combined with hydrochlorothiazide (OR=0.734, CI 0.603 to 0.887 in cohort 1; 0.702, CI 0.565 to 0.869 in cohort 2), and sitagliptin combined with metformin (OR=0.802, CI 0.696 to 0.922 in cohort 1; 0.826, CI 0.704 to 0.967 in cohort 2). Sitagliptin alone is also significant in cohort 1 (OR=0.658, CI 0.443 to 0.950).

In addition, we observe interesting patterns in cohort 2, which is designed to identify drugs associated with decreased hospitalization risk in SARS-CoV-2 positive patients: several vitamin or mineral supplementation items appear to have a protective effect, in addition to vitamin D and magnesium citrate, which were identified by both cohorts: vitamin B12 combinations (OR=0.618, CI 0.399 to 0.934), multivitamins for ocular use (OR=0.616, CI 0.376 to 0.976), and calcium-zinc combinations (OR=0.000, CI 0.000 to 0.892).

Several ophthalmic items also appear to be associated with significantly decreased odds for hospitalization, including artificial tears, hydroxypropyl-methylcellulose-based (OR=0.657, CI 0.490 to 0.871), or hyaluronic acid based (OR=0.322, CI 0.098 to 0.836); decreased OR are also found for items that may act as a physical barrier to the eye: eye care wipes, which are sterile wipes sold to clean the eyes (OR=0.285, CI 0.054 to 0.956), a retinol-based ointment used to treat cornea abrasion (OR=0.285, CI 0.054 to 0.956), and timolol drops used to treat glaucoma (OR=0.570, CI 0.328 to 0.949).

Also associated with decreased odds for hospitalization are several drugs based on an ACE inhibitor or an angiotensin receptor blocker (ARB), sometimes in combination with another compound. In addition to cilazapril and ramipril-hydrochlorothiazide that were highly ranked in both cohorts, cohort 1 identifies candesartan (OR=0.718, CI 0.544 to 0.934), and cohort 2 identifies valsartan with amlodipine (OR=0.821, CI 0.698 to 0.963), and losartan with hydrochlorothiazide (OR=0.783, CI 0.630 to 0.969).

Remarkably, several drugs acting on receptors to neurotransmitters also appear to decrease hospitalization risk: rizatriptan (OR=0.118, CI 0.003 to 0.750), bupropion (OR=0.399, CI 0.136 to 0.976), and methylphenidate (OR=0.444, CI 0.178 to 0.972).

In the Israeli population, the two groups that have been reported to be at higher risk are Ultra-Orthodox Jews and Arabs (Muhsen et al., 2021). Therefore, we performed additional analyses with the goal to eliminate membership in either of these groups as a potential confounder and to eliminate possible confounding in concurrently used medications. We performed multivariate conditional logistic regression (Materials and methods) in each of the cohorts. In each cohort, we modelized the OR for hospitalization, using ethnicity and purchased medications as explanatory factors. See Supplementary file 1. Either Ultra-Orthodox or Arab identity indeed appear to be each associated with increased risk for hospitalization. However, even after adjusting for the subgroup membership, most of the medications identified by individual Fisher’s exact tests maintain statistically significant protective effect.

Because of the established association between high BMI and COVID-19 severity, it is of interest to know whether any of the protective medications are especially protective in high BMI individuals. Therefore, we performed a subgroup analysis, by partitioning partition BMI into four ranges (see Materials and methods). The results are shown as a forest plot in Supplementary file 1-table 3. In general, the protective effects were seen in most or all BMI ranges and we did not see any striking association between a protective medication and high BMI.

In this large-scale retrospective study, we identified several drugs and products that are significantly associated with reduced odds for COVID-19 hospitalization, both in the general population and in patients with laboratory-proven SARS-CoV-2 infection. Several other research groups have recognized the potential for EHRs to enable large-scale studies in COVID-19 and the challenges of this sort of retrospective research are reviewed in Dagliati et al., 2021; Sudat et al., 2021. To give a few examples, EHRs have also been used to predict: (i) COVID-19 mortality based on pre-existing conditions (Estiri et al., 2021; Osborne et al., 2020), (ii) early diagnosis of COVID-19 based on clinical notes (Wagner et al., 2020), and (iii) eligibility of COVID-19 patients for clinical trials by matching trial criteria with patient records (Kim et al., 2021).

