Bo Yang1, Hongqing Yin2, Jianwei Wang3, Jiali Gan4, Jingfang Li2, Rui Han2, Ming Pei1, Lili Song5, Hongtao Yang6. 1. Department of Nephrology, First Teaching Hospital of Tianjin University of Traditional Chinese Medicine, Tianjin, 301617, China. 2. School of Traditional Chinese Materia Medica, Tianjin University of Traditional Chinese Medicine, Tianjin, 301617, China. 3. Department of Nephrology, Qian'an Traditional Chinese Medicine Hospital, Tianjin, 301617, He Bei, China. 4. Department of Pathology, School of Integrative Medicine, Tianjin University of Traditional Chinese Medicine, Tianjin, 301617, China. 5. School of Traditional Chinese Materia Medica, Tianjin University of Traditional Chinese Medicine, Tianjin, 301617, China. sll0204@163.com. 6. Department of Nephrology, First Teaching Hospital of Tianjin University of Traditional Chinese Medicine, Tianjin, 301617, China. tjtcmht@126.com.
Abstract
BACKGROUND: Restless legs syndrome (RLS) is a neuromotor disorder, and dialysis patients are more likely to develop RLS. RLS often causes sleep disorders, anxiety and depression in patients. It will increase the risk of death and severely affect the life of patients. At present, RLS has not received enough recognition and attention, and the misdiagnosis rate can reach more than 10%. METHODS: The discovery set selected 30 peritoneal dialysis (PD) patients and 27 peritoneal dialysis patients with RLS (PD-RLS). A metabolomics method based on ultra performance liquid chromatography tandem quadrupole time-of-flight mass spectrometric method (UPLC-Q-TOF/MS) was used to analyze the differential metabolites of the two groups. 51 PD patients and 51 PD-RLS patients were included in the validation set. The receiver operating characteristic (ROC) analysis was used to evaluate the early diagnostic biomarkers, and the correlation between the differential metabolites and laboratory test indexes was analyzed to explore the biological function of the differential metabolites. RESULTS: Through the integrated analysis, four metabolites can be used as markers for the diagnosis of PD-RLS, including Hippuric acid, Phenylacetylglutamine, N,N,N-Trimethyl-L-alanyl-L-proline betaine and Threonic acid. Through ROC analysis, it is found that they can be used as a metabolic biomarker panel, and the area under the curve of this combination is more than 0.9, indicating that the panel has good diagnostic and predictive ability. CONCLUSION: Metabolomics based on UPLC-Q-TOF/MS technology can effectively identify the potential biomarkers, and provide a theoretical basis for the early diagnosis, prevention and treatment on PD-RLS.
BACKGROUND: Restless legs syndrome (RLS) is a neuromotor disorder, and dialysis patients are more likely to develop RLS. RLS often causes sleep disorders, anxiety and depression in patients. It will increase the risk of death and severely affect the life of patients. At present, RLS has not received enough recognition and attention, and the misdiagnosis rate can reach more than 10%. METHODS: The discovery set selected 30 peritoneal dialysis (PD) patients and 27 peritoneal dialysis patients with RLS (PD-RLS). A metabolomics method based on ultra performance liquid chromatography tandem quadrupole time-of-flight mass spectrometric method (UPLC-Q-TOF/MS) was used to analyze the differential metabolites of the two groups. 51 PD patients and 51 PD-RLS patients were included in the validation set. The receiver operating characteristic (ROC) analysis was used to evaluate the early diagnostic biomarkers, and the correlation between the differential metabolites and laboratory test indexes was analyzed to explore the biological function of the differential metabolites. RESULTS: Through the integrated analysis, four metabolites can be used as markers for the diagnosis of PD-RLS, including Hippuric acid, Phenylacetylglutamine, N,N,N-Trimethyl-L-alanyl-L-proline betaine and Threonic acid. Through ROC analysis, it is found that they can be used as a metabolic biomarker panel, and the area under the curve of this combination is more than 0.9, indicating that the panel has good diagnostic and predictive ability. CONCLUSION: Metabolomics based on UPLC-Q-TOF/MS technology can effectively identify the potential biomarkers, and provide a theoretical basis for the early diagnosis, prevention and treatment on PD-RLS.
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