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Complex Haplotypes of the PGC-1 Gene Are Associated With Carbohydrate Metabolism and Type 2 Diabetes

¹ Department of Laboratory Medicine, Landeskliniken and Paracelsus Private Medical University, Salzburg, Austria
² Department of Internal Medicine, Landeskliniken and Paracelsus Private Medical University, Salzburg, Austria
³ Department of Neurology, Landeskliniken and Paracelsus Private Medical University, Salzburg, Austria
⁴ Department of Internal Medicine, Hospital Hallein, Hallein, Austria

	ABSTRACT

TOP ABSTRACT RESEARCH DESIGN AND METHODS RESULTS DISCUSSION REFERENCES

 
Peroxisome proliferator–activated receptor coactivator-1 (PGC-1) is a transcriptional coactivator implicated in transcriptional programs of hepatic gluconeogenesis, oxidative phosphorylation, and insulin release by ß-cells. To study associations of the PGC-1 gene locus with carbohydrate metabolism and type 2 diabetes in humans, we identified several polymorphisms in the promoter region that were located in a haplotype block distinct from a second haplotype block containing part of intron 2 and extending beyond exon 13. Each block contained five common haplotypes. Oral glucose tolerance testing revealed associations of promoter haplotype combinations with 30- and 60-min postload plasma glucose levels, whereas haplotypes in both blocks were associated with indexes of ß-cell function. The associations of promoter haplotypes are supported by functional studies showing that some polymorphisms are located in transcription factor binding sites and affect transactivation in an allele-specific manner. By comparing patients with type 2 diabetes and control subjects, we observed borderline significant differences of four-loci haplotype distributions in the downstream haplotype block. Moreover, the haplotype that was associated with the strongest insulin response to glucose conferred the lowest risk of type 2 diabetes (P < 0.01). Thus, the PGC-1 gene locus influences carbohydrate metabolism and contributes to type 2 diabetes in the population studied.

Studies in humans also support a role of PGC-1 in the pathogenesis of type 2 diabetes. Expression of PGC-1 and its target genes involved in oxidative phosphorylation were downregulated in muscle tissue of type 2 diabetic patients in comparison to control subjects (10). Moreover, skeletal muscle expression of PGC-1 and PGC-1ß, another PGC-1 family member (11), was reduced in both diabetic subjects and family history–positive nondiabetic subjects compared with control subjects (12). Hence, genetic influences that affect PGC-1 and -1ß expression in muscle may contribute to the disorder. Indeed, single nucleotide polymorphisms (SNPs) in the transcribed sequence of PGC-1 have been associated with type 2 diabetes in Danish, British, and Japanese but not in French populations (13–16). To gain further insight into the role of PGC-1 in humans, we identified and characterized sequence substitutions across its gene locus, determined their haplotype arrangement, and studied their association with carbohydrate metabolism and type 2 diabetes.

	RESEARCH DESIGN AND METHODS

TOP ABSTRACT RESEARCH DESIGN AND METHODS RESULTS DISCUSSION REFERENCES

 
For haplotype characterization, we studied 40- to 65-year-old male and 45- to 70-year-old female participants of the Salzburg Atherosclerosis Prevention Program in Subjects at High Individual Risk (SAPHIR). The overall study aim and recruitment procedures have been described (17). Associations of genotypes, haplotypes, and haplotype combinations with estimates of glucose metabolism were determined in a subset of 405 SAPHIR participants, who had fasting plasma glucose levels <110 mg/dl and underwent an oral glucose tolerance test (OGTT). After a 12-h overnight fast, subjects ingested a solution containing 75 g glucose, and plasma samples were obtained at 0, 30, 60, and 120 min for determination of glucose and insulin. Insulin sensitivity/resistance was estimated using the homeostasis model assessment (HOMA) insulin resistance index (HOMA_IR) (18), the composite insulin sensitivity index [S_i(comp)] (19), and the metabolic clearance rate and S_i proposed by Stumvoll et al. (20). For metabolic clearance rate and S_i calculations, average values of 60- and 120-min rather than 90-min glucose values were used. ß-Cell function was assessed by the HOMA-based insulin release index (HOMA_Secr), first-phase and second-phase insulin release (20), the CIR₃₀ (corrected insulin response at 30 min), the 30- to 0-min insulin ratio (I₃₀/I₀), the 30-min insulin-to-glucose ratio (I₃₀/G₃₀), and the insulinogenic index estimated as I₃₀ – I₀/G₃₀ – G₀ (I₃₀/G₃₀) (21). A total of 506 unrelated patients with type 2 diabetes were recruited from diabetes outpatient clinics of the Landeskliniken Salzburg and the Hospital Hallein. Patients who were <73 years of age and <63 years of age at the time of diabetes diagnosis were included. SAPHIR participants who were not using hypoglycemic medications and had fasting blood glucose levels <110 mg/dl served as control subjects. Study populations comprised only white Europeans, mainly of Bavarian or Austrian-German descent living in the same geographic region. Participants provided informed consent, and the study was approved by the local ethics committee.

