Background:

Insulin-like growth factor binding protein 5 (IGFBP5) binds to IGF and thus modulates IGF signaling pathway. We have shown earlier that the IGFBP5 gene was downregulated in the adipose tissue after 12-week carbohydrate diet with low insulinemic response.

Objective:

The aim was to examine the putative contribution of genetic variation of the IGFBP5 gene to the characteristics of metabolic syndrome and incidence of type 2 diabetes (T2DM) in the Finnish Diabetes Prevention Study (DPS).

Methods:

DPS is a longitudinal study where 522 subjects with impaired glucose tolerance were randomized to either lifestyle intervention group or control group. DNA was available from 507 subjects (mean body mass index (BMI) 31.24.5 kg/m², age 557 years). The eight single-nucleotide polymorphisms (SNPs) were selected from HapMap database and genotyped by Taqman allelic discrimination protocol. The main results were confirmed in a larger cross-sectional study population (METSIM). In addition, the gene expression of IGFBP5 was studied in two previously published study populations (FUNGENUT and GENOBIN) of 124 subjects with insulin resistance (BMI 32.23.5 kg/m², age 57.77.4 years).

Results:

Three out of eight IGFBP5 markers (rs9341234, rs3276 and rs11575134) were significantly associated with circulating adiponectin concentrations in men. Furthermore, mRNA expression studies of subcutaneous adipose tissue showed that mRNA concentrations of IGFBP5 correlated with adiponectin concentrations in all subjects and in women. None of the IGFBP5 SNPs were associated with T2DM.

Conclusions:

Our findings show that IGFBP5 has a gender-specific association with adiponectin, which may modulate the development of metabolic syndrome.

Introduction

Nutrients are known to interact with human genome¹ and may modulate the molecular pathways involved in the development of various chronic diseases.² Genetic polymorphisms can affect the expression of genes involved in a number of important metabolic processes and therefore the response to dietary factors may be dependent on genetic background.^{3, 4} Polymorphisms affecting nutrient metabolism may explain some of the inconsistencies among epidemiological studies relating diet to chronic diseases such as diabetes and cardiovascular disease.

Insulin-like growth factor (IGF) signaling pathway plays a crucial role in the regulation of cell growth, differentiation, apoptosis and aging.⁵ Growth in normal and malignant tissues has been linked to hyperinsulinemia as well as to the function of IGFs.⁶ In addition to insulin, also IGF-I regulates glucose metabolism, and is, after insulin, the most powerful natural peptide with glucose-lowering effects.⁷ Insulin stimulates IGF-I secretion from the liver, and increases insulin sensitivity and peripheral glucose uptake, decreases hepatic glucose production and improves the lipid profile.⁶

Insulin-like growth factor binding proteins (IGFBPs) bind to IGF and thus modulate IGF signaling.⁸ While all IGFBPs (IGFBP1-IGFBP7) are structurally related, their functions are non-redundant from each other and they exert their effects also independently from IGF.^{9, 10, 11} IGFBPs have been linked to several human disorders, such as different growth disorders^{12, 13} and insulin resistance.¹⁴ IGFBP3 is the major circulating IGFBP, which binds 75% of IGFs in healthy people, forming 140 kDa complexes.¹⁵ Similarly to IGFBP3, IGFBP5 is present in circulation at 10% of the molar concentration of IGFBP3, and forms similar complexes with IGFs.¹⁶ In humans, the IGFBP5 gene is located in chromosomal locus 2q33–q36.

We have recently shown that a 12 weeks diet with low-insulin response carbohydrate resulted in reduced expression of IGFBP5 mRNA in human subcutaneous adipose tissue (SAT) with simultaneous improvement in insulinogenic index.¹⁷ In addition, it has been shown that IGFBP1 and IGFBP3 respond postprandially to the glycemic load of the diet.¹⁸ Both of these findings indicate that IGFBPs are potential markers for nutritional changes in humans with healthy or disturbed metabolism. Therefore, we hypothesize that IGFBP5 could also be a candidate gene for T2DM and related disorders.

