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IL-6 promoter polymorphisms and quantitative traits related to the metabolic syndrome in KORA S4
Experimental Gerontology 41 (2006) 737–745 www.elsevier.com/locate/expgero
IL-6 promoter polymorphisms and quantitative traits related to the metabolic syndrome in KORA S4 Harald Grallert a, Cornelia Huth a,b, Melanie Kolz a, Christa Meisinger a, Christian Herder c, Klaus Strassburger d, Guido Giani d, H.-Erich Wichmann a,b, Jerzy Adamski e, Thomas Illig a,*, Wolfgang Rathmann d a
e
GSF National Research Center for Environment and Health, Institute of Epidemiology, Germany b IBE, Chair of Epidemiology, University of Munich, Germany c German Diabetes Clinic, German Diabetes Center, Leibniz Institute at the Heinrich-Heine-University, Du¨sseldorf, Germany d Institute of Biometrics and Epidemiology, German Diabetes Center, Leibniz Institute at the Heinrich-Heine-University, Du¨sseldorf, Germany GSF National Research Center for Environment and Health, Institute of Experimental Genetics, Genome Analysis Center, Neuherberg, Germany Received 3 March 2006; received in revised form 28 April 2006; accepted 2 May 2006 Available online 23 June 2006
Abstract Interleukin-6 (IL-6) is a pleiotropic cytokine which has been proposed as ‘‘cytokine for gerontologists’’ and linked to age-related metabolic disturbances such as the metabolic syndrome or type 2 diabetes. Polymorphisms located in the promoter region of IL-6 have been reported to be involved in the regulation of IL-6 transcription. This study investigates whether IL-6 promoter variants 174 G/C and 573 G/C are associated with quantitative traits related to the metabolic syndrome (International Diabetes Federation criteria) in a population of normoglycemic subjects (n = 878) from the latest KORA survey (KORA S4). Genotyping was performed using MALDI-TOF MS. Besides lower height (p = 0.01) the 174 CC genotype was independently associated with lower waist (p = 0.002) and hip (p = 0.01) circumferences in men. Furthermore, the 174 CC genotype was associated with BMI (p = 0.004) when adjusted for waist and hip circumference. The present study does not suggest associations with further components of the metabolic syndrome. The association with height seems to be the central factor indicating an influence of IL-6 on growth through impaired bone metabolism. However, the complex relationships need further investigation. 2006 Elsevier Inc. All rights reserved. Keywords: IL-6; Single nucleotide polymorphism; KORA S4; Metabolic syndrome; Height
1. Introduction The metabolic syndrome (MS) is a common metabolic disorder. The rising number of persons suffering from MS mainly results from the increasing prevalence of obesity. Besides central obesity, additional risk factors like insulin resistance, glucose intolerance, hypertension and dyslipidemia are characteristic for the MS (Isomma, 2003). Although the heritability of the MS components is high, the genetic mechanisms responsible for this clustering *
Corresponding author. Tel.: +49 89 3187 4249; fax: +49 89 3187 4567. E-mail address: [email protected] (T. Illig).
0531-5565/$ - see front matter 2006 Elsevier Inc. All rights reserved. doi:10.1016/j.exger.2006.05.002
are still unclear (Lin et al., 2005). The risk of developing type 2 diabetes (T2DM) or the MS increases with age. Likewise, the production of cytokines from monocytes and macrophages and levels of circulating acute-phase proteins increases with age (Bruunsgaard et al., 2001; Fagiolo et al., 1993). Aging was associated with low-grade elevations in levels of circulating inflammatory mediators like interleukin-6 (IL-6) acting as predictors of mortality independent of pre-existing morbidity (Krabbe et al., 2004). IL-6 is a multifunctional cytokine which is produced not only in monocytes and macrophages but in several cell types including most cells of the immune system, chondrocytes, osteoblasts, skeletal and smooth muscle cells,
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hepatocytes, islet b cells and adipocytes (Kamimura et al., 2003). This variety of cell types secreting IL-6 indicates the complexity of IL-6 function and regulation. IL-6 plays a central role in host defence as it functions as a main regulator of the acute phase response (Fey and Gauldie, 1990). Besides further functions in the immune, nervous and endocrine systems, IL-6 is involved in bone metabolism, hematopoiesis and cancer (Kamimura et al., 2003). As the receptor subunit gp130 is widely expressed, deregulated high-level production of IL-6 combined with its agonistic soluble receptor sIL-6R may induce an undesired inflammatory state in many organs and could thus cause various diseases. The association of MS with low-grade inflammation is well documented (Sutherland et al., 2004). Raised IL-6 levels