Achieving Secondary Prevention Low-Density Lipoprotein Particle Concentration Goals Using Lipoprotein Cholesterol-Based Data

BACKGROUND: Epidemiologic studies suggest that LDL particle concentration (LDL-P) may remain elevated at guideline recommended LDL cholesterol goals, representing a source of residual risk. We examined the following seven separate lipid parameters in achieving the LDL-P goal of <1000 nmol/L goal for very high risk secondary prevention: total cholesterol to HDL cholesterol ratio, TC/HDL, <3; a composite of ATP-III very high risk targets, LDL-C<70 mg/dL, non-HDL-C<100 mg/dL and TG<150 mg/dL; a composite of standard secondary risk targets, LDL-C<100, non-HDL-C<130, TG<150; LDL phenotype; HDL-C ≥ 40; TG<150; and TG/HDL-C<3. METHODS: We measured ApoB, ApoAI, ultracentrifugation lipoprotein cholesterol and NMR lipoprotein particle concentration in 148 unselected primary and secondary prevention patients. RESULTS: TC/HDL-C<3 effectively discriminated subjects by LDL-P goal (F = 84.1, p<10(-6)). The ATP-III very high risk composite target (LDL-C<70, nonHDL-C<100, TG<150) was also effective (F = 42.8, p<10(-5)). However, the standard secondary prevention composite (LDL-C<100, non-HDL-C<130, TG<150) was also effective but yielded higher LDL-P than the very high risk composite (F = 42.0, p<10(-5)) with upper 95% confidence interval of LDL-P less than 1000 nmol/L. TG<150 and TG/HDL-C<3 cutpoints both significantly discriminated subjects but the LDL-P upper 95% confidence intervals fell above goal of 1000 nmol/L (F = 15.8, p = 0.0001 and F = 9.7, p = 0.002 respectively). LDL density phenotype neared significance (F = 2.85, p = 0.094) and the HDL-C cutpoint of 40 mg/dL did not discriminate (F = 0.53, p = 0.47) alone or add discriminatory power to ATP-III targets. CONCLUSIONS: A simple composite of ATP-III very high risk lipoprotein cholesterol based treatment targets or TC/HDL-C ratio <3 most effectively identified subjects meeting the secondary prevention target level of LDL-P<1000 nmol/L, providing a potential alternative to advanced lipid testing in many clinical circumstances

Paraoxonase responses to exercise and niacin therapy in men with metabolic syndrome

Our purpose was to characterize changes in paraoxonase 1 (PON1) activity and concentration after single aerobic exercise sessions conducted before and after 6 weeks of niacin therapy in men with metabolic syndrome (MetS). Twelve men with MetS expended 500 kcal by walking at 65% of VO2max before and after a 6-week regimen of niacin. Niacin doses were titrated by 500 mg/week from 500 to 1500 mg/day and maintained at 1500 mg/day for the last 4 weeks. Fasting blood samples were collected before and 24 hours after each exercise session and analyzed for PON1 activity, PON1 concentration, myeloperoxidase (MPO), apolipoprotein A1, oxidized low-density lipoprotein (oLDL), lipoprotein particle sizes and concentrations. PON1 activity, PON1 concentration, MPO, and oLDL were unaltered following the independent effects of exercise and niacin (P > 0.05 for all). High-density lipoprotein particle size decreased by 3% (P = 0.040) and concentrations of small very low-density lipoprotein increased (P = 0.016) following exercise. PON1 activity increased 6.1% (P = 0.037) and PON1 concentrations increased 11.3% (P = 0.015) with the combination of exercise and niacin. Exercise and niacin works synergistically to increase PON1 activity and concentration with little or no changes in lipoproteins or markers of lipid oxidation.UCR::Vicerrectoría de Investigación::Unidades de Investigación::Ciencias Sociales::Centro de Investigación en Ciencias del Movimiento Humano (CIMOHU)UCR::Vicerrectoría de Docencia::Ciencias Sociales::Facultad de Educación::Escuela de Educación Físic

A novel Bayesian approach to quantify clinical variables and to determine their spectroscopic counterparts in 1H NMR metabonomic data

<p>Abstract</p> <p>Background</p> <p>A key challenge in metabonomics is to uncover quantitative associations between multidimensional spectroscopic data and biochemical measures used for disease risk assessment and diagnostics. Here we focus on clinically relevant estimation of lipoprotein lipids by ¹H NMR spectroscopy of serum.</p> <p>Results</p> <p>A Bayesian methodology, with a biochemical motivation, is presented for a real ¹H NMR metabonomics data set of 75 serum samples. Lipoprotein lipid concentrations were independently obtained for these samples via ultracentrifugation and specific biochemical assays. The Bayesian models were constructed by Markov chain Monte Carlo (MCMC) and they showed remarkably good quantitative performance, the predictive R-values being 0.985 for the very low density lipoprotein triglycerides (VLDL-TG), 0.787 for the intermediate, 0.943 for the low, and 0.933 for the high density lipoprotein cholesterol (IDL-C, LDL-C and HDL-C, respectively). The modelling produced a kernel-based reformulation of the data, the parameters of which coincided with the well-known biochemical characteristics of the ¹H NMR spectra; particularly for VLDL-TG and HDL-C the Bayesian methodology was able to clearly identify the most characteristic resonances within the heavily overlapping information in the spectra. For IDL-C and LDL-C the resulting model kernels were more complex than those for VLDL-TG and HDL-C, probably reflecting the severe overlap of the IDL and LDL resonances in the ¹H NMR spectra.</p> <p>Conclusion</p> <p>The systematic use of Bayesian MCMC analysis is computationally demanding. Nevertheless, the combination of high-quality quantification and the biochemical rationale of the resulting models is expected to be useful in the field of metabonomics.</p