The main aim of this study was to examine the putative contribution of the genetic variation in IGFBP5 to the characteristics of metabolic syndrome and T2DM in the Finnish Diabetes Prevention Study (DPS).

Materials and methods

Study I: the Finnish diabetes prevention study (DPS)

The DPS is a randomized, controlled, multicenter study started in Finland 1993. The study design and methods have been reported earlier in detail.^{19, 20} Briefly, main inclusion criteria were body mass index (BMI) >25 kg/m², age 40–64 years and impaired glucose tolerance (IGT).²¹ Five hundred and twenty-two subjects were randomized into an individualized lifestyle intervention group or a control group. DNA was available from 507 individuals (166 men and 341 women). The study protocol was approved by the Ethics Committee of the National Public Health Institute in Helsinki, Finland, and all the study participants gave written informed consent.

Study II: the metabolic syndrome in men study (METSIM)

The population is a random population-based sample of Finnish 50–70-year-old men living in the city of Kuopio in Eastern Finland.²² The diagnosis of type 2 diabetes was carried out according to the WHO's 1999 criteria.²³ The study was in accordance with the standards of the Helsinki Declaration. The Ethics Committee of the District Hospital Region of Northern Savo and Kuopio University Hospital approved the study plan. All participants gave their written informed consent.

Studies III–IV: FUNGENUT and GENOBIN study

We combined two previously published study populations, FUNGENUT¹⁷ and GENOBIN,^{24, 25} to examine the associations between gene expression of IGFBP5 and adiponectin at baseline. The participants had impaired fasting glycemia, or IGT and at least two other features of the metabolic syndrome according to the National Cholesterol Education Program criteria: waist circumference >102 cm (males), >88 cm (females); fasting serum triacylglycerol concentration 1.7 mmol l^-1; fasting serum high-density lipoprotein cholesterol <1.0 mmol l^-1 (males), <1.3 mmol l^-1 (females); blood pressure 130/80 mm Hg. Altogether 124 subjects (male N=61, female N=63) were studied. Characteristics of the study subjects are described in the Table 1.

Table 1 - Baseline characteristics of the individuals of the FUNGENUT and GENOBIN studies. Data are meanss.d.

Full table

Biochemical measurements

Study I

A medical history was taken, and a physical examination was performed at baseline and at annual follow-up visit.²⁰ Plasma glucose was measured at each center by standard methods. The serum insulin concentration was measured in a central laboratory by a radioimmunoassay method (Pharmacia, Uppsala, Sweden). Measurements from the baseline to the 4-year examination were used, including height, weight, BMI, waist and hip circumference, waist-to-hip ratio, sagittal and horizontal diameter, and 2-h oral glucose tolerance test with glucose and insulin concentrations before (0 min) and after a 75-g glucose load (120 min).²⁰ Serum total cholesterol, high-density lipoprotein cholesterol and triglycerides were determined using enzymatic assay methods.²⁰ Serum adiponectin concentrations (g ml^-1) measured using Human Adiponectin ELISA kit (B-Bridge International Inc., Sunnyvale, CA, USA) were available from Kuopio region, and from 237 (men n=83, women n=154), persons at baseline.

Study II

In METSIM study plasma adiponectin measurements were available from 2304 individuals of whom 1408 were normoglycemic, 378 had impaired fasting glucose, 180 had IGT and 337 had known or newly diagnosed diabetes. Adiponectin was determined with Human Adiponectin ELISA Kit (B-Bridge International Inc.).

Selection of SNPs and genotype analysis

The eight single-nucleotide polymorphisms (SNPs) (rs9341234, rs3276, rs7420849, rs3755137, rs11575134, rs6727330, rs9967835 and rs6738874) along the IGFBP5 gene (23.44 kb) and flanking regions (10 kb) were chosen using the HapMap TagSNP database (International HapMap Project data release 21).²⁶ The locations of the selected markers are shown in Figure 1. The coverage of the selected SNPs using the Tagger Software (http://www.broad.mit.edu/mpg/tagger/server.html)²⁷ was 80.8% (r²>0.8) and mean maximum r²82%. All eight markers were genotyped from 507 DPS subjects (166 men and 341 women), and the markers rs3276 and rs11575134 were performed from 2304 METSIM participants. Genotyping of the SNPs was performed with TaqMan allelic discrimination assays and ABI PRISM 7000 sequence detector according to the manufacturer's instructions (Applied Biosystems, Foster City, CA, USA).

