Title: Incorporating social determinants of health in the prediction of diabetes incidence and diabetes care outcomes in India (SOC-DIAB)

Funding Body: German Center for Diabetes Research 

Funding Period: 2026-2027

Partners: Prof Dr. Mohammed K. Ali, Emory Global Diabetes Research Center, Emory University; Prof. Dr. Nikkil Sudharsanan, Rudolf Mößbauer Tenure Track Professor on Behavioral Science for Disease Prevention and Health Care, Technical University of Munich

Objectives:

• We will develop prediction models in a low resource setting that allow to better target i) prevention approaches to people at risk of type 2 diabetes (T2D) and ii) treatment approaches to patients with T2D. We will extend prediction approaches by incorporating social determinants of health (SDoH) into machine learning (ML) methods in one of the largest cohort studies in South Asia, the CARRS cohort.

Background:

• SDoH are important predictors of diabetes incidence, self-management, treatment adherence, and complication rates. However, the value of incorporating SDoH into modern prediction methods to guide personalized treatment decisions in India is unknown. It is highly relevant to explore new approaches that comprehensively consider SDoH and clinical aspects to identify subgroups of individuals at risk to develop T2D and enhance the prediction of different outcomes in the diabetes care cascade in India, like the appropriate control of blood glucose, BP and cholesterol.

Methods

• We will apply modern ML algorithms to analyze how clinical together with behavioral and socioeconomic characteristics help to predict important outcomes in the diabetes prevention and care cascade in India, including the incidence of T2D in the general population and the control of cardiometabolic risk factors in patients with T2D. We will also identify socioeconomic clusters and clinical T2D phenotypes using modern ML and cluster analysis methods to determine the relevance of socioeconomic deprivation for diabetes outcomes.