Laboratory analyses.
Plasma glucose, insulin, cholesterol, triglyceride, HDL cholesterol, and HbA_1c levels were measured as described (22). Genomic DNA was extracted from peripheral white blood cells.

Identification of SNPs, genotyping, and haplotyping procedures.
SNPs in the promoter region of the human PGC-1 gene were identified by amplification of a 5,551-bp fragment from genomic DNA of six human subjects using 5'-CGCGGTACCCAATCCTAGAAGCGGTCGTCTTGG-3' and 5'-CGCACGCGTTCCAGCTCCTGAATGACGCCAG-3' as forward and reverse primers, respectively (NCBI genome annotation no. NT_006316). Restriction enzyme sites for MluI and KpnI, respectively, are in bold. The resulting PCR products were ligated into the pGL3 basic vector (Promega, Madison, WI). Individual clones and PCR products were sequenced from both directions by dye-terminator sequencing using an ABI Prism 310 genetic analyzer (Applied Biosystems, Foster City, CA). To delineate the phase of polymorphisms in 17 additional subjects, regions harboring SNPs were sequenced in PCR products and in one clone representing the sequence from one chromosome. Sequence differences between PCR products and clones thus provided the sequence of the second chromosome.

Four SNPs that unambiguously predicted phased haplotypes were typed in our populations. Typing procedures included restriction fragment–length polymorphism (RFLP) detection with or without artificially introduced restriction sites, using mismatch primers as well as real-time PCR. For the latter method, allele-specific hybridization probes conjugated with a fluorophore at the 5'-end were used with minor groove binder ligand and quencher moiety at the 3'-end (Applied Biosystems). For quality control, 30% of real-time PCR-based typings for SNP –1422 T/C were confirmed using an RFLP assay. Assay details are shown in Table 1 in the online appendix (available at http://diabetes.diabetesjournals.org).

View this table: [in this window] [in a new window]   TABLE 1 Standardized pairwise linkage disequlibrium coefficients (D') of polymorphic sites at the PGC-1 gene locus

To identify haplotype boundaries, regions in intron 2 as well as upstream and downstream regions of PGC-1 that contained several SNPs (www.ncbi.nlm.nih.gov/SNP/) were sequenced in eight subjects to identify polymorphisms with high information content. Four SNPs at –22174, +16022, +55180, and +114481 relative to the translational start site were typed in >84 randomly selected SAPHIR participants.

Plasmid constructs.
PGC-1 promoter reporter plasmids encompassing SNPs at positions –1984, –1774, –1679 and –1422 were constructed by amplification of a 732-bp fragment from genomic DNA of patients with known haplotypes. The reporter constructs –3959 TK-Luc, –3818 TK-Luc, –3614 TK-Luc, and –1422 TK-Luc were generated by amplifying 250- to 350-bp fragments encompassing the respective sites using genomic DNA from heterozygous subjects. Primers, PCR product fragment lengths, and restriction enzymes used for cloning are listed in Table 2 in the online appendix. Fragments were cloned into a reporter vector harboring the firefly luciferase gene under the control of the TK promoter (25,26).

View this table: [in this window] [in a new window]   TABLE 2 PGC-1 haplotype scores for indexes of insulin secretion

Cell culture and transfection.
INS-1E cells, kindly provided by Claes Wollheim (Geneva, Switzerland); PAZ6 cells; and HepG2 cells were cultured as described (17,22). Near-confluent cells were transfected with constant amounts of reporter constructs and 500 ng/well of HNF-1 or MEF2C expression plasmids or their empty pcDNA6/V5-His plasmid backbone using Lipofectamine 2000 reagent. pRL-CMV (1 ng/well; Promega) was cotransfected as transfection control. Transfection medium was replaced with fresh culture medium 12 h later. Cells were harvested 24 h after transfection, and firefly and Renilla luciferase activities were measured in cell extracts using a Lucy2 luminometer (Rosys Anthos, Salzburg, Austria) and the Dual-Luciferase reporter assay system (Promega).

