Friday, September 29, 2006

New Cholesterol Test Used In VA Hospital Research Study

Low-Density Lipoprotein and High-Density Lipoprotein Particle Subclasses Predict Coronary Events and Are Favorably Changed by Gemfibrozil Therapy in the Veterans Affairs High-Density Lipoprotein Intervention Trial

James D. Otvos, PhD; Dorothea Collins, ScD; David S. Freedman, PhD; Irina Shalaurova, MD;
Ernst J. Schaefer, MD; Judith R. McNamara, MT; Hanna E. Bloomfield, MD, MPH; Sander J. Robins, MD

Background—Changes in conventional lipid risk factors with gemfibrozil treatment only partially explain the reductions in coronary heart disease (CHD) events experienced by men in the Veterans Affairs High-Density Lipoprotein Intervention Trial (VA-HIT). We examined whether measurement of low-density lipoprotein (LDL) and high-density lipoprotein (HDL) particle subclasses provides additional information relative to CHD risk reduction.

Methods and Results—This is a prospective nested case-control study of 364 men with a new CHD event (nonfatal myocardial infarction or cardiac death) during a 5.1-year (median) follow-up and 697 age-matched controls. Nuclear magnetic resonance (NMR) spectroscopy was used to quantify levels of LDL and HDL particle subclasses and mean particle sizes in plasma obtained at baseline and after 7 months of treatment with gemfibrozil or placebo. Odds ratios for a 1-SD increment of each lipoprotein variable were calculated with adjusted logistic regression models. Gemfibrozil treatment increased LDL size and lowered numbers of LDL particles (5%) while raising numbers of HDL particles (10%) and small HDL subclass particles (21%). Concentrations of these LDL and HDL particles achieved with gemfibrozil were significant, independent predictors of new CHD events. For total LDL and HDL particles, odds ratios predicting CHD benefit were 1.28 (95% CI, 1.12 to 1.47) and 0.71 (95% CI, 0.61 to 0.81), respectively. Mean LDL and HDL particle sizes were not associated with CHD events.

