Original Article

International Journal of Obesity (2010) 34, 1011–1019; doi:10.1038/ijo.2010.27; published online 16 February 2010

A variant near the interleukin-6 gene is associated with fat mass in Caucasian men

N Andersson1,2, L Strandberg1, S Nilsson3, S Adamovic1, M K Karlsson4,5, Ö Ljunggren6, D Mellström7,8, N E Lane9, J M Zmuda10, C Nielsen11, E Orwoll11, M Lorentzon7,8, C Ohlsson7,8 and J-O Jansson1 for the Osteoporotic Fractures in Men (MrOS) Research Group

  1. 1Institute of Neuroscience and Physiology, Department of Physiology/Endocrinology, Sahlgrenska Academy, University of Gothenburg, Gothenburg, Sweden
  2. 2Food Science Unit, Department of Chemical and Biological Engineering, Chalmers University of Technology, Gothenburg, Sweden
  3. 3Department of Mathematical Statistics, Chalmers University of Technology, Gothenburg, Sweden
  4. 4Clinical and Molecular Osteoporosis Research Unit, Department of Clinical Sciences, Lund University, Lund, Sweden
  5. 5Department of Orthopedics, Malmö University Hospital, Malmö, Sweden
  6. 6Department of Medical Sciences, University of Uppsala, Uppsala, Sweden
  7. 7Department of Geriatrics, Center for Bone Research, Sahlgrenska Academy, University of Gothenburg, Gothenburg, Sweden
  8. 8Department of Internal Medicine, Sahlgrenska Academy, University of Gothenburg, Gothenburg, Sweden
  9. 9Department of Medicine, Center for Healthy Aging, University of California at Davis, Sacramento, CA, USA
  10. 10Department of Epidemiology, Graduate School of Public Health, University of Pittsburgh, Pittsburgh, PA, USA
  11. 11Bone and Mineral Unit, Department of Medicine, Oregon Health and Science University, Portland, OR, USA

Correspondence: Dr J-O Jansson, Institute of Neuroscience and Physiology/Endocrinology, Sahlgrenska Academy, University of Gothenburg, Box 434, SE-405 30 Gothenburg, Sweden. E-mail: john-olov.jansson@neuro.gu.se

Received 25 October 2009; Revised 28 December 2009; Accepted 4 January 2010; Published online 16 February 2010.

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Abstract

Context:

 

Regulation of fat mass appears to be associated with immune functions. Studies of knockout mice show that endogenous interleukin (IL)-6 can suppress mature-onset obesity.

Objective:

 

To systematically investigate associations of single nucleotide polymorphisms (SNPs) near the IL-6 (IL6) and IL-6 receptor (IL6R) genes with body fat mass, in support for our hypothesis that variants of these genes can be associated with obesity.

Design and Study Subjects:

 

The Gothenburg Osteoporosis and Obesity Determinants (GOOD) study is a population-based cross-sectional study of 18- to 20-year-old men (n=1049), from the Gothenburg area (Sweden). Major findings were confirmed in two additional cohorts consisting of elderly men from the Osteoporotic Fractures in Men (MrOS) Sweden (n=2851) and MrOS US (n=5611) multicenter population-based studies.

Main Outcome:

 

The genotype distributions and their association with fat mass in different compartments, measured with dual-energy X-ray absorptiometry.

Results:

 

Out of 18 evaluated tag SNPs near the IL6 and IL6R genes, a recently identified SNP rs10242595 G/A (minor allele frequency=29%) 3′ of the IL6 gene was negatively associated with the primary outcome total body fat mass (effect size −0.11 standard deviation (s.d.) units per A allele, P=0.02). This negative association with fat mass was also confirmed in the combined MrOS Sweden and MrOS US cohorts (effect size −0.05s.d. units per A allele, P=0.002). When all three cohorts were combined (n=8927, Caucasian subjects), rs10242595*A showed a negative association with total body fat mass (effect size −0.05s.d. units per A allele, P<0.0002). Furthermore, the rs10242595*A was associated with low body mass index (effect size −0.03, P<0.001) and smaller regional fat masses. None of the other SNPs investigated in the GOOD study were reproducibly associated with body fat.