Computational analyses and statistics.
For prediction of putative transcription factor binding sites, we used MatInspector professional software (www.genomatix.de), which locates individual matches by comparing the sequences with weight matrix descriptions of binding sites (27). The matrix library is based on Transfac release 3.3 (28). We considered matrix and core similarities >80 and >85%, respectively.

Group differences of continuous variables were ascertained by ANOVA. We made logarithmic transformations if the equal variance and normality assumptions of ANOVA were rejected. Measurements were adjusted for the effects of age and BMI as indicated. Two-way ANOVA was used to compare reporter-gene activities from variant and wild-type alleles as well as transactivation from PGC-1 promoter alleles by MEF2C and HNF-1. For post hoc comparisons of means, Tukey’s honest significant difference test was used.

Allele frequencies were estimated by gene counting. Agreement with Hardy-Weinberg expectations was tested using a ² goodness-of-fit test. The standardized pairwise linkage disequilibrium statistics (D') were estimated (24). For effects of individual SNPs on OGGT parameters, we used ANOVA with adjustments for age, sex, and BMI. In addition, multivariate regression models with glucose and insulin levels or derived indexes as outcome variables and genotypes, age, sex, and BMI as independent variables were used. Genotypes were given codes of 0, 1, and 2; 0, 1, and 1; or 0, 0, and 1 in the additive, dominant, or recessive models, respectively. Differences in genotype or allele frequencies between patients with type 2 diabetes and control subjects were determined using a ² distribution with 2 or 1 degrees of freedom. Differences in four-loci haplotype distributions between patients and control subjects were ascertained by the log-likelihood ratio test with 15 degrees of freedom (29). For testing of associations between haplotypes and quantitative or binary traits, we used haplo.score software (http://www.mayo.edu/statgen/). This method is based on efficient score statistics, which provides both global and haplotype-specific tests and allows the exclusion of rare haplotypes as well as adjustments for nongenetic covariates (30).

	RESULTS

TOP ABSTRACT RESEARCH DESIGN AND METHODS RESULTS DISCUSSION REFERENCES

 
We identified eight polymorphic sites between –1422 and –3959 (Table 3 in the online appendix and Fig. 1) by sequencing 12 promoter alleles up to –5551 relative to the translational start site. Of the 2⁸ (= 256) possible combinations of these SNPs, only five haplotypes were observed in the 46 alleles in which phased genotypes were determined (Fig. 1B). Thus, SNPs at –3959, –3690, –1984, and –1774 as well as SNPs at –3614 and –1679 showed complete linkage disequilibrium. Four SNPs (at –3959, –3818, –1679, and –1422) that permitted unambiguous assignments of phased haplotypes were typed in 1,608 SAPHIR participants. All polymorphisms fulfilled Hardy-Weinberg expectations. Variant allele frequencies of promoter polymorphisms are shown in Table 3 in the online appendix. Standardized pairwise linkage disequilibrium statistics (D') were highly significant for the promoter polymorphisms (Table 1), and the five haplotypes delineated by phased genotyping accounted for 97% of all haplotypes defined by the respective sites (Fig. 1B).

View this table: [in this window] [in a new window]   TABLE 3 Associations of PGC-1 haplotype blocks with type 2 diabetes

View larger version (25K): [in this window] [in a new window]   FIG. 1. Polymorphic sites and haplotype blocks in PGC-1. A: Linear map with arrows indicating translational initiation (ATG) and termination (TAA) sites as well as polyadenylation signals (AATAAA). , exons; sqsw;, the 3'-untranslated region. Scales differ between the promoter and the transcribed regions. SNP identifiers are relative to the translational start site in the genomic sequence. SNPs at +75656, +75918, +76058, and +94580 correspond to positions +1302, +1564, +1704, and +2962 relative to the translational start site in the mRNA sequence. Haplotype blocks 1 and 2 with their boundaries are shown beneath the SNPs. B: Structures and frequencies of the five common haplotypes in haplotype block 1. C: Structures and frequencies of the five common haplotypes in haplotype block 2. SNPs typed in 1,608 subjects and used for calculations of frequency distributions are in bold.

