Article

https://doi.org/10.1038/s41467-024-50656-8

Long-term risk of autoimmune diseases aftermRNA-based SARS-CoV2 vaccination in a
Korean, nationwide, population-based
cohort study
Received: 11 April 2024

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Accepted: 10 July 2024

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Seung-Won Jung 1,2, Jae Joon Jeon
Sung Jay Choe 1 & Solam Lee 1

1,2, You Hyun Kim

1,

The long-term association between mRNA-based coronavirus disease 2019
(COVID-19) vaccination and the development of autoimmune connective tissue diseases (AI-CTDs) remains unclear. In this nationwide, population-based
cohort study involving 9,258,803 individuals, we aim to determine whether theincidence of AI-CTDs is associated with mRNA vaccination. The study spansover 1 year of observation and further analyses the risk of AI-CTDs by stratifying demographics and vaccination proﬁles and treating booster vaccination
as time-varying covariate. We report that the risk of developing most AI-CTDs
did not increase following mRNA vaccination, except for systemic lupus erythematosus with a 1.16-fold risk in vaccinated individuals relative to controls.
Comparable results were reported in the stratiﬁed analyses for age, sex, mRNA
vaccine type, and prior history of non-mRNA vaccination. However, a booster
vaccination was associated with an increased risk of some AI-CTDs including
alopecia areata, psoriasis, and rheumatoid arthritis. Overall, we conclude that
mRNA-based vaccinations are not associated with an increased risk of most AICTDs, although further research is needed regarding its potential association
with certain conditions.

Infection with severe acute respiratory syndrome coronavirus 2 (SARSCoV-2) results in the development of coronavirus disease 2019
(COVID-19), and it has spread globally since 2020. COVID-19 has
emerged as a notable pandemic, causing a substantial burden on
public health, as > 50% of the world’s population has been infectedwith it, according to a 2022 global seroprevalence survey1,2.
Shortly after the COVID-19 outbreak, vaccines emerged as a crucial intervention to address the pandemic. These vaccines weredeveloped primarily by two main technological platforms replicating
incompetent adenoviral vectors and mRNA3. In particular, though theeffectiveness of vaccine wanes over time and as virus variants such as
Omicron, mRNA-based COVID-19 vaccines show generally signiﬁcant

efﬁcacy, preventing 46–92% of SARS-CoV-2 infections, 74–87% of
hospitalisations, and 62–92% of severe illnesses, as deﬁned by the
National Institutes of Health criteria4. Moreover, recent clinicalresearch or systematic reviews concerning mRNA COVID-19 vaccines
have conﬁrmed their generally favourable safety proﬁles5–7.
Although the COVID-19 vaccine has played a crucial role in combatting the pandemic, the mRNA COVID-19 vaccine is reportedly also
associated with adverse events, notably cardiac complications such as
myo- and pericarditis8–10. In particular, the potential association
between the mRNA COVID-19 vaccine and autoimmune connective
tissue diseases (AI-CTDs) has been actively studied. A few systemic
autoimmune diseases, such as autoimmune hepatitis and1

Department of Dermatology, Yonsei University Wonju College of Medicine, Wonju, Republic of Korea. 2These authors contributed equally: Seung-Won Jung,e-mail: wow8561@yonsei.ac.kr; solam@yonsei.ac.kr
Jae Joon Jeon.

Nature Communications | (2024)15:6181

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Articlehttps://doi.org/10.1038/s41467-024-50656-8

nephropathies, have been identiﬁed as being potentially associatedwith mRNA vaccines11,12. While previous studies have suggested an
association between mRNA vaccines and several systemic autoimmune diseases, there are limited studies demonstrating the development of AI-CTDs following mRNA vaccination in large populations
over a period of > 1 year, despite the low incidence and slow development of AI-CTDs. These uncertainties and adverse effects of mRNA
vaccines have heightened public scepticism regarding vaccination andnecessitated a risk-beneﬁt analysis of vaccination.
In this study, we aim to determine whether the incidence of AICTDs is associated with mRNA vaccination against SARS-CoV2.

