{"paper_id":"b2c17a3d-d830-45d7-aa54-77012b71544d","body_text":"1\n1 The effect of antidepressants on severity of COVID-19 in hospitalized patients:\n2 A systematic review and meta-analysis\n3\n4\n5 Hosein Nakhaee1, Reza Bayati1ϯ, Mohammad Rahmanian1ϯ, Amir Ghaffari Jolfayi1, Moein \n6 Zangiabadian2*, Sakineh Rakhshanderou3*\n7\n8 1Student Research Committee, School of Medicine, Shahid Beheshti University of Medical Sciences, \n9 Tehran, Iran\n10\n11 2Department of Microbiology, School of Medicine, Shahid Beheshti University of Medical\n12 Sciences, Tehran, Iran\n13\n14 3Environmental and Occupational Hazards Control Research Center, School of Public Health, Shahid \n15 Beheshti University of Medical Sciences, Tehran, Iran\n16\n17\n18\n19 ϯ These authors contributed equally\n20\n21\n22\n23 *Correspondence:\n24\n25 Moein Zangiabadian\n26 Zangiabadian1998@gmail.com\n27\n28 Sakineh Rakhshanderou\n29 s_rakhshanderou@sbmu.ac.ir\n30\n31\n . CC-BY 4.0 International licenseIt is made available under a \n is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity. (which was not certified by peer review)\nThe copyright holder for this preprint this version posted April 16, 2022. ; https://doi.org/10.1101/2022.04.11.22273709doi: medRxiv preprint \nNOTE: This preprint reports new research that has not been certified by peer review and should not be used to guide clinical practice.\n\n2\n32 Abstract\n33\n34 Introduction:\n35  Clinical depression and the subsequent low immunity is a comorbidity that can act as a risk factor \n36 for severity of COVID-19 cases. Antidepressants such as SSRI and SNRI are associated with \n37 immune-modulatory effects, which dismiss inflammatory response and reduce lung tissue damage. \n38 The current systematic review and meta-analysis aims to evaluate the effect of antidepressant drugs \n39 on prognosis and severity of COVID-19 in hospitalized patients.\n40\n41 Methods:\n42 A systematic search was carried out in PubMed/Medline, EMBASE, and Scopus up to January \n43 16, 2022. The following keywords were used: “COVID-19”, “SARS-CoV-2”, “2019-nCoV”, \n44 “SSRI”, “SNRI”, “TCA”, “MAOI”, and “Antidepressant”. The pooled risk ratio (RR) with 95% \n45 CI was assessed using a fixed or random-effect model. We considered P < 0.05 as statistically \n46 significant for publication bias. Data were analyzed by Comprehensive Meta-Analysis software, \n47 Version 2.0 (Biostat, Englewood, NJ).\n48\n49 Results:\n50 Twelve studies were included in our systematic review. Three of them were experimental with \n51 1751, and nine of them were observational with 290,950 participants. Seven out of twelve \n52 articles revealed the effect of antidepressants on reducing severity of COVID-19. SSRI \n53 medications, including Fluvoxamine, Escitalopram, Fluoxetine, and Paroxetine and also among \n54 the SNRI drugs Venlafaxine are also reasonably associated with reduced risk of intubation or \n55 death. There were four studies showing no significant effect and one study showing the negative \n56 effect of antidepressants on prognosis of covid-19. The meta-analysis on clinical trials showed \n57 that fluvoxamine could significantly decrease the severity outcomes of COVID-19 (RR: 0.745; \n58 95% CI: 0.580-0.956)\n59\n60 Conclusions: Most of the evidence supports that the use of antidepressant medications, mainly \n61 Fluvoxamine may decrease the severity and improve the outcome in hospitalizes patients with \n62 sars-cov-2. Some studies showed contradictory findings regarding the effects of antidepressants \n63 on severity of COVID-19. Further experimental studies should be conducted to clarify the effects \n64 of antidepressants on severity of COVID-19.\n65 Keywords: antidepressants –SSRI- COVID-19 -SARS-CoV-2\n66\n67\n . CC-BY 4.0 International licenseIt is made available under a \n is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity. (which was not certified by peer review)\nThe copyright holder for this preprint this version posted April 16, 2022. ; https://doi.org/10.1101/2022.04.11.22273709doi: medRxiv preprint \n\n3\n68 Introduction\n69\n70 After over two years since the first case of the novel coronavirus was detected in December 2019 \n71 in Wuhan, China, 483 million infected cases and 6.13 million deaths have been reported worldwide \n72 up to January 21, 2022 (1). Covid19 is an acute respiratory disease, resulting in progressive \n73 respiratory failure, and eventually leading to death. Common symptoms include fever, dry cough, \n74 myalgia and fatigue. Severe cases develop dyspnea, hemoptysis, acute respiratory distress \n75 syndrome which can result in death. The severity or mortality is higher in older population, with \n76 comorbidities such as diabetes, hypertension, cardiovascular diseases and weaker immune \n77 functions (2). During the COVID-19 pandemic with strict lockdown measures, psychiatric patients \n78 have suffered episodes of anxiety, depression, and stress disorders on higher scales.(3) Clinical \n79 depression and the subsequent low immunity can also act as risk factors for severity of COVID-\n80 19 cases (4). Patients with clinical depression have lower immunity compared to those of healthy \n81 controls. A history of depression is associated with higher risk of infection and also the increased \n82 risk remains consistent over time (5). The suggested mechanism of action is fewer circulating \n83 CD4+ T-cells and reduction in natural killer cell cytotoxic responses of lymphocyte proliferation \n84 in elderly patients with clinical depression (6). Treatment of depression as an underlying morbidity \n85 could decrease the risk of clinical deterioration of Covid-19 patients. On the other hand, \n86 antidepressants such as SNRI and SSRI which are widely used in treatment of psychiatric patients \n87 are shown to reduce corona virus infection rates as patients who were receiving these medications \n88 were less likely to test positive for COVID-19 (7). Antidepressants are also associated with less \n89 severe cases as they significantly reduced the risk of intubation or death in some cohort studies (8, \n90 9). Several mechanisms are suggested to justify this finding. Antidepressants could be associated \n91 with declined plasma levels of inflammatory cytokines, including IL-10, TNF-α, CCL-2, and IL-\n92 6, which are related to COVID-19 severity and mortality (10, 11). Also Some SSRI \n93 antidepressants, such as fluvoxamine which is a functional inhibitor of acid sphingomyelinase \n94 activity (FIASMA), may prevent the infection of epithelial cells with SARS-CoV-2 (12). In this \n95 study we aim to evaluate the effect of antidepressant drugs on prognosis and severity of COVID-\n96 19 in hospitalized patients.