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Low-Density Lipoprotein Subclass Distribution Pattern and Adiposity-Associated Dyslipidemia in Postmenopausal Women

Chicago Center for Clinical Research, Chicago, Illinois

Address reprint requests to: Kevin C. Maki, PhD, Chicago Center for Clinical Research, 515 North State Street, 27^th Floor, Chicago, Illinois 60610

	ABSTRACT

TOP FOOTNOTES ABSTRACT INTRODUCTION MATERIALS AND METHODS RESULTS CONCLUSIONS REFERENCES

 
Objective: A predominance of small, dense low-density lipoprotein (LDL) particles (subclass pattern B) is associated with increased risk for coronary heart disease and is characterized by elevated triglycerides and depressed high-density lipoprotein (HDL) cholesterol concentrations. The present analysis was undertaken to assess the impact of LDL subclass distribution pattern and adiposity on serum lipids in postmenopausal women.

Methods: Anthropometric measurements and fasting lipid data were obtained from 254 postmenopausal women 70 years of age or younger, not receiving sex hormone replacement, who were participating in a clinical trial designed to assess the influence of hormone replacement regimens on coronary heart disease risk markers.

Results: The prevalence of LDL subclass pattern B was 32%. Triglyceride levels were higher and HDL cholesterol lower (both p<0.001) in women with pattern B vs. pattern A, but total and LDL cholesterol levels did not differ. LDL subclass pattern contributed independently to the variance in HDL cholesterol (p<0.001) and log_e triglyceride (p<0.001) concentrations explained by anthropometric variables (waist circumference or body mass index). Compared to women with LDL subclass pattern A and waist circumference below the median value of 83.0 centimeters, those with pattern B and waist 83.0 centimeters had markedly lower HDL cholesterol levels [44.0 (41.6–47.4) vs. 57.2 (54.1–60.3) mg/dL, mean (95% CI)] and increased triglyceride concentrations [geometric mean 147.8 (131.6–165.7) vs. 95.4 (88.2–102.5) mg/dL].

Conclusions: These data suggest that adiposity and LDL subclass distribution pattern are independent determinants of plasma triglyceride and HDL cholesterol concentrations in postmenopausal women.

Key words: lipoproteins, hyperlipidemia, obesity, body fat distribution

Abbreviations: BMI=body mass index • EPAT=Eating Pattern Assessment Tool • HDL=high-density lipoprotein • LDL=low-density lipoprotein • Log_e=natural logarithm • MET=metabolic equivalents

	INTRODUCTION

TOP FOOTNOTES ABSTRACT INTRODUCTION MATERIALS AND METHODS RESULTS CONCLUSIONS REFERENCES

 
A predominance of small, dense low-density lipoprotein (LDL) particles (LDL subclass pattern B) is associated with a 2- to 3-fold increase in risk for coronary heart disease [1–4]. LDL subclass pattern B is also characterized by several abnormalities of the plasma lipid profile, notably elevated triglycerides and depressed high-density lipoprotein (HDL) cholesterol concentrations [1–4] as well as other metabolic disturbances including insulin resistance, glucose intolerance and a hypercoagulable state [5–7]. In addition, small, dense LDL particles may have heightened atherogenicity due to greater susceptibility to oxidative modification and higher affinity for arterial wall proteoglycans [8]. Thus, the increased risk of coronary heart disease associated with the LDL pattern B phenotype may be secondary to the atherogenic influence of small, dense LDL particles, the cluster of metabolic disturbances which accompany this phenotype or a combination of these factors [9–10].

Family studies have shown linkage of LDL particle size to loci on chromosomes 6, 11, 16 and 19 [8]. One-third to one-half of the variance in peak LDL particle diameter is explained by genetic factors [10–11]. Excess body fat, particularly abdominal fat, is associated with LDL subclass pattern B, as well as elevated triglycerides and depressed HDL cholesterol [12–14]. Katzel and colleagues studied the interaction between LDL subclass pattern and adiposity among 160 men [15]. Those with LDL subclass pattern B had higher triglycerides and lower HDL cholesterol at any level of adiposity (percent body fat), compared to those with LDL pattern A [15]. These data support the concept that the genetic factors underlying LDL subclass distribution amplify the unfavorable effects of obesity on triglyceride and HDL cholesterol concentrations in men. This may have implications for coronary heart disease risk, particularly in light of the rapidly growing body of evidence demonstrating the importance of triglyceride-rich lipoproteins in the atherogenic process [16].