Results
Study population
The primary cohort was established by combining the National Health
Insurance Service (NHIS) and Korea Disease Control and Prevention
Agency (KDCA) databases, which comprised the healthcare data of> 99% of the entire Korean population and their COVID-19 diagnosis

and vaccination proﬁles. In total, 9,258,803 individuals who hadreceived at least one dose of the mRNA-based COVID-19 vaccine were
included (Fig. 1). As COVID-19 vaccines have been administerednationwide in South Korea, the proportion of unvaccinated individuals
was minimal13. Therefore, the use of unvaccinated individuals ascomparators could have resulted in improper cohort selection andpotential selection bias. We consequently established a historicalcontrol cohort within mRNA-vaccinated individuals, but the observational period was shifted back 2 years from the date of the ﬁrst dose ofmRNA vaccination of the historical control cohort. In total, 4,445,333
and 4,444,932 patients were included in the vaccination and historicalcontrol cohorts, respectively, and all were observed for ≥ 1 year. The
baseline demographic and general health characteristics of eachcohort are summarised in Table 1. The covariates were well-balanced
after the inverse probability of treatment weighting (IPTW). The
COVID-19 vaccination proﬁles, such as the type of mRNA vaccine or
history of non-mRNA vaccination, are summarised in Supplementary
Table 1. The mean follow-up times for the vaccination and historical

The Korea Disease Control and Prevention AgencyCOVID-19-National Health Insurance Service (K-COV-N) cohort

15,076,899

Individuals who underwent general health examination in 2018

9,945,450

Randomly extracted participants

9,864,894

No missing data on general health examination

9,258,803

Individuals vaccinated with at least one dose ofmRNA-based COVID-19 vaccine until Dec 31, 2022
(either BNT162b2 or mRNA-1273)

50%
4,629,401

Vaccination cohort

50%
4,629,402

Historical control cohort

The study index assigned at
the date of the first dose ofmRNA vaccination

The study index assigned at thedate of the first dose of mRNA
vaccination minus 2 years

Observation up to Dec 31, 2022

Observation up to Dec 31, 2020

4,445,333

At least 365 days ofobservational period

Vaccination cohort
Fig. 1 | Flowchart of study population selection. This nationwide populationbased cohort study combined data from the Korea Disease Control and Prevention
Agency (KDCA) and the COVID-19 National Health Insurance Service (NHIS) cohort(K-COV-N cohort). The study included approximately 20% of the total South Korean
population. This primary cohort comprised all individuals vaccinated with at least
one dose of the mRNA-based COVID-19 vaccine (BNT162b2, Pﬁzer-BioNTech;mRNA-1273, Moderna) until 31 December 2022. Subsequently, half of the primarycohort was extracted to establish the vaccination cohort, and the study index was

Nature Communications | (2024)15:6181

4,444,932

At least 365 days ofobservational period

Historical control cohort
deﬁned as the date of the ﬁrst dose of the mRNA-based COVID-19 vaccine. A historical control cohort was established by extracting the other half of the primarycohort as the control, and its study index was assigned as the date of the ﬁrst doseof the mRNA-based COVID-19 vaccine minus 2 years. A total of 4,445,333 vaccination and 4,444,932 control cohorts were selected and observed until 31 December
2022 and 31 December 2020, respectively. Abbreviations: COVID-19, Coronavirus
2019 disease.

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Table 1 | Demographic and general health characteristics of the COVID-19 vaccination cohort and the historical control cohort
before and after inverse probability treatment weighting
Preweighting, patients, No. (%)
Characteristics

COVID-19 vaccination(N = 4,445,333)

Postweighting, weighted (%)

Historical control(N = 4,444,932)

SMD
0.141

COVID-19 vaccination(N = 4,445,333)

Historical control(N = 4,444,932)

SMD

Age, mean, ± SD, y53.42 ± 14.19

51.42 ± 14.19

52.42 (20.03)

52.41 (20.11)

0.001< 40, n (%)

1,773,871 (39.91)

2,010,298 (45.22)(42.86)(42.26)–40-59, n (%)

2,021,790 (45.48)

1,908,587 (42.94)(43.96)(44.56)> 60, n (%)

649,672 (14.61)

526,047 (11.84)

Sex, n (%)––(13.18)(13.18)

–

–

Male

2,384,742 (53.65)

0.000

2,383,958 (53.63)(53.63)(53.63)

Female–2,060,591 (46.35)

2,060,974 (46.37)(46.37)(46.37)

Insurance type, n (%)

–

–

–

–

0.000

Standard

4,369,010 (98.28)

4,367,357 (98.25)(98.27)(98.27)–

Medicaid

76,323 (1.72)

77,575 (1.75)(1.73)(1.73)

Income level quartile,n (%)a

–

–

0.000

0.002

0.001

–

0.000
–

Highest705,154 (15.84)

706,185 (15.89)(15.53)(16.19)Higher

1,078,272 (24.26)

1,079,213 (24.28)(24.57)(23.99)Lower

1,262,642 (28.40)

1,259,636 (28.34)(28.79)(27.96)Lowest1,400,265 (31.50)

1,399,903 (31.49)(31.10)(31.85)

Area of residence, n (%)

–

–

–

–

0.000

Urban area

1,932,306 (43.47)

1,933,980 (43.51)(43.49)(43.49)–Rural area

2,513,027 (56.53)

