Single Nucleotide Polymorphisms of PPARD in Combination With the Gly482Ser Substitution of PGC-1A and the Pro12Ala Substitution of PPARG2 Pr
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糖尿病学杂志 2006年第7期
1 Department of Medicine, University of Kuopio, Kuopio, Finland
2 Research Centre, Centre Hospitalier de l’Universitee de Montreeal, Htel-Dieu, Department of Medicine, University of Montreal, Quebec, Canada
FFA, free fatty acid; IGT, impaired glucose tolerance; LD, linkage disequilibrium; PGC-1, PPAR- coactivator 1; PPAR, peroxisome proliferatoreCactivated receptor; SNP, single nucleotide polymorphism
ABSTRACT
Peroxisome proliferatoreCactivated receptor (PPAR)- regulates fatty acid oxidation and improves insulin sensitivity. We screened six single nucleotide polymorphisms (SNPs) of the PPAR- gene (PPARD) for an association with the conversion from impaired glucose tolerance (IGT) to type 2 diabetes in 769 subjects participating in the STOP-NIDDM trial. A 2.7-fold increase in the risk of diabetes was observed in female carriers of the C allele of rs6902123 (95% CI 1.44eC5.30; adjusted P = 0.002). In the placebo group, subjects possessing both the 482Ser allele of the PPAR- coactivator-1 gene (PGC-1A) and the rare allele of two SNPs of PPARD (rs6902123 and rs3734254) had up to 2.5-fold increased risk for diabetes. Furthermore, women carrying the C allele of rs6902123 of PPARD and the Pro12Pro genotype of the PPAR-2 gene (PPARG2) had a 3.9-fold (95% CI 1.79eC8.63; P = 0.001)-higher risk for diabetes than women with protective genotypes. Expression levels of PPAR- in subcutaneous adipose tissue of 87 offspring of Finnish patients with type 2 diabetes did not differ among the genotype groups of SNPs of PPARD. We conclude that SNPs in PPARD modify the conversion from IGT to type 2 diabetes, particularly in combination with the SNPs of PGC-1A and PPARG2.
Peroxisome proliferatoreCactivated receptor (PPAR)- is a powerful regulator of lipid and glucose metabolism and energy homeostasis (1). Overexpression of PPAR- in adipose tissue leads to enhanced free fatty acid (FFA) oxidation, improved lipid profile, and protects from both genetically predisposed and high-fat dieteCinduced obesity (2). In skeletal muscle, the activation of PPAR- also induces fatty acid -oxidation and energy expenditure (3). Treatment with synthetic PPAR- activators attenuates plasma glucose and insulin levels in obese mice (3), reduces fasting insulin in insulin-resistant monkeys (4), and can enhance insulin-stimulated glucose uptake in cultured human myotubes (5) but not in skeletal muscle (6).
The physiological function of PPAR- appears to be quite similar to that of PPAR- coactivator 1 (PGC-1) (7). Indeed, many of the effects of PGC-1 on energy homeostasis may be mediated through PPAR-, as PGC-1 and PPAR- have a strong interaction (2). Moreover, PPAR- stimulates PGC-1 mRNA expression both in vitro and in vivo (3).
Cross-sectional studies have reported an association of single nucleotide polymorphisms (SNPs) of the PPAR- gene (PPARD) with lipid and glucose metabolism (8,9). Recently, we demonstrated that SNPs in PPARD were associated with skeletal muscle glucose uptake (10). One previous cross-sectional study (9) did not find association with diabetes. In the present study, we investigated the effect of the SNPs of PPARD and their interaction with common polymorphisms of the PGC-1 (PGC-1A) and PPAR-2 (PPARG2) genes on the risk of type 2 diabetes in the STOP-NIDDM trial.
RESEARCH DESIGN AND METHODS
The longitudinal, multicenter, double-blind, placebo-controlled STOP-NIDDM trial (mean follow-up 3.3 years) was carried out to evaluate the effect of acarbose on the conversion to type 2 diabetes in 1,429 subjects with impaired glucose tolerance (IGT) (11,12). DNA was available from 769 subjects. The informed consent form, approved by appropriate institutional review board, was signed by all participants of the study.
DNA analyses.
Selection of the SNPs was based on the data available from HapMap project’s website (13). Six SNPs were chosen from different haplotype blocks to cover the genomic region of PPARD. SNPs were genotyped using the TaqMan Allelic Discrimination Assays (Applied Biosystems, Foster City, CA) as previously described (10). Sequence information for oligonucleotide primers and probes is available from the authors.
Quantitative PCR.
Total RNA from subcutaneous adipose tissue of 87 offspring of Finnish patients with type 2 diabetes was isolated using Trizol reagent (Invitrogen) and Qiagen RNeasy Mini kit (Qiagen, Hilden, Germany). Total RNA was DNase treated using a DNA-free kit (Ambion) and transcribed to cDNA using random primers and a high-capacity cDNA archive kit (Applied Biosystems). Quantitative RT-PCRs were performed in a 7500 Real-Time PCR System (Applied Biosystems) using 6 ng (RNA equivalents) of cDNA as template, gene-specific primers, and probes (sequence information is available upon request). PPAR- (Hs00602622_m1) expressions were normalized to large ribosomal protein P0 (RPLP0; Hs99999902_m1) using standard curve method.
Statistical analysis.
