Single Nucleotide Polymorphisms of the Peroxisome Proliferator–Activated Receptor- Gene (PPARA) Influence the Conversion From Impaired Glucose Tolerance to Type 2 Diabetes
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2008年05月25日 16:14:22 Sunday
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作者：Laura Andrulionyt, Teemu Kuulasmaa, Jean-Louis Chiasson, Markku Laakso for the STOP-NIDDM Study Group* 作者单位：Department of Medicine, University of Kuopio and Kuopio University Hospital, Kuopio, Finland ;Research Centre, Centre Hospitalier de l'Université de Montréal, Htel-Dieu, and Department of Medicine, University of Montreal, Quebec, Canada
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【摘要】 Peroxisome proliferator–activated receptor (PPAR) , a transcription factor of the nuclear receptor superfamily, regulates fatty acid oxidation. We evaluated the association of single nucleotide polymorphisms (SNPs) of the PPAR- gene (PPARA) with the conversion from impaired glucose tolerance to type 2 diabetes in 767 subjects of the STOP-NIDDM trial in order to investigate the effect of acarbose in comparison with placebo on the prevention of diabetes. In the placebo group, the G (162V) allele of rs1800206 increased the risk for diabetes by 1.9-fold (95% CI 1.05–3.58) and was associated with elevated levels of plasma glucose and insulin. The effect of this allele on the risk of diabetes in the placebo group was enhanced by the simultaneous presence of the risk alleles of the PPAR-2, PPAR- coactivator 1, and hepatic nuclear factor 4 genes (odds ratios 2.2, 2.5, and 3.4, respectively). In the acarbose group, subjects carrying the minor G allele of rs4253776 and the CC genotype of rs4253778 of PPARA had a 1.7- and 2.7-fold increased risk for diabetes. Our data indicate that SNPs of PPARA increase the risk of type 2 diabetes alone and in combination with the SNPs of other genes acting closely with PPAR-.
【关键词】 IGT, impaired glucose tolerance; LD, linkage disequilibrium; PGC-1, PPAR- coactivator 1; PPAR, peroxisome proliferator–activated receptor; SNP, single nucleotide polymorphism
Key proteins involved in lipid metabolism are under the transcriptional control of peroxisome proliferator–activated receptor (PPAR) (1). Among the three PPAR subtypes, PPAR- is found predominantly in the liver, kidney, and heart, where it upregulates the expression of genes involved in fatty acid metabolism (1), particularly when activated by PPAR- coactivator 1 (PGC-1) (2).
PPAR- agonists lower plasma lipid levels, decrease intrahepatic and skeletal muscle lipid accumulation and adiposity, and normalize glucose and insulin concentrations, therefore markedly reducing insulin resistance and the risk of type 2 diabetes (3–5) in various rodent models of type 2 diabetes and insulin resistance, whereas gemfibrozil and fenofibrate can improve insulin sensitivity in humans (6,7).
Along with regulation of lipid and glucose metabolism, PPAR- is as an attractive candidate gene for type 2 diabetes. Among previous studies (8,9), only one cross-sectional study has reported an association of haplotype of PPARA with the age of diabetes diagnosis (10). Therefore, we evaluated the association of single nucleotide polymorphisms (SNPs) of PPARA with the conversion from impaired glucose tolerance (IGT) to type 2 diabetes in subjects of the STOP-NIDDM trial. Furthermore, the effects of SNPs of PPARA, along with the SNPs of PPAR- coactivator 1 (PGC-1A), PPAR-2, (PPARG2), and hepatic nuclear factor 4 (HNF4A) on the conversion to diabetes were investigated.
RESEARCH DESIGN AND METHODS
The STOP-NIDDM trial was a double-blind, placebo-controlled study that randomized 1,429 subjects with IGT to either acarbose or placebo groups (11). Annual oral glucose tolerance tests were performed to evaluate the conversion to diabetes. The entire population was followed up on an average of 3.3 ± 1.2 years. DNA was available from 767 subjects from seven countries (385 men and 382 women; 354 treated with acarbose and 413 with placebo). Their mean BMI was 30.8 ± 4.1 kg/m2 and age 54.7 ± 7.9 years. All participants signed an informed consent form, and the study was approved by appropriate institutional review boards.
DNA analyses.
We screened three SNPs (rs135559, rs1800206, and rs4253778) from the study of Flavell et al. (10). In addition, eight SNPs were selected using the Tagger software (12) (http://www.broad.mit.edu/mpg/tagger/faq.html) and data available from the HapMap project (13) (http://www.hapmap.org; Data Release #20/phase II, January 2006). In the genomic region of 100 kilo–base pairs (kbp) including the 93.2-kbp PPARA locus, 10 of all 11 selected SNPs captured up to 70% of common variants with minor allele frequency >5%; r2 > 0.8 (rs135539 not genotyped in HapMap).