2,510,952 (56.49)(56.51)(56.51)Hypertension

1,093,732 (24.61)

1,283,097 (28.86)

0.096(26.70)(26.68)0.000

Diabetes mellitus

700,779 (15.76)

542,836 (12.21)

0.103(13.97)(13.96)

0.000

Dyslipidemia

1,806,828 (40.65)

1,407,281 (31.66)

0.189(36.15)(36.14)0.000

Atopic dermatitis

38,558 (0.87)

27,588 (0.62)

0.029(0.74)(0.75)

0.000
0.000

0.001

Underlying disease, n (%)

Allergic rhinitis

201,939 (4.54)

176,682 (3.97)

0.028(4.26)(4.26)

Asthma77,511 (1.74)

68,092 (1.53)

0.017(1.64)(1.64)

0.000

Hypothyroidism

185,241 (4.17)

136,800 (3.08)

0.058(3.62)(3.62)

0.000

Hyperthyroidism

66,441 (1.49)

49,313 (1.11)

0.034(1.30)(1.30)

0.000

Hashimoto thyroiditis

29,350 (0.66)

21,710 (0.49)

0.023(0.58)(0.58)

0.000

Vitamin D deﬁciency

178,225 (4.01)

94,993 (2.14)

0.109(3.07)(3.07)

0.000

Hepatitis B

96,388 (2.17)

81,790 (1.84)

0.023(2.00)(2.00)

0.000

Hepatitis C

12,079 (0.27)

10,298 (0.23)

0.008(0.25)(0.25)

0.000

HIV infection

163 (0.00)

143 (0.00)

0.001(0.00)(0.00)

0.000

General health examination data, n (%)
Current smoker

851,737 (19.16)

852,265 (19.17)

0.000(19.16)(19.16)0.000

Drinkingc

2,840,913 (63.91)

2,839,130 (63.87)

0.001(63.86)(63.87)0.000

a

The income level was divided into quartiles based on health insurance premiums.
Body mass index was calculated as weight in kilograms divided by height in metres squared.
c
Drinking was deﬁned as routine alcohol consumption, regardless of the amount or frequency.
Abbreviations: COVID-19, Coronavirus 2019 disease; HIV, human immunodeﬁciency virus; SMD, absolute standardised mean difference.
b

control cohorts were 471.24 ± 66.16 days and 471.28 ± 66.15 days,
respectively.

Autoimmune connective tissue diseases following mRNA
vaccination
Cumulative incidence plots for the AI-CTDs are shown in Fig. 2, with
Supplementary Fig. 1 providing additional details, including cumulative incidence for positive and negative control outcomes, as well as
the cumulative number of events for each time point. The risks ofdeveloping incident AI-CTDs in the vaccination and historical control
cohorts are shown in Fig. 3. To mitigate the risk of type I error inducedby multiple comparisons, we employed a Bonferroni correction for 27
predeﬁned outcomes and used an adjusted 99.81% conﬁdence interval(99% CI) to determine statistical signiﬁcance. Individuals who had the

Nature Communications | (2024)15:6181

mRNA COVID-19 vaccine did not incur higher risks of developing most
AI-CTDs such as alopecia areata (adjusted hazard ratio [aHR], 1.00; 99%
CI, 0.96–1.04), alopecia totalis (aHR, 0.79; 99% CI, 0.68–0.93), psoriasis (aHR, 0.80; 99% CI, 0.77–0.84), vitiligo (aHR, 0.95; 99% CI,
0.88–1.02), anti-neutrophil cytoplasmic antibody (ANCA) associatedvasculitis (aHR, 1.09; 99% CI, 0.72–1.66), sarcoidosis (aHR, 1.06; 99% CI,
0.78–1.44), Behcet disease (aHR, 0.69; 99% CI, 0.58–0.82), Crohn’sdisease (aHR, 0.92; 99% CI, 0.77–1.09), ulcerative colitis (aHR, 0.97;
99% CI, 0.87–1.08), rheumatoid arthritis (aHR, 0.86; 99% CI,
0.84–0.89), systemic sclerosis (aHR, 1.01; 99% CI, 0.73–1.38), Sjogren’s
syndrome (aHR, 1.07; 99% CI, 0.96–1.18), ankylosing spondylitis (aHR,
0.95; 99% CI, 0.87–1.04), dermato/polymyositis (aHR, 1.02; 99% CI,
0.77–1.35), and bullous pemphigoid (BP) (aHR, 1.53; 99% CI,
0.90–2.60). However, individuals in the mRNA vaccination cohort
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Fig. 2 | Cumulative incidences of autoimmune connective tissue diseases outcomes. The cumulative incidence plot shows the cumulative incidences of autoimmune connective tissue diseases in mRNA-based COVID-19 vaccination cohortand historical control cohort. The shaded area shows a 95% conﬁdence interval forthe cumulative incidences. Additional information, including cumulative incidence
for positive and negative control outcomes, as well as the cumulative number of
events for each time point, was presented in Supplementary Fig. 1. Abbreviation:
ANCA, anti-neutrophil cytoplasmic antibody; COVID-19, coronavirus disease 2019.

were at considerably higher risk of developing systemic lupus erythematosus (SLE) (aHR, 1.16; 99% CI, 1.02–1.32) than those in the historical control cohort.

