New technology may change the diagnosis and management of diabetic neuropathy

15 November 2018

Diabetic neuropathy is one of the most common complications seen in patients suffering from diabetes worldwide, with rates of neuropathies seen in up to 50% of diabetic patients (Gordois et al., 2003a; Gordois et al., 2003b). Medoc has been developing different tools which, with the use of thermal and vibratory stimuli, are able to evaluate the type and level of impairment in different nerve fibers.

Medoc TSA-II NeuroSensory Analyzer and the VSA-3000 Vibratory Sensory Analyzer enable quantitative evaluation of the integrity of both small and large-caliber sensory nerve fibers and provide quantitative evidence of neuropathy in patients suffering from diabetic neuropathy. These advanced clinical tools allow physicians to intervene early in the progression of this condition, enhancing patients’ quality of life and reducing healthcare costs.

High blood glucose levels seen in diabetes often lead to nerve damage, including fibers in the autonomic nervous system, and most commonly small and large fibers in the peripheral sensory nervous system (Llewelyn, 2003). The occurrence and severity of diabetic polyneuropathy have been shown to be highly correlated with longer exposure to high blood glucose levels (Dyck et al., 1999).

The most common type of  neuropathy in diabetes  is called ‘distal symmetric polyneuropathy’(DSPN) and is defined as “symmetrical, length dependent sensorimotor polyneuropathy attributable to chronic hyperglycemia, associated cardiovascular risk covariates and microvessel alterations” (Dyck et al., 2011). Up to 26.4% of patients with peripheral neuropathy experience pain which arises as a result of the damage created in the somatosensory system (Greig et al., 2014).

Symptoms vary in their presentation depending on the type of sensory fibers that are damaged and the cumulative time of exposure to high blood glucose levels (Pop-Busui et al., 2017). Small nerve fibers tend to be the first to be involved and, as mentioned before, lead to pain and a sensation of burning (Albers et al., 2014). When large nerve fibers are also compromised, symptoms include numbness and tingling (paresthesia). These sensory deficits, together with alterations in blood flow, might lead to a common complication in diabetes,which is limb, and mostly foot ulceration, and ultimately, amputation.

To date, there are no effective treatment protocols that allow reversing diabetic neuropathies once they occur. Therefore, most researchers and clinicians have focused on developing prevention tools and early interventions. The most common preventive intervention is blood glucose levels monitoring, in order to maintain a normal glucose level and avoid early complications (Pop-Busui et al., 2017). However, this prevention intervention relies heavily on patients’ habits and their ability to follow very strict rules in their lifestyle, thus making it quite difficult to eliminate the increased risk of complications.

One of the gold-standard methods for the assessment of small fiber functionality is quantitative sensory testing (QST). Medoc is a leading QST device developer and manufacturer. Several diabetes studies have attempted to find measuring tools that could be adopted in order to quantitatively and qualitatively assess the presence and type of neuropathy in diabetic patients. Jia et al. (2014) found that QST is sensitive for the diagnosis of peripheral neuropathy, specifically when using warm detection thresholds. With the use of QST, clinicians can detect damage and dysfunction of nerve fibers early in the development of the disease. Additionally, QST can also be used as a tool to monitor the development of the neuropathy and the effectiveness of attempted treatments. Recent research has shown that the use of QST methods measuring c detection threshold (CDT) is an effective and validated assessment tool, providing a non-invasive way to recognize clinical and preclinical polyneuropathy in both type 1 and type 2 diabetes (Farooqi et al., 2016; Lysy et al., 2014).