\n97\n98 Method \n99\n100 This study was conducted and reported in accordance with the Preferred Reporting Items for \n101 Systematic Reviews and Meta-Analyses statement (13). The study was registered in the \n102 Systematic Review Registration: PROSPERO (pending registration ID: 313272).\n103\n104 Search strategy\n105 We searched Pubmed/Medline, Embase and Scopus for clinical studies reporting the effect of \n106 anti-depressants on reducing severity of hospitalized patients with covid-19, published up to \n107 January 16, 2022. We included clinical trials, cohort and case–control studies that were written \n108 in English. We used the following MeSH terms: “‘antidepressive agents’, ‘antidepressive agents, \n109 second generation’, ‘antidepressive agents, tricyclic’, ‘monoamine oxidase inhibitors’, ‘serotonin \n110 and noradrenaline reuptake inhibitors’, ‘serotonin uptake inhibitors’, ‘COVID-19’ and ‘SARS-\n111 CoV-2’” (Table S1&2&3). Keyword searches were done with combinations of the terms “SSRI”, \n112 “SNRI”, “TCA”, “MAOI”, “Antidepressant”, “2019 novel coronavirus” and “sars coronavirus \n . CC-BY 4.0 International licenseIt is made available under a \n is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity. (which was not certified by peer review)\nThe copyright holder for this preprint this version posted April 16, 2022. ; https://doi.org/10.1101/2022.04.11.22273709doi: medRxiv preprint \n\n4\n113 2”. Lists of references of selected articles and relevant review articles were hand-searched to \n114 identify further studies.\n115\n116 Study Selection\n117\n118 The records found through database searching were merged, and the duplicates were removed \n119 using EndNote X8 (Thomson Reuters, Toronto, ON, Canada). Two reviewers independently \n120 screened the records by title/abstract and full text to exclude those unrelated to the study \n121 objectives. Any disagreements were resolved by the lead investigator. Included studies met the \n122 following criteria: (i) patients were diagnosed with COVID-19 based on the WHO criteria; (ii) \n123 patients were received anti-depressants; and (iii) severity predictors (hospitalization due to \n124 COVID-19 and/or ARDS and/or need to NIV or mechanical ventilation and/or ICU admission \n125 and/or death). Conference abstracts, editorials, reviews, study protocols, molecular or \n126 experimental studies on animal models and studies with focusing on infection risk were \n127 excluded.\n128\n129 Data extraction\n130\n131 Two reviewers designed a data extraction form. These reviewers extracted data from all eligible \n132 studies, and differences were resolved by consensus. The following data were extracted: first \n133 author name; year of publication; study duration; type of study, country/ies where the research \n134 was conducted; demographics (i.e. age, sex); Detection test of COVID-19; anti-depressant type, \n135 dosage and frequency; Follow-up time; the definition of case and control; total number of \n136 controls and cases, severity indices, mechanism of action of anti-depressants against COVID-19 \n137 and Possibility of using anti-depressants in COVID-19 treatment.\n138\n139 Quality assessment \n140\n141 Two blinded reviewers assessed the quality of the studies using three different assessment tools \n142 (checklists): two for observational studies (case controls and cohorts) and one for experimental \n143 studies (14). Items such as study population, measure of exposures, confounding factors, extent \n144 of outcomes, follow-up data, and statistical analysis were evaluated.\n145\n146 Statistical Analysis\n147\n148  The pooled risk ratios (RRs) with 95% CI were assessed using random or fixed-effect models. \n149 The fixed-effects model was used because of the low estimated heterogeneity of the true effect \n150 sizes. The between-study heterogeneity was assessed by Cochran’s Q and the I2 statistic. \n151 Publication bias was evaluated statistically by using Egger’s and Begg’s tests as well as the \n152 funnel plot (p < 0.05 was considered indicative of statistically significant publication bias; funnel \n153 plot asymmetry also suggests bias) (15). All analyses were performed using “Comprehensive \n154 Meta-Analysis” software, Version 2.0 (Biostat, Englewood, NJ).\n155\n156  Results \n157\n158 The selection process of articles is shown in Figure 1. Twelve articles were included and \n159 classified into the followings: six cohort studies (8, 16-20), three case-control studies (21-23) and \n160 three clinical trials (24-26). There were 872 cases and 879 controls in clinical trials, 15950 cases \n . CC-BY 4.0 International licenseIt is made available under a \n is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity. (which was not certified by peer review)\nThe copyright holder for this preprint this version posted April 16, 2022. ; https://doi.org/10.1101/2022.04.11.22273709doi: medRxiv preprint \n\n5\n161 and 96638 controls in the cohort studies and 97,844 cases and 80518 controls in the case–control \n162 studies with the total population of 292701 in the whole studies (114666 cases and 178035 \n163 controls). Four studies were conducted in USA and other studies were designed in France, \n164 Turkey, Brazil, Hungary, Israel, Croatia, Scotland and Italy. The duration of studies, detection \n165 test of COVID-19 and other study characteristics are shown in table 1.