1.00–1.05), melanoma in situ (aHR, 1.21; 99% CI, 0.64–2.29), and tympanic membrane perforation (aHR, 0.84; 99% CI, 0.77–0.91)).

Stratiﬁed analysis by sex, age, and vaccination proﬁleValidation of the results using positive and negative control
outcomes
To validate these ﬁndings, we evaluated the risks of positive andnegative control outcomes associated with mRNA vaccination. For the
positive control outcomes, the risk of myocarditis (aHR, 7.20; 99% CI,
4.37–11.86), pericarditis (aHR, 2.75; 99% CI, 1.95–3.88), andGuillain–Barre syndrome (aHR, 1.62; 99% CI, 1.16–2.25) were considerably higher in the vaccination cohort than in the historical control
cohort (Fig. 3). Conversely, the risk of having negative control outcomes was not considerably higher in the vaccination cohort than in
the historical control cohort (benign skin tumour (aHR, 1.02; 99% CI,

Nature Communications | (2024)15:6181

In subgroup analyses, we compared the vaccination and historical control
cohorts stratiﬁed by sex, age (< 40 vs. ≥ 40), type of mRNA-based COVID19 vaccine (BNT162b2, Pﬁzer–BioNTech vs. mRNA-1273, Moderna), crossvaccination status with any history of non-mRNA COVID vaccination(ChAdOx1 nCoV-19 [AZD1222], Oxford–AstraZeneca or Ad26.COV2.S,
Janssen–Johnson & Johnson, or others) prior to mRNA vaccination, andany history of COVID-19 diagnosis. In general, there were no signiﬁcantdifferences between the two cohorts in the subgroup analyses for mostoutcomes (Figs. 4–5), with Supplementary Figs. 2–11 providing additional
details for each stratiﬁed analysis. However, women who had received themRNA vaccine had a signiﬁcantly higher risk of developing BP (aHR, 2.67;
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Fig. 3 | Risks of incident autoimmune skin and connective tissue disorders in
the mRNA-based COVID-19 vaccination cohort compared with the historicalcontrol cohort. To minimise the differences in baseline characteristics between
the vaccination and historical control cohorts, predeﬁned covariates, including
demographics, socioeconomic status, and comorbidities, were balanced using
inverse probability of treatment weighting. Subsequently, the incidence in thevaccination cohort compared to that in the historical control cohort was estimatedusing multivariable Cox proportional hazards analysis after adjusting for all predeﬁned covariates. The forest plot depicts adjusted hazard ratios (aHRs) inindividuals with mRNA-based COVID-19 vaccination compared with historicalcontrols, with the conﬁdence interval (CI) adjusted to 99.81% for Bonferroni correction but presented as 99% CI for simplicity. The point estimate (centre) represents the aHR, and the horizontal line (error bar) shows the range of the 99% CI. The
incidence rate was calculated as the number of events divided by 10,000 personyears, with the population at risk also presented. Abbreviations: aHR, adjusted
hazard ratio; ANCA, antineutrophil cytoplasmic antibody; CI, conﬁdence interval;
COVID-19, coronavirus disease 2019; HR, hazard ratio.

99% CI, 1.11–6.42) (Fig. 4a, b). In addition, aged ≥ 40 years who had
undergone mRNA vaccination tended to have a higher risk of developing
BP (aHR, 1.53; 99% CI, 0.90–2.61) (Fig. 4c, d). In the stratiﬁed analysis
based on the type of mRNA vaccine received, individuals who received
the BNT162b2 vaccine had a signiﬁcantly higher risk of developing SLE(aHR, 1.18; 99% CI, 1.02–1.36) (Fig. 5a, b). In addition, cross-vaccination
with non-mRNA vaccines did not independently affect the incident risk of
any AI-CTDs (Fig. 5c, d). In analyses according to the status of COVID-19
diagnosis, the incidence was not higher for almost all AI-CTDs, except SLE
in individuals with COVID-19 diagnosis (aHR, 1.23; 99% CI, 1.05–1.44)(Supplementary Figs. 6 and 7).

