Protein glycation – biomarkers of metabolic dysfunction and early-stage decline in health in the era of precision medicine
Author | Naila, Rabbani |
Author | Thornalley, Paul J. |
Available date | 2023-06-22T04:35:04Z |
Publication Date | 2021-06-30 |
Publication Name | Redox Biology |
Identifier | http://dx.doi.org/10.1016/j.redox.2021.101920 |
ISSN | 22132317 |
Abstract | Protein glycation provides a biomarker in widespread clinical use, glycated hemoglobin HbA1c (A1C). It is a biomarker for diagnosis of diabetes and prediabetes and of medium-term glycemic control in patients with established diabetes. A1C is an early-stage glycation adduct of hemoglobin with glucose; a fructosamine derivative. Glucose is an amino group-directed glycating agent, modifying N-terminal and lysine sidechain amino groups. A similar fructosamine derivative of serum albumin, glycated albumin (GA), finds use as a biomarker of glycemic control, particularly where there is interference in use of A1C. Later stage adducts, advanced glycation endproducts (AGEs), are formed by the degradation of fructosamines and by the reaction of reactive dicarbonyl metabolites, such as methylglyoxal. Dicarbonyls are arginine-directed glycating agents forming mainly hydroimidazolone AGEs. Glucosepane and pentosidine, an intense fluorophore, are AGE covalent crosslinks. Cellular proteolysis of glycated proteins forms glycated amino acids, which are released into plasma and excreted in urine. Development of diagnostic algorithms by artificial intelligence machine learning is enhancing the applications of glycation biomarkers. Investigational glycation biomarkers are in development for: (i) healthy aging; (ii) risk prediction of vascular complications of diabetes; (iii) diagnosis of autism; and (iv) diagnosis and classification of early-stage arthritis. Protein glycation biomarkers are influenced by heritability, aging, decline in metabolic, vascular, renal and skeletal health, and other factors. They are applicable to populations of differing ethnicities, bridging the gap between genotype and phenotype. They are thereby likely to find continued and expanding clinical use, including in the current era of developing precision medicine, reporting on multiple pathogenic processes and supporting a precision medicine approach. |
Language | en |
Publisher | Elsevier |
Subject | Glycated hemoglobin Fructosamine Methylglyoxal Diabetes Chronic kidney disease Machine learning |
Type | Article |
Volume Number | 42 |
Open Access user License | http://creativecommons.org/licenses/by-nc-nd/4.0/ |
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