© 1997 American Heart Association, Inc.
Articles |
Variability of Plasma HDL Subclass Concentrations in Men and Women Over Time
From the Life Sciences Division, Ernest Orlando Lawrence Berkeley National Laboratory, Berkeley, Calif.
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Abstract |
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Abstract Plasma HDL subclasses were examined by gradient gel electrophoresis in repeated samples to assess variability over time. Absorbance of the protein stain was used as an index of mass concentrations at 0.01-nm intervals within five HDL subclasses: HDL3c (7.2 to 7.8 nm), HDL3b (7.8 to 8.2 nm), HDL3a (8.2 to 8.8 nm), HDL2a (8.8 to 9.7 nm), and HDL2b (9.7 to 12 nm). Three separate longitudinal studies of men showed that repeated samples of HDL over time were correlated most strongly within HDL2b, somewhat less within HDL2a, and more weakly within HDL3a, HDL3b, and HDL3c. As in men, repeated samples in women from two studies were significantly correlated within the HDL2b, HDL2a, and HDL3b intervals. Plasma HDL2b levels were significantly more stable in men than in women. Although the variability of HDL subclass measurements includes both methodological and physiological sources, differences in laboratory measurement error do not appear to explain the differences in correlations among subclasses. Specifically, analysis of 288 replications from frozen aliquots suggested that laboratory error had the least effect on correlations involving HDL3 subclasses and only slightly greater effect on correlations involving HDL2 subclasses. Our results suggest that for plasma sampled over time, the stability of HDL subclass levels increases with particle size. Prior reports of subclass-specific correlations between HDL and other variables (eg, diet, exercise, and other lipids) are unlikely to be artifacts of laboratory precision but could arise from subclass differences in variability that are physiological.
Key Words: gradient gel electrophoresis • HDL
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Introduction |
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HDLs can be characterized by particle size in nondenaturing polyacrylamide gradient gels.1 At least five HDL subclasses have been identified by this method so far: HDL3c (7.2 to 7.8 nm in diameter), HDL3b (7.8 to 8.2 nm), HDL3a (8.2 to 8.8 nm), HDL2a (8.8 to 9.7 nm), and HDL2b (9.7 to 12 nm).1 Statistical analyses of these subclasses could yield significant results for some subclasses but not for others because of differences in biological variability (ie, the variation in a subclass measurement in an individual sampled over time) and differences in laboratory measurement error. For example, case-control and angiographic studies suggest that coronary heart disease (CHD) risk may be increased when the level of HDL2b is reduced relative to those of HDL3c and HDL3b.2 3 4 There will, however, be less statistical power to detect mean differences between case and control subjects for those subclasses that exhibit greater variation within individuals over time. Two subclasses could be equally involved in the etiology of the disease, yet one subclass could be discarded as a CHD risk factor because its measurement is less reproducible. Biological variability and measurement error also affect the correlations of subclass levels with other variables. Previous studies have shown that different HDL subclasses exhibit specific relations with exercise,5 6 7 weight loss,5 age,8 adiposity,7 8 insulin and non–insulin-dependent diabetes mellitus,7 alcohol intake,8 menarche and menopause,8 the menstrual cycle,9 postmenopausal estrogen replacement,8 other lipoproteins,10 11 and shared genetic and environmental influences within families.12 However, when subclass levels are measured to different degrees of laboratory precision, correlations with other variables will be attenuated to different degrees.13 We could erroneously conclude that a subclass is unrelated to adiposity, alcohol intake, or some other variable because it was measured less precisely than another subclass that showed a significant relationship. This report assesses the physiological variations in HDL subclass concentrations over time within individuals and the relative precision of the laboratory measurements of subclasses.
