Risk of Myocarditis After Sequential Doses of COVID-19 Vaccine and SARS-CoV-2 Infection by Age and Sex

Authors: Martina Patone, PhD; Xue W. Mei, PhD; Lahiru Handunnetthi, PhD; Sharon Dixon, MD; Francesco Zaccardi, PhD; Manu Shankar-Hari, PhD; Peter Watkinson, MD; Kamlesh Khunti, PhD; Anthony Harnden, PhD; Carol A.C. Coupland, PhD; Keith M. Channon, MD; Nicholas L. Mills, PhD; Aziz Sheikh, MD; Julia Hippisley-Cox, MD August 28, 2022 ORIGINAL RESEARCHARTICLECirculation. 2022;146:00–00. DOI: 10.1161/CIRCULATIONAHA.122.059970 xxx xxx, 20223Patone et al

BACKGROUND: Myocarditis is more common after severe acute respiratory syndrome coronavirus 2 infection than after COVID-19 vaccination, but the risks in younger people and after sequential vaccine doses are less certain.


A self-controlled case series study of people ages 13 years or older vaccinated for COVID-19 in England between December 1, 2020, and December 15, 2021, evaluated the association between vaccination and myocarditis, stratified by age and sex. The incidence rate ratio and excess number of hospital admissions or deaths from myocarditis per million people were estimated for the 1 to 28 days after sequential doses of adenovirus (ChAdOx1) or mRNA-based (BNT162b2, mRNA-1273) vaccines, or after a positive SARS-CoV-2 test.RESULTS: In 42842345 people receiving at least 1 dose of vaccine, 21242629 received 3 doses, and 5934153 had SARS-CoV-2 infection before or after vaccination. Myocarditis occurred in 2861 (0.007%) people, with 617 events 1 to 28 days after vaccination. Risk of myocarditis was increased in the 1 to 28 days after a first dose of ChAdOx1 (incidence rate ratio, 1.33 [95% CI, 1.09–1.62]) and a first, second, and booster dose of BNT162b2 (1.52 [95% CI, 1.24–1.85]; 1.57 [95% CI, 1.28–1.92], and 1.72 [95% CI, 1.33–2.22], respectively) but was lower than the risks after a positive SARS-CoV-2 test before or after vaccination (11.14 [95% CI, 8.64–14.36] and 5.97 [95% CI, 4.54–7.87], respectively). The risk of myocarditis was higher 1 to 28 days after a second dose of mRNA-1273 (11.76 [95% CI, 7.25–19.08]) and persisted after a booster dose (2.64 [95% CI, 1.25–5.58]). Associations were stronger in men younger than 40 years for all vaccines. In men younger than 40 years old, the number of excess myocarditis events per million people was higher after a second dose of mRNA-1273 than after a positive SARS-CoV-2 test (97 [95% CI, 91–99] versus 16 [95% CI, 12–18]). In women younger than 40 years, the number of excess events per million was similar after a second dose of mRNA-1273 and a positive test (7 [95% CI, 1–9] versus 8 [95% CI, 6–8]).CONCLUSIONS: Overall, the risk of myocarditis is greater after SARS-CoV-2 infection than after COVID-19 vaccination and remains modest after sequential doses including a booster dose of BNT162b2 mRNA vaccine. However, the risk of myocarditis after vaccination is higher in younger men, particularly after a second dose of the mRNA-1273 vaccine.

We recently reported an association between the first and second dose of COVID-19 vaccination and myocarditis, which generated considerable scientific, policy, and public interest.1 It added to evidence emerging from multiple countries that has linked exposure to BNT162b2 mRNA vaccine with acute myocarditis.2–8In the largest and most comprehensive analysis to date, we reported an increased risk of hospital admission or death from myocarditis after both adenoviral (ChAdOx1) vaccines and mRNA (BNT162b2 or mRNA-1273) vac-cines. It is important that we also demonstrated across the entire vaccinated population in England that the risk of myocarditis after vaccination was small compared with the risk after a positive SARS-CoV-2 test.1However, myocarditis is more common in younger people younger than the age of 40 years and in men in particular.9,10 Additional analyses stratified by age and sex are important because vaccine campaigns are rap-idly being extended to include children and young adults. Furthermore, given the consistent observation that the risk of myocarditis is higher after the second dose of vac-cine compared with the first dose,1,11 there is an urgent need to evaluate the risk associated with a booster dose because booster programs are accelerated internation-ally to combat the omicron variant.12Because new data were available, we have extended our analysis to include people ages 13 years or older and those receiving a booster dose to further evaluate the association between COVID-19 vaccination or infection and risk of myocarditis, stratified by age and sex.


Transparency and Openness Promotion This analysis makes use of multiple routinely collected health care data sources that were linked, deidentified, and held in a trusted research environment that was accessible to approved individuals who had undertaken the necessary governance training. Because of the sensitive nature of the data collected for this study, requests to access the dataset from qualified researchers trained in human subject confidentiality proto-cols may be sent to National Health Service Digital and the United Kingdom Health Security Agency. Simulated data and the analysis code are available publicly at https://github.com/qresearchcode/COVID-19-vaccine-safety. National Health Service Research Ethics Committee approval was obtained from the East Midlands–Derby Research Ethics Committee (Reference 18/EM/0400]. Anonymized data are analyzed, so there is no requirement for written informed consent. Data Sources We used the National Immunisation Database of COVID-19 vaccination to identify vaccine exposure. This includes vaccine type, date, and doses for all people vaccinated in England. We linked National Immunisation Database vaccination data, at the individual level, to national data for mortality (Office for National Statistics), hospital admissions (Hospital Episode Statistics and Secondary User’s service data), and SARS-CoV-2 infection data (Second Generation Surveillance System).Study Design and Oversight We undertook a self-controlled case series design, originally developed to examine vaccine safety.12 The analyses are conditional on each case, so any fixed characteristics during the study period, such as sex, ethnicity, or chronic conditions, are inherently controlled for. Age was considered as a fixed variable because the study period was short. Any time-varying factors, such as seasonal variation, need to be adjusted for in the analy-ses. Hospital admissions were likely to be influenced by the pressure on the health systems because of COVID-19, which was not uniform during the pandemic study period. To allow for these underlying seasonal effects, we split the study observation period into weeks and adjusted for week as a factor vari-able in the statistical models.Study Period and Population We included all people ages 13 years or older who had received at least 1 dose of ChAdOx1 (AstraZeneca), BNT162b2 (Pfizer), and mRNA-1273 (Moderna) vaccine and were admit-ted to hospital or died from myocarditis between December 1, 2020, and December 15, 2021.OutcomeThe primary outcome of interest was the first hospital admis-sion caused by the myocarditis, or death recorded on the death Clinical PerspectiveWhat Is New?•We performed an evaluation of the risk of myocar-ditis after COVID-19 vaccine in >42 million vacci-nated people 13 years or older, including 21 million people receiving a booster dose, stratified by age and sex.•We extend our previous findings demonstrating that the risk of hospitalization or death from myo-carditis after SARS-CoV-2 infection is substantially higher than the risk associated with a first dose of ChAdOx1, and a first, second, or booster dose of BNT162b2 mRNA vaccine.

•Associations were stronger in younger men <40 years for all vaccines and after a second dose of mRNA-1273 vaccine, where the risk of myocarditis was higher after vaccination than SARS-CoV-2 infection. What Are the Clinical Implications?•Our findings will inform recommendations on the type of vaccine offered to younger people and will help to shape public health policy on booster pro-grams enabling an informed discussion of the risk of vaccine associated myocarditis when considering the net benefit of vaccination.

Myocarditis After COVID-19 Vaccine and Infection certificate with the International Classification of Diseases, Tenth Revision code (Table S1) related to myocarditis within the study period (December 1, 2020, to December 15, 2022). We used the earliest date of hospitalization or date of death as the event date.ExposuresThe exposure variables were a first, second, or booster dose of the ChAdOx1, BNT162b2, or mRNA-1273 vaccines, and SARS-CoV-2 infection, defined as the first SARS-CoV-2–positive test in the study period. All exposures were included in the same model. We defined the exposure risk intervals as the following prespecified time periods: 0, 1 to 7, 8 to 14, 15 to 21, and 22 to 28 days after each exposure date, under the assumption that the adverse events under consideration are unlikely to be related to exposure later than 28 days after expo-sure. A pre-risk interval of 1 to 28 days before each exposure date was included to account for potential bias that might arise if the occurrence of the outcome temporarily influenced the likelihood of exposure. The baseline period for the vaccination exposures was the remaining time from December 1, 2020, until 29 days before the first dose date and from 29 days after the first or second dose until 29 days before the second or booster dose (if applicable), and from 29 days after the booster dose until December 15, 2021, or the censored date if earlier. We assumed that the risks might be different after each vac-cine dose, and hence we allowed for a dose effect, by defining a separate risk interval after each dose: 0, 1 to 7, 8 to 14, 15 to 21, or 22 to 28 days after the first, second, or booster dose. To avoid overlapping risk periods, we assumed that later expo-sures take precedence over earlier ones, except for the 1- to 28-day pre-risk period for the second or booster dose. A posi-tive SARS-CoV-2 test was considered as a separate exposure in the models, which allowed overlapping risk windows with vaccination exposure.Statistical AnalysisWe described the characteristics of the whole study population by vaccine dose and type, and in those with myocarditis strati-fied by age and sex.In vaccinated people with myocarditis, the self-controlled case series models were fitted using a conditional Poisson regression model with an offset for the length of the expo-sure risk period. Incidence rate ratios (IRR), the relative rate of hospital admissions or deaths caused by myocarditis in expo-sure risk periods relative to baseline periods, and their 95% CIs were estimated by the self-controlled case series model adjusted for calendar time. We investigated if associations between vaccine exposure and the myocarditis outcome were sex- or age-dependent by performing subgroup analyses strati-fied by sex and age (men age <40 years, men age≥ 40 years, women age <40 years, and women age ≥40 years). We also conducted analyses stratified by vaccination history, restricted to those who had the same type of vaccine in the first and sec-ond dose and by lag in days between the first and second dose (≤65, 66 to 79, and ≥80 days).We conducted sensitivity analyses to assess the robustness of results to assumptions, such as that the occurrence of an outcome event did not influence the probability of subsequent exposures by (1) excluding those who died from the outcome and (2) restricting analysis to the period after the first dose and (3) after the second dose, without censoring at death; and to assess potential reporting delays in the data by (4) restricting the study to the period up to December 1, 2021.We also performed sensitivity analyses (5) removing patients who had outcomes in the 28 days after a first dose, but before a second dose, and (6) removing patients who had outcomes in the 28 days after a second dose, but before a booster dose, because they are less likely to have a second dose if they experienced an adverse event after the first. Last, we conducted a sensitivity analysis (7) restricted to those with-out a positive SARS-CoV-2 test during the observation period.We used Stata (version 17) for these analyses.