Major strengths of our study include: (i) the large sample of hospitalized COVID-19 patients, (ii) the ability to collect comprehensive data about individual demographic and comorbidity characteristics and to build matched case and control populations, (iii) the ability to track hospitalizations and disease severity, owing to a central database established by the Israeli Ministry of Health, and (iv) the capacity to track which drugs and products have been acquired by patients in the period that have preceded SARS-CoV-2 infection, owing to comprehensive digital systems integration in CHS.

Another strength is the dual cohort design, with control individuals taken from the general population in the first cohort and from individuals positive for SARS-CoV-2 in the second cohort, with each using different matching criteria, mitigates potential bias that could affect each cohort. The two cohorts allowed us to evaluate the protective effect of drugs that act either by reducing the initial risk of infection or by reducing the risk of hospitalization in those infected. Analyses are based on items procured in the 35 days before the initial positive test. This window was chosen in accordance with the monthly renewal of prescription policy in place in CHS.

Limitations of this study are related to it being observational in nature. Best efforts were made to use matching so that patients in case and controls are similar regarding most of the known factors for disease severity, and notably, age, obesity, smoking, and baseline comorbidity. The cases and controls were not matched for ethnicity, which could be a substantial confounding factor. We aimed to get a sensible tradeoff between controlling for confounding factors by rigorous matching and keeping enough patients so that cohorts are representative of the general population. Our analysis is based on medication acquisition in pharmacies and does not ascertain that medications purchased were used. Notably, some of the drugs associated with a protective effect may have been stopped during patient’s hospitalization so that our analysis may have underestimated the full achievable benefits for some of the drugs. Conversely, since drugs tested here were acquired before patients were positive for SARS-CoV-2, the protective effect of some of the drugs may be fully attained only when treatment is started before or early in the infection.

The variable behavior of people during the pandemic has been an important factor that can affect the risk of exposure and the severity of infection. We tried to address this cause of variable risk by performing matching in two distinct cohorts and by using only PCR-positive patients in the second cohort. Nevertheless, behavioral factors, which could not measure, can still account for some of the observed differences.

Our analyses counted the purchase of each medication, but not the dose or the patient compliance. Therefore, we cannot comment on whether higher doses of the beneficial medications, such as rosuvastatin and ubiquinone, are associated with reduced risk.

The medications that are protective are prescribed for a variety of conditions. It is conceivable but unlikely that it is the medical condition, or comorbidity, that provides the protection rather than the medication itself. Three of the comorbidities that have been prominently suggested as relevant to COVID-19 severity and outcome include high BMI, diabetes, and hypertension. Therefore, at the helpful suggestion of the reviewers, we did both subgroup analysis and regression analysis to show that the protective effect of the most protective medications appears not to be associated with BMI (Supplementary file 1). The study design explicitly matched for diabetes and hypertension, so it follows that these two diseases are not associated with the protective effects of the drugs listed in Table 2A and B. However, we recognize the limitation that when the association between the medical condition and the prescription is very specific, such as flecainide for cardiac arrhythmia, we lack suitable data to separate the possible effects of the condition and the medication.

Bearing these strengths and potential limitations in mind, our analyses seem to indicate several viral vulnerability points, which can potentially be exploited to effectively reduce disease severity with drugs that are already available. The drugs identified as protective include ubiquinone, which is a food supplement with a very good safety profile that does not even require a prescription in our health system, and rosuvastatin and ezetimibe, two drugs prescribed routinely to reduce cholesterol and that have a very good safety profile. These findings are in line with previous reports that RNA viruses need cholesterol to enter cells, for virion assembly, and to maintain structural stability (Aizaki et al., 2008; Bajimaya et al., 2017; Rossman et al., 2010; Sun and Whittaker, 2003), and that prescribing statins may protect against infection with RNA viruses such as members of family Flaviviridae, including dengue virus, Zika virus, and West Nile virus (Gower and Graham, 2001; Osuna-Ramos et al., 2018; Whitehorn et al., 2016). The involvement of the cholesterol/ubiquinone pathway is further confirmed by the fact that risedronic acid, a drug acting on the enzyme farnesyl pyrophoshate synthase (Tsoumpra et al., 2015; Figure 2) which catalyzes the production of FPP from which the cholesterol and the ubiquinone synthesis pathways split (Buhaescu and Izzedine, 2007), is identified as protective as well, even though it is prescribed for osteoporosis regardless of the presence of hypercholesterolemia.

