Application of Surface-Enhanced Raman Spectroscopy and Machine Learning Omics Techniques in the Progression Assessment of Autosomal Dominant Polycystic Kidney Disease.

Introduction: The progression evaluation of autosomal dominant polycystic kidney disease (ADPKD) is critical to the treatment strategy and prognosis prediction of the disease, but the current evaluation methods are time-consuming and costly. Methods: ADPKD patients were recruited in Shanghai Changzheng Hospital from February 2023 to October 2023. Based on the Mayo imaging classification (MIC) model, patients with different disease progression rates were classified into slowly progressive (SP) group (MIC 1A, 1B) or rapidly progressive (RP) group (MIC 1C, 1D, and 1E). The Raman spectra of urine samples from ADPKD patients were obtained by Surface-enhanced Raman spectroscopy (SERS) protocol and the characteristics of urine Raman spectra from SP and RP groups were analyzed. The ADPKD progression prediction model was constructed based on principal component analysis (PCA) and support vector machine (SVM). Results: After adjusting for age, gender, and renal function, there were differences in Raman intensity of the six major Raman peaks (758/794/1,184/1,288/1,346/1,385 cm Conclusions: Urinary SERS detection can distinguish RP ADPKD patients from SP ones noninvasively, conveniently and quickly, with reasonable accuracy, sensitivity, and specificity. SERS combined with machine learning omics techniques might be a novel method to evaluate the progression of ADPKD.