For one-third of people with type 2 diabetes, complications have already set in by the time they are diagnosed. With early diagnosis, these complications could be delayed or even prevented through lifestyle interventions such as proper nutrition and regular exercise. Unfortunately, however, the initial diagnosis typically occurs well into the patient’s adulthood, after the disease has already caused harm. Genetic testing enables early diagnosis, but it is far too expensive for most patients to contemplate.
A team of researchers from Longwood University, Ohio University, and Touro University California have developed a novel, low-cost approach to early prediction of type 2 diabetes. This approach is based on measuring fluctuating asymmetry (FA), the small, random deviations from perfect symmetry in bilaterally paired structures such as fingerprints from the same fingers on opposing hands. Dr. Bjoern Ludwar, assistant professor at Longwood University, developed MATLAB® algorithms that apply wavelet analysis to quantify this asymmetry. The pilot study found a significant correlation between fingerprint FA scores and type 2 diabetes.
“When we began the study, we didn’t know what method for evaluating asymmetry would work best, or whether we would find any correlation at all,” says Ludwar. “We were working on a complex problem with lots of unknowns. MATLAB enabled us to rapidly try different approaches and techniques without spending months implementing my own wavelet operations or basic library functions.”