Figure 1.

Schematic presentation of the IGFBP5 gene and the locations of the single-nucleotide polymorphisms. White boxes, UTR; black boxes, coding region.

Full figure and legend (39K)

RNA isolation

Total RNA obtained from SAT was extracted with Trizol (Invitrogen, Carlsbad, CA, USA) followed by further purification with RNeasy Mini-Kit (Qiagen, Valencia, CA, USA) according to the manufacturer's instructions. RNA concentration and the A₂₆₀/A₂₈₀ ratio were determined using NanoDrop-1000 spectrophotometer (NanoDrop Technologies, Wilmington, DE, USA), the acceptable ratio of A₂₆₀/A₂₈₀ being 1.9–2.1. Integrity of the RNA was assessed using non-denaturating agarose gel electrophoresis.

cDNA synthesis and reverse transcriptase-PCR

Five g of total RNA was used as a template for reverse transcriptase reactions to generate cDNA using the High Capacity cDNA Archive Kit according to the manufacturer's protocol (Applied Biosystems). Reverse transcriptase-PCR was then performed as described earlier.¹⁷ Assays were based on TaqMan chemistry and ABI Prism 7500 SDS software (Applied Biosystems). The following assays, Hs01052296_m1 and Hs00605917_m1 (Applied Biosystems product codes for IGFBP5 and adiponectin, respectively) were ordered. Cyclophilin A (Hs99999904_m1) was used as an endogenous control gene.

Statistical methods

Haploview software²⁸ was used for linkage disequilibrium analysis, and association studies were performed with SPSS14.0 for Windows (SPSS, Chicago, IL, USA). The data are presented as meanss.d. (tables) or meanss.e.m. (figures), and the combination of P<0.05 and q-value <0.25 was considered statistically significant. Correction for multiple hypothesis testing was performed with false discovery rate using Q-value 1.0 software.²⁹ ₀ was estimated with bootstrap method using range from 0 to 0.9 by 0.05. Normal distribution of the variables was tested with Lilliefors-corrected Kolmogorov–Smirnov test. Serum adiponectin concentrations were transformed by natural logarithm to achieve normal distribution. The associations between SNPs of IGFBP5 and serum adiponectin concentrations were analyzed with the general linear model univariate analysis of variance. The results were adjusted for age, BMI and gender. To analyze the genotype effects, dominant model was used when the minor allele frequency was <5%. The differences in genotype frequencies among genders and groups among individuals with different genotypes were tested with Pearson's ²-test. To study the haplotype effects, the THESIAS 3.1 software were used.³⁰ The association of SNPs with the conversion of IGT to T2DM was analyzed using Cox regression. Pearson correlations were calculated to examine the association of IGFBP5 gene expression with adiponectin gene expression. The associations between SNPs and circulating adiponectin concentrations in METSIM study were analyzed using the Student's t-test.

Results

Allele frequencies and linkage disequilibrium

The success rate for genotyping was 100% for all markers. All SNPs were in Hardy–Weinberg equilibrium with P-values ranging from 0.078 to 0.752. The SNP rs9341234 was in linkage disequilibrium with rs3276, D' and r² being 0.957 and 0.728, respectively (Table 2). Overall, the allele frequencies did not differ from those reported in HapMap data for the Utah residents with ancestry from northern and western Europe (CEU) population (HapMap data release 21, April 2007). However, in the DPS data, the markers rs9341234 and rs3276 showed minor allele frequencies 0.024 and 0.030, respectively, whereas the HapMap data showed slightly higher minor allele frequencies (0.036 and 0.067, respectively).