administration on AI-CTDs are scarce15–17. Our study investigated theeffect of mRNA vaccination on the occurrence of AI-CTDs by observing
at least 1-year period in a nationwide population-based setting comprising more than 8 million individuals. In this analysis, the incidenceof most AI-CTDs was not associated with mRNA vaccination. However,
we observed an increased risk of developing some AI-CTDs after
booster vaccination.
We previously reported no signiﬁcant difference in the risk ofdeveloping AI-CTDs between the mRNA vaccination group and thehistorical control group at a mean follow-up of 100 days18. Our results
were generally aligned with the previous study, but we found some
gaps in an increased risk of some AI-CTDs, including SLE. This may be
attributed to differences in the demographic characteristics of thestudy population and observational periods across the studies. Given
the indolent course of AI-CTDs, these results suggest that long-term
surveillance for the development of AI-CTDs after mRNA vaccination
may be warranted.
Although the association between mRNA vaccination and SLE
remains unclear, there have been cases in which SLE has developed
following mRNA vaccination19. mRNA vaccination reportedly leads toelevated plasma anti-dsDNA antibody levels, and the extracellular selfDNA inﬂuences the pathogenesis of AI-CTDs, including SLE20,21.
Another study found that booster vaccinations increase circulatingcell-free DNA in B cells, T cells, and monocytes22. In addition, theobserved risk of SLE varied according to the type of mRNA vaccine andthe history of non-mRNA vaccination. Further studies are needed toelucidate whether factors such as mRNA dose may contribute to thesedifferences23,24.

Booster vaccination
In total, 2,284,342 individuals had the booster mRNA vaccination(3rd dose of mRNA vaccination) among the vaccination cohort. In
extended Cox proportional hazard analyses treating booster vaccination as time-varying covariate, the risk of alopecia areata (aHR,
1.12; 99% CI, 1.05–1.19), psoriasis (aHR, 1.16; 99% CI, 1.06–1.27), andrheumatoid arthritis (aHR, 1.14; 99% CI, 1.08–1.21) were greater inindividuals who had booster vaccination compared to those who
had not (Fig. 6).

Discussion
BNT162b2 and mRNA-1273 were the ﬁrst mRNA vaccines approved by
the US Food and Drug Administration (FDA) for combatting COVID1914. While prior studies have suggested that non-mRNA vaccinations
or COVID-19 infections increase the risk of patients developing autoimmune diseases, data on the long-term effects of mRNA vaccine

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Fig. 4 | Stratiﬁed analyses of the risks of incident autoimmune connective
tissue disorders in the mRNA-based COVID-19 vaccination cohort comparedwith the historical control cohort according to sex and age. The forest plot
depicts adjusted hazard ratios (aHRs) and 99% conﬁdence intervals (CIs) in individuals with mRNA-based COVID-19 vaccination compared with historical controls.
The point estimate (centre) represents the aHR, and the horizontal line (error bar)shows the range of the 99% CI. The incident risks of autoimmune disorderoutcomes were stratiﬁed by sex ((a) Male or (b) Female) and age ((c) < 40 years or(d) ≥ 40 years). Additional details, including unadjusted HRs and population at risk,
were provided in Supplementary Figures.; Male subgroup (Supplementary Fig. 2),
female subgroup (Supplementary Fig. 3), subgroup aged < 40 years (Supplementary Fig. 4), and subgroup aged ≥ 40 years (Supplementary Fig. 5). Abbreviations:
aHR, adjusted hazard ratio; ANCA, antineutrophil cytoplasmic antibody; CI, conﬁdence interval; COVID-19, coronavirus disease 2019.

The association between BP and mRNA vaccination remains to beelucidated, however, the vaccinated female population shows a 2.67fold higher risk of BP development than the non-vaccinated female
population in the subgroup analysis stratiﬁed by sex. The US caseseries of subepidermal blistering eruptions (including BP) followingmRNA vaccination reported it to be more common in women and after
the age of 4025. Similarly, our study tended to a higher risk of BP inwomen and in patients over 40 years of age following mRNA vaccination than in historical controls. This result may suggest the need tomonitor BP development in females who have received mRNA-based
vaccines.
Furthermore, our study found that booster vaccination was
associated with an increased risk of developing certain AI-CTDs, suchas alopecia areata, psoriasis, and rheumatoid arthritis, albeit the effectsize was small. This ﬁnding could be associated with autoimmune ﬂareups following repeated mRNA vaccination, which can cause subclinicaldiseases to become active and diagnosed26–28. The result of our study
may indicate the necessity for additional monitoring when administering booster vaccinations. However, it should be interpreted cautiously due to the potential healthy vaccine effect. In addition, booster
vaccinations have shown substantial safety and potential beneﬁts ofimproving humoral immune response preventing COVID-19 diagnosis
or reducing disease severity29. Moreover, an additional dose of thevaccine could serve as a strategy to address the limitation of its waning
efﬁcacy over time30. Therefore, our results are not sufﬁcient to discourage booster vaccination and suggest that regular and long-termmonitoring may be necessary to ensure the early detection and management of any emerging risks associated with repeated vaccinations.
This study has several strengths. First, we used the national
medical data of ~ 10 million people and national information on

