© 2000 American Heart Association, Inc.
Atherosclerosis and Lipoproteins |
Contribution of Apolipoprotein C-III Gene Variants to Determination of Triglyceride Levels and Interaction With Smoking in Middle-Aged Men
From the Division of Cardiovascular Genetics (D.M.W., P.J.T., R.M.F., S.E.H.), Department of Medicine, The Rayne Institute, University College London, and the MRC Epidemiology and Medical Care Unit (S.R.B., G.J.M.), Wolfson Institute of Preventive Medicine, St Bartholomew’s Hospital, London, UK.
Correspondence to Dr Dawn M. Waterworth, Division of Cardiovascular Genetics, Department of Medicine, The Rayne Institute, University College London, London WC1E 6JJ, UK. E-mail dwaterworth{at}lycos.com
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Abstract—Variation within and around the apolipoprotein C-III (APOC3) gene has been associated with elevated triglyceride (Tg) levels and cardiovascular disease. The associations of 4 polymorphic variants in the APOC3 gene (3238C>G in the 3' untranslated region [SstI], 1100C>T in exon 3, -482C>T in the insulin-responsive element, and -2854T>G in the APOC3-A4 intergenic region) with plasma Tg and cholesterol levels and their interaction with smoking have been investigated in the Second Northwick Park Heart Study (NPHSII), a large cohort of healthy men (n=2745). Analyzing the variants separately showed that 3238G, 1100T, and -482T alleles were all associated with raised Tg levels. For the 3238C>G and -482C>T sites, the Tg-raising effect appeared to depend on smoking status (test for interaction, P=0.042 and P=0.009, respectively), but for the 1100C>T site, the effect was constant irrespective of smoking status (test for interaction, P=0.27). The -2854T>G site was not associated with effects on Tg levels in this sample. Because all of the variants showed significant allelic association, regression modeling was used to quantify the relative size of each effect and to assess whether the effects of the separate variants were independent. The 1100C>T variant had an independent effect on Tg levels that was not influenced by smoking status (increase of 8.2% in Tg with each T1100 allele), whereas the -482C>T variant had a separate effect that was dependent on smoking (increase of 13.7% in Tg for each -482T allele in current smokers, 8.6% in exsmokers, and -7.4% in those who never smoked). The 3238C>G variant did not show a separate independent effect on Tg concentration. Thus, by use of the regression model, it was possible to estimate how mean Tg levels would vary in groups of individuals with respect to APOC3 genotype and smoking information. Analysis in this large group of healthy men has allowed the identification of a statistically robust APOC3 genotype-smoking interaction, which now warrants further molecular study.
Key Words: APO A1-C3-A4 gene cluster • apolipoprotein B • linkage disequilibrium • insulin-responsive element
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Introduction |
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Apolipoprotein C-III (apoC-III), a 79–amino acid glycoprotein, is synthesized by the liver and small intestine and is a major component of triglyceride (Tg)-rich lipoproteins (TRLs) and HDL. ApoC-III is important in the regulation of plasma Tg concentration.1 Accumulating evidence has suggested that a subset of apoB-containing particles, such as VLDL and IDL, are related to the progression of coronary artery disease (CAD).2 ApoC-III and serum Tg levels are positively correlated.3 4 Furthermore, apoC-III acts as a marker of TRL metabolism, and the ratio of apoC-III HDL to VLDL has been found to be negatively associated with the progression of atherosclerosis in a number of studies.5 6
The gene for apoC-III has been mapped to chromosome 11q23.37 and is flanked by the genes for apoA-I and apoA-IV in a 15-kb gene cluster.8 The rare allele of the polymorphic SstI site (3238G) in the 3'-untranslated region of the apolipoprotein C-III (APOC3) gene has frequently been associated with raised apoC-III and Tg levels3 9 and with CAD.10 Because no function has been attributed to this polymorphism, it was thought that this effect was due to linkage disequilibrium with another functional variant. When variation in an insulin-responsive element (IRE) in the APOC3 promoter was identified, this was thought to be a strong candidate for the SstI effect, because loss of insulin regulation could conceivably result in overexpression of apoC-III.11 However, in a number of association studies, the IRE polymorphisms could not explain fully the effect observed at the SstI site.9 10 12
ApoC-III inhibits lipoprotein lipase (LPL) in vitro,13 and inhibition of LPL-activated lipolysis by VLDL-associated apoC-III may prolong the time that the arterial wall is exposed to this atherogenic particle.2 The precise function and mechanism of action of apoC-III in lipid metabolism is still unclear, but some recent insight has come from apoC-III transgenic mice studies. The apoc3 knockout mice exhibited a 70% reduction in fasting Tg levels, and postprandial hypertriglyceridemia was abolished.14 Human APOC3 transgenic mice had a 40% increase in apoC-III levels, which resulted in a doubling of plasma Tg.15 This effect is thought to be due to substitution of apoE by apoC-III on VLDL particles, affecting LDL receptor or LDL receptor–related protein recognition. However, this assumption was contradicted by the absence of severe hypertriglyceridemia in apoE knockout mice.16 An investigation into APOC3 transgenic/apoe null mice17 showed a marked decrease in VLDL glycosaminoglycan binding that was independent of apoE. The investigators suggested that the predominant mechanism of apoC-III–induced hypertriglyceridemia appeared to be decreased lipolysis at the cell surface. In addition, hepatic production of VLDL-Tg was moderately increased, and increased nonesterified fatty acid (NEFA) levels were also observed.