\n166\n167\n168\n169 Quality of the included studies\n170\n171 The checklists for observational studies (14) showed that the included observational studies had a \n172 low risk of bias except Bora et al. study (16). (Table 2 & 3) In contrast, the checklist for \n173 experimental studies (14) showed that the included experimental studies had a high risk of bias for \n174 randomization, group concealment and participants, treatment delivers and outcome assessors \n175 blinding except Lenze et al. study (25). (Table 4)\n176\n177 Patient Characteristics\n178\n179 According to nine studies (8, 16-18, 21-25), the mean age of total patients was 58.1 years old \n180 with 40 and 56.5 years old in case and control groups in eight studies, respectively (8, 17, 18, 20, \n181 21, 23-25). 49.6 percent of patients were female according to eleven studies (8, 16-21, 23-26). \n182 This number was 52% in case groups and 49.3% in control groups according to nine studies (17-\n183 21, 23-26). Cases and controls were matched with age, sex, comorbidities, smoking status, BMI \n184 category and etc. The definition of case and control groups and severity outcomes are shown in \n185 table 5.\n186\n187 Interventions and exposures Characteristics\n188\n189 Seven studies (18, 20, 21, 23-26) only used or surveyed SSRIs as anti-depressant in case groups \n190 (fluvoxamine in five studies (18, 20, 24-26), fluoxetine in two studies (18, 23) and Escitalopram \n191 in one study (21)). In remain five studies (8, 16, 17, 19, 22) different types of SSRIs, SNRIs and \n192 TCAs were used or surveyed. Anti-depressant type, dosage, frequency and follow-up time are \n193 shown in table 6.\n194\n195 Effect of anti-depressants on severity outcomes of COVID-19\n196\n197 After adjustment, seven studies (8, 16, 18, 20, 21, 23, 25) showed significant association \n198 between anti-depressant use and reducing severity outcomes of COVID-19. On the other hand, \n199 five studies (17, 19, 22, 24, 26) presented that either there are not any significant effect for anti-\n200 depressants to reduce disease severity or anti-depressants are associated with severe COVID-19 \n201 in hospitalized patients. In the study that was conducted by Bora et al. (16) anti-depressants were \n202 associated with reducing mortality, regardless of the anti-depressant type. Hoertel et al. (8) \n203 suggested that antidepressant use (HR:0.56; 95% CI: 0.43–0.73) is significantly and substantially \n204 associated with reduced risk of intubation or death, independently of patient characteristics, \n205 clinical and biological markers of disease severity, and other psychotropic medications. They \n206 found that SSRI (HR:0.51; 95% CI:0.316–0.72) and non-SSRI (HR:0.65; 95% CI, 0.45–0.93) \n207 antidepressants, and specifically the SSRIs escitalopram, fluoxetine, and paroxetine, the SNRI \n . CC-BY 4.0 International licenseIt is made available under a \n is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity. (which was not certified by peer review)\nThe copyright holder for this preprint this version posted April 16, 2022. ; https://doi.org/10.1101/2022.04.11.22273709doi: medRxiv preprint \n\n6\n208 venlafaxine and the α2-antagonist antidepressants mirtazapine are significantly associated with \n209 reduced risk of intubation or death. Escitalopram is also effective in reducing severity according \n210 to Israel et al. (21) study. Fluoxetine  use  was  associated  with  an  important (70%)  decrease  \n211 of  mortality  (OR:0.33; 95% CI:0.16–0.68) and threefold survival in fluoxetine group according \n212 to Nemeth et al. (23) study. Also another study by Oskotsky et al. (18) showed similar result that \n213 fluoxetine could reduce risk of mortality (RR:0.72; 95% CI:0.54-0.97). On the other hand, \n214 Rauchman et al. (19) mentioned that prior use of SSRIs or SNRIs did not reduce mortality \n215 (OR:0.96; 95% CI:0.79-1.16). McKeigue et al. (22) proposed that TCAs (RR:1.1; 95% CI: 0.94-\n216 1.27) and SSRIs (RR:1.18; 95% CI:1.03-1.36) like other anti-depressants (RR: 1.76; 95% CI:1.5-\n217 2.07) increase the risk of severe COVID-19 because of their anti-cholinergic effect that is likely \n218 to increase risk of pneumonia. However, Fei et al. (17) represented that mortality rate, respiratory \n219 failure related to pneumonia and renal failure as a comorbidity within the anti-depressant treated \n220 subgroup is not lower than in the other patient of the sample but suggested that anti-depressant \n221 treated patient subgroup show high mean age and high size of medical comorbidity. Although, it \n222 is shown in this study that ARDS is significantly lower for the anti-depressant treated subgroup \n223 so mild significant lower employment of protease inhibitors and endotracheal intubation is \n224 needed in this subgroup because of lower level of IL-6 in anti-depressant treated patient that \n225 resulted from inhibition function of acid sphingomyelinase induced by antidepressants.\n226\n227 Specific effect of fluvoxamine on severity outcomes of COVID-19\n228\n229 Four studies have discussed about the relation of using fluvoxamine and COVID-19 severity. \n230 One cohort study (20) and three clinical trials (24-26). Lenze et al. (25) represented that patients \n231 treated with fluvoxamine, compared with placebo, had a lower likelihood of clinical deterioration \n232 over 15 days (absolute difference:8.7% 95% CI:1.8%-16.4%). Also serious adverse events in \n233 fluvoxamine group were less than placebo group (1.3% vs 6.9%). They suggested that this effect \n234 is because of the influence of fluvoxamine on the S1R-IRE1 pathway and anti-inflammatory\n235 (Cytokine reduction) actions resulting from S1R activation. Also according to Seftel et al. (20) \n236 study the incidence of subsequent  hospitalization was lower in fluvoxamine group (0% vs \n237 12.5%) and elevated respiratory rates were improved faster by day 7 in this group so \n238 fluvoxamine seems to be promising as early treatment for COVID-19 to prevent clinical \n239 deterioration requiring hospitalization and to prevent possible long haul symptoms persisting \n240 beyond 2 weeks. On the other hand, Reis et al. (26) suggested that There were no significant \n241 differences between