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Genetic Variants at the Resistin Locus and Risk of Type 2 Diabetes in Caucasians

Section on Genetics and Epidemiology, Research Division, Joslin Diabetes Center (X.M., J.H.W., A.D.), and Department of Medicine, Harvard Medical School (X.M., A.D.), Boston, Massachusetts 02215; Unit of Endocrinology, Ospedale Casa Sollievo della Sofferenza (E.T.), 71013 San Giovanni Rotondo, Italy; and Department of Clinical Science, University La Sapienza (E.T.), 00161 Rome, Italy

Address all correspondence and requests for reprints to: Alessandro Doria, M.D., Ph.D., Section on Genetics, Epidemiology, Joslin Diabetes Center, One Joslin Place, Boston, Massachusetts 02215. E-mail: . alessandro.doria{at}joslin.harvard.edu

Abstract

Resistin is a newly identified hormone secreted by adipocytes that inhibits insulin action on peripheral tissues. The aim of our study was to investigate whether genetic variability at this locus is associated with the risk of type 2 diabetes. By sequencing 32 subjects with type 2 diabetes, we identified 8 single nucleotide polymorphisms (SNPs) in the 5'-flanking region and introns of the resistin gene. Allele and genotype distributions were determined for all 8 SNPs in 312 cases with type 2 diabetes and 303 nondiabetic controls, all of Caucasian origin. No significant association with type 2 diabetes was found at any of the polymorphic loci. However, an interactive effect of genotype at SNP 6 (IVS2 + 181GA) and obesity was a significant determinant of type 2 diabetes risk in this population. The relative risk of diabetes for the A/A genotype was 4.8 (95% confidence interval, 1.1–21.0) in individuals above the median for body weight, but only 0.7 (95% confidence interval, 0.2–2.1) in those below the median. This difference between relative risks was significant (² = 4.5; P = 0.03). A similar, but much weaker, interaction with obesity was observed for SNPs in linkage disequilibrium with SNP6. In conclusion, resistin does not appear to be a major gene for type 2 diabetes. However, our data suggest a synergistic effect of sequence differences at the resistin locus and obesity on risk of type 2 diabetes. Further studies are needed to confirm this finding in other populations.

THE CONTRIBUTION OF genetic factors to the development of insulin resistance and type 2 diabetes has been known for many years, but the nature of the genes involved is uncertain (1). Cellular physiology studies indicate that an important role may be played by genes coding for naturally occurring inhibitors of insulin-action such as TNF, -Heremans-Schmid glycoprotein, membrane glycoprotein PC-1, Ras associated with diabetes, and resistin (2, 3, 4, 5, 6). Because of the links between obesity and type 2 diabetes, particular emphasis has been placed on inhibitors that are produced by the adipose tissue.

Resistin is a novel 12.5-kDa, cysteine-rich protein that belongs to a family of resistin-like molecules also known as FIZZ (found in inflammatory zone) proteins (6, 7, 8). It is secreted by adipocytes and is detectable in serum (6). Although resistin’s function is still under study, there is evidence that this protein acts as a hormone inhibiting insulin action (6, 9). Administration of recombinant resistin to wild-type mice impairs glucose tolerance and insulin action, whereas an increase in insulin sensitivity is noted when animals are treated with resistin-neutralizing antibodies (6). In vitro results in 3T3-L1 adipocytes are similar, suggesting that resistin may also have autocrine effects. In the same cells, insulin inhibits resistin expression (10).

Although resistin’s role in human obesity is still unclear (11), circulating levels of resistin are markedly increased in obese, insulin-resistant mice, and are decreased by treatment with insulin-sensitizing peroxisome proliferative-activated receptor- activators (6). These findings point to increased resistin secretion as one of the possible mechanisms by which obesity may lead to insulin resistance and type 2 diabetes. Thus, genetic variants affecting resistin function, expression, or secretion may modulate the risk of diabetes associated with a given body weight. Here we report the results of a screening for polymorphisms in the resistin gene. We present preliminary evidence that a variant at this locus may interact with obesity in determining the risk of type 2 diabetes.