The present analysis was designed to test the hypothesis that postmenopausal women with LDL subclass pattern B would have greater disturbances of the plasma lipid profile (higher triglycerides and lower HDL cholesterol) than women with LDL pattern A at any level of adiposity. A secondary objective was to assess and compare the utility of body mass index and waist circumference as indicators of adiposity-related dyslipidemia in postmenopausal women.

	MATERIALS AND METHODS

TOP FOOTNOTES ABSTRACT INTRODUCTION MATERIALS AND METHODS RESULTS CONCLUSIONS REFERENCES

 
The dataset used for these analyses consisted of information collected at baseline from a group of 270 postmenopausal women who participated in a clinical trial designed to assess the influence of three hormone replacement regimens on coronary heart disease risk markers. All subjects provided written informed consent and the study protocol was approved by an institutional review board (Schulman Associates, Cincinnati, OH). An additional 229 women were screened but did not qualify for participation.

Eligible women were less than 71 years of age with natural or surgically-induced menopause at least 12 months prior to randomization, confirmed by a plasma estradiol level <20 pg/mL. Exclusion criteria included use of hormone replacement or lipid-altering agents within 10 weeks of the baseline plasma lipid measurements. Also excluded were women whose body mass index was >31.5 kg/m², who were heavy smokers (>20 cigarettes per day) or alcohol users (>14 alcoholic drinks per week) or who engaged in substance abuse. Women with uncontrolled hypertension (systolic pressure >160 mm Hg, or diastolic pressure >95 mm Hg) or elevated triglycerides (>350 mg/dL at two consecutive visits) were excluded. Other medical conditions excluding participation were history of stroke, pancreatitis, gallbladder disease, thrombophlebitis or thromboembolic disorders, myocardial infarction within six months, abnormal genital bleeding of unknown etiology, an abnormal mammogram suspicious for malignancy, the presence of hepatic enzymes more than twice the upper limit of normal, diabetes mellitus or other endocrine disease (except hypothyroidism adequately treated with a stable dose of thyroid replacement), and significant psychiatric disorders. Women using beta-adrenergic blockers, high doses of thiazide diuretics (>25 mg/d of hydrochlorothiazide or its equivalent), erythromycin, immunosuppressants, systemic corticosteroids or anticoagulants were also excluded.

Blood for baseline biochemical measures, including a plasma lipid profile, glucose, insulin, and hemoglobin A_1C, was collected after an overnight fast at two baseline visits, approximately 14 days apart. The mean of two values obtained on separate days was used in the analyses for all biochemical variables except LDL subclass distribution pattern and hemoglobin A_1C, which were measured once at the final baseline visit. Plasma lipid profiles included total cholesterol, HDL cholesterol, triglycerides and calculated values for LDL cholesterol. LDL subclass pattern was determined once from a plasma sample obtained at the final baseline visit.

Biochemical Analyses
Except for LDL subclass distribution, all biochemical assays were completed by Quest Nichols Institute, San Juan Capistrano, CA. Quest Nichols Institute participates in the Centers for Disease Control and Prevention/National Heart, Lung, and Blood Institute lipid measurement standardization program. LDL subclass distribution measurements were completed by Atherotech, Inc., Birmingham, AL.

The Vertical Auto Profile II method was used to assess the concentration of cholesterol carried in large, buoyant (LDL₁ and LDL₂) and small, dense (LDL₃ and LDL₄) LDL particles, as described elsewhere in detail [17–18]. Briefly, the Vertical Auto Profile II method utilizes single vertical spin density gradient ultracentrifugation to separate the various plasma lipoprotein fractions. After centrifugation, the cholesterol content of the tube is continuously analyzed and digitized. A cholesterol absorbance curve profile is generated by plotting digitized absorbance units on the Y axis and the relative gradient position on the X axis. A deconvolution program is used to separate the different lipoprotein classes and subclasses. Subjects with >50% of their LDL cholesterol in the small, dense fractions (LDL₃ + LDL₄) were classified as having the small, dense LDL phenotype (LDL subclass pattern B).

