ATHEROSCLEROSIS
Compared to non-diabetics, patients with type 2 diabetes (T2D) have a 4-fold increased risk for cardiovascular disease (CVD) during their lifetime and have a greater overall plaque burden and higher rate of multi-vessel disease. Although patients with T2D face a significant risk for developing CVD, the mechanisms underlying this risk are poorly understood. In epidemiological studies, traditional risk factors like, smoking, hypertension, and LDL cholesterol, HDL cholesterol, total cholesterol, and triglyceride levels, do not explain the risk associated with CVD in type 2 diabetic patients. Moreover, intervention studies showed that increased mortality is observed even when plasma cholesterol levels are aggressively lowered with statin treatment, hypertension is controlled, or with aggressive glycemic control.
Macrophages may represent an important cellular link between T2D and atherosclerosis. Macrophages are inappropriately activated during obesity and insulin resistance (IR), and macrophages that accumulate excess cholesterol (foam cells) are causatively linked to initiation, progression, and rupture of atherosclerotic plaques.
Using an integrative approach that combines cell-based studies, controlled animal studies, and human observation, we are exploring macrophage pathways linking obesity/T2D to atherogenesis and developing therapeutics to safely target them.
Selected Publications
Reardon, C.A., Lingaraju, A., Schoenfelt, K.Q., Zhou, G., Cui, C., Jacobs-El, H., Babenko, I., Hoofnagle, A., Czyz, D., Shuman, H., Vaisar, T., & Becker, L. (2018). Obesity and insulin resistance promote atherosclerosis through an IFN-regulated macrophage protein network. Cell Rep. 23, 3021-3030.
Becker, L., Gharib, S.A., Wijsman, E., Vaisar, T., Oram, J.F., & Heinecke, J.W. (2010). A macrophage sterol-responsive network linked to atherogenesis. Cell Metab. 11, 125-135. [Research Highlight (2010): Nature 463, 713.]
Heinecke, N.L., Pratt, B.S., Vaisar, T., & Becker, L. (2010). PepC: Proteomics software for identifying differentially expressed proteins based on spectral counting. Bioinformatics 26, 1574-1575.