Lipids and Pulmonary Function in the Third National Health and Nutrition Examination Survey

Dominic J. Cirillo, Yuri Agrawal and Patricia A. Cassano

From the Division of Nutritional Sciences, Cornell University, Ithaca, NY.


    ABSTRACT
 TOP
 ABSTRACT
 INTRODUCTION
 MATERIALS AND METHODS
 RESULTS
 DISCUSSION
 REFERENCES
 
Studies considering the association between total cholesterol and noncardiovascular mortality, particularly from respiratory disease, yield inconclusive findings. To explore this question, the relation of lipids to pulmonary function, specifically forced expiratory volume in 1 second (FEV1), was investigated in the Third National Health and Nutrition Examination Survey. Conducted in the United States in 1988–1994 among adults aged >=17 years, this survey measured serum lipids, FEV1, and confounding factors including smoking and antioxidants. Multiple linear regression analysis explored the relation of FEV1/height2 to low density lipoprotein (LDL) cholesterol, high density lipoprotein (HDL) cholesterol, and their respective apolipoproteins (apo) B and A-I. A standard deviation increase in HDL cholesterol or apo A-I was associated with an FEV1 increase of 43 ml (95% confidence interval (CI): 30, 56) or 29 ml (95% CI: 11, 47), respectively, for an average-height adult. A standard deviation increase in LDL cholesterol or apo B was associated with an FEV1 decrease of -24 ml (95% CI: -43, -5) or -53 ml (95% CI: -74, -32), respectively, adjusted for serum antioxidant status. The lipid subfractions were differentially associated with FEV1 consistent with the possibility that LDL cholesterol contributes to endogenous oxidative burden while HDL cholesterol attenuates inflammatory tissue damage. Whether these associations are causal remains to be determined.

antioxidants; apoproteins; cholesterol; forced expiratory volume; lipoproteins, HDL; lipoproteins, LDL

Abbreviations: apo, apolipoprotein; FEV1, forced expiratory volume in 1 second; HDL, high density lipoprotein; LDL, low density lipoprotein; NHANES III, Third National Health and Nutrition Examination Survey


    INTRODUCTION
 TOP
 ABSTRACT
 INTRODUCTION
 MATERIALS AND METHODS
 RESULTS
 DISCUSSION
 REFERENCES
 
Plasma total cholesterol has been reported to have a U-shaped association with all-cause mortality (1GoGoGo–4Go). Although increased total cholesterol raises the risk of cardiovascular disease, a lower level of serum cholesterol is associated with an increased risk of noncardiovascular causes of mortality, including cancer and respiratory disease (1GoGoGo–4Go). Several studies found that reverse causality, whereby subclinical disease causes cholesterol lowering, accounted for the apparent inverse relation between cholesterol and cancer risk (1Go, 5GoGo–7Go). A remaining question is whether the association of cholesterol with other noncardiovascular outcomes, particularly respiratory mortality, is causal.

An overview of 19 observational cohort studies reported a 30–40 percent increase in the risk of noncardiovascular, noncancer mortality among subjects with lower total cholesterol (<160 mg/dl) and an inverse relation of total cholesterol to respiratory disease mortality (4Go). The associations were unchanged after deaths occurring early in the follow-up period were excluded and after adjustment for age, dia-stolic blood pressure, cigarette smoking, body adiposity, and alcohol intake. More recently, Iribarren et al. reported an inverse association of serum total cholesterol with chronic bronchitis and emphysema mortality (3Go). The associations were somewhat inconsistent but remained after early deaths were excluded and after adjustment for confounding factors. Frank et al. observed an inverse association between total cholesterol and chronic obstructive pulmonary disease mortality that persisted after eliminating the first 5 years of follow-up. They found that the excess risk was limited to subjects whose cholesterol levels were below about 160 mg/dl, but they observed no relation of cholesterol to chronic obstructive pulmonary disease mortality over the rest of the cholesterol range (6Go). On the other hand, the prospective Whitehall Study found that the association of low cholesterol levels with respiratory causes of death did not persist in adjusted analyses (8Go). However, inclusion of pulmonary function (forced expiratory volume in 1 second (FEV1)) as a confounding factor may compromise the validity of this analysis if the relation of cholesterol to the risk of respiratory disease mortality is mediated by pulmonary function.