Between December 1, 2020, and December 15, 2021, there were 42 842 345 people vaccinated with at least 1 dose of ChAdOx1 (n=20 650 685), BNT162b2 (n=20 979 704), or mRNA-1273 (n=1 211 956) (Table 1). Of these, 39 118 282 received a sec-ond dose of ChAdOx1 (n=20 080 976), BNT162b2 (n=17 950 086), or mRNA-1273 (n=1 087 220), and 21 242 629 people received a third vaccine dose: ChAdOx1 (n=53 606), BNT162b2 (n=17 517 692), and mRNA-1273 (n=3 671 331).Among people receiving at least 1 vaccine dose, 5 934 153 (13.9%) tested positive for SARS-CoV-2, including 2 958 026 (49.8%) before their first vac-cination.Of the 42 842 345 people in the study population, 2861 (0.007%) were hospitalized or died from myocar-ditis during the study period; 345 (<0.001%) patients died within 28 days from a hospital admission with myo-carditis or with myocarditis as cause of death recorded in the death certificate. A total of 617 (0.001%) of these events occurred 1 to 28 days after any dose of vaccine (Table 2). Of the 524 patients admitted to the hospital with myocarditis in the 1 to 28 days after any first or sec-ond vaccine dose, 151 (28.8%) had received a booster dose: 34.4% (79/230) of those who had ChAdOx1 in the first or second dose and 29.7% (72/243) of those who had BNT162b2 in the first or second dose (Table 2). Of the 5 934 153 patients with a SARS-CoV-2 infection, 195 (0.003%) were hospitalized or died with myocarditis in the 1 to 28 days after the positive test; 114 (58.5%) of these events occurred before vaccination (Table S2).Vaccine-Associated MyocarditisIn the study period, we observed 140 and 90 patients who were admitted to the hospital or died of myocardi-tis after a first and second dose of ChAdOx1 vaccine, respectively. Of these, 40 (28.6%) and 11 (12.2%)‚ re-spectively, died with myocarditis or within 28 days from hospital admission. Similarly, there were 124, 119, and 85 patients who were admitted to the hospital or died

After COVID-19 Vaccine and Infection Table 1.Baseline Demographic Characteristics of People Receiving ChAdOx1, BNT162b2, or mRNA-1273 Vaccines or Testing Positive for SARS-CoV-2 Virus (in Those Vaccinated) in England Between December 1, 2020, and December 15, 2021 ChAdOx1BNT162b2mRNA-1273ChAdOx1BNT162b2mRNA-1273ChAdOx1BNT162b2mRNA-1273SARS- CoV-2 positive*One dose (n=42 842 345)Two doses (n=39 118 282)Booster doses (n=21 242 629)(n= 5 934 153)% (n)% (n) % (n)% (n)% (n)% (n)% (n)% (n)% (n)% (n)Total no. of people20 650 68520 979 7041 211 95620 080 97617 950 0861 087 22053 60617 517 6923 671 3315 934 153SexWomen49.5(10 215 079)49.1(10 295 561)38.7(469 114)49.5(9 945 533)50.1(9 000 748)39.5(429 705)61.2(32 792)54.2(9 489 364)48.4(1 778 317)52.3(3 103 168)Men43.3(8 933 572)40.4(8 476 032)42.0(508 416)43.3(8 697 560)39.8(7 148 539)42.1(457 629)34.8(18 674)41.4(7 244 858)44.2(1 623 230)40.5(2 405 336)Not recorded7. 3(1 502 034)10.5(2 208 110)19.3(234 426)7. 2(1 437 882)10.0(1 800 799)18.4(199 886)4.0(2140)4.5(783 471)7. 3(269 784)7. 2(425 649)Age, yMean age (SD)54.9 (14.8)43.0 (22.4)32.3 (9.7)55.0 (14.7)46.5 (21.7)32.7 (9.8)63.1 (17.0)61.8 (15.9)53.7 (12.4)41.4 (18.0)13–17 <0.1(10 214)10.6(2 219 006)0.1(838)<0.1(9105)2.6(468 569)0.1 (623)0.1 (31)0.1(23 826)0.1(2961)8.3(493 728)18–29 5.2(1 081 177)24.4(5 127 151)43.1(521 916)5.1(1 022 847)24.9(4 472 159)41.3(449 436)3.7(1964)3.6(624 465)4.0(146 688)21.6(1 279 933)30–39 7. 9(1 634 841)21.5(4 517 781)35.6(431 515)7. 8(1 556 785)23.1(4 146 117)36.1(392 581)5.8(3102)6.1(1 067 916)8.6(315 936)18.3(1 084 406)40–49 22.1(4 564 393)8.5(1 784 664)18.4(222 849)22.0(4 414 864)9.3(1 665 983)19.5(212 187)11.5 (6171)11.1(1 949 092)19.2(706 004)19.4(1 152 196)50–59 2 7. 5(5 673 878)8.0(1 684 013)1.8(22 320)2 7. 6(5 549 187)9.1(1 636 430)1.9(20 463)19.9(10 644)20.8(3 635 337)35.3(1 295 168)16.7(989 499)60–69 19.8(4 083 887)8.5(1 777 370)0.7(8330)20.0(4 013 588)9.8(1 753 552)0.7(8145)19.3(10 371)22.5(3 938 515)24.8(910 586)8.5(505 389)70–79 13.4(2 763 041)9.4(1 979 901)0.3(3241)13.5(2 717 638)10.9(1 959 318)0.3(2789)22.6(12 090)23.1(4 049 042)6.5(237 287)4.2(248 415)80–89 3.1(630 457)7. 7(1 621 129)0.1(842)3.0(604 788)8.9(1 591 216)0.1(837)12.5 (6710)10.8(1 888 973)1.3(47 228)2.2(132 459)90+ 1.0(208 753)1.3(268 563)<0.1(103)1.0(192 162)1.4(256 698)<0.1(158)4.7(2523)1.9(340 498)0.3(9473)0.8(48 117)Not recorded<0.1 (44)<0.1 (125)<0.1 (2)<0.1 (11)<0.1 (44)<0.0 (1)0<0.1 (29)0<0.1 (11)Women age groups, y <40 14.8(1 510 119)51.7(5 325 910)7 7. 9(365 443)14.4(1 437 517)45.9(4 131 123)76.4(328 311)9.2(3020)10.9(1 032 366)14.2(252 054)4 7. 6(1 477 776)≥40 85.2(8 704 960)48.3(4 969 651)22.1(103 671)85.5(8 508 009)54.1(4 869 604)23.6(101 394)90.8(29 772)89.1(8 456 981)85.8(1 526 263)52.4(1 625 385) Not recorded<0.1 (16)<0.1 (59)0<0.1 (7)<0.1 (21)0000<0.1 (7)Men age groups, y<40 11.2(998 025)56.2(4 762 038)78.2(397 521)10.9(949 865)49.4(3 533 806)76.7(35 074)8.8(1650)7. 5(541 432)10.5(171 132)46.2(1 110 723)≥40 88.8(7 935 546)43.8(3 712 994)21.8(110 895)89.1(7 747 692)50.8(3 614 721)23.3(106 834)91.2(17 024)92.5(6 703 416)89.5(1 452 098)53.8(1 294 609) Not recorded<0.1 (21)<0.1 (42)<0.1 (2)<0.1 (3)<0.1 (12)<0.1 (1)000<0.1 (4)EthnicityWhite6 7. 9(14 012 353)63.6(13 344 722)53.0(642 168)68.0(13 656 716)64.2(11 530 182)54.0(587 123)74.3(39 827)73.6(12 891 303)69.6(2 553 453)66.9(3 971 366)Indian2.0(406 066)2.2(469 302)1.1(13 385)2.0(395 171)2.2(394 274)1.1(11 902)2.1(1141)2.0(354 433)1.4(51 193)2.6(153 403)Pakistani1.2(253 523)1.6(335 100)1.0(12 213)1.2(239 511)1.4(249 446)0.9(9732)0.9(477)0.6(109 038)0.5(19 186)2.0(118 522)Bangladeshi0.5 (96 392)0.5 (111 314)0.5 (5966)0.5 (92 835)0.5 (83 524)0.5 (4902)0.4 (217)0.2 (43 360)0.3 (10 775)0.7 (40 093)Other Asian0.9 (177 629)1.1 (238 245)1.0 (11 859)0.9 (171 863)1.1 (191 996)1.0 (10 365)0.8 (436)0.7 (128 434)0.6 (23 284)1.1 (67 392)Caribbean0.6 (117 507)0.5 (96 994)0.4 (4265)0.6 (110 470)0.4 (80 146)0.3 (3296)1.3 (706)0.4 (77 095)0.3 (11 820)0.5 (28 327)(Continued)ORIGINAL RESEARCHARTICLECirculation. 2022;146:00–00. DOI: 10.1161/CIRCULATIONAHA.122.059970xxx xxx, 20225Patone et alMyocarditis After COVID-19 Vaccine and Infectionof myocarditis after a first, second, and third dose of BNT162b2 vaccine, respectively. Of these, 22 (17.7%), 14 (11.8%), and 13 (15.3%) patients died with myo-carditis or within 28 days from hospital admission. Last, there were 11, 40, and 8 patients who were admitted to the hospital for myocarditis after, respectively, a first, second, and third dose of mRNA-1273 vaccine. None of these patients died with myocarditis or within 28 days from hospital admission with myocarditis (Table2).In the overall population, we confirmed our previous findings that the risk of hospitalization or death from myocarditis was higher after SARS-CoV-2 infection than vaccination and was greater after the first 2 doses of mRNA vaccine than after adenovirus vaccine (Table3; Table S3; Figure). There was an increased risk of myo-carditis at 1 to 28 days after the first dose of ChAdOx1 (IRR, 1.33 [95% CI, 1.09–1.62]) and BNT162b2 (IRR, 1.52 [95% CI, 1.24–1.85]).There was an increased risk of myocarditis at 1 to 28 days after a second dose of BNT162b2 (IRR, 1.57 [95% CI, 1.28–1.92]) and mRNA-1273 (IRR, 11.76 [95% CI, 7.25–19.08]); and after a booster dose of BNT162b2 (IRR, 1.72 [95% CI, 1.33–2.22]) and mRNA-1273 (IRR, 2.64 [95% CI, 1.25–5.58]).Vaccine-Associated Myocarditis in MenOf the 17918020 men vaccinated in England in the study period, 6158584 (34.4%) were younger than 40 years, and 11759 436 (65.6%) were 40 years or older (Table1). Analysis restricted to younger men age younger than 40 years showed an increased risk of myocarditis Black African0.9 (185 852)1.0 (218 158)1.0 (12 121)0.9 (176 094)0.9 (164 260)0.9 (9258)1.1 (588)0.6 (98 216)0.5 (16 997)1.0 (57 157)Chinese0.3 (63 180)0.3 (70 206)0.4 (5176)0.3 (61 902)0.3 (58 438)0.5 (4902)0.3 (149)0.3 (47 390)0.3 (11 899)0.2 (11 732)Other1.8 (378 719)2.4 (502 815)2.6 (31 811)1.8 (363 257)2.2 (388 674)2.5 (27 107)1.7 (902)1.4 (245 301)1.4 (50 501)2.3 (138 024)Not recorded24.0(4 959 464)26.7(5 592 847)39.0(472 992)24.0(4 813 156)26.8(4 809 146)38.5(418 633)1 7. 1(9163)20.1(3 523 123)25.1(922 223)22.7(1 348 137)History of myocarditis Previous myo-carditis<0.1 (1837)<0.1 (1632)<0.1 (69)<0.1 (1778)<0.1 (1511)<0.1 (56)<0.1 (18)<0.1 (1885)<0.1 (272)<0.1 (687)COVID-19 status†No COVID-1986.3(17 815 732)86.0(18 052 842)85.8(1 039 833)86.3(17 334 448)8 7. 3(15 674 125)86.2(937 147)88.4(47 367)90.5(15 846 583)88.0(3 230 055)…COVID-19 previous vac-cination5.9(1 227 131)7. 8(1 629 334)8.4(101 484)5.9(1 183 882)6.5(1 170 434)7. 8(85 166)6.3(3398)4.7 (815 805)5.3(194 056)49.8(2 958 026)COVID-19 after first dose0.7(143 526)2.8(594 914)3.2(38 200)0.5(99 981)2.2(401 516)3.0(32 222)0.9(456)0.6 (108 097)0.4(15 316)13.1(776 725)COVID-19 after second dose6.7(1 383 490)3.0(638 578)2.7(32 215)6.9(1 381 868)3.6(639 976)3.0(32 452)1.8(969)3.5 (621 836)5.8(213 627)34.6(2 054 331)COVID-19 after booster dose0.4(80 807)0.3(64 035)<0.1(224)0.4(80 796)0.4(64 035)<0.1(233)2.6(1416)0.7(125 372)0.5(18 277)2.4(145 071)No. of dosesOne dose only2.3(467 328)14.8(3 114 034)11.9(144 026)………………12.8(761 515)Two doses only36.0(7 430 747)45.1(9 464 269)80.8(979 495)36.5(7 328 422)53.2(9 550 989)91.7(996 599)………51.5(3 054 000)Two doses + booster61.8(12 752 610)40.0(8 401 400)7. 3(88 435)63.5(12 752 553)46.8(8 399 097)8.3 (90 621)100.0(53 606)100.0(17 517 692)100.0(3 671 331)35.7(2 118 638)Type of vaccinesTwo doses of ChAdOx19 7. 0(20 040 458)……99.8(20 040 458)……83.0(44 472)55.8(9 780 549)79.1(2 903 545)46.2(2 741 419)Two doses of BNT162b2…84.9(17 815 058)……99.2(17 815 058)…5.1(2760)43.7(7 653 274)19.6(720 535)38.0(2 256 069)Two doses of mRNA-1273……8 7. 5(1 060 277)……9 7. 5(1 060 277)<0.1(8)0.3(45 269)1.2(42 783)2.5(146 385)*Among vaccinated individuals. †Determined by a SARS-CoV-2 test. Table 1.ContinuedChAdOx1BNT162b2mRNA-1273ChAdOx1BNT162b2mRNA-1273ChAdOx1BNT162b2mRNA-1273SARS- CoV-2 positive*One dose (n=42 842 345)Two doses (n=39 118 282)Booster doses (n=21 242 629)(n= 5 934 153)% (n)% (n) % (n)% (n)% (n)% (n)% (n)% (n)% (n)% (n)