associated with expanded adipose tissue mass have been demonstrated in obese subjects as well as in diabetic patients, particularly in subjects also having features of the metabolic syndrome (Pickup et al., 1997). In the pathophysiology of the MS raised IL-6 levels were hypothesized to act on several key parameters. IL-6 was postulated to contribute to insulin resistance, increased glucose production in the liver, inhibition of the insulinmediated glucose uptake in skeletal muscle and to be involved in the development of hypertension (Eckel et al., 2005). Several polymorphisms in the IL-6 promoter region were analyzed for association with T2DM. Numerous studies on the promoter polymorphism 174 G/C have yielded controversial results (Kristiansen and MandrupPoulsen, 2005). Functional analyses have indicated a role of this polymorphism in IL-6 expression (Fishman et al., 1998; Terry et al., 2000). Recent studies on an age-related association of the 174 G/C polymorphism on IL-6 expression with controversial results have not contributed to clarity (Krabbe et al., 2004). This analysis investigates whether the 174 G/C and 573 G/C IL-6 promoter polymorphisms are associated with quantitative traits related to the International Diabetes Federation (IDF)-defined metabolic syndrome in fasting non-diabetic elderly participants of the large population-based study KORA S4. 2. Materials and methods
excluded in the present analysis. Oral glucose tolerance tests (OGTT) were performed in 1353 participants (Rathmann et al., 2003). After the exclusion of all subjects with impaired fasting glucose (IFG; fasting glucose levels of 100–125 mg/dl), impaired glucose tolerance (IGT; fasting glucose levels below 140 mg/dl and 2 h glucose levels of 140–199 mg/dl) and newly diagnosed diabetes, 878 normoglycemic (fasting/2 h OGTT serum glucose <110 mg/dl/ <140 mg/dl) individuals (417 male, 461 female) were included in the present analysis. Body weight was measured in light clothing to the nearest 0.1 kg and height to the nearest 0.1 cm. Waist circumference was measured at the maximum abdominal girth to the nearest 0.1 cm. Blood pressure in a sitting position was measured at the right arm three times, after 15 min rest, using an automatic device (OMRON HEM 705-CP). The mean of the second and third measurement was used for analysis. Blood glucose was assessed using a hexokinase method (Gluco-quant, Roche Diagnostics, Mannheim, Germany). HDL cholesterol was measured using the phosphotungstic acid method (Boehringer–Mannheim, Germany). Triglycerides were assessed with the Boehringer GPO-PAP assay. Serum IL-6 levels were measured as described in Muller et al. (2002) and in Herder et al. (2005a). In a genomic control approach, 210 SNPs were tested in KORA S4. No major population stratification within the population could be detected. 2.2. Definition of the metabolic syndrome The metabolic syndrome was defined according to the International Diabetes Federation (IDF) for Europid persons by presence of central obesity (waist circumference >94 cm in men, >80 cm in women) and two of four further factors (IDF, 2005). These are (1) raised triglyceride levels (P150 mg/dl) or specific treatment for this lipid abnormality, (2) reduced HDL cholesterol (<40 mg/dl in men, <50 mg/dl in women) or treatment for this abnormality, (3) raised blood pressure (BP) (systolic BP P 130 mmHg or diastolic BP P 85 mmHg) or treatment of previously diagnosed hypertension, (4) raised fasting plasma glucose (P100 mg/dl) or previously diagnosed T2DM (which is not relevant for the present study as subjects with known T2DM were excluded).
2.1. Study population 2.3. Genotyping The KORA (Cooperative Health Research in the Region of Augsburg) Survey S4 (formerly known as S2000) is a population-based study of adults performed in Southern Germany (Wichmann et al., 2005). This survey was conducted under the same conditions as the previous three surveys within the WHO MONICA Augsburg project (Holle et al., 2005). One thousand six hundred and fifty-three subjects were included in the 55- to 74-year age group. One hundred and thirty-one individuals with known diabetes and 169 subjects who were not fasting were
Genomic DNA of KORA participants was extracted from blood leukocytes using the Puregene DNA Isolation Kit (Gentra Systems, MN 55441, USA) according to the manufacturer’s recommendation. Genotyping for the 174 G/C (rs1800795) and 573 G/C (rs1800796, denoted as 572 in previous studies) IL-6 promoter variants was carried out by means of matrix-assisted laser desorption ionization-time of flight (MALDI-TOF) analysis of allele-dependent primer extension products as described
H. Grallert et al. / Experimental Gerontology 41 (2006) 737–745
elsewhere (Weidinger et al., 2004). Control genotyping of 384 subjects was performed with the TaqMan method.