Conclusions—The effects of gemfibrozil on NMR-measured LDL and HDL particle subclasses, which are not reflected by conventional lipoprotein cholesterol measures, help to explain the demonstrated benefit of this therapy in patients with low HDL cholesterol. (Circulation. 2006;113:&NA;-.)
Key Words: coronary disease  drugs  lipoproteins  risk factors  spectroscopy
The Veterans Affairs High-Density Lipoprotein Intervention Trial (VA-HIT) demonstrated that the fibric acid derivative gemfibrozil reduced the 5-year rate of new coronary heart disease (CHD) events in men with known CHD and low levels of both high-density lipoprotein cholesterol (HDL-C) and low-density lipoprotein cholesterol (LDL-C).1 Overall, HDL-C was raised 6% and triglycerides lowered 31% by gemfibrozil, but only the increased HDL-C predicted the reduction in CHD events.2 LDL-C concentrations neither predicted the development of new CHD events nor were lowered by gemfibrozil.
Editorial p ●●●
Clinical Perspective p ●●●
Individuals with low HDL-C and elevated triglycerides, such as those in VA-HIT, characteristically have LDL particles that are smaller and contain less cholesterol than average.
3,4 As a result, numbers of atherogenic LDL particles are frequently elevated even though LDL-C levels are not.5 CHD risk in several prospective studies has been found to be better explained by concentrations of LDL particles, assessed by nuclear magnetic resonance (NMR) or plasma levels of apolipoprotein B (apoB), than by LDL-C.6–13 Some evidence also suggests that LDL particle subclasses may differ in atherogenicity, with small, dense LDL usually,4 but not
always,14 associated with greater CHD risk than large, buoyant LDL. Angiographic trials with fibrates demonstrating reduced progression of coronary artery stenosis have shown that these
drugs, although not appreciably changing the concentration of LDL-C, increase LDL particle size and decrease plasma apoB Received May 27, 2005; revision received December 5, 2005; accepted January 13, 2006.
From LipoScience, Inc, Raleigh, NC (J.D.O., I.S.); Veterans Affairs Medical Center Cooperative Studies Program Coordinating Center, West Haven, Conn (D.C.); Division of Nutrition, Centers for Disease Control and Prevention, Atlanta, Ga (D.S.F.); Lipid Research Laboratory, Tufts University School of Medicine, Boston, Mass (E.J.S., J.R.M.); Center for Chronic Disease Outcomes Research, Veterans Affairs Medical Center, Minneapolis, Minn (H.E.B.); and Boston University School of Medicine, Boston, Mass (S.J.R.). Guest Editor for this article was Robert H. Eckel, MD. Correspondence to Sander Robins, MD, Framingham Heart Study, 73 Mt Wayte Ave, Framingham, MA 01702. E-mail sjrobins@bu.edu © 2006 American Heart Association, Inc. Circulation is available at http://www.circulationaha.org DOI: 0.1161/CIRCULATIONAHA.105.565135 1 Coronary Heart Disease levels.15–17 Furthermore, the increase in HDL-C brought about by fibrates results from increased levels of the smaller
HDL3 subclass.2,18,19 As yet, there have been no trials with fibrates showing that a reduction in major clinical CHD events with these drugs may be related to changes in LDL or HDL subclass concentrations or particle size distributions. In the present nested case-control substudy of VA-HIT, we assessed the effects of gemfibrozil treatment on LDL and HDL subclass particle numbers and mean particle sizes as measured by NMR spectroscopy. We also examined whether levels of these subclasses measured at baseline or during the trial were related to CHD events and whether these relations differed in subjects with diabetes or insulin resistance.
Methods
Subjects
The rationale, design, and methods for VA-HIT have been described previously.1 Briefly, men younger than 74 years with an established diagnosis of CHD were recruited at 20 Veterans Affairs medical centers throughout the United States. Lipid eligibility criteria were HDL-C 40 mg/dL, LDL-C 140 mg/dL, and triglycerides 300 mg/dL. A total of 2531 subjects were randomized to either gemfibrozil (1200 mg/d) or placebo and were treated for a median of 5.1
years. The primary end point was nonfatal myocardial infarction (MI) or CHD death.
For this analysis, the case subjects (n364) were those study participants who experienced a nonfatal MI or CHD death during the trial from whom baseline and follow-up stored plasma samples were available. The control subjects (n697) matched for age were selected from among the remaining study participants who remained free of CHD events during follow-up. An equal percentage (43%) of the 364 cases with a CHD event selected for this analysis and the 494
total CHD cases in the entirety of VA-HIT were treated with gemfibrozil. VA-HIT was approved by the Human Rights Committee of the Cooperative Studies Program Coordinating Center, by each of the 20 study site’s institutional review boards, and by the Cooperative Studies Program Evaluation Committee. All subjects gave written, informed consent. Laboratory Analyses
Blood samples were collected from subjects, after a 12- to 14-hour fast, into tubes containing 0.1% EDTA. Plasma was isolated, frozen, and sent to the VA-HIT central laboratory (Tufts University, Boston, Mass) for lipid analyses and for long-term storage at 80°C before
subsequent analyses of apolipoproteins in the central laboratory and lipoprotein particle subclasses at LipoScience, Inc (Raleigh, NC). Unlike the lipid values reported for the entire VA-HIT population,2 which were the averages of 2 baseline samples obtained 1 to 2 weeks
apart and 4 follow-up samples obtained at 4, 7, 12, and 18 months during the trial, values reported here represent a single baseline and 7-month follow-up measurement, except for the follow-up apolipoprotein values, which were obtained at the 12-month visit.
Total cholesterol, HDL-C, triglycerides, and glucose were measured by standardized automated methods, and LDL-C was calculated by the Friedewald equation.20 Insulin was determined as total immunoreactive insulin.21 ApoA-1 and apoB levels were determined with immunoturbidimetric assays.22,23 Diabetes was defined by history or by a fasting glucose 126 mg/dL. Insulin resistance was defined by a homeostasis model assessment of insulin resistance
value 10.2, as previously described.21 All insulin, glucose, and lipid assays had between-run coefficients of variation (CVs) of 5%. Between-run CVs were 7% to 11% for apoB and 4% for
apoA-1.22,23 LDL and HDL subclass particle concentrations and mean LDL and HDL particle diameters were measured with an automated NMR spectroscopic assay as previously described24,25 and recently modified. 26 In brief, the particle concentrations of lipoprotein subclasses of different size are derived from the measured amplitudes of the distinct lipid methyl group NMR signals they emit. Concentrations (nmol/L for LDL particles and mol/L for HDL particles) of the following subclasses were analyzed in this study: small LDL (18.0 to
21.2 nm), large LDL (21.2 to 23.0 nm), intermediate-density lipoprotein (IDL) (23.0 to 27.0 nm), large HDL (8.8 to 13.0 nm), medium HDL (8.2 to 8.8 nm), and small HDL (7.3 to 8.2 nm). Weightedaverage lipoprotein particle sizes in nanometers were calculated from the subclass levels, and the diameters were assigned to each subclass. Very low-density lipoprotein (VLDL) subclass data are not included in this analysis because there was evidence that NMR detection of
these triglyceride-rich particles was altered to a variable extent by the freeze-thaw cycles to which the plasma specimens were subjected. LDL and HDL subclass levels measured by NMR are unaffected by frozen storage25 and multiple freeze-thaw cycles (J.D. Otvos, PhD,
unpublished data, 2005). Reproducibility of the NMR-measured lipoprotein particle parameters
was determined by replicate analyses of plasma pools. Between-run CVs for low-normal concentrations were 4% for total LDL and HDL particle concentrations, 0.5% for LDL and HDL size, 8% for large and small LDL subclasses, and 5% for large and small HDL subclasses. Higher CVs for IDL (20%) and medium HDL (35%) subclasses reflect their typically low concentrations. For all biochemical and NMR analyses, samples were
handled in a fully blinded fashion such that investigators had no knowledge of case or control status. All results of laboratory analyses were sent to the VA Cooperative Studies Coordinating
Center (West Haven, Conn) and entered into the VA-HIT centralized database.
Statistical Analyses Baseline characteristics were compared between cases and controls,
and lipids, apolipoproteins, and NMR lipoprotein particle measures were compared by treatment with t tests. Odds ratios for 1-SD increments of baseline and on-trial concentrations of lipids, apolipoproteins, and NMR lipoprotein particle measures, as well as changes in these variables, were assessed with the use of logistic regression with adjustment for treatment group, age, hypertension, smoking, body mass index, and diabetes. Separate analyses were
performed on the subset of subjects (n395) with insulin resistance or diabetes (excluding those treated with insulin). Because of the multiple statistical tests that were performed, which could inflate the type 1 error rate, we used 0.01 as the criterion for statistical significance. To examine whether the relation of each lipid/lipoprotein variable to CHD differed by treatment group, we included interaction (product) terms in our regression models. The authors had full access to the data and take full responsibility for its integrity. All authors have read and agree to the anuscript as written.
Results
Baseline characteristics of the cases and controls, shown in Table 1, were similar to those of the entire VA-HIT population. As previously documented, a relatively large proportion of subjects in this trial had diabetes or insulin resistance, with a greater prevalence in cases compared with controls. Lipid and apolipoprotein changes in the gemfibrozil treatment group (Table 2) were virtually identical to those reported previously for the whole study population.2 Triglycerides
decreased 30%, HDL-C increased 6%, and LDL-C was minimally increased. Both plasma apoB and non–HDL cholesterol (non–HDL-C), reflecting the combined levels of VLDL and LDL, underwent small reductions. As Table 2 shows, the concentration of total LDL particles
(LDL-P) measured by NMR was decreased 5% by gemfibrozil. This reduction was caused by a significant 20% decrease in the number of small LDL particles, from 967 to 777 2 Circulation March 28, 2006 nmol/L, which was partially offset by a 36% increase in the number of large LDL particles. As a result of this alteration in LDL subclass composition, average LDL particle size increased significantly from 20.4 to 20.9 nm. Total HDL particles (HDL-P) in the gemfibrozil treatment group were increased as a result of increased numbers of small HDL
particles offsetting reductions in large- and medium-size HDL subclass particles.
Table 3 shows the odds ratios (ORs) for a new CHD event associated with a 1-SD increment of each lipid or lipoprotein particle variable measured at baseline and during the trial in
the combined gemfibrozil and placebo treatment groups, TABLE 1. Baseline Characteristics of Cases, Controls, and Entire VA-HIT Population
Characteristic Cases
(n364)
Controls
(n697)
Entire Population
(n2531) P*
Age, y 64.3 (7.1) 64.5 (6.9) 64.2 (7.2) 0.64 Body mass index, kg/m2 29.2 (4.7) 29.1 (4.9) 29.0 (4.8) 0.43 Waist, cm 103 (12) 103 (12) 103 (12) 0.48 Hypertension, % 56.6 58.4 56.9 0.57
