Integrated metabolomic and cytokine profiling reveals biomarkers across the clinical spectrum of lupus nephritis

Introduction Lupus nephritis (LN) represents one of the most frequent and severe manifestations of systemic lupus erythematosus (SLE), highlighting an urgent need for novel non-invasive approaches to improve its diagnosis and clinical monitoring. Objectives To characterize serum and urinary metabolomic profiles, together with urinary cytokine expression, in patients with lupus nephritis (LN) across the clinical spectrum from active disease, post-induction response, and sustained remission. Methods We enrolled 62 patients fulfilling the 2019 ACR/EULAR SLE criteria, divided into three groups: active LN (n=21), post-induction responders at 6 months (n=21), and sustained responders with ≥2 years of remission (n=20). Untargeted metabolomic profiling of serum and urine was performed using GC-MS, and urinary cytokines were measured by luminometry. Statistical analyses included Kruskal-Wallis tests and multinomial logistic regression. Results Median age was 36 years; 87% were female. Twelve serum metabolites and ten urinary metabolites differed significantly across groups. Active LN patients showed increased urinary threonate and elevated cytokines (15 out of 17 measured, including IL-10). Serum glycerol was significantly higher in post-induction responders compared to sustained responders, while urinary sucrose was lower in this group. Logistic regression identified urinary threonate as discriminative for active LN, whereas serum glycerol (OR 1.05, 95% CI 1.048–1.050) and urinary sucrose (OR 0.98, 95% CI 0.9802–0.981) differentiated post-induction from sustained response. Increased urinary IL-10 was strongly associated with active LN, while G-CSF discriminated post-induction from sustained responders. Discussion and conclusions The combined analysis of serum/urinary metabolomic profiles and urinary cytokines provides discriminatory power to differentiate active LN from remission states, supporting their potential role as non-invasive biomarkers for diagnosis and treatment monitoring.