Conclusions:

 

The IL6 gene polymorphism rs10242595*A is associated with decreased fat mass in three combined cohorts of 8927 Caucasian men.

Keywords:

IL6; IL6R; SNP; rs10242595

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Introduction

Interleukin-6 (IL-6) is a cytokine that mainly stimulates immune responses, such as B-cell proliferation and acute-phase production by the liver.1 The immune modulating effects of IL-6 have been shown with the IL-6-neutralizing drug tocilizumab that has beneficial effects on autoimmune diseases such as juvenile and adult rheumatoid arthritis.2, 3 However, IL-6 also has some anti-inflammatory properties such as inhibition of the effects of tumor necrosis factor-α.4, 5

The IL-6 receptor (IL-6R) belongs to the type I cytokine receptor group of transmembrane receptors and is expressed on the surface of cell. The IL-6R complex consists of two parts, the ligand-binding IL-6R (also known as IL-6Rα, encoded by the IL6R gene) and the IL-6 signal transducer (previously designated gp130, encoded by IL6ST gene). The IL-6R specifically binds IL-6 and is expressed on few cell types including immune cells and hepatocytes. IL6ST is much less specific than the IL-6R, as it is a component of several cytokine receptors, and is expressed on the surface of most cell types.6 It has been claimed that pro-inflammatory and other pathophysiological effects of IL-6 to a large extent are exerted through so-called trans-signaling, that is, when a complex between IL-6 and a soluble isoform of the IL-6R is activating membrane bound IL-6ST in various cell types.6 The physiological effects of IL-6 have been suggested to be exerted via the classic route, with free IL-6 acting on membrane bound IL-6R/IL6-ST dimers.6

Recent results indicate that IL-6, in addition to regulating various immune functions, also affects metabolic functions, including fat metabolism. IL-6 is one of several so-called adipokines that are produced and released by white adipose tissue, and has been assumed, together with other adipokines, to contribute to obesity-related metabolic and cardiovascular disturbances.7 Conversely, IL-6 is released from working skeletal muscle in the absence of substantial release of other pro-inflammatory cytokines, such as tumor necrosis factor-α. It has been suggested that IL-6 in this context exerts beneficial effects on the carbohydrate and fat metabolism as well as the exercise capacity.5, 8 In addition, IL-6 knockout mice as well as IL-6/IL-1 double knockout mice get obese, indicating that endogenous IL-6 exerts beneficial effects on fat mass in healthy individuals.8, 9, 10 The obesity-preventing mechanism by IL-6 is unknown, but there are indications that endogenous IL-6 is of importance for leptin sensitivity in healthy individuals, and that the site of its action is in the hypothalamus.10, 11, 12, 13, 14 More recent data are in line with effects by IL-6 through the paraventricular nucleus of the hypothalamus.15 This in turn may lead to activation of the sympathetic nerve system and increased energy expenditure.16, 17

In early studies, there was an association between a supposedly functional polymorphism of the IL6 gene promoter, the −174G/C single nucleotide exchange (rs1800795), described by Fishman et al.,18 and body mass index (BMI) as reported by for instance Grallert et al.19 However, the association with BMI did not reach the level of significance in two meta-analyses on more than 25000 subjects per study.20, 21 Nevertheless, in a recent study of more than 3000 individuals, an association between variants in the IL6 gene and BMI has been shown.21 Moreover, there was an association between rs1800795 and total and regional fat masses as determined by dual-energy X-ray absorptiometry (DXA) in a cohort with 3014 elderly men.22 A possible reason for the above-described discrepancy could be not yet clarified interference by other parameters such as gender, age, cohort and other gene polymorphisms. For example, the studied single nucleotide polymorphisms (SNPs) may be in linkage disequilibrium (LD) with yet unknown functional polymorphisms in the IL6 that are the primary regulators of body fat.23

In the IL6R there is a nonsynonymous SNP (rs8192284, also-called rs2228145) that seems to affect the proteolytic cleavage of a part of the extracellular domain of the IL-6R. This SNP has been associated with BMI and blood glucose in some, but not all studies, possibly due to differences between ethnic groups.24, 25, 26, 27, 28 In summary, the issue of possible associations between IL-6 system gene polymorphisms and obesity does not seem to be settled.