Subjects and Methods

Polymorphism screening

The search for sequence differences was conducted in 32 individuals of Caucasian origin with type 2 diabetes. This number of subjects, corresponding to 64 chromosomes, provided 95% power to detect single nucleotide polymorphisms (SNPs) with a minor allele frequency as low as 0.05. Six overlapping fragments covering the resistin gene (956 bp of the 5'-flanking region, all four exons, including 5'- and 3'-untranslated regions, and all three introns) were amplified from each individual by PCR. Reactions were performed on 65 ng DNA in 50 µl containing 10 mM Tris-HCl (pH 8.3), 50 mM KCl, 1.5 mM MgCl₂, 0.2 mM of each dNTP, 0.4 µM forward and reverse primers, and 0.035 U/µl polymerase (PE Applied Biosystems, Foster City, CA) for 30 cycles (60 sec at 95 C, 45 sec at 55–62 C, and 45 sec at 72 C) in an MJ Research, Inc. thermal cycler (Cambridge, MA). Primers and annealing temperatures for each fragment are indicated in Table 1. After purification, PCR fragments were sequenced in both directions using the Big-Dye Terminator Cycle Sequencing Kit (version 2.0, PE Applied Biosystems) using an ABI 310 Genetic Analyzer.

Genotype and allele frequencies of all SNPs that were identified were determined in 312 unrelated cases with type 2 diabetes and 303 unrelated nondiabetic controls. All subjects were of Caucasian origin. The study and informed consent procedures were approved by the Joslin committee on human studies. Subjects with type 2 diabetes were randomly selected from Joslin Clinic patients, aged 40–70 yr, who met the following criteria: 1) diabetes diagnosed according to WHO criteria after age 35 yr, and 2) insulin treatment not required for at least 2 yr after diabetes diagnosis. Blood samples for DNA extraction were drawn from these subjects during a visit to the Joslin Clinic. Controls consisted of unrelated, nondiabetic spouses of subjects with type 2 diabetes and nondiabetic parents of patients with type 1 diabetes, who are enrolled in family studies currently underway at Joslin. All controls had a negative history for type 2 diabetes, were not currently taking any glucose-lowering medications, and had fasting glucose less than 6.1 mmol/liter or a hemoglobin A_1c value less than 6.1%. Height and weight were used to calculate body mass index and percent ideal body weight (%IBW = body mass index x 4.39 for males or 4.76 for females) (12). Characteristics of cases and controls are summarized in Table 2. The association between SNP6 and fasting insulin levels was tested in 388 nondiabetic Caucasian residents of the Gargano area (east coast of Italy). Subjects were recruited among the employees of the hospital Casa Sollievo della Sofferenza (San Giovanni Rotondo, Italy), who had fasting plasma glucose less than 7 mmol/liter at screening and were not taking any medications. Included were 145 males and 243 females with a mean age of 38 ± 12 yr.

Genotypes were determined at each polymorphic site by PCR, followed by dot-blotting and allele-specific hybridization. DNA fragments containing each SNP were amplified by PCR from genomic DNA using the same primers and conditions that were used for the mutation screening. PCR fragments were dot-blotted on nylon membranes in duplicate, hybridized with ³²P-labeled allele-specific 17-mers according to standard protocols, and autoradiographed overnight. Genotypes were inferred by comparing the autoradiograms of membranes hybridized with the two different allele probes. Microsatellite genotypes were determined by fragment analysis using the ABI 310 genetic analyzer (PE Applied Biosystems).

Data analysis

Allele frequencies were computed from genotype frequencies. The distribution of genotypes and alleles were compared between study groups by ² tests. Haplotype frequencies were estimated by gene counting as previously described (13). As a descriptive measure of association between genotypes and outcomes, odds ratios were calculated along with 95% confidence intervals. Odds ratios were compared using the Breslow-Day test.

Results

The 5'-flanking region, the 4 exons and the 3 introns of the resistin gene were screened for polymorphisms. In the 2400 bp that were examined, we identified a total of 8 SNPs, or one every 300 bp, and a (GAT)_n microsatellite. Four SNPs were placed in the 5'-flanking region, 2 in intron 2, and 2 in intron 3 (Table 3). Two of these SNPs (SNP2 and SNP4) were rare, with allele frequencies of 0.008 in the 303 controls (Table 4). The remaining 6 SNPs were more frequent, with allele frequencies ranging from 0.09–0.43. The microsatellite was located in the 3'-untranslated region and had 4 alleles, 1 of which accounted for 92% of chromosomes (Table 4). All genotype frequencies were in Hardy-Weinberg equilibrium. Significant linkage disequilibrium was present between pairs of frequent SNPs, with 4 haplotypes accounting for more than 80% of control chromosomes. For all polymorphisms, only small differences in allele and genotype distributions were observed among the 312 cases with type 2 diabetes and the 303 nondiabetic controls (Table 4). A similar pattern was observed if data were adjusted for age and gender and when SNPs were considered together as haplotypes.

