Post-COVID Individuals Have A 42.6 Percent Higher Likelihood Of Acquiring Autoimmune Diseases

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A news German study has found that people who have been exposed to the SARS-CoV-2 virus have a 42.6 percent higher likelihood of acquiring autoimmunity and various autoimmune diseases.

The study findings were published on a preprint server and are currently being peer reviewed.
https://www.medrxiv.org/content/10.1101/2023.01.25.23285014v1

To our knowledge, this is the largest cohort studies to investigate the association between SARS-CoV-2 infection and the subsequent development of autoimmune diseases.

The excess risk for any newly diagnosed autoimmune disease was 4.50 per 1000 person-years in this study. The highest IRRs were found for rather uncommon autoimmune diseases of the vasculitis group. For the more common autoimmune diseases, the highest risks were found for rheumatoid arthritis, Sjögren disease, Graves’ disease and Hashimoto thyroiditis, with an increase of approximately 40% compared to a matched cohort without SARS-CoV-2 infection.

Those without a prior autoimmune disease and COVID-19 had a 43% higher likelihood of developing an incident autoimmune disease than controls, while those with any preexisting autoimmune disease and COVID-19 had a 23% higher likelihood of being diagnosed with another autoimmune disease.

As expected, absolute incidence rates of any autoimmune disease were higher among women compared to men, among older compared to younger individuals and among those without preexisting autoimmune disease. Comparing persons with and without COVID-19, the IRR increased with the severity of COVID-19 as indicated by hospitalization and particularly by ICU/ventilation treatment versus COVID-19 patients in the outpatient sector.

Additionally, a higher IRR for a new-onset autoimmune disease was observed in children and adolescents than in adults with/without COVID-19. However, differences between age groups did not reach statistical significance.

Only other cohorts study was found to investigate the onset of 11 autoimmune diseases after a SARS-CoV2-Infection. It reported a hazard ratio of 1.22 (95%-CI=1.10-1.34). Due to the median follow-up of only 0.29 years only 3 of 11 autoimmune diseases were significant[22].

There are several hypotheses regarding the pathogenesis of post-COVID-19, although different mechanisms most likely underlie the complex clinical picture involving multiple organ systems.

Drawing parallels to other post infectious syndromes, possible mechanisms include persistence of the virus or viral or remnants, latent virus reactivation, long-lasting tissue damage due to microclotting and chronic inflammation, and autoimmunity [31].

According to current knowledge, autoimmunity following viral infection may be triggered by mechanisms such as epitope spreading, bystander activation, molecular mimicry, and cryptic epitopes [32]. SARS-CoV-2 shares characteristics of other viruses associated with the development of autoimmunity.

Acosta-Ampudia and Anaya summarized these hypotheses as follows.

1. Superantigen activity: The S protein of SARS-CoV-2 contains sequence and structure motifs similar to those of a bacterial superantigen and can bind directly to the T-cell receptor.

2. Molecular mimicry: Accumulating evidence demonstrates that the virus has structural similarity to host-derived components.

3. Neutrophil extracellular trap (NET) formation.

4. Type I interferon (IFN) response.

5. “Overt immunity” which describes the appearance of multiple autoantibodies and diverse autoimmune diseases that are significantly associated with SARS-CoV-2 [33]. These mechanisms are in line with several serological studies demonstrating the onset of IgG autoantibodies[34, 35] or emergence of self-reactive B cells[36] as a response to SARS-CoV-2.

Moreover, autoantibodies generated during infection are negatively correlated with SARS-CoV-2 antibodies but positively correlated with hyperinflammation markers during acute illness as well as biomarkers for certain post-acute conditions[37].

These findings highlight a potential link between autoreactivity, severity of COVID-19 and susceptibility to post-acute sequelae. Indeed, serological studies have found persisting patterns of autoreactivity in severe COVID-19 cases even after most autoimmunological markers have subsided after the acute phase [34, 36].

This suggests latent autoimmunity acquired by some patients, which may lead to de novo autoimmune diseases in the long run [23, 35].

Early clinical case studies reported few cases of onset autoimmune diseases following COVID-19. There is growing consensus regarding the relevance of long-term studies on this matter [23, 38].

Two recently conducted systematic reviews and meta-analyses for example show the association between diabetes mellitus and SARS-CoV-2 infection and conclude that the excess risk of type 1 diabetes is small but relevant from a public health perspective although the underlying mechanisms remain to be elucidated in order to prove a causal relationship [39, 40].

Results of the present study regarding the excess risk of newly diagnosed type 1 diabetes in relation to documented SARS-CoV-2 infection are in line with these previous findings.

We also found the overall excess risk for a first autoimmune disease to be 4.50 per 1000 person-years, which is much smaller than previously proposed for other potential chronic sequelae of COVID-19.

For cardiovascular diseases, the excess risk was 45.29[13]; for mental diseases, it was 36.48 [14]; and for neurologic disorders, it was estimated to be 70.69 per 1000 people[15]. One reason for this could be that autoimmune diseases are less frequent and the detection time is much longer than that for other diseases. The much larger IRR for hospitalized patients and patients with ICU/ventilation was also reported elsewhere [11, 17].

Strengths and limitations

The main strength of our analysis is its large dataset including more than 600,000 COVID-19 patients and a minimum follow-up period of three to 15 months. This unselected sample from all over Germany covers both outpatient and inpatient care and thus constitutes a unique and comprehensive source of evidence.

Our analysis is based on confirmed diagnoses documented by ambulatory physicians and hospital discharge diagnosis. Accordingly, our results are not subject to possible distortions resulting from selective, incomplete, or inadequate self-reporting of symptoms but instead rely on information provided by medical professionals.

To avoid confounding the relationships between outcomes and exposure, we applied matching on relevant covariates, age, sex, previous autoimmune disease and several prevalent diseases and utilization of outpatient and inpatient care. The results were confirmed by the fact that estimates of individual outcome definitions were similar with and without additional consideration of disease-specific medication.

Due to the observational nature of our study, we could not determine a causal interpretation of the results. We could not exclude the possibility that our results were affected by unmeasured confounding, although we minimized differences between the COVID-19 and control cohorts by matching. Vaccination status could not be validly assessed in German claims data.

Our results may also have been subject to greater symptom awareness of individuals following SARS-CoV-2 infection or detection bias that may have arisen if the health status of individuals after the onset of COVID-19 was more closely monitored and better documented by physicians.

Individuals with a mild or asymptomatic course of COVID-19 were likely to be underrepresented in our study because SARS-CoV-2 infections may not have been documented[41], especially in the first months of the pandemic. The resulting selection of more severe COVID-19 cases may have led to higher incidence estimates in this cohort.

In addition, individuals with undocumented SARS-CoV-2 infection may have been included in the control cohort. To the extent that post-COVID also occurred in individuals with undocumented infections, this misclassification induced an overestimation of incidence rates in the control group and, thus, a bias toward the null in estimators of incidence rate ratios.

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