The global health burden of Diabetes is quite high, due to our increasingly sedentary lifestyle and unhealthy diet. Diabetes is also part of a larger etiology known as Cardiometabolic Syndrome. Our group has been developing several non-invasive tools and machine learning algorithms that can help predict the different stages of Diabetes, and also provide people with feedback regarding their current status along the cardiometabolic spectrum: Healthy –> Prediabetes –> Diabetes –> Advanced Diabetes with Complications.
In order to develop and validate these tools, our group has some strong international partners, including the Aditya-Jyot Foundation for Twinkling Little Eyes (AJFTLE), located in Mumbai, India, and S-VYASA University, located outside Bangalore, India.
With AJFTLE, we are validating our non-invasive diabetes screening tools to predict more advances stages of diabetes. Since fundus (retina) imaging is very time-intensive and costly, we have been creating a simple screening tool for diabetic retinopathy that does not require a fundus camera. Patients with the positive screening results are then referred to the mobile clinic for fundus imaging. This relieves a great deal of the burden on the health care system for community retinopathy screening.
With S-VYASA, we are particularly interested in using our non-invasive screening tools to identify early stages of diabetes so that yoga interventions can be made. S-VYASA has done significant research showing how early-stage diabetes in oldr adults can be reversed through the practice of Yoga. (the full yoga therapy is not just the asanas, as is popularly stereotyped in most western countries; true yoga therapy also includes pranayama (breathing) and meditation, along with improvement in diet).
Students: Saadiyah Husnoo, Kobbie Ofori-Atta, Shivani Chauhans, John Mofor.