The evidence from clinical trials is equivocal. Early overviews of clinical trials of cholesterol-lowering medication (4Go) reported higher noncardiovascular mortality at lower cholesterol levels (9Go). The associations were marginally statistically significant, spread over several causes of death, and unrelated to the strength of the intervention, casting some doubt on the likelihood of a causal relation (4Go). Two recent meta-analyses reported no association of cholesterol-lowering treatment with noncardiovascular outcomes (10Go, 11Go).

Thus, the relation of cholesterol to pulmonary outcomes is unclear. While previous studies examined respiratory mortality or hospitalization for respiratory disease, to the best of our knowledge published studies have addressed neither the association of cholesterol with pulmonary function nor the possibly distinct effects of the lipid subfractions. Using data from the Third National Health and Nutrition Examination Survey (NHANES III), we investigated the association between lipids and pulmonary function, specifically FEV1. Both high density lipoprotein (HDL) and low density lipoprotein (LDL) cholesterol were considered independently, as were their respective apolipoproteins (apo) A-I and B.


    MATERIALS AND METHODS
 TOP
 ABSTRACT
 INTRODUCTION
 MATERIALS AND METHODS
 RESULTS
 DISCUSSION
 REFERENCES
 
Study population
NHANES III was conducted in the United States from 1988 through 1994 on a nationwide probability sample of approximately 33,994 persons aged >=2 months. Complete details of the survey design and examination procedures have been published by the National Center for Health Statistics (12Go). For this study, the sample was restricted to adult subjects aged >=17 years, yielding an initial sample size of 18,162.

Data collection
Trained interviewers administered a detailed socioeconomic and medical history questionnaire, including a complete smoking history (number of years of smoking, current number of cigarettes smoked per day, number of years since quitting, etc.). Further measurements were taken at mobile examination centers, including medical examinations and blood sample collection.

Both serum total cholesterol and HDL cholesterol were measured throughout the duration of the survey (1988–1994), the former enzymatically and the latter following precipitation of the other lipoproteins (13Go). Both assays were performed by using a Hitachi 704 Analyzer (Boehringer Mannheim Diagnostics, Indianapolis, Indiana). LDL cholesterol was calculated by using the Friedewald equation: (LDL cholesterol) = total cholesterol - (HDL cholesterol) - triglyceride/5 (14Go). This calculation is valid for only those subjects fasting >=9 hours prior to blood collection, with serum triglyceride levels of <=400 mg/dl; thus, calculated LDL cholesterol levels are available for a subgroup of the total sample. Apo A-I and B were measured in phase I of the survey (1988–1991); both were assayed by either radial immunodiffusion or rate immunonephelometry (13Go, 15Go), and the results were adjusted according to the World Health Organization–International Federation of Clinical Chemistry method (13Go). Serum vitamin C was measured by high-performance liquid chromatography with electrochemical detection, and serum vitamin E ({alpha}-tocopherol) and ß-carotene were measured by isocratic high-performance liquid chromatography with detection at two different wavelengths (13Go). Serum selenium was measured by using atomic absorption spectrophotometry. C-reactive protein was quantified by using latex-enhanced nephelometry (13Go) on a Behring Nephelometer Analyzer System (Behring Diagnostics Inc., Somerville, New Jersey), with a lower limit of detection of 0.21 mg/dl.

The medical examination included spirometry assessments that met the American Thoracic Society minimum spirometry recommendations (16Go). The US National Institute for Occupational Safety and Health spirometry system incorporated a customized Ohio Censored 822 or 827 dry rolling seal spirometer with breathing tubes and a calibration syringe; an attached computer was programmed to analyze the expiratory curves, calculate the key pulmonary parameters, and determine the acceptability of the tests. Subjects were excluded from testing if they had had chest or abdominal surgery within the past 3 weeks or if they had a history of myocardial infarction. Each eligible subject performed about five trials, and reproducibility was checked according to the American Thoracic Society standards. FEV1 was recorded as the highest value from all acceptable maneuvers.

Statistical analysis
For all models, the dependent variable was FEV1 divided by height squared. For each lipoprotein component (serum total cholesterol, LDL cholesterol, HDL cholesterol, apo B, and apo A-I), a series of regression models was considered. Initial models assessed the association of the lipoprotein component with FEV1, adjusting for potential confounding factors (age, race, sex, income, and cigarette smoking represented by eight variables to fully characterize length of exposure and dose). Further models assessed mediating variables and effect modification by considering, for example, body mass index (weight in kilograms divided by height in meters squared), serum antioxidant nutrients, and C-reactive protein. Finally, the lipoprotein subfractions were included simultaneously in a single model to assess the independence of their effects. Fasting status of the subject was considered, and analyses of the subgroup that fasted >=9 hours were compared with analyses including all subjects. Limiting analyses to fasting subjects made no difference in the regression results; thus, only those analyses that included all subjects are reported in this paper (with the exception of LDL cholesterol, which was calculated for fasting subjects only).