Based on the obesity observed in IL-6 knockout mice (IL6/),8, 9, 10 the aim of the present study was to investigate carefully whether polymorphisms in the IL-6 system genes are associated with body fat mass in humans. We used several means to obtain novel information compared with earlier literature. We used a gene-tagging approach to find common genetic variants in IL6 and IL6R, the two genes that are important for the unique biological effects of IL-6. The tag SNPs were investigated in relation to total body fat mass measured by DXA, a more specific measure than BMI.29 To increase statistical power, we investigated three different populations (8927 subjects in total) that were relatively homogeneous for age, gender and ethnicity.

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Subjects and methods

Study subjects: young adult men

The population-based Gothenburg Osteoporosis and Obesity Determinants (GOOD) study was initiated to determine environmental and genetic factors involved in the regulation of bone and fat mass. Study subjects were randomly identified using national population registers, contacted by telephone, and asked to participate in this study. Men aged 18–20 years from the greater Gothenburg area in Sweden were approached. There were no specified exclusion criteria. Almost half (49%) of the study candidates agreed to participate and were enrolled (n=1068, mean age 18.9±0.6 years).30, 31 Informed consent was obtained from all study participants. The study was approved by the ethics committee at the University of Gothenburg.

A total of 1049 subjects with both successful genotyping and available data on body composition (Table 1) were included in the initial screening of variants in the IL6 and IL6R genes and their association with total body fat mass.


Study subjects: elderly men

The major findings in the initial screening study were replicated in two large cohorts of elderly men (Osteoporotic Fractures in Men (MrOS) Sweden and MrOS US) with available data on body composition (Table 1). Study subjects of the population-based MrOS Sweden cohort (n=3014, men aged 69–81 years) were randomly identified using national population registers, contacted and asked to participate.32 A total of 2851 subjects with both successful genotyping and available data on body composition were included in the first replication analysis.

The MrOS US cohort consists of 5995 community-dwelling, ambulatory men aged greater than or equal to65 years.33, 34 A total of 5611 MrOS US subjects with both successful genotyping and available data on body composition (Table 1) were included in this study.

Assessment of body composition

In the GOOD cohort, lean tissue mass and fat masses for total body, arm, leg and trunk were determined by using DXA (Lunar Prodigy DXA; GE Lunar Corp., Madison, WI, USA). In the MrOS Sweden cohort, lean tissue mass and total fat mass were determined using the Lunar Prodigy DXA (n=1997) for subjects investigated in Malmö (n=998) and Uppsala (n=999) or the Hologic DXA Hologic QDR 4500/A-Delphi (Hologic, Whaltman, MA, USA) (n=953) for subjects investigated in Gothenburg. The QDR 4500 Hologic instrument was also used at all six MrOS US clinical sites.

Assessment of covariates

A standardized questionnaire was used to collect information about amount of physical activity and smoking. In the GOOD cohort, physical activity was assessed as hours of physical activity per week, as previously described.31 In the MrOS Sweden cohort physical activity was the subject's average total daily walking distance (in km), including both walking as a means of exercise and leisure, and as a means of outdoor transportation in activities of daily life.32 In the MrOS US cohort, physical activity was assessed as the self-reported number of city blocks walked each day, including both walking as a means of exercise and walking as a part of daily routine. To be able to merge the measure of physical activity for the MrOS Sweden and MrOS US into a common variable, it was assumed that 1 city block=200 meters.