Smokers are insulin resistant and show impaired lipid metabolism, with a reduced Tg clearance after a mixed meal.18 Smoking also acutely increases the activity of the sympathetic nervous system and thus raises the concentrations of circulating catecholamines.19 Therefore, smoking will effect the function of hormone sensitive lipase (HSL) and LPL, both of which are under the control of insulin and catecholamines.20 Elevated catecholamine concentrations activate HSL, leading to energy mobilization and increased levels of NEFAs in the circulation, and downregulate LPL.21
To determine which APOC3 polymorphism(s) has the greatest impact on lipid concentrations, we investigated 4 different polymorphic sites that span the APOC3 gene region. In addition to the 3238C>G (SstI) and -482C>T (IRE) sites, we examined 2 other sites. The first is a 1100C>T base change in exon 3, where the T allele has been reported to be associated with higher apoC-III levels in healthy subjects22 and with elevated concentrations of apoC-III, plasma Tgs, VLDL, and IDL particles in patients with familial combined hyperlipidemia.23 The second is a novel base-change -2854T>G,9 which extends our investigation further upstream into the APOC3-A4 region. Because the effects of smoking on insulin resistance and lipid intolerance are recognized, we investigated the possibility of an interactive effect between genotype and smoking on the various lipid traits available.
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Methods |
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Sample Population
The Second Northwick Park Heart Study (NPHSII) is a large prospective study of healthy middle-aged (50- to 61-year-old) men from 9 UK general practices. Of the initial cohort of 3052 men, 2745 were white and had DNA available for genotyping. The study has been ongoing for 9 years, and men were followed up annually for lipids (5 years) and throughout the study for CAD events. Details of the study have been described previously.24 Briefly, 9 general practices participated in the study, and all men with a history of unstable angina, myocardial infarction, cerebrovascular disease, life-threatening malignancy, or regular medication with aspirin or anticoagulants were excluded. Each respondent attended in the nonfasting state after having been instructed to avoid a cooked breakfast or a heavy meal before examination. They had refrained from smoking and vigorous exercise from midnight beforehand. Baseline characteristics and demographic information were ascertained by means of a questionnaire completed at entry to the study. From this, subjects were classified as never smokers, exsmokers (cessation for minimum of 1 year), or current smokers.
Serum Tg and cholesterol concentrations were determined by automated enzymatic procedures with reagents from Sigma Chemical Co. ApoB and apoA-I were measured by immunoturbidometry with reagents from Incstar.
Polymorphism Detection
All variants analyzed were restriction
fragment length polymorphisms, and assays were performed as
described previously.25 The
fragments were separated by using 5% to 10% polyacrylamide
microtiter array diagonal gel
electrophoresis.26
Data Analysis
Data were entered onto an EXCEL spreadsheet
(Microsoft) and tested for deviation from Hardy-Weinberg equilibrium by
using a 
2 test. Linkage disequilibrium
(
) was calculated by EXCEL and the method of Chakravarti et
al.27 Statistical
analysis was performed by using STATA (Intercooled STATA
Version 5.0, STATA Corp). Cholesterol, Tg, apoA-I, and apoB
were available at baseline, and cholesterol and Tg were
available for 5 years. ApoB and apoA-I were only available in a
subsample for technical reasons (there were no significant differences
in any of the characteristics between those with and those without
apoA-I and apoB measures). Tg and apoB levels were logarithmically
transformed. Differences in clinical and biochemical characteristics
according to smoking status were analyzed by 1-way ANOVA. The
Welch test was used when there was evidence of unequal group variances.
