Xuan Liu1, Xiuqing Gao2, Rui Zhang3, Ziyan Liu4, Na Shen5, Yanbo Di5, Tao Fang5, Huanming Li6, Fengshi Tian7. 1. Pharmacy Department, Tianjin 4th Center Hospital, Tianjin 300140, China; Central Laboratory, Tianjin 4th Center Hospital, Tianjin 300140, China; Tianjin Fourth Central Hospital, Tianjin 300140, China. Electronic address: liuxuan0727bo@163.com. 2. Pharmacy Department, Tianjin 4th Center Hospital, Tianjin 300140, China; Hospital Acquired Infection Control Department, Tianjin 4th Center Hospital, Tianjin 300140, China; Tianjin Fourth Central Hospital, Tianjin 300140, China. Electronic address: gaoxiuqing0809@163.com. 3. Hospital Acquired Infection Control Department, Tianjin 4th Center Hospital, Tianjin 300140, China; Tianjin Fourth Central Hospital, Tianjin 300140, China. 4. Pharmacy Department, Tianjin 4th Center Hospital, Tianjin 300140, China; Tianjin Fourth Central Hospital, Tianjin 300140, China. 5. Central Laboratory, Tianjin 4th Center Hospital, Tianjin 300140, China; Tianjin Fourth Central Hospital, Tianjin 300140, China. 6. Department of Cardiology, Tianjin 4th Center Hospital, Tianjin 300140, China; Tianjin Fourth Central Hospital, Tianjin 300140, China. Electronic address: lihuanming0716@163.com. 7. Department of Cardiology, Tianjin 4th Center Hospital, Tianjin 300140, China; Tianjin Fourth Central Hospital, Tianjin 300140, China. Electronic address: fengshitian0801011@126.com.
Abstract
INTRODUCTION: Diabetes mellitus (DM) and metabolic syndrome (MetS) are systemic metabolic disorders, which have risk factors for diabetic cardiovascular and cerebral microvascular disease. It is very important to screen the metabolic biomarkers between DM and MetS patients, which can make patients benefit to a greater extent and prevent the occurrence of disease in advance. OBJECTIVES: Diabetes mellitus (DM) and metabolic syndrome are a complex, chronic illness with a pronounced impact on the quality of life of many people. However, understanding the metabolic changes in patients and identifying high-risk individuals is crucial for prevention and disease management strategies. METHODS: In this study, a nontargeted metabolomics approach based on UPLC-Q-TOF/MS was used to find the differential metabolites in serum samples from patients with DM and MetS. RESULTS: Metabonomic analysis reveals metabolic differences between DM and HC with significant differences more than 60 metabolites. While, more than 65 metabolites have significant differences between MetS and HC. The independent disturbed pathway in the DM group was the FoxO signaling pathway. The independent disturbed pathways in the MetS group were the alpha-linolenic acid metabolism, glycerophospholipid metabolism and pyrimidine metabolism. The independent disturbed metabolites and the logistic regression result showed that betaine, alpha-linolenic acid, d-mannose, l-glutamine and methylmalonic acid can be used as a combinatorial biomarker to distinguish DM from healthy control. L-isoleucine, l-glutamine, PC(16:0/16:0), alpha-d-glucose, ketoisocaproic acid, d-mannose, uridine can be used as a combinatorial biomarker in MetS. CONCLUSION: Our findings, on one hand, provide critical insight into the pathological mechanism of DM and MetS. On the other hand, supply a combinatorial biomarker to aid the diagnosis of diseases in clinical usage.
INTRODUCTION:Diabetes mellitus (DM) and metabolic syndrome (MetS) are systemic metabolic disorders, which have risk factors for diabetic cardiovascular and cerebral microvascular disease. It is very important to screen the metabolic biomarkers between DM and MetSpatients, which can make patients benefit to a greater extent and prevent the occurrence of disease in advance. OBJECTIVES:Diabetes mellitus (DM) and metabolic syndrome are a complex, chronic illness with a pronounced impact on the quality of life of many people. However, understanding the metabolic changes in patients and identifying high-risk individuals is crucial for prevention and disease management strategies. METHODS: In this study, a nontargeted metabolomics approach based on UPLC-Q-TOF/MS was used to find the differential metabolites in serum samples from patients with DM and MetS. RESULTS: Metabonomic analysis reveals metabolic differences between DM and HC with significant differences more than 60 metabolites. While, more than 65 metabolites have significant differences between MetS and HC. The independent disturbed pathway in the DM group was the FoxO signaling pathway. The independent disturbed pathways in the MetS group were the alpha-linolenic acid metabolism, glycerophospholipid metabolism and pyrimidine metabolism. The independent disturbed metabolites and the logistic regression result showed that betaine, alpha-linolenic acid, d-mannose, l-glutamine and methylmalonic acid can be used as a combinatorial biomarker to distinguish DM from healthy control. L-isoleucine, l-glutamine, PC(16:0/16:0), alpha-d-glucose, ketoisocaproic acid, d-mannose, uridine can be used as a combinatorial biomarker in MetS. CONCLUSION: Our findings, on one hand, provide critical insight into the pathological mechanism of DM and MetS. On the other hand, supply a combinatorial biomarker to aid the diagnosis of diseases in clinical usage.
Authors: Ulla Sovio; Gemma L Clayton; Emma Cook; Francesca Gaccioli; D Stephen Charnock-Jones; Deborah A Lawlor; Gordon C S Smith Journal: J Clin Endocrinol Metab Date: 2022-07-14 Impact factor: 6.134