Diabetes, % 37.1 28.8 30.4 0.006 Insulin resistance, % 35.2 27.8 30.0 0.002 Current smoker, % 22.0 17.7 20.4 0.09 LDL-C, mg/dL 113.3 (21.4) 110.8 (23.1) 111.1 (22.2) 0.10 HDL-C, mg/dL 30.9 (5.5) 31.5 (5.2) 31.8 (5.3) 0.05 Triglycerides, mg/dL 166.9 (72.8) 161.4 (66.9) 160.6 (68.0) 0.41 Glucose, mg/dL 119 (40) 114 (34) 115 (37) 0.03 Values are mean (SD) or percentage. The prevalences of hypertension and smoking were obtained by history. Diabetes was defined by history or by a fasting glucose 126 mg/dL. Insulin resistance was defined by a homeostasis model assessment of insulin resistance value 10.2 and does not include subjects with diabetes being treated with insulin.21 *P value is for the comparison of cases and controls. For body mass index and triglycerides, a nonparametric Wilcoxon test was used. For all other variables, t tests or 2 tests were used. TABLE 2. Treatment Effects on Lipids, Lipoprotein Particle Numbers, and Lipoprotein Particle Sizes Placebo (n546) Gemfibrozil (n515) Variable Baseline On-Trial* Baseline On-Trial P† Lipids and apolipoproteins LDL-C, mg/dL 111.6 (23.1) 112.1 (26.7) 111.7 (22.1) 114.7 (25.9) 0.14 HDL-C, mg/dL 31.2 (5.0) 31.3 (6.1) 31.5 (5.5) 33.4 (6.7) 0.0001
Triglycerides, mg/dL 167.1 (69.5) 176.7 (84.2) 159.3 (68.3) 111.3 (59.3) 0.0001 Non–HDL-C, mg/dL 144.6 (24.1) 147.0 (29.9) 143.6 (23.8) 137.0 (28.6) 0.0001 ApoB, mg/dL 96.8 (20.6) 93.6 (17.9) 95.4 (21.7) 89.0 (19.4) 0.0002 ApoA-1, mg/dL 105.9 (15.9) 108.8 (16.2) 104.9 (16.6) 107.8 (16.4) 0.35 NMR lipoprotein particle measures LDL particle No., nmol/L 1364 (315) 1463 (342) 1352 (316) 1290 (331) 0.0001 IDL particles, nmol/L 34 (25) 35 (27) 32 (24) 33(28) 0.29 Large LDL particles, nmol/L 346 (214) 345 (239) 354 (217) 480 (231) 0.0001 Small LDL particles, nmol/L 984 (391) 1083 (444) 967 (406) 777 (425) 0.0001
LDL particle size, nm 20.4 (0.7) 20.3 (0.8) 20.4 (0.8) 20.9 (0.7) 0.0001 HDL particle No., mol/L 25.2 (4.3) 26.6 (4.5) 25.1 (4.6) 27.6 (5.0) 0.0005 Large HDL particles, mol/L 2.7 (1.7) 2.3 (1.6) 2.7 (1.7) 2.3 (1.6) 0.71 Medium HDL particles, mol/L 1.9 (2.5) 2.0 (2.6) 1.9 (2.3) 0.7 (1.6) 0.0001 Small HDL particles, mol/L 20.7 (5.2) 22.4 (5.3) 20.4 (5.2) 24.6 (5.4) 0.0001 HDL particle size, nm 8.5 (0.3) 8.4 (0.3) 8.5 (0.3) 8.4 (0.3) 0.68 Values are mean (SD). *On-trial data are for samples obtained at the 7-month visit; apolipoproteins are the values at 12 months. †P values are for comparison of on-trial values in the placebo vs gemfibrozil groups. Otvos et al LDL and HDL Subclasses and Coronary Events 3
assessed by separate logistic regression models that were adjusted for major nonlipid CHD risk factors and treatment group. Models that included the interaction between treatment
and each lipid/lipoprotein variable indicated that the associations did not differ significantly between the placebo and gemfibrozil groups (data not shown). Neither baseline nor on-trial levels of HDL-C, triglycerides, or LDL-C were significant predictors of CHD risk. Among other lipid/apolipoprotein variables, baseline, but not on-trial, concentrations of apoA-1 and the ratios of apoB to apoA-1 and total cholesterol to HDL-C predicted a CHD end point. Among NMR lipoprotein measures, both baseline and on-trial levels of LDL-P and HDL-P were strong, independent predictors of a new CHD event. A 1-SD increment of LDL-P (350 nmol/L) during the trial was associated with an OR of 1.28 (95% CI, 1.12 to 1.47; P0.0003), whereas the
OR of a 1-SD increment of HDL-P (4.8 mol/L) was 0.71 (95% CI, 0.61 to 0.81; P0.0001). Further adjustment for LDL-C, HDL-C, and triglycerides did not appreciably change
these relations (data not shown). The small HDL-P subclass, which constituted the majority of the total HDL particles in VA-HIT men, was also a significant predictor of CHD end
points. On-trial numbers of small LDL particles were positively associated with events (P0.03) but did not achieve the P0.01 significance level. Neither the large nor the average LDL and HDL particle subclasses were related to CHD events (Table 3). Additional analyses (results not
shown) indicated that no change (by concentration or percentage) in any of the lipid, apolipoprotein, or lipoprotein particle variables was a significant predictor of CHD risk.
We also conducted separate analyses (not shown) identical to those in Tables 2 and 3 for the subset of subjects (n395) with diabetes or insulin resistance. In agreement with results
reported for the whole VA-HIT population,21 gemfibrozil induced a somewhat smaller increase in HDL-C (3% versus 6%) and a smaller decrease in triglycerides (26% versus 30%) in this subgroup. LDL-P also decreased less (2% versus 5%), and the increase in LDL size was smaller (0.3 versus 0.5 nm). However, the gemfibrozil-induced changes in HDL-P and the HDL-P subclasses were very similar to those seen in the entire study population. Furthermore, relations of lipid, apolipoprotein, and lipoprotein particle parameters to CHD events in the diabetes/insulin resistance subgroup were virtually identical to those shown in Table 3. None of the on-trial lipid or apolipoprotein variables predicted CHD events in this subgroup, whereas both LDL-P (OR1.30; 95% CI, 1.05 to 1.61) and HDL-P (OR0.68; 95% CI, 0.54 to 0.86) did.
To assess the independence of relations of LDL and HDL particle subclasses with CHD events and correct for any confounding caused by the intercorrelations among these variables, all 6 LDL and HDL subclasses (including IDL) TABLE 3. Lipids, Apolipoproteins, and Lipoprotein Particle Parameters as Individual Predictors of CHD Events Baseline On-Trial Variable OR* (95% CI) P OR (95% CI) P Lipids and apolipoproteins LDL-C 1.10 (0.97–1.25) 0.15 1.08 (0.95–1.23) 0.25 HDL-C 0.91 (0.80–1.03) 0.14 0.95 (0.83–1.08) 0.42 Triglycerides 1.07 (0.94–1.22) 0.29 1.04 (0.90–1.19) 0.63 Non–HDL-C 1.14 (1.00–1.30) 0.05 1.09 (0.96–1.25) 0.17
Total cholesterol:HDL-C ratio 1.19 (1.05–1.35) 0.008 1.14 (1.01–1.30) 0.05 ApoB 1.12 (0.99–1.27) 0.08 1.07 (0.94–1.23) 0.31 ApoA-1 0.84 (0.74–0.96) 0.01 0.91 (0.80–1.04) 0.18
ApoB:apoA-1 ratio 1.24 (1.10–1.41) 0.0008 1.14 (0.99–1.30) 0.06 NMR lipoprotein particle measures LDL particle No. 1.20 (1.05–1.37) 0.006 1.28 (1.12–1.47) 0.0003 IDL particles 0.99 (0.87–1.12) 0.84 1.17 (1.03–1.33) 0.02 Large LDL particles 1.08 (0.95–1.23) 0.27 1.06 (0.93–1.22) 0.35 Small LDL particles 1.11 (0.98–1.27) 0.11 1.17 (1.02–1.34) 0.03 LDL particle size 0.97 (0.85–1.10) 0.64 0.96 (0.84–1.10) 0.57 HDL particles 0.78 (0.69–0.90) 0.0004 0.71 (0.61–0.81) 0.0001 Large HDL particles 0.98 (0.86–1.11) 0.74 0.94 (0.83–1.07) 0.37
Medium HDL particles 0.96 (0.84–1.09) 0.54 1.04 (0.92–1.19) 0.52 Small HDL particles 0.82 (0.72–0.94) 0.004 0.74 (0.64–0.85) 0.0001 HDL Particle Size 1.00 (0.88–1.14) 0.97 0.91 (0.79–1.04) 0.15 *ORs (95% CIs) were calculated for a 1-SD increment in each lipid/lipoprotein variable in separate logistic regression models adjusted for treatment group, age, hypertension, smoking, body mass index, and diabetes. For placebo and gemfibrozil groups combined, n1061 subjects. 4 Circulation March 28, 2006 were included in the same regression models that were
additionally adjusted for treatment and other risk factors (Table 4). Both large and small LDL subclass particle numbers were now strongly and independently predictive of CHD outcomes, both at baseline and during the trial. ORs for small and large LDL-P during the trial were 1.41 (95% CI, 1.14 to 1.73; P0.001) and 1.34 (95% CI, 1.11 to 1.62; P0.002), respectively. Stronger associations with CHD events were also seen for all 3 HDL subclasses when these variables were considered jointly with LDL subclasses in the same model (Table 4) rather than individually in separate models (Table 3). The extent to which the risk of new CHD events is related to different measures of the ratio of atherogenic to antiatherogenic lipoprotein particles is shown in Table 5. The relative risk of those in the highest quartile of ratio of total cholesterol to HDL-C or ratio of apoB to apoA-1 was significantly elevated (relative risk1.5; 95% CI, 1.1 to 2.0 for both lipid ratios). However, a stronger, graded risk relationship was seen for the ratio of LDL-P to HDL-P (relative risk2.4 for the highest versus lowest quartile; 95% CI, 1.8 to 3.3). Similar results were obtained when the gemfibrozil and placebo groups were examined separately (results not shown). Discussion VA-HIT was undertaken to determine whether treatment aimed at raising HDL-C (and lowering triglycerides), rather than lowering LDL-C, would reduce CHD events in men with coronary disease. To simplify interpretation of the study results,
2 aspects of the study design were intended to “uncouple” HDL-C from LDL-C as contributors to any achieved treatment benefit.1 First, subjects recruited into the trial had not only low
HDL-C (mean, 32 mg/dL) but low-risk levels of LDL-C (mean, 111 mg/dL). Second, gemfibrozil was chosen as the treatment drug for its ability both to increase HDL-C and to not appreciably
change levels of LDL-C. VA-HIT achieved these objectives. Not only did this treatment produce a significant 22% reduction in CHD events, but this result was accomplished without a
decrease in LDL-C.1 Subsequent regression analyses confirmed TABLE 4. NMR Lipoprotein Subclass Particle Parameters as Multivariable Predictors of CHD Events Baseline On-Trial
Variable OR* (95% CI) P OR (95% CI) P Large LDL particles 1.31 (1.09–1.57) 0.003 1.34 (1.11–1.62) 0.002 Small LDL particles 1.44 (1.20–1.73) 0.0001 1.41 (1.14–1.73) 0.001
IDL particles 0.98 (0.86–1.12) 0.78 1.13 (0.97–1.30) 0.11 Large HDL particles 0.95 (0.82–1.11) 0.53 0.92 (0.79–1.07) 0.30 Medium HDL particles 0.82 (0.70–0.96) 0.02 0.82 (0.69–0.97) 0.02 Small HDL particles 0.71 (0.60–0.84) 0.0001 0.67 (0.57–0.79) 0.0001 *ORs (95% CIs) were calculated for a 1-SD increment in each lipoprotein subclass parameter at
baseline and on-trial with the use of logistic regression models that included all lipoprotein particle parameters in 1 model. All models were additionally adjusted for treatment group, age, hypertension, smoking, body mass index, and diabetes. TABLE 5. Risk of CHD Events According to Quartile of Lipid/Lipoprotein Ratios Quartile of Plasma Level Ratio 1 2 3 4 P for Trend
TC:HDL-C Median (range) 4.1 (2.1–4.6) 5.1 (4.6–5.5) 5.9 (5.5–6.5) 7.2 (6.5–12.5)
Relative risk* (95% CI) 1 1.3 (0.9–1.7) 1.2 (0.9–1.6) 1.5 (1.1–2.0) 0.27 P 0.13 0.27 0.01
ApoB:ApoA-1 Median (range) 0.66 (0.26–0.73) 0.79 (0.73–0.85) 0.90 (0.85–0.97) 1.1 (0.97–1.9) Relative risk (95% CI) 1 1.3 (0.9–1.8) 1.6 (1.2–2.2) 1.5 (1.1–2.0) 0.37
P 0.04 0.004 0.02 LDL-P:HDL-P Median (range) 34.4 (13.9–40.5) 46.4 (40.5–51.2) 55.3 (51.3–61.2) 71.0 (61.3–127.7) Relative risk (95% CI) 1 1.6 (1.2–2.3) 1.8 (1.3–2.6) 2.4 (1.8–3.3) 0.009 P 0.005 0.0005 0.0001 TC indicates triglycerides. *Logistic regression models used on-trial values of lipid/lipoprotein ratios and were adjusted for treatment group, age, hypertension, smoking, body mass index, and diabetes. Otvos et al LDL and HDL Subclasses and Coronary Events 5 that LDL-C levels at baseline and during the trial were not
related to new CHD events.2 In contrast, levels of HDL-C achieved with therapy predicted CHD outcomes and explained some of the gemfibrozil treatment benefit. Measuring the cholesterol content of LDL and HDL particles (LDL-C and HDL-C) is the traditional way that levels of these atherogenic and antiatherogenic lipoproteins are assessed. In this study we used an alternative
