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基于代谢组学和机器学习方法在唇腺中鉴定的免疫脂质组学特征诊断原发性干燥综合征。

An immuno-lipidomic signature revealed by metabolomic and machine-learning approaches in labial salivary gland to diagnose primary Sjögren's syndrome.

机构信息

Department of Internal Medicine and Clinical Immunology, University Hospital, Angers, France.

Mitolab, MitoVasc Institute, CNRS 6015, INSERM U1083, University of Angers, Angers, France.

出版信息

Front Immunol. 2023 Jul 14;14:1205616. doi: 10.3389/fimmu.2023.1205616. eCollection 2023.

Abstract

INTRODUCTION

Assessing labial salivary gland exocrinopathy is a cornerstone in primary Sjögren's syndrome. Currently this relies on the histopathologic diagnosis of focal lymphocytic sialadenitis and computing a focus score by counting lym=phocyte foci. However, those lesions represent advanced stages of primary Sjögren's syndrome, although earlier recognition of primary Sjögren's syndrome and its effective treatment could prevent irreversible damage to labial salivary gland. This study aimed at finding early biomarkers of primary Sjögren's syndrome in labial salivary gland combining metabolomics and machine-learning approaches.

METHODS

We used a standardized targeted metabolomic approach involving high performance liquid chromatography coupled with mass spectrometry among newly diagnosed primary Sjögren's syndrome (n=40) and non- primary Sjögren's syndrome sicca (n=40) participants in a prospective cohort. A metabolic signature predictive of primary Sjögren's syndrome status was explored using linear (logistic regression with elastic-net regularization) and non-linear (random forests) machine learning architectures, after splitting the data set into training, validation, and test sets.

RESULTS

Among 126 metabolites accurately measured, we identified a discriminant signature composed of six metabolites with robust performances (ROC-AUC = 0.86) for predicting primary Sjögren's syndrome status. This signature included the well-known immune-metabolite kynurenine and five phospholipids (LysoPC C28:0; PCaa C26:0; PCaaC30:2; PCae C30:1, and PCaeC30:2). It was split into two main components: the first including the phospholipids was related to the intensity of lymphocytic infiltrates in salivary glands, while the second represented by kynurenine was independently associated with the presence of anti-SSA antibodies in participant serum.

CONCLUSION

Our results reveal an immuno-lipidomic signature in labial salivary gland that accurately distinguishes early primary Sjögren's syndrome from other causes of sicca symptoms.

摘要

简介

评估唇腺外分泌功能障碍是原发性干燥综合征的基石。目前,这依赖于局灶性淋巴细胞性涎腺炎的组织病理学诊断,并通过计算淋巴细胞灶数来计算灶评分。然而,这些病变代表原发性干燥综合征的晚期阶段,尽管早期识别原发性干燥综合征并进行有效治疗可以防止唇腺的不可逆损伤。本研究旨在通过代谢组学和机器学习方法在唇腺中寻找原发性干燥综合征的早期生物标志物。

方法

我们使用标准化的靶向代谢组学方法,在一个前瞻性队列中对新诊断的原发性干燥综合征(n=40)和非原发性干燥综合征干燥症(n=40)参与者进行高效液相色谱-质谱联用分析。使用线性(逻辑回归与弹性网络正则化)和非线性(随机森林)机器学习架构,在将数据集分为训练、验证和测试集后,探索预测原发性干燥综合征状态的代谢特征。

结果

在准确测量的 126 种代谢物中,我们确定了一个由六种代谢物组成的鉴别特征,具有预测原发性干燥综合征状态的稳健性能(ROC-AUC=0.86)。该特征包括众所周知的免疫代谢物犬尿氨酸和五种磷脂(LysoPC C28:0;PCaa C26:0;PCaaC30:2;PCae C30:1 和 PCaeC30:2)。它分为两个主要成分:第一个包含磷脂的成分与唾液腺中淋巴细胞浸润的强度有关,而第二个由犬尿氨酸代表的成分与参与者血清中抗 SSA 抗体的存在独立相关。

结论

我们的结果揭示了唇腺中的免疫脂质组学特征,能够准确地区分早期原发性干燥综合征与其他干燥症状的原因。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/b860/10375713/23b08556470a/fimmu-14-1205616-g001.jpg

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