Fine mapping of a linkage peak with integration of lipid traits identifies novel coronary artery disease genes on chromosome 5

Coronary artery disease (CAD), and one of its intermediate risk factors, dyslipidemia, possess a demonstrable genetic component, although the genetic architecture is incompletely defined. We previously reported a linkage peak on chromosome 5q31-33 for early-onset CAD where the strength of evidence for linkage was increased in families with higher mean low density lipoprotein-cholesterol (LDL-C). Therefore, we sought to fine-map the peak using association mapping of LDL-C as an intermediate disease-related trait to further define the etiology of this linkage peak. The study populations consisted of 1908 individuals from the CATHGEN biorepository of patients undergoing cardiac catheterization; 254 families (N = 827 individuals) from the GENECARD familial study of early-onset CAD; and 162 aorta samples harvested from deceased donors. Linkage disequilibrium-tagged SNPs were selected with an average of one SNP per 20 kb for 126.6-160.2 MB (region of highest linkage) and less dense spacing (one SNP per 50 kb) for the flanking regions (117.7-126.6 and 160.2-167.5 MB) and genotyped on all samples using a custom Illumina array. Association analysis of each SNP with LDL-C was performed using multivariable linear regression (CATHGEN) and the quantitative trait transmission disequilibrium test (QTDT; GENECARD). SNPs associated with the intermediate quantitative trait, LDL-C, were then assessed for association with CAD (i.e., a qualitative phenotype) using linkage and association in the presence of linkage (APL; GENECARD) and logistic regression (CATHGEN and aortas)

El Niño, tropical Atlantic warmth, and Atlantic hurricanes over the past 1500 years

Author Posting. © The Author(s), 2009. This is the author's version of the work. It is posted here by permission of Nature Publishing Group for personal use, not for redistribution. The definitive version was published in Nature 460 (2009): 880-883, doi:10.1038/nature08219.Atlantic Tropical Cyclone (TC) activity, as measured by annual storm counts, reached anomalous levels over the past decade. The short nature of the historical record and potential issues with its reliability in earlier decades, however, has prompted an ongoing debate regarding the reality and significance of the recent rise. Here, we place recent activity in a longer-term context, by comparing two independent estimates of TC activity over the past 1500 years. The first estimate is based on a composite of regional sedimentary evidence of landfalling hurricanes, while the second estimate employs a previously published statistical model of Atlantic TC activity driven by proxy-reconstructions of past climate changes. Both approaches yield consistent evidence of a peak in Atlantic TC activity during Medieval times (around AD 1000) followed by a subsequent lull in activity. The Medieval peak, which rivals or even exceeds (within uncertainties) recent levels of activity, results in the statistical model from a ‘perfect storm’ of La Niña-like climate conditions and relative tropical Atlantic warmth.M.E.M. and Z.Z. acknowledge support from the ATM programme of the National Science Foundation (grant ATM-0542356). J.P.D. acknowledges support from the EAR and OCE programmes of the National Science Foundation (grants EAR-0519118 and OCE-0402746), the Risk Prediction Initiative at the Bermuda Institute for Ocean Sciences, and the Inter-American Institute for Global Change Research

Computational Lipidology: Predicting Lipoprotein Density Profiles in Human Blood Plasma

Monitoring cholesterol levels is strongly recommended to identify patients at risk for myocardial infarction. However, clinical markers beyond “bad” and “good” cholesterol are needed to precisely predict individual lipid disorders. Our work contributes to this aim by bringing together experiment and theory. We developed a novel computer-based model of the human plasma lipoprotein metabolism in order to simulate the blood lipid levels in high resolution. Instead of focusing on a few conventionally used predefined lipoprotein density classes (LDL, HDL), we consider the entire protein and lipid composition spectrum of individual lipoprotein complexes. Subsequently, their distribution over density (which equals the lipoprotein profile) is calculated. As our main results, we (i) successfully reproduced clinically measured lipoprotein profiles of healthy subjects; (ii) assigned lipoproteins to narrow density classes, named high-resolution density sub-fractions (hrDS), revealing heterogeneous lipoprotein distributions within the major lipoprotein classes; and (iii) present model-based predictions of changes in the lipoprotein distribution elicited by disorders in underlying molecular processes. In its present state, the model offers a platform for many future applications aimed at understanding the reasons for inter-individual variability, identifying new sub-fractions of potential clinical relevance and a patient-oriented diagnosis of the potential molecular causes for individual dyslipidemia