technique, NMR spectroscopy, which “counts” the numbers of LDL and HDL particles (LDL-P and HDL-P) and their subclasses.24 With this different measure of LDL and HDL concentration, we gained new insights into the lipoprotein particle altering effects of fibrate therapy in VA-HIT and how these alterations might affect clinical outcomes. Gemfibrozil significantly reduced total LDL particle numbers by changing the subclass distribution, lowering numbers of small LDL particles while raising to a lesser extent numbers of the larger, more cholesterol-rich particles (Table 2). Despite the decrease in LDL-P, LDL-C remained unchanged because the cholesterol content of the LDL had increased as a result of the gemfibrozil-induced shift toward larger particles. Similarly, although gemfibrozil raised HDL cholesterol levels only modestly (6%),
there was a more substantial 10% increase in total HDL particle number and a 21% increase in numbers of the small, relatively cholesterol-poor HDL subclass. Notably, we found that both LDL and HDL particle numbers measured during the trial had significant, independent
associations with new CHD events, whereas LDL and HDL cholesterol levels did not. A possible reason is that LDL and HDL cholesterol levels have more sources of variability, with levels differing either because of differences in cholesterol composition (amount of cholesterol
per particle due to particle size or core lipid compositional differences),5 differences in particle number, or some combination of the two. Because only men with low levels of LDL-C and HDL-C were enrolled in VA-HIT, the range of lipid levels was restricted compared with the general
population. As a result, the impact of lipoprotein cholesterol compositional heterogeneity on relations of lipid levels with CHD risk was likely magnified in this study population. Whatever the reason, our results clearly indicate that in this secondary prevention study of men with
low HDL and LDL, CHD risk is better reflected by numbers of lipoprotein particles than by the amount of cholesterol these particles contain. The same conclusion applies to the subset of men in VA-HIT who had diabetes or insulin resistance. It was shown previously that these subjects, despite experiencing smaller changes in HDL-C, derived substantially more benefit from gemfibrozil treatment than those without insulin resistance.21 We found that on-trial levels of HDL-P and LDL-P were equally predictive of CHD events in this subgroup and the study
population as a whole. Other studies have also reported that CHD risk appears to be more strongly related to the number of circulating LDL particles than to the measured concentration of cholesterol in LDL.6,11–13 Many of these have used plasma apoB concentration to approximate LDL particle number because, despite apoB being present on VLDL as well as LDL particles, at least 90% of apoB is on LDL even in hypertriglyceridemic patients.27 In VA-HIT, gemfibrozil comparably lowered levels of both apoB (6%) and NMR-measured LDL-P (5%), yet
in regression analyses previously conducted on the entire study population2 and in this nested case-control substudy, levels of apoB with gemfibrozil were not significantly related to the development of a CHD event. Two other studies in which both apoB and LDL-P were measured have also reported stronger disease associations for LDL-P.7,28 We are unsure why apoB is less strongly related to CHD outcomes than LDL-P in VA-HIT. One contributing factor may be the
greater analytic reproducibility of the LDL-P measurement compared with the apoB immunoassay. Another is that, as previously mentioned, plasma apoB provides only an estimate
of LDL particle number because some apoB resides on non-LDL particles. Of note, LDL-P and apoB were correlated less strongly in this study (r0.56) than in the Framingham Offspring Study (r0.86)22 and Women’s Health Study (r0.70),7 in part because the range of LDL concentrations in VA-HIT was more restricted. Although gemfibrozil appreciably increased average LDL particle size in VA-HIT by 0.5 nm, this parameter did not predict a significant reduction in CHD events. This observation is consistent with the results of several observational and intervention studies showing that quantitative measures of LDL particle number, assessed by either NMR or apoB measurement, are more strongly associated with CHD than is
small LDL size.6–8,10,16,17 Although large LDL-P in our study was not related to CHD events when considered individually (Table 3), it became strongly predictive when confounding
due to its correlations with small LDL-P and the HDL subclasses was overcome by inclusion of these interrelated variables in the same model (Table 4). Similar observations have been made by others.14 Our findings with regard to prediction of CHD events by LDL and HDL particle subclasses are highly concordant with those of the Bezafibrate Coronary Atherosclerosis Intervention Trial (BECAIT), an angiographic trial that studied a similar population of male CHD patients with low HDL-C.17 The only on-trial predictors of angiographic outcomes were levels of the small HDL subclass (HDL3-C) and apoB. Significantly, and in agreement with VA-HIT, despite the large impact of fibrate treatment on triglyceride levels and LDL particle size, neither of these parameters was related to the angiographic outcomes. In agreement with the results of other studies with fibrates, we found that increased levels of the small HDL subclass accounted for the increase in HDL-C18,19 brought about by drug treatment and that on-trial numbers of the small HDL particles, but not large HDL particles, predicted CHD events.17,29 Although it is unclear by which mechanism(s) the increase in numbers of HDL particles with this therapy leads to clinical benefit, it may be suggested that higher numbers of HDL particles might
promote greater cholesterol efflux and protection of LDL from oxidative changes.30
In this nested case-control substudy, HDL-C levels at baseline (31.5 mg/dL) and on-trial levels (33.4 mg/dL) 6 Circulation March 28, 2006 matched exactly the mean values reported for the entire study population of VA-HIT.2 However, although the findings in the whole trial showed that achieved levels of HDL-C were predictive of CHD events (RR0.89; 95% CI, 0.81 to 0.97),
we found a weaker, nonsignificant association (RR0.95) in this substudy. We have no certain explanation for this difference but speculate that it may be because we used only a single on-trial measure of HDL-C, whereas the original study used the mean of 4 on-trial determinations.2 Random measurement variability, which is partially offset by averaging multiple determinations, would be expected to attenuate associations with clinical outcomes.
In summary, this case-control substudy nested within VA-HIT shows that fibrate therapy produces favorable changes in the numbers of plasma LDL and HDL subclass particles that can occur independently of any change in the cholesterol content of these lipoproteins. These lipoprotein particle changes may help to explain the reduction in major CHD events and CHD death achieved in this trial. We believe this demonstration of a significant beneficial effect of fibrate therapy in a population with low LDL-C should provide impetus for considering the potential benefit of this kind of therapy even in the absence of substantial changes in conventional lipid measures.
Acknowledgments
VA-HIT was supported by the Department of Veterans Affairs Office of Research and Development Cooperative Studies Program, by a supplemental grant from Parke-Davis, and by an R03 grant, HL069111. We acknowledge the contributions of the VA-HIT investigators and study personnel and thank the VA-HIT subjects for their participation in this trial. Disclosures
Drs Otvos and Shalaurova are employees of LipoScience, Inc. Dr Schaefer has been a consultant to LipoScience, Inc. The other authors report no conflicts.
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Collins D. Insulin resistance and cardiovascular events with low HDL
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Diabetes Care. 2003;26:1513–1517.
22. Contois JH, McNamara JR, Lammi-Keefe CJ, Wilson PW, Massov T,
Schaefer EJ. Reference intervals for plasma apolipoprotein A-1 as
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Schaefer EJ. Reference intervals for plasma apolipoprotein B as
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24. Otvos JD. Measurement of lipoprotein subclass profiles by nuclear
magnetic resonance spectroscopy. Clin Lab. 2002;48:171–180.
25. Freedman DS, Otvos JD, Jeyarajah EJ, Shalaurova I, Cupples LA, Parise
H, D’Agostino RB, Wilson PWF, Schaefer EJ. Sex and age differences in
lipoprotein subclasses measured by nuclear magnetic resonance spectroscopy:
the Framingham Study. Clin Chem. 2004;50:1189 –1200.
26. Festa A, Williams K, Hanley AJG, Otvos JD, Goff DC, Wagenknecht LE,
Haffner SM. Nuclear magnetic resonance (NMR) lipoprotein abnormalities
in prediabetic subjects in the Insulin Resistance Atherosclerosis
Study (IRAS). Circulation. 2005;111:3465–3472.
Otvos et al LDL and HDL Subclasses and Coronary Events 7
27. Sniderman A, Vu H, Cianflone K. Effect of moderate hypertriglyceridemia
on the relation of plasma total and LDL apoB levels. Atherosclerosis.
1991;89:109 –116.
28. Deguchi H, Pecheniuk NM, Elias DJ, Averell PM, Griffin JH. Highdensity
lipoprotein deficiency and dyslipoproteinemia associated with
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29. Syvänne M, Nieminen MS, Frick MH, Kauma H, Majahalme S,
Virtanen V, Kesäniemi A, Pasternack A, Ehnholm C, Taskinen M-R.
Associations between lipoproteins and the progression of coronary and
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with low baseline levels of HDL cholesterol. Circulation. 1998;98:
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disease. Arterioscler Thromb Vasc Biol. 2004;24:1755–1760.
CLINICAL PERSPECTIVE
A highly prevalent lipid abnormality in patients with coronary heart disease (CHD) is low high- density lipoprotein cholesterol (HDL-C) coupled with a relatively low low-density lipoprotein cholesterol (LDL-C). The Veterans Affairs HDL Intervention Trial (VA-HIT) has been one of the few clinical trials to address specifically the risk associated with low HDL-C in conjunction with low LDL-C. Men with established CHD and low levels of both HDL-C and LDL-C were treated with the fibric acid derivative gemfibrozil, which raised HDL-C and lowered triglycerides without affecting levels of LDL-C. This treatment resulted in a significant reduction in major CHD events, a benefit only partially explained by the HDL-C increase and triglyceride decrease. In this case-control substudy of VA-HIT, we used NMR spectroscopy to investigate the effects of gemfibrozil on numbers of LDL and HDL subclass particles. Despite having no effect on LDL-C, gemfibrozil markedly increased the size of LDL particles and reduced their overall number. LDL-C levels were unrelated to CHD events, but LDL particle numbers at baseline and during the trial were strong, independent predictors of a new CHD event. LDL particle size, in contrast, was not related to events. Total HDL particle numbers and numbers of the small HDL particle subclass were increased by gemfibrozil, and on-trial concentrations of both of these HDL particle parameters predicted CHD events. In summary, gemfibrozil produced favorable changes in both LDL and HDL particle numbers that were not reflected by changes in the cholesterol content of these lipoproteins but that predicted a significant reduction in CHD events. 8 Circulation March 28, 2006

