Evaluating the performance of a multiparametric IgA assay for celiac disease diagnosis.

IF 3.7 2区 医学 Q1 MEDICAL LABORATORY TECHNOLOGY
Caterina Maria Gambino, Luisa Agnello, Fabio Del Ben, Anna Maria Ciaccio, Salvatore Milano, Roberta Vassallo, Francesco Cacciabaudo, Aurelio Seidita, Pasquale Mansueto, Antonio Carroccio, Marcello Ciaccio
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Abstract

Objectives: Celiac disease (CD) is a systemic autoimmune disorder triggered by gluten in genetically predisposed individuals. Accurate diagnosis remains challenging due to clinical heterogeneity and reliance on invasive biopsy. This study aimed to evaluate the diagnostic performance of a novel multiparametric membrane-based enzyme immunoassay (AESKUBLOTS®) for the simultaneous detection of IgA antibodies targeting eight CD-related antigens.

Methods: A retrospective, single-centre study was conducted on 180 participants: 80 with CD (30 untreated, 50 on gluten-free diet, GFD), 50 with non-celiac wheat sensitivity (NCWS), and 50 healthy controls (HC). Serum samples were analysed using the AESKU assay. Diagnostic accuracy was assessed via ROC curve analysis and 5-fold cross-validation, examining individual markers and a composite antibody score.

Results: The assay demonstrated high diagnostic performance, particularly in untreated CD patients. Anti-tTG neo IgA showed the highest accuracy (AUC=0.93), followed by anti-tTG IgA (AUC=0.92). A composite score of ≥4 positive markers yielded an AUC of 0.99, while ≥6 positive markers achieved 100 % specificity and PPV, with 76.7 % sensitivity. Notably, anti-mTG IgA levels were elevated in all CD patients regardless of diet, suggesting potential utility in monitoring or identifying ongoing mucosal immune activity.

Conclusions: This multiparametric IgA assay offers a sensitive, specific, and non-invasive diagnostic tool for CD. Larger, prospective studies are warranted to confirm the clinical utility and expand the applicability to broader populations.

评估乳糜泻诊断中多参数IgA检测的性能。
目的:乳糜泻(CD)是一种在遗传易感个体中由麸质引发的系统性自身免疫性疾病。由于临床异质性和对侵入性活检的依赖,准确诊断仍然具有挑战性。本研究旨在评估一种新型多参数膜基酶免疫分析法(AESKUBLOTS®)同时检测针对8种cd相关抗原的IgA抗体的诊断性能。方法:对180名参与者进行回顾性单中心研究:80名乳糜泻患者(30名未经治疗,50名无麸质饮食,GFD), 50名非乳糜泻小麦敏感(NCWS), 50名健康对照(HC)。血清样品采用AESKU法分析。通过ROC曲线分析和5倍交叉验证评估诊断准确性,检查个体标记物和复合抗体评分。结果:该检测显示出很高的诊断性能,特别是在未经治疗的乳糜泻患者中。Anti-tTG neo IgA准确率最高(AUC=0.93),其次是Anti-tTG IgA (AUC=0.92)。≥4个阳性标记物的综合评分的AUC为0.99,而≥6个阳性标记物的特异性和PPV为100 %,敏感性为76.7 %。值得注意的是,无论饮食如何,所有乳糜泻患者的抗mtg IgA水平均升高,这表明在监测或识别正在进行的粘膜免疫活动方面具有潜在的效用。结论:这种多参数IgA检测为乳糜泻提供了一种敏感、特异性和非侵入性的诊断工具。有必要进行更大规模的前瞻性研究,以确认其临床实用性,并扩大其在更广泛人群中的适用性。
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来源期刊
Clinical chemistry and laboratory medicine
Clinical chemistry and laboratory medicine 医学-医学实验技术
CiteScore
11.30
自引率
16.20%
发文量
306
审稿时长
3 months
期刊介绍: Clinical Chemistry and Laboratory Medicine (CCLM) publishes articles on novel teaching and training methods applicable to laboratory medicine. CCLM welcomes contributions on the progress in fundamental and applied research and cutting-edge clinical laboratory medicine. It is one of the leading journals in the field, with an impact factor over 3. CCLM is issued monthly, and it is published in print and electronically. CCLM is the official journal of the European Federation of Clinical Chemistry and Laboratory Medicine (EFLM) and publishes regularly EFLM recommendations and news. CCLM is the official journal of the National Societies from Austria (ÖGLMKC); Belgium (RBSLM); Germany (DGKL); Hungary (MLDT); Ireland (ACBI); Italy (SIBioC); Portugal (SPML); and Slovenia (SZKK); and it is affiliated to AACB (Australia) and SFBC (France). Topics: - clinical biochemistry - clinical genomics and molecular biology - clinical haematology and coagulation - clinical immunology and autoimmunity - clinical microbiology - drug monitoring and analysis - evaluation of diagnostic biomarkers - disease-oriented topics (cardiovascular disease, cancer diagnostics, diabetes) - new reagents, instrumentation and technologies - new methodologies - reference materials and methods - reference values and decision limits - quality and safety in laboratory medicine - translational laboratory medicine - clinical metrology Follow @cclm_degruyter on Twitter!
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