Most diabetic patients die of cardiovascular disease (CVD), with CVD rates in diabetic persons two to four times those of the non-diabetic population. This increased CVD risk, along with the increasing prevalence of obesity and increasing numbers of elderly in the U.S. population, means that diabetes-associated CVD will become an even greater public health problem in the future. Diabetes mellitus is a complex metabolic disorder with generally evaluated levels of insulin resistance and variable levels of circulating insulin, which is often accompanied by abnormalities in carbohydrate, lipid, and protein metabolism and elevated blood pressure. While glucose control appears important for Type 2 patients, it is critical not to overlook treatment of associated CVD risk factor abnormalities. The effects on prevention of cardiovascular disease events of intensive control of established CVD risk factors (blood pressure and blood lipids), and intensive control of blood glucose, in Type 2 diabetes are unknown. The study is a multi-center randomized clinical trial of about 10,000 adult patients with Type 2 diabetes mellitus followed for four to seven years. The primary objective of this research program is to assess whether the rate of major cardiovascular disease events can be reduced by: (1) intensive control of blood sugar compared with conventional control; (2) intensive control of blood pressure compared with conventional control; and (3) intensive control of blood lipids compared with conventional control. Other objectives include the comparative cost effectiveness of the above interventions and their impact on health-related quality of life.

Project Start
1999-09-30
Project End
2008-09-30
Budget Start
1999-09-30
Budget End
2001-12-31
Support Year
Fiscal Year
1999
Total Cost
Indirect Cost
Name
Wake Forest University Health Sciences
Department
Public Health & Prev Medicine
Type
Schools of Medicine
DUNS #
041418799
City
Winston-Salem
State
NC
Country
United States
Zip Code
27106
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Casanova, Ramon; Saldana, Santiago; Chew, Emily Y et al. (2014) Application of random forests methods to diabetic retinopathy classification analyses. PLoS One 9:e98587

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