Berkman Diabetes Clinical Innovation Grant Awarded to Develop Liver Disease Screening in the Adult Diabetes Clinic
Metabolically-dysfunction-associated steatotic liver disease (MASLD), known as non-alcoholic fatty liver disease before 2024, includes a range of conditions caused by a build-up of fat in the liver. It is strongly associated with type 2 diabetes (T2D) and obesity. MASLD remains underdiagnosed and undertreated. Many people living with T2D are unaware they have it.
Endocrinologist Madona Azar, MD, in partnership with hepatologists Deepika Devuni, MD, and Anita Krishnarao, MD, was awarded the 2023 Herman G. Berkman Diabetes Clinical Innovation Fund grant to create a program to screen adults with diabetes and prediabetes in the UMass Memorial diabetes clinic.
Their project implemented a screening tool called the Fibrosis-4 (Fib-4) Index. It utilizes clinically available data to determine the patient's risk of liver fibrosis.
“This project will not only raise awareness about MASLD, but also encourage screening and identify high-risk patients so they can be treated,” said Dr. Azar. “We also want diabetes providers to start thinking of Fib4 as an automatic standard to monitor after the A1c test.”
Diabetes care team providers have had access to the online screening tool since it went live in the Fall of 2023. Data analysis is reviewed quarterly to monitor progress and is presented to the care team to reinforce the benefits.
Previous Berkman Fund Recipients
Clinical Study on Diabetes Management for Pregnant Women to Improve Outcomes for Maternal and Infant Health
This study aimed to compare continuous glucose monitoring (CGM) with traditional fingerstick testing in pregnant women with type 2 diabetes to determine which method improves blood sugar control and pregnancy outcomes. Gianna Wilkie, MD, led the randomized trial at UMass Memorial Medical Center, evaluating glucose control, patient satisfaction, and perinatal outcomes, including rates of infants born larger than expected, with the goal of reducing future diabetes and obesity risk across generations.
AI Diabetic Retinopathy Screening in Primary Care
This project is implementing an artificial intelligence (AI)- based diabetic retinopathy screening program in Family Medicine clinics to detect eye disease and improve comprehensive care for people living with diabetes. Recent studies have identified AI-based algorithms as promising tools for screening and early detection of diabetic retinopathy, helping those at risk. This study, led by optometrist Juan Ding, OD, PhD, is testing the diagnostic accuracy of a handheld AI-assisted camera for primary care physicians to screen at-risk individuals for retinal changes indicative of diabetic retinopathy.
The GOOD-ER Program
This randomized clinical trial provided continuous glucose monitors (CGM) to people with diabetes who were currently not using one and arrived at the Emergency Room with high or low blood sugar, or other diabetes related complications. The study, led by endocrinologist Dr. Mark O’Connor and emergency physician Dr. Laurel O'Connor, analyzed whether CGM effectively prevents people from returning to the ER for diabetes-related issues, compared with a control group that does not wear a device to monitor their blood sugar.
Improving Inpatient Blood Glucose Management
This project aimed to implement a carbohydrate-counting system for hospitalized inpatients with diabetes across the UMass Memorial Health system. Endocrinologist Dr. Leslie Domalik evaluated whether adopting a flexible meal dosing option based on carb counting would improve the outcomes of hospitalized patients with diabetes. By coordinating the timing of blood glucose testing, insulin dosing, and the administration of rapid-acting mealtime insulin, she wanted to ensure carbohydrate counts are listed for all food served to hospitalized patients and to better coordinate insulin delivery with meal delivery.
Identifying Diabetes Patients and Leveraging Underutilized Services to Improve Care’ (ID PLUS Care)
This project focused on a multidisciplinary, collaborative approach to improve care access, quality, and management for at-risk patients with diabetes. They monitored Electronic Health Record data to identify UMass Medicare Accountable Care Organization patients at risk for negative outcomes and proactively contacted patients to nudge them towards recommended services. It led to Dr. Daniel Amante receiving a three-year KL2 Mentored Career Development Training grant to develop a Diabetes Mellitus program using Behavioral economics to Optimize Outreach and Self-management support with Technology (DM-BOOST).