Plasma cholesterol, triglyceride and glucose concentrations were determined with a Hitachi 914 analyzer (Boehringer Mannheim, Indianapolis, Indiana) which employs enzymatic methods. HDL cholesterol was quantified after precipitation of lower-density lipoproteins with phosphotungstate and magnesium. LDL cholesterol in mg/dL was calculated using the following equation: LDL cholesterol=total cholesterol-HDL cholesterol-triglycerides/6.25 [19]. This equation loses accuracy when the plasma triglyceride level exceeds 400 mg/dL. Accordingly, no LDL cholesterol value was calculated in cases where triglycerides were above this level. Hemoglobin A_1C was measured with a VARIANT Analyzer (Bio-Rad Laboratories, Hercules, CA) by ion exchange high performance liquid chromatography. Plasma insulin concentration was assessed by radioimmunoassay (Linco Scientific, St. Charles, MO).

Questionnaires
Subjects completed a standard medical history questionnaire that was used to identify possible exclusion criteria and to assess smoking and alcohol consumption habits. The Stanford Seven-Day Physical Activity Recall questionnaire was used to estimate energy expended during sleep, light, moderate, hard and very hard activities [20]. Hours of activity in each category were multiplied by constants to produce estimates of energy expenditure. Estimated energy expenditure from each of these categories was then summed to produce a physical activity score in metabolic equivalent-hours per week (one metabolic equivalent-hour represents approximately one kilocalorie per kilogram of body weight). Dietary intake was assessed with section one of the Eating Pattern Assessment Tool^TM that consists of questions relating to intake of foods in 11 categories [21]. Lower scores indicate lower consumption of foods high in fat, saturated fats and cholesterol. A score of approximately 28 or below is consistent with the dietary recommendations of the National Cholesterol Education Program.

Anthropometric Measurements
Body weight and height were measured in light clothes without shoes. Body mass index was calculated as weight in kilograms divided by squared height in meters. Waist was measured in duplicate at the minimum circumference between the lowest rib and the iliac crest. If values differed by more than 0.5 cm, a third measurement was obtained, and the two closest values were averaged.

Statistical Methods
Statistical analyses were completed using the Statview 4.5 (Abacus Concepts, Berkeley, CA) and JMP 3.1 (SAS Institute, Cary NC) software packages. Plasma insulin, triglycerides and physical activity score were not normally distributed. Natural logarithm transformations produced acceptable distributions for insulin and triglycerides, but not physical activity score. Accordingly, physical activity score was ranked, and the ranks were used in multivariate analyses. Analysis of variance, Mann-Whitney U and Pearson chi-square tests were employed to assess differences in characteristics of subjects with LDL subclass patterns A and B.

Least squares linear regression models were fit for log_e triglyceride, HDL cholesterol and LDL cholesterol in order to test the null hypothesis that the regression lines for waist circumference and body mass index on plasma lipid levels were coincident for women with LDL subclass patterns A and B [22]. A single regression model approach was used as described by Kleinbaum and colleagues [22]:

where y is the lipid variable under investigation, x₁ is an anthropometric variable (waist or body mass index) and x₂ is LDL subclass pattern (0 = A, 1 = B). If the coincident lines hypothesis was rejected, additional tests were run to assess possible differences in slopes and intercepts. F-ratios calculated for these tests used the mean squared error from the full model as the denominator [22]. Separate regression models were also fit for women with the two LDL subclass patterns. Correlation coefficients are reported to express the strength of the relationship between anthropometric measures and plasma lipid variables within LDL subclass categories. Analysis of variance was employed to assess the influence of adiposity and LDL subclass distribution pattern on mean serum lipid concentrations using a median split to classify women into "high" and "low" categories for waist and body mass index.

