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High-density lipoprotein subclass measurements improve mortality risk prediction, discrimination and reclassification in a cardiac catheterization cohort.

ABSTRACT: Recent failures of HDL cholesterol (HDL-C)-raising therapies to prevent cardiovascular disease (CVD) events have tempered the interest in the role of HDL-C in clinical risk assessment. Emerging data suggest that the atheroprotective properties of HDL depend on specific HDL particle characteristics not reflected by HDL-C. The purpose of this study was to determine the association of HDL particle concentration (HDL-P) and HDL subclasses with mortality in a high-risk cardiovascular population and to examine the clinical utility of these parameters in mortality risk discrimination and reclassification models.Using nuclear magnetic resonance spectroscopy, we measured HDL-P and HDL subclasses in 3972 individuals enrolled in the CATHGEN coronary catheterization biorepository; tested for association with all-cause mortality in robust clinical models; and examined the utility of HDL subclasses in incremental mortality risk discrimination and reclassification.Over an average follow-up of eight years, 29.6% of the individuals died. In a multivariable model adjusted for ten CVD risk factors, HDL-P [HR, 0.71 (0.67-0.76), p = 1.3e-24] had a stronger inverse association with mortality than did HDL-C [HR 0.93 (0.87-0.99), p = 0.02]. Larger HDL size conferred greater risk and the sum of medium- and small-size HDL particles (MS-HDL-P) conferred less risk. Furthermore, the strong inverse relation of HDL-P levels with mortality was accounted for entirely by MS-HDL-P; HDL-C was not associated with mortality after adjustment for MS-HDL-P. Addition of MS-HDL-P to the GRACE Risk Score significantly improved risk discrimination and risk reclassification.HDL-P and smaller HDL subclasses were independent markers of residual mortality risk and incremental to HDL-C in a high-risk CVD population. These measures should be considered in risk stratification and future development of HDL-targeted therapies in high-risk populations.

SUBMITTER: McGarrah RW

PROVIDER: S-EPMC4764426 | biostudies-other | 2016 Mar

REPOSITORIES: biostudies-other

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High-density lipoprotein subclass measurements improve mortality risk prediction, discrimination and reclassification in a cardiac catheterization cohort.

McGarrah Robert W RW Craig Damian M DM Haynes Carol C Dowdy Z Elaine ZE Shah Svati H SH Kraus William E WE

Atherosclerosis 20160111

<h4>Background and aims</h4>Recent failures of HDL cholesterol (HDL-C)-raising therapies to prevent cardiovascular disease (CVD) events have tempered the interest in the role of HDL-C in clinical risk assessment. Emerging data suggest that the atheroprotective properties of HDL depend on specific HDL particle characteristics not reflected by HDL-C. The purpose of this study was to determine the association of HDL particle concentration (HDL-P) and HDL subclasses with mortality in a high-risk car ...[more]

PMID: 26803432

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Project description:Multiple single biomarkers have been associated with poor outcomes in acute lung injury; however, no single biomarker has sufficient discriminating power to clearly indicate prognosis. Using both derivation and replication cohorts, we tested novel risk reclassification methods to determine whether measurement of multiple plasma biomarkers at the time of acute lung injury diagnosis would improve mortality prediction in acute lung injury.Analysis of plasma biomarker levels and prospectively collected clinical data from patients enrolled in two randomized controlled trials of ventilator therapy for acute lung injury.Intensive care units of university hospitals participating in the National Institutes of Health Acute Respiratory Distress Syndrome Network.Subjects enrolled in a trial of lower tidal volume ventilation (derivation cohort) and subjects enrolled in a trial of higher vs. lower positive end-expiratory pressure (replication cohort).None.The plasma biomarkers were intercellular adhesion molecule-1, von Willebrand factor, interleukin-8, soluble tumor necrosis factor receptor-1, and surfactant protein-D. In the derivation cohort (n = 547), adding data on these biomarkers to clinical predictors (Acute Physiology and Chronic Health Evaluation III score) at the time of study enrollment improved the accuracy of risk prediction, as reflected by a net reclassification improvement of 22% (95% confidence interval 13% to 32%; p < .001). In the replication cohort (n = 500), the net reclassification improvement was 17% (95% confidence interval 7% to 26%; p < .001). A reduced set of three biomarkers (interleukin-8, soluble tumor necrosis factor receptor-1, and surfactant protein-D) had nearly equivalent prognostic value in both cohorts.When combined with clinical data, plasma biomarkers measured at the onset of acute lung injury can improve the accuracy of risk prediction. Combining three or more biomarkers may be useful for selecting a high-risk acute lung injury population for enrollment in clinical trials of novel therapies.

Dataset Information

High-density lipoprotein subclass measurements improve mortality risk prediction, discrimination and reclassification in a cardiac catheterization cohort.

Publications

High-density lipoprotein subclass measurements improve mortality risk prediction, discrimination and reclassification in a cardiac catheterization cohort.

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