Anti-Cytosolic 5'-Nucleotidase 1A in the Diagnosis of Patients with Suspected Idiopathic Inflammatory Myopathies: An Italian Real-Life, Single-Centre Retrospective Study.

autoantibodies idiopathic inflammatory myopathies inclusion body myositis

Journal

Biomedicines
ISSN: 2227-9059
Titre abrégé: Biomedicines
Pays: Switzerland
ID NLM: 101691304

Informations de publication

Date de publication:
12 Jul 2023
Historique:
received: 07 06 2023
revised: 04 07 2023
accepted: 10 07 2023
medline: 29 7 2023
pubmed: 29 7 2023
entrez: 29 7 2023
Statut: epublish

Résumé

Anti-cytosolic 5'-nucleotidase 1A (anti-cN1A) antibodies were proposed as a biomarker for the diagnosis of inclusion body myositis (IBM), but conflicting specificity and sensitivity evidence limits its use. Our study aimed to assess the diagnostic accuracy of anti-cN1A in a cohort of patients who underwent a myositis line immunoassay for suspected idiopathic inflammatory myopathies (IIM). We also assessed the agreement between two testing procedures: line immunoassay (LIA) and enzyme-linked immunoassay (ELISA). We collected retrospective clinical and serological data for 340 patients who underwent a myositis antibody assay using LIA (EUROLINE Autoimmune Inflammatory Myopathies 16 Ag et cN-1A (IgG) line immunoassay) and verification with an anti-cN1A antibody assay using ELISA (IgG) (Euroimmun Lubeck, Germany). The serum samples of 20 (5.88%) patients (15 females, 5 males, mean age 58.76 ± 18.31) tested positive for anti-cN1A using LIA, but only two out of twenty were diagnosed with IBM. Seventeen out of twenty tested positive for anti-cN1A using ELISA (median IQR, 2.9 (1.9-4.18)). Our study suggests excellent concordance between LIA and ELISA for detecting anti-cN1A antibodies. LIA may be a rapid and useful adjunct, and it could even replace ELISA for cN1A assay. However, the high prevalence of diseases other than IBM in our cohort of anti-cN1A-positive patients did not allow us to consider anti-cN1A antibodies as a specific biomarker for IBM.

Sections du résumé

BACKGROUND BACKGROUND
Anti-cytosolic 5'-nucleotidase 1A (anti-cN1A) antibodies were proposed as a biomarker for the diagnosis of inclusion body myositis (IBM), but conflicting specificity and sensitivity evidence limits its use. Our study aimed to assess the diagnostic accuracy of anti-cN1A in a cohort of patients who underwent a myositis line immunoassay for suspected idiopathic inflammatory myopathies (IIM). We also assessed the agreement between two testing procedures: line immunoassay (LIA) and enzyme-linked immunoassay (ELISA).
MATERIALS AND METHODS METHODS
We collected retrospective clinical and serological data for 340 patients who underwent a myositis antibody assay using LIA (EUROLINE Autoimmune Inflammatory Myopathies 16 Ag et cN-1A (IgG) line immunoassay) and verification with an anti-cN1A antibody assay using ELISA (IgG) (Euroimmun Lubeck, Germany).
RESULTS RESULTS
The serum samples of 20 (5.88%) patients (15 females, 5 males, mean age 58.76 ± 18.31) tested positive for anti-cN1A using LIA, but only two out of twenty were diagnosed with IBM. Seventeen out of twenty tested positive for anti-cN1A using ELISA (median IQR, 2.9 (1.9-4.18)).
CONCLUSIONS CONCLUSIONS
Our study suggests excellent concordance between LIA and ELISA for detecting anti-cN1A antibodies. LIA may be a rapid and useful adjunct, and it could even replace ELISA for cN1A assay. However, the high prevalence of diseases other than IBM in our cohort of anti-cN1A-positive patients did not allow us to consider anti-cN1A antibodies as a specific biomarker for IBM.

Identifiants

pubmed: 37509600
pii: biomedicines11071963
doi: 10.3390/biomedicines11071963
pmc: PMC10377506
pii:
doi:

Types de publication

Journal Article

Langues

eng

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Auteurs

Brunetta Porcelli (B)

UOC Laboratorio Patologia Clinica, Policlinico S. Maria alle Scotte, AOU Senese, 53100 Siena, Italy.
Dipartimento Biotecnologie Mediche, Università degli Studi di Siena, 53100 Siena, Italy.

Miriana d'Alessandro (M)

Respiratory Diseases Unit, Department of Medical and Surgical Sciences & Neurosciences, University of Siena, 53100 Siena, Italy.

Latika Gupta (L)

Department of Clinical Immunology and Rheumatology, Sanjay Gandhi Postgraduate Institute of Medical Sciences, Lucknow 226014, India.

Silvia Grazzini (S)

Rheumatology Unit, Department of Medicine, Surgery and Neurosciences, University of Siena, 53100 Siena, Italy.

Nila Volpi (N)

Neurology and Clinical Neurophysiology Unit, Department of Medical, Surgical and Neurological Sciences, University of Siena, 53100 Siena, Italy.

Maria Romana Bacarelli (MR)

UOC Laboratorio Patologia Clinica, Policlinico S. Maria alle Scotte, AOU Senese, 53100 Siena, Italy.

Federica Ginanneschi (F)

Neurology and Clinical Neurophysiology Unit, Department of Medical, Surgical and Neurological Sciences, University of Siena, 53100 Siena, Italy.

Giovanni Biasi (G)

Rheumatology Unit, Department of Medicine, Surgery and Neurosciences, University of Siena, 53100 Siena, Italy.

Francesca Bellisai (F)

Rheumatology Unit, Department of Medicine, Surgery and Neurosciences, University of Siena, 53100 Siena, Italy.

Marta Fabbroni (M)

Rheumatology Unit, Department of Medicine, Surgery and Neurosciences, University of Siena, 53100 Siena, Italy.

David Bennett (D)

Respiratory Diseases Unit, Department of Medical and Surgical Sciences & Neurosciences, University of Siena, 53100 Siena, Italy.

Claudia Fabiani (C)

Ophthalmology Unit, Department of Medicine, Surgery and Neurosciences, University of Siena, 53100 Siena, Italy.

Luca Cantarini (L)

Rheumatology Unit, Department of Medicine, Surgery and Neurosciences, University of Siena, 53100 Siena, Italy.

Elena Bargagli (E)

Respiratory Diseases Unit, Department of Medical and Surgical Sciences & Neurosciences, University of Siena, 53100 Siena, Italy.

Bruno Frediani (B)

Rheumatology Unit, Department of Medicine, Surgery and Neurosciences, University of Siena, 53100 Siena, Italy.

Edoardo Conticini (E)

Rheumatology Unit, Department of Medicine, Surgery and Neurosciences, University of Siena, 53100 Siena, Italy.

Classifications MeSH