All regression analyses were adjusted for the complex survey design by using the PROC SURVEYREG procedure in SAS software (SAS Institute, Inc., Cary, North Carolina). Sample weights were generated in NHANES III to account for oversampling in certain subgroups and for nonresponse. As per National Center for Health Statistics instructions, specific weight variables were used in each analysis: total sample weights were used in HDL cholesterol analyses, phase I sample weights were used in the apolipoprotein analyses, and fasting sample weights were used in LDL cholesterol analyses (12Go). The weights were incorporated into all regression analyses by using the WEIGHT statement in SAS software.


    RESULTS
 TOP
 ABSTRACT
 INTRODUCTION
 MATERIALS AND METHODS
 RESULTS
 DISCUSSION
 REFERENCES
 
The initial sample for this analysis consisted of 18,162 adults aged 17–90 years. Subjects were excluded from further consideration if they had an unreliable FEV1 measurement (i.e., the second-largest FEV1 reading not within 5 percent or 100 ml, whichever was greater, of the largest FEV1 reading), missing FEV1 data, or missing height data (total excluded = 1,678). To address the possibility of reverse causality (i.e., disease per se causes an altered cholesterol level), we excluded remaining subjects with a physician diagnosis of prevalent respiratory disease (814 subjects reported asthma only, 158 reported emphysema only, 529 reported chronic bronchitis only, and 381 reported some combination). Subjects with cancer (all forms except nonmalignant skin cancer) were also excluded (some with respiratory disease had cancer, and 467 reported cancer only), yielding a final study population of 14,135.

The excluded subjects were similar to the final study subjects regarding most variables considered (table 1). Excluded subjects were on average older, a finding not unexpected since one of the reasons for exclusion was prevalent disease. The differences between the two groups concerning other variables were largely the result of the older age and/or the disease status of excluded subjects.


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TABLE 1. General characteristics of subjects included and excluded in analyses of lipids and pulmonary function, Third National Health and Nutrition Examination Survey, 1988–1994

 
Each lipoprotein component was first considered in a separate model. The baseline models were adjusted for confounding variables that were not postulated to mediate the relation between lipids and FEV1 (table 2). Both HDL components, HDL cholesterol and apo A-I, were positively associated with FEV1. Both LDL components, LDL cholesterol and apo B, were inversely associated with FEV1, although the association of LDL cholesterol was weak before serum antioxidants were considered. The association of total cholesterol reflected the association of the LDL components. Given that the low- and high-density subfractions were associated with FEV1 in opposite directions, subsequent analyses focused on the subfractions. The linearity of these associations was assessed by adding square terms to the regression models. Only the LDL components showed statistically significant nonlinearity, but the nonlinear component was weak, suggesting a slight attenuation of the inverse association at higher lipid levels.


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TABLE 2. Predicted difference in FEV1* (ml) associated with one standard deviation increase in serum lipid variables, Third National Health and Nutrition Examination Survey, 1988–1994

 
In these data, serum antioxidants were positively associated with pulmonary function, as reported previously by Hu and Cassano (17Go). Serum antioxidants had strong positive associations with LDL cholesterol and apo B (correlation coefficients, 0.2–0.6) and weak positive associations with the HDL components (correlation coefficients, 0.0–0.2). Thus, the association of lipids with FEV1 adjusted for antioxidants was an important consideration (table 2), particularly for the lipid-soluble antioxidants vitamin E and ß-carotene. Adjusting the regression models for serum antioxidants (vitamin E, ß-carotene, vitamin C, and selenium) had little or no effect on the coefficients for either HDL cholesterol or apo A-I but strengthened the association of the LDL components with pulmonary function. The inverse association of LDL cholesterol was strengthened about threefold and the inverse association of apo B about twofold. Negative confounding by the antioxidants partly masked the inverse association of the LDL components with pulmonary function.

