The diagnostic performance of a novel flow cytometry-based assay for the diagnosis of systemic lupus erythematosus

IF 2.9 3区医学 Q2 MEDICAL LABORATORY TECHNOLOGY

Clinica Chimica Acta Pub Date : 2025-10-13 DOI:10.1016/j.cca.2025.120662

Yanling Zhao , Minghua Zhan , Jianing Chen , Honglin Xu , Futai Feng , Zhou Bai , Fang Wang , Yongjing Cheng , Xuan Zhang , Yudong Liu

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引用次数: 0

Abstract

Objectives

There is continuous need to identify novel biomarkers that can provide early and accurate diagnosis of systemic lupus erythematosus (SLE) due to its highly heterogenous nature and lack of specific clinical manifestations.

Methods

A total of 316 subjects comprising 127 patients with SLE, 129 patients with other autoimmune diseases as disease controls (DCs) and 60 healthy controls (HCs) were enrolled to determine the clinical relevance of the surface expression levels of CD169 on monocytes, CD317 on B cells and CD177 on neutrophils by flow cytometry for the diagnosis of SLE.

Results

The flow cytometric assay based on the combination of three molecules displayed a favorable diagnostic performance with an area under curve (AUC) of 0.9243. The sensitivities and specificities for this assay were 79.2 % and 96.2 % in the training cohort, and 82.1 % and 97.3 % in the validation cohort, respectively. Importantly, the diagnostic performance of this assay was independent of anti-double stranded DNA (dsDNA) antibodies with an AUC of 0.9329. The clinical performance of this assay was validated in an independent test cohort with a sensitivity and a specificity of 88.9 % and 95.9 %, respectively.

Conclusion

Our findings may have important clinical relevance in the diagnosis of SLE, especially when the suspects were tested negative for anti-dsDNA antibodies. Our study may shed light on a new direction other than detection of autoantibodies for the diagnosis of SLE in clinical settings.

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一种新的基于流式细胞术的系统性红斑狼疮诊断方法的诊断性能。

目的：由于系统性红斑狼疮（SLE）的高度异质性和缺乏特异性临床表现，一直需要识别新的生物标志物，以提供早期和准确的诊断。方法：采用流式细胞术检测单核细胞表面CD169、B细胞表面CD317、中性粒细胞表面CD177表达水平与SLE诊断的临床相关性，共纳入316例SLE患者(127例SLE患者、129例其他自身免疫性疾病患者作为疾病对照（DCs）和60例健康对照（hc）。结果：三种分子联合应用的流式细胞术诊断效果良好，曲线下面积（AUC）为0.9243。该方法在训练组的敏感性和特异性分别为79.2% %和96.2 %，在验证组的敏感性和特异性分别为82.1 %和97.3% %。重要的是，该检测的诊断性能不依赖于抗双链DNA （dsDNA）抗体，AUC为0.9329。该方法的临床表现在一个独立的测试队列中得到验证，其敏感性和特异性分别为88.9% %和95.9% %。结论：我们的研究结果可能对SLE的诊断具有重要的临床意义，特别是当疑似患者的抗dsdna抗体检测呈阴性时。我们的研究可能为临床诊断SLE提供了除自身抗体检测之外的新方向。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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来源期刊

Clinica Chimica Acta 医学-医学实验技术

CiteScore

10.10

自引率

2.00%

发文量

1268

审稿时长

23 days

期刊介绍： The Official Journal of the International Federation of Clinical Chemistry and Laboratory Medicine (IFCC) Clinica Chimica Acta is a high-quality journal which publishes original Research Communications in the field of clinical chemistry and laboratory medicine, defined as the diagnostic application of chemistry, biochemistry, immunochemistry, biochemical aspects of hematology, toxicology, and molecular biology to the study of human disease in body fluids and cells. The objective of the journal is to publish novel information leading to a better understanding of biological mechanisms of human diseases, their prevention, diagnosis, and patient management. Reports of an applied clinical character are also welcome. Papers concerned with normal metabolic processes or with constituents of normal cells or body fluids, such as reports of experimental or clinical studies in animals, are only considered when they are clearly and directly relevant to human disease. Evaluation of commercial products have a low priority for publication, unless they are novel or represent a technological breakthrough. Studies dealing with effects of drugs and natural products and studies dealing with the redox status in various diseases are not within the journal''s scope. Development and evaluation of novel analytical methodologies where applicable to diagnostic clinical chemistry and laboratory medicine, including point-of-care testing, and topics on laboratory management and informatics will also be considered. Studies focused on emerging diagnostic technologies and (big) data analysis procedures including digitalization, mobile Health, and artificial Intelligence applied to Laboratory Medicine are also of interest.