COVID-19 infection and vaccination proﬁles. Second, the risk of incident AI-CTDs was measured with a large sample size and longer
observation period, more than the mean follow-up period of 471 days,
than that used in previous studies18. Third, we designed a historicalcontrol cohort to minimise selection bias and examined the reliabilityof the analysis by evaluating positive and negative outcome controls.
Fourth, we considered several confounding factors such as sex, age,
type of mRNA vaccine, cross-vaccination, and COVID-19 diagnosis
status to consider their potential impacts and designed an analysis
treating booster vaccination as a time-varying covariate to account for
its variability during the observation.
However, this study has some limitations. First, the analysis was
conducted on individuals belonging to a single ethnic group. Since
autoimmune disease-associated single nucleotide polymorphisms
vary by ethnicity, our results may not be generalisable to otherpopulations31. Second, although our study has one of the longest
follow-up periods among mRNA vaccine studies reported to date, thisduration may still be considered too short, given that the developmentof AI-CTDs can take years to decades after trigger exposure32. Moreover, the observation period of 2 years before the index date may nothave been long enough to identify pre-existing AI-CTDs due to theirindolent onset. Therefore, some incident cases in this study could have
had their onset prior to the observation. Third, considering the global
decline in the use of healthcare services during the COVID-19 pandemic, some outcomes of interest may have been underdiagnosed
during this period33,34. Nevertheless, we investigated negative control
outcomes to address these concerns. Fourth, potential misclassiﬁedcases related to using ICD-10 code claim data could be existed in ourstudy. To mitigate this risk, we considered patients with three or more
visits under the same ICD-10 code as having AI-CTDs.

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Fig. 5 | Stratiﬁed analyses of the incidence of autoimmune connective tissue
disorders in the mRNA-based COVID-19 vaccination cohort compared with thehistorical control cohort according to the type of mRNA vaccine and the history of cross-vaccination with non-mRNA vaccination before the study index.
The forest plot depicts adjusted hazard ratios (aHRs) and 99% conﬁdence intervals(CIs) in individuals with mRNA-based COVID-19 vaccination compared with historical controls. The point estimate (centre) represents the aHR, and the horizontal
line (error bar) shows the range of the 99% CI. The incident risks of autoimmune
disorder outcomes were stratiﬁed by the type of mRNA vaccine ((a) BNT162b2 or(b) mRNA-1273) and the history of cross-vaccination ((c) Only mRNA vaccination or(d) Cross-vaccination with non-mRNA vaccination (AZD12222 or Ad26.COV2.S)).
Additional details, including unadjusted HRs and population at risk, were provided
in Supplementary Figures.; Subgroup who received the BNT162b2 vaccine (Supplementary Fig. 8), subgroup who received the mRNA-1273 vaccine (Supplementary
Fig. 9), subgroup who received only mRNA-based vaccines (Supplementary Fig. 10),and subgroup who had a history of cross-vaccination with any non-mRNA vaccines(Supplementary Fig. 11). Abbreviations: aHR, adjusted hazard ratio; ANCA, antineutrophil cytoplasmic antibody; CI, conﬁdence interval; COVID-19, coronavirus
disease 2019.

In conclusion, our study results suggest that mRNA vaccination is
generally not associated with a higher risk of most AI-CTDs. However,given that the risk of SLE and BP was increased in certain demographic
conditions such as age and sex, long-term monitoring is necessary
after mRNA vaccination for the development of AI-CTDs. Our results
can provide clinical insights into mRNA therapeutics, and further
research is needed regarding the association between mRNA-based
vaccines and AI-CTDs35.

such as the type, dose, and vaccination date. This study was approvedby the Korean National Institute for Bioethics Policy, and the researchnumber for this study was KDCA-NHIS-2023-1-500.

