2018
DOI: 10.1002/biof.1423
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Studies of advanced glycation end products and oxidation biomarkers for type 2 diabetes

Abstract: Advanced glycation end products (AGEs) are formed upon nonenzymatic reactions of sugars or their metabolites with proteins and other cellular constituents. Many AGEs are long lived. Recent findings suggest that AGEs may predict diabetes and its complications and thus may warrant further study. The objective of this study was to assess the validity of our experimental procedures for measuring AGEs in stored blood sample and to conduct a pilot study for developing AGE biomarkers for diabetes and/or age-related c… Show more

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Cited by 27 publications
(27 citation statements)
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“…Glucosepane is the major quantitative crosslink formed in protein glycation, formed from FL residues [42]. It is a marker of glycemic control [43] and the major glycation adduct released from joint proteins during early-stage development of osteoarthritis [41]. Pentosidine is pentose sugar-derived AGE and intense fluorophore.…”
Section: Protein Ageomics and Other Modifications As Reporters Of Metmentioning
confidence: 99%
“…Glucosepane is the major quantitative crosslink formed in protein glycation, formed from FL residues [42]. It is a marker of glycemic control [43] and the major glycation adduct released from joint proteins during early-stage development of osteoarthritis [41]. Pentosidine is pentose sugar-derived AGE and intense fluorophore.…”
Section: Protein Ageomics and Other Modifications As Reporters Of Metmentioning
confidence: 99%
“…We also identified the cross-linking AGE glucosepane as a potential plasma biomarker of AMD [148]. Plasma glucosepane may also be a biomarker of diabetes [149]. The accumulation of AGEs with a high glycemic index diet was not irreversible.…”
Section: New Directions and Approachesmentioning
confidence: 99%
“…Improved glycemic markers may emerge from a combination of biomarker responses assessed, particularly by machine learning techniques with data-driven selection of optimum biomarker combinations, and further validation of candidate markers in blood samples collected and stored under conventional routine clinical conditions for ease of clinical translation (e.g., use of machine learning in plasma metabolomics studies to identify metabolite combinations that predict progression to diabetes [ 115 ] and application of an LC-MS/MS assay of AGEs in stored repository blood samples [ 116 ]).…”
Section: Discussionmentioning
confidence: 99%