fluvoxamine and placebo for viral clearance at day 7 (RR:0·67; 95% \n242 CI:0·42–1·06), hospitalizations due to COVID-19 (RR:0·77; 95% CI:0·55–1·05), time to \n243 hospitalization (RR:0·79; 95% CI:0·58–1·06), mechanical ventilation (RR:0·77; CI:0·45–1·30), \n244 time on mechanical ventilation (RR:1·03; 95% CI: 0·64–1·67), death (RR:0·69; 95% CI:0·36–\n245 1·27) and time to death (RR:0·80; 95%CI:0·43–1·51). However, according to Calusic et al. (24) \n246 study, there was no statistically significant differences between groups were observed regarding \n247 the number of days on ventilator support, duration of ICU or total hospital stay. But overall \n248 mortality was lower in the fluvoxamine group (HR: 0.58; 95% CI: 0.36–0.94). Although only \n249 mortality in women were significantly reduced in fluvoxamine group (HR: 0.40; 95% CI: 0.16–\n250 0.99) and there were no significant differences in men mortality (HR: 0.69; 95% CI: 0.39–1.24).\n251\n . CC-BY 4.0 International licenseIt is made available under a \n is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity. (which was not certified by peer review)\nThe copyright holder for this preprint this version posted April 16, 2022. ; https://doi.org/10.1101/2022.04.11.22273709doi: medRxiv preprint \n\n7\n252 Statistical focus on fluvoxamine\n253\n254 The meta-analysis on clinical trials showed that fluvoxamine could significantly decrease the \n255 severity outcomes of COVID-19 (RR: 0.745; 95% CI: 0.580-0.956) (figure 2). There was no \n256 evidence of publication bias (p > 0.05) (figure 3).\n257\n258\n259\n260 Discussion\n261\n262 Our systematic review study included cohort, case-control, and clinical trials and showed \n263 contradictory findings regarding the effect of antidepressant drugs on severity and mortality in \n264 covid-19 patients. Although various subgroups of antidepressants mainly reduce the risk of ARDS \n265 and mortality in hospitalized covid-19 patients compared to control groups, some studies reported \n266 no difference between case and control groups. On the other hand, one study proposed that \n267 antidepressants such as SSRIs and TCAs increase the risk of severe COVID-19 due to their \n268 anticholinergic effect that is likely to cause pneumonia.\n269 Our meta-analysis on clinical trials also demonstrated that fluvoxamine could significantly \n270 decrease the severity outcomes of COVID-19 and as Seftel et al. (20) study proposed that it can \n271 be a therapeutic choice to prevent clinical deterioration requiring hospitalization for COVID-19 \n272 patients.\n273 It has been proven that patients with clinical depression have lower immunity and thus more \n274 vulnerability to infection than healthy controls. (5)  The suggested mechanism of action is fewer \n275 circulating CD4+ T-cells and reduction in natural killer cell cytotoxic responses of lymphocyte \n276 proliferation in elderly patients with clinical depression. There is also an abnormal regulation of \n277 the hypothalamus–pituitary–adrenal axis, sympathetic–adrenal–medullary axis, and \n278 hypothalamic-pituitary ovarian axis in patients with clinical depression (6). On the other hand, \n279 severe cases of COVID-19 infection are associated with an increase in the production of anti-\n280 inflammatory cytokines such as IL-6, IL-8, IL-10, and proliferation of CD8+ cytotoxic T-cells \n281 derived from CD4+ T-cells, which act in neutralizing the virus and inflammation of the lung (27). \n282 Thus it is justified to consider clinical depression and the subsequent low immunity as risk factors \n283 for the severity of COVID-19 cases.\n284 In covid-19 patients with clinical depression, anti-depression treatment could improve immune \n285 responses by neutralizing the comorbidity (28). Still, there are several other mechanisms through \n286 which antidepressants might lower the risk of severe outcomes.\n287\n288 Inhibition of serotonin transporter\n289 SSRI and SNRIs are shown to have anti-inflammatory effects in both preclinical and clinical \n290 studies.(11, 29, 30) this might be owing to serotonin transporter inhibition.(31) Serotonin is \n291 involved in anti-inflammatory cytokine production. (32) For example, it can increase the release \n292 of the IL-10, which is a potent cytokine with reputable anti-inflammatory assets (33) IL-10 also \n293 moderates the levels of TNF-α and IL-6(34) . Which are both responsible for the inflammatory \n294 response in covid-19 infection. Serotonin also reduces the production of inflammatory cytokines \n295 such as TNF-α and interferon-gamma (IFN-γ) by human blood leucocytes (35, 36). High serotonin \n296 transporter expression in the lungs(37) brings up its possible function in lung inflammation. \n297 Serotonin inhibits IL-12 and TNF-α release in human alveolar macrophages, but it increases IL-\n . CC-BY 4.0 International licenseIt is made available under a \n is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity. (which was not certified by peer review)\nThe copyright holder for this preprint this version posted April 16, 2022. ; https://doi.org/10.1101/2022.04.11.22273709doi: medRxiv preprint \n\n8\n298 10 production via 5-HT2 receptors (38). Therefore, SSRIs and SNRIs may influence COVID-19 \n299 patients’ lung function. Considering that other antidepressants with the ability to block serotonin \n300 transporters did not show similar beneficial effects for COVID-19 patients, it is unlikely that \n301 serotonin transporter inhibition may play a significant role in SSRIs’ beneficial effects for COVID-\n302 19 patients. However, the anti-inflammatory effects of serotonin transporter inhibition may be a \n303 factor for the beneficial effects of SSRIs such as fluoxetine and fluvoxamine (31).\n304\n305\n306\n307 Acid sphingomyelinase (ASM)\n308 Many antidepressants, such as fluoxetine, fluvoxamine, sertraline, paroxetine, and amitriptyline, \n309 directly inhibit acid sphingomyelinase activity (11, 39, 40)which is necessary for epithelial cells \n310 to uptake SARS-CoV-2 (41). This could explain the findings of Hoertel et al. study (8) , although \n311 considering that there are other drugs such as chlorpromazine with ASM inhibition activity that \n312 didn’t show the same protective effects on mortality in COVID-19 patients (42), this mechanism \n313 is unlikely a major factor for said effect.