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3. Results 3.1. Characteristics of the study participants
2.4. Statistical analysis Violation of the Hardy–Weinberg equilibrium (HWE) was tested by Fisher’s exact test. All statistical analyses were carried out for the whole population and stratified by sex. Quantitative traits approximating normal distribution on the original scale are presented as means ± standard deviation whereas traits not approximately normally distributed are given as median and 25th/75th percentiles. Traits that did not accomplish normal distribution after logarithmic transformation were analyzed by Kruskal–Wallis tests. Quantitative traits which were normally distributed were analyzed by linear regression. For the 174 G/C polymorphism dominant, recessive and separate pairwise comparison (nominal) models were used. According to the low minor allele frequency the 573 G/C polymorphism was analyzed only by a dominant model. For analysis of blood pressure subjects with hypertension or medication against hypertension were generally excluded. Association of the genotypes with the IDF-defined MS was assessed by logistic regression. Multiple regression models were also fitted to adjust for potential confounders. Correlations were calculated by Pearson’s or Spearman’s correlation coefficient r2 for traits which were normally or not normally distributed. p-values of p < 0.01 were considered statistically significant. All analyses were carried out using SAS (V. 9.1 Cary, NC, USA).
Characteristics of the normoglycemic elderly KORA S4 participants stratified by sex are shown in Table 1. Significant sex differences were observed for waist circumference, waist-to-hip ratio, height, weight, fasting plasma glucose, triglycerides, uric acid and blood pressure, which were all higher in men, and for hip circumference, percent body fat, HDL cholesterol levels and HbA1c, which were higher in women (p < 0.01). No significant sex differences were observed for BMI, 2 h glucose, LDL cholesterol levels, fasting insulin, HOMA-IR, leukocyte count and IL-6. Except for IL-6 levels, HOMA IR, fasting insulin and triglycerides, all quantitative traits were normally distributed. Comparing age categories (55–64 and 65–74 years) there was a trend towards higher IL-6 levels in the elderly (men p = 0.03; women p = 0.02). 3.2. Genotyping results Genotype distributions of both polymorphisms were in HWE (p > 0.05). Genotyping success rates were >98%, with an error rate <1% in 210 routine duplicates. No discordant genotypes were found in quality control genotyping with the TaqMan method for both polymorphisms (data not shown). Major allele frequencies of 57% G for the 174 and 95% G for the 573 polymorphism corresponded to allele frequencies in dbSNP from the National Center for Biotechnology Information (NCBI).
Table 1 Characteristics of normoglycemic survey participants aged 55–74 years by sex: KORA Survey 4, Augsburg, Germany
Age (years) Body mass index (kg/m2) Height (cm)a Weight (kg)a Body fat (%)a WHRa Waist circumference (cm)a Hip circumference (cm)a Serum uric acid (mg/dl)a HDL cholesterol (mmol/L)a LDL cholesterol (mmol/L) Fasting plasma glucose (mg/dl)a Two-hour plasma glucose (mg/dl) Systolic blood pressure (mmHg)a Diastolic blood pressure (mmHg)a Leukocytes (·10 3/ll) HbA1c (%)a HOMA-IR Fasting insulin (mU/L) Triglycerides (mg/dl)a Interleukin-6 (pg/ml)
N (m/w)
Men
Women
(417/460) (414/460) (416/461) (414/461) (412/457) (416/460) (416/460) (416/460) (417/460) (416/459) (415/460) (417/460) (417/460) (290/337) (290/337) (417/460) (417/460) (416/460) (416/460) (412/456) (413/460)
63.6 ± 5.6 27.4 ± 3.3 172.3 ± 6.3 81.4 ± 10.9 32.18 ± 4.09 0.95 ± 0.05 98.6 ± 8.9 103.7 ± 6.2 6.13 ± 1.26 1.40 ± 0.34 3.97 ± 0.10 97.1 ± 6.8 100.4 ± 21.3 134.3 ± 17.6 80.1 ± 9.6 6.07 ± 1.56 5.52 ± 0.33 2.06 (1.49/2.98) 8.70 (6.15/12.15) 113.0 (79.5/155.5) 1.88 (0.95/3.24)
63.4 ± 5.4 27.9 ± 4.6 159.3 ± 6.1 70.8 ± 12.1 39.67 ± 5.06 0.83 ± 0.06 88.3 ± 10.8 106.6 ± 9.4 4.78 ± 1.07 1.71 ± 0.44 4.02 ± 1.07 94.2 ± 7.0 103.3 ± 20.6 125.9 ± 18.1 76.7 ± 9.4 5.79 ± 1.31 5.60 ± 0.33 2.05 (1.48/3.01) 8.70 (6.60/12.90) 101.0 (78.0/137.0) 1.62 (0.94/2.61)
a p < 0.01 for sex differences. Data are presented as means ± standard deviations or medians (25th/75th percentiles) for traits that were not normally distributed (insulin, HOMA-IR, triglycerides, and IL-6).