Associations between genotype and Tg, cholesterol,
apoB, and apoA-I were initially examined by ANOVA and included tests
for smoking interactions.
Regression modeling was used to investigate the effect of any interaction between APOC3 variants and smoking on levels of baseline Tg. Initial investigations concentrated on each polymorphism separately. A codominant model was assumed for each of the 4 variants such that for each variant, the effect of possessing 2 rare alleles would be twice that of possessing 1 rare allele. In other words, the effect on mean Tg associated with the possession of a rare allele would be independent of the second allele. To incorporate this structure into the regression modeling, each variant was coded as 0, 1, or 2 according to the number of rare alleles present. The variants were then included in the models as continuous variables, which enabled the parameter estimates and standard errors associated with the rare alleles to be calculated directly. As coded, the effect of each allele on mean Tg corresponded exactly with that of a 1-unit increase in each variable. For each polymorphism, the assumption of codominance was checked by including a second variable, coded 1 for homozygous carriers of the rare allele and 0 for others, in the regression model. Assessing the significance these additional terms produced no evidence against a codominant structure. The significance of all the parameters in the resulting models, including the interaction between smoking and genotype, was assessed with an F test. Smoking was included as a categorical variable with 3 levels (see Sample Population). Adjustment was also made for possible confounding factors, which included body mass index (BMI, as a logged continuous variable) and clinic (as a fixed categorical variable with 9 levels).
Finally, to quantify the relative size of the effects
observed in the separate models and to assess whether the 4 variants
were acting on Tg independently, all the polymorphisms were
included in 1 regression model by use of a stepwise procedure. The
significance of the variables was again assessed by F test. Raw
parameter estimates (on the additive log scale) and 95%
CIs from the final model are given in
Table 3
. For the purposes of the model, it was assumed that
there was no allelic interaction between the 4
variants.
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Results |
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Baseline clinical and biochemical characteristics are shown in Table 1
. Smoking status was ascertained for 2715 of
the participants: 867 men (32%) had never smoked, 1075 (40%) were
exsmokers, and 773 (28%) were current smokers. The 3 groups did not
differ significantly in mean age, cholesterol, and apoA-I
or apoB levels. However, current smokers had the lowest mean BMI
compared with exsmokers, who had the highest, and never smokers, who
had intermediate measures (ANOVA,
P<0.0001). Exsmokers had mean
Tg concentrations with a magnitude similar to that of current smokers,
with never smokers having much lower levels in comparison (ANOVA,
P<0.0001). Clinic (general
practice) was also found to be associated with smoking status
(2 test,
P=0.032; data not tabulated).
In addition, both BMI (correlation,
P<0.0001) and clinic (1-way
ANOVA, P<0.0001) were highly
significantly associated with Tg levels. As a result, BMI and clinic
were included in the regression modeling as possible
confounders.
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The locations of the 4 polymorphic sites in the
APOC3 gene region and the
allele frequencies are shown in
Figure 1. The genotype distribution did not
deviate from the expected Hardy-Weinberg equilibrium for any of the
polymorphisms studied. The rare allele frequencies (and CI)
were as follows: 0.08 (0.07 to 0.09) for 3238G, 0.21 (0.20 to 0.22) for
1100T, 0.24 (0.23 to 0.25) for -482T, and 0.33 (0.32 to 0.35) for
-2854G. The linkage disequilibrium between the markers is shown in
Figure 1 (all values were highly statistically significant).
All the polymorphisms showed strong allelic association, with
coinheritance of the rare alleles.
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No associations were found between the
APOC3 polymorphisms and
cholesterol and apoB and apoA-I levels (data not shown).