Table 2 - The pairwise linkage disequilibrium values (D' and r²-values) of the IGFBP5 SNPs in the participants of the Finnish DPS.

Full table

The genetic association between IGFBP5 and circulating adiponectin concentrations

At baseline of the DPS study, the markers rs9341234 and rs3276 were associated with serum adiponectin concentrations (n=237) in all subjects (P=0.002, q=0.250 and P=0.007, q=0.280, respectively) (Figure 2). Individuals who were homozygotes for rs9341234-T and rs3276-G had higher adiponectin serum concentrations than individuals with other genotypes of these markers. SNP^*gender interactions were significant with all studied markers, and therefore data was stratified according to the gender and analyzed separately. Association between SNPs and adiponectin concentration with markers rs9341234, rs3276 and rs11575134 were specifically seen in men (P=0.001, q=0.001 for rs9341234; P=0.001, q=0.001 for rs3276; P=0.001, q=0.074 for rs11575134), whereas in women the associations were not significant (Figure 2, Table 3).

Figure 2.

Associations of the IGFBP5 markers rs9341234 (a and b), rs3276 (c and d) and rs11575134 (e) with the concentrations of serum adiponectin at baseline in the Finnish Diabetes Preventions Study. All results are adjusted with age, body mass index and gender. Data are meanss.e.m.

Full figure and legend (38K)

Table 3 - Association of IGFBP5 SNPs with concentrations of serum adiponectin (g ml^-1) in the Finnish Diabetes Prevention Study divided according to genotypes of IGFBP5.

Full table

To study the haplotype effects on adiponectin concentrations in men, we constructed a three-marker haplotype (rs9341234, rs3276 and rs11575134). Men with haplotype TGA alleles of the respective SNPs had significantly lower (-1.822) adiponectin concentration (P=0.017, adjusted for BMI and age) when compared with those with major haplotype TGG (Table 4). The results of haplotype analyses were in line with the single marker analyses, but they did not reveal a haplotype that would explain the results substantially more than individual SNPs.

Table 4 - Association of the three-marker haplotypes consisted of markers individually associated with serum adiponectin concentration (rs9341234, rs3276 and rs11575134).

Full table

The genetic associations observed in the DPS study were replicated in a larger cross-sectional study population of men (n=2604) recruited from Kuopio area (the METSIM study) by genotyping the SNPs rs3276 and rs11575134 that were previously associated with circulating adiponectin concentrations in DPS. Rs9341234 was not included because it is in strong linkage disequilibrium with rs3276 (Table 2). Significant differences in adiponectin concentrations according to genotype of rs11575134 among diabetic men (dominant model, P=0.029) and men with impaired fasting glucose (dominant model, P=0.043) were observed (Figure 3). The persons with GA/AA genotypes had lower adiponectin concentrations than those with GG genotype. These results are in line with those obtained from DPS study, which was composed of persons with IGT. However, we were not able to replicate the results obtained from DPS with rs3276 in the METSIM study. None of the selected markers was associated with the risk of type 2 diabetes in the DPS.

Figure 3.

Association of IGFBP5 marker rs11575134 with the concentration of plasma adiponectin in (a) normoglycemic men, (b) men with impaired fasting glucose, (c) men with impaired glucose tolerance and (d) men with T2DM in METSIM study. Data are meanss.e.m.

Full figure and legend (112K)

Gene expression correlations in SAT

Because of the observed strong association between the sequence variation of IGFBP5 and serum adiponectin concentrations, we studied whether the mRNA expression of IGFBP5 and adiponectin were also correlated. For this purpose we utilized baseline data from FUNGENUT and GENOBIN study populations at baseline.^{17, 24} The persons recruited to these studies had metabolic syndrome and impaired glucose metabolism similarly to that of the participants in the DPS. The mRNA concentrations of IGFBP5 and adiponectin were highly correlated in all subjects (P<0.001, r²=0.368) and in women (P=0.001, r²=0.425) (Figure 4).

Figure 4.

