Metabolite Biomarkers and Predictive Model Analysis for Patients with Type 2 Diabetes Mellitus With and Without Complications
Objective: Understanding the pathogenesis of type 2 diabetes mellitus including the interaction between the inherent susceptibility, lifestyles, and environment is believed to cast hope to predict, prevent, and personalize cure for type 2 diabetes mellitus and its complications. To identify the diff...
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2023
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2-s2.0-85171802568 Kek T.L.; Rofiee M.S.; Ghani R.A.; Nor N.A.M.; Salleh M.Z. Metabolite Biomarkers and Predictive Model Analysis for Patients with Type 2 Diabetes Mellitus With and Without Complications 2023 Endocrinology Research and Practice 27 3 10.5152/erp.2023.23224 https://www.scopus.com/inward/record.uri?eid=2-s2.0-85171802568&doi=10.5152%2ferp.2023.23224&partnerID=40&md5=58dc7dee7565f5b1407096d5c650f52b Objective: Understanding the pathogenesis of type 2 diabetes mellitus including the interaction between the inherent susceptibility, lifestyles, and environment is believed to cast hope to predict, prevent, and personalize cure for type 2 diabetes mellitus and its complications. To identify the differentially expressed metabolites as potential diabetes-associated metabolite biomarkers that identify individuals with and without diabetes. Methods: Sixty-four subjects were recruited to identify the systemic metabolic changes and biomarkers related to type 2 diabetes mellitus, and the related complications (ischemic heart disease and chronic kidney disease) using quadrupole time-of-flight liquid chromatography coupled to mass spectrometry. The top 5 biomarkers were identified, and the prediction accuracies for models developed by 4 algorithms were compared. Result: Tyrosine, tryptophan, glycerophospholipid, porphyrin and chlorophyll, sphingolipid metabolism, and glyco sylph ospha tidyl inosi tol-a nchor biosynthesis were the lipids and amino acid-related pathways differentially regulated in the type 2 diabetes mellitus patients compared to normal subjects and patients with complications. Hydroxyprolyl-leucine and N-palmitoyl threonine were higher in patients; 4,4ʹ- Thiob is-2- butan one, geran yl-hy droxy benzo ate, and Sesamex were higher in patients with chronic kidney disease complications; Asp Glu Trp, Trp Met Met were higher in patients with type 2 diabetes mellitus and ischemic heart disease compared to those normal subjects without risk. Random forest produced a consistently higher accuracy of more than 70% in the prediction for all the comparison groups. Pathways perturbated and biomarkers differentially regulated in individuals with risks or with the existing conditions of type 2 diabetes mellitus and its complications of ischemic heart disease and chronic kidney disease were identified using time-of-flight liquid chromatography coupled to mass spectrometry. Conclusion: Metabolomics is a new emerging field that provides comprehensive phenotypic information on the disease and drug response of a patient. It serves as a potential comprehensive therapeutic drug monitoring approach to be adopted in the near future for pharmaceutical care. © 2023, AVES. All rights reserved. AVES 28226135 English Article All Open Access; Gold Open Access |
author |
Kek T.L.; Rofiee M.S.; Ghani R.A.; Nor N.A.M.; Salleh M.Z. |
spellingShingle |
Kek T.L.; Rofiee M.S.; Ghani R.A.; Nor N.A.M.; Salleh M.Z. Metabolite Biomarkers and Predictive Model Analysis for Patients with Type 2 Diabetes Mellitus With and Without Complications |
author_facet |
Kek T.L.; Rofiee M.S.; Ghani R.A.; Nor N.A.M.; Salleh M.Z. |
author_sort |
Kek T.L.; Rofiee M.S.; Ghani R.A.; Nor N.A.M.; Salleh M.Z. |
title |
Metabolite Biomarkers and Predictive Model Analysis for Patients with Type 2 Diabetes Mellitus With and Without Complications |
title_short |
Metabolite Biomarkers and Predictive Model Analysis for Patients with Type 2 Diabetes Mellitus With and Without Complications |
title_full |
Metabolite Biomarkers and Predictive Model Analysis for Patients with Type 2 Diabetes Mellitus With and Without Complications |
title_fullStr |
Metabolite Biomarkers and Predictive Model Analysis for Patients with Type 2 Diabetes Mellitus With and Without Complications |
title_full_unstemmed |
Metabolite Biomarkers and Predictive Model Analysis for Patients with Type 2 Diabetes Mellitus With and Without Complications |
title_sort |
Metabolite Biomarkers and Predictive Model Analysis for Patients with Type 2 Diabetes Mellitus With and Without Complications |
publishDate |
2023 |
container_title |
Endocrinology Research and Practice |
container_volume |
27 |
container_issue |
3 |
doi_str_mv |
10.5152/erp.2023.23224 |
url |
https://www.scopus.com/inward/record.uri?eid=2-s2.0-85171802568&doi=10.5152%2ferp.2023.23224&partnerID=40&md5=58dc7dee7565f5b1407096d5c650f52b |
description |
Objective: Understanding the pathogenesis of type 2 diabetes mellitus including the interaction between the inherent susceptibility, lifestyles, and environment is believed to cast hope to predict, prevent, and personalize cure for type 2 diabetes mellitus and its complications. To identify the differentially expressed metabolites as potential diabetes-associated metabolite biomarkers that identify individuals with and without diabetes. Methods: Sixty-four subjects were recruited to identify the systemic metabolic changes and biomarkers related to type 2 diabetes mellitus, and the related complications (ischemic heart disease and chronic kidney disease) using quadrupole time-of-flight liquid chromatography coupled to mass spectrometry. The top 5 biomarkers were identified, and the prediction accuracies for models developed by 4 algorithms were compared. Result: Tyrosine, tryptophan, glycerophospholipid, porphyrin and chlorophyll, sphingolipid metabolism, and glyco sylph ospha tidyl inosi tol-a nchor biosynthesis were the lipids and amino acid-related pathways differentially regulated in the type 2 diabetes mellitus patients compared to normal subjects and patients with complications. Hydroxyprolyl-leucine and N-palmitoyl threonine were higher in patients; 4,4ʹ- Thiob is-2- butan one, geran yl-hy droxy benzo ate, and Sesamex were higher in patients with chronic kidney disease complications; Asp Glu Trp, Trp Met Met were higher in patients with type 2 diabetes mellitus and ischemic heart disease compared to those normal subjects without risk. Random forest produced a consistently higher accuracy of more than 70% in the prediction for all the comparison groups. Pathways perturbated and biomarkers differentially regulated in individuals with risks or with the existing conditions of type 2 diabetes mellitus and its complications of ischemic heart disease and chronic kidney disease were identified using time-of-flight liquid chromatography coupled to mass spectrometry. Conclusion: Metabolomics is a new emerging field that provides comprehensive phenotypic information on the disease and drug response of a patient. It serves as a potential comprehensive therapeutic drug monitoring approach to be adopted in the near future for pharmaceutical care. © 2023, AVES. All rights reserved. |
publisher |
AVES |
issn |
28226135 |
language |
English |
format |
Article |
accesstype |
All Open Access; Gold Open Access |
record_format |
scopus |
collection |
Scopus |
_version_ |
1809678016885817344 |