Prediction of Type 2 Diabetes at Pre-diabetes Stage by Mass Spectrometry: A Preliminary Study

International Journal of Peptide Research and Therapeutics(2022)

Cited 1|Views4
No score
Abstract
Globally, diabetes mellitus is considered as emerging health emergency of twenty-first century. According to the International Diabetic Federation (IDF), 537 million people are currently living with diabetes and this number is projected to increase up to 783 million by 2045 (IDF Report 10th edition 2021). Individuals with type 2 diabetes remain asymptomatic for years or have already lost 50% to 70% of their pancreatic beta cells at the time of diagnosis. The aim of the present study was to discover more sensitive potential protein biomarker candidates capable of predicting progression to hyperglycemia before its onset (at pre-diabetes stage). To accomplish this, nano-liquid chromatography mass spectrometry (nano-LCMS) technique was applied to explore proteome of serum sample from people with normal glucose level (HbA1c ≤ 5.7%), pre-diabetes (HbA1c > 5.7% to < 6.5%), and type 2 diabetes (HbA1c ≥ 6.5%). Differential protein expression of 57 proteins were found between the study groups. Among these 12 proteins were found to be significantly differentially expressed. Proteins SERPINC1, APOA4, AHSG, ITIH4, A2M, VTN, HBB, JCHAIN, and IGHM were found common and with significantly higher expression in both pre-diabetes and diabetes groups as compared to the control. While proteins APOA2, CFB, and APCS were found exclusively significantly high in pre-diabetes group as compared to the diabetes group. The GO analysis resulted that these proteins are known to be performing the functions of modulator proteins, transfer/ carrier proteins, and most importantly are involved in inflammatory processes along with regulation of hydrolases and endopeptidases. The identified proteins hold potential for being early screening/diagnostic biomarker candidates of type 2 diabetes after further validation.
More
Translated text
Key words
Type 2 diabetes,Prediabetes,Proteomics,Mass spectrometry
AI Read Science
Must-Reading Tree
Example
Generate MRT to find the research sequence of this paper
Chat Paper
Summary is being generated by the instructions you defined