Further models tested for modification of the lipid-FEV1 association by lipid-soluble antioxidants. Both vitamin E and ß-carotene were statistically significant effect modifiers of the apo B-FEV1 association. For subjects whose log vitamin E levels were one standard deviation below the mean, a standard deviation increase in apo B was associated with a -69 ml change in FEV1, whereas for subjects whose log vi-tamin E levels were one standard deviation above the mean, a similar increase in apo B was associated with a -45 ml change in FEV1. Coefficients for apo B were similarly different across levels of ß-carotene. There was no evidence that lipid-soluble antioxidants modified the association of either LDL cholesterol or the HDL components with FEV1.

Lifestyle factors, including alcohol intake, adiposity, and exercise, were also considered as confounding variables. Alcohol intake (assessed by questionnaire; answering yes or no to the following question: "Did you drink on 12 or more occasions in the past year?") was positively associated with FEV1 and with slightly higher HDL cholesterol levels. Adjustment for alcohol intake attenuated the association of HDL cholesterol with FEV1 by about 30 percent. Adjusting either physical activity (answering the following questionnaire query: "Compared with most (men/women) your age, would you say that you are more active, less active, or about the same?") or body mass index had little or no effect on the HDL cholesterol-FEV1 association. In adjusted analyses, none of the lifestyle variables had any effect on the association of LDL lipoprotein components with FEV1.

The possible differential association of lipids with FEV1 across demographic subgroups was considered, although there was no strong a priori hypothesis for such effect modification. For men, there were slightly larger effects for all the lipids, but, overall, the direction and approximate magnitude of the coefficients were the same across race and sex groups, as confirmed in stratified analyses. Similarly, slight differences were found in the magnitude of the LDL cholesterol-FEV1 and apo B-FEV1 associations across the age range. The associations were strongest in the middle adult years and were slightly weaker at younger and older ages.

Undiagnosed disease may cause changes in the lipid profile, contributing to reverse causality. C-reactive protein, an acute-phase reactant, was considered as a biologic marker of an underlying pathophysiologic process (table 2). C-reactive protein was strongly negatively associated with FEV1 (for a person of height 1.7 m, ß = -66 ml for each milligram-per-deciliter increase in loge-tranformed C-reactive protein (95 percent confidence interval: -79, -55)). However, C-reactive protein had little or no relation to lipids (all correlation coefficients were <0.1), and adding C-reactive protein to the model had little or no effect on the lipoprotein-FEV1 associations. Underlying, but undiagnosed respiratory disease can be predicted by low FEV1 (18Go); thus, further analyses excluded subjects with the lowest pulmonary function (the lowest 5 percent of FEV1 percent predicted, in which observed FEV1 is compared with what would be predicted based on age, height, and sex (16Go)). Both the HDL cholesterol-FEV1 and LDL cholesterol-FEV1 associations were only slightly attenuated, and there was little or no change in either of the apolipoprotein coefficients.

HDL cholesterol and LDL cholesterol are inversely associated (in these data, r = -0.14); therefore, further analyses considered the independence of their effects (table 3). When HDL cholesterol and LDL cholesterol were included in a single model, both retained their respective positive and negative associations with FEV1. The apolipoproteins had little or no correlation in these data; consequently, inclusion of both apo A-I and apo B in a single model made little or no difference in their respective associations with FEV1.


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TABLE 3. Predicted difference in FEV1* (ml) associated with one standard deviation increase in serum lipid variables modeled simultaneously, Third National Health and Nutrition Examination Survey, 1988–1994

 
We compared the full sample and the sample excluding those subjects with low cholesterol (i.e., total cholesterol >= 160 mg/dl; n = 11,162). The lipid-FEV1 associations were essentially unchanged, except that the effect size for LDL cholesterol was reduced by a third (p = 0.2). In both groups, the lipid-FEV1 associations were about the same across the age range (both age and age-squared interaction terms were included). For the low-density components, the association was slightly larger nearer the mean age, but the direction of the effect was unchanged in all models. Next, we considered the low-cholesterol group separately because several past studies have suggested an increased risk of adverse respiratory outcomes in this region of the distribution (total cholesterol < 160 mg/dl; the number of subjects with lipid data ranged from 1,000 to 2,000). In the low-cholesterol group, we found statistically significant effect modification by age for HDL cholesterol, apo B, and apo A-I. In each case, the direction of the effect was reversed among older persons. In young and middle age, a lower apo B level was associated with higher FEV1; however, at older ages, a lower apo B level was associated with lower FEV1. In young and middle-age subjects, a higher HDL cholesterol level was associated with higher FEV1, but, in older subjects, a higher HDL cholesterol level was associated with lower FEV1. The pattern of results was similar for apo A-I. In simultaneous models considering both HDL cholesterol and apo B, the association of each variable was attenuated by about 25 percent.


