Purpose

The lacrimal exocrinopathy of primary Sjögren's syndrome (pSS) is one of the main causes of severe dry eye syndrome and a burden for patients. Early recognition and treatment could prevent irreversible damage to lacrimal glands. The aim of this study was to find biomarkers in tears, using metabolomics and data mining approaches, in patients with newly-diagnosed pSS compared to other causes of dry eye syndrome.

Methods

A prospective cohort of 40 pSS and 40 non-pSS Sicca patients with dryness was explored through a standardized targeted metabolomic approach using liquid chromatography coupled with mass spectrometry. A metabolomic signature predictive of the pSS status was sought out using linear (logistic regression with elastic-net regularization) and non-linear (random forests) machine learning architectures, after splitting the studied population into training, validation and test sets.

Results

Among the 104 metabolites accurately measured in tears, we identified a discriminant signature composed of nine metabolites (two amino acids: serine, aspartate; one biogenic amine: dopamine; six lipids: Lysophosphatidylcholine C16:1, C18:1, C18:2, sphingomyelin C16:0 and C22:3, and the phoshatidylcholine diacyl PCaa C42:4), with robust performances (ROC-AUC = 0.83) for predicting the pSS status. Adjustment for age, sex and anti-SSA antibodies did not disrupt the link between the metabolomic signature and the pSS status. The non-lipidic components also remained specific for pSS regardless of the dryness severity.

Conclusion

Our results reveal a metabolomic signature for tears that distinguishes pSS from other dry eye syndromes and further highlight nine key metabolites of potential interest for early diagnosis and therapeutics of pSS.

中文翻译：

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泪液代谢组学突出了新的潜在生物标志物，用于区分干燥综合征和干眼症的其他原因

目的

原发性干燥综合征 (pSS) 的泪道外分泌病变是严重干眼综合征的主要原因之一，也是患者的负担。早期识别和治疗可以防止对泪腺造成不可逆的损害。本研究的目的是使用代谢组学和数据挖掘方法，在新诊断的 pSS 患者与干眼症的其他原因相比，发现眼泪中的生物标志物。

方法

通过使用液相色谱和质谱联用的标准化靶向代谢组学方法，对 40 pSS 和 40 名非 pSS Sicca 干燥患者的前瞻性队列进行了探索。在将研究人群分成训练、验证和测试集之后，使用线性（具有弹性网络正则化的逻辑回归）和非线性（随机森林）机器学习架构寻找预测 pSS 状态的代谢组学特征。

结果

在眼泪中准确测量的 104 种代谢物中，我们确定了一个由 9 种代谢物组成的判别特征（两种氨基酸：丝氨酸、天冬氨酸；一种生物胺：多巴胺；六种脂质：溶血磷脂酰胆碱 C16:1、C18:1、C18:2、鞘磷脂C16:0 和 C22:3，以及磷脂酰胆碱二酰基 PCaa C42:4)，在预测 pSS 状态方面具有强大的性能 (ROC-AUC = 0.83)。年龄、性别和抗 SSA 抗体的调整不会破坏代谢组学特征和 pSS 状态之间的联系。无论干燥严重程度如何，非脂质成分也保持对 pSS 的特异性。

结论

我们的研究结果揭示了眼泪的代谢组学特征，可将 pSS 与其他干眼症区分开来，并进一步突出了对 pSS 的早期诊断和治疗具有潜在意义的九种关键代谢物。