After COVID-19 Vaccine and Infectionafter a first dose of BNT162b2 (IRR, 1.85 [95% CI, 1.30–2.62]) and mRNA-1273 (IRR, 3.06 [95% CI, 1.33–7.03]); and a second dose of ChAdOx1 (IRR, 2.73 [95% CI, 1.62–4.60]), BNT162b2 (IRR, 3.08 [95% CI, 2.24–4.24]), and mRNA-1273 (IRR, 16.83 [95% CI, 9.11–31.11]). The risk of myocarditis for older men 40 years or more was associated with a booster dose of both mRNA vaccines, BNT162b2 (IRR, 2.15 [95% CI, 1.46–3.17]) and mRNA-1273 (IRR, 3.76 [95% CI, 1.41–10.02]) (Table 3).Vaccine-Associated Myocarditis in WomenOf the 20 979 754 women vaccinated in England in the study period, 7 201 472 (34.3%) were younger than 40 Table 2. Demographic and Clinical Characteristics of Patients Who Were Admitted to the Hospital for Myocarditis in the 1 to 28 Days After a COVID-19 Vaccine First Dose, Second Dose, and Booster Dose or SARS-CoV-2 Infection Among the Vaccinated Population in England from December 1, 2020, Until December 15, 2021VariableBaselineRisk set (1–28 days after exposure)ChAdOx1BNT162b2mRNA-1273 First dose Second dose Booster dose First dose Second dose Booster dose First dose Second dose Booster dose Total no. of people22441409001241198511408Sex Women40.4 (907)40.7 (57)26.7 (24)…41.1 (51)28.6 (34)45.9 (39)*** Men59.4 (1333)59.3 (83)73.3 (66)…58.1 (72)70.6 (84)54.1 (46)>5>5>5 Not recorded0.2 (4)00…0.8 (1)0.8 (1)0000Age Mean age (SD)53.8 (19.7)57.5 (17.5)54.2 (18.0)…48.7 (24.3)45.0 (24.8)67.2 (15.8)27.0 (9.5)24.9 (6.3)61.8 (14.8) <40 y26.3 (590)14.3 (20)25.6 (23)…46.8 (58)58.8 (70)7.1 (6)>5>5≥40 y73.7 (1654)85.7 (120)74.4 (67)…53.2 (66)41.2 (49)92.9 (79)>5Deaths with myocarditis or within 28 days of hospital admission with myocarditis No. of deaths10.9 (245)28.6 (40)12.2 (11)…17.7 (22)11.8 (14)15.3 (13)……… Mean age of death (SD), y68.7 (14.3)62.1 (17.4)65.2 (10.4)…67.8 (20.4)69.2 (21.6)78 (8.7)……… No. of deaths Women38.2 (92)35.0 (14)…57.1 (12)46.1 (6)……… Men61.8 (149)65.0 (26)>5…42.9 (9)53.9 (7)> 5……… Not recorded0.2 (4)000.8 (1)0.8 (1)0COVID-19 status (positive SARS-CoV-2 test) No COVID-19…72.9 (102)82.2 (74)…71.8 (89)88.2 (105)81.2 (69)54.5 (6)90.0 (36)100.0 (8) COVID-19 previous vac-cination…12.9 (18)11.1 (10)…10.5 (13)8.2 (7)… COVID-19 after first dose…11.4 (16)…15.3 (19)… COVID-19 after second dose…5.6 (5)…5.0 (6)… COVID-19 after booster dose…7.1 (6)…No. of doses One …45.7 (64)…53.2 (66)90.9 (10)* Two …23.6 (33)60.0 (54)…16.9 (21)70.6 (84)97.5 (39)* Two + booster…30.7 (43)40.0 (36)…29.8 (37)29.4 (35)100.0 (85)100.0 (8)Type of first 2 doses received ChAdOx1…50.7 (71)98.9 (89)………49.4 (42)……62.5 (5) BNT162b2…………43.5 (54)99.2 (118)50.6 (43)……* mRNA-1273………………100.0 (40)Lag between first and second doses (days)≤655.7 (8)16.7 (15)…8.1 (10)47.9 (57)24.7 (21)55.0 (22)* 6 6–7931.4 (44)55.6 (50)…25.8 (32)32.8 (39)54.1 (46)…22.5 (9)*≥8017.1 (24)27.8 (25)…12.9 (16)19.3 (23)21.2 (18)…22.5 (9)Cells with counts <5 are suppressed. ORIGINAL RESEARCH

After COVID-19 Vaccine and Infection Table 3. Incidence Rate Ratios (IRR [95% CI]) for Main Analysis and by Age Group (Age 40 Years or Older, Younger Than 40 Years) and Sex (Female and Male) for Myocarditis in Predefined Risk Periods Immediately Before and After Exposure to Vacci-nation and Before and After a Positive SARS-CoV-2 Test Result, Adjusted for Calendar Time From December 1, 2020, to December 15, 2021 (if 1 or no events, IRR has not been estimated and reported as n/a).Time periodChAdOx1 nCoV-19 vaccineBNT162b2 mRNA vaccinemRNA-1273 vaccine Positive SARS-CoV-2 test (before vaccine)Positive SARS-CoV-2 test (vaccinated) Events I RR (95% CI) Events I RR (95% CI)Events IRR (95% CI)Events IRR (95% CI)Events IRR (95% CI)Main analysis 1–28 days: first dose/positive test before any vaccination1401.33 (1.09–1.62)1241.52 (1.24–1.85)111.85 (0.93–3.66)11411.14 (8.64–14.36)815.97 (4.54–7.87) 1–28 days: second dose900.93 (0.74–1.17)1191.57 (1.28–1.92)4011.76 (7.25–19.08) 1–28 days: booster dose*n/a851.72 (1.33–2.22)82.64 (1.25–5.58)Women 1–28 days: first dose/positive test before any vaccination571.32 (0.97–1.81)511.59 (1.16–2.20)*1.07 (0.23–4.90)4714.23 (9.34–21.68)326.87 (4.38–10.78) 1–28 days: second dose240.54 (0.35–0.83)341.04 (0.72–1.50)*3.95 (1.20–13.04) 1–28 days: booster dose*n/a391.55 (1.06–2.27)*1.51 (0.35–6.47)Men 1–28 days: first dose/positive test before any vaccination831.33 (1.03–1.72)721.47 (1.14–1.90)92.35 (1.09–5.08)679.71 (7.03–13.40)495.55 (3.91–7.88) 1–28 days: second dose661.26 (0.96–1.65)841.93 (1.51–2.45)3614.98 (8.61–26.07) 1–28 days: booster dose*n/a461.89 (1.34–2.67)63.57 (1.48–8.64)Age <40 y 1–28 days: first dose/positive test before any vaccination201.31 (0.79–2.16)581.79 (1.33–2.41)102.76 (1.32–5.75)205.25 (3.11–8.86)81.18 (0.56–2.48) 1–28 days: second dose231.69 (1.06–2.71)702.59 (1.96–3.44)3913.97 (8.07–24.19) 1–28 days: booster dose*n/a61.53 (0.64–3.64)*n/aAge ≥40 y 1–28 days: first dose/positive test before any vaccination1201.21 (0.97–1.51)661.28 (0.97–1.71)*n/a9414.87 (10.98–20.14)7310.52 (7.61–14.54) 1–28 days: second dose670.72 (0.55–0.93)490.85 (0.62–1.16)*n/a 1–28 days: booster dose*n/a791.96 (1.48–2.59)72.97 (1.32–6.69)Women age <40 y 1–28 days: first dose/positive test before any vaccination71.20 (0.51–2.84)141.65 (0.91–2.97)*2.68 (0.54–13.25)79.80 (3.70–25.97)63.98 (1.52–10.42) 1–28 days: second dose/posi-tive test after any vaccination*0.32 (0.08–1.37)91.16 (0.57–2.34)*4.75 (1.11–20.40) 1–28 days: booster dose*n/a*0.83 (0.19–3.64)*n/aMen age <40 y 1–28 days: first dose/positive test before any vaccination131.34 (0.72–2.48)431.85 (1.30–2.62)83.06 (1.33–7.03)134.35 (2.31–8.21)*0.39 (0.09–1.60) 1–28 days: second dose212.73 (1.62–4.60)603.08 (2.24–4.24)3616.83 (9.11–31.11) 1–28 days: booster dose*n/a*2.28 (0.77–6.80)*n/a(Continued )ORIGINAL