We used the SPSS program (version 11.0 for Windows; SPSS, Chicago, IL) in data analysis and gave the results as either means ± SD or percentages. NoneCnormally distributed parameters were logarithmically transformed. Two-tailed Student’s t test, nonparametric, 2, and Fisher’s exact tests were used to compare variables between the groups, when appropriate. Logistic regression analysis was used to evaluate the effect of SNPs on the conversion to diabetes. Linkage disequilibrium (LD) statistics were calculated and haplotype blocks were visualized by using Haploview software (14) (available at http://www.broad.mit.edu/mpg/haploview/). Haplotype estimation from unrelated individuals was performed by using SNPHAP (available at http://www-gene.cimr.cam.ac.uk/clayton/software/).
RESULTS
The LD statistics (D', r2) and the minor allele frequencies of the SNPs of PPARD are shown in Fig. 1. As the number of homozygous subjects for the rare allele of each SNP was small, they were combined with heterozygous subjects in all statistical analyses. Frequencies of genotypes of four SNPs of PPARD followed the Hardy-Weinberg expectations. However, genotype distributions of rs2076167 and rs1053049 deviated from the Hardy-Weinberg equilibrium (P < 0.025 and <0.015, respectively). No genotyping errors were found. Despite that, SNPs rs2076167 and rs1053049 (both in tight LD with rs3734254; inter-SNP D' > 0.98, r2 > 0.8; Fig. 1) were excluded from further statistical analyses.
The association of four SNPs of PPARD with the conversion to type 2 diabetes is presented in Table 1. Because rs6902123 had an interaction with sex in its effect on the conversion to diabetes (logistic regression analysis, P = 0.008), we show the results separately for men and women. The C allele of rs6902123 was more frequent among women who converted to diabetes compared with women who did not. In logistic regression analysis, the presence of the C allele in women increased the risk for the conversion to diabetes by 2.47-fold (95% CI 1.32eC4.63; P = 0.005), which remained statistically significant even after the adjustment for age, treatment group, smoking, weight at baseline, and weight change (odds ratio [OR] 2.76 [95% CI 1.44eC5.30]; P = 0.002). In haplotype analysis based on different combinations of the four SNPs, none of the haplotypes increased the risk for the conversion to type 2 diabetes beyond that of individual SNPs (data not shown). The expression levels of PPAR- in subcutaneous adipose tissue of 87 offspring of Finnish patients with type 2 diabetes did not differ between the genotype groups of SNPs of PPARD (online appendix Table 1 [available at http://diabetes.diabetesjournals.org]).
Next, we investigated whether the SNPs of PPARD further increased the risk for the conversion to diabetes in carriers of the Gly482Ser (rs8192687) substitution of PGC-1A, which was previously shown to increase the risk of diabetes in participants of the STOP-NIDDM trial (15). To this aim, study participants were classified into three groups: subjects having both the nonrisk genotype of PPARD and the nonrisk genotype of PGC-1A (Gly482Gly) (the reference group), subjects with one risk allele of either PPARD or PGC-1A, and subjects possessing both the risk allele of PPARD and the 482Ser risk allele of PGC-1A. Subjects of the placebo group possessing both the 482Ser allele of PGC-1A and the C allele of two PPARD SNPs (rs6902123 and rs3734254) had a 2.56- and 2.15-fold higher risk for the conversion to type 2 diabetes compared with those having neither risk genotypes (Table 2). ORs varied from 2.30 to 2.92 after the adjustment for confounding factors. The presence of one risk allele (that of either PGC-1A or PPARD; Table 2) in subjects of the placebo group resulted in a 1.5 increase in the risk of diabetes, a finding similar to that observed earlier for the 482Ser allele of PGC-1A alone (15). The simultaneous presence of the PPARD risk allele and the 482Ser allele of PGC-1A increased the risk up to 2.2-fold, suggesting an additive effect. Furthermore, carriers of one or both risk alleles of PPARD and PGC-1A benefited more from the intervention (acarbose) than did noncarriers.
In women, the simultaneous presence of the two risk genotypes (the Pro12Pro genotype of PPARG2 [rs1801282] and the C allele of rs6902123 of PPARD) increased the risk for the conversion from IGT to diabetes by 3.93-fold (95% CI 1.79eC8.63; P = 0.001) compared with women having the protective genotypes (the 12Ala allele of PPARG2 and the TT genotype of rs6902123 of PPARD) (P = 0.006 for the interaction between the combined genotypes and sex). Adjustment for confounding factors increased the risk to 5.06-fold (2.20eC11.60; P < 0.001). Other SNPs of PPARD, together with the Pro12Pro genotype of PPARG2, had no additive effect on the conversion to type 2 diabetes.
DISCUSSION
This is the first study demonstrating that an SNP rs6902123 of PPARD alone and in combination with the Pro12Ala polymorphism of PPARG2 and two SNPs (rs6902123 and rs3734254) of PPARD in combination with the Gly482Ser substitution of PGC-1A predicted the conversion from IGT to type 2 diabetes.
PPAR- plays a prominent role in energy homeostasis. Overexpression of PPAR- in skeletal muscle (3) and adipocytes (2) markedly increase fat catabolism in both tissues. Given the important role of FFAs in the development of type 2 diabetes (16), the upregulation of the oxidative phosphorylation pathway by PPAR- is beneficial in alleviation of insulin resistance and adiposity (1). PPAR- agonists increase glucose transport (5) and consequently decrease insulin and glucose levels (2,3). Therefore, variants in PPARD could potentially modify glucose metabolism. Indeed, we recently reported that SNPs of PPARD significantly modulated glucose uptake in skeletal muscle, measured by positron emission tomography (10). In the present study, the C allele of rs6902123 increased the risk of diabetes by 2.5-fold. The association was observed only in women but not in men. This sex difference remains unexplained.