Methods
Data sourceThis nationwide population-based cohort study was conducted using
data from the KDCA COVID-19 NHIS (K-COV-N) cohort. The NHIS
database provided comprehensive data consisting of demographics,
insurance eligibility data (insurance type and area of residence),socioeconomic status (income level), inpatient and outpatient
healthcare records (disease diagnoses and procedures), prescriptions,and national health examination results (alcohol use and smoking) of> 99% of the total population of South Korea36. The underlying diseaseof a population was conﬁrmed when that disease was identiﬁed at ≥ 3
inpatient or outpatient visits using the corresponding International
Classiﬁcation of Diseases, Tenth Revision (ICD-10) diagnostic codes.
The corresponding ICD-10 codes for the underlying diseases are
summarised in Supplementary Table 2. Medical records that canidentify past medical history before the index date and incident events
during the observation period have been available since January 1,
2016. The South Korean government manages the NHIS COVID-19
registry, and the KDCA provides data regarding the COVID-19 vaccine,

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Study population
In the NHIS database, 15,076,899 individuals, approximately 30% of
the total South Korean population, underwent a general healthexamination in 2018. According to the data provider’s regulations, wewere required to limit our study population to 10 million due to privacy
and data capacity constraints. Consequently, we randomly selected
9,945,450 participants, approximately 20% of the total population ofSouth Korea. After excluding individuals with incomplete general
health examination reports, 9,258,803 individuals vaccinated with atleast one dose of the mRNA-based COVID-19 vaccine (BNT162b2 or
mRNA-1273) until 31 December 2022 were selected. We subsequently
extracted half of the primary cohort to establish the vaccination
cohort, whose index was the date of administration of the ﬁrst dose of
the mRNA-based COVID-19 vaccine. As COVID-19 vaccination was
conducted nationwide in South Korea, as of October 2022, the overall
vaccination coverage rate among adults meeting the requirements forthe primary series of each COVID-19 vaccine introduced in South Korea
was 96.6%13. Therefore, using unvaccinated individuals as controls forcomparison could have led to inappropriate cohort selection andpotential selection bias. Instead, our study uses historical controls ascomparators. The other half of the primary cohort was used to form
historical control cohorts, while the observational period of the control group was shifted back by 2 years from the date of the ﬁrst dose ofmRNA vaccination of individuals. The two cohort groups were7

Articlehttps://doi.org/10.1038/s41467-024-50656-8

Fig. 6 | Risks of incident autoimmune connective tissue diseases within mRNAbased COVID-19 vaccination cohort according to prior history of booster
vaccination. The forest plot depicts adjusted hazard ratios (aHRs) with 99% conﬁdence intervals (CIs) in individuals within the vaccination cohort according toprior history of booster vaccination, deﬁned by the administration of 3rd additionaldose of the mRNA-based COVID-19 vaccine following the completion of the twodose primary series of the same mRNA-based COVID-19 vaccine. The point estimate(centre) represents the aHR, and the horizontal line (error bar) shows the range of
the 99% CI. Among the vaccination cohort, 2,284,342 individuals were vaccinatedwith a booster dose and the extended Cox proportional hazard analyses treating
booster vaccination as a time-varying covariate were conducted for the variabilityof vaccination status during the observation period. The numbers of events ofautoimmune disorder outcomes and population at risk were presented for eachgroup divided based on booster vaccination status, with the number of events
speciﬁcally shown before and after the booster vaccination in the individuals withbooster vaccination group. Abbreviations: aHR, adjusted hazard ratio; ANCA,
antineutrophil cytoplasmic antibody; CI, conﬁdence interval; COVID-19, coronavirus disease 2019; HR, hazard ratio.

followed up from the respective study index date to disease diagnosis,
emigration, death, or the end of the study period. The vaccinationgroup was observed until 31 December 2022, and the historical cohort
group until 31 December 2020.

covariates, including demographics and socioeconomic status, such asage, sex, insurance type (standard vs Medicaid), income level (divided
into quartiles based on health insurance premiums), area of residence(urban vs rural area), and general health examination data mentioned
earlier. In the general health examination data, we established thecurrent smoking status and deﬁned drinking as routine alcohol consumption, regardless of the amount or frequency. In addition, we set
several chronic diseases as predeﬁned covariates and listed their corresponding ICD-10 codes in Supplementary Table 2. The covariates
were balanced between the two cohorts using IPTW.

Outcomes
We assessed the incidence and risk of developing AI-CTDs associatedwith the mRNA-based COVID-19 vaccine during the follow-up period
by restricting the study population to patients without a history of therespective outcomes before the study index date. The occurrence ofeach outcome disease was deﬁned when that disease was identiﬁed at≥ 3 in- or outpatient visits using the corresponding ICD-10 diagnostic
codes. We established and evaluated three positive control outcomes(myocarditis, pericarditis, and Guillain–Barre syndrome), which arereportedly signiﬁcantly associated with the COVID-19 vaccine, as well
as negative control outcomes (benign skin tumour, melanoma in situ,
and tympanic membrane perforation), which are less likely to be
associated with the COVID-19 vaccine, to ensure the validity of ourstudy37. The corresponding ICD-10 codes of autoimmune connective
tissue diseases and predeﬁned positive and negative control outcomesare summarised in Supplementary Table 2.

