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(Stroke. 2004;35:2036.)
© 2004 American Heart Association, Inc.

Original Contributions

Genetic Variation in PPARG Encoding Peroxisome Proliferator-Activated Receptor Associated With Carotid Atherosclerosis

Khalid Z. Al-Shali, MBBS; Andrew A. House, MD; Anthony J.G. Hanley, PhD; Hafiz M.R. Khan, PhD; Stewart B. Harris, MD; Bernard Zinman, MD; Mary Mamakeesick, RPN; Aaron Fenster, PhD; J. David Spence, MD Robert A. Hegele, MD

From the Robarts Research Institute (K.Z.A.-S., H.M.R.K., A.F., J.D.S., R.A.H.), London, Ontario, Canada; the Department of Medicine (A.A.H.), University of Western Ontario, London, Ontario, Canada; the Department of Medicine and Samuel Lunenfeld Research Institute (A.J.G.H., B.Z.), Mount Sinai Hospital and University of Toronto, Ontario, Canada; the Thames Valley Family Practice Research Unit (S.B.H.), University of Western Ontario, London, Ontario, Canada; and the Sandy Lake Health and Diabetes Project (M.M.), Ontario, Canada.

Correspondence to Dr Robert A. Hegele, Blackburn Cardiovascular Genetics Laboratory, Robarts Research Institute, 406-100 Perth Dr, London, Ontario, Canada N6A 5K8. E-mail hegele{at}robarts.ca

	Abstract

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Background and Purpose— Peroxisome proliferator-activated receptor is a crucial molecule in atherogenesis because it is associated with metabolic risk factors such as obesity and diabetes and also plays a key role in subcellular metabolism of arterial wall macrophage foam cells. Genetic variation in PPARG has been associated with metabolic and cardiovascular end points.

Methods— We investigated the relationship between 2 common PPARG polymorphisms, namely P12A and c.1431C>T, and carotid atherosclerosis in a sample of 161 Canadian aboriginal people. Dependent variables were carotid intima media thickness (IMT), assessed using B-mode ultrasonography, and total carotid plaque volume (TPV), assessed using 3D ultrasound.

Results— Using multivariate analysis, we found that subjects with 1 PPARG A12 allele had less carotid IMT than others (0.72±0.03 versus 0.80±0.02 mm; P=0.0045), with no between-genotype difference in TPV. In contrast, subjects with the PPARG c.1431T allele had greater TPV than others (124±18.4 versus 65.1±23.7 mm³; P=0.0079), with no between-genotype difference in IMT.

Conclusions— The findings show an association between PPARG genotypes and carotid arterial phenotypes, and further reflect the prevailing view that the PPARG A12 allele protects against deleterious phenotypes. Also, whereas IMT and TPV are somewhat correlated with each other, they might also represent distinct traits with discrete determinants representing different stages of atherogenesis.

Key Words: atherosclerosis • carotid arteries • polymorphism • ultrasonography

	Introduction

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Peroxisome proliferator-activated receptor (PPAR) is a member of the nuclear hormone receptor superfamily of ligand-activated transcription factors.¹ It is an important regulator of adipogenesis; by forming a heterodimer with retinoid X receptor, it triggers the adipocyte differentiation program by binding to specific transcription elements in various metabolic genes.^2,3 Furthermore, the pharmacological PPAR agonist thiazolidinedione (TZD) drugs appear to be antiatherogenic at multiple levels, including generalized improvement of metabolism, and beneficial effects on vascular wall components, such as macrophages.^4–6 For instance, TZD activation of PPAR induces cholesterol efflux from macrophages by inducing ATP-binding cassette protein A1.⁷ For these reasons, association analysis has been performed using common single nucleotide polymorphisms (SNPs) in PPARG, such as the Pro12Ala (P12A; MIM 601487.0002) SNP in the adipocyte-specific PPAR2 isoform,^8–11 and the silent exon 6 c.1431C>T (codon 478 CAC [His]CAT[His]; MIM 601487.0009) SNP.^12–14 A meta-analysis of 8 case–control studies and 2 family-based studies found that the PPARG A12 allele was associated with significantly reduced risk of type 2 diabetes.⁹ The PPARG A12 allele was also associated with significantly reduced risk of myocardial infarction.¹¹ In contrast, the PPARG c.1431T allele has been less consistently associated with traits such as obesity¹² and coronary heart disease (CHD),^13,14 although associations of vascular metabolic traits with this allele have tended to be unfavorable.

Because of these associations involving PPARG alleles A12 and c.1431T, we investigated their association with atherosclerosis in 161 Canadian aboriginal subjects, an isolated founder population.

