Metabolomics Signature in Prediabetes and Diabetes: Insights From Tandem Mass Spectrometry Analysis

Author:

Jabbar Al‐Rikabi Saad Ayyal1,Etemadi Ali23ORCID,Morad Maher Mohammed1,Nowrouzi Azin1,Panahi Ghodratollah1,Mondeali Mozhgan4,Toorani‐ghazvini Mahsa3,Nasli‐Esfahani Ensieh5,Razi Farideh6,Bandarian Fatemeh6

Affiliation:

1. Department of Clinical Biochemistry, School of Medicine Tehran University of Medical Sciences Tehran Iran

2. Endocrinology and Metabolism Research Center, Endocrinology and Metabolism Clinical Sciences Institute Tehran University of Medical Sciences Tehran Iran

3. Medical Biotechnology Department, School of Advanced Technologies in Medicine Tehran University of Medical Sciences Tehran Iran

4. Department of Medical Genetics, School of Medicine Tehran University of Medical Sciences Tehran Iran

5. Diabetes Research Center Endocrinology and Metabolism Clinical Sciences Institute, Tehran University of Medical Sciences Tehran Iran

6. Metabolomics and Genomics Research Center Endocrinology and Metabolism Molecular‐Cellular Sciences Institute, Tehran University of Medical Sciences Tehran Iran

Abstract

ABSTRACTObjectiveThis study investigates the metabolic differences between normal, prediabetic and diabetic patients with good and poor glycaemic control (GGC and PGC).DesignIn this study, 1102 individuals were included, and 50 metabolites were analysed using tandem mass spectrometry. The diabetes diagnosis and treatment standards of the American Diabetes Association (ADA) were used to classify patients.MethodsThe nearest neighbour method was used to match controls and cases in each group on the basis of age, sex and BMI. Factor analysis was used to reduce the number of variables and find influential underlying factors. Finally, Pearson's correlation coefficient was used to check the correlation between both glucose and HbAc1 as independent factors with binary classes.ResultsAmino acids such as glycine, serine and proline, and acylcarnitines (AcylCs) such as C16 and C18 showed significant differences between the prediabetes and normal groups. Additionally, several metabolites, including C0, C5, C8 and C16, showed significant differences between the diabetes and normal groups. Moreover, the study found that several metabolites significantly differed between the GGC and PGC diabetes groups, such as C2, C6, C10, C16 and C18. The correlation analysis revealed that glucose and HbA1c levels significantly correlated with several metabolites, including glycine, serine and C16, in both the prediabetes and diabetes groups. Additionally, the correlation analysis showed that HbA1c significantly correlated with several metabolites, such as C2, C5 and C18, in the controlled and uncontrolled diabetes groups.ConclusionsThese findings could help identify new biomarkers or underlying markers for the early detection and management of diabetes.

Publisher

Wiley

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