Lipoprotein subclass measurements by nuclear magnetic resonance spectroscopy improve the prediction of coronary artery disease in Type 1 diabetes. A prospective report from the Pittsburgh Epidemiology of Diabetes Complications Study.
OBJECTIVE
To examine whether nuclear magnetic resonance lipoprotein spectroscopy improves the prediction of coronary artery disease in patients with Type 1 diabetes, independently of conventional lipid and other risk factors.


METHODS
A prospective nested case-control design of subjects with childhood onset Type 1 diabetes from the Pittsburgh Epidemiology of Diabetes Complications Study was used. 59 controls were age-, sex- and duration-matched to 59 incident cases of coronary artery disease (fatal or non-fatal myocardial infarction, angina, coronary stenosis >50%) occurring during 10 years of follow-up. Lipid mass and particle concentrations of VLDL, LDL, and HDL subclasses, grouped into three size categories (large, medium, and small), were assessed prior to event with nuclear magnetic resonance spectroscopy.


RESULTS
Univariate analyses showed that both lipid mass and particle concentrations of all three VLDL subclasses, small LDL, medium LDL, and medium HDL were increased in CAD cases compared to controls, while large HDL was decreased. Mean LDL and HDL particle sizes were lower in cases. In multivariate models using conventional lipid and non-lipid risk factors, triglycerides and overt nephropathy were the strongest predictors of CAD. Nuclear magnetic resonance measures further improved the prediction, i.e. large HDL particle concentration (OR=0.43, p=0.030), medium HDL mass (OR=3.79, p=0.026) and total VLDL particle concentration (OR=2.33, p=0.033).


CONCLUSIONS
While these results underscore the importance of triglycerides and overt nephropathy in CAD risk in Type 1 diabetic patients, they also suggest that nuclear magnetic resonance lipoprotein spectroscopy could further refine its prediction and show novel findings concerning HDL subclasses.
Soedamah-Muthu
S S
SS
Department of Epidemiology, Graduate School of Public Health, University of Pittsburgh, Pittsburgh, PA, USA.
Chang
Y-F
YF
Otvos
J
J
Evans
R W
RW
Orchard
T J
TJ
D000328:Adult; D017668:Age of Onset; D000704:Analysis of Variance; D001794:Blood Pressure; D008076:Cholesterol, HDL / Q000097:blood; D008078:Cholesterol, LDL / Q000097:blood; D003327:Coronary Disease / Q000453:epidemiology*; D003922:Diabetes Mellitus, Type 1 / Q000097:blood*; D003925:Diabetic Angiopathies / Q000453:epidemiology*; D003928:Diabetic Nephropathies / Q000453:epidemiology*; D006801:Humans; D008074:Lipoproteins / Q000097:blood* / Q000145:classification*; D008075:Lipoproteins, HDL / Q000097:blood / Q000145:classification; D008077:Lipoproteins, LDL / Q000097:blood / Q000145:classification; D008079:Lipoproteins, VLDL / Q000097:blood / Q000145:classification; D009682:Magnetic Resonance Spectroscopy / Q000379:methods; D011237:Predictive Value of Tests; D012042:Registries; D012307:Risk Factors; D014280:Triglycerides / Q000097:blood
D008076:Cholesterol, HDL; D008078:Cholesterol, LDL; D008074:Lipoproteins; D008075:Lipoproteins, HDL; D008077:Lipoproteins, LDL; D008079:Lipoproteins, VLDL; D014280:Triglycerides