	Methods

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Study Sample
The Sandy Lake community is located at the 55th parallel of latitude in the subarctic boreal forest of central Canada. Residents are self-defined as Oji-Cree on the basis of their language, which is derived from Ojibway and Cree. Baseline demographic, clinical, and biochemical attributes from an ongoing study of diabetes risk and complications have been described.^15–17 Briefly, all community members aged 10 years were studied with medical history and physical examination. The participation rate was 72%. Fasting blood samples were sent to Toronto for analysis. In 2001, 278 adult community members free of coronary heart disease had ultrasound (US) assessment of the carotid arteries. Of these, 161 had baseline demographic data and sufficient DNA for PPARG genotyping, and this subset was demographically representative of the overall sample (data not shown). All subjects provided informed consent, and the study received approval from the Sandy Lake Band Council and the institutional review boards of the University of Toronto and the University of Western Ontario.

DNA Analysis
We used published methods to genotype the PPARG P12A and c.1431C>T SNPs.^8–14 Briefly, for the P12A SNP genotype, we amplified exon B using primers 5'-ACT CTG GGA GAT TCT CCT ATT GGC and 5'-CTG GAA GAC AAA CTA CAA GAG, with the underlined mismatched base introducing an HaeIII recognition site, allowing for restriction digestion and size polymorphism to distinguish between alleles. Samples were amplified for 28 cycles, each of which consisted of denaturing at 94°C for 20 s, annealing at 56°C for 20 s, and extension at 72°C for 20 s. After HaeIII (New England Biolabs), digestion of the resulting 155-bp fragment, the P12 allele yielded 2 fragments with sizes 132 and 23 bp, although the A12 allele yielded only a single 155-bp fragment.

For the PPARG c.1431C>T SNP genotype, we amplified PPARG exon 6 using primers 5'-CTG AAT GTG AAG CCC ATT GAA and 5'-GTG GCT CAG GAC TCT CTG CTA G. Samples were amplified for 30 cycles, each of which consisted of denaturing at 94°C for 30 s, annealing at 56°C for 30 s, and extension at 72°C for 30 s. After Pml I (New England Biolabs) digestion of the resulting 251-bp fragment, the c.1431C allele yielded 2 fragments with sizes 145 and 106 bp, although the c.1431T allele yielded only a single 251-bp fragment.

General US Logistics
US images were obtained using an HDI-5000 US machine and an L12–5 transducer (both from Advanced Technology Laboratories) that had been flown to the Sandy Lake community and housed within the diabetes research center. A single certified operator used the same instrument during a 4-week period to obtain carotid US images suitable for determination of intima media thickness (IMT) and total carotid plaque volume (TPV) from each participant.

IMT Measurement
A single observer, blinded to subjects’ vascular risk, measured combined thickness of intima and media of the far wall of both common carotid arteries. The anterolateral longitudinal far walls of the common carotid arteries were recorded with the head 45° in the contralateral direction and the probe between 30° and 45° to the horizontal. The sonographer used the minimum gain necessary to clearly visualize lumen-intima and media-adventitia echoes, which were of best quality when the image plane was parallel to the carotid artery axis. These views were played back using an image processing board and a specialized recorder with digital memory permitting digitization of a full video frame in still mode. Still images were analyzed using computerized edge-detection software (Prowin).¹⁸ Using a step-wise algorithm, conditional sets of "edges" (consisting of lumen-intima and media-adventitia echoes) were located within the image and then tested for "edge strength." Weak edge points were deleted, thus minimizing identification of spurious edge points resulting from image noise. Once all acceptable edge points were identified, boundary gaps were filled by linear interpolation. The distance between lumen-intima and media-adventitia boundaries was then measured to calculate IMT. Mean IMT was computed from 80 to 120 measurements over a 10-mm span ending 5 mm proximal to the transition between the common carotid and bulb regions. Intraoperator and interoperator coefficients of variation of 3.0% and 3.1%, respectively, and intraoperator and interoperator intraclass correlations were both 0.97.

TPV Measurement
Two-dimensional US images of the carotid arteries were obtained from each of the 278 Oji-Cree subjects enrolled in the study. The resulting 2D images that were parallel to each other within a known regular spatial interval and constant transducer angle for each subject were reconstructed immediately into a 3D volume to verify scan quality.^19,20 Three-dimensional US images were acquired with a mechanical linear scanning system and analyzed with L3Di visualization software (Life Imaging Systems Inc). Each 3D image was displayed using multiplanar texture mapping, allowing plaques to be viewed in various orientations. Plaque volumes were measured using manual planimetry: each 3D image was "sliced" transversely at an interslice distance of 1 mm, moving from 1 plaque edge to the other. Plaques were identified on the basis of visible changes in morphology in which local thickening of the intimal layer exceeded 1.0 mm. Plaque boundaries were traced using a mouse-driven cross-haired cursor. Slice areas were summed and multiplied by interslice distance to calculate plaque volume. TPV was the sum of all plaque volumes between the clavicle and angle of the jaw for both carotids. Intraobserver and interobserver reliability were 0.94 (n=40) and 0.93 (n=40), respectively.

