AHSG Gene Variation is not Associated with Regional Body Fat Distribution – A Magnetic Resonance Study

Abstract

Obesity-resistance in AHSG-knockout mice indicate an important role of α2-Heremans-Schmid glycoprotein/fetuin-A (AHSG) in the development of obesity. We studied whether genetic variation within AHSG affects whole-body adiposity and regional fat distribution in humans. We genotyped 321 subjects at increased risk for type 2 diabetes for five single nucleotide polymorphisms (SNP) rs2248690, rs4831, rs2070635, rs4917, and rs1071592. Body fat distribution and ectopic hepatic and intramyocellular lipids were assessed by magnetic resonance techniques. AHSG levels were determined by immunoturbidimetry. The five chosen SNPs covered 100% of common genetic variation (minor allele frequency ≥0.05) within AHSG (r²≥0.8). All SNPs were significantly associated with AHSG levels (p<0.0001), except for rs4831 (p=0.9) after adjustment for gender, age, and body mass index (BMI). AHSG levels were associated with liver fat content (p=0.0160) and BMI (p=0.0247) after adjustment for gender and age. While rs2248690 was nominally associated with BMI in the dominant model (p=0.0432), none of the SNPs was associated with regional fat distribution. Common genetic variation within AHSG does not appear to influence regional body fat distribution, but may affect whole-body adiposity in humans.

α2-Heremans-Schmid glycoprotein - fetuin-A - visceral adipose tissue - SNP - insulin resistance - insulin sensitivity

References

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Methods

Subjects

The 321 non-diabetic subjects (subject characteristics shown in [Table 1]) at an increased risk for type 2 diabetes, due to family history of diabetes (at least one second-degree relative with type 2 diabetes mellitus), history of gestational diabetes, overweight, impaired fasting glucose (IFG), or impaired glucose tolerance (IGT), were recruited from an ongoing study on the pathophysiology of type 2 diabetes (Stefan et al., 2008). 80.9% of the subjects had a family history of diabetes. Relatedness among subjects was less than 1%. All subjects were anthropometrically characterized by magnetic resonance imaging (MRI) and magnetic resonance spectroscopy (MRS) and metabolically characterized by an oral glucose tolerance test (OGTT). A subgroup of 227 subjects was additionally characterized by a hyperinsulinemic-euglycemic clamp. The participants gave informed written consent to the study. The protocol was approved by the Ethics Committee of the University of Tübingen.

Analysis of the AHSG gene and selection of single nucleotide polymorphisms (SNPs) for genotyping

Using the publically available phase II data of the International HapMap Project derived from a population of Utah residents with ancestry from northern and western Europe (release #22, April 2007, www.hapmap.org/index.html.en, (The International HapMap Project, 2003)), we screened in silico the complete AHSG gene spanning 8.24 kb (7 exons, 6 introns, located on human chromosome 3q27.3), as well as 3 kb of its 5’- and 3 kb of its 3’-flanking regions. Among 33 informative SNPs, five SNPs were manually chosen as representative covering 100% of the common genetic variation (MAF≥0.05) of the locus with D’=1.0 and r²≥0.8. The five genotyped SNPs were: rs2248690 A/T (located in the 5’ flanking region), rs4831 C/G (located in exon 1, leading to the silent mutation Leu13Leu), rs2070635 A/G (located in intron 4), rs4917 C/T (located in exon 6, leading to the missense mutation Thr248Met), and rs1071592 C/A (located in exon 7, leading to the silent mutation Thr270Thr).

Genotyping of the study population

For genotyping, DNA was isolated from whole blood using a commercial DNA isolation kit (NucleoSpin, Macherey & Nagel, Düren, Germany). SNPs were genotyped using the TaqMan assay (Applied Biosystems, Foster City, CA, USA). The TaqMan genotyping reaction was amplified on a GeneAmp PCR system 7000 (50°C for 2 min, 95°C for 10 min, followed by 40 cycles of 95°C for 15 s and 60°C for 1 min), and fluorescence was detected on an ABI Prism sequence detector (Applied Biosystems, Foster City, CA, USA). Quality control was performed in the manner reported earlier (Stefan et al., 2005). The overall genotyping success rate was 99.1% (rs2248690: 98.8%, rs4831: 99.7%, rs2070635: 99.1%, rs4917: 98.8%, and rs1071592: 99.1%), and rescreening of 4.0% of subjects gave 100% identical results.

OGTT and hyperinsulinaemic-euglycaemic clamp

Both assays were performed as formerly described in detail ([Stefan et al., 2005]).

Determination of blood parameters

Plasma glucose, insulin, and C-peptide concentrations were measured as described earlier (Stefan et al., 2005). For determination of AHSG an immunoturbidimetric method was used with specific polyclonal goat anti-human fetuin-A antibodies to human fetuin-A (BioVendor Laboratory Medicine, Modreci, Czech Republic). This method was evaluated in a side-by-side comparison with an enzyme-linked immunosorbent assay (intra-assay coefficient of variation 3.5% and inter-assay coefficient of variation 5.4%, BioVendor Laboratory Medicine, Modreci, Czech Republic) (Stefan et al., 2006) showing a r² of 0.88.

Body composition and body fat distribution

Percentage of body fat, body mass index (BMI), and waist circumference were measured as described earlier (Stefan et al., 2005).

Body fat depots were quantified using magnetic resonance imaging as described earlier (Machann et al., 2005).

Determination of IMCL by magnetic resonance spectroscopy

Neutral lipids within the muscle cell (IMCL) and those interlaced between the muscle fibers were measured by magnetic resonance spectroscopy as described earlier (Machann et al., 2005).

Determination of hepatic fat content

Hepatic fat content was determined by localized proton-magnetic resonance spectroscopy as described earlier (Machann et al., 2005).

Calculations

Insulin sensitivity from the OGTT (in arbitrary units), and clamp-derived insulin sensitivity (in arbitrary units) were calculated as reported earlier (Stefan et al., 2005).

Statistical analyses

Usually, data are given as means±SD. Distribution was tested for normality using the Shapiro-Wilk W test. Log-transformation of non-normally distributed variables, i.e., age, BMI, total adipose tissue (TAT), non-visceral adipose tissue (NVAT), visceral adipose tissue (VAT), hepatic lipids, IMCL tibialis, IMCL soleus, ISI (OGTT), ISI (clamp), and AHSG plasma levels, was performed prior to simple and multivariate linear regression analyses. In multivariate linear regression models, the trait was chosen as dependent variable, and gender, age, BMI, and genotype were tested as independent variables. Taking into account that 5 SNPs were tested in parallel, a Bonferroni corrected p-value<0.01 was considered statistically significant. The statistical software package JMP 7.0 (SAS Institue, Cary, NC, USA) was used. In the dominant model, the study was sufficiently powered (1-β>0.8) to detect effect sizes of 0.31≤d≤0.35 (two-tailed t-test) dependent on the SNP tested. Power calculation was performed using G*power software available at www.psycho.uni-duesseldorf.de/aap/projects/gpower. Analysis of linkage disequilibrium (LD) (D’, r²) was performed using the JLIN program provided by the Western Australian Institute for Medical Research (www.genepi.org.au/jlin (Carter et al., 2006)). Hardy-Weinberg equilibrium was tested using χ² test.

References

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Correspondence