After COVID-19 Vaccine and Infection years, and 13 778 282 (65.7%) were 40 years or older (Table 1). Analysis restricted to women younger than 40 years showed an increased risk of myocarditis after a second dose of mRNA-1273 (IRR, 4.75 [95% CI, 1.11–20.40]). For women 40 years or older, there was an in-creased risk of myocarditis associated with a first (IRR, 1.57 [95% CI, 1.05–2.33]) and third (IRR, 1.76 [95% CI, 1.17–2.65]) dose of BNT162b2 vaccine. It is important that for all subgroups, the higher risk of myocarditis was found in the 1 to 7 days or 8 to 14 days after vaccination (Table S4).Vaccine-Associated Myocarditis by Vaccination History Analyses restricted to people who had the same type of vaccine for the first and second doses (Table S5) showed that for patients having a first and second dose of ChAdOx1, there was an increased risk of myocarditis associated with a booster dose of BNT162b2 (IRR, 1.78 [95% CI, 1.22–2.60]) and mRNA-1273 (IRR, 2.97 [95% CI, 1.13–7.82]). For patients who had a first and second dose of BNT162b2 vaccine, there was an increased risk of myocarditis after the second dose of BNT162b2 (IRR, 1.53 [95% CI, 1.24–1.88]). Last, for patients who had a first and second dose of mRNA-1273 vaccine, there was an increased risk of myocarditis after a second dose of mRNA-1273 (IRR, 8.63 [95% CI, 3.98–18.75]).The risk after a second dose of BNT162b2 was higher for people who received the first 2 doses within 65 days of each other (IRR, 2.16 [95% CI, 1.60–2.91]) compared with people who received the first 2 doses with a longer lag: between 66 and 79 days (IRR, 1.01 [95% CI, 0.71–1.44]) and 80 days or more (IRR, 1.40 [95% CI, 0.88–2.21]). The risk after a second dose of mRNA-1273 was higher when the lag was of 80 or more days (IRR, 22.80 [95% CI, 7.48–69.48]) compared with when the lag was 65 days or less (IRR, 7.41 [95% CI, 3.98–13.77) (Table S6).SARS-CoV-2 Infection–Associated Myocarditis There was an increased risk of myocarditis in the 1 to 28 days after a SARS-CoV-2–positive test, which was higher if infection occurred before vaccination (IRR, 11.14 [95% CI, 8.64–14.36]) than in vaccinated individuals (IRR, 5.97 [95% CI, 4.54–7.87]). The risk of myocarditis associated with a SARS-CoV-2–positive test before vaccination was higher in people 40 years or older (IRR, 14.87 [95% CI, 10.98–20.14]) than in-dividuals younger than 40 years (IRR, 5.25 [95% CI, 3.11–8.86]), but no significant difference was observed between risks in women (IRR, 14.23 [95% CI, 9.34–21.68]) and men (IRR, 9.71 [95% CI, 7.03–13.40), al-though the point estimate for women was higher than the equivalent for men. A similar pattern of risk of myo-carditis was associated with a SARS-CoV-2–positive test occurring in vaccinated individuals; however, in this case, the increased risk was substantially lower and in particular was not observed for individuals younger than 40 years (IRR, 1.18 [95% CI, 0.56–2.48]) (Table 3).Absolute and Excess Risks After the first dose of the ChAdOx1 and BNT162b2 vaccines, an additional 2 (95% CI, 1–3) and 2 (95% CI, 1–3) myocarditis events per million people vaccinated would be anticipated, respectively. After the second dose of BNT162b2 and mRNA-1273, an additional 2 (95% CI, 2–3) and 34 (95% CI, 32–35) myocar-ditis events per million people would be anticipated, Women age ≥40 y 1–28 days: first dose/positive test before any vaccination501.30 (0.92–1.84)371.57 (1.05–2.33)*n/a4017.29 (10.70–27.96)268.65 (5.13–14.59) 1–28 days: second dose220.55 (0.35–0.86)250.98 (0.63–1.52)*n/a 1–28 days: booster dose*n/a371.76 (1.17–2.65)*2.00 (0.46–8.72)Men age ≥40 y 1–28 days: 1st dose/positive test before any vaccination701.16 (0.87–1.54)291.05 (0.69–1.59)*n/a5413.40 (9.04–19.88)4711.77 (7.77–17.85) 1–28 days: second dose450.85 (0.61–1.19)240.77 (0.49–1.18)*n/a 1–28 days: booster dose*n/a422.15 (1.46–3.17)53.76 (1.41–10.02)Day 0 of each exposure has been removed because of small numbers.*Cells with counts <5 are suppressed. Table 3. Continued Time periodChAdOx1 nCoV-19 vaccineBNT162b2 mRNA vaccinemRNA-1273 vaccine Positive SARS-CoV-2 test (before vaccine)Positive SARS-CoV-2 test (vaccinated) Events IRR (95% CI)Events IRR (95% CI)Events IRR (95% CI)Events IRR (95% CI) EventsIRR (95%

After COVID-19 Vaccine and Infectionres pectively. After a booster dose of BNT162b2 and mRNA-1273, an additional 2 (95% CI, 1–3) and 1 (95% CI, 0–2) myocarditis events per million people would be anticipated, respectively. These estimates compare with an additional 35 (95% CI, 34–36) and 23 (95% CI, 21–24) myocarditis events per million people in the 1 to 28 days after a SARS-CoV-2–posi-tive test before vaccination and in vaccinated individu-als, respectively (Table 4; Figure).In men younger than 40 years, we estimate an additional 4 (95% CI, 2–6) and 14 (95% CI, 5–17) myocarditis events per million in the 1 to 28 days after a first dose of BNT162b2 and mRNA-1273, respectively; and an additional 14 (95% CI, 8–17), 11 (95% CI, 9–13) and 97 (95% CI, 91–99) myocarditis events after a second dose of ChAdOx1, BNT162b2, and mRNA-1273, respectively. These estimates compare with an additional 16 (95% CI, 12–18) myocarditis events per million men younger than 40 years in the 1 to 28 days after a SARS-CoV-2–positive test before vaccination (Table 4; Figure).Robustness of the ResultsOverall, our main findings were not sensitive to censoring because of death (Table S7, sensitivity analyses 1 through 3), and IRRs for the second dose of vaccination agreed with main results when we removed those who had the outcome after the first dose of any vaccine, but before the second dose (Table S7, sensitivity analysis 5). Similarly, IRRs for the booster dose of vaccination agreed with main results when we removed those who had the outcome af-ter the second dose of any vaccine, but before the booster dose (Table S7, sensitivity analysis 6). There was no bias caused by possibly not complete data near the end of the study period (Table S7, sensitivity analysis 4). Estimates for vaccines exposures agreed with the main analysis when restricted to patients who never tested positive to SARS-CoV-2 (Table S8, sensitivity analysis 7).


a population of >42 million vaccinated individuals, we re-port several new findings that could influence public health Figure. Risk of myocarditis in the 1 to 28 days after COVID-19 vaccines or SARS-CoV-2.(Left) Incidence rate ratios with 95% CIs and (right) number of excess myocarditis events for million people with 95% CIs in the 1 to 28 day risk periods after the first, second, and booster doses of ChAdOx1, BNT162b2,and mRNA-1273 vaccine or a positive SARS-CoV-2 test in (top) a population of 42 842 345 vaccinated individuals and (bottom) younger men (age <40 years), older men (age ≥40 years), younger women (age <40 years), and older women (aged ≥40 years).ORIGINAL

First, the risk of myocar-ditis is substantially higher after SARS-CoV-2 infection in unvaccinated individuals than the increase in risk observed after a first dose of ChAdOx1nCoV-19 vaccine, and a first, second, or booster dose of BNT162b2 vaccine. Second, although the risk of myocarditis with SARS-CoV-2 infec-tion remains after vaccination, it was substantially reduced, suggesting vaccination provides some protection from the cardiovascular consequences of SARS-CoV-2. Third, in contrast with other vaccines, the risk of myocarditis ob-served 1 to 28 days after a second dose of mRNA-1273 vaccine was higher and similar to the risk after infection. Last, vaccine-associated myocarditis was largely restrict-ed to men younger than 40 years with 1 exception; both younger men and women were at increased risk of myo-carditis after a second dose of mRNA-1273.Vaccination against COVID-19 has both major public health and economic benefits. Although the net benefit of vaccination for the individual or on a population level should not be framed exclusively around the risks of myocarditis, quantifying this risk is important, particularly in young people who are less likely to have a severe ill-ness with SARS-CoV-2 infection. Multiple studies have identified an increase in myocarditis after exposure to the BNT162b2 mRNA vaccine.1–8,13 Some of our find-ings are confirmatory, but we also demonstrate that the risk of myocarditis is not restricted to this vaccine but is observed after vaccination with adenovirus and other mRNA vaccines and after a booster dose.It is important to place our findings into context. One of the strengths of our analysis is that we quantify the risk of myocarditis associated with both vaccination and SARS-CoV-2 infection in the same population. Myocarditis is an uncommon condition. The risk of vaccine-associated myocarditis is small, with up to an additional 2 events per million people in the 28-day period after exposure to all vaccine doses other than mRNA-1273. This is substan-tially lower than the 35 additional myocarditis events observed with SARS-CoV-2 infection before vaccination. Furthermore, vaccination reduced the risk of infection associated myocarditis by approximately half, suggest-ing that the prevention of infection associated myocarditis may be an additional longer-term benefit of vaccination.The risk of vaccine-associated myocarditis is con-sistently higher in younger men, particularly after a second dose of mRNA-1273, where the number of additional events during 28 days was estimated to be 97 per million people exposed. An important consid-eration for this group is that the risk of myocarditis after a second dose of mRNA-1273 was higher than the risk after infection. Indeed, in younger women, although the relative risks of myocarditis were lower than in younger men, the number of additional events per million after a second dose of mRNA-1273 was similar to the number after infection. These findings may justify some reconsideration of the selection of vaccine type, the timing of vaccine doses, and the net benefit of booster doses in young people, particularly in young men. However, there are some important caveats that need to be considered. First, the num-ber of people vaccinated with mRNA-1273 was small compared with those receiving other types of vaccine, Table 4. Measures of the Effect of Vaccinations and SARS-CoV-2 Infections Presented as Excess Events Per 1 Million Exposed Excess myocarditis events per 1 000 000 exposed (95% CI)Main analysis Age <40 yAge ≥40 y Women Men Age <40 yAge ≥40 y Women Men Women Men ChAdOx1 First dose2 (1–3)………2 (0–4)………… Second dose…4 (0–6)…………14 (8–17)…… Booster dose………………………BNT162b2 First dose2 (1–3)2 (1–3)…2 (1–3)3 (1–4)…4 (2–6)3 (0–4)… Second dose2 (1–3)5 (4–5)……6 (4–7)…11 (9–13)…… Booster dose2 (1–3)…2 (2–3)1 (0–2)3 (2–4)……2 (1–3)3 (2–4)mRNA-1273 First dose…7 (3–9)……10 (1–14)…14 (5–17)…… Second dose34 (32–35)43 (41–44)…7 (2–9)73 (70–76)7 (1–9)97 (91–99)…… Booster dose1 (0–2)…1 (1–2)…3 (1–3)………3 (1–3)SARS-CoV-2 Positive test (before vaccine)35 (34–36)10 (9–11)63 (62–64)28 (27–29)50 (48–51)8 (6–8)16 (12–18)51 (49–52)85 (82–87) Positive test (vaccinated)23 (21–24)…39 (38–40)17 (16–19)34 (30–36)7 (3–8)…26 (24–27)61 (58–63)Only significant increased risks were reported during the 1 to 28 days after exposure. When incidence rate ratios were not significant during the 1 to 28 days after vaccine, absolute measures are not given.