Statistical Analysis
SAS version 6.12 (SAS Institute) was used to evaluate the association between IMT and plaque volume and the PPARG P12A and c.1431C>T SNPs. Distributions of IMT and TPV measurements were significantly non-normally distributed in this data set. Therefore, for parametric statistical analyses, IMT and TPV variables were transformed and subjected to analysis of normality. After transformation, 1/IMT and the square root of TPV were normally distributed. The transformed IMT and TPV were used for parametric statistical analyses, but the untransformed values are presented in the tables. ANOVA, which was performed using the general linear models procedure, was used to determine the sources of variation, with F tests computed from the type III sums of squares. This form of sums of squares is applicable to unbalanced study designs and reports the effect of an independent variable after adjusting for all other variables included in the model.^16–18 The dependent variables were transformed mean IMT and TPV from both carotid artery systems in each subject. Independent variables were PPARG P12A genotype, PPARG c.1431C>T genotype, age, sex, body mass index (BMI), diabetes status, total cholesterol, current smoking, and hypertension. The general linear model procedure for least-squares means was used to determine the level of significance in pairwise plaque volume comparisons as well as pairwise IMT and TPV comparisons between genotype classes. Least-squares means, also known as population marginal means, are the values for class means after adjustment for all covariates in the model. Deviation of genotype frequencies from those predicted by the Hardy–Weinberg law was tested by ² analysis. Linkage disequilibrium between PPARG genotypes was estimated using a modification of the method of Hill and Robertson as described.²¹

	Results

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Baseline Demographic Features
Clinical attributes of the 161 subjects overall and according to sex are shown in Table 1. None of the discrete or quantitative traits were significantly different between males and females, although there was a trend toward a higher proportion of females affected with diabetes or impaired glucose tolerance (P=0.063). The simple Pearson correlation coefficient between untransformed carotid artery quantitative traits was 0.474 (P<0.0001). This increased somewhat to 0.644 (P<0.0001) when transformed values (ie, 1/IMT and square root of TPV) were used. However, the correlations between US measurements, although highly significant, were only moderate in degree (r<0.7).

View this table: [in this window] [in a new window]   TABLE 1. Baseline Demographics of 161 Oji-Cree Study Subjects

Allele and Genotype Frequencies
Genotype and allele frequencies for PPARG P12A and c.1431C>T SNPs are shown in Table 2. There was no significant deviation of the frequencies of both PPARG genotypes from those predicted by the Hardy–Weinberg law. The correlation coefficient r of nonrandom allelic association on the basis of Hill and Robertson’s linkage disequilibrium constant was 0.34 (²=133; P<0.0001). Thus, there was significant but modest and incomplete linkage disequilibrium between these 2 SNP markers. We constructed haplotypes using phase information derived from inheritance but found no significant associations with the haplotypes (data not shown). Therefore, the 2 SNPs were treated as separate variables in subsequent analyses.

View this table: [in this window] [in a new window]   TABLE 2. PPARG Genotype and Allele Frequencies

Determinants of Carotid IMT and TPV
ANOVA in Table 3 showed that transformed IMT was significantly associated only with age and with PPARG P12A genotype in this sample, each with a nominal P<0.05. Sex and hypertension tended to be associated with IMT. ANOVA in Table 3 also showed that transformed TPV was significantly associated with age, BMI, the presence of diabetes, and PPARG c.1431C>T genotype in this sample, each with a nominal P<0.05.

View this table: [in this window] [in a new window]   TABLE 3. Determinants of Carotid Ultrasound Traits in Oji-Cree (ANOVA)

The significant associations detected using ANOVA were evaluated by comparison of least-squares means for genotype classes (Table 4). Subjects heterozygous for the A12 allele had significantly less carotid IMT than homozygotes for the P12 allele (0.72±0.03 versus 0.80±0.02 mm; P=0.0045), but there was no association between this genotype and carotid TPV. In contrast, subjects heterozygous or homozygous for c.1431T allele (ie, dominant model for c.1431T) had significantly more carotid TPV than homozygotes for the c.1431C allele (124±18.4 versus 65.1±23.7 mm³; P=0.0079), but there was no association between this genotype and carotid IMT.