    DISCUSSION
 TOP
 ABSTRACT
 INTRODUCTION
 MATERIALS AND METHODS
 RESULTS
 DISCUSSION
 REFERENCES
 
Prior research has suggested an association between a low level of serum total cholesterol and an increased risk of noncardiovascular mortality, particularly respiratory disease mortality (3Go, 4Go). If such a relation does indeed exist, it might operate through an association of serum lipids with pulmonary function, given that FEV1 is an excellent predictor of the risk of respiratory disease and respiratory disease mortality (19Go). In the NHANES III data, the total cholesterol-FEV1 association paralleled the LDL findings: both were inversely associated with FEV1 (lower levels were associated with better pulmonary function). Conversely, the HDL components were positively associated with FEV1 (lower levels were associated with worse pulmonary function). Findings were similar for both of the cholesterol and apolipoprotein components, and the density subfractions were independently associated with FEV1.

Although the literature suggests an increased risk of all-cause and respiratory disease mortality with lower cholesterol levels, we found that associations with total cholesterol may be misleading. The correlation of total cholesterol with LDL cholesterol was lower in the group with low cholesterol compared with the full sample (0.67 vs. 0.93), and the correlation of total cholesterol with HDL cholesterol was higher (0.16 vs. 0.09). In the low-cholesterol group, these associations also varied by age (for the HDL cholesterol–total cholesterol association, r = 0.20 in the young, low-cholesterol group vs. r = 0.02 in the older, low-cholesterol group), suggesting that what is conveyed by total cholesterol differs by both age and average cholesterol level. Moreover, the lipid subfractions were differentially associated with FEV1, suggesting that total cholesterol is a poor summary variable in any case.

Could the differential associations of the lipid subfractions with pulmonary function reflect an underlying difference in the biology? LDL may have an adverse effect by contributing to endogenous oxidative burden and hence to the pathophysiology of chronic obstructive pulmonary disease (20Go). In contrast, HDL may have a positive effect through its role in immune regulation. HDL has been shown to bind to bacterial endotoxin as well as to relieve inflammation (21Go, 22Go), suggesting a potential role for HDL in preventing lung tissue damage. Cholesterol also is a constituent of lung surfactant; however, the relation of surfactant properties to FEV1, or the possibly distinct relations of the lipid subfractions to surfactant, is unclear.

We stratified the sample by total cholesterol, since previous studies found an excess risk of noncardiovascular mortality primarily for persons whose total cholesterol levels were <160 mg/dl. In the low-cholesterol group, there was heterogeneity by age: among young and middle-age persons with low cholesterol, the findings were similar to those for the total sample: higher LDL components were associated with worse lung function, and higher HDL components were associated with better lung function. However, for older persons with low cholesterol, lower LDL components were associated with worse lung function, a result consistent with the finding that lower total cholesterol is associated with a greater risk of respiratory disease (and, in this subgroup, total cholesterol had little or no correlation with HDL components so would mainly reflect LDL components). There may be additional heterogeneity among the subgroup of older persons with low total cholesterol, given that, for some, levels may be stably low while for others they may be acutely low and/or declining (23Go). If a biologic relation underlies observations that lower cholesterol increases the risk of respiratory outcomes, advancing our understanding rests on further consideration of effect modification by age, lipid subfractions, and longitudinal patterns in lipid subfractions.

These NHANES III findings may reflect a biologic process or may have resulted from reverse causality and/or confounding not adequately addressed in this cross-sectional study. If preexisting disease that compromises pulmonary function also affects serum cholesterol (23Go, 24Go), the disease may induce the association of the lipid subfractions with FEV1. Although this issue cannot be fully addressed in this cross-sectional study, analyses excluding all prevalent cases of respiratory disease and then also excluding subjects with poor respiratory functioning provided some insights. Excluding such subjects had little or no effect on either the HDL-FEV1 or the LDL-FEV1 association.

Other analyses explored the extent to which these associations may be explained by confounding factors. The lipoprotein components may be markers for some other aspect of diet or lifestyle that may affect both lung function and the levels of HDL and LDL. A series of models addressed known predictors of both pulmonary function and lipid level to identify confounding factors that might account for the associations we observed. None of these models identified important sources of confounding, although some lifestyle variables were poorly measured in these data (for example, alcohol intake); thus, residual confounding is indeed possible. Confounding may also occur if an underlying condition that led to pathologically high lipid levels might, through other mechanisms, affect pulmonary function. Excluding subjects with clinically abnormal total cholesterol levels (defined as either the top 5 percent or higher than 220 mg/dl) had little or no effect on the reported associations. Since chronic inflammation has been shown to cause hypocholesterolemia in older people (23Go), a proxy for inflammation (C-reactive protein) was considered, but no changes were observed in any of the lipoprotein-FEV1 associations.

