Insulin resistance-related factors, but not glycemia, predict coronary artery disease in type 1 diabetes: 10-year follow-up data from the Pittsburgh Epidemiology of Diabetes Complications Study.
OBJECTIVE
To determine the independent risk factors for coronary artery disease (CAD) in type 1 diabetes by type of CAD at first presentation.


METHODS
This is a historical prospective cohort study of 603 patients with type 1 diabetes diagnosed before 18 years of age between 1950 and 1980. The mean age and duration of diabetes at baseline were 28 (range 8-47) and 19 years (7-37), respectively, and patients were followed for 10 years. Patients with prevalent CAD were excluded from the study. Electrocardiogram (ECG) ischemia was defined by Minnesota Code (MC) 1.3, 4.1-3, 5.1-3, or 7.1; angina was determined by Pittsburgh Epidemiology of Diabetes Complications (EDC) study physician diagnosis; and hard CAD was determined by angiographic stenosis > or =50%, revascularization procedure, Q waves (MC 1.1-1.2), nonfatal myocardial infarction (MI), or CAD death.


RESULTS
A total of 108 incident CAD events occurred during the 10-year follow-up: 17 cases of ECG ischemia, 49 cases of angina, and 42 cases of hard CAD (5 CAD deaths, 25 nonfatal MI or major Q waves, and 12 revascularization or > or =50% stenosis). Blood pressure, lipid levels, inflammatory markers, renal disease, and peripheral vascular disease showed a positive gradient across the groups of no CAD, angina, and hard CAD (P < 0.01, trend analysis, all variables), although estimated glucose disposal rate (eGDR) and physical activity showed inverse associations (P < 0.01, trend analysis, both variables). In addition, depressive symptomatology predicted angina (P = 0.016), whereas HbA(1) showed no association with subsequent CAD.


CONCLUSIONS
These data suggest that although the standard CAD risk factors are still operative in type 1 diabetes, greater glycemia does not seem to predict future CAD events. In addition, depressive symptomatology predicts angina and insulin resistance (eGDR) predicts hard CAD end points.
Orchard
Trevor J
TJ
Department of Epidemiology, Graduate School of Public Health, University of Pittsburgh, Pennsylvania, USA. tjo+@pitt.edu
Olson
Jon C
JC
Erbey
John R
JR
Williams
Katherine
K
Forrest
Kimberly Y-Z
KY
Smithline Kinder
Leslie
L
Ellis
Demetrius
D
Becker
Dorothy J
DJ
D000328:Adult; D017668:Age of Onset; D015415:Biomarkers / Q000097:blood; D001786:Blood Glucose / Q000378:metabolism*; D003327:Coronary Disease / Q000453:epidemiology*; D003404:Creatinine / Q000097:blood; D003922:Diabetes Mellitus, Type 1 / Q000150:complications*; D003925:Diabetic Angiopathies / Q000453:epidemiology*; D005260:Female; D005500:Follow-Up Studies; D006801:Humans; D015994:Incidence; D007333:Insulin Resistance / Q000502:physiology*; D008297:Male; D011237:Predictive Value of Tests; D013997:Time Factors
D015415:Biomarkers; D001786:Blood Glucose; D003404:Creatinine