Second, the average age of those receiving this vaccine was younger at 32 years compared with other vaccines where recipients were in their mid-40s and 50s. The observed excess risk related to mRNA-1273 may in part be a result of the higher probability of myocarditis in this younger age group. Our findings are consistent with 2 recent studies from the United States and Denmark in which the risks of myocarditis after mRNA-1273 and BNT162b2 were compared.7,14 In the Vaccine Adverse Event Reporting System, 1991 cases of myocarditis were reported to August 31, 2021, with a median age of 21 years and 82% male.14 Although our findings are not directly com-parable because the Vaccine Adverse Event Reporting System dataset relies on clinician reporting, the risks of myocarditis were higher after a second dose of both BNT162b2 and mRNA-1273 and were greater for mRNA-1273 in most younger age groups. In Denmark, a population-based study that applied both case-control and self-controlled case series study methods observed a greater increase in the risk of myocarditis or myopericarditis 1 to 28 days after mRNA-1273 (adjusted hazard ratio, 3.92 [95% CI, 2.30–6.68]) than after BNT162b2 (adjusted hazard ratio, 1.34 [95% CI, 0.90–2.00]).7 They also observed the risk was largely confined to those younger than 40 years and was present for both younger men and women for mRNA-1273. The reasons for male predominance in myocarditis is not known but may relate to sex hormone differences in both the immune response and myocarditis, or to the underdiagnosis of cardiac dis-ease in women.15,16This study has several strengths. First, the United Kingdom offered an ideal place to carry out this study given that 3 types of COVID-19 vaccination have been rolled out at the same speed and scale as each other. Second, this was a population-based study of data recorded prospectively and avoided recall and selection biases linked to case reports. Third, the large sample size provided sufficient power to investigate these rare outcomes, which could not be assessed through clini-cal trials. Fourth, the self-controlled case series study design removes potential confounding from fixed char-acteristics, and the breakdown of our study period into weekly blocks accounted for temporal confounding. Of note, the estimated IRRs were consistently <1 in the pre-exposure period before vaccination and >1 in the pre-risk period before a SARS-CoV-2–positive test. This was expected because events are unlikely to happen shortly before vaccination (relatively healthy people are receiving the vaccine) and more likely to happen before a SARS-CoV-2–positive test (as a standard procedure, patients admitted to the hospital are tested for SARS-CoV-2). We also assessed the robustness of our results through several sensitivity analyses.There are some limitations to consider. First, the number of people receiving a booster dose of ChAdOx1 or mRNA-1273 vaccine was too small to evaluate the risk of myocar-ditis. Second, we relied on hospital admission codes and death certification to define myocarditis, and it is possible that we might have over- or underestimated risk because of misclassification. Third, although we were able to include 2 230 058 children age 13 to 17 years in this analysis, the number of myocarditis events was small (56 events in all periods and 16 events in the 1 to 28 days after vac-cination) in this subpopulation and precluded a separate evaluation of risk. It should also be noted that only the first occurrence of myocarditis in the study period is used in this analysis. Therefore, the results found for the risk of myo-carditis after a third dose do not include repeated instances of myocarditis in the same individual. A comparison of rates of death with myocarditis between those infected with SARS-CoV-2 or vaccinated was not possible, given that for this analysis, we have included only people who had been vaccinated. Therefore, a patient with COVID-19 who died after myocarditis before receiving a vaccination will not be included, and rates of myocarditis death after SARS-CoV-2 will be under estimated.In summary, the risk of hospital admission or death from myocarditis is greater after SARS- CoV2 infection than COVID-19 vaccination and remains modest after sequential doses including a booster dose of BNT162b2 mRNA vaccine. However, the risk of myocarditis after vaccination is higher in younger men, particularly after a second dose of the mRNA-1273 vaccine.

ARTICLE INFORMATIONReceived March 10, 2022; accepted June 7, 2022.AffiliationsNuffield Department of Primary Health Care Sciences (M.P., X.W.M., S.D., A.H., C.A.C.C., J.H.-C.), Wellcome Centre for Human Genetics (L.H.), British Heart Foundation Centre of Research Excellence, National Institute for Health Research, Oxford Biomedical Research Centre, Radcliffe Department of Medicine, John Rad-cliffe Hospital (K.M.C.): National Institute for Health Research Biomedical Research Centre, Oxford University Hospitals National Health Service Trust (P.W.); University of Oxford. School of Immunology and Microbial Sciences, King’s College London, Centre for Inflammation Research (M.S.-H.). Leicester Real World Evidence Unit, Diabetes Research Centre (F.Z., K.K.), University of Leicester. Usher Institute (M.S.-H., N.L.M., A.S.), British Heart Foundation University Centre for Cardiovascular Sci-ence (N.L.M.), University of Edinburgh. Centre for Academic Primary Care, School of Medicine, University of Nottingham (C.A.C.C.)

Understanding COVID-19 through genome-wide association studies

Authors: Tom H. Karlsen  Nature Genetics volume 54, pages368–369 (2022)Cite this article

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Defining the most appropriate phenotypes in genome-wide association studies of COVID-19 is challenging, and two new publications demonstrate how case-control definitions critically determine outcomes and downstream clinical utility of findings.

Exploring self-reported data from more than 700,000 participants in a direct-to-consumer ancestry genetics company, in this issue of Nature Genetics, Roberts et al. report how several commonly used phenotype definitions in COVID-19 genetics studies converge to represent either susceptibility to infection by the SARS-CoV-2 virus or risk of severe COVID-19 disease1. For pragmatic reasons, early genome-wide association studies (GWAS) in COVID-19 focused on hospitalized cases compared with unscreened and often previously genotyped controls2,3. While allowing for rapid assessments during the first and very challenging wave of the pandemic, such study designs are biased towards the biology of complications in COVID-19. The emphasis on patients with mild or no symptoms, including identification of household COVID-19 exposure as a high-risk measure, allowed the authors to conduct a deep investigation of susceptibility to SARS-CoV-2 infection through comparisons such as exposed individuals who tested positive for COVID-19 versus exposed individuals who tested negative. Not only did these assessments corroborate the controversial ABO locus as a bona fide susceptibility gene for SARS-CoV-2 infection2,4, they also suggested the presence of a hitherto unexplored pool of protective variants.

In a dedicated query of rare variants (minor allele frequency (MAF) < 0.005), also reported in this issue of Nature Genetics, Horowitz et al. identified an association signal between a non-coding X chromosome variant (rs190509934) upstream of angiotensin-converting enzyme 2 (ACE2) and protection against SARS-CoV-2 infection5. The authors go on to substantiate their finding using RNA sequencing – data from liver tissue, showing that the protective allele leads to an almost 40% reduction in ACE2 expression levels in carriers. The association inherently holds considerable plausibility, with the membrane-bound ACE2 serving as the binding site for the SARS-CoV-2 spike glycoprotein, initiating virus cell entry6. Furthermore, Horowitz et al.5 and Roberts et al.1 utilize rich phenotype data to dissect the chromosome 3p21.31 association into a susceptibility signal and a severity signal, which localize to SLC6A20 and LZTFL1, respectively, as also observed by others7SLC6A20 encodes the sodium–imino-acid (proline) transporter 1 (SIT1), which functionally interacts with ACE2 (ref. 8), and the risk allele has been shown to associate with increased expression of SLC6A20 (ref. 2). Along with data suggesting that the receptor-binding domain of the SARS-CoV-2 spike protein preferentially interacts with blood group A9, which is encoded by the risk variant at the ABO locus, genetics of the susceptibility to SARS-CoV-2 infection appear to converge on the cell entry apparatus for the virus.

Critical illness in COVID-19 develops in fewer than 10% of individuals infected with SARS-CoV-2 (ref. 10). Given the window from the first symptoms of COVID-19 to onset of severe disease with respiratory failure (typically about one week)10, prediction of a severe disease course following SARS-CoV-2 infection is of considerable clinical interest as well as from a therapeutic point of view. Reliable risk stratification may guide therapeutic interventions during this lead-in period, characterized by enhanced viral replication. These interventions potentially include antiviral therapies, convalescent plasma, neutralizing monoclonal antibodies or — possibly more important for hospitalized patients — immunomodulating drugs.

Horowitz et al. found that a high genetic risk score (top 10%) based on six established severity variants was associated with a 1.65-fold and 1.75-fold higher risk of severe disease, in individuals with or without the presence of clinical risk factors such as age and diabetes, respectively5. Others have found an odds ratio of 2.0 for the impact of the rs10490770 risk allele at the 3p21.31 locus on the combined end-point of death or severe respiratory failure in an overall COVID-19 patient population11, with almost double the effect size in individuals 60 years or younger (odds ratio of 3.5). These magnitudes are comparable with those associated with clinical risk factors. Findings of lower age in individuals homozygous for the chromosome 3p21.31 risk variant support enhanced utility of genetic risk stratification in the young patient population2.

The execution of GWAS in COVID-19 has been remarkably nimble, due in part to robust collaborative networks set up during past GWAS12, as well as the utilization of previously genotyped study populations such as the UK Biobank, AncestryDNA and 23andme1,3,4,5. The rapid phenotyping undertaken by several biobanks and direct-to-consumer genetics companies during the COVID-19 pandemic is unprecedented, and the resulting publications deserve acknowledgement as a form of ‘population-level testing’ for genetic clues in emerging diseases. The orchestration of projects by the COVID-19 Host Genetics Initiative has also been an important catalyzer of activities13. Figure 1 summarizes published and peer-reviewed GWAS articles on COVID-19. However, even at time of writing, the meta-analysis of the sixth data freeze of the COVID-19 Host Genetics Initiative has been released online, reporting on a total of 23 loci involving in COVID-19 susceptibility (7 loci) and severity (15 loci); adding 10 new loci to the consortium’s own publication only 3 months ago7. The 22-month period that has passed since the publication of the first COVID-19 GWAS2 appears even more impressive in comparison with the 7 years of Crohn’s disease genetics — spanning from the 2001 nucleotide-binding oligomerization domain 2 (NOD2) susceptibility gene discovery to a 2008 meta-analysis14,15 — that it took to achieve the same amount of insight. Further exemplified by the 20-year history of genetics of Crohn’s disease, translational studies of GWAS findings take time, but may reveal new and unexpected aspects of pathophysiology. It is in this context that the rapid unravelling of COVID-19 genetics becomes important. Some of the loci hold immediate biological plausibility (for example, ACE2 and some of the chemokines), whereas the underlying mechanisms of others remain obscure. Following this recent sprint of COVID-19 GWAS to which Horowitz et al.5 and Roberts et al.1 significantly contribute, the subsequent translational ultramarathon of biological studies can begin — and with this a deeper understanding of the pathophysiology of SARS-CoV-2 infection and its complications will emerge. Vaccination has proven the ultimate protection against SARS-CoV-2 infection. The hope is that the biological insights provided by COVID-19 GWAS will facilitate identification and development of novel treatment options of not only hospitalized and critically ill COVID-19 patients, but also treatment modalities that can prevent hospitalization.

figure 1
Fig. 1: Genetic loci from COVID-19 GWAS in peer-reviewed publications to date.