SNPs of PPARD also increased the effect of the Gly482Ser substitution of PGC-1A on the risk of type 2 diabetes. This is not surprising since PPAR- appears to have a strong interaction with PGC-1 (2,7). Similarly to PGC-1 (7,17), PPAR- is also induced by fasting (18) and physical exercise (19), and it promotes the formation of highly oxidative skeletal muscle fibers (19). Moreover, PPAR- itself can increase expression of PGC-1 and lead to mitochondrial biogenesis and enhanced oxidative phosphorylation (3). The Gly482Ser substitution of PGC-1A has been linked to increased risk of diabetes (15,20), but no association of SNPs of PPARD with diabetes has been reported. The novel finding of our study was that SNPs of PPARD together with the Gly482Ser polymorphism of PGC-1A increased the risk of diabetes by 2.2-fold. Interestingly, acarbose prevented the progression from IGT to diabetes in subjects carrying one or both risk alleles of PPARD and PGC-1A (Table 2).
What could be the mechanisms via which acarbose modifies the effect of SNPs of PPARD and PGC-1A on the risk of diabetes Postprandial hyperglycemia and high FFAs contribute to the production of reactive oxygen species (16,21), which increase mitochondrial mass, mainly by overexpression of nuclear respiratory factors and PGC-1 (22). Both acarbose and PGC-1 have a protective effect on reactive oxygen species formation, either by alleviating postprandial hyperglycemia and high FFAs or by transcriptionally regulating the mitochondrial antioxidant defense system (23). Thus, acarbose may interact with SNPs of PPARD and PGC-1A and reduce oxidative stress and the development of diabetes.
Whether the Gly482Ser substitution of PGC-1A is a functional variant remains unclear (24). As determined with the Genomatix Program (availabe at http://www.genomatix.de), the binding sites for known transcription factors are not altered by this polymorphism. Similarly, SNPs of PPARD included in the present study did not cause alterations in transcription factor binding sites and did not affect RNA expression levels of PPAR- in adipose tissue samples. It is possible, however, that above-mentioned polymorphisms of PGC-1A and PPARD are in strong LD with functional variants in these genes or in other genes nearby.
The simultaneous presence of the C allele of rs6902123 of PPARD and the Pro12Pro genotype of PPARG2 increased the risk of diabetes by 3.9-fold in the present study, suggesting a gene-to-gene interaction. Both PPAR- and PPAR-2 are activated by PGC-1 (2,7) and seem to interfere with adipocyte differentiation (25). However, the mechanisms explaining the interaction of PPARD and PPARG2 on the risk of diabetes remain to be determined.
The effects of the PPARD, PGC-1A, and PPARG2 polymorphisms on the risk of diabetes in the present study might be overestimated because of multiple comparisons causing type I error. The threshold of statistical significance after the Bonferroni correction (one phenotype: conversion to diabetes and six polymorphisms analyzed) is P = 0.008. However, even after this adjustment, ORs for the conversion to diabetes were still mostly significant (P = 0.006eC0.024, Table 2; P = 0.001 for the risk alleles of PPARD and PPARG2).
In summary, we have shown for the first time that SNPs of PPARD together with the Gly482Ser polymorphism of PGC-1A and the Pro12Ala genotype of PPARG2 are associated with the risk of type 2 diabetes. Moreover, we have demonstrated that the treatment with acarbose was able to reduce the risk of diabetes in carriers of the risk genotypes in PPARD and PGC-1A.
ACKNOWLEDGMENTS
DNA analyses were funded by an unrestricted grant from Bayer to M.L.
We thank Leena Uschanoff and Kaija Eirola for their skillful technical assistance in genotyping the subjects. We are grateful to Teemu Kuulasmaa, MSc, for his help in statistical analyses of the data.
FOOTNOTES
A complete list of STOP-NIDDM trial members can be found in ref. 12.
J.-L.C. and M.L. have received honoraria/consulting fees and grant/research support from Bayer.
Additional information on this article can be found in an online appendix available at http://diabetes.diabetesjournals.org.