\n314\n315 Sigma-1 receptor\n316 sigma-1 receptor, an endoplasmic reticulum (ER) protein, is a cellular factor mediating the early \n317 steps of viral RNA replication, thus essential for virus replication at the early stage of infection \n318 (43, 44). A recent study showed that several compounds for sigma-1 and sigma-2 receptors are \n319 active in protein-protein interactions between SARS-CoV-2 and human proteins and could be \n320 promising inhibitors for SARS-CoV-2 replication (45).\n321 On the other hand, animal studies indicated that Some SSRIs, such as fluvoxamine, sertraline, \n322 fluoxetine, and citalopram, have a high to moderate affinity for sigma-1 receptors in the rat brain. \n323 (45). Binding of Sigma-1 receptor agonists (i.e., fluvoxamine and fluoxetine) can result in sigma-\n324 1 receptor chaperone activity in the cells (46, 47). Another study demonstrated that binding of \n325 fluoxetine to the sigma-1 receptor in the endoplasmic reticulum decreases cytokine activity and \n326 enhances survival in preclinical models of sepsis and inflammation (48). Given the crucial role of \n327 the chaperone activity by sigma-1 receptor agonist in SARS-CoV-2 replication, it is possible that \n328 the SSRIs (i.e., fluvoxamine, fluoxetine, escitalopram) with sigma-1 receptor agonisms could be \n329 COVID-19 prophylactic drugs (8).\n330\n331 Another mechanism proposed to explain the anti-inflammatory effects of SSRIs is increasing \n332 melatonin levels due to cytochrome P450 enzyme CYP1A2 inhibition by fluvoxamine (49, 50). \n333 Melatonin which is naturally synthesized in the pineal gland and immune cells from the amino \n334 acid tryptophan, has anti-inflammatory, immunomodulatory, and antioxidant effects and is a \n335 therapeutic candidate to treat covid-19 (51).\n336\n337\n338 Among the articles indicating no significant effect for antidepressants, Fei et al. (17) and Calusic \n339 et al. (24) studies were limited by small sample size. Calusic et al. (24) and Reis et al. (26) \n340 studies couldn’t entirely rule out selection bias. Fei et al. also stated that control group showed \n341 higher mean age and high size of medical comorbidities. Patients with antidepressant therapy \n342 have worse health conditions and immune responses than general population. This issue can have \n343 an impact on prognosis of covid-19 (17).\n . CC-BY 4.0 International licenseIt is made available under a \n is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity. (which was not certified by peer review)\nThe copyright holder for this preprint this version posted April 16, 2022. ; https://doi.org/10.1101/2022.04.11.22273709doi: medRxiv preprint \n\n9\n344 Another important clinical issue is to either continue or discontinue SSRIs when a COVID-19 \n345 patient is hospitalized or admitted to an ICU. There is evidence showing adverse ICU outcomes \n346 after discontinuation of SSRIs. This is probably due to increased agitation and need for sedation \n347 among these patients that can result in respiratory depression (52, 53) . SSRI use may sometimes \n348 be contraindicated in ICU patients due to ECG changes and abnormal coagulation effects(54). A \n349 systematic review regarding the use of antidepressants in Critical Care, suggested that there may \n350 be excess morbidity in critically ill SSRI/SNRI users, but whether this is due to chronic effects, \n351 ongoing use, or drug withdrawal is unclear (53).\n352 Reis et al (26) study suggested the modulatory effect of fluvoxamine on systemic inflammation \n353 as lower respiratory tract infections and hospital admissions were reported less frequently in \n354 patients in the fluvoxamine group compared to those in the placebo group.\n355\n356 Finally, efficacy of fluvoxamine in COVID-19 patients could be dependent on the timing of \n357 treatment, where increased effectiveness could be achieved if treatment is initiated earlier during \n358 SARSCoV-2 infection.\n359\n360 Limitations\n361\n362 Most of the included articles in this study were not specific about antidepressant drugs and didn’t \n363 evaluate each drug separately. They mainly studied antidepressants in groups like SSRIs, SNRIs, \n364 and TCAs. We reviewed antidepressants but could only arrange a meta-analysis for fluvoxamine \n365 which was studied in three clinical trial which two of them had a high risk of bias. Our meta-\n366 analysis was limited to a small population of about 2000 persons. We also generally expressed our \n367 results about severity and outcome and couldn’t arrange a subgroup analysis for each outcome or \n368 sex group.\n369\n370 Suggestions\n371 As there is strong evidence of the link between antidepressant use and improving outcomes of \n372 covid-19, it seems legible to conduct more research on the subject, aiming to find more therapeutic \n373 options to treat covid-19. Future studies should focus on antidepressants separately and be more \n374 specific about the outcome in different patient groups. We also need to arrange more clinical trials \n375 with larger populations to confirm the efficacy of a candidate certainly. SSRIs such as Fluoxetine \n376 and Fluvoxamine are supported by stronger evidence and could be favorable options for future \n377 research programs.