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Friday, September 22, 2006

NMR LipoProfile Test Used in HIV Research


ISSN: 1524-4636
Copyright © 2005 American Heart Association. All rights reserved. Print ISSN: 1079-5642. Online 7272 Greenville Avenue, Dallas, TX 72514
Arteriosclerosis, Thrombosis, and Vascular Biology is published by the American Heart Association.
DOI: 10.1161/01.ATV.0000152233.80082.9c 2004;
Arterioscler. Thromb. Vasc. Biol. 2005;25;399-405; originally published online Dec 2,
Donald A. Wiebe and James M. Sosman
James H. Stein, Michelle A. Merwood, Jennifer B. Bellehumeur, Patrick E. McBride,

HIV Infection
Postprandial Lipoprotein Changes in Patients Taking Antiretroviral Therapy for
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Postprandial Lipoprotein Changes in Patients Taking
Antiretroviral Therapy for HIV Infection
James H. Stein, Michelle A. Merwood, Jennifer B. Bellehumeur, Patrick E. McBride,
Donald A. Wiebe, James M. Sosman
Objective—Dyslipidemia is common among patients receiving antiretroviral therapy for HIV infection. The purpose of this
study was to determine whether postprandial lipemia contributes to the dyslipidemia observed in HIV-positive patients
taking antiretroviral therapy.

Methods and Results—A standardized fat load was administered to 65 subjects (group 1 35 HIV-positive subjects receiving protease inhibitors [PIs]; group 2 20 HIV-positive subjects not receiving PIs; group 3 10 HIV-negative controls). Serum triglycerides, retinyl palmitate, and lipoproteins were measured using enzymatic and nuclear magnetic resonance spectroscopic techniques. Compared with HIV-negative controls, peak postprandial retinyl palmitate and
large very low-density lipoprotein (VLDL) levels occurred later in both HIV-positive groups, and a delayed decrease in serum triglycerides was observed. However, postprandial areas under the curve (AUCs) for triglycerides, retinyl palmitate, chylomicrons, and large VLDL were similar. Postprandial AUCs for intermediate-density lipoproteins (IDLs) and low-density lipoproteins (LDLs) were higher in group 1 than groups 2 and 3 (all P0.035).

Conclusions—Postprandial clearance of triglyceride-rich lipoproteins is delayed in HIV-positive individuals receiving antiretroviral therapy. Compared with HIV-positive individuals not on PIs, those taking PIs do not have increased postprandial triglyceride-rich lipoproteins but do have increased postprandial IDLs and LDLs. (Arterioscler Thromb Vasc Biol. 2005;25:399-405.)
Key Words: human immunodeficiency virus  lipids  lipoproteins  metabolism  protease inhibitors

The dramatic immunologic and clinical benefits associated with use of highly active antiretroviral therapy (HAART) have led to its widespread acceptance for treatment of patients with HIV infection.1 Although many patients taking HAART develop metabolic changes that may increase cardiovascular risk, it is unclear whether HAART, its pharmacological components, HIV infection per se, or agingassociated risk factors account for the increased risk of
cardiovascular disease observed in patients taking antiretroviral therapy.2–8
Patients taking HAART frequently have hypercholesterolemia and hypertriglyceridemia, and increased concentrations of very low-density lipoproteins (VLDLs) and intermediatedensity
lipoproteins (IDLs) have been observed in patients taking HIV protease inhibitors (PIs).9–11 In the pre-HAART era, decreases in cholesterol-containing lipoproteins were observed with hypertriglyceridemia that was, at least in part, related to disease progression and impaired clearance of triglycerides.12 Triglyceride-rich lipoproteins and their cholesterol-rich remnants promote accumulation of cholesterol in the arterial wall and adversely affect high-density lipoprotein (HDL) and low-density lipoprotein (LDL) composition and cholesterol concentrations.13,14 In individuals without HIV infection, postprandial lipemia is a risk factor for the development and progression of coronary artery disease (CAD).14
Subjects with CAD have delayed clearance of triglyceride rich lipoproteins and their remnants, resulting in postprandial lipemia.14–16
Several potential mechanisms by which HAART could lead to dyslipidemia have been proposed, including some related to decreased lipoprotein clearance; however, it is not known whether postprandial lipemia contributes to the dyslipidemia and increased cardiovascular risk observed in patients on HAART.2,17–19