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COVID-19 outcomes and the human genome

Authors: Michael F. Murray MDEimear E. Kenny PhDMarylyn D. RitchiePhDDaniel J. Rader MDAllen E. Bale MDMonica A. Giovanni MS, CGC & Noura S. Abul-Husn MD, PhD Genetics in Medicine  volume 22, pages1175–1177


In the COVID-19 pandemic, the opportunity to link host genomic factors to the highly variable clinical manifestations of SARS-CoV-2 infection has been widely recognized.1,2 The overt motivation for this research is the clinical implementation of any new insights to improve clinical management and foster better patient outcomes.

Human infection is a complex interaction between the microbe, the environment, and the human host.3 Variation in the human genome has only rarely been linked to complete resistance to infection by a specific microbe; far more commonly host genomic variability has been linked to complications associated with infections (see Table 1).3,4,5 In this pandemic, the ability to identify host genomic factors that increase susceptibility or resistance to the complications of COVID-19 and to translate these findings to improved patient care should be the goal.Table 1 Sample characteristics.

Full size table

Several approaches can be taken to uncover relevant host genomic factors. Familial and population-based linkage analyses and analyses of extreme phenotypes can uncover monogenic variants contributing to COVID-19 clinical outcomes.6 Genome-wide association studies (GWAS)7,8 and multiomic-based approaches can be used to uncover common variants and biological networks underlying host-pathogen interactions. Likewise, data derived from genomes, such as HLA haplotypes, ABO blood groups, and polygenic risk scores (PRS),9 can be used to understand COVID-19 susceptibility, resistance, and complications. Furthermore, biobanks linking genomic data to electronic health records (EHRs)10 can be leveraged to investigate the impact of these genomic factors on the clinical course of SARS-CoV-2 infected patients.

Many recognize that this area of research needs to go forward in a manner that is proactively inclusive of traditionally underserved populations to both avoid the exacerbation of existing health-care disparities and to optimize discovery. Past efforts have demonstrated the value of this type of inclusion, as was seen in the extension of a CCR5-associated delta 32 correlation to HIV-1 infection in individuals with European ancestry to a promoter variant in CCR5 linked to perinatal HIV-1 transmission in individuals with African ancestry.11,12,13

As host genomic factors are discovered, new strategies supporting rapid clinical implementation should be trialed to realize improvement in outcomes for SARS-CoV-2 infected patients. Implementation will require an infrastructure to deliver relevant genomic results to infected patients and their health-care providers to guide clinical management. This commentary examines the types of genomic factors that might be identified in emerging COVID-19 discovery and implementation research, based on decades of genomic discovery, research into other human infections, and advances in genomic medicine.


In this fast-moving pandemic, we believe there will be at least two phases to defining COVID-19 related phenotypes. Currently in the United States, we are in an initial phase when important limitations influence the ability of research teams to ascertain and appropriately define phenotypes of interest. These limitations include (1) the absence of widespread viral and serologic testing to accurately distinguish those who have been infected from those who have not, (2) the lack of knowledge about infection exposure at a community level, and (3) institutional limits to recruiting human subjects in a time of social distancing. Heterogeneity of testing strategies and their sensitivity, and nascent regulatory oversight may pose challenges in clear and reproducible definitions of COVID-19-related phenotypes. In the second phase, adequate serologic testing may allow for increased numbers and more accurate discrimination of cases and controls, as well as the ability to define additional clinical phenotypes of interest (e.g., asymptomatic seropositive individuals). The use of telemedicine, which has expanded for health-care delivery during the pandemic, in addition to community outreach efforts, can overcome barriers to recruitment in this infectious disease outbreak.

To find important genotype–phenotype correlations, there will need to be phenotypes that are ascertained in a manner that is clear, quantitative, and reproducible, and there will need to be adequate sampling from well-defined cases and controls. One rubric that can be used for phenotyping during this initial phase of COVID-19 host genomic research is the Ordinal Scale for Clinical Improvement proposed by the World Health Organization (WHO) in their blueprint for therapeutic trials (see Supplemental Table 1).14 For instance, this scale can be applied across research groups and across health systems in order to allow phenotypic groupings of COVID-19 patients based on (1) need for hospitalization, (2) need for oxygen supplementation, (3) progression to respiratory failure, or (4) mortality, and these phenotypes could be readily extracted from EHRs. In the current initial phase of the COVID-19 pandemic, difficulties with the enrollment and appropriate scoring of uninfected, asymptomatic, or mildly affected patients (categories 0–2 in Supplemental Table 1) are anticipated. Specifically, asymptomatic positives will be mistakenly scored as 0 instead of 1 without either viral screening or serologic testing. In addition, patients who would be scored 0–2 are difficult to recruit and consent given the social distancing limitations that are currently in place. As serologic testing becomes more sophisticated, widespread, and robust, it is anticipated that COVID-19-related phenotyping will become more standard, facilitating reproducible and scalable COVID-19 research.


At least three lines of inquiry might inform the nomination of candidate genes for intensive interrogation with COVID-19 phenotypes: (1) what do we know about the microbial life cycle, (2) what do clinical observations in patients suggest with regard to biological pathways that are likely being triggered, and (3) what does the literature teach us about host genetics in infection that could apply to this novel infection. For example, the cellular surface receptor for SARS-CoV-2 virus is encoded by the ACE2 gene, and critical amino acid residues in the binding interaction have been described.15,16 This and other insights into host–pathogen interactions will elucidate specific variants, genes, and pathways underlying interindividual COVID-19 susceptibility and response. Genes and pathways related to COVID-19 could also include other viral receptor genes (e.g., TMPRSS2) (unpublished data: https://doi.org/10.1101/2020.03.30.20047878), inflammatory and immune response pathways (e.g., IL-6 pathway), and genes involved in hypercoagulability and acute respiratory distress syndrome.17 Other genes that may be of interest include genes associated with ABO blood group (e.g., FUT2) in light of a report on an association between blood groups and COVID-19 in China (unpublished data: https://doi.org/10.1101/2020.03.11.20031096) as well as similar associations in the past.18 Research into the genetics of the interplay between viral infection and common diseases (e.g., diabetes and heart disease) is also of interest to many investigators. As our understanding of genes underlying SARS-CoV-2 infectivity and biological mechanisms grows, we will better elucidate their potential involvement in disease susceptibility and clinical outcomes.


In tandem, the global scientific community has rapidly mobilized collaborative efforts to advance unbiased genome-wide COVID-19 host genomic discovery through large-scale genomic studies. For example, the COVID-19 Host Genetics Initiative is organizing analytical activities across a growing network of over 120 studies to identify genomic determinants of COVID-19 susceptibility and severity.1 It is difficult at this stage to estimate the number of research participants needed to identify host genomic factors related to the COVID-19 novel pathogenic exposure. If we assume that the effect size and allele frequency of genetic variants important for COVID-19 susceptibility, resistance, and/or complications are as variable as other host factors in infectious conditions (i.e., Supplemental Table 1), then the number of cases and controls needed to have statistical power to identify associations could vary widely. Collaborative efforts like the COVID-19 Host Genetics Initiative should be well-powered for the unbiased discovery of novel genes and pathways. Such efforts foster data aggregation and sharing broadly among the research community and are likely to greatly impact the speed with which COVID-19 discoveries can be made and disseminated worldwide.

In aggregate, knowledge of host genomic factors could lead to improved care for patients with COVID-19, through risk stratification, as well as targeted prevention and treatment options. For example, GWAS discovery efforts could yield PRS for COVID-19 clinical outcomes, which could be used in the context of other clinical data to risk stratify patients early in the disease course. Host genomic factors could be linked to variability in the protective immune response and have implications for vaccination strategies, or could be used to optimally select patients for novel therapeutic treatments and trials. However, as it can take many years for genomic discoveries to directly benefit patients,10 in parallel we need to prepare our health systems with infrastructure to rapidly integrate high quality, clinically relevant COVID-19 host genomic findings into the care of individuals with SARS-CoV-2 infection.


The COVID-19 pandemic currently threatens to overwhelm health-care systems and undermine economies. There is no proven therapeutic and no vaccine for the novel coronavirus causing this pandemic. In this moment, we emphasize the sentiments voiced by the COVID-19 Host Genetics Initiative, namely that “[i]nsights into how to better understand and treat COVID-19 are desperately needed. Given the importance and urgency in obtaining these insights, it is critical for the scientific community to come together around this shared purpose.” 1

As the community works together to develop a COVID-19 host genomics research engine, we are poised for novel discovery and advances in genomic medicine. A model to understand human genomic variants linked to COVID-19 outcomes can be conceived as a continuum from ultrarare to common. We offer Supplemental Table 2 as a way to think about findings that can be expected from this research.19,20 It is imperative that the research community prioritize high-quality and reproducible findings, even under the pressure for expediency, and be mindful of ethical, legal, or social issues that could emerge related to the COVID-19 impact among different groups within society.


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COVID long-haulers: Study shows who is most at risk, impact on local communities

Authors:  Hiroshima University Medical Express Posted June 9. 2022

A Japanese research team looking at COVID-19’s lingering impacts on survivors and local communities found that having a mild case of COVID-19, smoking status, comorbidities, or your sex aren’t significant predictors to tell if you are less likely to develop long-term symptoms, but age is.

“The prevalence of sequelae did not significantly differ by sex, severity of COVID-19, place of medical care, smoking status, or comorbidities,” the research team, led by Hiroshima University Professor and Executive Vice President Junko Tanaka, said in their findings published in Scientific Reports.

The cross-sectional study explored four areas to investigate what recovery and community life are like for COVID-19 survivors. These areas are the persistence of symptoms, psychological distress, impairments in work performance, and experiences of stigma and discrimination. Some 127 patients who recovered from COVID-19 at two hospitals in Hiroshima Prefecture, Japan participated in the study between August 2020 to March 2021.

Although they found that smoking history and comorbidities were not significantly related to the frequency of long-term symptoms in the multivariate analysis, the researchers believe that these factors should be continued to be examined in the future since only 18 were smokers among the study participants. As for comorbidities, hypertension was reported only in 19 of the participants and diabetes in 13.

COVID-19 severity is not a risk factor

Persistent symptoms of COVID-19 were identified in over half of the participants at a median of 29 days after onset. Meanwhile, half of those with mild cases experienced lingering symptoms.

“The most important finding is that the percentage of patients with some sequelae after approximately one month from the onset of COVID-19 was as high as 52%, and even among those with mild disease, the rate was as high as 49.5%,” study first-author Aya Sugiyama, assistant professor at Hiroshima University’s Graduate School of Biomedical and Health Sciences, said.

Their findings are consistent with previous studies reporting that 53% to 55% of non-hospitalized COVID-19 patients get lingering symptoms.

“Several reports have pointed out that COVID-19 severity is not associated with sequelae. These findings suggest that COVID-19 patients should be followed up for persistent symptoms regardless of the severity of COVID-19,” the researchers said.