Genotyping

In all three cohorts studied, genotyping was completed using genomic DNA prepared from whole blood.

GOOD cohort
 

Altogether 19 SNPs with minor allele frequencygreater than or equal to5% in the IL6 (n=9, Table 2) and the IL6R (n=10, Supplementary Table S1) were selected from HapMapData Rel 21/phasell (http://hapmap.ncbi.nlm.nih.gov/) using a pair-wise correlation method (r2greater than or equal to0.80) including the sequence 10kb upstream and 5kb downstream of each gene. The SNPs were genotyped using the GoldenGate assay 35 from Illumina Inc. (San Diego, CA, USA). The genotyping was performed by the SNP Technology Platform in Uppsala, Sweden (www.genotyping.se). Of the genotyped SNPs, all 19 had a genotype call rate of greater than or equal to99% in the study subjects. One of these SNPs, rs12700386, was not polymorphic, leaving 18 SNPs for further analysis. The reproducibility of the genotyping was 100% according to duplicate analysis of 5% of the samples. There were no deviations from Hardy–Weinberg equilibrium (HWE) for these markers (P>0.05, Table 2; Supplementary Table S1).


MrOS Sweden cohort
 

rs10242595 in the IL6 and rs4075015 in the IL6R were analyzed using the Sequenom MassARRAY platform (San Diego, CA, USA). The overall call rate was greater than or equal to98%, and 167 samples were run in duplicates with 100% genotyping concordance rate. Both the SNPs, rs10242595 and rs4075015, were in HWE.

MrOS US cohort
 

Genotyping was performed using TaqMan technology (Applied Biosystems, Foster City, CA, USA). Genotypes were called under standard conditions on a 7900HT Real-Time PCR instrument. All genotype calls were determined by two independent investigators, and only concordant calls were used. The average genotyping call rate was 98.2%. The genotyping concordance rate among 849 replicate samples was 99.9%. There was no deviation in HWE for rs10242595, the only SNP analyzed in this cohort.

Statistics

All SNPs were checked for HWE with χ2-analysis. LD between the SNPs was measured by D′ (Figure 1 for the IL6; Supplementary Figure S1 for the IL6R). We performed multiple linear regressions with total body fat mass for each SNP. Covariates for these calculations were determined as factors significantly associated with the primary outcome total fat in each cohort. Backward selection was then used to determine whether these covariates were suitable. Effect sizes and P-values for the remaining covariates are shown in Supplementary Table S2. Power calculations for the MrOS cohorts were based on the estimates for the two SNPs that were significantly associated with total fat mass in the GOOD cohort.

Figure 1.
Figure 1 - Unfortunately we are unable to provide accessible alternative text for this. If you require assistance to access this image, please contact help@nature.com or the author

The linkage disequilibrium (LD) pattern of the interleukin-6 gene (IL6, also known as interferon-β2) based on results from tag-single nucleotide polymorphism (SNP) genotyping in the Gothenburg Osteoporosis and Obesity Determinants (GOOD) cohort. The location of each tested SNP along the chromosome is indicated on top and the number in each square indicates the magnitude of LD expressed as D′ between respective pairs of SNPs. Color scheme: bright red squares indicate strong LD (D′=1); squares colored by lighter shades of pink indicate weaker LD; white indicates very weak or no LD (D′=0). Modified from Haploview.37

Full figure and legend (169K)

Covariates in the GOOD cohort
 

Linear regression under the assumption of an additive model was used to analyze the relationships between total body fat mass and the SNPs (DD=0, Dd=1, dd=2, where D and d are the major and minor alleles, respectively). Current physical activity and total body lean mass were used as covariates on log-transformed response, in a similar way as performed previously36, 37 (Table 3; Supplementary Table S2).