The costs of publication of this article were defrayed in part by the payment of page charges. This article must therefore be hereby marked "advertisement" in accordance with 18 U.S.C. Section 1734 solely to indicate this fact
REFERENCES
Grimaldi PA: Regulatory role of peroxisome proliferator-activated receptor delta (PPAR delta) in muscle metabolism: a new target for metabolic syndrome treatment Biochimie 87:5eC8, 2005
Wang YX, Lee CH, Tiep S, Yu RT, Ham J, Kang H, Evans RM: Peroxisome-proliferator-activated receptor delta activates fat metabolism to prevent obesity. Cell 113:159eC170, 2003
Tanaka T, Yamamoto J, Iwasaki S, Asaba H, Hamura H, Ikeda Y, Watanabe M, Magoori K, Ioka RX, Tachibana K, Watanabe Y, Uchiyama Y, Sumi K, Iguchi H, Ito S, Doi T, Hamakubo T, Naito M, Auwerx J, Yanagisawa M, Kodama T, Sakai J: Activation of peroxisome proliferator-activated receptor delta induces fatty acid beta-oxidation in skeletal muscle and attenuates metabolic syndrome. Proc Natl Acad Sci U S A 100:15924eC15929, 2003
Oliver WR Jr, Shenk JL, Snaith MR, Russell CS, Plunket KD, Bodkin NL, Lewis MC, Winegar DA, Sznaidman ML, Lambert MH, Xu HE, Sternbach DD, Kliewer SA, Hansen BC, Willson TM: A selective peroxisome proliferator-activated receptor delta agonist promotes reverse cholesterol transport. Proc Natl Acad Sci U S A 98:5306eC5311, 2001
Kramer DK, Al-Khalili L, Perrini S, Skogsberg J, Wretenberg P, Kannisto K, Wallberg-Henriksson H, Ehrenborg E, Zierath JR, Krook A: Direct activation of glucose transport in primary human myotubes after activation of peroxisome proliferatoreCactivated receptor . Diabetes 54:1157eC1163, 2005
Terada S, Wicke S, Holloszy JO, Han DH: The PPAR-delta activator GW505516 has no acute effect on glucose transport in skeletal muscle. Am J Physiol Endocrinol Metab 290:E607eCE611, 2006
Puigserver P, Spiegelman BM: Peroxisome proliferator-activated receptor-gamma coactivator 1 alpha (PGC-1 alpha): transcriptional coactivator and metabolic regulator. Endocr Rev 24:78eC90, 2003
Skogsberg J, Kannisto K, Cassel TN, Hamsten A, Eriksson P, Ehrenborg E: Evidence that peroxisome proliferator-activated receptor delta influences cholesterol metabolism in men. Arterioscler Thromb Vasc Biol 23:637eC643, 2003
Shin HD, Park BL, Kim LH, Jung HS, Cho YM, Moon MK, Park YJ, Lee HK, Park KS: Genetic polymorphisms in peroxisome proliferator-activated receptor associated with obesity. Diabetes 53:847eC851, 2004
Vanttinen M, Nuutila P, Kuulasmaa T, Pihlajamaki J, Hallsten K, Virtanen KA, Lautamaki R, Peltoniemi P, Takala T, Viljanen AP, Knuuti J, Laakso M: Single nucleotide polymorphisms in the peroxisome proliferatoreCactivated receptor gene are associated with skeletal muscle glucose uptake. Diabetes 54:3587eC3591, 2005
Chiasson JL, Gomis R, Hanefeld M, Josse RG, Karasik A, Laakso M: The STOP-NIDDM trial: an international study on the efficacy of an -glucosidase inhibitor to prevent type 2 diabetes in a population with impaired glucose tolerance: rationale, design, and preliminary screening data. Diabetes Care 21:1720eC1725, 1998
Chiasson JL, Josse RG, Gomis R, Hanefeld M, Karasik A, Laakso M: Acarbose for prevention of type 2 diabetes mellitus: the STOP-NIDDM randomised trial. Lancet 359:2072eC2077, 2002
Altshuler D, Brooks LD, Chakravarti A, Collins FS, Daly MJ, Donnelly P: A haplotype map of the human genome. Nature 437:1299eC1320, 2005
Barrett JC, Fry B, Maller J, Daly MJ: Haploview: analysis and visualization of LD and haplotype maps. Bioinformatics 21:263eC265, 2005
Andrulionyte L, Zacharova J, Chiasson JL, Laakso M: Common polymorphisms of the PPAR-gamma2 (Pro12Ala) and PGC-1alpha (Gly482Ser) genes are associated with the conversion from impaired glucose tolerance to type 2 diabetes in the STOP-NIDDM trial. Diabetologia 47:2176eC2184, 2004
Boden G, Laakso M: Lipids and glucose in type 2 diabetes: what is the cause and effect Diabetes Care 27:2253eC2259, 2004
Lin J, Wu H, Tarr PT, Zhang CY, Wu Z, Boss O, Michael LF, Puigserver P, Isotani E, Olson EN, Lowell BB, Bassel-Duby R, Spiegelman BM: Transcriptional co-activator PGC-1 alpha drives the formation of slow-twitch muscle fibres. Nature 418:797eC801, 2002
Holst D, Luquet S, Nogueira V, Kristiansen K, Leverve X, Grimaldi PA: Nutritional regulation and role of peroxisome proliferator-activated receptor delta in fatty acid catabolism in skeletal muscle. Biochim Biophys Acta 4:43eC50, 2003
Luquet S, Lopez-Soriano J, Holst D, Fredenrich A, Melki J, Rassoulzadegan M, Grimaldi PA: Peroxisome proliferator-activated receptor delta controls muscle development and oxidative capability. FASEB J 17:2299eC2301, 2003
Ek J, Andersen G, Urhammer SA, Gaede PH, Drivsholm T, Borch-Johnsen K, Hansen T, Pedersen O: Mutation analysis of peroxisome proliferator-activated receptor-gamma coactivator-1 (PGC-1) and relationships of identified amino acid polymorphisms to type II diabetes mellitus. Diabetologia 44:2220eC2226, 2001
Gavin JR 3rd: Pathophysiologic mechanisms of postprandial hyperglycemia. Am J Cardiol 88:4HeC8H, 2001
Lee HC, Yin PH, Chi CW, Wei YH: Increase in mitochondrial mass in human fibroblasts under oxidative stress and during replicative cell senescence. J Biomed Sci 9:517eC526, 2002
Valle I, Alvarez-Barrientos A, Arza E, Lamas S, Monsalve M: PGC-1alpha regulates the mitochondrial antioxidant defense system in vascular endothelial cells. Cardiovasc Res 66:562eC573, 2005
Esterbauer H, Oberkofler H, Linnemayr V, Iglseder B, Hedegger M, Wolfsgruber P, Paulweber B, Fastner G, Krempler F, Patsch W: Peroxisome proliferatoreCactivated receptor- coactivator-1 gene locus: associations with obesity indices in middle-aged women. Diabetes 51:1281eC1286, 2002