Age significantly modified the association between LDL and FEV1. FEV1 declines with age, while LDL cholesterol increases with age up to late middle age, after which it plateaus and then declines. The LDL-FEV1 association was strongest in the age range in which both lung function and lipid levels are changing with age (in opposite directions, hence the inverse association). HDL components do not vary much by age, and no difference was found in the HDL-FEV1 association by age. Further research should explore this finding.

If the effect of lipoproteins on noncardiovascular mortality was due solely to cholesterol's correlation with antioxidants, as suggested in a recent review (1Go), then controlling for four serum antioxidants known to affect lung function would be expected to attenuate the association of FEV1 with the lipoprotein components. The dramatic increase in the magnitude of the LDL-FEV1 association suggested that there was in fact negative confounding by the serum antioxidants. In cardiovascular disease, the mode of action of LDL involves oxidation of the LDL particle into a more potent atherogenic agent (25Go), a process that can be reversed by antioxidants (26Go). We hypothesized that, at lower levels of antioxidants, there might be increased levels of oxidized LDL contributing to the overall levels of endogenous oxidative burden in the lung. Thus, we tested antioxidant-LDL interactions and found that, at lower levels of both vitamin E and ß-carotene, the association of apo B with FEV1 was strengthened, as hypothesized.

From a measurement perspective, apo B is thought to have less "noise" than LDL cholesterol as a measure of the LDL subfraction because of the variable density of LDL cholesterol on LDL particles (15Go). In addition, there may be different levels of measurement error in the two constituents given that apo B was measured directly, while LDL cholesterol levels were calculated by using the Friedewald equation (14Go). Similarly, HDL cholesterol may have less noise than apo A-I as a measure of the HDL subfraction because of the dynamic exchange of apo A-I proteins between HDL particles (15Go). In models that considered these lipoprotein components simultaneously, apo B and HDL cholesterol retained strong, significant, independent associations with pulmonary function, perhaps reflecting the measurement issues described above, although the degree of association between the particle components (LDL cholesterol and apo B: r = 0.85; HDL cholesterol and apo A-I: r = 0.76) precludes a definitive conclusion. Subsequent research should consider whether there may be distinct functional roles in lung tissue for the lipid versus protein component of the lipoproteins.

A strength of this study is that it used data from a nationally representative population sample with high-quality measurement (13Go, 27Go). An interesting finding is that the lipoprotein subfractions have differential associations with pulmonary function. The fact that previous attempts to relate serum total cholesterol levels with respiratory outcomes were inconsistent may be partly explained by the lack of consideration given to the lipid subfractions. Because a single measurement of the lipid components contains noise (i.e., random error) relative to the usual, long-term average level, our findings may have been affected by regression dilution bias, and the true magnitude of these associations may be larger.

Further research should consider how the range of population cholesterol levels affects the cholesterol-FEV1 association (e.g., by studying a naturally low cholesterol population). Such research should also shed light on whether chronically lower cholesterol levels are qualitatively different from acutely lowered cholesterol levels achieved through drug treatment. Future studies should also investigate the time course of the cholesterol-FEV1 relation by using longitudinal data (e.g., by studying changes in lipids in relation to the age-related decline in pulmonary function).


    ACKNOWLEDGMENTS
 
Supported in part by the US Department of Agriculture, Cooperative State Research, Education and Extension Service (NYC-399305), by Hatch Federal Formula Funds (NYC-399420), and by the National Institutes of Health (R03-HL66539).

The authors thank Unhee Lim for helpful assistance with data analysis.


    NOTES
 
Correspondence to Dr. Patricia A. Cassano, 209 Savage Hall, Division of Nutritional Sciences, Cornell University, Ithaca, NY 14853 (e-mail: pac6{at}cornell.edu).


    REFERENCES
 TOP
 ABSTRACT
 INTRODUCTION
 MATERIALS AND METHODS
 RESULTS
 DISCUSSION
 REFERENCES
 

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Received for publication February 9, 2001. Accepted for publication January 11, 2002.