The investigators felt that the deconvolution model employed to assess LDL subclasses provided a poor fit to the observed data for 12 subjects. Separate analyses were completed for which these women were excluded. Since doing so did not materially alter the results, only data from the full study sample are presented.

	RESULTS

TOP FOOTNOTES ABSTRACT INTRODUCTION MATERIALS AND METHODS RESULTS CONCLUSIONS REFERENCES

 
LDL subclass distribution was not measured for 12 of the 270 women randomized because an inadequate volume of plasma was available. An additional four women were excluded from the analyses because data for height were unavailable. Therefore, the analyses presented herein represent data from 254 subjects.

Characteristics of the study sample categorized by LDL subclass pattern are shown in Table 1. The prevalence of LDL subclass pattern B was 32%. Women with pattern B were slightly, but not significantly, older than those with pattern A. Dietary fat intake, as indicated by the Eating Pattern Assessment Tool, alcohol consumption and prevalence of current cigarette smoking did not differ between LDL subclass groups. Body mass index (p=0.037) and waist circumference (p=0.031) were significantly higher among women with LDL pattern B, while physical activity score was lower (p=0.007). The race/ethnicity of subjects in both LDL subclass categories was predominantly caucasian (non-Hispanic white). Use of antihypertensive medication and history of atherosclerotic disease were infrequent in both groups (<8%). Differences were not significant, but the prevalence of these characteristics tended to be higher among subjects with LDL subclass pattern A.

View larger version (28K): [in this window] [in a new window]   Fig. 1. Results of regression analyses for the relationship between waist circumference and loge triglycerides according to LDL subclass distribution pattern.

View larger version (27K): [in this window] [in a new window]   Fig. 2. Results of regression analyses for the relationship between waist circumference and HDL cholesterol according to LDL subclass distribution pattern.

Waist circumference alone explained 8.6% of the variance in HDL cholesterol and 5.2% of the variance in log_e triglyceride concentration (p<0.001 for both). The addition of LDL subclass pattern significantly (p<0.001) increased the variance explained in HDL cholesterol and log_e triglyceride concentrations to 14.1% for each. Body mass index alone explained 10.2% (p<0.001) of the variance in HDL cholesterol and 5.9% (p<0.001) of the variance in log_e triglyceride concentration. The combination of body mass index and LDL subclass pattern explained 16.3% of the variance in HDL cholesterol and 15.6% of the variance in log_e triglyceride concentration (p<0.001 for the additional variance explained by LDL subclass pattern in both models).

Analysis of variance using waist or body mass index designated "low" and "high" based on a median split for the entire study sample and LDL subclass distribution pattern as independent variables was performed with lipid values as dependent variables. Results from these analyses are shown in Table 4. No significant main effects were present for anthropometric measures or LDL subclass pattern with respect to LDL cholesterol concentration. Significant main effects were present for anthropometric measures (waist and body mass index) and LDL subclass pattern for both HDL cholesterol and triglycerides. As was the case for the linear regression analysis, the interaction terms for waist or body mass index with LDL subclass pattern did not reach the 5% level of significance with regard to HDL cholesterol or triglyceride concentrations (p values all 0.14 or higher).

	DISCUSSION

The lipid profile which characterizes LDL subclass pattern B (elevated triglycerides, depressed HDL cholesterol and a predominance of small, dense LDL particles) reflects an underlying metabolic state which may influence responsiveness to preventive therapies and prove useful for guiding treatment selection. Katzel and colleagues [23] found that a 10 kg weight loss produced changes of -15% and -34% in triglyceride levels among obese men with LDL subclass patterns A and B, respectively (p<0.01). However, the increase in HDL cholesterol was smaller among men with LDL pattern B (16% vs. 10%, p<0.05).

