Diagnostics, cilt.16, sa.1, 2026 (SCI-Expanded, Scopus)
Background: Acute renal colic, most often caused by ureteral stones, is a common cause of emergency admissions. While non-contrast computed tomography (CT) is the diagnostic gold standard, its use is limited by radiation exposure, cost, and accessibility. Growth Differentiation Factor-15 (GDF-15) is a stress-induced cytokine elevated in various acute pathologies. This study investigated the diagnostic and predictive value of serum and urine GDF-15 in patients with acute renal colic due to ureteral stones. Methods: In this prospective observational study (January 2024–March 2025), 76 patients presenting with sudden-onset flank pain were enrolled. A total of 41 patients with radiologically confirmed ureteral stones formed the stone-positive group, and 35 patients without urinary pathology served as controls. Serum and urine GDF-15 levels were measured by ELISA, along with routine laboratory tests. CT was used to assess stone characteristics, hydronephrosis grade, and ureteral wall thickness. Group comparisons were performed using the Mann–Whitney U test, correlations with Spearman’s test, and diagnostic performance with ROC analysis. Results: Both serum and urine GDF-15 levels were significantly higher in stone-positive patients (p < 0.001). Urine GDF-15 demonstrated excellent diagnostic accuracy (AUC = 0.986; sensitivity = 92.7%; specificity = 91.4), while serum GDF-15 showed moderate performance (AUC = 0.767). GDF-15 levels showed modest positive correlations with CRP and were numerically higher in patients with ureteral wall thickness > 1 mm and proximal stones. No significant association was found with spontaneous stone passage (p > 0.05). Conclusions: Urine GDF-15 shows promising diagnostic accuracy for ureteral stones and may serve as a non-invasive adjunctive tool when imaging is limited. While associated with inflammation and stone location, it did not predict spontaneous stone passage. These findings support its potential as a clinical biomarker, though further large-scale validation is required.