Matsusue K, Peters JM, Gonzalez FJ: PPARbeta/delta potentiates PPARgamma-stimulated adipocyte differentiation. FASEB J 18:1477eC1479, 2004(Laura Andrulionyt, Paula )
2 Research Centre, Centre Hospitalier de l’Universitee de Montreeal, Htel-Dieu, Department of Medicine, University of Montreal, Quebec, Canada
FFA, free fatty acid; IGT, impaired glucose tolerance; LD, linkage disequilibrium; PGC-1, PPAR- coactivator 1; PPAR, peroxisome proliferatoreCactivated receptor; SNP, single nucleotide polymorphism
ABSTRACT
Peroxisome proliferatoreCactivated receptor (PPAR)- regulates fatty acid oxidation and improves insulin sensitivity. We screened six single nucleotide polymorphisms (SNPs) of the PPAR- gene (PPARD) for an association with the conversion from impaired glucose tolerance (IGT) to type 2 diabetes in 769 subjects participating in the STOP-NIDDM trial. A 2.7-fold increase in the risk of diabetes was observed in female carriers of the C allele of rs6902123 (95% CI 1.44eC5.30; adjusted P = 0.002). In the placebo group, subjects possessing both the 482Ser allele of the PPAR- coactivator-1 gene (PGC-1A) and the rare allele of two SNPs of PPARD (rs6902123 and rs3734254) had up to 2.5-fold increased risk for diabetes. Furthermore, women carrying the C allele of rs6902123 of PPARD and the Pro12Pro genotype of the PPAR-2 gene (PPARG2) had a 3.9-fold (95% CI 1.79eC8.63; P = 0.001)-higher risk for diabetes than women with protective genotypes. Expression levels of PPAR- in subcutaneous adipose tissue of 87 offspring of Finnish patients with type 2 diabetes did not differ among the genotype groups of SNPs of PPARD. We conclude that SNPs in PPARD modify the conversion from IGT to type 2 diabetes, particularly in combination with the SNPs of PGC-1A and PPARG2.
Peroxisome proliferatoreCactivated receptor (PPAR)- is a powerful regulator of lipid and glucose metabolism and energy homeostasis (1). Overexpression of PPAR- in adipose tissue leads to enhanced free fatty acid (FFA) oxidation, improved lipid profile, and protects from both genetically predisposed and high-fat dieteCinduced obesity (2). In skeletal muscle, the activation of PPAR- also induces fatty acid -oxidation and energy expenditure (3). Treatment with synthetic PPAR- activators attenuates plasma glucose and insulin levels in obese mice (3), reduces fasting insulin in insulin-resistant monkeys (4), and can enhance insulin-stimulated glucose uptake in cultured human myotubes (5) but not in skeletal muscle (6).
The physiological function of PPAR- appears to be quite similar to that of PPAR- coactivator 1 (PGC-1) (7). Indeed, many of the effects of PGC-1 on energy homeostasis may be mediated through PPAR-, as PGC-1 and PPAR- have a strong interaction (2). Moreover, PPAR- stimulates PGC-1 mRNA expression both in vitro and in vivo (3).
Cross-sectional studies have reported an association of single nucleotide polymorphisms (SNPs) of the PPAR- gene (PPARD) with lipid and glucose metabolism (8,9). Recently, we demonstrated that SNPs in PPARD were associated with skeletal muscle glucose uptake (10). One previous cross-sectional study (9) did not find association with diabetes. In the present study, we investigated the effect of the SNPs of PPARD and their interaction with common polymorphisms of the PGC-1 (PGC-1A) and PPAR-2 (PPARG2) genes on the risk of type 2 diabetes in the STOP-NIDDM trial.
RESEARCH DESIGN AND METHODS
The longitudinal, multicenter, double-blind, placebo-controlled STOP-NIDDM trial (mean follow-up 3.3 years) was carried out to evaluate the effect of acarbose on the conversion to type 2 diabetes in 1,429 subjects with impaired glucose tolerance (IGT) (11,12). DNA was available from 769 subjects. The informed consent form, approved by appropriate institutional review board, was signed by all participants of the study.
DNA analyses.
Selection of the SNPs was based on the data available from HapMap project’s website (13). Six SNPs were chosen from different haplotype blocks to cover the genomic region of PPARD. SNPs were genotyped using the TaqMan Allelic Discrimination Assays (Applied Biosystems, Foster City, CA) as previously described (10). Sequence information for oligonucleotide primers and probes is available from the authors.
Quantitative PCR.
Total RNA from subcutaneous adipose tissue of 87 offspring of Finnish patients with type 2 diabetes was isolated using Trizol reagent (Invitrogen) and Qiagen RNeasy Mini kit (Qiagen, Hilden, Germany). Total RNA was DNase treated using a DNA-free kit (Ambion) and transcribed to cDNA using random primers and a high-capacity cDNA archive kit (Applied Biosystems). Quantitative RT-PCRs were performed in a 7500 Real-Time PCR System (Applied Biosystems) using 6 ng (RNA equivalents) of cDNA as template, gene-specific primers, and probes (sequence information is available upon request). PPAR- (Hs00602622_m1) expressions were normalized to large ribosomal protein P0 (RPLP0; Hs99999902_m1) using standard curve method.
Statistical analysis.
We used the SPSS program (version 11.0 for Windows; SPSS, Chicago, IL) in data analysis and gave the results as either means ± SD or percentages. NoneCnormally distributed parameters were logarithmically transformed. Two-tailed Student’s t test, nonparametric, 2, and Fisher’s exact tests were used to compare variables between the groups, when appropriate. Logistic regression analysis was used to evaluate the effect of SNPs on the conversion to diabetes. Linkage disequilibrium (LD) statistics were calculated and haplotype blocks were visualized by using Haploview software (14) (available at http://www.broad.mit.edu/mpg/haploview/). Haplotype estimation from unrelated individuals was performed by using SNPHAP (available at http://www-gene.cimr.cam.ac.uk/clayton/software/).