Methods
Subjects
The University of Wisconsin institutional review board approved this study. Subjects included adults with HIV infection on a stable antiretroviral regimen for 3 months who had evidence of dyslipidemia, including serum triglycerides 150 mg/dL and either HDL cholesterol 40 mg/dL or LDL cholesterol 130 mg/dL. These subjects were recruited into 2 groups: group 1 (HIV positive and on antiretroviral therapy, including PIs) and group 2 (HIV positive and
on antiretroviral therapy without PIs for 6 months). A control set of HIV negative subjects also was recruited (group 3). Exclusion criteria included current use of lipid-lowering therapy, diabetes Original received September 3, 2004; final version accepted November 22, 2004.
From the University of Wisconsin Medical School, Madison.
Correspondence to James H. Stein, MD, University of Wisconsin Medical School, 600 Highland Ave, G7/341 CSC (MC 3248) Madison, WI 53792.
E-mail jhs@medicine.wisc.edu
© 2005 American Heart Association, Inc.
Arterioscler Thromb Vasc Biol. is available at http://www.atvbaha.org DOI: 10.1161/01.ATV.0000152233.80082.9c 399
mellitus, hypothyroidism, creatinine 2 mg/dL, current use of glucocorticoids or anabolic steroids, and malignancy or opportunistic infection in the past 12 weeks.
Oral Lipid Load
After a minimum of 12 hours of fasting (except medications), subjects were admitted to the General Clinical Research Center at 7:00 AM and drank 236 mL of water. Vital signs were measured and an 18-gauge intravenous catheter was placed. After blood was drawn
for fasting tests, subjects consumed a milkshake composed of heavy whipping cream (190 g; Sysco Grade A), ice cream (90 g; Babcock vanilla 12% butterfat), chocolate-flavored syrup (30 g; Richardson’s or Hershey’s), concentrated protein supplement (25 g; nonfat dry
milk powder), safflower oil (22 g), Lactaid (McNeil-PPC), and Aquasol (Amur Pharmaceuticals). The milkshake composition was adjusted to a body surface area of 2.0 m2. On average (SD), each milkshake contained 1175 (98) calories, with 101 (9) g of fat, 55 (5) g of carbohydrate, and 17 (1) g of protein. The average fat composition included 51 (5) g saturated, 6 (1) g polyunsaturated, and 38 (3) g monounsaturated fat, with 296 (25) g of cholesterol and 51
(4) g of sugar. Laboratory tests were repeated 2, 4, 6, 8, and 10 hours later.
Measurement of Lipids and Lipoproteins
Serum triglycerides were measured using a glycerol kinase– based enzymatic procedure on a Hitachi Modular DP. Retinyl palmitate levels were measured by high-performance liquid chromatography. Apolipoprotein E genotyping was performed using the polymerase
chain reaction with fluorescent monitoring. A blood sample was collected into a lavender-topped tube and immediately centrifuged at 3000 rpm for 15 minutes. Plasma was transferred to a cryovial, refrigerated at 4°C, and shipped within 24 hours in a refrigerated box
to LipoScience, Inc. for NMR spectroscopic lipoprotein analysis, which was performed within 48 hours of sample collection.13
Other Laboratory Tests
Serum glucose levels were measured using a colorimetric enzymatic procedure on a Hitachi Modular DP. Fasting serum insulin was measured using a chemiluminescent immunoassay on a Diagnostic Products Corporation Immulite 2000 analyzer. Homeostasis model assessment of insulin resistance (HOMA-IR) was calculated as fasting serum insulin (U/mL)fasting plasma glucose (after conversion to mmol/L)/22.5. CD4 cell counts were measured by flow
cytometry. Plasma HIV RNA titers were measured by b-DNA hybridization.
Data Analysis
All variables are described by meanSE unless otherwise noted. Baseline comparisons between the 3 groups initially were performed using 1-way repeated-measures ANOVA; however, subjects in group 3 were younger than in groups 1 and 2, so baseline betweengroup
differences were re-evaluated using analysis of covariance and Tukey–Kramer multiple comparison tests, adjusted for age.  2 or Fisher exact tests for proportions were performed for categorical data. Student t tests were used to compare pre-HAART data between groups 1 and 2. To evaluate response to the oral fat load, areas under the curve (AUCs) were calculated from plots of lipid and lipoprotein values that were measured at baseline and every 2 hours through
hour 10. Between-group AUC differences were evaluated by repeated-measures ANOVA, with group contrasts determined using the general linear model, adjusted for age. In addition, incremental AUCs were determined sequentially in a similar fashion (ie, hours 2 through 10, 4 through 10, 6 through 10, and 8 through 10). Preliminary analysis after recruitment of 49 subjects led to a revised sample size estimate that 62 subjects (including 32 in group 1) would
provide 80% power to show significant between-group differences in AUCs for chylomicrons and serum triglycerides (0.05).
Results
Subject Characteristics
Of 65 subjects, 35 were HIV positive and receiving PIs (group 1), 20 were HIV positive and not receiving PIs (group 2), and 10 were HIV negative (group 3) (Table 1). The mean age was 40.41.1 years, 54 (83%) subjects were men, 52 (80%) were white, 7 were black (11%), and the remainder were Hispanic or Asian. Subjects in group 3 were younger than those in groups 1 and 2 (P0.001); therefore all subsequent between-group comparisons were adjusted for age. Distributions of sex and race did not differ significantly between groups. A family history of premature CAD was reported in 11 (17%) subjects, 29 (45%) currently used cigarettes, and 12 (18%) had hypertension. The prevalences of these CAD risk factors were similar in all groups. Insulin levels and HOMA-IR were lower in group 3 than in either HIV-positive group; however, these differences were only significant compared with group 1. Group 1 also had the highest waist circumference (P0.028 versus group 2). The most common apolipoprotein E genotype was 3/3 (52%) followed by 3/4 (22%). No subjects had the 2/2 genotype. Apolipoprotein E allele frequencies did not differ between groups. The average duration of HIV treatment, CD4 cell count, and median HIV RNA titer, including the percentage completely suppressed (50 copies/mL; 46%), were similar in groups 1 and 2. In group 1, the most commonly used PI was
ritonavir (n20), which, in all subjects, was used to boost serum levels of another PI (12 lopinavir; 5 indinavir; 2 amprenavir; and 1 saquinavir). Remaining group 1 subjects were receiving nelfinavir (10), indinavir (4), and amprenavir (1). In group 1, 5 subjects also were taking nevirapine, and 1 was taking efavirenz. The most commonly used nucleoside reverse transcriptase inhibitors were lamivudine (83%), stavudine (57%), and abacavir (23%). Less than 20% were taking zidovudine, didanosine, or tenofovir. In group 2, 6 subjects were taking nevirapine (30%), and 7 were taking efavirenz (35%). The most commonly used nucleoside reverse transcriptase inhibitors in group 2 were similar to group 1: lamivudine 95%; stavudine 40%; zidovudine 50%; and abacavir 35%. Less than 20% were taking didanosine or tenofovir. There were no significant differences between groups 1 and 2 in the frequency of use of any individual nucleoside or non-nucleoside reverse transcriptase inhibitors. Before starting on HAART, total cholesterol levels (149.56.1 versus 152.510.1 mg/dL; P0.250), glucose
levels (89.76.1 versus 85.54.1 mg/dL; P0.372), weight (74.03.1 versus 68.4.43.7 kg; P0.303), and body mass index (23.50.9 versus 22.41.1 kg/m2; P0.491) were similar in both groups of HIV-positive subjects (group 1 versus group 2). Baseline Lipids and Lipoproteins
Baseline serum triglycerides and retinyl palmitate levels in groups 1 and 2 were not significantly different (P0.100) (Table 2). Serum triglycerides in group 1 were greater than in group 3 (P0.040). Retinyl palmitate levels were low in all groups but were marginally higher in group 1 than in group 3 400 Arterioscler Thromb Vasc Biol. February 2005 (P0.049). Baseline lipoprotein concentrations also were similar in groups 1 and 2. The only significant difference was in the concentration of LDL particles (P0.042), with significantly higher values in group 1 than in group 3 (P0.014) and a trend for higher values than in group 2 (P0.091). Other significant between-group differences were only when compared with the control group (group 3). Baseline particle sizes were similar in groups 1 and 2, with smaller LDL and HDL particles than group 3. Postprandial Changes in Lipids and Lipoproteins All 3 groups experienced parallel rises in serum triglycerides, retinyl palmitate, and large VLDL, with peak triglycerides and chylomicron concentrations observed at hour 4 (Figure 1 and Table 3). After hour 4, concentrations of these parameters decreased rapidly in group 3 but remained elevated in groups 1 and 2, with peak retinyl palmitate and large VLDL concentrations not occurring until hour 6 (Figure 1). The postprandial concentration curves for groups 1 and 2 were nearly superimposable for these parameters. Postprandial AUCs did not differ significantly between groups 1 and 2. However, the serum triglycerides postprandial AUC for group 1 was higher than for group 3 (P0.021; Table 3). Postprandial AUCs for medium VLDL were higher for both
HIV-positive groups than controls, but did not differ between groups 1 and 2 (Table 3). Thus, differences in postprandial triglyceride metabolism were not observed between groups 1
and 2, but delayed clearance was seen in both HIV-positive groups compared with HIV-negative controls. Significant between-group differences were noted in the postprandial AUCs for IDL (P0.020) and LDL particles (P0.031; Table 3). For these parameters, the postprandial
AUCs for group 1 were higher than for groups 2 and 3. For LDL particles, the postprandial curves were relatively flat, with slight decreases in the second and fourth postprandial
hours (Figure 2). Postprandial differences seemed to reflect baseline values; however, the differences between groups 1 and 2 increased enough to reach statistical significance
(P0.032). For IDL, postprandial levels increased in both HIV-positive groups, but more in group 1 than 2 (P0.020) or group 3 (P0.017; Figure 2). This difference was especially notable between the fourth and sixth postprandial hours, when IDL increased in group 1 but decreased in groups
2 and 3. The postprandial AUC for small LDL particles was highest among subjects in group 1 and was significantly higher than in group 3 (P0.030) but did not differ significantly
from group 2. Incremental Postprandial AUC Differences Between Groups 1 and 2
In incremental AUC analyses (numerical data not shown), differences in triglycerides, retinyl palmitate, chylomicrons, large VLDL, and small VLDL were not seen between groups
1 and 2 across any time increment. Between-group differences in medium VLDL postprandial AUCs trended toward significance at hours 2 through 10 and 4 through 10 only.
Postprandial AUC differences in IDL and LDL particle concentrations between groups 1 and 2 remained statistically significant throughout each time increment because of con-
TABLE 1. Subject Characteristics Group 1 (HIV, on PIs) Group 2 (HIV, not on PIs) Group 3
(HIV) P* n 35 20 10 — Age (years) 42.51.3 41.61.8 31.12.5 0.001 (1 vs 3, 0.001)
(2 vs 3, 0.001) Sex (% male) 83 85 80 0.794 Body surface area (kg/m2) 1.930.03 1.870.03 1.970.05 0.124 (2 vs 3, 0.038) Systolic blood pressure (mm Hg) 128.02.4 128.33.2 124.84.6 0.831 Hypertension (%) 26 15 0 0.101 Glucose (mg/dL) 96.61.9 95.02.4 89.83.4 0.233 Insulin (U/mL) 12.81.3 10.61.7 5.62.5 0.039 (1 vs 3, 0.012)
HOMA-IR (units) 3.20.4 2.50.5 1.20.7 0.052 (1 vs 3, 0.017) Waist circumference (cm) 90.91.6 83.82.1 89.32.9 0.028 (1 vs 2, 0.008) Diabetes mellitus (%) 3 5 0 0.770
Current smoking (%) 46 55 0 0.114 Family history of premature CAD (%) 23 15 10 0.124
Duration of HIV treatment (years) 5.70.5 5.10.7 — 0.453 CD4 cell count (cells/mL) 42345 47459 — 0.500 HIV RNA titer (median copies/mL, % undetectable) 75 (45.7) 67 (45.8) — 0.226 *Statistically significant between-group differences (P0.05) are in parentheses. Stein et al Postprandial Lipoproteins in HIV 401 sistently higher values among subjects in group 1. Consistent significant differences in postprandial AUCs were not observed
for LDL subclasses or lipoprotein particle sizes. Exploratory Analyses Exploratory analyses to determine whether there were differences in baseline and postprandial responses between the 12 subjects taking lopinavir/ritonavir (the most commonly used ritonavir-boosted PI) and the 10 subjects taking nelfinavir (the most commonly used nonritonavir-boosted PI) were
performed. These subjects were of similar age. Subjects taking nelfinavir had higher CD4 cell counts (P0.001) and a higher prevalence of hypertension (P0.028); however, no
significant differences or trends (P0.010) between the groups taking these PIs were observed for lipids or lipoproteins at baseline or after the fat load. Similar results were obtained when comparing all 20 subjects in group 1 who were taking ritonavir (the PI most implicated in hyperlipidemia), with the remaining 15 not taking this PI. Also, use of stavudine did not significantly influence postprandial AUCs for the parameters in Figures 1 and 2. Discussion
In this study, postprandial serum triglycerides, retinyl palmitate, chylomicrons, and large VLDL concentrations in HIVpositive individuals taking PIs were similar to HIV-positive individuals not taking PIs. However, compared with HIVnegative subjects, both HIV-positive groups had more persistent peak serum triglyceride concentrations and later peak concentrations of retinyl palmitate and large VLDL. For these markers of triglyceride metabolism, postprandial concentration curves for groups 1 and 2 were nearly superimposable, and the postprandial AUCs did not differ significantly. Between the fourth and sixth postprandial hours, the decreasing chylomicron concentrations with increasing large VLDLs/remnants and IDLs suggest that clearance of postprandial lipoproteins is delayed in HIV-positive patients on
antiretroviral therapy, with chylomicron triglycerides being hydrolyzed by lipoprotein lipases and subsequent conversion to chylomicron remnants, large VLDLs, and IDLs, with
delayed hepatic removal. The delayed peak in retinyl palmitate levels (at hour 6) in groups 1 and 2 also supports this conclusion. Evaluating postprandial chylomicron and triglyceride metabolism by measuring retinyl palmitate levels is based on the observation that in humans, retinyl esters circulate with chylomicrons and their remnants, are taken up by hepatocytes,
and do not recycle in VLDLs.20,21 Because of experimental evidence that retinyl esters can be transferred from chylomicrons to other lipoprotein fractions, we also assessed lipoprotein concentrations using NMR spectroscopy, another validated technique for assessing triglyceride-rich lipoproteins after an oral fat load.13,22,23 In agreement with the findings using retinyl palmitate levels, NMR assessment of TABLE 2. Baseline Lipids and Lipoproteins
Group 1 Group 2 Group 3 P* Serum triglycerides (mg/dL) 233.135.2 183.245.9 78.864.9 0.176 (1 vs 3, 0.040) Retinyl palmitate (mg/dL) 0.1160.029 0.0610.038 0.0000.053 0.170 Chylomicrons (mg/dL) 5.81.2 3.81.6 1.32.3 0.258 Large VLDL (mg/dL) 83.022.5 54.429.8 15.642.1 0.408 Medium VLDL (mg/dL) 81.211.0 72.414.5 20.720.5 0.071 (1 vs 3, 0.012) (2 vs 3, 0.044) Small VLDL (mg/dL) 11.42.0 11.42.7 10.43.8 0.978 LDL particles (mmol/L) 1470.187.3 1221.5115.4 996.8163.2 0.042
(1 vs 3, 0.014) IDL (mg/dL) 4.91.2 1.61.6 0.02.3 0.080 (1 vs 3, 0.032)
Large LDL (mg/dL) 42.07.4 30.39.8 43.613.9 0.602 Medium LDL (mg/dL) 37.86.2 47.68.2 43.411.6 0.629 Small LDL (mg/dL) 39.88.2 24.910.8 7.215.3 0.199
(1 vs 3, 0.066) Large HDL (mg/dL) 24.72.4 23.03.1 18.44.5 0.539 Small HDL (mg/dL) 15.30.9 17.81.1 18.21.6 0.123 VLDL size (nm) 57.82.0 53.52.7 51.53.8 0.261
LDL size (nm) 20.50.5 20.60.7 20.71.0 0.033 (1 vs 3, 0.005) (2 vs 3, 0.012)
HDL size (nm) 8.80.3 8.80.3 10.10.5 0.092 (1 vs 3, 0.020) (2 vs 3, 0.031)
* Statistically significant between-group differences (P0.05) are in parentheses.
402 Arterioscler Thromb Vasc Biol. February 2005 postprandial chylomicron and large VLDL remnant concentrations showed similar postprandial AUCs among both groups of HIV-positive patients. These findings also suggest that postprandial metabolism of chylomicrons and their remnants do not differ significantly between patients with HIV infection currently using or not using PIs. The only postprandial AUC differences observed between HIV-positive subjects receiving and not receiving PIs were in the concentrations of IDL and LDL particles. Postprandial curves for LDL particles initially reflected baseline differences between groups and were higher in group 1. Although this general relationship was maintained throughout the
postprandial period, differences between groups 1 and 2 increased, whereas the differences between groups 2 and 3 tended to decrease, so the postprandial AUCs for LDL particles were significantly higher in group 1 than in group 2 or 3. The LDL particle concentration is the most powerful of the NMR-derived lipoprotein concentrations for predicting cardiovascular risk in the fasting state; however, associations between CAD and postprandial lipoprotein measured using
NMR spectroscopy have not been described previously.13,24,25 Similarly, baseline IDL concentrations were highest among subjects taking PIs. After the lipid load, the increase in IDLs
was most dramatic and sustained in group 1, and the postprandial AUC for IDLs was significantly higher than in groups 2 and 3. Although disorders associated with increased
IDL levels (measured using other techniques) have been associated with atherosclerosis, associations with CAD have not been described between IDL concentrations measured by
NMR spectroscopy or postprandial IDL levels.13 Nevertheless, as a cholesterol-rich remnant lipoprotein, it is likely that increased IDL levels contribute to atherosclerosis. Overall, these findings suggest that HIV-positive subjects receiving antiretroviral therapy have impaired clearance of postprandial triglyceride-rich lipoproteins, but that the dyslipidemia observed in patients receiving PIs also may be related to impaired clearance of IDLs and LDLs.2,10 Increased postprandial lipemia predicts the development and progres-Figure 1. Age-adjusted postprandial changes in serum triglycerides, retinyl palmitate, chylomicrons, and large VLDL. For statistical significance, see text and Table 3. TABLE 3. Postprandial AUC Values Group 1 Group 2 Group 3 P* Serum triglycerides (mg/dL) 3389362 3092478 1565677 0.114
(1 vs 3, 0.021) Retinyl palmitate (mg/dL) 15.21.4 15.21.8 10.62.6 0.355 Chylomicrons (mg/dL) 44775 40899 267140 0.688 Large VLDL (mg/dL) 1231201 1026266 447376 0.265 Medium VLDL (mg/dL) 76884 653111 240157 0.036 (1 vs 3, 0.004)
(2 vs 3, 0.035) Small VLDL (mg/dL) 10015 11320 9628 0.841 LDL particles (mmol/L) 14362693 11846917 108241297 0.031 (1 vs 2, 0.032) (1 vs 3, 0.019) IDL (mg/dL) 5610 1514 519 0.020 (1 vs 2, 0.017) (1 vs 3, 0.020) Large LDL (mg/dL) 41766 31488 445124 0.588 Medium LDL (mg/dL) 36057 49275 487106 0.321 Small LDL (mg/dL) 38766 20887 78123 0.082 (1 vs 3, 0.030) Large HDL (mg/dL) 25527 23930 21542 0.732 Small HDL (mg/dL) 1387.0 1609.3 16013.1 0.126 *Statistically significant between-group differences (P0.05) are in parentheses. Stein et al Postprandial Lipoproteins in HIV 403 sion of CAD; however, postprandial lipoprotein metabolism is complex, and most studies have focused only on postprandial triglyceride metabolism.14–16,20–22 Patients with increased postprandial hypertriglyceridemia have disordered handling
not only of exogenous-derived triglyceride-rich lipoproteins but also hepatic-derived triglyceride-rich lipoproteins.26,27 Postprandial hypertriglyceridemia in hypertriglyceridemic
patients with CAD appears to be attributable to impaired metabolism of VLDLs rather than accumulation of chylomicrons and their remnants.27 In this context, it is interesting
that postprandial medium VLDL concentrations were higher in both HIV-positive groups than in HIV-negative controls. These findings also are consistent with a previous report of
decreased triglyceride clearance in patients with advanced HIV infection not on HAART.12
Strengths of this study include the use of an HIV-negative control group, demonstration of the expected postprandial curves for serum triglycerides and retinyl palmitate, and verification and elucidation of postprandial lipoprotein metabolism by the newer NMR technology.23 Other strengths include statistical validation of the AUC data by incremental AUC analysis and similar apolipoprotein E genotypes in all 3 groups. Also, age, body surface area, systolic blood pressure,
glucose, duration of HIV treatment, CD4 cell count, and HIV RNA titer (including percentage completely suppressed) were similar between both HIV-positive groups. Although subjects
in group 1 had larger waist circumferences, their fasting insulin and HOMA-IR levels were not significantly different from group 2.
Limitations
Because subjects with diabetes mellitus or on lipid-lowering medications were excluded, the magnitude of the dyslipidemia in this study was not as severe as in previous studies of HAART and lipoproteins. It is possible that differences in postprandial lipoprotein metabolism among patients taking HAART with more significant metabolic abnormalities were not detected.11 In this study, levels of apolipoprotein B-48, apolipoprotein B-100, and “triglyceride-rich remnant lipoproteins” using newer assays were not measured as in other studies of postprandial lipemia.12,20,21,26–28 Similarly, levels and activity of enzymes involved in triglyceride metabolism were not assayed. Although the HIV-negative controls were significantly younger than both HIV-positive groups, all between-group comparisons were adjusted statistically for
age. A fourth arm of HIV-positive individuals not on HAART was not included because impaired triglyceride clearance has already been demonstrated in this group, and the sample size for a 4-way comparison would have been prohibitive.12 Finally, the prevalence of stavudine use in this study was somewhat higher than current usage patterns; however, it did not appear to affect postprandial lipoprotein metabolism in group 1 or 2 and was reflective of nucleoside reverse
transcriptase inhibitor use when this study started (summer 2002).
Conclusions
Postprandial clearance of triglyceride-rich lipoproteins is delayed in HIV-positive individuals receiving antiretroviral therapy. Compared with HIV-positive individuals not on PIs, those taking PIs do not have increased postprandial triglyceride-rich lipoproteins but do have ncreased postprandial IDLs and LDLs. In this regard, postprandial hypertriglyceridemia
does not contribute to the increased cardiovascular risk observed among HIV-positive patients receiving PIs relative to HIV-positive subjects not taking PIs, but may contribute to the increased cardiovascular risk observed in patients with HIV infection per se. The finding that subjects taking HIV PIs had increased postprandial concentrations of atherogenic IDL and LDL particles is unique and merits further study.
Acknowledgments
This work was funded in part by the National Center for Research Resources (K23 RR16176) and the University of Wisconsin General Clinical Research Center (M01 RR03186-1551).
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Stein et al Postprandial Lipoproteins in HIV 405