Screening of SNPs of PPARA was performed with TaqMan Allelic Discrimination Assays (Applied Biosystems). Genotyping reaction was amplified on a GeneAmp PCR system 2700 (95°C for 10 min, followed by 40 cycles of 95°C for 15 s and 60°C for 1 min), and fluorescence was detected on an ABI Prism 7,000 sequence detector (Applied Biosystems). The genotyping success rate was 100%, and rescreening of 7% of subjects gave 100% identical results. TaqMan Allelic Discrimination Assays were also used to genotype 8 SNPs of HNF4A (14), of which rs4801424 G/C (C risk allele), rs2425637 G/T (G risk allele), rs2071197 G/A (G risk allele), and rs3818247 G/T (T risk allele) have been associated with the conversion to type 2 diabetes (14). Screening methods for polymorphisms of PGC-1A (rs8192678 G/A: Gly482Ser; 482Ser risk allele) and PPARG2 (rs1801282 C/G: Pro12Ala; Pro12Pro risk genotype) have been previously described (15).
Statistical analysis.
The SPSS program (SPSS, Chicago, IL) version 11.0 for Windows was used in data analysis. Results are expressed as either means ± SD or percentages. Not normally distributed parameters were logarithmically transformed. Categorical variables were compared using the 2 and Fisher's exact tests under the dominant and recessive models and continuous variables with the two-tailed Student's t test, ANOVA, or nonparametric tests when appropriate. Logistic regression analyses and further adjustment for appropriate covariates were applied to evaluate whether the SNPs predicted the development of type 2 diabetes. P values <0.05 were considered statistically significant. The Haploview program (16) (available at http://www.broad.mit.edu/mpg/haploview/) was used to calculate and visualize the linkage disequilibrium (LD) statistics and haplotype blocks among the SNPs. Haplotype estimation from unrelated individuals was performed using SNPHAP (available at http://www-gene.cimr.cam.ac.uk/clayton/software/). Permutation analysis was carried with the R 2.3.0 (17) and Design package 2.0-12 (http://biostat.mc.vanderbilt.edu/s/design.html).
A permutation test was applied to all results giving statistically significant results. The variable "conversion to diabetes" was permuted, and analyses were repeated 1,000 times, each time resampling the actual dataset. P values of the permutation test (the number by which the permuted test statistics exceeded the nominal P values divided by the number of permutations performed) for each test of interest varied from <0.001 to 0.048, suggesting that all nominal P values reported are likely to be statistically significant.
RESULTS
Eleven SNPs covering the genomic region of PPARA are shown in Fig. 1A and LD statistics (r2) among the SNPs with minor allele frequency of the SNPs in Fig. 1B. The distributions of the genotypes of all SNPs were in Hardy-Weinberg equilibrium and did not differ between the placebo and acarbose groups or between men and women.
Table 1 shows the conversion to diabetes (in percentages) for all 11 SNPs under the dominant model. Analyses under the recessive model gave significant results for rs4253778 only (Table 1). A significant interaction was observed between rs1800206, rs4253776, and the treatment group in their effects on the conversion to diabetes. Therefore, the results are presented separately for the placebo and acarbose groups. In the placebo group, carriers of the rare G (162V) allele of rs1800206 converted to diabetes more often than carriers of the common CC genotype (P = 0.044). In the acarbose group, the conversion to diabetes was higher in carriers of the rare G allele of rs4253776 (P = 0.044). Moreover, a higher conversion rate (P = 0.030) was observed in subjects with the CC genotype of rs4253778 than in subjects having the G allele in the acarbose group (Table 1, recessive model).
The presence of the G (162V) allele of rs1800206 increased the risk of developing diabetes in the placebo group (odds ratio [OR] 1.93 [95% CI 1.05–3.58], P = 0.035) (Table 2). Adjustment for confounding factors, including country of origin in the multivariate logistic regression model, did not abolish the statistical significance (P = 0.032). In the acarbose group, a 1.7-fold increase in the risk for the development of diabetes was observed in carriers of the minor G allele of rs4253776 in comparison with carriers of the AA genotype (dominant model) and a 2.7-fold increase in the risk in carriers of the CC genotype of rs4253778 in comparison with carriers of the common G allele (recessive model) (Table 2). In addition, in the placebo group, haplotypes C-G-C and A-G-C, based on SNPs rs135539, rs1800206, and rs4253778 (as reported in ref. 10), but no other haplotypes based on different three SNPs combinations), increased the risk of developing diabetes by 4.58-fold (P = 0.010) and 3.18-fold (P = 0.020), respectively, compared with the C-C-C haplotype (Table 2).
Subjects in the placebo group possessing the minor G (162V) allele of rs1800206 had higher follow-up values of fasting (6.6 vs. 6.2 mmol/l, P = 0.020) and 2-h glucose (10.7 vs. 9.6 mmol/l, P = 0.012), higher baseline values of fasting insulin (113.7 vs. 94.3 pmol/l, P = 0.012), and a larger increase in 2-h glucose levels during the follow-up (1.14 vs. 0.38 mmol/l, P = 0.029). Similarly, in the acarbose group, the carriers of the risk G allele of rs4253776 had significantly higher fasting and 2-h plasma glucose levels and a larger increase in glucose levels and body weight during the follow-up, whereas carriers of the risk CC genotype of rs4253778 gained more weight and had a larger increase in waist circumference during the follow-up (data not shown).