Mean baseline Tg levels, according to
APOC3 genotype and
smoking status, are displayed in
Table 2 and
Figure 2. Adjustment for baseline BMI and general practice
clinic was made by using regression analysis of the individual
logged Tg concentrations. Corresponding means have undergone
exponentiation and are displayed as geometric mean Tg concentrations.
In general, smokers who carry the rare alleles of any of the 4
polymorphisms had the highest Tg levels. Considering the 4 apoC-III
variants separately, carriers of the 1100T allele had elevated Tg
levels regardless of smoking status, whereas carriers of the 3238G
allele had similarly raised Tg levels, but the effect was more
pronounced in current smokers. Carriers of the -482T allele had
lower levels if they had never smoked and higher levels if they were
current smokers. In carriers of the -2854G allele, an elevation
in Tg levels was confined to current smokers. Thus, the data suggest
that the elevation in Tg levels in carriers of the 3238G and -482T
alleles was being modified by smoking.
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Separate codominant regression models were used to assess
the significance of each of the
APOC3 polymorphisms in
determining Tg levels. Significant interaction was found between
smoking and the -482C>T
(P=0.009) and 3238C>G
(P=0.042) sites (see
Table 2
). Possession of the 1100T allele was
significantly associated with raised mean Tg levels
(P<0.0001). However, no
additional evidence was found to suggest that smoking modified the
Tg-raising effects of the 1100T allele (test for interaction,
P=0.26). There was no
statistical evidence to suggest that the -2854T>G variant was
associated with Tg levels
(P=0.096). Because all 4
APOC3 variants are in allelic
association, it is unclear which polymorphisms were more likely to
be etiologic and which were merely markers for these effects.
Therefore, additional regression modeling was performed to ascertain
whether any of these polymorphisms were having independent effects
on Tg and to characterize the smoking interaction in more
detail.
When the 3238C>G
(SstI) variant was included in
a model with the -482C>T (IRE) variant, the interaction between
smoking and -482C>T genotype remained significant
(P=0.0045). However, there was
no evidence for further interaction between 3238C>G and smoking
(P=0.48), although the main
Tg-raising effect of the G3238 allele was still observed
(P=0.001), irrespective of
smoking status. After addition of the (exon3) 1100C>T, the evidence
for interaction between -482C>T and smoking was still strong
(P=0.005), and there was also
strong evidence for a smoking-independent raising effect of the 1100T
allele on Tg levels
(P=0.001). However, the effect
of the 3238G allele on Tg was no longer present
(P=0.15). Therefore, the effect
of 3238C>G on Tg levels was not found to be independent of -482C>T
or 1100C>T, but the -482C>T and 1100C>T sites were found to be
acting independently of each other. The -2854T>G variant did not
appear to affect Tg levels and was not included in the final model
shown in
Table 3.
The main genetic effect of the 1100C>T site, irrespective
of smoking status, was to raise Tg levels by 8.2% [exp(0.079)=1.082]
for each 1100T allele compared with the 1100CC genotype.
The effect on Tg levels of the -482T allele was dependent on
smoking habit. The main effect at the -482C>T site is given for the
baseline group of never smokers, for whom possession of each -482T
allele lowered Tg levels by 7.4% [exp(-0.077)=0.926]. Smoking
modified this result such that in exsmokers the effect on Tg levels was
8.6% [exp(0.083)=1.086] greater than this baseline estimate, giving
an overall raising effect of 0.6% [exp(-0.077+ 0.083)=1.006] for
each -482T allele, and in current smokers, it was 13.7% greater
[exp(0.128)=1.137], indicating an elevation of 5.2% for each -482T
allele [exp(-0.077+ 0.128)=1.052]. Because genotype is
fixed but smoking status may be altered, the effect of smoking on Tg
levels with use of the model is shown for each -482C>T
genotype
(Table 4).