RESULTS
The LD statistics (D', r2) and the minor allele frequencies of the SNPs of PPARD are shown in Fig. 1. As the number of homozygous subjects for the rare allele of each SNP was small, they were combined with heterozygous subjects in all statistical analyses. Frequencies of genotypes of four SNPs of PPARD followed the Hardy-Weinberg expectations. However, genotype distributions of rs2076167 and rs1053049 deviated from the Hardy-Weinberg equilibrium (P < 0.025 and <0.015, respectively). No genotyping errors were found. Despite that, SNPs rs2076167 and rs1053049 (both in tight LD with rs3734254; inter-SNP D' > 0.98, r2 > 0.8; Fig. 1) were excluded from further statistical analyses.
The association of four SNPs of PPARD with the conversion to type 2 diabetes is presented in Table 1. Because rs6902123 had an interaction with sex in its effect on the conversion to diabetes (logistic regression analysis, P = 0.008), we show the results separately for men and women. The C allele of rs6902123 was more frequent among women who converted to diabetes compared with women who did not. In logistic regression analysis, the presence of the C allele in women increased the risk for the conversion to diabetes by 2.47-fold (95% CI 1.32eC4.63; P = 0.005), which remained statistically significant even after the adjustment for age, treatment group, smoking, weight at baseline, and weight change (odds ratio [OR] 2.76 [95% CI 1.44eC5.30]; P = 0.002). In haplotype analysis based on different combinations of the four SNPs, none of the haplotypes increased the risk for the conversion to type 2 diabetes beyond that of individual SNPs (data not shown). The expression levels of PPAR- in subcutaneous adipose tissue of 87 offspring of Finnish patients with type 2 diabetes did not differ between the genotype groups of SNPs of PPARD (online appendix Table 1 [available at http://diabetes.diabetesjournals.org]).
Next, we investigated whether the SNPs of PPARD further increased the risk for the conversion to diabetes in carriers of the Gly482Ser (rs8192687) substitution of PGC-1A, which was previously shown to increase the risk of diabetes in participants of the STOP-NIDDM trial (15). To this aim, study participants were classified into three groups: subjects having both the nonrisk genotype of PPARD and the nonrisk genotype of PGC-1A (Gly482Gly) (the reference group), subjects with one risk allele of either PPARD or PGC-1A, and subjects possessing both the risk allele of PPARD and the 482Ser risk allele of PGC-1A. Subjects of the placebo group possessing both the 482Ser allele of PGC-1A and the C allele of two PPARD SNPs (rs6902123 and rs3734254) had a 2.56- and 2.15-fold higher risk for the conversion to type 2 diabetes compared with those having neither risk genotypes (Table 2). ORs varied from 2.30 to 2.92 after the adjustment for confounding factors. The presence of one risk allele (that of either PGC-1A or PPARD; Table 2) in subjects of the placebo group resulted in a 1.5 increase in the risk of diabetes, a finding similar to that observed earlier for the 482Ser allele of PGC-1A alone (15). The simultaneous presence of the PPARD risk allele and the 482Ser allele of PGC-1A increased the risk up to 2.2-fold, suggesting an additive effect. Furthermore, carriers of one or both risk alleles of PPARD and PGC-1A benefited more from the intervention (acarbose) than did noncarriers.
In women, the simultaneous presence of the two risk genotypes (the Pro12Pro genotype of PPARG2 [rs1801282] and the C allele of rs6902123 of PPARD) increased the risk for the conversion from IGT to diabetes by 3.93-fold (95% CI 1.79eC8.63; P = 0.001) compared with women having the protective genotypes (the 12Ala allele of PPARG2 and the TT genotype of rs6902123 of PPARD) (P = 0.006 for the interaction between the combined genotypes and sex). Adjustment for confounding factors increased the risk to 5.06-fold (2.20eC11.60; P < 0.001). Other SNPs of PPARD, together with the Pro12Pro genotype of PPARG2, had no additive effect on the conversion to type 2 diabetes.
DISCUSSION
This is the first study demonstrating that an SNP rs6902123 of PPARD alone and in combination with the Pro12Ala polymorphism of PPARG2 and two SNPs (rs6902123 and rs3734254) of PPARD in combination with the Gly482Ser substitution of PGC-1A predicted the conversion from IGT to type 2 diabetes.
PPAR- plays a prominent role in energy homeostasis. Overexpression of PPAR- in skeletal muscle (3) and adipocytes (2) markedly increase fat catabolism in both tissues. Given the important role of FFAs in the development of type 2 diabetes (16), the upregulation of the oxidative phosphorylation pathway by PPAR- is beneficial in alleviation of insulin resistance and adiposity (1). PPAR- agonists increase glucose transport (5) and consequently decrease insulin and glucose levels (2,3). Therefore, variants in PPARD could potentially modify glucose metabolism. Indeed, we recently reported that SNPs of PPARD significantly modulated glucose uptake in skeletal muscle, measured by positron emission tomography (10). In the present study, the C allele of rs6902123 increased the risk of diabetes by 2.5-fold. The association was observed only in women but not in men. This sex difference remains unexplained.