To purchase a NMR LipoProfile test simply order online at LabSafe at http://www.lab-safe.com/lab-tests/test/532/ or visit our website at www.LabSafe.com

For more information, or to speak with a member of our professional Medical Staff, call LabSafe toll free at 1-888-333-LABS.

Friday, September 15, 2006

Breaking News: LabSafe Deploys SLI Systems' Search Solutions to Put Critical Medical Information in Customers' Hands More Quickly

Breaking News: LabSafe Deploys SLI Systems' Search Solutions to Put Critical Medical Information in Customers' Hands More Quickly

Tuesday, September 12, 2006

Market Wire Press Release for LabSafe

LabSafe Deploys SLI Systems' Search Solutions to Put Critical Medical Information in Customers' Hands More Quickly

Online Laboratory Products and Services Provider Generates More Sales Through SLI's Managed Site Search and Automated SEO Offerings

CUPERTINO, CA -- (MARKET WIRE) -- September 12, 2006 -- SLI Systems, Inc., a provider of on-demand search services for Internet and e-commerce sites, today announced that LabSafe (http://www.labsafe.com/), an online provider of medical laboratory products and services, has implemented the company's hosted site search and automated search engine optimization (SEO) offerings. Since deploying SLI Systems' Learning Search and Site Champion just a few months ago, LabSafe has seen a steady increase in revenues derived directly from the managed solutions, by delivering more relevant search results to site visitors and increasing its ranking on searches conducted on Internet search engines like Google and Yahoo!

LabSafe has experienced an added benefit from working with SLI Systems in eliminating frequent problems it encountered with its previous site search solution, stemming from too many search queries that overloaded the company's servers and brought them to a standstill. Because SLI's offerings are hosted entirely on SLI's own servers, which can handle upwards of 20 million searches a day, LabSafe no longer worries about the negative impact of abundant search-bot activity during peak traffic periods, and the issues with site performance have completely disappeared.

"Our mission is to expand the field of preventative medicine by increasing consumer access to affordable laboratory tests and laboratory testing education," said Brian Lunn, CEO of LabSafe. "If people can't find what they're looking for on our site, then we're not doing a good job of putting critical information in their hands. SLI Systems has been a life saver to us -- and our customers -- because they deliver the results people look for when searching on our site. The increases in sales and positive feedback we've seen are evidence."

About Learning Search and Site Champion
Based on SLI's patented "Learning Search" advanced analytics engine, the company's hosted site search and automated SEO solutions "learn" from user behavior by tracking the search terms people use on a retailer's site and the resulting items they click on. The SLI solutions give retailers greater insight into what terms they should be both linking products to and including in product descriptions, and which products are the most popular for various keywords. As a result, retailers can ensure the products people are searching for show up in the right searches -- whether those searches are conducted on their own sites or on an Internet search engine like Google.

Learning Search is a hosted site search solution that continually tracks visitors' aggregate search terms and the corresponding items clicked on, and uses that data to deliver results based on popularity. For example, if someone searches on "Blood Test," Learning Search automatically ranks the items so that those with historically higher click-rates are listed at the top. Learning Search also shows how many products exist and in what categories, and gives visitors the option to sort the results by ascending or descending prices, or filter by category. Learning Search is supplemented with a new, free Site Search Feedback Tool, which is designed to help e-commerce and other Web sites better understand the quality of their site search functionality based on customer input. More information is available at http://www.sli-systems.com/feedback.

Site Champion is an automated search engine optimization service that integrates with Learning Search, tracking visitors' search terms and using them to automatically create related search links for each page of a retailer's site. When visitors click on these 'related search' links they are presented with site search results for that term, directing them to additional relevant content and commerce opportunities -- making it easier for a site's visitors to find what they want, and helping retailers generate more sales. The links also drive more natural search traffic to the retailer's site by giving the retailer's results pages links that are crawled by search engine spiders.

"It's a good feeling to know we're helping companies like LabSafe that have such a worthwhile purpose," said Dr. Shaun Ryan, CEO of SLI Systems. " The benefit of our managed offerings is that there's no up-front or ongoing work for the retailer; they just get to sit back and watch the positive results come in. This makes it easy for companies like LabSafe to stay focused on the important work they do."

More than 100 online retailers and other companies are using SLI Systems' hosted offerings, including NBC, Tupperware, Fright Catalog, NRS (Northwest River Supplies), and many more.

About SLI Systems
SLI Systems is a leading provider of managed site search services for e-commerce and other Internet sites that learn from user behavior to improve visitors' search experiences. SLI Systems' hosted site search, and automated SEO and SEM solutions empower businesses to enhance customer satisfaction while increasing sales, reducing costs and yielding valuable customer information. Unlike traditional search software, SLI Systems' patented technology continuously "learns" from the behavior of visitors over time to deliver more relevant results. Current customers include Etronics.com, NBC, Tupperware, ULTA, Chiasso and others. SLI Systems is a privately held company, with offices in Silicon Valley, London, and Christchurch, New Zealand. For more information, visit http://www.sli-systems.com/.

FOR MORE INFORMATION, CONTACT:
Nancy MacGregor Hill
RealTime Communications
510-733-6228
Email Contact
SOURCE: SLI Systems, Inc.

LabSafe
1-888-333-LABS
Email Contact
website@LabSafe.com
SOURCE: LabSafe

Friday, September 08, 2006

Have You Been Exposed To Heavy Metals?

Heavy Metal

Due to natural disasters and other recent catastrophes, we at LabSafe have had numerous inquires regarding heavy metal and other toxin exposures.

The Environmental Protection Agency (EPA) monitors the environment for health hazards and public safety. Heavy Metals include mercury, lead, arsenic and chromium to name a few. We are exposed to these toxins daily, and in low levels these toxins cause no problems. However, the EPA has issued warnings for Alabama, Louisiana, Mississippi, and Texas due to high levels of some of these toxins.

Some of the harmful effects of mercury are cancer, damage to the stomach and large intestines, permanent damage to the brain and kidneys, permanent harm to unborn children, lung damage, and increased blood pressure and heart rate.

Chromium exposure through the inhalation of insoluble chromium compounds may produce pneumoconiosis with impairment of pulmonary function. Exposure to the inorganic soluble salts can precipitate skin ulcerations, dermatitis, perforation of the nasal septum, and respiratory sensitization. Acute exposure to these salts may result in local tissue necrosis and kidney damage.

Symptoms of high levels of arsenic exposure include headache, feeling tired, confusion, hallucination, vomiting, diarrhea, digestive system bleeding seizures, and coma.

Symptoms of high lead exposure include fatigue, depression, heart failure, abdominal pain, gout, kidney failure, high blood pressure, wrist or foot weakness, reproductive problems, and anemia.

A heavy metal test panel, offered by LabSafe, can monitor exposure and detect if your levels are higher than the acceptable limits. Call LabSafe toll free at 1-888-333-LABS or visit us online at www.LabSafe.com (click on the category Testing For Toxins or simply follow this link: http://www.labsafe.com/lab-tests/test/483/ ) for more information. As with all tests the results should be reviewed with your Physician.

Thursday, September 07, 2006

Facts About Menopause

Menopause

A woman’s body goes through several changes during menopause. Some of the more common symptoms of menopause occur when estrogen levels start to drop. Women may experience:

1. hot flashes;
2. rapid mood swings ranging from depression to euphoria;
3. decreased libido and sex drive;
4. increased frequency or sudden urge to urinate;
5. vaginal dryness with pain during intercourse;
6. excessive bone loss, leading to a higher incidence of fractures of the hip and spinal column;
7. a higher risk for heart disease (because the levels of LDL “bad” cholesterol in the blood may rise).

