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Articles by Ryle S. Perera
Total Records ( 1 ) for Ryle S. Perera
  Nimalie J. Perera , Jennifer C. Burns , Ryle S. Perera , Barry Lewis and David R. Sullivan
 

Background

Low plasma levels of high-density-lipoprotein cholesterol (HDL-C) and high triglyceride (TG) are strongly associated with cardiovascular disease (CVD). Clinical recognition of this high-risk population demands accurate measurement of HDL-C, whereas cost and clinical demand dictate that optimal HDL-C measurement requires fully automated methods that avoid manual precipitation. Commercial techniques use specific reagents to selectively expose and “directly” measure cholesterol in HDL. However, these “direct” methods may experience interference from the cholesterol content of triglyceride-rich-lipoproteins (TRL), leading to analytical overestimation of HDL-C, with subsequent underestimation of low-density-lipoprotein cholesterol (LDL-C) and of CVD risk.

Objective

The aim of this study was to develop a method to overcome this interference.

Methods

Serum/Li+-heparin plasma samples from consecutive patients were analyzed for HDL-C by the comparison of three generations of the Roche Diagnostics, HDL-C assay on a Hitachi-917 or Modular-PPE analyzer. HDL-C measurement was performed before and after removal of TRL by ultracentrifugation (“direct” HDL-C and HDL-UC, respectively). We examined the effect of TG on the relationship between HDL-UC and “direct” HDL-C. Analysis of variance multiregression analysis was performed for each generation of the commercial assay.

Results

We observed progressive TG interference that increased “direct” HDL-C by 10% to 15% or more in moderately hypertriglyceridemic samples (<600 mg/dL). Predictive equations were derived for each generation of the assay to estimate HDL-C in the absence of TRL.

Conclusions

This study casts doubt on the specificity of “direct” HDL-C assays in the presence of hypertriglyceridemia. The use of assay-specific correction formulae to adjust for interference from TRL reduces the overestimation of HDL-C that influences CVD risk calculation, treatment, and follow-up of patients.

 
 
 
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