The DOI matches its Crossref record; 12 of the 12 references it cites were found in a registry or an index.
Biomarkers in diabetes: hemoglobin A1c, vascular and tissue markers
Open accessAbstract
Biomarkers are conventionally defined as ‘biological molecules that represent health and disease states.’ They typically are measured in readily available body fluids (blood or urine), lie outside the causal pathway, are able to detect sub-clinical disease, and are used to monitor clinical and sub-clinical disease burden and response to treatments. Biomarkers can be “direct” endpoints of the disease itself, or “indirect” or surrogate endpoints. New technologies (such as metabolomics, proteomics, genomics) bring a wealth of opportunity to develop new biomarkers. Other new technologies enable the development of non-molecular, functional or bio-physical tissue-based biomarkers. Diabetes mellitus is a complex disease affecting almost every tissue and organ system, with metabolic ramifications extending far beyond impaired glucose metabolism. Biomarkers may reflect the presence and severity of hyperglycemia (i.e. diabetes itself), or the presence and severity of the vascular complications of diabetes. Illustrative examples are considered in this brief review. In blood, hemoglobin A1c (HbA1c) may be considered as a biomarker for the presence and severity of hyperglycemia, implying diabetes or pre-diabetes, or, over time, as a “biomarker for a risk factor”, i.e. hyperglycemia as a risk factor for diabetic retinopathy, nephropathy, and other vascular complications of diabetes. In tissues, glycation and oxidative stress resulting from hyperglycemia and dyslipidemia lead to widespread modification of biomolecules by advanced glycation end products (AGEs). Some of these altered species may serve as biomarkers, whereas others may lie in the causal pathway for vascular damage. New non-invasive technologies can detect tissue damage mediated by AGE formation: these include indirect measures such as pulse wave analysis (a marker of vascular dysfunction) and more direct markers such as skin autofluorescence (a marker of long-term accumulation of AGEs). In the future, we can be optimistic that new blood and tissue-based biomarkers will enable the detection, prevention, and treatment of diabetes and its complications long before overt disease develops.
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Citations
Cites
International Expert Committee report on the role of the A1c assay in the diagnosis of diabetes
The International Expert Committee (2009) · Diabetes Care · doi:10.2337/dc09-9033 · Cited by 3,185
Diabetes Prevention Program Research Group (2009) · The Lancet · doi:10.1016/s0140-6736(09)61457-4 · Cited by 3,020 · cited 1×
“In the Diabetes Prevention Program Outcomes Study, 10-year follow-up of diabetes incidence and weight loss showed lower HbA1c and fasting glucose concentrations in the metformin (n5 924) and intensive lifestyle intervention (n 5 910) groups than the placebo group (n 5 932).(27) Mean HbA1c values were lower in the inte…”
Translating the A1C assay into estimated average glucose values
Nathan, D. M., Kuenen, J., Borg, R., Zheng, H., Schoenfeld, D., Heine, R. J., & for the A1c-Derived Average Glucose (ADAG) Study Group (2008) · Diabetes Care · doi:10.2337/dc08-0545 · Cited by 1,812
Long-term effects of intensive glucose lowering on cardiovascular outcomes
The ACCORD Study Group (2011) · New England Journal of Medicine · doi:10.1056/nejmoa1006524 · Cited by 1,035
The Role of Advanced Glycation End Products in Progression and Complications of Diabetes
Goh, S.-Y., & Cooper, M. E. (2008) · The Journal of Clinical Endocrinology & Metabolism · doi:10.1210/jc.2007-1817 · Cited by 987
Postprandial Hyperglycemia and Diabetes Complications
Ceriello, A. (2005) · Diabetes · doi:10.2337/diabetes.54.1.1 · Cited by 945
What if Minkowski Had Been Ageusic? An Alternative Angle on Diabetes
McGarry, J. D. (1992) · Science · doi:10.1126/science.1439783 · Cited by 657
Herman, W. H., Ma, Y., Uwaifo, G., Haffner, S., Kahn, S. E., Horton, E. S., Lachin, J. M., Montez, M. G., Brenneman, T., Barrett-Connor, E., et al. (2007) · Diabetes Care · doi:10.2337/dc06-2003 · Cited by 574
Gong, Q., Gregg, E. W., Wang, J., An, Y., Zhang, P., Yang, W., Li, H., Li, H., Jiang, Y., Shuai, Y., et al. (2011) · Diabetologia · doi:10.1007/s00125-010-1948-9 · Cited by 251
Metabolomics and Cardiovascular Biomarker Discovery
Rhee, E. P., & Gerszten, R. E. (2012) · Clinical Chemistry · doi:10.1373/clinchem.2011.169573 · Cited by 232
Cited by
Aslam, H., Trakman, G., Dissanayake, T., Todd, E., Harrison, P., Alby, C., Jabeen, T., Gamage, E., Travica, N., Marshall, S., et al. (2026) · Journal of Translational Medicine · doi:10.1186/s12967-025-07428-9 · Cited by 30
Meyer, J. D., Hayney, M. S., Coe, C. L., Ninos, C. L., & Barrett, B. P. (2019) · Journal of Sport and Exercise Psychology · doi:10.1123/jsep.2018-0214 · Cited by 29
Metadata
- Authors
- Lyons, T. J., & Basu, A.
- Venue
- Translational Research
- Publisher
- Elsevier BV
- Published
- 2012-04
- Volume
- 159
- Issue
- 4
- Pages
- 303-312
- ISSN
- 1878-1810 (ISSN-L)
- 1931-5244
- OpenAlex
- W2002445251
- Cited by
- 250
- References
- 12
- FWCI
- 6.82
- Type
- journal article
- Language
- en
- Open access
- green
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Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
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