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Approved Research

Multivariate analysis of modifiable risk factors associated with cardiovascular disease for adults with type 2 diabetes mellitus

Principal Investigator: Dr Ydo Wexler
Approved Research ID: 73272
Approval date: September 29th 2021

Lay summary

Insulin resistance is the fundamental defect of type 2 diabetes, and often leads to conditions that are associated with reduced heart health and adverse heart events. It was reported that approximately 80% of all patients with diabetes die of heart diseases. To date many studies identified the increased risk stemming from insulin resistance, and found it to accelerate cardiovascular disease. It was found that even people with minor insulin resistance suffer from increased risk for heart attacks and strokes.

In recent years studies demonstrated that focusing on medications for controlling blood sugar may increase in the longer run the risk of cardiovascular events.  Instead, making changes related to regular, rigorous exercise and diet greatly decreased insulin resistance and allowed far more patients to balance their blood sugar levels. Studies showed that using lifestyle intervention in combination with specific common non-insulin medication lead to significant improvement in heart health in patients with type 2 diabetes.

However, changes in lifestyle are sometimes hard to make to the full extent. Therefore, for a successful intervention to work, one needs to identify the minimal changes in behavior possible for an individual, which can reduce the risk for heart events.

In our study we wish to uncover the extent to which small changes of several behaviors can affect the risk of heart attacks and stroke, and compare that with larger change in a single behavior. This will help people with type 2 diabetes and healthcare professionals decide what lifestyle behaviors are best to change and customize this to the patient based on their abilities and the likelihood they can complete those changes.

We estimate this research study will take about 9 months to complete, and we intend to apply cutting-edge machine-learning tools to go beyond what was found in the space hitherto.