Diabetes, although a disease in and of itself, also puts the individual at risk to a number of other diseases, some of which can be serious or life-threatening. Studies of diabetes pathogenesis have revealed alanine aminotransferase levels as a predictive biomarker for diabetes, along with other biomarkers such as GGT, triglyceride, plasminogen activator inhibitor, tissue plasminogen activator antigen, ferritin, C-reactive protein, and sex hormone-binding globulin (SHBG), to name just a few.1 There are also studies identifying numerous predictive biomarkers of diabetes. Can we reliably test for a biomarker or subset of biomarkers for diabetes before its onset?
We have been reliant on HbA1c as a biomarker for managing diabetes for over two decades. Yet still, approximately 20%-30% of diabetics develop nephropathy and now account for almost half the patients in the U.S. with end-stage renal disease.2 HbA1c has also been implicated in a number of other diseases, such as cardiovascular disease and circulating cholesterol, triglycerides and lipids. It is unclear whether or not the association between HbA1c and diabetes is independent of these other co-conditions.3
So far, with all of these studies looking for a biomarker for diabetes, it remains difficult to apply the findings in a clinical setting. Should patient management be our priority and should our focus shift towards finding a biomarker for the vast array of complications that can arise from being diabetic, such as blood vessel damage, increased risk of cardiovascular disease, stroke, and blindness?4 Although regular screening and checkups are a part of diabetic maintenance, could we be applying proteomics technology better to predicting an individual’s risk of developing complications?
Alkhalaf et al.5 have produced data suggesting urine collagen fragments as biomarkers for diabetes-induced renal damage. These may be useful as early predictors of diabetic nephropathy. The study looked at type 2 diabetes patients with nephropathy and a control group of type 2 diabetics without nephropathy. Candidate biomarkers were sequenced using CEMS/MS or LC-MS/MS (Thermo Scientific). The group was able to achieve high specificity for diabetic neuropathy, particularly compared with the currently used urinary albumin measures. The strength of the data certainly instills confidence that this method for detecting neuropathy could significantly improve diagnosis at earlier stages of this co-diabetic disease.
Similarly, a number of studies have looked at earlier detection of retinopathy, another risk associated with diabetes.6
Studies such as this one provide argument for an approach to finding a biomarker for diabetes that is more focussed on emergent complications than on biomarkers for the disease itself, which remain elusive.
1. Sattar, N. (2012) ‘Biomarkers for diabetes prediction, pathogenesis or pharmacotherapy guidance? Past, present and future possibilities‘, Diabetic Medicine, 29 (1), (pp. 5-13)
2. Molitch, M.E., et al. (2004) ‘Nephropathy in diabetes‘, Diabetes Care, 27 (Suppl 1), (pp. 79-83)
3. Caveney, E.J. and Cohen, O.J. (2011) ‘Diabetes and biomarkers‘, Journal of Diabetes Science and Technology, 5 (1), (pp. 192-197)
4. Better Health Channel, http://www.betterhealth.vic.gov.au/bhcv2/bhcarticles.nsf/pages/Diabetes_complications?open
5. Alkhalaf, A., et al. (2010) ‘Multicentric validation of proteomic biomarkers in urine specific for diabetic nephropathy‘, PLoS ONE, 5 (10), (p. e13421)
6. Wautier, M.P., et al. (2003) ‘N(carboxymethyl)lysine as a biomarker for microvascular complications in type 2 diabetic patients‘, Diabetes & Metabolism, 29 (1), (pp. 44-52)