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3.3. IL-6 promoter polymorphisms and the metabolic syndrome Three hundred and thirty-two subjects (37.8%) of the analyzed population fulfilled the IDF criteria for the metabolic syndrome although no subjects with diabetes, IFG or IGT were included. Comparing this group with the remaining subjects, no statistically significant association between the 174 G/C and 573 G/C variants and the metabolic syndrome could be demonstrated ( 174 CC vs. GG OR (95% CI) = 1.22 (0.80–1.85), GC vs. GG OR (95% CI) = 1.08 (0.79–1.49); 573 GC/CC vs. GG OR (95% CI) = 0.87 (0.54–1.39)). 3.4. The
174 G/C polymorphism and quantitative traits
The results of the nominal linear regression for the 174 G/C polymorphism are displayed in Table 2. There was no significant association with age. The CC genotype was significantly associated with lower height in men (b = 2.23 cm, p = 0.01). A negative association between the C allele and waist circumference was also observed for men when adjusted for BMI and age. CC genotype carriers showed reduced waist circumference compared to GG genotype carriers in the nominal model (b = 1.91 cm, p = 0.002). This association resists additional adjustment for smoking, physical activity and alcohol intake (CC vs. GG: b = 1.90 cm, p = 0.003). The b-estimate of the recessive model shows a significant association (CC vs. GG/GC b = 1.47 cm, p = 0.008) but with a b-estimate of the GC heterozygotes of 0.74 cm the association could also be additive (b = 0.96 cm p = 0.003). A similar behavior was observable for hip circumference. The association results for waist and hip circumference are independent of BMI, although BMI and waist circumference (r2 = 0.87 within each gender group) and BMI and hip circumference (r2 = 0.81 men; r2 = 0.89 women) are highly correlated. After additional adjustment for waist and hip circumference, the C allele is associated with higher BMI in men (CC vs. GG: b = 0.65 kg/m2, p = 0.004; GC vs. GG: b = 0.41 kg/m2, p = 0.02) pointing to a dominant association (GC/CC vs. GG: b = 0.47 kg/m2, p = 0.004). For all other traits analyzed by linear regression modeling, there was no significant association for the 174 G/C polymorphism. The Kruskal–Wallis test did not show any significant association for IL-6 levels, fasting insulin levels or HOMA-IR with the 174 G/C polymorphism. 3.5. The
573 G/C polymorphism and quantitative traits
The results for the 573 G/C polymorphism are presented in Table 3. The C allele carriers of the 573 G/C polymorphism showed a trend toward association with younger age (b = 1.37 years, p = 0.03). In addition, carriers of a C allele without medication against hypertension
(n = 609) also showed a trend toward higher diastolic blood pressure (b = 3.00, p = 0.03). After additional adjustment for smoking, physical activity and alcohol intake no essential change was observed (data not shown). None of the other traits analyzed by linear regression, nor IL-6 levels (all subjects: p = 0.60; men: p = 0.13; women: p = 0.59), insulin levels (all subjects: p = 0.28; men: p = 0.77; women: p = 0.19) or HOMA-IR (all subjects: p = 0.45; men: p = 0.63; women: p = 0.22) analyzed by the Kruskal–Wallis test, were significantly associated with the 573 G/C polymorphism. 