We also tested whether rs1800206 (L162V) of PPARA had an effect on the risk of diabetes associated previously with polymorphisms of PGC-1A (Gly482Ser), PPARG2 (Pro12Ala) (15), and HNF4A (eight SNPs) (14). Genotypes of rs1800206 were therefore combined with those of other SNPs to form three new genotype combinations having no (0), one of two (1), and both (2) risk alleles of rs1800206 and SNPs of either PGC-1A, PPARG2, or HNF4A. No statistically significant interactions between the SNPs were observed with respect to diabetes risk. In the placebo group, the risk for the conversion to type 2 diabetes increased by 2.5-, 2.2-, and 3.4-fold with the presence of the G (162V) allele of rs1800206 and the risk alleles of either PGC-1A (482Ser), PPARG2 (Pro12Pro), or HNF4A (G allele of rs2425637), respectively, compared with subjects without risk alleles (Table 3).
DISCUSSION
This is the first large prospective study to report that SNPs of PPARA predict the development of type 2 diabetes in subjects with IGT. The minor G (162V) allele of rs1800206 was associated with a 1.9-fold (P = 0.035) higher risk of diabetes in the placebo group (in six of seven different populations included in the study). The same allele of rs1800206 further increased the risk of diabetes beyond that associated previously with the SNPs of PGC-1A, PPARG2 (15), and HNF4A (14). In addition, acarbose was more effective in preventing the progression from IGT to diabetes in subjects carrying either one or both risk alleles of PPARA and PGC-1A or HNF4A, indicating a gene-treatment interaction.
Activation of PPAR- favors lipid sparing (1) and improves insulin sensitivity and -cell function by decreasing adiposity, skeletal muscle, hepatic steatosis, plasma free fatty acids, and triglycerides (3–5). Moreover, recent studies show that PPAR- deficiency decreases the mean area of the pancreatic -cells and diminishes insulin secretion in response to glucose, whereas PPAR- agonists reduce lipotoxicity in human islets and prevent the progression to diabetes (19). It is therefore possible that the pancreatic effects of PPAR- are responsible for the results observed in our study.
rs1800206 of PPARA is likely to be functional. Transcriptional studies have demonstrated that the C to G change in codon 162 (L162V) of exon 5 encodes a more active PPAR- in vitro (8,20). However, the L162V polymorphism has different effects on gene transcription depending on the concentration of ligand. In the presence of high concentrations, transcriptional activity of the G (162V) allele is higher compared with that of the C allele (8,20), whereas at low concentrations results are the opposite (20). In our study and a previous study (10) the presence of the G (162V) allele increased the risk of diabetes. The reasons for this finding remain unexplained, but it is possible that the effect of rs1800206 is influenced by gene-environment interactions regulating the concentrations of ligands for PPAR-. Furthermore, the L162V substitution could be in LD with another SNP, causing the association.
What could be the mechanisms by which acarbose and SNPs in PPARA as well as in PGC-1A, PPARG2, and HNF4A regulate the risk of diabetes High levels of free fatty acids and postprandial hyperglycemia (21) increase the production of reactive oxygen species and insulin resistance. PPAR- protects from oxidative stress, and PGC-1 prevents the formation of reactive oxygen species by transcriptionally regulating the mitochondrial antioxidant defense system (22). Since acarbose alleviates postprandial hyperglycemia and lowers the levels of free fatty acids, it could interact with SNPs of PPARA and PGC-1A and thus prevent the development of diabetes.
The Gly482Ser substitution of PGC-1A (15,23), the Pro12Ala substitution of PPARG2 (15,24), and SNPs of HNF4A (14) have been associated previously with the risk of diabetes. PGC-1 coactivates PPAR- in the transcriptional control of fatty acid oxidation proteins (2), whereas hepatic expression of PPAR- is regulated by HNF4- (hepatic nuclear factor 4) (25). Genetically determined defects in one or several factors of this transcriptional network might impair their function and thus affect the entire system. In the placebo group, the simultaneous presence of the minor G allele (162V) of rs1800206 of PPARA with the 482Ser allele of PGC-1A (rs8192678), the Pro12Pro genotype of PPARG2 (rs1801282), or the G allele of HNF4A (rs2425637) increased the risk of diabetes by 2.5-, 2.2-, and 3.4-fold, respectively. However, no statistically significant interaction between the SNPs with respect to the conversion to diabetes were observed. Mechanisms explaining possible transcriptional interactions on the risk of diabetes are currently unclear.
Examination of many genetic variations and their interactions on several variables raises the possibility of finding false-positive results. Bonferroni correction is conservative and likely to result in overadjustment. All statistically significant results survived the permutation test, suggesting that it is unlikely that our findings have occurred by chance.
In conclusion, the present study shows that SNPs of PPARA modulate the progression to type 2 diabetes in subjects with IGT. Moreover, the combination of rs1800206 of PPARA with SNPs of PGC-1A, PPARG2, and HNF4A had an additive effect on the risk of diabetes.
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