Covariates in the MrOS Sweden cohort
 

Linear regressions were performed assuming additive models for rs10242595 (GG=0, AG=1, AA=2) and for rs4075015 (AA=0, AT=1, TT=2). Correction was carried out for study site (subjects were collected in three geographically separated regions), age, current physical activity, current smoking status and height. One limitation in the MrOS Sweden study is that the clinical sites used DXA machines from different manufacturers (see assessment of body composition above). In the multiple linear regression analysis we have used site as a covariate that to a large extent should compensate for this discrepancy. Moreover, when the subjects not measured with the Hologic DXA machine (that is, MrOS Sweden from Uppsala and Malmö) were excluded from the calculation of combined elderly men (Table 4), the results remain very similar (see Results section). A subanalysis regarding type of DXA machine in MrOS Sweden could not be performed due to greatly reduced the sample size and power (not shown).


Covariates in the MrOS US cohort
 

Linear regression for rs10242595 was performed in a similar way as for MrOS Sweden. All covariates were the same as for MrOS Sweden with the addition of race.

Information about effect sizes and standard errors (s.e.m.), as well as confidence intervals (CI) and covariates used for adjustments in the three cohorts and the three cohorts combined is given in table legends (Table 3, Table 4 and Table 5).


Softwares
 

Haploview (version 4.1) was used for LD measurements, to generate figures representing LD patterns in the IL6 and IL6R based on tag-SNP genotypes from the GOOD cohort, and for a haplotype analysis using case–control model. Haplotypes were also analyzed by sliding window approach using Helix Tree (Golden Helix, Inc., Bozeman, MT, USA; version 7).36 For linear and multiple linear regression analyses we used SPSS (SPSS Inc., Chicago, IL, USA; version 17.0.0). Values are given as mean±s.d. All tests were two tailed and conducted at the 5% significance level.

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Results

Characteristics of the SNPs in the IL6 and IL6R genes

To investigate whether SNPs in the IL-6 system are associated with body fat, we analyzed several SNPs in the IL6 and IL6R genes in relation to the primary outcome total body fat mass as measured by DXA. We successfully genotyped 18 SNPs distributed across the IL6 (n=8) and IL6R (n=10). The general SNP localizations within the genes, allele and genotype frequencies, and HWE are shown in Table 2 for the IL6 SNPs and in Supplementary Table S1 for the IL6R SNPs. χ2-Analysis showed no deviation from HWE for any of the 18 investigated SNPs. The allelic frequencies obtained are consistent with the dbSNP allelic frequencies for the European population.

Pair-wise LD tests between consecutive SNPs, genotyped in the GOOD cohort, showed that the LD (expressed as D′) was mostly strong and significant across the IL6 (also known as interferon-β2). rs10242595, located about 2kb downstream from the IL6 coding region, had weak LD with the other SNPs, with the exception for rs2069861 and rs1800795 (Figure 1). In the IL6R gene, we could define two distinct LD blocks (Supplementary Figure S1).

Associations between individual SNPs in the IL6 and IL6R and fat mass

The SNP associated with body fat close to IL6 was rs10242595 (effect size −0.11s.d. units change per A allele, P=0.02, Pc=0.19) with adjustment for covariates as presented in the legend of Table 3. In the IL6R gene, rs4075015 was associated with fat mass (effect size −0.09s.d. units per T allele, P=0.03, Pc=0.17; Supplementary Table S3). Based on these estimates, the power to detect an association was approximately 95% in MrOS Sweden and >99% in MrOS US for both SNPs.

