UCSF

INTRODUCTION: Assessing labial salivary gland exocrinopathy is a cornerstone in primary Sjögrens 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 Sjögrens syndrome, although earlier recognition of primary Sjögrens syndrome and its effective treatment could prevent irreversible damage to labial salivary gland. This study aimed at finding early biomarkers of primary Sjögrens 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 Sjögrens syndrome (n=40) and non- primary Sjögrens syndrome sicca (n=40) participants in a prospective cohort. A metabolic signature predictive of primary Sjögrens 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 Sjögrens 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 Sjögrens syndrome from other causes of sicca symptoms.