SNPs of PPARD also increased the effect of the Gly482Ser substitution of PGC-1A on the risk of type 2 diabetes. This is not surprising since PPAR- appears to have a strong interaction with PGC-1 (2,7). Similarly to PGC-1 (7,17), PPAR- is also induced by fasting (18) and physical exercise (19), and it promotes the formation of highly oxidative skeletal muscle fibers (19). Moreover, PPAR- itself can increase expression of PGC-1 and lead to mitochondrial biogenesis and enhanced oxidative phosphorylation (3). The Gly482Ser substitution of PGC-1A has been linked to increased risk of diabetes (15,20), but no association of SNPs of PPARD with diabetes has been reported. The novel finding of our study was that SNPs of PPARD together with the Gly482Ser polymorphism of PGC-1A increased the risk of diabetes by 2.2-fold. Interestingly, acarbose prevented the progression from IGT to diabetes in subjects carrying one or both risk alleles of PPARD and PGC-1A (Table 2).
What could be the mechanisms via which acarbose modifies the effect of SNPs of PPARD and PGC-1A on the risk of diabetes Postprandial hyperglycemia and high FFAs contribute to the production of reactive oxygen species (16,21), which increase mitochondrial mass, mainly by overexpression of nuclear respiratory factors and PGC-1 (22). Both acarbose and PGC-1 have a protective effect on reactive oxygen species formation, either by alleviating postprandial hyperglycemia and high FFAs or by transcriptionally regulating the mitochondrial antioxidant defense system (23). Thus, acarbose may interact with SNPs of PPARD and PGC-1A and reduce oxidative stress and the development of diabetes.
Whether the Gly482Ser substitution of PGC-1A is a functional variant remains unclear (24). As determined with the Genomatix Program (availabe at http://www.genomatix.de), the binding sites for known transcription factors are not altered by this polymorphism. Similarly, SNPs of PPARD included in the present study did not cause alterations in transcription factor binding sites and did not affect RNA expression levels of PPAR- in adipose tissue samples. It is possible, however, that above-mentioned polymorphisms of PGC-1A and PPARD are in strong LD with functional variants in these genes or in other genes nearby.
The simultaneous presence of the C allele of rs6902123 of PPARD and the Pro12Pro genotype of PPARG2 increased the risk of diabetes by 3.9-fold in the present study, suggesting a gene-to-gene interaction. Both PPAR- and PPAR-2 are activated by PGC-1 (2,7) and seem to interfere with adipocyte differentiation (25). However, the mechanisms explaining the interaction of PPARD and PPARG2 on the risk of diabetes remain to be determined.
The effects of the PPARD, PGC-1A, and PPARG2 polymorphisms on the risk of diabetes in the present study might be overestimated because of multiple comparisons causing type I error. The threshold of statistical significance after the Bonferroni correction (one phenotype: conversion to diabetes and six polymorphisms analyzed) is P = 0.008. However, even after this adjustment, ORs for the conversion to diabetes were still mostly significant (P = 0.006eC0.024, Table 2; P = 0.001 for the risk alleles of PPARD and PPARG2).
In summary, we have shown for the first time that SNPs of PPARD together with the Gly482Ser polymorphism of PGC-1A and the Pro12Ala genotype of PPARG2 are associated with the risk of type 2 diabetes. Moreover, we have demonstrated that the treatment with acarbose was able to reduce the risk of diabetes in carriers of the risk genotypes in PPARD and PGC-1A.
ACKNOWLEDGMENTS
DNA analyses were funded by an unrestricted grant from Bayer to M.L.
We thank Leena Uschanoff and Kaija Eirola for their skillful technical assistance in genotyping the subjects. We are grateful to Teemu Kuulasmaa, MSc, for his help in statistical analyses of the data.
FOOTNOTES
A complete list of STOP-NIDDM trial members can be found in ref. 12.
J.-L.C. and M.L. have received honoraria/consulting fees and grant/research support from Bayer.
Additional information on this article can be found in an online appendix available at http://diabetes.diabetesjournals.org.
The costs of publication of this article were defrayed in part by the payment of page charges. This article must therefore be hereby marked "advertisement" in accordance with 18 U.S.C. Section 1734 solely to indicate this fact
REFERENCES
Grimaldi PA: Regulatory role of peroxisome proliferator-activated receptor delta (PPAR delta) in muscle metabolism: a new target for metabolic syndrome treatment Biochimie 87:5eC8, 2005
Wang YX, Lee CH, Tiep S, Yu RT, Ham J, Kang H, Evans RM: Peroxisome-proliferator-activated receptor delta activates fat metabolism to prevent obesity. Cell 113:159eC170, 2003
Tanaka T, Yamamoto J, Iwasaki S, Asaba H, Hamura H, Ikeda Y, Watanabe M, Magoori K, Ioka RX, Tachibana K, Watanabe Y, Uchiyama Y, Sumi K, Iguchi H, Ito S, Doi T, Hamakubo T, Naito M, Auwerx J, Yanagisawa M, Kodama T, Sakai J: Activation of peroxisome proliferator-activated receptor delta induces fatty acid beta-oxidation in skeletal muscle and attenuates metabolic syndrome. Proc Natl Acad Sci U S A 100:15924eC15929, 2003