Among the promoter polymorphisms typed, two sites displayed significant associations with oral glucose tolerance test parameters in glucose-tolerant subjects. Clinical characteristics of the study population are shown in Table 4 in the online appendix. The SNP at –3959 SNP was associated with differences in 30-min postload insulin levels (Fig. 2) and parameters reflecting insulin release, such as first-phase, second-phase, and 30-min insulin-to-glucose ratio (P < 0.02). The SNP at –1679 was associated with differences in 30- and 60-min postload glucose levels (Fig. 3) and the integrated area of glucose (P < 0.015, ANOVA). Linear regression models were most consistent with additive and variant-allele recessive effects at the –3959 and the 1679 sites, respectively. Neither polymorphism was associated with measures of insulin sensitivity such as HOMA_IR, S_i(comp), metabolic clearance rate, or S_i (data not shown). Haplotype-specific analyses showed a consistent pattern of associations with parameters reflecting the insulin secretory response to glucose (Table 2), but not with 30- or 60-min glucose levels and integrated glucose areas. However, common four-loci promoter haplotype combinations showed associations with 30- and 60-min postload glucose levels and integrated postload glucose areas (Table 5 in the online appendix), a result compatible with the apparently recessive effect of the –1679 variant allele. None of the four SNPs in haplotype block 2 displayed an association with any of the OGTT parameters, but global and haplotype-specific statistics showed significant associations with calculated parameters of insulin response to glucose (Table 2). Haplotype block 2 combinations showed similar associations (data not shown).

View larger version (21K): [in this window] [in a new window]   FIG. 2. Associations of –3959 T/C genotypes with OGTT glucose and insulin levels and effects on PGC-1 gene expression in INS1-E cells. Time-dependent values for plasma glucose (A) and insulin (B) in response to 75 g glucose in glucose-tolerant patients differing by –3959 T/C genotypes. Error bars indicate SE. For T/T, T/C, and C/C subjects, n was 238, 110, and 13, respectively. *P < 0.02, ANOVA, adjusted for age, sex, and BMI. C: INS1-E cells were transiently transfected with –3959T TK-Luc and –3959C TK-Luc. Results are means + SD and representative for one of two experiments, each performed in quadruplicate. **P < 0.001. D: Transcription factors and their binding matrices in relation to the PGC-1 promoter region encompassing the –959 T/C polymorphism. Variable nucleotides in the PGC-1 sequence and respective nucleotides in binding sequences of candidate transcription factors are in bold. Numbers refer to nucleotide positions relative to the transcriptional start site. The core sequences of binding sites are in upper case letters. Nucleotides underlined exhibit a high percentage of conservation. Binding matrices allowing for more than one nucleotide at a position are given in International Union of Pure and Applied Chemistry (IUPAC) standard codes for nucleic acids: w = A,T; r = A/G; s = C/G, and m = A,C.

View larger version (20K): [in this window] [in a new window]   FIG. 3. Associations of the –1679/–3614 alleles with OGGT glucose and insulin levels and effects on PGC-1 gene expression in HepG2 cells. Time-dependent values for plasma glucose (A) and insulin (B) in response to 75 g glucose in glucose-tolerant subjects by –1679 A/G genotypes. Error bars indicate SE. For A/A, G/A, and G/G subjects, n was 162, 161, and 38, respectively. *P < 0.015, ANOVA. C: HepG2 cells were transiently cotransfected with –3614A TK-Luc or –3614C TK-Luc and HNF-1 expression plasmid or the empty pcDNA6/V5-His backbone as indicated. **P < 0.001. D: HNF-1 binding matrix relative to the –3614 A/C polymorphic site. Variable nucleotides in the PGC-1 sequence and the respective nucleotide in the HNF-1 consensus binding sequence are in bold. IUPAC standard code for nucleic acids: w = A and m = A,C.

The polymorphic site at –1679 was associated with postload glucose levels and had little effect in transient expression studies (Fig. 1 in the online appendix), but it was in perfect linkage disequilibrium with the SNP at –3614, which is located within a potential HNF-1 binding site (Fig. 3). Because HNF-1 has been implicated in the regulation of hepatic gluconeogenesis (31), we performed transient transfection studies using the –3614A TK-Luc and –3614C TK-Luc reporter constructs as well as cotransfections with the HNF-1 expression plasmid in HepG2 cells. PGC-1 expression from the variant –3614C allele was significantly decreased in both the absence and presence of HNF-1 overexpression (Fig. 3C). Thus, the reduced promoter activity of the –3614 variant allele paralleled the lower 30- and 60-min postload glucose levels in subjects homozygous for the –1679 variant allele (Fig. 3A). Reporter gene activities of the variant and wild-type alleles were similar in PAZ6 or INS1-E cells (data not shown). Because overexpression of HNF-1 in HepG2 cells resulted in an approximately threefold induction of luciferase activity from both the –3614A TK-Luc and –3614C TK-Luc constructs (Fig. 3C), HNF-1 may participate in the regulation of PGC-1 expression.