\n378\n379 Conclusion\n380\n381 Among the studied experiment, seven studies showed anti-depressant ability to reduce the severity \n382 of covid-19, and five studies didn’t mention any significant effect on it. The covid-19 reducing \n383 effect of the mentioned medications can be concluded from the decline in the risk of intubation \n384 and death, clinical outcomes, and biological markers demonstrating the disease’s severity. Among \n385 the SSRI medication, the most common studied drug was fluvoxamine, which has a less \n386 subsequent hospitalization rate, less likelihood of 15-day-deterioration, and fewer adverse events \n387 than placebo, attributable to the S1R-IRE1 pathway and anti-inflammatory actions. Other SSRI \n388 medications, including Escitalopram, Fluoxetine, and Paroxetine and also among the SNRI drugs \n389 Venlafaxine are also reasonably associated with reduced risk of intubation or death. Still, they \n390 didn’t study as much as fluvoxamine. Although most of the evidence supports the effectiveness \n . CC-BY 4.0 International licenseIt is made available under a \n is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity. (which was not certified by peer review)\nThe copyright holder for this preprint this version posted April 16, 2022. ; https://doi.org/10.1101/2022.04.11.22273709doi: medRxiv preprint \n\n10\n391 hypothesis of anti-depressant drugs, a single study proposed that some anti-depressants can \n392 intensify the severity of COVID-19 via anticholinergic effects that induce pneumonia. This issue \n393 should be investigated more precisely in future studies.\n394\n395\n396\n397\n398\n399\n400 DATA AVAILABILITY STATEMENT\n401\n402 The original contributions presented in the study are included in the article/supplementary \n403 material. Further inquiries can be directed to the corresponding authors.\n404\n405 AUTHOR CONTRIBUTIONS\n406\n407 MZ, HN: designed the study. MZ, RB, MR, and AG: performed the search, study selection, and \n408 data synthesis. MZ, HN: wrote the first draft of the manuscript. SR: revised the article. All \n409 authors contributed to the article and approved the submitted version.\n410\n411 CONFILICT OF INTREST:\n412 The mentioned in this article, have no involvement in any organization or entity with any \n413 financial interest nor non-financial interest such as personal or professional relationships, \n414 affiliations, knowledge or beliefs) in the subject matter discussed in this article.\n415\n416 ACKNOWLEDGMENTS\n417\n418 This study is related to the MPH project From the Department of Public Health, School of Public \n419 Health and Safety, Shahid Beheshti University of Medical Sciences, Tehran, Iran.\n420\n . CC-BY 4.0 International licenseIt is made available under a \n is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity. (which was not certified by peer review)\nThe copyright holder for this preprint this version posted April 16, 2022. ; https://doi.org/10.1101/2022.04.11.22273709doi: medRxiv preprint \n\n11\n421\n422 Figure1. Flow chart of study selection for inclusion in the systematic review and meta-analysis.\n423\nRecords identified through databases (n=2041)\nPubMed/Medline: 91\nEmbase: 1528\nScopus: 422\nRecords after duplicates removed (n=1572)\nTitle and abstract of records \nscreened\n(n=1572)\nExcluded irrelevant\n (n=1556)\nFull-text articles assessed \nfor eligibility\n(n=16)\nStudies included\n(n=12)\nExcluded irrelevant (n=3)\nReason for exclusion:\nFocus on infection risk (n=1)\nWithout specific data for anti-\ndepressants (n=1)\nFull text not found (n=1)\nWithout data for severity \noutcomes (n=1)\nScreeningIncluded Eligibility Identification\n . CC-BY 4.0 International licenseIt is made available under a \n is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity. (which was not certified by peer review)\nThe copyright holder for this preprint this version posted April 16, 2022. ; https://doi.org/10.1101/2022.04.11.22273709doi: medRxiv preprint \n\n12\n424 Table 1. Study characteristics\n425\n426\n427 ABBREVIATIONS \n428 PCR: polymerase chain reaction/ RT-PCR: reverse transcription polymerase chain reaction/ ICD 10: \n429 International Statistical Classification of Diseases and Related Health Problems, Tenth Revision/ N/R: not reported\n430\n431\n432\nFirst \nauthor\nStudy \ndesign\nPublication \nyear country Study duration detection test of \nCOVID-19\nLenze et al. \n(25)\nRandomized \nClinical Trial\n2020 USA April 10, 2020, to \nAugust 5, 2020 PCR\nReis et al. (26) Randomized \nClinical Trial\n2021 Brazil June 2, 2020 to Aug \n5, 2021\nRT-PCR\nCalusic et al. \n(24)\ncohort trial 2021 Croatia April and May 2021, PCR\nIsrael et al. \n(21) case-control 2021 Israel November 30, 2020 \nto December 31, \n2020.\nRT-PCR\nMcKeigue\net al. (22) case-control 2021 Scotland June 6, 2020 to June \n14, 2020 N/R\nNemeth et al. \n(23) case-control 2021 Hungary March 17, 2021  to \nApril 22, 2021\nantigen or  polymerase  chain \nreaction  test\nRauchman et \nal. (19)\nretrospective \ncohort\n2021 USA March\n2020 to March 2021 N/R\nHoertel et al. \n(8)\nretrospective \ncohort\n2021 France January 24, 2020 to \nApril 1, 2020 RT-PCR\nFEI et al. (17) Prospective \nCohort\n2021 Italy N/R N/R\nOskotsky et \nal. (18)\nretrospective \ncohort\n2020 USA January to \nSeptember 2020\nlaboratory test for SARS-\nCoV-2 (nucleic acid \namplification tests and \nimmunoassays) and/or by  \nICD-10  (for COVID-19 \nconfirmed by laboratory \ntesting)\nSeftel et al. \n(20)\nProspective \nCohort\n2021 USA November–\nDecember 2020\nPCR\nBora et al. \n(16)\nretrospective \ncohort\n2021 Turkey October 1, 2020 to \nJanuary 1, 2021\nthe ICD-10 classification \nconfirmed by the test\nresults\n . CC-BY 4.0 International licenseIt is made available under a \n is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity. (which was not certified by peer review)\nThe copyright holder for this preprint this version posted April 16, 2022. ; https://doi.org/10.1101/2022.04.11.22273709doi: medRxiv preprint \n\n13\n433\n434 Table 2. Quality assessment of cohort studies\n435\n436 1. Were the two groups similar and recruited from the same population? \n437 2. Were the exposures measured similarly to assign people to both exposed and unexposed groups? \n438 3. Was the exposure measured in a valid and reliable way? \n439 4. Were confounding factors identified? \n440 5. Were strategies to deal with confounding factors stated? \n441 6. Were the groups/participants free of the outcome at the start of the study? \n442 7. Were the outcomes measured in a valid and reliable way? \n443 8. Was the follow-up time reported and sufficient to be long enough for outcomes to occur? \n444 9. Was follow-up complete, and, if not, were the reasons to loss to follow-up described and explored? \n445 10. Were strategies to address incomplete follow-up utilized? \n446 11. Was appropriate statistical analysis used\n447 Table 3. Quality assessment of case-control studies\nauthor 1 2 3 4 5 6 7 8 9 10\nIsrael\n(Cohort1) yes yes yes yes yes yes yes yes yes yes\nIsrael\n(Cohort2) yes yes yes yes yes yes yes yes yes yes\nMcKeigue yes yes yes yes yes yes yes yes yes yes\nNemeth yes yes yes yes yes no no yes yes yes\n448\n1. Were the groups comparable other than the presence of disease in cases or the absence of disease in controls?