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The regression model allows an estimate to be made of the
effect of a particular genetic profile combined with smoking
information for calculation of the effects in groups of subjects most
at risk of high Tg and the subsequent risk of CAD. For example, the
most advantageous genotype and smoking combination in men would
be the following: possession of the
APOC3 genotype 1100CC
and of -482TT and to have never smoked; their estimated mean Tg would
be lowered by 7.4% for each -482T allele
[exp(-0.077+-0.077)=0.86], a total Tg-lowering effect of 14%
(compared with the combination of 1100CC, -482CC, and never having
smoked). The most harmful combination would be the
APOC3 genotype 1100TT,
-482TT, and current smoking; their estimated mean Tg will be raised
by 8.2% for each 1100T allele, lowered by 7.4% for each -482T
allele, raised by 7.6% for being current smokers, and raised by an
additional 13.3% for each -482T allele plus being current
smokers [exp(0.079+ 0.079+-0.077+-0.077+0.074+0.128+0.128)=1.40]
Thus, the estimated effect of this harmful genotype and smoking
status is a Tg-raising effect of 
40% (compared with 1100CC,
-482CC, and never having smoked).
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Discussion |
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The SstI site (3238C>G) in the 3' untranslated region of the APOC3 gene was first described in 1983,28 and its association with hypertriglyceridemia has been confirmed in the majority of studies and a review.10 We have confirmed this relationship between the APOC3 SstI polymorphism and serum Tg concentration in this large study of healthy middle-aged men (NPHSII). However, other polymorphisms both within the APOC3 gene itself and in the promoter region also show associations with Tg levels.22 23 29 This may be due, at least partly, to the allelic association between the rare alleles of the 4 APOC3 variants. We have confirmed these genetic effects and observed that they are modified by smoking. Failure to allow for smoking would have obscured the association with the -482C>T variant and in part explain why other studies have failed to show significant and separate effects associated with this variant over and above that of the 3238C>G (SstI).10 12 In addition, the inclusion of smoking as a covariate (as is usually the case), as opposed to testing for an interaction, would also result in the concealment of any effect. Other reasons for the disparity between the different studies may be due to differences in either age or sex.
Because this cohort is composed of unrelated individuals, family-based mapping methods are not feasible. To help to determine the etiologic variants, we have used regression modeling to distinguish the separate effects on Tg levels. This approach also allowed us to include environmental information, such as smoking and BMI, rendering the model more realistic. We have established that the 1100T allele has a raising effect on serum Tg concentrations and that the -482T allele has a separate raising effect that varied according to smoking habit. We have also shown that the effect of the 3238G allele on Tg is not independent of the 1100T and -482T allele effects, whereas the -2854T>G site does not effect Tg levels in this sample of men. In attempting to ascertain the specific contribution of APOC3 variants in hypertriglyceridemia, this approach has proved useful in indicating which variants are more likely to be important in determining Tg levels and therefore reduce the number of variants that require further investigation in functional studies. Furthermore, the interaction with smoking has been localized to the -482C>T variant, a site in an IRE already known to be functional,11 lending support to the present findings. The model also allowed us to estimate the effect on Tg concentration of a particular APOC3 genotype and smoking status. Because Tg concentration is thought to be related to the risk of CAD,2 this model will be a useful component in the generation of a comprehensive risk profile for CAD. This extension of the regression model approach will also be a useful paradigm for the investigation of other loci where fine mapping of variants in allelic association is required and gene-environment interactions may be crucial. Furthermore, compared with family studies, the present study illustrates the advantages of using large cohorts of sex- and age-specific unrelated individuals to determine the effect of etiologic variants on quantitative phenotypes. We believe this method to be superior to a haplotype analysis, because in unrelated subjects, haplotypes can only be inferred, making assumptions about the data that may not be correct. A haplotype analysis would also not allow us to distinguish separate effects of different variants, which is our primary question.
The mechanism of the Tg-raising effect associated with the 1100C>T variant is unknown. The 1100C>T base change is in exon 3 and does not encode an amino acid change. Therefore, it is not likely to be functional and may be in linkage disequilibrium with another variant, possibly situated in the APOA1-C3 intergenic region (none have so far been reported).
There are numerous reports and a review30 of the association between smoking and elevated Tg concentrations. However, the mechanisms involved in this process remain unclear. Smoking has been shown to elicit insulin resistance and poor handling of dietary fat, with an impaired Tg clearance after a mixed meal.18 The increase in circulating catecholamines from the sympathetic nervous system and the subsequent hyperinsulinemia will affect the regulation of both HSL and LPL, enzymes that regulate Tg metabolism.19
Smoking influences Tg metabolism directly through activation of HSL by catecholamines. HSL is the rate-limiting step for mobilization of adipose tissue Tg, which is released into the plasma and circulates as NEFA complexed to albumin.20 The rate of NEFA delivery to the liver is a major determinant of VLDL-Tg secretion. Elevated levels of circulating NEFA stimulate apoB secretion from the liver, leading to an increased levels of VLDL31 and, consequently, elevated serum Tg levels.