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Methods |
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Blood was drawn from subjects who had abstained from all food and vigorous activity during the preceding 12 to 14 hours. Plasma was prepared from venous blood collected in tubes containing 1.4 mg/mL of disodium EDTA. The plasma fraction of d
1.20 kg/L was
obtained after a single-spin ultracentrifugation
(114 000g, 24 hours, 15°C; Beckman rotor, Beckman
Instruments). Electrophoresis of HDL was performed on Pharmacia
electrophoresis apparatus (GE 4-II) by using slab gradient
gels (PAA 4/30, Pharmacia).1 14 Time for electrophoresis
of HDL was generally 24 hours. A mixture of four globular proteins (HMW
Calibration Kit) was run on the central lane to calibrate particle
size. Thyroglobulin was added to each plasma sample to identify a
common reference on all lanes of the gel. The thyroglobulin peak was
identified for each sample and assigned a migration distance of 0. The
migration peak of BSA, one of the protein standards of the calibration
lane, was assigned a migration distance of 1 (the assigned values are
arbitrary and do not affect the calculations that follow). The HDL
migration distances (Rf's) were measured as the
fraction of the migration distance between the BSA and thyroglobulin
peaks. The gradient gels were then stained for protein with Coomassie
Brilliant Blue G-250 and scanned with a densitometer (model RFT,
Transidyne Corp) at a wavelength of 603 nm. A computer file of
absorbance versus Rf was obtained for 1000
equidistant points along the gradient gel. Originally, we proposed
using the known concentration of the thyroglobulin added to each HDL
sample to correct for differences in sample volume and stain
uptake.10 In subsequent studies,5 6 7 8 9 11 12 we
made no adjustment for the absorbance of HDL for the area of the
thyroglobulin peak because this adjustment was not found to decrease
the variance of the HDL measurements. Standard least-squares regression was used to fit a quadratic equation with the Rf's of the thyroglobulin, ferritin, lactic acid dehydrogenase, and BSA as the independent variables and the natural logarithm of their hydrated molecular diameters (17.0, 12.2, 8.16, and 7.1 nm, respectively) as the dependent variables. Calculus (transformation of variables) was then used to transform the HDL distribution from the Rf to the particle diameter scale.10 Specifically, in addition to finding the corresponding diameter for each Rf, it was necessary to multiply the height of the distribution by the Jacobian of the transformation.10 The height of the distribution curve (absorbance) at each diameter value was determined by interpolation for each 0.01-nm increment between 7.2 and 12 nm.10
Total variation (biological plus methodological) was assessed from correlations between repeated HDL subclass measurements in the control groups of three longitudinal clinical trials: Study 1, which included 42 men, aged 30 to 59 years, 20% to 60% over Metropolitan Life Insurance Table ideal weight,15 who were sampled at baseline, 7 months, and one year5 ; Study 2, which included 38 men, aged 25 to 49 years, with a body mass index (weight in kilograms divided by the square of height in meters) between 28 and 34 kg/m2 and 36 premenopausal women, all of whom were sampled 12 months apart6 ; and Study 3, which included 125 men and 12 women with angiographically defined coronary atherosclerosis who received usual physician care and who were sampled at baseline and at 1 year.16
Laboratory measurement error was assessed from frozen aliquots of the 1.20 g/mL>d>1.063 g/mL plasma, which was drawn annually from a 60-year-old man over a 5-year period. These samples were included on approximately every fourth gradient gel run at our laboratory, yielding 288 replications. These were used to calculate the SD for the laboratory measurement error (square root of the average variance for the five frozen aliquots) and coefficient of variation (square root of the average squared coefficient of variations for the five frozen aliquots). The laboratory error includes day-to-day variation in staining and destaining and calibration errors for assigning diameter values to Rf's.
Statistical Methods
Correlations between repeated samples in individuals were
determined by Pearson correlation and by ANOVA for estimating the
reliability of a measurement.17 The effect of the
laboratory measurement error on the correlation coefficient was
assessed by the attenuation coefficient.13 18
Specifically, when HDL is correlated with another variable
y, then the laboratory error in measuring HDL will attenuate
the true correlation and cause its value to be biased toward 0. Assume
that the observed value of HDL (HDLobs) is equal to its
true value (HDLtrue) plus its laboratory measurement error
(ie, HDLobs=HDLtrue+measurement error). Let
2HDLtrue represent the variance for
HDLtrue, 2HDLobs the variance
for HDLobs, and 2error the
laboratory measurement error variance of HDL. Then
2HDLobs=2HDLtrue+2error.