Taken together, our findings lend (albeit indirect) support to the possibility that SARS-CoV-2 hijacks the cholesterol synthesis pathway, possibly to boost production of the cellular cholesterol it needs as an RNA virus. The fact that ubiquinone protects against severe disease suggests that SARS-CoV-2 may tilt the mevalonate pathway toward cholesterol synthesis and away from ubiquinone synthesis. Such a pathway imbalance would ultimately result in deficiency of ubiquinone that could lead to cell death unless counteracted by ubiquinone supplementation.

It is remarkable that the protective effect of anti-cholesterol drugs was observed mostly for rosuvastatin – and in cohort 1 for pravastatin – but not for other statins. Rosuvastatin was found to significantly increase 25-OH vitamin D levels in the blood (Yavuz et al., 2009), much more than what could be observed with other statins. Yavuz and Ertugrul, 2012 suggested that the increase in 25-OH vitamin D observed following rosuvastatin treatment could be mediated by the Niemann-Pick C1 like 1 (NPC1L1) membrane transporter that is involved in intestinal absorption of vitamin D. Interestingly, the NPC1L1 membrane transporter is also the target of ezetimibe, identified by our study to decrease significantly the hospitalization risk of COVID-19 patients.

In both cohorts, we observed a significant decrease of the odds for hospitalization for COVID-19 patients treated with either vitamin D or magnesium citrate. Vitamin D deficiency has been shown to be associated with increased risk for COVID-19 in multiple studies (Israel et al., 2020; Merzon et al., 2020). Magnesium is needed for vitamin D activation (Uwitonze and Razzaque, 2018) and its levels in drinking water in Israel are low, as water is produced in great part through desalination of sea water (Koren et al., 2017). The decreased hospitalization rate revealed here for patients taking magnesium supplementation may suggest a role for supplementation of this element along with vitamin D. Hospitalization risk was also found to be decreased in patients taking vitamin B12 and calcium-zinc, as identified by other studies (Ragan et al., 2020; Trasino, 2020; Wessels et al., 2020).

Another medication that was associated with decreased odds for hospitalization is flecainide, an antiarrhythmic drug that blocks sodium channels in the heart, and inhibits ryanodine receptor 2, a major regulator of sarcoplasmic release of stored calcium ions. It may prevent apoptosis by release of calcium from the endoplasmic reticulum (ER) once the cell mitochondria cease to function. An expert review recommended that patients with arrhythmia who get COVID-19 should continue flecainide treatment if already prescribed (Ci et al., 2020). In our study, the protective effect observed in both cohorts is even more marked for severe patients, suggesting that this drug, which can be given intravenously (Antonelli et al., 2006), could be administered to patients in respiratory distress, if the protective effect is confirmed in clinical trials.

Several drugs acting as ACE inhibitors or ARBs appeared to slightly decrease the odds for hospitalization, either alone or in combination (cilazapril, ramipril-hydrochlorothiazide, losartan-hydrochlorothiazide, valsartan amlodipine). These results are consistent with ACE and ARBs treated patients shown to not have an increased risk for COVID-19 (Morales et al., 2021), and there is therefore no reason to discontinue these medications to decrease COVID-19 risk. Our findings also confirm and substantially extend recent EHR-based findings about the favorable association between metformin use and COVID-19 outcomes (Bramante et al., 2021).

In addition, several drugs acting on synapses (escitalopram, bupropion, mirabegron, and timolol) were associated with decreased risk of hospitalization. This is consistent with SARS-CoV-2 invading neuronal cells (Iroegbu et al., 2020; Meinhardt et al., 2021; Song et al., 2021), as manifest by symptoms of loss of smell and taste, where it may spread throughout the nervous system across synapses. Decreased neurotransmitter internalization may therefore reduce the infectious potential of the virus.

Interestingly, items that could improve the physical barrier of the eye surface were among the top items decreasing odds of hospitalization, including eye wipes, artificial tears, and eye ointments. Interestingly, the protective effect for these items was observed foremost among patients from cohort 2 in which controls are already SARS-CoV-2 positive. This suggests that these barrier items could not only protect against the initial risk of infection, but, notably, also reduce disease severity in patients already infected. The beneficial effect observed here for many different ophthalmologic preparations raises the possibility that autoinoculation of the virus to the eyes, prevented by these items, has a role in the virulence of SARS-CoV-2. The possibility that invasion of the central nervous system by the virus through the eyes could increase the risk of COVID-19 complications is also supported by the fact that eyeglass wearers were shown previously to be at decreased risk for COVID-19 hospitalization (Zeng et al., 2020). Until the meaning of these findings is fully understood, it may be helpful to advise COVID-19 patients to avoid touching their eyes in order to reduce the risk of complications.