The insulin responsiveness at the APOC3 locus is ablated in vitro in those individuals who carry the -482T (IRE) rare allele, resulting in inappropriate expression of apoC-III.11 Postprandial insulin production normally downregulates apoC-III expression and consequently removes its inhibitory effect on LPL, but this downregulation will be reduced in carriers of the -482T allele. Therefore, constitutive expression of apoC-III will lead to reduced activity of LPL, and the increase in circulating NEFA in individuals who smoke21 may also affect LPL activity, because increased local NEFA at the endothelial cell site may result in the dissociation of LPL from the endothelial cells and hence lower its activity.32 This reduced activity of LPL in combination with a decreased apoE-mediated lipoprotein uptake by elevated apoC-III levels (caused by displacement of apoE by apoC-III on the lipoprotein particle15 or decrease in glycosaminoglycan binding17 ) would result in a reduced TRL removal from the circulation. Thus, it is conceivable that smoking and elevated apoC-III levels could act synergistically to increase the concentration of circulating TRL.
In interpreting these results, it is important to consider the details of the study design. Subjects were requested to consume only a light breakfast; therefore, they were not fasting but postprandial to a varying degree. However, they will not have consumed a substantial amount of fat or carbohydrate, thus limiting postprandial metabolic sequelae. Furthermore, this increase in variability of the Tg measure (compared with fasting) will consequently add "noise" and therefore make associations more difficult to find. Thus, given that we did find robust associations, the validity of our results are not in question. We see no obvious reason why this situation should make interpretation of the data more difficult, and one might even argue that the fasting situation is more artificial and our study design is more realistic.
When the smoking interactions are interpreted, it is important to remember that lifestyle habits also vary between smokers and nonsmokers (eg, physical activity, diet, and alcohol consumption); it is possible that some of these factors may be contributing to these effects attributed to smoking. Likewise, the intermediate effect on Tgs observed in exsmokers may be confounded by lifestyle differences that are maintained (or started) after smoking cessation.
As indicated by the transgenic mouse studies, the majority of the effect of apoC-III can be observed only postprandially. Therefore, it is possible that other polymorphic sites may contribute to the regulation of apoC-III on lipid levels postprandially. In particular, because insulin levels are highest postprandially, it is reasonable to expect the IRE polymorphism to have its major effect at this time. The metabolism of Tg-rich lipoproteins is a highly variable and dynamic process according to both time and level of nutrition. A full postprandial study with the inclusion of smoking information would more fully elucidate the relative contributions of these polymorphisms. We have recently performed such a study by use of the European Atherosclerosis Research Study (EARS) II cohort, a group of young healthy male subjects (n=800) from an offspring study, in whom both an oral glucose tolerance test and an oral fat tolerance test have been performed.25 These results showed that the -2854T>G variant modulates response to an oral fat tolerance test and that the -482C>T variant modulates response to an oral glucose tolerance test, indicating substrate-specific effects from distinct polymorphic sites. It is interesting that no association with fasting Tgs was observed in this group of young healthy men, suggesting that insulin resistance may be the primary defect coded for by the -482C>T and that the effect on Tgs seen in this middle-aged group may be secondary. In addition, no significant associations were observed with the -2854T>G variant in this cohort, but the effect of this variant was only seen in the stimulated situation (after an oral fat tolerance test).
Thus, we have established that genetic variations in the APOC3 promoter and coding region influence serum Tg concentrations under different physiological conditions, including a smoking-specific effect at the -482C>T variant. In middle-aged men, a group at high risk for CAD, regression modeling has allowed us to determine a useful predictive model for estimating Tg levels, which also incorporates smoking information, known to be one of the strongest risk factors for CAD.
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Acknowledgments |
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D.M.W., R.M.F., P.J.T., S.E.H., and S.R.B. are funded by the British Heart Foundation (RG/95007, PG/95190, and PG/96184). G.J.M. is funded by the Medical Research Council. We thank Prof Deborah Ashby for statistical advice.
Received January 25, 2000; accepted May 22, 2000.
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