Replication of major findings in MrOS Sweden and MrOS US

Multiple linear regression analysis showed a borderline association between rs10242595*A and a decrease in the primary outcome total body fat mass (effect size −0.044s.d. units per A allele, P=0.10 for the fully adjusted model; Table 4). There was no such association for the rs4075015*T (effect size 0.034s.d. units per T allele, P=0.23; data not shown) and no further calculations were carried out on this SNP. Having found a borderline association between total body fat mass and rs10242595*A in the elderly Caucasian subjects of the MrOS Sweden cohort (n=2851), we expanded the replication effort to include also the MrOS US cohort (n=5609). The A allele of rs10242595 was significantly associated with low total body fat in the Caucasian subpopulation of the MrOS US cohort (effect size −0.047s.d. units per A allele, P=0.009), and this was also seen in the MrOS US cohort as a whole (effect size −0.037 units per A allele, P=0.03). rs10242595*A was significantly associated with decreased total body fat in combined elderly Caucasian men from MrOS Sweden and MrOS US cohorts (effect size −0.046s.d. units per A allele, P=0.002) and in combined Caucasian men from GOOD, MrOS Sweden and MrOS US (effect size −0.052s.d. units per A allele, P=0.0002). When only subjects measured with the Hologic DXA machine (effect size −0.042, P=0.01 in Caucasian subjects; n=5994) or only subjects measured with the Lunar Prodigy DXA machine (effect size 0.048, P=0.006 in Caucasian subjects; n=3046) were included in the combined cohorts, similar results were obtained.

In addition to the primary outcome total body fat mass, this analysis showed associations between the rs10242595*A variant and decreases of secondary outcomes total fat percent, body weight, BMI as well as for regional measures of fat (Table 5). There was also a negative correlation between the A allele and most of the fat-related parameters in the GOOD study (data not shown). The A allele was associated with increased lean body mass in Caucasians in the combined MrOS Sweden and US study (Table 5). However, there was no correlation between the A allele and lean body mass in the GOOD study (data not shown). The effect sizes for the different parameters, calculated as s.d. units per A allele, are also shown in Table 5. In the MrOS US Study a decrease in body weight by 0.03s.d. units per A allele equals a body weight decrease of about 0.5% per A allele, or about 0.4kg body weight per A allele for a man weighing 80kg. There was no association between rs10242595*A on one hand and serum levels of insulin, glucose and fats on the other (data not shown).

Haplotype analysis showed no association between IL6 or IL6R genes and body fat mass as calculated by case–control analysis or a sliding window approach (not shown).

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Discussion

Although IL-6 is mostly associated with pathophysiologic adaptations during inflammation,1 there is evidence that endogenous IL-6 suppresses fat mass in healthy experimental animals.8, 9, 10 In this translational candidate gene study we performed a systematic investigation of how genetic variations in the IL6 and IL6R genes are associated with total body fat mass and regional fat parameters, as measured by DXA, in a well-characterized population of 1049 healthy young men within a 2-year age span. Using this approach, we identified one SNP (rs10242595) in the IL6 gene, not previously associated with any phenotypic changes, which was clearly associated with body fat mass and this observation was confirmed in two other cohorts consisting of 7779 Caucasian elderly men.

As discussed above, IL-6 knockout mice develop obesity,8, 9, 10 but there are several other reasons to investigate possible associations between the IL6 gene system polymorphisms and metabolic functions in humans. IL-6 belongs to the same family of proteins as leptin38 and ciliary neurotrophic factor, both of which are known to suppress obesity. A variant of ciliary neurotrophic factor suppressed body fat in clinical phase 2 studies in humans, but the clinical development of ciliary neurotrophic factor as an antiobesity drug was later discontinued due to technical problems with appearances of neutralizing antibodies to the drug.39 Moreover, IL-6 is released into the blood from adipose tissue in humans and there is a positive correlation between serum IL-6 levels and body fat.40, 41 IL-6 is also released from working skeletal muscle and has been suggested to be of importance for the metabolic adaptation to exercise.5, 42, 43 In resting lean individuals, the level of IL-6 may be higher in cerebrospinal fluid than in serum suggesting that IL-6 is also produced by the brain.14 Finally, the recent introduction of the IL-6 receptor antibody tocilizumab for treatment of autoimmune diseases makes it necessary to foresee possible side effects, including metabolic ones, after suppression of endogenous IL-6.2, 3

