A Closer Look At New Developments In Diabetes

David A. Farnen, BS, and Stephanie C. Wu, DPM, MSc

The prevalence of diabetes is increasing rapidly and is expected to reach epidemic proportion over the next decade. Recent research estimates that the number of people diagnosed with diabetes will rise from 23.7 million to 44.1 million between 2009 and 2034.1 The Centers for Disease Control and Prevention (CDC) further predict that up to one-third of U.S. adults could have diabetes by 2050 if Americans continue to gain weight and avoid exercise.2

   Diabetes is associated with a myriad of complications with foot ulcerations being the most common. An estimated 15 percent of all patients with diabetes will develop foot ulcers.3 About half of these ulcers become infected and 20 percent of those patients will end up with some form of lower extremity amputation.3 With the prevalence of diabetes dramatically increasing, billions of dollars are spent in the field of diabetes research for the early diagnosis, prevention and management of this disease.

   With that said, here is a closer look at current research in the field of diabetes and emerging methods of disease management.

What You Should Know About Biomarkers For Diabetes

Researchers are constantly studying biomarkers to help predict the possibility of developing certain diseases. Biomarkers can indicate a change in the expression or state of a protein that correlates with the risk or progression of a disease, or with the susceptibility of the disease to a given treatment.

   Recently, researchers from the United Kingdom have reported that microRNA (MiR) can help identify people who are likely to develop type 2 diabetes even before the onset of symptoms.4 MicroRNAs are classes of approximately 22 non-coding nucleotide regulatory ribonucleic acid (RNA) molecules that play important roles in controlling the developmental and physiological processes.5 Specifically, microRNAs regulate gene expression including differentiation and development by either inhibiting translation or inducing target degradation. MicroRNAs can also help serve as diagnostic markers to identify those who are at high risk of developing coronary and peripheral arterial disease.

   In a study of 822 people, researchers identified five specific microRNA molecules with an abnormally low concentration in blood in people with diabetes and in those who subsequently went on to develop the disorder.6 One molecule in particular, microRNA 126 (MiR-126), was among the most reliable predictors of current and future diabetes. MiR-126 is known to help with angiogenesis and regulate the maintenance of vasculature. Healthy blood vessel cells are able to release substantial quantities of MiR-126 into the bloodstream.

   However, when endothelial damage occurs, the cells retain MiR-126 and subsequently release less MiR-126 into the bloodstream. A decrease in plasma MiR-126 can therefore be an indicator of blood vessel damage and cardiovascular disease. Researchers also found that levels of MiR were lower when they gave large amounts of sugar to mice with a genetic propensity to develop diabetes.6 The MiR test can directly assess vascular endothelial damage secondary to diabetes and has a fairly low cost at around $3 per test. Clinicians may possibly be able to use this in conjunction with conventional tests in the near future.

   Plasma thrombin activatable fibrinolysis inhibitor (TAFI) antigen is another biomarker that may participate in arterial thrombosis in cardiovascular diseases and may be involved in the mechanism of vascular endothelial damage in patients with diabetes.

   Erdogan and colleagues investigated the association of plasma TAFI antigen level in the development of diabetic foot ulcers in people with type 2 diabetes.7 Specifically, researchers determined TAFI antigen levels in plasma samples in 50 patients with diabetic foot ulcers, 34 patients with diabetes but without diabetic foot ulcers, and 25 healthy individuals. The diabetic foot ulcer group and the diabetic non-ulcer group were similar in terms of mean age and sex distribution.

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