A woman of menopausal age might have the following laboratory tests ordered:

1. Follicle-stimulating hormone (FSH) test, to learn whether she is approaching or has gone through menopause;

2. Estradiol test, to measure ovarian production of estrogen and to evaluate whether the menstrual cycle is normal and if she is fertile;

3. Thyroid function testing (free T4 and TSH tests) to test the function of the thyroid gland, which can slow with age;

4. Lipid profile, to test for triglycerides and the good (HDL) and bad (LDL) cholesterol levels in the blood to assess for cardiovascular disease;

5. Complete blood count (CBC), to determine the adequacy of the number of red and white blood cells in the blood;

6. Chemistry tests for liver and kidney function, to see if she can tolerate hormone replacement therapy; and

7. If a woman has risk factors or symptoms of diabetes, her doctor may also order a glucose test to learn whether the sugar levels in the blood are too high.

Knowing what is going on with your body is the first step in understanding menopause.

To purchase any of the above tests, click on their link where you may simply order online at the LabSafe website at www.LabSafe.com

For more information, or to speak with a member of our professional Medical Staff, call LabSafe toll free at 1-888-333-LABS.

Wednesday, September 06, 2006

Can Herbal Remedies or "Liver Flushes" Damage Your Liver?

Liver Panel Test

A liver panel, also known as liver (hepatic) function tests or LFT, It is used to check for liver damage or disease. It includes:

• ALT – an enzyme mainly found in the liver; the best test for detecting hepatitis

• AST – an enzyme found in the liver and a few other places, particularly the heart and other muscles in the body; indicates liver damage

• Bilirubin – (especially significant if a person has jaundice); indicates if liver is filtering and processing properly

• Albumin-measures the main protein made by the liver and tells how well the liver is making these proteins

• Total protein- measures protein including antibodies made to help fight infection; can be an indicator of cirrhosis or other medical disorders.

The liver panel test may also be ordered when a person has been or may have been exposed to a hepatitis virus; has a family history of liver disease; has excessive alcohol intake; or is taking a drug that can cause liver damage. The liver is unique as the only internal human organ capable of natural regeneration of lost tissue. As little as 25% of a remaining liver can regenerate into a whole liver again. The liver is the largest organ in the body, weighing about three to four pounds, and making up about 2-3% of the total body weight. It is the “mother” of the body and does what most mothers do—a vast complex multi-systematic performance of coordination—including that of a ‘watchdog’, grocer, housekeeper, body-guard, bodybuilder, energy plant supervisor, and sanitation engineer - to name but a few.

The following list names just a few of the liver functions. The liver performs several roles in carbohydrate, protein, and lipid (fats} metabolism. It also aids in the breakdown of insulin and other hormones, toxic substances and most medicinal products. The liver aids in blood clotting. It stores nutrients, including glucose (in the form of glycogen), vitamin B12, iron, and copper. The liver also has an important role in vitamin storage. High concentrations of riboflavin or vitamin B1 are found in the liver. 95% of the body’s vitamin A stores are concentrated in the liver. The liver also contains small amounts of vitamin C, most of the body’s vitamin D stores, and vitamins E and K.

A liver test panel may be ordered when symptoms suspicious of a liver condition are noticed. These include: jaundice, dark urine, or light-colored bowel movements; nausea, vomiting, and/or diarrhea; loss of appetite; vomiting of blood; bloody or black bowel movements; swelling or pain in the belly; unusual weight change; or fatigue or loss of stamina. One or more of these tests may also be ordered when a person has been or may have been exposed to, or is taking a drug that can cause liver damage.

Liver flushes and herbal liver cleansers are available without a prescription and are gaining in popularity. Many people are concerned that their livers may be damaged or stressed, and they seek to detoxify their liver in order to be healthier, rid their body of toxins, and have higher energy levels. However, many herbal remedies can actually damage the liver and many others may provide little or no benefit. Taking a liver panel test before taking the herbal remedy can help you understand if your liver is healthy, and set a baseline for comparison afterwards. After taking the herbal remedy, a second, follow-up liver panel can be done to help understand if the remedy has hurt or helped your liver.


To purchase a Liver Function test simply order online at LabSafe at http://www.labsafe.com/lab-tests/test/412/ or visit our website at www.LabSafe.com

For more information, or to speak with a member of our professional Medical Staff, call LabSafe toll free at 1-888-333-LABS.

Tuesday, September 05, 2006

What is the Prostatic Acid Phosphatase Test?

Prostatic Acid Phosphatase

Prostatic Acid Phosphatase (PAP) is an enzyme that is normally present only in small amounts in the blood. It may be found at higher levels in some patients with prostate cancer, especially if the cancer has spread beyond the prostate. However, blood levels may also be elevated in patients with certain benign prostate conditions or early stage cancer. Although PAP was originally found to be produced by the prostate, elevated PAP levels have since been associated with testicular cancer, leukemia, and non-Hodgkin’s lymphoma, as well as noncancerous conditions such as Gauchers disease, Paget’s disease, osteoporosis, cirrhosis of the liver, pulmonary embolism and hyperparathyroidism. A simple blood test is available to detect PAP levels.

Prostatic acid phosphatase is used as a prostate tumor marker. PAP in conjunction with PSA measurements are useful in assessing the prognosis of prostate cancer. Measurement of two markers allows identification of prostate cancer patients who have an elevation of PAP but not of PSA, and thus help monitoring the course of disease and response to treatment. PAP is more specific than PSA and less false-positives are seen due to Benign Prostatic Hyperplasia (BPH).

Source. MedlinePlus, National Institute of Health.

To purchase a Prostatic Acid Phosphatase test simply order online at LabSafe at http://www.labsafe.com/lab-tests/test/481/ or visit our website at www.LabSafe.com

For more information, or to speak with a member of our professional Medical Staff, call LabSafe toll free at 1-888-333-LABS.

Friday, September 01, 2006

Why Testosterone, Steroids, or Anti-Aging Drugs Can Be Dangerous

Testosterone
Testosterone is often thought of as only being important in males. In men, the hormone is produced by the testicles and is responsible for the proper development of male sexual characteristics. However, testosterone is also important in females, and in both sexes it is essential for maintaining muscle mass, adequate levels of red blood cells, bone growth, sense of well-being and sexual function.

In men, the amount of testosterone in the body gradually declines with age. This natural decline starts after age 30 and continues throughout life. Other causes of lowered testosterone levels include, but are not limited to: injury or infection to the testes, pituitary gland dysfunction, stress, medications, and alcoholism. Advances in the medical field of Endocrinology have led many of our nation's top doctors to characterize the natural drop in male testosterone levels as a condition called "andropause." Simply put, andropause can be considered the male version of menopause in females.

Without adequate testosterone a man may lose his sex drive, experience erectile dysfunction, feel depressed, have a decreased sense of well-being, and have difficulty concentrating. Other changes that can occur are a decrease in muscle mass with an increase in body fat, changes in cholesterol levels, osteoporosis and mild anemia.

A testosterone blood test may help detect the andropause condition by measuring the amount of testosterone in the blood. The result is compared with the expected testosterone levels (based on a man's age).

LabSafe offers a simple and relatively inexpensive testosterone blood test that can give valuable insight into your current testosterone levels. Knowing your testosterone level may help you and your physician understand if medications such as Viagra, Human Growth Hormone, Testosterone Replacement Therapy, Anti Aging therapy, or other medications are appropriate for you.

If you are taking testosterone or steriods, it is imperative that you regularly have your testosterone blood levels tested. Testosterone therapy has been associated with serious heart disease and cardiovascular problems, gynecomastia (swelling and tenderness of the breasts in men), dangerous mood swings and emotional problems, and more. Taking supplemental testosterone when your body is already making enough testosterone can be very dangerous to your body. It can cause a man's testicles to shrink as the body cuts back natural testosterone production to adjust your testosterone levels back to normal.

If you are taking testosterone or steroids when your body is making enough natural testosterone, then you are "doping." Doping is very dangerous and unfortunately is often not taken seriously enough. Many people do it in an effort to enhance athletic performance, and some believe it will help their sex drive or make them feel "better." Young athletes often mistakenly believe that they are young and healthy enough that steroids won't cause them any harm, or that they can contol it with the option to stop taking steroids when they want. But a person can not see the damage that steroids can do to their internal body and often there are no immediate symptoms. For example, unnecessary steroids can cause the heart to change its structure, function, and tissue composition. You can't see these changes and most often you can't feel them either, but the effects are there and do indeed increase your cardiovascular risk, particularly for heart attacks. We often hear of strong young athletes who suddenly die of a heart attack.

However, your doctor has a wide variety of options to help you while at the same time using FDA approved, legal, proven, and safe therapies, which may include medications such as Viagra, vitamin and amino acid supplementation, or possibly even testoserone. Before you seek any testosterone, steriod or antiaging drug therapy it is imperative that you discuss this with your licensed Medical Doctor.

In the past several years there has been an increasing problem with internet based companies who sell steroids, Anti-Aging therapy, Human Growth Hormone (HGH), etc. Many of these outfits have a doctor who authorizes steroid or HGH therapy without ever seeing their patient. The patient feels a false sense of security because a doctor has written a prescription for them, but what they fail to realize is that these companies are simply in business to make money. Often times, the sales people they are talking to do not even have any medical training or background. To make matters worse, they often fail to tell their own doctor that they are taking steroids. Again, before you take any medications, consult your own doctor. If you presently fall into this category, consult your doctor immediately and your doctor can advise you if and how you should stop taking these medications. Suddenly stopping or starting medications can present risks and you should always consult your own doctor.

Symptoms people are experiencing that lead them to choose steroid therapy can often be caused by other underlying medical conditions. For example, a person may say they feel tired or just don't have the same stamina they used to have. Well, that could be due to a number of medical issues ranging from cancer to thyroid problems. Taking steroids may actually make some of these conditions worse. Again, it is essential that you consult your own doctor about symptoms you are having.

LabSafe does not sell or market any medications and therefore has no financial incentive for your blood test results to turn out one way or another. For this reason many people choose to have their HGH, IGF-1 (Insulin-like Growth Factor 1), or testosterone blood testing performed through LabSafe. As with all LabSafe lab tests, regardless of your test results you should share them with your own doctor.

To purchase aTestosterone test, IGF-1 test, or HGH test, simply order online at LabSafe at http://www.labsafe.com/lab-tests/test/74/ or visit our website at http://www.labsafe.com/

For more information, or to speak with a member of our professional Medical Staff, call LabSafe toll free at 1-888-333-LABS.