View this table: [in this window] [in a new window]   TABLE 4. Carotid Ultrasound Traits Shown According to PPARG Genotype in Oji-Cree

	Discussion

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In Canadian Oji-Cree, we report: (1) cardiovascular risk factors and noninvasive measurements of carotid arterial structural changes, namely IMT and TPV; (2) moderate correlation between IMT and TPV determinations; (3) allele frequencies of PPARG P12A and c.1431C>T genotypes, and modest pairwise linkage disequilibrium between them; (4) an association of IMT with age and PPARG P12A genotype, with A12 heterozygotes having lower mean IMT; and (5) an association of TPV with age, BMI, diabetes, and PPARG c.1431T, with c.1431T carriers having significantly higher TPV than homozygotes for c.1431C. Thus, PPARG variation is associated with carotid artery structural changes measured noninvasively. The association of reduced IMT with the A12 allele may be related to improved profile of intermediate metabolites attributed to this allele,⁸ although the association of increased TPV with the silent, nonfunctional c.1431T allele is probably the result of linkage disequilibrium with another DNA change.

Although the genetic association findings will require replication in other study samples, our findings are consistent with some previous observations. For instance, we showed that the A12 allele was associated with a lower mean IMT. A recent study in 154 Japanese subjects with type 2 diabetes found a similar relationship between carotid IMT and the A12 allele.²² Furthermore, the A12 allele was associated with 25% reduction in myocardial infarction risk in a prospective study.¹¹ The A12 allele also seems to be beneficial with respect to metabolic traits; a meta-analysis showed A12 to be associated with decreased diabetes risk.⁹ Another study showed that A12 was associated with improved insulin sensitivity.¹⁰ In our relatively small study sample, PPARG A12 was not associated with either type 2 diabetes or obesity measures (data not shown). Together, association studies support the idea that PPARG A12, which has in vitro functional consequences,²³ is associated with less severe vascular or metabolic phenotypes.

We also found that the PPARG c.1431T allele was associated with increased TPV after adjustments for age, BMI, sex, diabetes status, total cholesterol, current smoking, hypertension, and PPARG codon 12 genotype. However, associations between this SNP and vascular phenotypes have been somewhat inconsistent. The c.1431T allele has been associated previously with reduced¹³ and unchanged¹⁴ risk of CHD. Furthermore, the c.1431T allele has been associated previously with increased^24,25 and decreased²⁶ obesity indices. These inconsistencies may be attributable to the fact that this SNP is silent at the amino acid level and is unlikely to have any direct mechanistic link with specific phenotypes. Instead, this silent SNP might be in linkage disequilibrium with an unmeasured functional variation either within or flanking the PPARG gene or in another gene at this locus. Because there was only modest linkage disequilibrium between the PPARG P12A and c.1431C>T SNPs in this sample, and because there was no independent association of P12A with TPV, it is unlikely that the P12A SNP explains the association of c.1431T with TPV in this sample.

Although both US traits were significantly associated with age, IMT was significantly associated only with PPARG P12A genotype, although TPV was significantly associated with BMI, diabetes, and PPARG c.1431C>T genotype. Furthermore, the simple correlation between IMT and TPV, although statistically significant, was moderate. Thus, different US-derived measures of carotid artery morphology, although somewhat correlated, probably represent distinct intermediate traits with unique determinants and relationships with risk factors. The use of any particular US trait as a surrogate marker for "atherosclerosis" might lead to different conclusions regarding the role of specific risk factors in a particular patient sample. Recently, we showed that total carotid plaque area and percent carotid stenosis measured by US were moderately well correlated and had different associations with specific risk factors.²⁷ In the same way, IMT and TPV in this study likely reflected different attributes of atherosclerosis.

The results of this small study indicate that although genetic variation in PPARG is associated with atherosclerosis, specific relationships with IMT and TPV differ somewhat. In addition to small sample size, the limitations include the relatively young age of the sample, the generalizability of the study, and the fact that the internal carotid arteries and bifurcations were not assessed. PPARG is emerging as a focal determinant of metabolic and vascular pathways that determine atherosclerosis risk. If replicated, associations with PPARG could be used in risk prediction algorithms. Also, IMT and TPV may be different stages along a continuum that might reflect different attributes of the atherosclerotic process, and therefore, their use as surrogates for atherosclerosis might lead to different conclusions in a particular sample. Future work in individual study samples, with careful and extensive collection of intermediate phenotypes and genetic markers, may help clarify whether these traits actually reflect different aspects of atherosclerosis.

	Acknowledgments

 
This work was supported by grants from the Heart and Stroke Foundation of Ontario, the Canadian Institutes for Health Research (CIHR; FRN 44087), the Canadian Genetic Diseases Network, and the Blackburn Group. The authors acknowledge the expert assistance of Maria DiCicco, Janine Boere, Edith Fiddler, and Ken Goodwin. R.A.H. and A.F. hold Canada Research Chairs (tier I). R.A.H. is a Career Investigator of the Heart and Stroke Foundation of Ontario. A.J.G.H. was supported through a CIHR Postdoctoral fellowship. S.B.H. is a Career Scientist of the Ontario Ministry of Health.

Received April 30, 2004; revision received June 3, 2004; accepted June 28, 2004.

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