4. Discussion This study investigated two IL-6 promoter polymorphisms ( 174 G/C and 573 G/C) for association with quantitative traits related to the IDF-defined metabolic syndrome. The results of this study do not support the hypothesis that the 174 G/C polymorphism is associated with age. For the 573 G/C polymorphism there was a tendency for the C allele to be associated with lower age. Our data suggest a negative association of the 174 C allele with height in men. Moreover, for men lower waist and hip circumferences as well as higher BMI were associated with the 174 C allele. For the 573 C allele the data showed a further tendency to be associated with higher diastolic blood pressure. Low-grade inflammation has been postulated as the link between insulin resistance, obesity and diabetes (Dandona et al., 2004). As IL-6 is one of the major cytokines in low-grade inflammation, the physiological and pathophysiological effects of IL-6 have been examined extensively. Elevated circulating levels of IL-6 have been suggested to cause insulin resistance and T2DM (Herder et al., 2005b; Hu et al., 2004; Pickup et al., 2000; Pradhan et al., 2001; Spranger et al., 2003). In vitro there is evidence for a role of IL-6 in causing impaired insulin signaling in adipocytes (Kristiansen and Mandrup-Poulsen, 2005; and references therein). High IL-6 levels have been one of the most powerful predictors of morbidity and mortality in the elderly (Ferrucci et al., 1999; Harris et al., 1999). However, IL-6 has also been proposed to have an enhancing effect on glucose and lipid metabolism (Steensberg et al., 2000; Van Hall et al., 2003; Wallenius et al., 2003). There is increasing evidence for an age-related effect of the 174 G/C polymorphism (Bonafe et al., 2001; Fishman et al., 1998; Olivieri et al., 2003). Men with 174 GG genotype were proposed to be disadvantaged for longevity (Bonafe et al., 2001; Ross et al., 2003). This was proposed to be due to higher IL-6 expression (Olivieri et al., 2003) but another study observed lower IL-6 expression in this context (Rea et al., 2003). According to its influence on diseases of aging, IL-6 was proposed to be ‘‘a cytokine for gerontologists’’ (Ershler, 1993). In contrast, some studies propose no influence of the IL-6 variant in aging (Wang et al., 2001). Likewise, the present study reveals no significant impact of the 174 IL-6 promoter polymorphism on
H. Grallert et al. / Experimental Gerontology 41 (2006) 737–745 Table 2 Estimated associations in the nominal linear regression model and scores of the Kruskal–Wallis test for the IL-6 traits related to the metabolic syndrome GC vs. GG Total (A) Linear regression Age (years) n = 857 (408/449) BMI (kg/m2) n = 857 (406/451) BMI (kg/m2)a n = 857 (406/451) Waist circumference (cm)b n = 857 (406/451) Waist circumference (cm) n = 857 (406/451) Hip circumference (cm)b n = 857 (406/450) Hip circumference (cm) n = 857 (406/450) Height (cm) n = 859 (408/451) Weight (kg) n = 857 (406/451) Body fat (%) n = 851 (404/447) WHR n = 857 (406/451) Serum uric acid (mg/dl) n = 857 (406/451) HDL cholesterol (mmol/L) n = 855 (405/450) LDL cholesterol (mmol/L) n = 854 (404/450) Triglycerides (log) n = 849 (401/448) Fasting glucose (mg/dl) n = 857 (406/451) Two-hour glucose (mg/dl) n = 857 (406/451) Systolic blood pressure (mmHg) n = 611 (283/328) Diastolic blood pressure (mmHg) n = 611 (283/328) Leukocytes (·10 3/ll) n = 857 (406/451) HbA1c (%) n = 857 (406/451)