Covariates
Although both the vaccination and historical control cohorts were
derived from the same primary cohort, there could be residual differences in baseline characteristics that were potentially linked to theoccurrence of disease outcomes. Therefore, we considered predeﬁned

Nature Communications | (2024)15:6181

Statistical analysis
The baseline demographic characteristics are presented as means withstandard deviations and frequencies with percentages, depending on
the variable types. Propensity scores for individuals were estimatedbased on predeﬁned covariates representing the possibility of
belonging to the vaccination cohort. These scores were used to calculate the inverse probability of treatment weights, obtained by
dividing the probability of belonging to the vaccination cohort by
1 minus the probability of being in the vaccination cohort: the probability of belonging to the vaccination cohort / (1–the probability of
being in the vaccination cohort). The covariate balance was assessedusing standardised mean differences before and after the applicationof the IPTWs. Subsequently, the risk of predeﬁned outcomes in thevaccination cohort was estimated and compared with that in the historical control cohort. Statistical analysis involved multivariable Cox
proportional hazards analysis after adjusting for all predeﬁned
8Article
covariates used to calculate the IPTWs. Each analysis for outcomes
included only the population at risk by excluding those who had
already been diagnosed with the target outcome at the index date or
before. Stratiﬁed subgroup analyses were conducted according to thesex, age (< 40 vs ≥ 40), type of mRNA-based COVID-19 vaccine(BNT162b2 vs mRNA-1273), history of having a non-mRNA COVID
vaccine such as the viral vector vaccine (ChAdOx1 nCoV-19 (AZD1222)
or Ad26.COV2.S) prior to mRNA vaccination, and whether COVID-19
was diagnosed or not. We also aimed to further ascertain the impact ofbooster vaccination, deﬁned by the administration of 3rd additionaldose of the mRNA-based vaccine following the completion of the twodose primary series of the same mRNA-based vaccine, on the incidenceof AI-CTDs. As an individual’s vaccination status may change during
the observation, we conducted extended Cox proportional hazard
analyses with booster vaccination as a time-varying covariate. To
minimise the risk of false ﬁndings, such as type I errors due to multiple
comparisons, we applied the Bonferroni correction for 27 predeﬁned
outcomes. Consequently, statistical signiﬁcance was determinedbased on the adjusted conﬁdence interval for 27 comparisons. All
statistical analyses were conducted using SAS statistical software(version 9.4; SAS Institute, Cary, NC, USA) and R statistical software(version 3.4.1; R Foundation for Statistical Computing, Vienna,
Austria).

Reporting summary
Further information on research design is available in the Nature
Portfolio Reporting Summary linked to this article.

Data availability
The datasets analysed during the current study are available in the
National Health Insurance Service in South Korea. This protects the
conﬁdentiality of the data and ensures that Information Governance isrobust. Applications to access health data in South Korea should besubmitted to the National Health Insurance Service in South Korea.
Information can be found at https://nhiss.nhis.or.kr/bd/ab/
bdaba000eng.do.

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Acknowledgements
This study used the KDCA and NHIS databases for policy and academic
research. The research number for this study was KDCA-NHIS-2023-1500. The KDCA is the Korea Disease Control and Prevention Agency,
Republic of Korea. The NHIS is part of the National Health Insurance
Service of the Republic of Korea. Funding: This research was
supported by funding from the Research Programme of the Korea
Medical Institute. This research was also supported by a grant from the
Korea Health Technology Research and Development Project throughthe Korea Health Industry Development Institute (KHIDI), the Ministryof Health & Welfare, Republic of Korea (grant number: HI23C1506; S.L.),and by a grant from the National Research Foundation of Korea(NRF) grant funded by the Korea government (MSIT; no. RS-202300249120; S.L.).

Author contributions
S.J.C. and S.L. have full access to all data in the study and takeresponsibility for the integrity and accuracy of the data analysis. S.W.J.
and S.L. were involved in the conceptualisation and design of the study.
S.W.J., J.J.J., Y.H.K., S.J.C. and S.L. contributed to the study methodology
and data curation. S.W.J. and J.J.J. were involved in the analysis, datainterpretation, and draughting of the manuscript. S.J.C. and S.L. contributed to the data validation, visualisation, and critical revision of themanuscript. S.W.J., J.J.J., Y.H.K., S.J.C. and S.L. administered the project.

Nature Communications | (2024)15:6181

https://doi.org/10.1038/s41467-024-50656-8

Competing interests
The authors declare no competing interests.

Additional information
Supplementary information The online version contains
supplementary material available athttps://doi.org/10.1038/s41467-024-50656-8.
Correspondence and requests for materials should be addressed toSung Jay Choe or Solam Lee.
Peer review information Nature Communications thanks Allison Naleway and the other anonymous, reviewer(s) for their contribution to thepeer review of this work. A peer review ﬁle is available.
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