Wu Yaling, Wang Zijie, Xing Mengmeng, Li Bingyan, Liu Zhiyuan, Du Peng, Yang Huinan, Wang Xiaolei
Department of Gastroenterology, Shanghai Tenth People's Hospital, Tongji University School of Medicine, Shanghai, 200072, People's Republic of China.
School of Energy and Power Engineering, University of Shanghai for Science and Technology, Shanghai, 200093, People's Republic of China.
J Inflamm Res. 2022 Feb 9;15:897-910. doi: 10.2147/JIR.S341871. eCollection 2022.
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.
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.
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, =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, <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 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.
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.
克罗恩病（CD）是一种慢性复发性肠道炎症性疾病，需要反复进行侵入性检查。目前缺乏方便且无创的CD诊断工具。表面增强拉曼光谱（SERS）能够快速提供各种样本中的特定代谢物信息。我们之前的研究表明，尿液拉曼光谱可以无创地区分CD患者和健康对照。在本研究中，我们进一步探讨了尿液拉曼光谱在识别CD患者疾病特征方面的价值。
通过SERS分析尿液样本，以获取100例活动期CD（aCD）患者和88例非活动期CD（iCD）患者光谱的特定变化。通过主成分分析和支持向量机（PCA-SVM）评估SERS产生的分类器模型的准确性，以研究光谱差异和疾病特征。
基于一组16条特定拉曼光谱建立了分类器模型，用于预测aCD和iCD患者之间的疾病活动，其效能高于粪便钙卫蛋白（AUC值，0.864对0.596，P = 0.02）。在留一法交叉验证后，分类器模型仍获得75.5%的准确率。相关性分析表明，其与内镜检查结果呈负相关（r = -0.616，P < 0.0001）。我们进一步建立了用于识别疾病部位的分类器模型，以区分结肠型和回肠型CD，准确率为63.6%，1643 cm波段强度显著增加；还建立了用于预测肿瘤坏死因子抑制剂反应者治疗前后光谱变化的模型，基于一组11条特定光谱，准确率为91.2%。SERS观察到CD患者尿液中氨基酸、蛋白质、脂质及其他化合物的代谢变化。
尿液拉曼光谱的特定变化可反映尿液代谢变化。它有潜力成为一种方便、无创的用于CD患者疾病特征诊断的有前景的工具。