0.03 0.39 0.33 0.41
174 G/C polymorphism on quantitative
CC vs. GG Men
(0.94) (0.22) (0.01) (0.59)
741
0.08 0.64 0.41 0.76
Women (0.90) (0.08) (0.02) (0.44)
0.16 0.16 0.26 0.10
(0.98) (0.74) (0.17) (0.93)
Total 0.09 0.06 0.26 0.77
Men (0.88) (0.89) (0.13) (0.44)
0.50 0.03 0.65 2.02
Women (0.55) (0.95) (0.004) (0.11)
0.30 0.10 0.14 0.33
(0.70) (0.88) (0.56) (0.83)
0.40 (0.30)
0.74 (0.14)
0.22 (0.70)
0.61 (0.22)
1.91 (0.002)
0.56 (0.45)
0.23 (0.73)
0.28 (0.68)
0.70 (0-53)
0.87 (0.31)
1.46 (0.10)
0.38 (0.79)
0.70 (0.02)
0.68 (0.11)
1.03 (0.04)
0.55 (0.16)
1.39 (0.01)
0.19 (0.77)
0.74 (0.11)
1.33 (0.05)
0.19 (0.76)
0.75 (0.21)
2.23 (0.01)
0.58 (0.48)
0.48 (0.59)
0.71 (0.56)
0.27 (0.84)
0.75 (0.52)
2.10 (0.19)
0.42 (0.84)
0.07 (0.83)
0.41 (0.31)
0.25 (0.65)
0.33 (0.47)
0.11 (0.83)
0.59 (0.40)
0.0034 (0.36) 0.09 (0.29)
0.001 (0.83) 0.11 (0.43)
0.006 (0.30) 0.08 (0.45)
0.0006 (0.89) 0.13 (0.26)
0.006 (0.30) 0.18 (0.33)
0.007 (0.34) 0.07 (0.63)
0.019 (0.51)
0.035 (0.36)
0.002 (0.97)
0.012 (0.76)
0.005 (0.92)
0.020 (0.72)
0.10 (0.22)
0.19 (0.09)
0.01 (0.93)
0.04 (0.73)
0.18 (0.22)
0.11 (0.46)
0.016 (0.65)
0.016 (0.78)
0.018 (0.68)
0.002 (0.96)
0.034 (0.64)
0.048 (0.40)
0.82 (0.12)
0.88 (0.25)
0.74 (0.32)
1.11 (0.11)
1.07 (0.28)
1.20 (0.22)
0.83 (0.60)
2.60 (0.28)
0.83 (0.70)
0.92 (0.66)
2.80 (0.37)
0.71 (0.80)
3.04 (0.06)
1.15 (0.62)
4.25 (0.06)
3.22 (0.13)
3.95 (0.19)
2.28 (0.44)
0.08 (0.92)
1.27 (0.41)
0.91 (0.45)
0.29 (0.80)
0.46 (0.79)
0.13 (0.75)
0.02 (0.88)
0.25 (0.17)
0.19 (0.18)
0.12 (0.40)
0.11 (0.63)
0.14 (0.44)
0.005 (0.85)
0.05 (0.16)
0.04 (0.28)
0.003 (0.93)
0.03 (0.56)
0.03 (0.50)
Total
Men p-value
Medians
Women
N = 858 (408/450)
Medians
(B) Kruskal–Wallis test IL6 levels GG GC CC
p-value
1.67 (0.91/2.52) 1.74 (1.02/3.07) 1.68 (0.89/3.02)
0.20
1.73 (0.89/2.55) 2.02 (1.20/3.55) 1.78 (0.81/3.42)
HOMA-IR GG GC CC
2.04 (1.47/2.92) 2.05 (1.50/3.04) 2.13 (1.47/3.00)
81
2.02 (1.44/2.90) 2.05 (1.51/3.08) 2.22 (1.47/2.97)
76
2.09 (1.48/2.93) 2.04 (1.47/3.02) 2.08 (1.47/3.02)
95
Fasting insulin GG GC CC
8.70 (6.30/12.60) 8.70 (6.60/12.45) 9.15 (6.30/12.60)
90
8.70 (6.00/12.00) 8.70 (6.30/12.30) 9.30 (6.15/11.70)
87
8.70 (6.53/12.90) 8.70 (6.60/12.53) 9.08 (6.60/13.50)
96
0.07
Medians
1.66 (0.91/2.49) 1.60 (0.95/2.72) 1.64 (0.89/2.72)
p-value
0.95
(A) Linear regression: b-values (p-values) are given after adjustment for BMI and age. The traits BMI, height, weight and body fat were only adjusted for age. The trait age was only adjusted for gender in the whole group; aadditionally adjusted for waist and hip circumference. bAdjustment without BMI. All analyses for the total group were additionally adjusted for sex. p-value <0.01 was regarded as significant. Statistical significant values are shown in bold. (B) Kruskal–Wallis test: medians (25th/75th percentiles).