\n2. Were cases and controls matched appropriately?\n3. Were the same criteria used for identification of cases and controls?\n4. Was exposure measured in a standard, valid and reliable way?\n5. Was exposure measured in the same way for cases and controls?\n6. Were confounding factors identified? \n7. Were strategies to deal with confounding factors stated?\n8. Were outcomes assessed in a standard, valid and reliable way for cases and controls?\n9. Was the exposure period of interest long enough to be meaningful?\n10. Was appropriate statistical analysis used?\n449\nauthor 1 2 3 4 5 6 7 8 9 10 11\nRauchman yes yes yes no no yes yes yes yes yes yes\nHoertel yes yes yes no yes yes yes yes yes no yes\nFEI yes yes yes no no yes yes yes yes yes yes\nOskotsky yes yes yes no yes unclear yes yes yes yes yes\nSeftel yes yes yes no yes yes yes no yes yes yes\nBora yes yes yes no no yes yes unclear unclear unclear yes\n . CC-BY 4.0 International licenseIt is made available under a \n is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity. (which was not certified by peer review)\nThe copyright holder for this preprint this version posted April 16, 2022. ; https://doi.org/10.1101/2022.04.11.22273709doi: medRxiv preprint \n\n14\n450 Table 4. Quality assessment of the experimental studies\nauthor 1 2 3 4 5 6 7 8 9 10 11 12 13\nLenze yes yes yes Yes yes yes yes yes yes yes yes yes yes\nCalusic unclear unclear yes Unclear unclear unclear yes yes yes yes yes yes yes\nReis yes yes yes Unclear unclear unclear yes yes yes yes yes yes yes\n451\n452 1. Was true randomization used for assignment of participants to treatment groups? \n453 2. Was allocation to treatment groups concealed? \n454 3. Were treatment groups similar at baseline? \n455 4. Were participants blind to treatment assignment? \n456 5. Were those delivering treatment blind to treatment assignment? \n457 6. Were outcome assessors blind to treatment assignment? \n458 7. Were treatment groups treated identically other than the intervention of interest? \n459 8. Was follow-up complete, and, if not, were differences between groups in terms of their follow-up adequately described and \n460 analyzed? \n461 9. Were participants analyzed in the groups to which they were randomized? \n462 10. Were outcomes measured in the same way for treatment groups? \n463 11. Were outcomes measured in a reliable way? \n464 12. Was appropriate statistical analysis used? \n465 13. Was the trial design appropriate and were any deviations from the standard randomized controlled trial design accounted for \n466 in the conduct and analysis of the trial? \n467\n468\n . CC-BY 4.0 International licenseIt is made available under a \n is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity. (which was not certified by peer review)\nThe copyright holder for this preprint this version posted April 16, 2022. ; https://doi.org/10.1101/2022.04.11.22273709doi: medRxiv preprint \n\n15\n469 Table 5. Patient characteristics\nAge Gender\n(F/M %)\nFirst \nauthor\ncase contr\nol\ncase Control\nmatching Case definition Control \ndefinition\nSeverity \nindices\nMean:46 71.7/28.3Lenze et \nal. (25) 46 45 70/30 74/26\n1:1\nAge, sex\nAdults with SARS CoV-\n2 infection who received \nFluvoxamine and were \nsymptomatic within 7 days of \nthe first dose\nof study medication \nAdults with SARS \nCoV-\n2 infection who \nreceived placebo \ncapsules and were \nsymptomatic \nwithin 7 days of \nthe first dose\nof study \nmedication\noxygen saturation \n<92% plus \nsupplemental \noxygen\nneeded and \nhospitalization \nrelated to dyspnea \nor hypoxia\nand/or ventilator \nsupport needed \nfor ≥3 days\n57.5/42.5Reis et \nal. (26)\nMedian:50\n55/45 60/40\n1:1\nBMI, Age, \nco-\nmorbidities\npatients were allocated to \nfluvoxamine\npatients were \nallocated to \nplacebo\nHospitalized for \nCOVID-19, need \nto mechanical \nventilation and/or \nmortality\nMean:65.7 33.3/66.7Calusic \net al. \n(24) 65.6 65.9 33/67 33/67\n1:1\nAge, sex,  co-\nmorbidities,  \nvaccination \nstatus, disease \nseverity\npatients over the age of\n18 with positive SARS-CoV-2 \nPCR test and acute\nclinical condition consistent \nwith COVID-19 requiring ICU \nadmission who received \nfluvoxamine + standard therapy\npatients over the \nage of\n18 with positive \nSARS-CoV-2 PCR \ntest and acute\nclinical condition \nconsistent with \nCOVID-19 \nrequiring ICU \nadmission who \nreceived standard \ntherapy\nDays of hospital \nstay, Days of ICU \nstay, Days on \nventilator support  \nand/or mortality\nMean: 64.7 49.9/50.1Israel et \nal. (21)\nGroup 1 64.6 64.8 50/50 50/50\n1:5\nAge, sex, co-\nmorbidities, \nBMI, \nsmoking \nstatus  \nHospitalized COVID-19 \npatients\npatients were \nchosen among the \ngeneral population\noverall risk for \nhospitalization \ndue to COVID-19\nMean: 65.7 48.6/51.4Israel et \nal. (21)\nGroup 2 65.7 65.7 49/51 49/51\n1:2\nAge, sex, co-\nmorbidities, \nBMI, \nsmoking \nstatus  \nHospitalized COVID-19 \npatients\npatients who had a \npositive test for \nSARS-CoV-2\nbut had not been \nhospitalized\nRisk for COVID-\n19\nhospitalization in \npatients who had a \nproven infection \nwith the virus.\nMcKeig\nue\net al. \n(22)\nMean:61.2 N/R 1:10\nsex\nA positive nucleic acid test \nfollowed by entry to critical \ncare or death within 28 days or \na death certificate with \nCOVID-19 as underlying \ncause.\nControls were alive \nand had not yet \ntested positive on \nthe date that the \ncase first tested \npositive.