In earlier studies, several groups, including ours, found an association between the well-described polymorphism in the IL6 promoter, −174G/C SNP (rs1800795), and BMI. However, this effect could not be confirmed in two large meta-analyses20, 21 recently, it was reported by Qi et al.21 that certain haplotypes as well as some individual SNPs in the IL6 gene were associated with BMI and waist circumference in two cohorts with about 3000 men and women in total. rs2069827, the individual SNP most associated with BMI in this study,21 was in strong LD (r2=0.92) with one of our tag SNPs (rs11766273, Tables 2 and 3), but the latter was not associated with body fat mass in the young adult men from the GOOD study. The interrelation between the present IL6 gene polymorphism and the variants reported by Qi et al. and functional consequences of all of these polymorphisms need to be clarified. One possibility is that they are merely in LD with yet unknown functional polymorphisms. Some polymorphisms of the IL6R gene have also been associated with metabolic parameters.24, 25, 26, 27, 28 In this study, we saw no reproducible associations between polymorphisms of the IL6R gene, including the nonsynonymous rs2228145 (also known as rs8192284), and total body fat mass.

There are some strengths and weaknesses of this study. One strength is that the major finding of this study, significant association of rs10242595*A with decreased fat mass in young adult men, was replicated in two other population-based studies consisting of 7779 elderly Caucasian men. The statistical power increases not only due to the comparatively large sample, but also due to the fact that the cohorts are homogenous. Moreover, the fact that the effect size of the rs10242595*A on fat was similar (approximately −0.04s.d. units per A allele) in each one of the two replication cohorts with men of similar age (MrOS Sweden and MrOS US) may support the validity of the present findings. The measurement of body fat with DXA is considerably more specific than the surrogate parameter BMI used in many other studies.29 As discussed earlier, the theoretical background for considering IL6 as a candidate gene for obesity is strong. A clear drawback is that rs10242595 is likely to be only in LD with a functional polymorphism of the IL6 gene. However, it seems unlikely that rs10242595 is affecting another gene then IL6. The closest gene to rs10242595, besides IL6, is the translocase of outer mitochondrial membrane 7 homolog (yeast, TOMM7) gene that is located 81kb 3′ of IL6. It would be important to determine if this association holds true in women and other ethnic groups as well. In the MrOS USA cohort there were no significant associations between the rs101242595 and body fat in comparatively small groups of non-Caucasian subjects, but more detailed studies on considerably larger cohorts are needed to investigate this issue.

In summary, several earlier studies have indicated that IL-6 is important for metabolism. We now found that the IL6 polymorphism rs10242595*A, which has not been described to be in association with phenotypes earlier, is associated with decreased fat mass in three combined cohorts of 8927 Caucasian men. The relation of the present data to possible metabolic side effects of IL-6-neutralizing therapy2, 3 may be a topic of future investigation.

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Conflict of interest

The authors declare no conflict of interest.

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Acknowledgements

This work was supported by Swedish Research Council (no. K2007-54X-09894-16-3), EC FP6 funding (contract no. LSHM-CT-2003-503041), Johan och Jakob Söderbergs Foundation, Marcus Borgströms Foundation, Nilsson-Ehle Foundation, NovoNordisk Foundation, Swedish Medical Society, Swedish Society for Medical Research and Sahlgrenska Center for Cardiovascular and Metabolic Research (CMR, no. A305:188) which is supported by the Swedish Strategic Foundation. The MrOS US study was supported, in part, by Grants R01-AR049747 from the National Institute of Arthritis and Musculoskeletal and Skin Diseases (NIAMS). The Osteoporotic Fractures in Men Study was supported by the National Institutes of Health (NIH) funding. The following Institutes provide support: the National Institute of Arthritis and Musculoskeletal and Skin Diseases (NIAMS), the National Institute on Aging (NIA), the National Center for Research Resources (NCRR) and NIH Roadmap for Medical Research under the following grant numbers: U01 AR45580, U01 AR45614, U01 AR45632, U01 AR45647, U01 AR45654, U01 AR45583, U01 AG18197, U01 AG027810, UL1 RR024140, AR052000 and AR048841.

Supplementary Information accompanies the paper on International Journal of Obesity website

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