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Table 3 Estimated effects of the IL-6 573 G/C polymorphism on quantitative traits related to the metabolic syndrome – comparing genotype effects of the minor allele in a dominant model (CC/GC vs. GG) N (m/w) Age (years) BMI (kg/m2) Height (cm) Weight (kg) Body fat (%) WHR Waist circumference (cm) Hip circumference (cm) Serum uric acid (mg/dl) HDL cholesterol (mmol/L) LDL cholesterol (mmol/L) Triglycerides (log) Fasting glucose (mg/dl) Two-hour glucose (mg/dl) Systolic blood pressure (mmHg) Diastolic blood pressure (mmHg) Leukocytes (·10 3/ll) HbA1c (%)
858 858 860 858 853 858 858 857 858 856 855 848 858 858 609 609 858 858
(411/447) (411/447) (413/447) (411/447) (409/444) (411/447) (411/447) (411/446) (411/447) (410/446) (409/446) (406/442) (411/447) (411/447) (285/324) (285/324) (411/447) (411/447)
All
Men
1.37 (0.03) 0.21 (0.65) 1.43 (0.04) 0.78 (0.57) 0.30 (0.56) 0.004 (0.45) 0.70 (0.23) 0.25 (0.58) 0.18 (0.16) 0.017 (0.70) 0.01 (0.90) 0.014 (0.79) 0.183 (0.82) 0.74 (0.76) 1.91 (0.45) 3.00 (0.026) 0.12 (0.49) 0.011 (0.77)
Women
1.27 (0.15) 0.08 (0.87) 1.75 (0.07) 1.35 (0.43) 0.27 (0.64) 0.004 (0.54) 0.92 (0.18) 0.53 (0.36) 0.22 (0.26) 0.053 (0.31) 0.06 (0.70) 0.028 (0.72) 0.210 (0.84) 1.48 (0.66) 5.46 (0.09) 4.19 (0.022) 0.04 (0.86) 0.047 (0.37)
1.47 (0.12) 0.37 (0.65) 1.05 (0.30) 0.09 (0.96) 0.34 (0.69) 0.004 (0.62) 0.41 (0.66) 0.09 (0.89) 0.14 (0.41) 0.026 (0.71) 0.10 (0.57) 0.005 (0.94) 0.160 (0.89) 0.13 (0.97) 2.21 (0.57) 1.49 (0.46) 0.20 (0.38) 0.082 (0.16)
b-values (p-values) are given after adjustment for BMI and age. The traits BMI, height, weight and body fat were only adjusted for age. The trait age was only adjusted for gender in the whole group; aadditionally adjusted for waist and hip circumference. bAdjustment without BMI. All analyses for the total group were additionally adjusted for sex. p-value <0.01 was regarded as significant.
age. However, the scope of the present study for investigating associations on longevity is limited as the highest investigated age of 74 years is rather moderate. The data for the 573 G/C variant which was not investigated for longevity in previous studies point to the direction that the C allele carriers might be disadvantaged in this context. This effect might correspond to the previously reported association with higher IL-6 levels in elderly people for the 573 C allele (Brull et al., 2001). Although an impact of IL-6 promoter variants on IL-6 levels has been proposed (Ferna´ndez-Real et al., 2000), the present study gives no evidence of an association with IL-6 levels for any variant in the population-based elderly cohort. In the present study, the 174 C allele is associated with reduced waist circumference in men. The fact that this association only occurs when BMI is included in the adjustment is a consequence of the opposite estimated BMI effect. The BMI-adjusted association of the 174 C allele with hip circumference was in the same direction in men as with waist circumference. Estimated effects of waist and hip circumferences, independent of BMI, in the same direction are unexpected, because larger waist circumference indicates increased cardiovascular risk whereas a larger hip circumference has a protective influence. However, risk estimates of waist and hip circumferences have been proposed to be independent of each other and of BMI (Lissner et al., 2001). The associations with waist and hip circumference and BMI seem to be due to lower height which is associated with the C allele in men, and suggest an association with a particular stature (Fig. 1). Thus, men carrying a 174 C allele may be smaller but not lighter than men carrying no C allele, as weight and body fat mass do not differ significantly between these
IL-6 levels inflammation bone metabolism height
GG
weight waist
CC GC
hip BMI Fig. 1. Hypothetical model for the association of the 174 C allele with height in men. C allele carriers might develop enhanced IL-6 expression levels. These would induce inflammatory processes which may inhibit bone metabolism. Thus, men with a 174 C allele would show impaired growth. Lower height may negatively influence waist and hip circumference. As weight of C allele carriers did not differ from the GG homozygotes, the C allele carriers have increased BMI.
genotype groups. Hence, the distribution of fat mass over the body might differ. IL-6 and related cytokines have profound effects on bone metabolism by regulating osteoclast and osteoblast development and function (Manolagas, 1998). Men who carry a C allele might have impaired regulation of bone metabolism, inhibiting growth. An explanation of the gender-specific association might be hormonal protection from bone mineral loss by estrogens in women. As estrogens can inhibit IL-6 expression (Bruunsgaard et al., 1999) men could also be more affected by deregulated IL-6 expression. In this context, several other variants showed gender-specific associations with height.
