Approved research

Assessing metformin use in T2D patients to suppress autoimmune diseases

Lay summary

Summary: Type 2 Diabetes is projected to become the biggest epidemic disease in the world. Studies have shown correlation between T2D and different autoimmune diseases. People diagnosed with prediabetes and diabetes are prescribed with metformin. As metformin has immune modulatory effects, therefore identifying the cohorts with T2D+autoimmune and metformin will help to understand whether metformin has evidence to delay autoimmune diseases (and which) in the broader population as capture in this real-world dataset. Aims: (i) Evaluate demographics, clinical, medications, and socio-determinants of T2D+autoimmune disease type cohorts that are treated with and without metformin. (ii) Identify key features that can be used as markers for each cohort using ANOVA/MANOVAs. (iii) The methodology developed in (i) and (ii) using the UK Biobank will be implemented on another similar INPC USA dataset. (iv) The key finding using UK Biobank and INPC USA dataset will be compared/contrast. (v) The key features will be tested as markers using ML. Scientific Rationale: As metformin is prescribed as first line of drug for the treatment of prediabetes and diabetes and studies have reported immune-modulatory effects of metformin for T2D patients. In this project we plan to study the cohorts that are treated with/without/ before/after metformin as preventative measures for autoimmune disease. Completion of the study can show if metformin can really help to control the progression of T2D to autoimmune disease. On comparing two different population cohorts across world can help to understand the prevalence and the similarity/differences. The key features identified using statistical approaches will be evaluated as markers using different machine learning methods. Project Duration: We estimate a total 2 years to complete the project. Public Health Impact: As it is projected by 2045 nearly 10%-12% of the world population will be affected with diabetes. Moreover, studies have shown the association of autoimmune diseases with T2D. Both these disease types effect the quality of life and incur lot of medication expenditure. This study can assist better understanding if metformin has evidence to delay onset of specific autoimmune diseases (and which) and in which patient populations. Based on these results it could lead to (1) identification of new markers of risk for specific autoimmune disease, (2) evidence for further study of metformin in specific autoimmune disease and patient populations, or (3) additional electronic (Machine Learning) tools to identify patients at risk of developing autoimmune disease assisting in clinical monitoring and treatment.