Although interconnectedness of atherosclerotic cardiovascular disease, rheumatoid arthritis, and diabetes is widely reported these diseases are rarely combined in the same prognostic or diagnostic model. These diseases, while distinct in their primary manifestations, often co-occur, creating a compounded health challenge. The synergy among these conditions creates a vicious cycle: as one condition worsens, it increases the severity of the others, leading to a cascade of health complications. This complex interaction highlights the necessity for integrated treatment strategies that address all facets of each condition, rather than tackling them in isolation.
Atherosclerotic cardiovascular disease primarily involves the build-up of plaques in the arterial walls, leading to narrowed or blocked arteries, which can precipitate heart attacks or strokes. Cardiovascular disease is the leading cause of death in the world; however, its risk escalates in the presence of rheumatoid arthritis and diabetes. Rheumatoid arthritis exacerbates cardiovascular risk. The inflammatory processes that damage joint tissues also fuel plaque formation in the arteries, thereby not only amplifying the severity of atherosclerosis but potentially accelerating its progression. Diabetes, marked by chronic high blood sugar levels, contributes to cardiovascular disease by promoting the thickening of arterial walls and the formation of fatty deposits. The high glucose environment acts like a stimulator for atherosclerotic plaque growth, making the arterial walls stickier and more prone to damage.
The project is structured around two primary aims.
Aim 1: Develop advanced machine learning, and deep learning techniques (ML/DL) to integrate all data modalities to detect and predict atherosclerotic cardiovascular disease, rheumatoid arthritis, and diabetes spectrum of disease development. We will adapt ML/DL algorithms to clinical needs addressing data heterogeneity, missingness and competing events.
Aim 2: Apply these methodological advancements to develop novel, clinically relevant models to predict and diagnose outcomes on the cardiovascular-autoimmune-type2 diabetes spectrum.