Thus, for
Corr (HDLtrue,y)=True Correlation Between HDLtrue and y and
Corr (HDLobs,y)=Observed Correlation Between
HDLobs and y, the attenuation coefficient is
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2HDLtrue,
2HDLobs, and
2error are estimated by their sample moments
(ie, the squared SDs s2HDLtrue,
s2HDLobs, and s2error,
respectively). |
Results |
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TopAbstractIntroductionMethodsResultsDiscussionReferences |
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Laboratory Precision
Fig 1
displays three different assessments of the
error associated with the laboratory measurement of protein-stained HDL
by gradient gel electrophoresis. The top panel compares the SD for the
laboratory measurement error (ie, the between-run SD) with the pooled
cross-sectional SD for the men from studies 1, 2, and 3. The SDs are
plotted separately for each diameter value between 7.2 and 12 nm. The
middle panel displays the attenuation coefficient for the correlation
coefficient, and the bottom panel displays the coefficient of
variation. The approximate subclass intervals described by Blanche et
al1 are provided for reference.
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The attenuation coefficient is the ratio of the observed correlation to
the true correlation, and the percent reduction in the true correlation
brought about by laboratory error in measuring HDL is
100x(1-attenuation coefficient). For example, Fig 1
shows that at 8.0
nm (within HDL3b), the observed correlation for HDL versus
other variables is expected to be 97% of the true correlation (ie,
laboratory measurement error decreases the correlation between HDL and
other variables by 3%). The figure also suggests that percent
reduction in the correlation coefficient is <15% for all diameter
values between 7.2 and 11.5 nm. In addition, (1) overall, correlations
involving larger HDL3c and HDL3b are the least
affected by measurement error; (2) percent reduction within the
HDL2a range becomes larger with increasing diameter; and
(3) correlations within the HDL2b interval show the least
reduction at 
10.5 nm.
The bottom panel displays the coefficient of variation, which compares the SD of the measurement error relative to a sample mean. The coefficient of variation is lowest for HDL2a and HDL2b and becomes progressively larger for HDL3a, HDL3b, and HDL3c.
Variation in Subclass Levels in Persons Sampled Over Time
The top panel of Fig 2 displays the correlation of
HDL subclasses sampled twice or three times over 1 year in three
samples of men. The graph suggests that the replicate samples were
correlated most strongly within HDL2b, somewhat less within
HDL2a, and more weakly within HDL3a,
HDL3b, and HDL3c. The bottom panel presents
the pooled correlation19 for the three studies of men. The
pooled correlations showed that HDL2b was the least
variable, HDL2a somewhat more variable, and
HDL3 subclasses (particularly HDL3c and
HDL3a) the most variable. The bottom panel also
compares the pooled correlation for men with the pooled correlation for
women.20 As in men, repeated samples from women were
significantly correlated within the HDL2b,
HDL2a, and HDL3b intervals. Between
HDL3b and HDL2a, there was an interval of
weaker correlation, presumably of HDL3a, which may be due
to the intrinsic variation in the HDL3a subclass or the
accumulated variation from three overlapping distributions within this
region (ie, HDL3b, HDL3a, and
HDL2a). Plasma HDL2b levels were significantly
more stable in men than women.
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Discussion |
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TopAbstractIntroductionMethodsResultsDiscussionReferences |
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In three separate studies, we found that plasma HDL concentrations sampled over a 1-year period in men were correlated most strongly within HDL2b, modestly within HDL2a, and more weakly within the HDL3 subclasses. The differences in correlations could not be attributed to laboratory measurement error; ie, laboratory error was found to have the least effect on correlations involving the HDL3 subclasses and only a slightly greater effect for correlations involving the HDL2 subclasses. The regions of increasing and decreasing stability over repeated samples correspond to recognized subclass intervals of particle diameter.1 Plasma HDL2b levels were significantly more stable in men than women. The more variable HDL2b levels in women are unlikely to be the consequence of variations associated with the menstrual cycle, which previous investigators have associated with variations in HDL2a rather than HDL2b levels.9