Correlations between gene expression of IGFBP5 and adiponectin in adipose tissue at baseline of the FUNGENUT and GENOBIN studies in (a) all subjects, (N=115; P<0.001, r²=0.368) (b) in women (N=61; P=0.001, r²=0.425, corrected with fat mass) and (c) in men (N=54; P=0.291, r²=0.291). Gene expression data are represented relative to the endogenous control cyclophilin A1.

Full figure and legend (39K)

Discussion

In this study, we hypothesized that IGFBP5 may be linked with characteristics of metabolic syndrome. Therefore, we examined the association of genetic variation of IGFBP5 with features of metabolic syndrome, in overweight individuals who have IGT and thus, were at high risk for developing T2DM (the DPS study population). Our main results showed that three SNPs (rs9341234, rs3276 and rs11575134) of the IGFBP5 gene were strongly associated with serum adiponectin concentrations particularly in men with IGT. To confirm this finding, the association was replicated in a larger cross-sectional METSIM study population with the SNPs rs3276 and rs11575134. Moreover, we found a positive correlation between IGFBP5 and adiponectin gene expression in SAT in all subjects and in women with metabolic syndrome in FUNGENUT and GENOBIN studies.

Adiponectin, a circulating peptide mostly expressed in and secreted from SAT,³¹ has a crucial role in the modulation of glucose metabolism in insulin-sensitive tissues. It is well known that circulating adiponectin concentrations are decreased in human obesity, insulin resistance and T2DM.^{32, 33} Tworoger et al. have shown that sex hormones, specifically estrogens are inversely associated with adiponectin concentrations.³⁴ Moreover, they have demonstrated that IGFBP1, sex hormone-binding globulin and C-peptide have a strong and independent association with adiponectin. As in this study, gender-specific association between IGFBP5 and adiponectin was demonstrated both in genetic and in expression level, we can speculate that IGFBP5 could mediate gender-specific effects on adiponectin (or vice versa) production and/or secretion through interacting with sex hormones.

Differentially distributed adipose tissue depots among genders^{35, 36} could also explain the gender-specific differences. Chan et al.¹⁴ have demonstrated that upregulation of IGFBP3 in vitro induced insulin resistance in human visceral adipose tissue but not in SAT. Thus, it is possible that IGFBP5 could harbor adipose tissue depot-specific functions. Unfortunately, we did not have visceral adipose tissue available to test if there is a correlation between IGFBP5 and adiponectin.

We have recently shown that expression of IGFBP5 is modulated by diet in human SAT along with the improved insulinogenic index.¹⁷ Also other IGFBPs are reported to have similar functions. Brand-Miller et al.¹⁸ have shown that the glycemic index of foods acutely changes the IGFBP1 and IGFBP3 concentrations in circulation. Specifically, the change in serum IGFBP3 concentration was significantly higher after the low-glycaemic index meal than after the high-glycaemic index meal. In contrast, serum IGFBP1 declined markedly after both meals, but the mean change at 4 h was significantly more prolonged after the low-glycaemic index meal than after the high-glycaemic index meal. These studies indicate that IGF–IGFBP signaling pathway could be a target for nutritional factors that modulate glucose metabolism and insulin action.

Recently, IGFBPs have also been linked to carcinogenesis. The genetic variation of IGFBP5–IGFBP2 region in chromosome 2 was shown to be associated with breast cancer in a population of African origin.³⁷ Furthermore, the overexpression of IGFBP2 and IGFBP5 are associated with the progression of breast cancer.³⁸

These reports indicate that lifestyle changes may modulate the IGF–IGFBP signaling contributing to the development of chronic diseases such as obesity, insulin resistance and cancer. This is the first report on the association between genetic variations of the IGFBP5 gene and adiponectin. The exact role of IGFBP5, the most conserved gene of this family¹¹ in adipose tissue remains to be determined. Thus, it is of a great interest to study further the molecular mechanisms between the IGFBP5 and adiponectin in adipose tissue and their relation with metabolic syndrome and obesity.

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