Older age is a factor

The prevalence of lingering symptoms varied by age group in the study, but the researchers found that older patients are significantly more likely to become long-haulers compared to those aged 40 and below. This is consistent with previous studies showing that long-haul symptoms were more likely with increasing age.

They also discovered age-dependent differences in the prevalence of symptoms. Patients aged 60 and above were more likely than other age groups to report fatigue, palpitations, dry eyes or mouth, dyspnea, and sputum production.

The researchers noted how long-haul symptoms are common in organs with high ACE2 expression. ACE2, the major cell entry receptor for SARS-CoV-2, is extensively expressed in numerous human organs such as the mouth, liver, and lungs.

“COVID-19 affects various tissues and organs, such as those in the respiratory, cardiovascular, and neurological systems,” they stated in the paper.

Common symptoms reported by long-haulers in the study included disorders in their sense of smell (15%) and taste (14.2%), cough (14.2%), and fatigue (11%).

Recovery and community life

Their findings also found that sex was not a risk factor for long-haul COVID symptoms, a contrast to another study in the BMJ that pointed out how they are twice as common in females as in males.

Sex and the presence of long-haul symptoms, however, were found to be predictors of psychological distress. Some 45% of females and 17.9% of males scored ≥ 5 on the Kessler Psychological Distress Scale (K6), meaning the risk of psychological distress was higher in women than men.

Stigma and discrimination due to COVID-19 were reported by 43.3% of participants. The most common complaints were being treated as contagious despite being cured (61.8%), harmful rumors (29.1%), and verbal harassment (25.5%).

Meanwhile, 29.1% of study participants had possible impairments in their job performance, suggesting that post–COVID-19 conditions may influence productivity at work to only a limited extent.

The researchers noted how their findings revealed significant health impacts of long-haul COVID symptoms in local communities. They hope to conduct a large-scale and long-term study.

“We would like to elucidate how long the aftereffects last and whether the actual aftereffects differ by viral variant,” Sugiyama said.

These symptoms and risk factors may predict whether you could become a ‘COVID-19 long hauler,’ study suggests

Authors: Adrianna Rodriguez USA TODAY March 11, 2021

A new study suggests coronavirus symptoms felt in the first week of infection may be a predictor of how long they will last.

Patients with COVID-19 who felt more than five symptoms in their first week of illness were more likely to become a “COVID-19 long hauler,” which researchers qualified as having symptoms for longer than 28 days, according to the study published Wednesday in the peer-reviewed journal Nature Medicine.

The five symptoms experienced during the first week that were most predictive of becoming a long hauler were fatigue, headache, hoarse voice, muscle pain and difficulty breathing.

Researchers from King’s College London, Massachusetts General Hospital and Boston Children’s Hospital asked COVID-19 patients from the U.K., U.S. and Sweden to report their symptoms through a smartphone application from March to September 2020.

Out of more than 4,000 participants, about 13% of patients reported symptoms lasting more than 28 days, 4% for more than 8 weeks and 2% more than 12 weeks.

Out of the patients who reported symptoms for more than four weeks, “a third of those will have symptoms at 8 weeks and then a third of those at 12 weeks,” said study co-author Dr. Christina Astley, a physician scientist at Boston Children’s Hospital. “If you think about it, 1 in 20 people who have COVID-19 will have symptoms lasting 8 weeks or more.” 

The likelihood of having persistent symptoms was significantly associated with increasing age, rising from 9.9% of individuals 18 to 49, to 21.9% in those above 70. Anosmia, or the loss of smell, was the most common symptom in older age groups.

Women also were more likely to have long COVID-19 than men, with 14.9% of female study participants reporting symptoms 28 days after initial infection, compared with 9.5% of men.

While the study attempted to identify risk factors and markers that may indicate long COVID-19, doctors are finding it can happen to anyone at any age, said Dr. Michael Wechsler, a pulmonologist at National Jewish Health.

“It can happen in any age group, but it’s most alarming to younger people who are otherwise healthy and not used to these symptoms,” he said.

COVID long haulers:Dr. Anthony Fauci aims to answer ‘a lot of important questions’ in new nationwide initiative

The study found two main patterns among study participants. One group of COVID-19 long haulers exclusively reported fatigue, headache and upper respiratory issues, such as shortness of breath, sore throat, cough and loss of smell. However, a second group of long haulers had persistent multi-system complaints, such as a fever or gastrointestinal symptoms.

Weschler sees a wide array of symptoms in the clinic that caters to COVID-19 long haulers at National Jewish Health. Similar clinics have popped up in hospitals across the country to accommodate the growing number of COVID-19 patients who report symptoms months after recovery.

“Long COVID is common. It affects a large proportion of patients and has a wide distribution of symptoms,” he said. “It’s important to make people aware that all these different side effects and symptoms can occur.”

The study comes a few weeks after Dr. Anthony Fauci announced the U.S. government was launching nationwide initiative to study long COVID-19, which he called Post Acute Sequelae of SARS-CoV-2 (PASC).

A study published in JAMA Network Open on Feb. 19 found that about 30% of COVID-19 patients reported persistent symptoms as long as nine months after illness.

“(There are) a lot of important questions that are now unanswered that we hope with this series of initiatives we will ultimately answer,” he said during a White House briefing Feb. 24.

Link between fever, diarrhea, severe COVID-19, and persistent anti-SARS-CoV-2 antibodies

Authors: By Dr. Liji Thomas, MD Jan 7 2021

Ever since the coronavirus disease 2019 (COVID-19) pandemic began, there have been many attempts to understand the nature and duration of immunity against the causative agent, the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2).

A new preprint research paper appearing on the medRxiv* server describes a link between the persistence of neutralizing antibodies against the virus, disease severity, and specific COVID-19 symptoms.

Permanent immunity is essential if the pandemic is to end. In the earlier SARS epidemic, antibodies were found to last for three or more years after infection in most patients. With the current virus, it may last for six or more months at least, as appears from some reports. Other researchers have concluded that immunity wanes rapidly over the same period, with some patients who were tested positive for antibodies becoming seronegative later on. This discrepancy may be traceable to variation in testing methods, sample sizes and testing time points, as well as disease severity.

Study details

The current study looked at a population of over a hundred convalescent COVID-19 patients, testing most of them for antibodies at five weeks and three months from symptom resolution.

The researchers used a multiplex assay that measured the Immunoglobulin G (IgG) levels against four SARS-CoV-2 antigens, one from SARS-CoV, and four from circulating seasonal coronaviruses. In addition, they carried out an inhibition assay against SARS-CoV-2 spike receptor-binding domain (RBD)-angiotensin-converting enzyme 2 (ACE2) binding and a neutralization assay against the virus. The antibody titers were then plotted against various clinical features and demographic factors.

Antibody titers higher in COVID-19 convalescents

The researchers found that severe disease is correlated with advanced age and the male sex. Patients with underlying vascular disease were more likely to be hospitalized with COVID-19, but those with asthma were relatively spared.

Convalescent COVID-19 patients had higher IgG levels against all four SARS-CoV-2 antigens, relative to controls, and in 98% of cases, at least one of the tests was likely to show higher binding compared to controls. IgGs targeting the viral spike and RBD were likely to be much more discriminatory between SARS-CoV-2 patients and controls. Interestingly, anti-SARS-CoV IgG, as well as anti-seasonal betacoronavirus antibodies, were likely to be higher in these patients.

Anti-spike and anti-nucleocapsid IgG levels, as well as neutralizing antibody titers, were higher in convalescent hospitalized COVID-19 patients than in convalescent non-hospitalized patients, and the titers were positively associated with disease severity.Antibodies against SARS-CoV-2 persist three months after COVID-19 symptom resolution. Sera from COVID-19 convalescent subjects (n=79) collected 5 weeks (w) and 3 months (m) after symptom resolution were subjected to multiplex assay to detect IgG that binds to SARS-CoV-2 S, NTD, RBD and N antigens (A), to RBD-ACE2 binding inhibition assay (B), and to SARS-CoV-2 neutralization assay (C). Dots, lines, and asterisks in red represent non-hospitalized (n=67) and in blue represent hospitalized (n=12) subjects with lines connecting the two time points for individual subjects (*p<0.05 and **p<0.01 by paired t test).Antibodies against SARS-CoV-2 persist three months after COVID-19 symptom resolution. Sera from COVID-19 convalescent subjects (n=79) collected 5 weeks (w) and 3 months (m) after symptom resolution were subjected to multiplex assay to detect IgG that binds to SARS-CoV-2 S, NTD, RBD and N antigens (A), to RBD-ACE2 binding inhibition assay (B), and to SARS-CoV-2 neutralization assay (C). Dots, lines, and asterisks in red represent non-hospitalized (n=67) and in blue represent hospitalized (n=12) subjects with lines connecting the two time points for individual subjects (*p<0.05 and **p<0.01 by paired t test).

Clinical correlates of higher antibody titer

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When antibody titers in non-hospitalized subjects were compared with clinical and demographic variables, they found that older males with a higher body mass index (BMI) and a Charlson Comorbidity Index score >2 were likely to have higher antibody titers. COVID-19 symptoms that correlated with higher antibody levels in these patients comprise fever, diarrhea, abdominal pain and loss of appetite. Chest tightening, headache and sore throat were associated with less severe symptoms.

The link between the specific symptoms listed above with higher antibody titers could indicate that they mark a robust systemic inflammatory response, which in turn is necessary for a strong antibody response. Diarrhea may mark severe disease, but it is strange that in this case, it was not more frequent in the hospitalized cohort. Alternatively, diarrhea may have strengthened the immune antibody response via the exposure of the virus to more immune cells via the damaged enteric mucosa. More study is required to clarify this finding.

Potential substitute for neutralizing assay

The binding assay showed that the convalescent serum at five weeks inhibited RBD-ACE2 binding much more powerfully than control serum. Neutralizing activity was also higher in these sera, but in 15% of cases, convalescent patients showed comparable neutralizing antibody titers to those in control sera. On the whole, however, there was a positive association between neutralizing antibody titer, anti-SARS-CoV-2 IgG titers, and inhibition of ACE2 binding.

Persistent immunity at three months

This study also shows that SARS-CoV-2 antibodies persist in these patients at even three months after symptoms subside, with persistent IgG titers against the SARS-CoV-2 spike, RBD, nucleocapsid and N-terminal domain antigens. Binding and neutralization assays remained highly inhibitory throughout this period. The same was true of antibodies against the other coronaviruses tested as well, an effect that has been seen with other viruses and could be the result of cross-reactive anti-SARS-CoV-2 antibodies. Alternatively, it could be due to the activation of memory B cells formed in response to infection by the seasonal beta-coronaviruses.


IgG titers, particularly against S and RBD, and RBD-ACE2 binding inhibition better differentiate between COVID-19 convalescent and naive individuals than the neutralizing assay,” the researchers concluded.

These could be combined into a single diagnostic test, they suggest, with extreme sensitivity and specificity. The correlation with neutralizing antibody titers could indicate that the neutralizing assay, which is more expensive, sophisticated and expensive, as well as more dangerous for the investigators, could be replaced by the other antibody tests without loss of value.

