Background Autoimmune diseases (ADs) often co-occur within individuals and families, indicating shared genetic risk factors. However, the composition of genetic overlap across autoimmunity is largely unknown. Methods This nationwide study included 6,336,615 individuals born in Sweden between 1932 and 1983, comprising 3,839,400 full-sibling pairs. Based on national heath-registers, 22 ADs were identified from 1969-2013. Aggregation and co-aggregation of ADs among siblings was used to estimate pairwise genetic correlations of ADs under a liability-threshold model. Network analysis and principal component analysis was used to characterize the structure of shared genetic risk across ADs. Results A total of 707,995 individuals (11.2%) were diagnosed with at least one AD. The studied ADs formed a network of significant genetic correlations (mean rg = 0.24, range 0.08-0.84) with clusters of more closely related ADs (rg ≥ 0.3). We found no evidence of a significant universal factor predisposing to autoimmunity. Conclusions This study demonstrates that ADs share substantial cluster-specific genetic overlap that largely aligns with affected tissue types, leading to distinct groupings of connective tissue diseases, gastrointestinal disorders, and endocrinopathies, whereas diseases of the nervous system show limited genetic cohesion. This suggests that shared biological mechanisms may drive coaggregation within disease groups. Clinically, these insights highlight the importance of monitoring patients and their relatives for related autoimmune disorders. Funding The Swedish Society of Medicine, Region Värmland's County Research Council, The Swedish Research Council, the Knut and Alice Wallenberg Foundation, The Regional Agreement on medical training and Clinical research (ALF) between Stockholm County Council and Karolinska Institutet.
Daniel Eriksson, R. Kuja-Halkola, M. Holmqvist et al.· Journal of Clinical Investig...· 0 citations
Systemic autoimmune rheumatic diseases (SARDs) are a heterogeneous group of autoimmune conditions characterized by immune system dysregulation leading to chronic inflammation and tissue damage. The overlapping clinical manifestations make differential diagnosis challenging, highlighting the need for novel biomarkers to facilitate early diagnosis, stratification, and personalized treatment. Five SARDs including idiopathic inflammatory myopathies (n = 210), rheumatoid arthritis (n = 84), systemic sclerosis (n = 100), Sjögren disease (n = 99), and systemic lupus erythematosus (n = 99), as well as healthy controls (n = 400) and controls with acute infectious diseases (n = 218) were selected for plasma protein profiling using Olink Explore 1536. Differential abundance analysis and machine learning were used to identify proteins with both known and novel association to SARDs. The five SARDs share hundreds of proteins with consistently altered abundance compared to both healthy and infectious controls, reflecting common underlying molecular dysregulation. Despite the overlap, we identify multiple proteins with higher abundance specific to individual SARDs. Machine learning further enables accurate classification of the five SARDs, identifying a panel of 48 proteins with high discriminatory performance, several of which are also supported by differential abundance analysis. Altogether, this explorative cross-sectional study demonstrates the importance of a pan-disease approach, including also infectious and healthy controls, to identify robust and disease-informative protein panels for improved classification of SARDs. Protein levels from this study are available open access through the Human Protein Atlas, facilitating further plasma proteome research on autoimmune disease. Systemic autoimmune rheumatic diseases (SARDs) are a group of diseases caused by a malfunctioning immune system that attacks and damages the body´s own tissues and organs. They can be difficult to distinguish and diagnose correctly because they share similar symptoms; therefore, additional molecular indicators could be helpful to aid in diagnosis. In this study, blood samples were collected from patients representing five SARDs. These samples were measured using a technology named Olink Explore, which measures around 1500 proteins in the blood at the same time. Using protein measurements from 592 patients, as well as from control groups, we identified proteins that may help distinguish the five diseases from one another. The proteins, if confirmed in future studies, could help clinicians in the diagnosis of these diseases with higher precision. Kenrick et al. investigate levels of more than 1500 proteins in blood across five systemic autoimmune rheumatic diseases using proximity extension assay. They demonstrate the importance of a pan-disease approach and identify disease-specific proteins that differentiate systemic autoimmune rheumatic diseases.
J. Kenrick, C. Preger, M. B. Álvez et al.· Communications Medicine· 0 citations
We use cookies to run the site and, with your consent, for analytics and to show ads.
See our Cookie Policy.