\nentry to critical \ncare and/or \nmortality\n . CC-BY 4.0 International licenseIt is made available under a \n is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity. (which was not certified by peer review)\nThe copyright holder for this preprint this version posted April 16, 2022. ; https://doi.org/10.1101/2022.04.11.22273709doi: medRxiv preprint \n\n16\n470\n471 ABBREVIATIONS\n472 BMI: body mass index/ ICU: intensive care unit/ PCR: polymerase chain reaction/ N/R: not reported\n473\n474\n475\n476\n477\n478\nMean: 66 54.6/45.4Nemeth \net al. \n(23) 65 67 53/47 44/56\n1:1.5\nN/R\nadult  patients  hospitalized  \nwith  moderate  or  severe \nCOVID-19  pneumonia who \nreceived fluoxetine as an \nadjuvant medication in \ncombination with antiviral \ndrugs    \nadult  patients  \nhospitalized  with  \nmoderate or severe  \nCOVID-19  \npneumonia who \nreceived antiviral \ndrugs    \nmortality between  \nhospital days  2  \nand  28\n45.9/54.1Rauchm\nan et al. \n(19)\n>=18\n61/39 44/56\n1:10\nAge, sex,  \nprimary race\nadult patients 18 and over \nadmitted with a diagnosis of \nCOVID-19\nand on an antidepressant drug \nduring admission\nadult patients 18 \nand over admitted \nwith a diagnosis of \nCOVID-19\nand not on an \nantidepressant drug \nduring admission\nmortality\nMean:57.6Hoertel \net al. (8) 73.7 56.8\n49.2/50.8 1:14.5\nN/R\nPatients who receiving any \nantidepressant during the first \n48 h of hospital admission and \nbefore the end of the index \nhospitalization or intubation or \ndeath\nPatients not \nreceiving any \nantidepressant \nduring the first 48 \nh of hospital \nadmission and \nbefore the end of \nthe index \nhospitalization or \nintubation or death\nintubation or\ndeath\nMean:70 40.3/59.7FEI et \nal. (17) 80.1 69.1 56/44 39/61\n1:11\nN/R\npatient treated with an \nantidepressant  before \nadmission until discharge from \nhospital\npatient not treated \nwith antidepressant\nnoninvasive \nventilation (NIV),\nintubation,\nICU admission,\nmortality\nMean:52 50.1/49.9Oskotsk\ny et al. \n(18) 63.1 51.6 59/41 50/50\n1:21\nAge, sex, race \n&  ethnicity, \nco-\nmorbidities\nPatients with COVID-19 and a \nmedication order for an SSRI at \nleast once within a period of 10 \ndays before and 7 days after \ntheir first recorded COVID-19 \ndiagnosis\npatients with \nCOVID-19 and no \nSSRI orders\nmortality\nMedian:42 24.8/75.2Seftel et \nal. (20) Mean\n:44\nMea\nn:43\n23/77 27/73\n1.5:1\nDemographic \nfeatures\npatients treated with \nfluvoxamine for 14 days\npatients not treated \nwith fluvoxamine\nHospitalization,\nICU care and /or \nmortality\nBora et \nal. (16)\nMean:47.6 46.6/53.4 1:9 patients treated by \nantidepressants with no  limit to \nthe types of these medications\npatients not treated \nby antidepressants\nmortality\n . CC-BY 4.0 International licenseIt is made available under a \n is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity. (which was not certified by peer review)\nThe copyright holder for this preprint this version posted April 16, 2022. ; https://doi.org/10.1101/2022.04.11.22273709doi: medRxiv preprint \n\n17\n479 Table 6. Interventions and exposures Characteristics\nFirst \nauthor\nAnti-depressant type Dosage Frequency Follow-up time\nLenze et \nal. (25)\nFluvoxamine 50 mg ( in the evening immediately after the baseline\nassessment and confirmation of eligibility)\n100 mg (for 2 days)\n100 mg ( through day 15 then stopped)\n---\ntwice daily\n3 times daily\n30 days\nReis et \nal. (26)\nFluvoxamine 100mg (for 10 days) twice daily 28days\nCalusic \net al. \n(24)\nFluvoxamine 100 mg (for 15 days)\n50 mg (After Day15)\n3 times daily\ntwice daily\n22 days\nIsrael et \nal. (21)\nEscitalopram N/R N/R 35 days\nMcKeig\nue\net al. \n(22)\nTCAs & SSRIs & other \nanti-depressants\nN/R N/R N/R\nNemeth \net al. \n(23)\nFluoxetine 20  mg once  daily N/R\nRauchm\nan et al. \n(19)\nCitalopram, Desvenlafaxine, \nDuloxetine, Escitalopram, \nFluoxetine, Paroxetine, \nSertraline, Venlafaxine\nN/R N/R N/R\nHoertel \net al. (8)\nCitalopram\nEscitalopram\nCitalopram or\nEscitalopram\nFlouxetine\nFluvoxamine\nParoxetine\nSertraline\nVortioxetine\nVenlafaxine\nDuloxetine\nN/R N/R 18.4 days\nFEI et \nal. (17)\nSertraline\nEscitalopram\nCitalopram\nParoxetine\nVenlafaxine \nDuloxetine \nEscitalopram + Venlafaxine\nN/R N/R 3 months\nOskotsk\ny et al. \n(18)\nFluoxetine\nFlouxetine or  Fluvoxamine\nSSRI other than Flouxetine \nor Fluvoxamine\n30.2mg/d\n29mg/d\n30.4mg/d\nN/R 8 months\nSeftel et \nal. (20)\nFluvoxamine 50- to 100-mg loading dose\n50 mg\n---\ntwice daily\n14 days\n . CC-BY 4.0 International licenseIt is made available under a \n is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity. (which was not certified by peer review)\nThe copyright holder for this preprint this version posted April 16, 2022. ; https://doi.org/10.1101/2022.04.11.22273709doi: medRxiv preprint \n\n18\n480\n481 ABBREVIATIONS\n482 SSRI: Selective serotonin re-uptake inhibitors/ TCA: Tricyclic antidepressant\n483\nBora et \nal. (16)\nDuloxetine, Escitalopram,\nFluoxetine, Fluvoxamine, \nMirtazapine, Paroxetine,\nSertraline, Venlafaxine,\ntricyclic antidepressants\nN/R N/R N/R\n . CC-BY 4.0 International licenseIt is made available under a \n is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity. (which was not certified by peer review)\nThe copyright holder for this preprint this version posted April 16, 2022. ; https://doi.org/10.1101/2022.04.11.22273709doi: medRxiv preprint \n\n19\n484\n485\n486\n487 Figure 2. Pooled RR for clinical trials\n488\n489\n490 Figure 3. the funnel plot of analysis\n491\n . CC-BY 4.0 International licenseIt is made available under a \n is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity. (which was not certified by peer review)\nThe copyright holder for this preprint this version posted April 16, 2022. ; https://doi.org/10.1101/2022.04.11.22273709doi: medRxiv preprint \n\n20\n492 References\n493 1. 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(which was not certified by peer review)\nThe copyright holder for this preprint this version posted April 16, 2022. ; https://doi.org/10.1101/2022.04.11.22273709doi: medRxiv preprint","source_license":"CC-BY-4.0","license_restricted":false}