Supplementum 279: Abstracts of the annual meeting of the Swiss Society of Rheumatology (Lausanne, Switzerland, September 5−6, 2024)

Background: Despite the progress in explaining the clinical heterogeneity in systemic sclerosis (SSc) based on SSc-specific antibodies, better understanding of additional risk factors is needed.SSc non-specific antibodies might represent additional markers to improve the stratification of SSc patients. Objective:We aimed to evaluate the prevalence of anti-Ro/SSA antibodies in the EUSTAR database and study their association with disease phenotype and clinical outcomes, focusing on lung involvement.Methods: SSc patients from the EUSTAR database with available data on anti-Ro/SSA antibodies, were included.Clinical characteristics of patients with or without anti-Ro/SSA antibodies were compared at baseline.Multivariable logistic regression models were built to identify factors associated with lung fibrosis.The progression of lung fibrosis was defined by FVC% decline from baseline of 10% or a FVC% decline of 5-9% in association with a DLco% decline of 15% or by a decline of FVC >5% in patients with lung fibrosis or by the development of lung fibrosis de novo.Prognostic factors for progression of lung fibrosis and death during follow-up were tested by multivariate Cox proportional hazards regression.Results: Among the 4'421 patients fulfilling the inclusion criteria, 661 (15.2%) had positive anti-Ro/SSA antibodies.Anti-Ro/SSA antibodies were positively associated (p <0.001) with anti-SSB, anti-U1RNP antibodies, and rheumatoid factor.Patients with anti-Ro/SSA antibodies more frequently presented with muscular involvement (18% vs 12.5%, p <0.001), and lung fibrosis (56.2% vs 47.8%, p = 0.001) at baseline.Over a median follow-up of 2.7 years,14'066 visits were recorded.In multivariable logistic regression, anti-SSA independently predicted the presence of lung fibrosis in at least one follow up visit (OR 1.24, p = 0.006) and lower DLCO in patients with lung fibrosis (regression coefficient: -1.9, p = 0.049).Anti-SSA antibodies had a trend to predict lower FVC in patients with lung fibrosis (p = 0.082).Anti-SSA antibodies did not predict the progression of lung fibrosis or death during the follow-up.Conclusions: Anti-SSA antibodies are detected in 15% of SScpatients and represent an independent risk factor for the presence of lung fibrosis.They are also predictive of more severe lung involvement.These data support the inclusion of anti-SSA antibodies in routine clinical practice to improve the risk-stratification of SSc patients.

Authors

Publication Details

Journal
Swiss Medical Weekly
Published
2024-09-02
DOI
https://doi.org/10.57187/s.4175
Citations
3
Primary Topic
Immunodeficiency and Autoimmune Disorders
Type
article
Field-Weighted Citation Impact
0.72
Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

Supplementum 279: Abstracts of the annual meeting of the Swiss Society of Rheumatology (Lausanne, Switzerland, September 5−6, 2024)

Swiss Society of Rheumatology
3 citations
Swiss Medical Weekly
Immunodeficiency and Autoimmune Disorders
0.72
article

Supplementum 279: Abstracts of the annual meeting of the Swiss Society of Rheumatology (Lausanne, Switzerland, September 5−6, 2024)

Swiss Society of Rheumatology
article en
3 citations

Abstract

Background: Despite the progress in explaining the clinical heterogeneity in systemic sclerosis (SSc) based on SSc-specific antibodies, better understanding of additional risk factors is needed.SSc non-specific antibodies might represent additional markers to improve the stratification of SSc patients. Objective:We aimed to evaluate the prevalence of anti-Ro/SSA antibodies in the EUSTAR database and study their association with disease phenotype and clinical outcomes, focusing on lung involvement.Methods: SSc patients from the EUSTAR database with available data on anti-Ro/SSA antibodies, were included.Clinical characteristics of patients with or without anti-Ro/SSA antibodies were compared at baseline.Multivariable logistic regression models were built to identify factors associated with lung fibrosis.The progression of lung fibrosis was defined by FVC% decline from baseline of 10% or a FVC% decline of 5-9% in association with a DLco% decline of 15% or by a decline of FVC >5% in patients with lung fibrosis or by the development of lung fibrosis de novo.Prognostic factors for progression of lung fibrosis and death during follow-up were tested by multivariate Cox proportional hazards regression.Results: Among the 4'421 patients fulfilling the inclusion criteria, 661 (15.2%) had positive anti-Ro/SSA antibodies.Anti-Ro/SSA antibodies were positively associated (p <0.001) with anti-SSB, anti-U1RNP antibodies, and rheumatoid factor.Patients with anti-Ro/SSA antibodies more frequently presented with muscular involvement (18% vs 12.5%, p <0.001), and lung fibrosis (56.2% vs 47.8%, p = 0.001) at baseline.Over a median follow-up of 2.7 years,14'066 visits were recorded.In multivariable logistic regression, anti-SSA independently predicted the presence of lung fibrosis in at least one follow up visit (OR 1.24, p = 0.006) and lower DLCO in patients with lung fibrosis (regression coefficient: -1.9, p = 0.049).Anti-SSA antibodies had a trend to predict lower FVC in patients with lung fibrosis (p = 0.082).Anti-SSA antibodies did not predict the progression of lung fibrosis or death during the follow-up.Conclusions: Anti-SSA antibodies are detected in 15% of SScpatients and represent an independent risk factor for the presence of lung fibrosis.They are also predictive of more severe lung involvement.These data support the inclusion of anti-SSA antibodies in routine clinical practice to improve the risk-stratification of SSc patients.

Swiss Medical WeeklyVol. 154(9)
Openalex Percentile: Top 29%
Immunodeficiency and Autoimmune Disorders
0.72
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.