HDL subclasses may show different degrees of stability within individuals sampled over time because of differences in genetic and metabolic determinants of subclasses. Between 40% and 60% of plasma HDL cholesterol variability has been attributed to genetic variation,21 22 23 with 25% and 22% attributed to allelic variations in genes that encode hepatic lipase and the apolipoprotein AI/CIII/AIV cluster, respectively.23 The importance of low hepatic lipase levels to the formation of large HDL particles is suggested by studies of mice with inactivated hepatic lipase genes,24 transgenic rabbits with overexpressed hepatic lipase,25 and patients with hepatic lipase deficiency.26 We have reported elsewhere that among all subfractions, HDL2b exhibits the strongest correlation between parents and offspring and that the offspring's HDL2b levels are more strongly correlated with their father's than their mother's HDL2b level.12 This could explain the consistency of HDL2b levels over repeated samples in men (the genetic or enduring effects of family environment remaining constant) and the stronger consistency of the HDL2b measurement in men than women (the inheritance of HDL2b being stronger in men than women).12 It is also true that a better estimate of the paternal than the maternal phenotype may account for the stronger correlation of the offspring's HDL2b level with their father's rather than their mother's.12 Our previous analyses also showed that plasma HDL3b levels were correlated among siblings and between parents and offspring, suggesting that inheritance (genetic or family environment) may also contribute to the stability of HDL3b over time.12
Subclasses with a more rapid turnover may be more sensitive to perturbations than are subclasses that have a longer residence time in plasma. For particles that contain both apoA-I and apoA-II (HDL3b, HDL3a, and HDL2a)27 and particles that lack apoA-II (HDL3c, HDL3a, and HDL2b),28 the HDL3 species are catabolized more rapidly than are the HDL2. This could explain the greater stability of HDL2b vis-á-vis HDL3c and HDL3a and the greater stability of HDL2a vis-á-vis HDL3b and HDL3a. Differences in HDL2a and HDL2b stability could also reflect differing degrees of interactions with other lipoproteins or lipases. For example, when incubated with hepatic lipase, lipolysis of triglycerides and hydrolysis of phosphatidycholine in HDL2 (A-I with A-II) is reported to be substantially greater for HDL2 (A-I with A-II) than HDL2 (A-I without A-II)29 (ie, presumably greater for HDL2a than HDL2b).
The statistical analyses of protein-stained HDL could show
differences between subclasses that are due to laboratory measurement
error and biological variability rather than
physiological differences. Analyses of Figs 1 and 2 show the extent that these two factors could affect statistical
results. Measurement error will bias the correlation coefficient toward
0 (ie, attenuate the correlation so that it is less likely to reach
significance). For example, we have shown that adiposity levels in
cross-sectional samples and changes in weight in longitudinal studies
correlate significantly with HDL2b but not
HDL2a.5 7 8 We have also described significant
correlations among siblings and as noted above between father and
offspring for HDL2b but not HDL2a. However, the
differences in significance between HDL2b and
HDL2a are unlikely to be artifacts of differences in
measurement precision, since the degree of attenuation is at least as
great for HDL2b as for HDL2a (Fig 1). The power
to detect significant group differences in longitudinal studies is a
function of the within-subject and between-subject variances, which can
be derived from the correlation of two or more measurements in
individuals sampled over time.17 Fig 2 shows that HDL
subclasses exhibit different degrees of variability over time. In men,
correlations between replicate samples over time generally increase
with particle diameter. This results in less statistical power to
detect differences or correlations in HDL3 compared with
HDL2 subclasses. For example, reductions in
HDL3b concentrations during exercise-induced weight
loss5 and increased dietary fat intake30 are
larger in magnitude than are the increases in HDL2b;
however, the significances of the HDL3b decrease is less,
presumably because the variability over time is greater for
HDL3b than for HDL2b.
Measurement error and within-person variability does not affect the probability of a type I error (false-positive) but increases the probability of a type II error, which could lead to misinterpretation. Our analyses suggest that HDL subclasses exhibit only minor differences in laboratory precision but major differences in the stability of the measurements within individuals over time. Thus, observed differences in subclasses are unlikely to arise as artifacts of laboratory imprecision but could reflect biological variability in subclass levels over time.
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Acknowledgments |
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This study was supported in part by the National Dairy Promotion and Research Board and administered in cooperation with the National Dairy Council, grants HL-52617, HL-45652, HL-49857, and HL-28292 and program project HL-18574 from the National Heart, Lung, and Blood Institute and conducted at Stanford University and Lawrence Berkeley Laboratory (Department of Energy DE-AC03-76SF00098 to the University of California). We wish to thank Laura Holl, Charlotte Brown, and Bahareh Sahami for laboratory analysis of gradient gel electrophoresis and the staff of the Stanford Coronary Risk Intervention Project.
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Footnotes |
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Reprint requests to Paul T. Williams, PhD, Life Sciences Division, Ernest Orlando Lawrence Berkeley National Laboratory, One Cyclotron Rd, Bldg 934, Berkeley, CA 94720.
Received March 4, 1996; accepted July 14, 1996.
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