The Specific Changes of Urine Raman Spectra Can Serve as Novel Diagnostic Tools for Disease Characteristics in Patients with Crohn’s Disease - Wu Y; Wang Z; Xing M; Li B; Liu Z; Du P; Yang H; Wang X (Dove Medical Press, 2022)
Yaling Wu,1 Zijie Wang,2 Mengmeng Xing,2 Bingyan Li,2 Zhiyuan Liu,2 Peng Du,3 Huinan Yang,2 Xiaolei Wang1 1Department of Gastroenterology, Shanghai Tenth People's Hospital, Tongji University School of Medicine, Shanghai, 200072, People's Republic of China; 2School of Energy and Power Engineering, University of Shanghai for Science and Technology, Shanghai, 200093, People's Republic of China; 3Department of Colorectal Surgery, Xinhua Hospital, Shanghai Jiaotong University School of Medicine, Shanghai, 200092, People's Republic of ChinaCorrespondence: Xiaolei WangDepartment of Gastroenterology, Shanghai Tenth People's Hospital, Tongji University School of Medicine, Shanghai, 200072, People's Republic of China, Tel +86-21-66313573, Email wangxiaolei@tongji.edu.cn; Huinan YangSchool of Energy and Power Engineering, University of Shanghai for Science and Technology, Shanghai, 200093, People's Republic of China, Tel +86-21-55272638, Email yanghuinan@usst.edu.cnPurpose: Crohn's disease (CD) is a chronic recurrent intestinal inflammatory disease that requires repeated invasive examinations. Convenient and noninvasive diagnostic tools for CD are lacking. Surface-enhanced Raman spectroscopy (SERS) can rapidly provide specific metabolite information in various samples. Our previous study has showed urine Raman spectrum can distinguish CD patients from healthy controls noninvasively. In this study, we further investigated the value of urine Raman spectra on identifying the disease characterizations in patients with CD.Patients and Methods: Urine samples were analyzed by SERS to acquire specific changes of the spectra from 100 active CD (aCD) patients and 88 inactive CD (iCD) patients. The accuracy of classifier models yielded by SERS was assessed by principal component analysis and support vector machine (PCA-SVM) to investigate spectral differences and disease characterizations.Results: Given a panel of 16 specific Raman spectra, the classifier model was established to predict disease activity between patients with aCD and iCD and achieved higher efficacy than fecal calprotectin (AUC value, 0.864 vs 0.596, P=0.02). After leave-one-patient-out cross-validation, the classifier model still obtained 75.5% of accuracy. The correlation analysis showed it had negative correlation with endoscopic results (r=− 0.616, P< 0.0001). We further established the classifier model in identifying disease location to discriminate colonic-type from ileal-type CD with 63.6% of accuracy with the significantly increased intensity of 1643 cm− 1 band, and the model to predict the spectra changes of before and after treatment in tumor necrosis factor inhibitor responders with 91.2% of accuracy with a panel of 11 specific spectra. The metabolic changes of amino acids, proteins, lipids, and other compounds in urine levels were noted by SERS in patients with CD.Conclusion: The specific changes of urine Raman spectra can reflect changes in urine metabolism. It has the potential value on being the promising diagnostic tool for disease characterizations in CD patients by a convenient and noninvasive way.Keywords: Crohn's disease, urine, Raman spectra, disease characterization