In conclusion, this study shows apparently protective effects for several medications and dietary supplements, such as rosuvastatin, ezetimibe, ubiquinone, risedronate, vitamin D, and magnesium. We suggest to further investigate these, and other products identified by this study, in prospective trials aimed to reduce disease severity in COVID-19 patients. In the meantime, we believe that the observed protective effects of these drugs provide important evidence supporting their safe continuation for COVID-19 patients.

Data were obtained from patients' electronic health records, and IRB approval restrains its use to researchers inside Clalit Health Services. For further information regarding data availability, researchers may contact Dr. Lavie gillav@clalit.org.il. This study is based on real-world patient drug purchases, and it cannot be made available due to patient privacy concerns. R code used to produce Figure 1 is available as a supplementary file named ‘Source code 1’.

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Author details

1. Ariel Israel

   Division of Planning and Strategy, Clalit Health Services, Tel Aviv, Israel

   Contribution

   Conceptualization, Data curation, Software, Investigation, Methodology, Writing - original draft, Writing - review and editing

   For correspondence

   dr.ariel.israel@gmail.com

   Competing interests

   No competing interests declared

   "This ORCID iD identifies the author of this article:" 0000-0003-4389-8896

2. Alejandro A Schäffer

   Cancer Data Science Laboratory, National Cancer Institute, National Institutes of Health, Bethesda, United States

   Contribution

   Conceptualization, Writing - original draft, Writing - review and editing

   Competing interests

   No competing interests declared

   "This ORCID iD identifies the author of this article:" 0000-0002-2147-8033

3. Assi Cicurel

   1. Division of Planning and Strategy, Clalit Health Services, Tel Aviv, Israel
   2. Clalit Health Services, Southern District and Faculty of Health Sciences, Ben-Gurion University of the Negev, Beer-Sheva, Israel

   Contribution

   Conceptualization, Investigation, Writing - original draft, Writing - review and editing

   Competing interests

   No competing interests declared

4. Kuoyuan Cheng

   Cancer Data Science Laboratory, National Cancer Institute, National Institutes of Health, Bethesda, United States

   Contribution

   Conceptualization, Investigation, Writing - review and editing

   Competing interests

   No competing interests declared

5. Sanju Sinha

   Cancer Data Science Laboratory, National Cancer Institute, National Institutes of Health, Bethesda, United States

   Contribution

   Conceptualization, Investigation, Writing - review and editing

   Competing interests

   No competing interests declared

6. Eyal Schiff

   Sheba Medical Center, Tel-Aviv University, Ramat Gan, Israel

   Contribution

   Conceptualization, Investigation, Writing - review and editing

   Competing interests

   No competing interests declared

7. Ilan Feldhamer

   Division of Planning and Strategy, Clalit Health Services, Tel Aviv, Israel

   Contribution

   Validation, Investigation, Writing - review and editing

   Competing interests

   No competing interests declared

8. Ameer Tal

   Division of Planning and Strategy, Clalit Health Services, Tel Aviv, Israel

   Contribution

   Software, Validation, Investigation, Writing - review and editing

   Competing interests

   No competing interests declared

9. Gil Lavie

   1. Division of Planning and Strategy, Clalit Health Services, Tel Aviv, Israel
   2. Ruth and Bruce Rappaport Faculty of Medicine, Technion – Israel Institute of Technology, Haifa, Israel

   Contribution

   Conceptualization, Supervision, Investigation, Methodology, Writing - original draft, Writing - review and editing

   Contributed equally with

   Eytan Ruppin

   Competing interests

   No competing interests declared

10. Eytan Ruppin

    Cancer Data Science Laboratory, National Cancer Institute, National Institutes of Health, Bethesda, United States

    Contribution

    Conceptualization, Supervision, Investigation, Methodology, Writing - original draft, Writing - review and editing

    Contributed equally with

    Gil Lavie

    For correspondence

    eytan.ruppin@nih.gov

    Competing interests

    No competing interests declared

    "This ORCID iD identifies the author of this article:" 0000-0002-7862-3940

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