The SNP at –1422T is located within a putative MEF2C binding site previously identified in the murine PGC-1 promoter (6,7). The SNP at –1774 is contained within another potential MEF2C binding site not conserved in the murine promoter. Contransfection studies with or without MEF2C expression plasmids revealed that constructs harboring the variant nucleotide at position –1422 increased reporter gene activity in HepG2 cells, in comparison to constructs harboring the wild-type allele (Fig. 1 in the online appendix).

To ascertain associations of the PGC-1 gene locus with type 2 diabetes, we determined genotype distributions in type 2 diabetic patients and control subjects. Clinical data of patients and control subjects are shown in Table 6 in the online appendix. None of the eight polymorphic sites displayed associations with type 2 diabetes in sex-specific or combined analyses in our population (data not shown). Four-loci haplotype distributions of haplotype block 2, but not haplotype block 1, showed borderline significant differences (Table 3). Score testing revealed that the common haplotype in block 2 that carries wild-type nucleotides at all four polymorphic sites was associated with the lowest risk of type 2 diabetes (P < 0.01). The same haplotype was associated with the strongest insulin secretory response to glucose (Table 2). Conversely, the common block 2 haplotype that contained variant nucleotides at the three distal polymorphic sites, and displayed the lowest scores for insulin secretory indexes, exhibited the highest score for type 2 diabetes among common haplotypes. Thus, effects of PGC-1 on the insulin response to glucose are likely to contribute to its association with type 2 diabetes.

	DISCUSSION

TOP ABSTRACT RESEARCH DESIGN AND METHODS RESULTS DISCUSSION REFERENCES

 
Previous studies in animal models and humans strongly support a role of PGC-1 in the pathogenesis of type 2 diabetes. In this work, we have delineated SNPs within contiguous regions of the PGC-1 gene promoter and the transcribed sequence. The SNPs in the promoter region comprise a haplotype block that is distinct from a second haplotype block harboring SNPs downstream of intron 2. Each block contains five common haplotypes. Other studies that applied high marker densities over contiguous genome regions also demonstrated blocks of variable length. Such blocks typically contain only few haplotypes separated by sites at which recombinations occurred in the ancestors of current populations (32,33). In glucose-tolerant subjects, haplotype block 1 showed discrete associations with postload glucose levels, whereas both haplotype blocks were associated with indexes of the insulin secretory response to glucose. Moreover, a common specific haplotype in block 2 revealed associations with ß-cell function and type 2 diabetes risk.

Our transient transfection studies in human cell lines identified several promoter SNPs that modulate promoter function, but it is likely that other functional sites in haplotype block 1 might exist that affect carbohydrate metabolism and/or type 2 diabetes risk. Nucleotides at –1422 and –1774 are located within MEF2 binding sites. Even though both sites are required for full transactivation, only the –1422 site displayed allele-specific promoter effects, but by itself it showed no associations with OGGT parameters or type 2 diabetes. Because MEF2 has been implicated in skeletal muscle GLUT4 expression (5,34), and transcriptional responses of the PGC-1 promoter to MEF2 may play an important role in programs controlling muscle fiber-type conversion (7,8) and cardiac energy production (6), the –1422 site may contribute to the pathogenesis of other disease phenotypes or to type 2 diabetes in other populations.

The –1679 polymorphic site as well as common haplotype combinations reflecting the integrated effects of promoter polymorphisms were associated with differences in 30- and 60-min postload glucose levels, and functionality of the perfectly linked polymorphic site at –3614 was established by transient transfection studies in HepG2 cells. Whether the association of the –3614/–1679 SNPs with decreased postload glucose levels reflects changes in glucose uptake or glucose production is not known, but both possibilities appear plausible. PGC-1 has been shown to participate in insulin-regulated hepatic gluconeogenesis by coactivating HNF-4 and FOXO1 (2–4). Moreover, hepatic gluconeogenesis contributes to glucose effectiveness ascertained in human studies (35,36). Conversely, PGC-1 enhances oxidative phosphorylation and glucose uptake in skeletal muscle (5,10,12). Hence, the variant allele or another functional site in linkage disequilibrium with it may have tissue-specific effects and enhance PGC-1 expression in skeletal muscle. Our cotransfection studies further suggest that HNF-1 contributes to the regulation of PGC1- promoter activity. HNF-1 and HNF-4, which is coactivated by PGC-1, have been shown to activate the expression of each other (37). Mice lacking HNF-4 in liver exhibit higher PGC-1 mRNA levels than control mice (38). It is thus possible that HNF-1 is part of a regulatory loop that controls PGC-1 expression in liver.