Dreon et al. [24] showed that men with LDL subclass pattern B while consuming a reference diet high in fat (46% of energy) had a more favorable plasma lipid response upon switching to a low-fat diet (24% of energy) than did men with pattern A during the reference diet phase. Men with pattern B had larger reductions in LDL cholesterol and apolipoprotein B and a trend toward a smaller decline in HDL cholesterol. The same group of investigators showed that the LDL cholesterol response to switching from a self-selected diet to one low in fat and high in carbohydrate differed according to parental LDL subclass pattern in premenopausal women [25]. Women with two parents having LDL subclass pattern B showed the largest LDL cholesterol change (-36 mg/dL). Those with one pattern B parent showed an intermediate response (-9 mg/dL), while the LDL response was minimal (-2 mg/dL) in women whose parents both had LDL subclass pattern A. In the Stanford Coronary Risk Intervention Program, intensive risk factor modification (vs. usual care) retarded coronary artery disease progression among subjects with a predominance of dense LDL (subclass pattern B) at baseline (0.008 vs. 0.054 mm/y, p=0.007). No benefit was observed among subjects with a predominance of buoyant LDL upon entry (0.038 vs. 0.0039 mm/y) [26].

The present study shows a clear, additive influence of LDL subclass pattern B on the dyslipidemia associated with increased adiposity. Trends were also present in this sample toward multiplicative interactions, i.e., greater worsening of the lipid profile (HDL cholesterol and triglycerides) with increasing waist circumference or body mass index among women with LDL subclass pattern B, compared to those with pattern A.

The women studied were taking part in a clinical trial, and the range of adiposity in the sample was restricted because a trial exclusion criterion prevented enrollment of women with body mass index >31.5 kg/m². Restriction of the range of adiposity would tend to reduce the power to detect non-parallelism in the regression lines. Therefore, it is likely that the failure to detect a significant multiplicative interaction is due to insufficient statistical power. Additional research will be needed covering a wider range of adiposity to more fully characterize the influence of LDL subclass pattern on the dyslipidemia associated with increased adiposity.

A secondary objective of the current study was to compare the utility of waist circumference and body mass index for predicting adiposity-related alterations in the serum lipid profile. Body mass index reflects total adiposity, whereas waist circumference is a measure of both total and abdominal adiposity. These two measures were strongly correlated in our sample (r=0.77, p<0.001), and both were significantly associated with increased triglyceride and depressed HDL cholesterol concentrations. Neither waist circumference nor body mass index correlated significantly with the LDL cholesterol level.

Our group has previously shown that the LDL cholesterol concentration was directly related to measures of adiposity in a group of younger (18 to 49 years) men, but that this relationship was absent in men 50 and older [27]. Most women in the present sample were 50 years of age or older. Thus, adiposity does not appear to be a determinant of the LDL cholesterol level among older persons of either gender. Based on these data it might be anticipated that weight loss would not produce the same degree of LDL cholesterol-lowering among older individuals that it does in young adults. Indeed, a meta-analysis of trials investigating blood lipid responses to weight loss showed that the mean LDL cholesterol response was larger for younger subjects (-25 mg/dL) than those middle-aged or older (-8 mg/dL) [28].

	CONCLUSIONS

TOP FOOTNOTES ABSTRACT INTRODUCTION MATERIALS AND METHODS RESULTS CONCLUSIONS REFERENCES

 
In the current study, LDL subclass pattern B and anthropometric indicators of adiposity (waist or body mass index) were independent predictors of HDL cholesterol and triglyceride levels in postmenopausal women. However, no significant relationship was observed between LDL cholesterol and measures of adiposity. Non-significant trends were present toward greater worsening of HDL cholesterol and triglyceride levels with increasing adiposity among women with LDL subclass pattern B, compared to those with pattern A. Body mass index and waist circumference showed similar relationships to triglyceride and HDL cholesterol concentrations, suggesting that either measurement may be used for assessing the risk of adiposity-related dyslipidemia in postmenopausal women.

	FOOTNOTES

TOP FOOTNOTES ABSTRACT INTRODUCTION MATERIALS AND METHODS RESULTS CONCLUSIONS REFERENCES

 
Funding for this research was provided by Novo Nordisk Pharmaceuticals, Inc., Princeton, NJ.

Received June 1, 1999. Accepted November 1, 1999.

	REFERENCES

TOP FOOTNOTES ABSTRACT INTRODUCTION MATERIALS AND METHODS RESULTS CONCLUSIONS REFERENCES

 

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