Towards a Novel Diagnostic Tool for Autoimmune Rheumatic Diseases: Classification of SLE Using FTIR Spectroscopy and Machine Learning

Background/Objective: Systemic lupus erythematosus (SLE) is a complex autoimmune disease with clinical and serological heterogeneity. Current laboratory assays show variable performance for disease detection and risk stratification, resulting in diagnostic delays exceeding six years. There is an unmet need for improved diagnostic and monitoring tools. This study examines the clinical application of Fourier-transform infrared (FTIR) spectroscopy as a rapid, label-free technique for classifying SLE and other connective tissue diseases (CTDs). Methods: Serum samples from patients with SLE, Sjögren’s syndrome (SS), undifferentiated CTD (UCTD), and healthy controls (HCs) were analysed by FTIR spectroscopy followed by genetic algorithm–linear discriminant analysis (GA-LDA) modelling at two time points: baseline and six-month follow-up. Results: FTIR spectroscopy reliably differentiated CTDs from HCs with accuracies of 94.2% (inclusive of all time points), 99.3% at baseline, and 97.5% at follow-up. SLE patients were successfully separated from disease controls, achieving classification accuracies of 100% at baseline and 97.4% at follow-up. Subgroup analysis also demonstrated good classification accuracies for SLE (81.7%), SS (82.9%), and UCTD (86.1%), with discriminatory spectral features aligning with disease-related biochemical assignments. Notably, wavenumbers associated with proteins, carbohydrates, lipids, and DNA showed increased absorbance intensities in CTD patients, potentially reflecting immune dysregulation and metabolic changes. Conclusions: FTIR spectroscopy can reliably distinguish patients with SLE from both other CTDs and healthy individuals and may offer greater diagnostic utility than conventional serological testing. Identification of longitudinal variations in wavenumber biomarkers further supports the clinical potential of this technique and suggests possible future applications in therapeutic monitoring and risk stratification within rheumatological autoimmune disorders.