The effects of the –3959 variant site were tissue-specific, since the less common allele reduced reporter gene activity in INS1-E cells but not in HepG2 or PAZ6 cells. An inverse association between PGC-1 expression and insulin secretion is supported by recent studies (9). The influence of this SNP was also apparent in a four-loci haplotype context because the haplotype harboring the –3959 variant nucleotide revealed the highest scores for insulin secretion indexes.

Haplotypes of block 2 revealed strong associations with derived estimates of the insulin secretory response to glucose. Associations of +1564G/A with lipid oxidation and early insulin secretion have been reported in Pima Indians (39). Even though we did not observe such an association, the results in Pima Indians are not inconsistent with our findings because haplotype block size, allele frequencies, and linkage disequilibrium relationships differ among populations (31). The absence of associations with individual polymorphisms in our population suggests that the block 2 SNPs studied are not major functional sites. Such site(s) may be located in introns and affect mRNA expression or splicing. Indeed, a PGC-1 isoform devoid of exon 8 and induced by exercise has been observed in rat skeletal muscle (40). Using RNase protection assays, we have recently observed distinct PGC-1 isoforms lacking specific interfaces in various human tissues (M. Xie, H.O., M. Roden, W.P., unpublished observations), but their pathophysiological significance awaits further analyses.

Given the role of PGC-1 in mitochondrial biogenesis and oxidative phosphorylation (10,12), the lack of associations with indexes of insulin resistance in either haplo-type block was surprising. Imprecision in the ascertainment of insulin resistance may have contributed (41). Furthermore, combined effects of both haplotype blocks could not be determined because of sample size constraints. Interestingly, muscle-specific overexpression of PGC-1 in transgenic mice was shown to reduce GLUT4 expression (42).

The borderline-significant different haplotype distributions in block 2 between patients with type 2 diabetes and control subjects suggest, but do not prove, that the PGC-1 gene locus contributes to the susceptibility for type 2 diabetes. Importantly, the haplotype that displayed the lowest score for type 2 diabetes was associated with the highest scores for indexes of ß-cell function, thus providing physiological plausibility and internal consistency of results. Associations of the +1564 site with type 2 diabetes have been reported in a Danish population (13), but these results are at variance with studies in French and Japanese populations as well as with our findings. Nevertheless, two-loci haplotypes comprising the +1564 SNP displayed associations with diabetes in Japanese subjects. Among the three European populations, variant allele frequencies in subjects with type 2 diabetes were similar and ranged from 0.363 to 0.373, but they were slightly higher in French and Austrian than in Danish nondiabetic subjects (0.343 and 0.348 vs. 0.306). However, in all four populations, variant allele frequencies were higher in patients than in control subjects. It is therefore conceivable that the +1564 polymorphic site is not the main causal site, but is contained within haplotype block 2, that contributes to type 2 diabetes in several populations.

	ACKNOWLEDGMENTS

 
This study was supported by Jubilaeumsfondsprojekt no. 9364 and no. 10678 from the Oesterreichische Nationalbank, and by grants from the Medizinische Forschungsgesellschaft Salzburg and the Verein für Medizinische Fortbildung, Salzburg.

We acknowledge the critical reading of the manuscript by Dr. S.M. Soyal.

	FOOTNOTES

 
Additional information for this article can be found in an online appendix at http://diabetes.diabetesjournals.org.

Address correspondence and reprint requests to Wolfgang Patsch, MD, Department of Laboratory Medicine, LandesklinikenParacelsus Private Medical University Salzburg, Müllner Hauptstr. 48, A-5020 Austria. E-mail: w.patsch{at}lks.at

Received for publication October 1, 2003 and accepted in revised form February 9, 2004

Abbreviations: HOMA, homeostasis model assessment; HNF, hepatocyte nuclear factor; IUPAC, International Union of Pure and Applied Chemistry; OGTT, oral glucose tolerance test; MEF2, MADS box transcription enhancer factor-2; PGC-1, peroxisome proliferator–activated receptor coactivator-1; RFLP, restriction fragment–length polymorphism; SAPHIR, Salzburg Atherosclerosis Prevention Program in Subjects at High Individual Risk; SNP, single nucleotide polymorphism

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