In short, the study shows that specific antibodies persist for three months at least following recovery; antibody titers correlate with COVID-19-related fever, loss of appetite, abdominal pain and diarrhea; and are also higher in older males with more severe disease, a higher BMI and CCI above 2. Further research would help understand the lowest protective titer that prevents reinfection, and the duration of immunity.

*Important Notice

medRxiv publishes preliminary scientific reports that are not peer-reviewed and, therefore, should not be regarded as conclusive, guide clinical practice/health-related behavior, or treated as established information.Journal reference:

Clinical determinants of the severity of COVID-19: A systematic review and meta-analysis




We aimed to systematically identify the possible risk factors responsible for severe cases.


We searched PubMed, Embase, Web of science and Cochrane Library for epidemiological studies of confirmed COVID-19, which include information about clinical characteristics and severity of patients’ disease. We analyzed the potential associations between clinical characteristics and severe cases.


We identified a total of 41 eligible studies including 21060 patients with COVID-19. Severe cases were potentially associated with advanced age (Standard Mean Difference (SMD) = 1.73, 95% CI: 1.34–2.12), male gender (Odds Ratio (OR) = 1.51, 95% CI:1.33–1.71), obesity (OR = 1.89, 95% CI: 1.44–2.46), history of smoking (OR = 1.40, 95% CI:1.06–1.85), hypertension (OR = 2.42, 95% CI: 2.03–2.88), diabetes (OR = 2.40, 95% CI: 1.98–2.91), coronary heart disease (OR: 2.87, 95% CI: 2.22–3.71), chronic kidney disease (CKD) (OR = 2.97, 95% CI: 1.63–5.41), cerebrovascular disease (OR = 2.47, 95% CI: 1.54–3.97), chronic obstructive pulmonary disease (COPD) (OR = 2.88, 95% CI: 1.89–4.38), malignancy (OR = 2.60, 95% CI: 2.00–3.40), and chronic liver disease (OR = 1.51, 95% CI: 1.06–2.17). Acute respiratory distress syndrome (ARDS) (OR = 39.59, 95% CI: 19.99–78.41), shock (OR = 21.50, 95% CI: 10.49–44.06) and acute kidney injury (AKI) (OR = 8.84, 95% CI: 4.34–18.00) were most likely to prevent recovery. In summary, patients with severe conditions had a higher rate of comorbidities and complications than patients with non-severe conditions.


Patients who were male, with advanced age, obesity, a history of smoking, hypertension, diabetes, malignancy, coronary heart disease, hypertension, chronic liver disease, COPD, or CKD are more likely to develop severe COVID-19 symptoms. ARDS, shock and AKI were thought to be the main hinderances to recovery.

For More Information: https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0250602

Neurologic Manifestations Associations of COVID-19

High-quality epidemiologic data is still urgently needed to better understand neurologic effects of COVID-19.

Authors: Shraddha Mainali, MD; and Marin Darsie, MD VIEW/PRINT PDF

Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection continues to prevail as a deadly pandemic and unparalleled global crisis. More than 74 million people have been infected globally, and over 1.6 million have died as of mid-December 2020. The virus transmits mainly through close contacts and respiratory droplets.1 Although the mean incubation period is 3 to 9 days (range, 0-24 days), transmission may occur prior to symptom onset, and about 18% of cases remain asymptomatic.2 The highest rates of coronavirus disease 2019 (COVID-19) in the US have been reported in adults age 18 to 29 and 50 to 64 years, representing 23.8% and 20.5% of cases, respectively.3 Although adults age 65 and older make up only 14.6% of total cases in the US, they account for the vast majority of deaths (79.9%).3 Similarly, men appear to be more vulnerable to the disease, accounting for 69% of intensive care unit (ICU) admissions and 58% of deaths despite nearly equal disease prevalence between men and women.4 In terms of ethnicity, Black Americans account for 15.6% of COVID-19 infections and 19.7% of related deaths, whereas Hispanic/Latinx Americans account for 26.3% of COVID-19 infections and 15.7% of COVID-19 deaths, despite these groups comprising 13.4% and 16.7% of the US population, respectively.3,5

The most commonly reported symptoms are fever, dry cough, fatigue, dyspnea, and anorexia.2 Numerous studies have also reported a spectrum of neurologic dysfunctions, including mild symptoms (eg, headache, anosmia, and dysgeusia) to severe complications (eg, stroke and encephalitis). Despite the prolific reports of neurologic associations and complications of COVID-19 in the face of a raging pandemic with limited resources, there is a significant lack of control for important confounders including the severity of systemic disease, exacerbation or recrudescence of preexisting neurologic disease, iatrogenic complications, and hospital-acquired conditions. Moreover, given the ubiquity of the virus, it is challenging to parse COVID-19–related complications from coexisting conditions. There is an urgent need for high-quality epidemiologic data reflecting COVID-19 prevalence by age, sex, race, and ethnicity on a local, state, national, and international level.

Neurologic and Neuropsychiatric Manifestations of COVID-19

Prevalence estimates of acute neurologic dysfunctions caused by COVID-19 are widely variable, with reports ranging from 3.5% to 36.4%.6 A recent study from Chicago showed that in those with COVID-19 who develop neurologic complications, 42% had neurologic complaints at disease onset, 63% had them during hospitalization, and 82% experienced them during the course of illness.7 Considering the widespread nature of the pandemic, with millions infected globally, neurologic complications of COVID-19 could lead to a significant increase in morbidity, mortality, and economic burden.

People over age 50 with comorbidities (eg, hypertension, diabetes, and cardiovascular disease) are prone to neurologic complications.2,8 Common nonspecific symptoms include headache, fatigue, malaise, myalgia, nausea, vomiting, confusion, anorexia, and dizziness. COVID-19 is known characteristically to affect taste (dysgeusia) and smell (anosmia) in the absence of coryza with variable prevalence estimates ranging from 5% to 85%.9 Since the first report on hospitalized individuals in Wuhan, China, numerous other reports have indicated a spectrum of mild-to-severe neurologic complications, including cerebrovascular events, seizures, demyelinating disease, and encephalitis.8,10-13 As a result of fragmented data from across the world with diverse neurologic manifestations and multiple potential mechanisms of injury, the classification of neurologic dysfunctions in COVID-19 is complex and varies across the literature. Here we present 2 pragmatic classification approaches based on 1) type and site of neurologic manifestations disease categories.

For More Information: https://practicalneurology.com/articles/2021-jan/neurologic-manifestations-associations-of-covid-19

Lack of antibodies against seasonal coronavirus OC43 nucleocapsid protein identifies patients at risk of critical COVID-19

Authors: MartinDugasa1TanjaGrote-Westrickb1UtaMerledMichaelaFontenaylmAndreas E.KremerhFrankHansesijRichardVollenbergcEvaLorentzenbShilpaTiwari-BecklerdJérômeDucheminlSyrineEllouzelMarcelVetterhJuliaFürsthPhilippSchusterkTobiasBrixaClaudia M.DenkingerfgCarstenMüller-TidoweHartmutSchmidtcJoachimKühnb1


Does prior infection with seasonal human coronavirus OC43 protect against critical COVID-19?•

Findings: In an international multi-center study inpatients without anti-HCoV OC43 NP antibodies had an increased risk of critical disease.•

Meaning: Prior infections with seasonal HCoV OC43 have a protective effect against critical COVID-19.



The vast majority of COVID-19 patients experience a mild disease. However, a minority suffers from critical disease with substantial morbidity and mortality.


To identify individuals at risk of critical COVID-19, the relevance of a seroreactivity against seasonal human coronaviruses was analyzed.


We conducted a multi-center non-interventional study comprising 296 patients with confirmed SARS-CoV-2 infections from four tertiary care referral centers in Germany and France. The ICU group comprised more males, whereas the outpatient group contained a higher percentage of females. For each patient, the serum or plasma sample obtained closest after symptom onset was examined by immunoblot regarding IgG antibodies against the nucleocapsid protein (NP) of HCoV 229E, NL63, OC43 and HKU1.


Median age was 60 years (range 18-96). Patients with critical disease (n=106) had significantly lower levels of anti-HCoV OC43 nucleocapsid protein (NP)-specific antibodies compared to other COVID-19 inpatients (p=0.007). In multivariate analysis (adjusted for age, sex and BMI), OC43 negative inpatients had an increased risk of critical disease (adjusted odds ratio (AOR) 2.68 [95% CI 1.09 – 7.05]), higher than the risk by increased age or BMI, and lower than the risk by male sex. A risk stratification based on sex and OC43 serostatus was derived from this analysis.


Our results suggest that prior infections with seasonal human coronaviruses can protect against a severe course of COVID-19. Therefore, anti-OC43 antibodies should be measured for COVID-19 inpatients and considered as part of the risk assessment for each patient. Hence, we expect individuals tested negative for anti-OC43 antibodies to particularly benefit from vaccination against SARS-CoV-2, especially with other risk factors prevailing.

For More Information: https://www.sciencedirect.com/science/article/pii/S1386653221001141

Lack of antibodies against seasonal coronavirus OC43 nucleocapsid protein identifies patients at risk of critical COVID-19

Authors: Martin Dugas 1Tanja Grote-Westrick 2Uta Merle 3Michaela Fontenay 4Andreas E Kremer 5Frank Hanses 6Richard Vollenberg 7Eva Lorentzen 8Shilpa Tiwari-Heckler 9Jérôme Duchemin 10Syrine Ellouze 11Marcel Vetter 12Julia Fürst 13Philipp Schuster 14Tobias Brix 15Claudia M Denkinger 16Carsten Müller-Tidow 17Hartmut Schmidt 18Phil-Robin Tepasse 19Joachim Kühn 20


Background: The vast majority of COVID-19 patients experience a mild disease. However, a minority suffers from critical disease with substantial morbidity and mortality.

Objectives: To identify individuals at risk of critical COVID-19, the relevance of a seroreactivity against seasonal human coronaviruses was analyzed.

Methods: We conducted a multi-center non-interventional study comprising 296 patients with confirmed SARS-CoV-2 infections from four tertiary care referral centers in Germany and France. The ICU group comprised more males, whereas the outpatient group contained a higher percentage of females. For each patient, the serum or plasma sample obtained closest after symptom onset was examined by immunoblot regarding IgG antibodies against the nucleocapsid protein (NP) of HCoV 229E, NL63, OC43 and HKU1.

Results: Median age was 60 years (range 18-96). Patients with critical disease (n=106) had significantly lower levels of anti-HCoV OC43 nucleocapsid protein (NP)-specific antibodies compared to other COVID-19 inpatients (p=0.007). In multivariate analysis (adjusted for age, sex and BMI), OC43 negative inpatients had an increased risk of critical disease (adjusted odds ratio (AOR) 2.68 [95% CI 1.09 – 7.05]), higher than the risk by increased age or BMI, and lower than the risk by male sex. A risk stratification based on sex and OC43 serostatus was derived from this analysis.

Conclusions: Our results suggest that prior infections with seasonal human coronaviruses can protect against a severe course of COVID-19. Therefore, anti-OC43 antibodies should be measured for COVID-19 inpatients and considered as part of the risk assessment for each patient. Hence, we expect individuals tested negative for anti-OC43 antibodies to particularly benefit from vaccination against SARS-CoV-2, especially with other risk factors prevailing.

For More Information: https://pubmed.ncbi.nlm.nih.gov/33965698/