What question did this study set out to answer?

The central aim is to evaluate advances in diagnostics for primary Sjögren’s syndrome, focusing on biomarkers and clinical translation.

April 1, 2026Open Access

Precision Diagnostics in Primary Sjögren’s Syndrome: Advances in Biomarkers, Epigenetic Markers, Immune Pathways, and Clinical Translation

Key Points

The central aim is to evaluate advances in diagnostics for primary Sjögren’s syndrome, focusing on biomarkers and clinical translation.
Assessment of new autoantibody targets related to primary Sjögren’s syndrome.
Exploration of non-invasive biomarkers using salivary and tear fluid proteomics.
Integration of machine learning and artificial intelligence with multi-omics data for diagnostic accuracy.
Evaluation of AI-assisted methods for quantifying glandular inflammation.
Identification of novel targets like DTD2 and RESF1 alongside established biomarkers.
Non-invasive biomarkers show potential for earlier disease detection than conventional serologies.
Machine learning models achieve similar diagnostic accuracy to biopsies using multi-omics.
AI techniques enhance biomarker discovery and prediction of treatment outcomes.

Abstract

Primary Sjögren’s syndrome (pSS) is a systemic autoimmune disease defined by exocrine gland infiltration and systemic involvement. The management of pSS is hampered by three persistent challenges: seronegativity, heterogeneity, and delayed diagnosis. Up to one-third of patients lack anti-Sjögren’s-syndrome-related antigen A/B (SSA/SSB) autoantibodies, contributing to misclassification and delayed recognition. Recent studies have expanded the autoantibody repertoire, identifying novel targets such as anti-D-aminoacyl-tRNA deacylase 2 (DTD2), anti-retroelement silencing factor-1 (RESF1), and anti-calreticulin (CALR), as well as multiplex panels including anti-salivary protein-1 (SP-1), anti-parotid secretory protein (PSP), and anti-carbonic anhydrase VI (CA6). These can detect disease before conventional seroconversion, thus offering diagnostic value for seronegative cases. The greatest challenge remains early detection, as the current reliance on biopsy and late-appearing serologies overlooks subclinical disease. In this context, non-invasive fluid biomarkers are transformative, with salivary and tear fluid proteomics (β2-microglobulin, clusterin, matrix metalloproteinase-9), exosomal micro ribonucleic acid (miRNAs), and metabolomic fingerprints providing sensitive indicators of glandular dysfunction and immune activation. When combined with machine learning, integrated multi-omics panels can achieve diagnostic accuracies comparable to biopsy while enabling prognostic stratification. Emerging approaches also leverage artificial intelligence (AI) to refine biomarker discovery and clinical translation. AI-assisted ultrasonography enables reproducible quantification of glandular inflammation, while the application of integrative AI models to multi-omics datasets can identify biomarker signatures with superior predictive accuracy. Such tools have the potential to accelerate early diagnosis, automate risk prediction, and guide precision therapeutics in real time. The future use of biomarker panels in clinical practice should reduce the time to diagnosis, thereby facilitating the anticipation of risk and the provision of therapy based on the underlying cause. In this review, we describe how pSS exemplifies some of the problems inherent in contemporary autoimmunity. This multifaceted and diverse condition is now well-positioned to benefit from integrative, biomarker-driven methodologies, which should lead to improved patient outcomes.

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