View this table: [in this window] [in a new window]   Table 5. Resistin SNP 6 genotype distributions in type 2 diabetes cases (T2DM) and nondiabetic controls (NDM) in lean (%IBW < median) and obese (%IBW median) subjects

Discussion

We investigated the resistin gene as a candidate locus for susceptibility to type 2 diabetes in a study of 312 cases and 303 controls. Although none of the resistin polymorphisms was associated with diabetes, we found suggestive evidence of an interaction between the genotype at this locus and the effect of obesity on the risk of type 2 diabetes. Among obese individuals, homozygotes for the minor allele of SNP6 have a 4- to 5-fold risk of diabetes (P < 0.02), whereas among lean individuals, the risk of diabetes is independent of this SNP. Although this interaction is statistically significant, its contribution to the prevalence of type 2 diabetes is small due to the low frequency of the predisposing genotype in the population.

A possible explanation for these findings is that the A/A genotype of SNP6 enhances resistin expression, but this does not affect the risk of diabetes unless resistin production is already amplified by adiposity. Although the obesity side of this hypothesized interaction seems well established (6, 11), the SNP6 component is less clear. This polymorphism is in an intron, which traditionally has not been considered to have regulatory functions. However, effects of intronic polymorphisms on gene expression have been reported, the most notable example being SNP43 in intron 3 of the calpain 10 gene (14, 15, 16, 17). This polymorphism, which is associated with type 2 diabetes in Mexican-Americans, affects the binding of nuclear factors from liver and islets and regulates calpain 10 expression in vitro and in vivo (17, 18). The same could be true for SNP6 of the resistin gene. Alternatively, this polymorphism may be a marker in linkage disequilibrium with another polymorphism affecting resistin expression. In this case, the causal SNP might be placed outside the boundaries of the region that we screened, as none of the other identified SNPs interacted with obesity as strongly as SNP6.

Although these findings are interesting, it is important to note that among nondiabetic individuals from Italy we did not find an interactive effect of SNP6 and obesity on fasting insulinemia, a marker of insulin resistance. One possibility is that the resistin polymorphism affects the risk of diabetes through mechanisms unrelated to insulin sensitivity. Type 2 diabetes results from two different pathophysiological defects, namely insulin resistance and a failure of the ß-cell to compensate for insulin resistance (1, 19). Because of this complexity, association studies considering the phenotypes insulin resistance and type 2 diabetes do not necessarily produce similar results. Although resistin is not known to act on the ß-cell, this cannot be totally excluded on the basis of the data available at this time. If we postulate instead that insulin resistance is the only mechanism through which resistin polymorphisms can affect type 2 diabetes risk, these conflicting findings might be due to weaker linkage disequilibrium in the Italian population between SNP6 (if this is a mere marker) and a putative causal variant. If the interaction between SNP6 and obesity is age dependent, another possible explanation is the younger age of nondiabetic individuals from Italy compared with the cases and controls from Boston (38 ± 12 vs. 61 ± 11 yr). Unfortunately, there was not enough power to test this hypothesis because of the paucity of homozygotes for the SNP6 minor allele. Finally, it is possible that the original finding of interaction between SNP6 and obesity on type 2 diabetes risk is, in fact, a type 1 error. However, this seems unlikely. If we examine all nine polymorphisms in a multiple logistic analysis, which takes into account both the multiplicity of SNPs and their linkage disequilibrium with each other, the overall P value for association with diabetes in the obese stratum is still significant (P = 0.03).

In conclusion, resistin does not appear to be a major gene for type 2 diabetes. However, our data suggest that genetic variability at this locus interacts with obesity in determining the risk of type 2 diabetes. If this trend is confirmed in other populations, these results may provide a basis for identifying individuals who would benefit most from weight reduction programs. Studying the cellular pathways involved in this interaction may also provide additional insights into resistin’s function and the cellular mechanisms by which increased adiposity leads to insulin resistance and type 2 diabetes. Knowledge of these mechanisms might suggest additional strategies for preventing type 2 diabetes and its burden of morbidity and mortality.

Acknowledgments

Footnotes

This work was supported by NIH Grants RO1-DK-55523 (to A.D.), RO1-DK-47475 (to J.H.W.), and Grant DK-36836 (to Genetics Core, Joslin’s Diabetes Endocrinology Research Center) and a Research Award from the American Diabetes Association (to A.D.).

Abbreviations: %IBW, Percent ideal body weight; SNP, single nucleotide polymorphism.

Received January 28, 2002.

Accepted June 5, 2002.

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