Oliver WR Jr, Shenk JL, Snaith MR, Russell CS, Plunket KD, Bodkin NL, Lewis MC, Winegar DA, Sznaidman ML, Lambert MH, Xu HE, Sternbach DD, Kliewer SA, Hansen BC, Willson TM: A selective peroxisome proliferator-activated receptor delta agonist promotes reverse cholesterol transport. Proc Natl Acad Sci U S A 98:5306eC5311, 2001
Kramer DK, Al-Khalili L, Perrini S, Skogsberg J, Wretenberg P, Kannisto K, Wallberg-Henriksson H, Ehrenborg E, Zierath JR, Krook A: Direct activation of glucose transport in primary human myotubes after activation of peroxisome proliferatoreCactivated receptor . Diabetes 54:1157eC1163, 2005
Terada S, Wicke S, Holloszy JO, Han DH: The PPAR-delta activator GW505516 has no acute effect on glucose transport in skeletal muscle. Am J Physiol Endocrinol Metab 290:E607eCE611, 2006
Puigserver P, Spiegelman BM: Peroxisome proliferator-activated receptor-gamma coactivator 1 alpha (PGC-1 alpha): transcriptional coactivator and metabolic regulator. Endocr Rev 24:78eC90, 2003
Skogsberg J, Kannisto K, Cassel TN, Hamsten A, Eriksson P, Ehrenborg E: Evidence that peroxisome proliferator-activated receptor delta influences cholesterol metabolism in men. Arterioscler Thromb Vasc Biol 23:637eC643, 2003
Shin HD, Park BL, Kim LH, Jung HS, Cho YM, Moon MK, Park YJ, Lee HK, Park KS: Genetic polymorphisms in peroxisome proliferator-activated receptor associated with obesity. Diabetes 53:847eC851, 2004
Vanttinen M, Nuutila P, Kuulasmaa T, Pihlajamaki J, Hallsten K, Virtanen KA, Lautamaki R, Peltoniemi P, Takala T, Viljanen AP, Knuuti J, Laakso M: Single nucleotide polymorphisms in the peroxisome proliferatoreCactivated receptor gene are associated with skeletal muscle glucose uptake. Diabetes 54:3587eC3591, 2005
Chiasson JL, Gomis R, Hanefeld M, Josse RG, Karasik A, Laakso M: The STOP-NIDDM trial: an international study on the efficacy of an -glucosidase inhibitor to prevent type 2 diabetes in a population with impaired glucose tolerance: rationale, design, and preliminary screening data. Diabetes Care 21:1720eC1725, 1998
Chiasson JL, Josse RG, Gomis R, Hanefeld M, Karasik A, Laakso M: Acarbose for prevention of type 2 diabetes mellitus: the STOP-NIDDM randomised trial. Lancet 359:2072eC2077, 2002
Altshuler D, Brooks LD, Chakravarti A, Collins FS, Daly MJ, Donnelly P: A haplotype map of the human genome. Nature 437:1299eC1320, 2005
Barrett JC, Fry B, Maller J, Daly MJ: Haploview: analysis and visualization of LD and haplotype maps. Bioinformatics 21:263eC265, 2005
Andrulionyte L, Zacharova J, Chiasson JL, Laakso M: Common polymorphisms of the PPAR-gamma2 (Pro12Ala) and PGC-1alpha (Gly482Ser) genes are associated with the conversion from impaired glucose tolerance to type 2 diabetes in the STOP-NIDDM trial. Diabetologia 47:2176eC2184, 2004
Boden G, Laakso M: Lipids and glucose in type 2 diabetes: what is the cause and effect Diabetes Care 27:2253eC2259, 2004
Lin J, Wu H, Tarr PT, Zhang CY, Wu Z, Boss O, Michael LF, Puigserver P, Isotani E, Olson EN, Lowell BB, Bassel-Duby R, Spiegelman BM: Transcriptional co-activator PGC-1 alpha drives the formation of slow-twitch muscle fibres. Nature 418:797eC801, 2002
Holst D, Luquet S, Nogueira V, Kristiansen K, Leverve X, Grimaldi PA: Nutritional regulation and role of peroxisome proliferator-activated receptor delta in fatty acid catabolism in skeletal muscle. Biochim Biophys Acta 4:43eC50, 2003
Luquet S, Lopez-Soriano J, Holst D, Fredenrich A, Melki J, Rassoulzadegan M, Grimaldi PA: Peroxisome proliferator-activated receptor delta controls muscle development and oxidative capability. FASEB J 17:2299eC2301, 2003
Ek J, Andersen G, Urhammer SA, Gaede PH, Drivsholm T, Borch-Johnsen K, Hansen T, Pedersen O: Mutation analysis of peroxisome proliferator-activated receptor-gamma coactivator-1 (PGC-1) and relationships of identified amino acid polymorphisms to type II diabetes mellitus. Diabetologia 44:2220eC2226, 2001
Gavin JR 3rd: Pathophysiologic mechanisms of postprandial hyperglycemia. Am J Cardiol 88:4HeC8H, 2001
Lee HC, Yin PH, Chi CW, Wei YH: Increase in mitochondrial mass in human fibroblasts under oxidative stress and during replicative cell senescence. J Biomed Sci 9:517eC526, 2002
Valle I, Alvarez-Barrientos A, Arza E, Lamas S, Monsalve M: PGC-1alpha regulates the mitochondrial antioxidant defense system in vascular endothelial cells. Cardiovasc Res 66:562eC573, 2005
Esterbauer H, Oberkofler H, Linnemayr V, Iglseder B, Hedegger M, Wolfsgruber P, Paulweber B, Fastner G, Krempler F, Patsch W: Peroxisome proliferatoreCactivated receptor- coactivator-1 gene locus: associations with obesity indices in middle-aged women. Diabetes 51:1281eC1286, 2002
Matsusue K, Peters JM, Gonzalez FJ: PPARbeta/delta potentiates PPARgamma-stimulated adipocyte differentiation. FASEB J 18:1477eC1479, 2004(Laura Andrulionyt, Paula )