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Polymorphisms in the parathyroid hormone type 1 receptor gene (PTHR1) (Scillitani et al., 2006), the vitamin D receptor (d’Alesio et al., 2005), and the estrogen receptor alpha (ESR1) were associated with height in women (Schuit et al., 2004) as well as variants in the renin–angiotensin system (Chaves et al., 2004). For men a polymorphic CA repeat in the insulin-like growth factor I (IGF-I) (Rietveld et al., 2004) was associated with height. Furthermore, variants in the aromatase gene (CYP19) were associated with height, which is even sustained considering interactions with variants in a locus on the Y chromosome (Ellis et al., 2001). If height was reduced more considerably than weight, BMI would increase. This phenomenon would explain the higher BMI in the present study, which is significantly associated with the C allele when adjustment is made for the controversially estimated effects of waist and hip circumference. In a previous study, the 174 CC genotype was associated with higher BMI in T2DM subjects but not in healthy subjects, indicating a higher risk of developing T2DM (Stephens et al., 2004). Controversially, Hamid et al. (2005) have reported an association of the G allele carriers with higher BMI in a large study of glucose-tolerant subjects. In 2003 Sesso et al. found hypertension to be associated with low-grade inflammation. In the present study, a trend was observed towards higher diastolic blood pressure for the 573 C allele carriers, especially in men. Blood pressure might be influenced by IL-6 expression but to the authors’ knowledge no other study has yet observed an association of this polymorphism with hypertension. Thus, the observed trend could represent a chance finding. Many of the controversies that were found for the 174 G/C polymorphism might be due to insufficient statistical power or lack of correction for multiple testing. Ethnic and gender differences and genetic heterogeneity might bias association studies as well. For the present study, the significance level was lowered to 0.01. As traits and SNPs are correlated no consistent Bonferroni correction was performed. Furthermore, Barbieri et al. (2005) suggest additive effects of IL6 and PPARc variants. Thus, interactions with the effects of other variants should be investigated in further studies. The KORA population analyzed in this study is a population-based German adult study which benefits from the high-quality phenotyping of the internationally approved MONICA/KORA surveys. In particular, the subgroup analyzed comprised an OGTT and fasting measurement of parameters relevant for the metabolic syndrome. The advantages of the study are its size and the phenotyping in the field of metabolic parameters. To our knowledge it is the second largest association study of IL-6 variants on glucose-tolerant subjects besides a Danish study (Hamid et al., 2005). In conclusion, single nucleotide polymorphisms in the promoter region of IL-6 were associated with components of the metabolic syndrome in German Caucasians. In men, the 174 CC genotype seems to be associated with lower height, waist and hip circumference and higher BMI. As
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height seems to be the central factor causing these associations, a role of the 174 G/C polymorphism in bone metabolism is indicated. However, to improve the understanding of the complex relationships further studies are necessary. Acknowledgements The OGTT study was partly funded by the German Federal Ministry of Health, the Ministry of School, Science and Research of the State of North-Rhine-Westfalia, and the Anna Wunderlich-Ernst Ju¨hling Foundation (W.R., G.G.). Parts of this work were supported by the German Ministry of Education and Research (BMBF)/ National Genome Research Network (NGFN) and the Deutsche Forschungsgemeinschaft (Wi621/12-1). The KORA Survey 4 was financed by the GSF, which is funded by the German Federal Ministry of Education, Science, Research and Technology and the State of Bavaria. The authors are indebted to K. Papke (head of KORA Study Center) and B. Schwertner (survey organization) and their co-workers for organizing and conducting the data collection. We are grateful to the KORA Study Group (Head: Professor H.E. Wichmann) for initiating the KORA Survey 4. We also thank all participants of the OGTT study. References Barbieri, M., Rizzo, M.R., Papa, M., Acampora, R., De Angelis, L., Olivieri, F., Marchegiani, F., Franceschi, C., Paolisso, G., 2005. Role of interaction between variants in the PPARG and interleukin-6 genes on obesity related metabolic risk factors. Exp. Gerontol. 40 (7), 599–604. Bonafe, M., Olivieri, F., Cavallone, L., Giovagnetti, S., Mayegiani, F., Cardelli, M., Pieri, C., Marra, M., Antonicelli, R., Lisa, R., Rizzo, M.R., Paolisso, G., Monti, D., Franceschi, C., 2001. A genderdependent genetic predisposition to produce high levels of IL-6 is detrimental for longevity. Eur. J. Immunol. 31 (8), 2357–2361. Brull, D.J., Montgomery, H.E., Sanders, J., Dhamrait, S., Luong, L., Rumley, A., Lowe, G.D., Humphries, S.E., 2001. Interleukin-6 gene 174g > c and 572g > c promoter polymorphisms are strong predictors of plasma interleukin-6 levels after coronary artery bypass surgery. Arterioscler. Thromb. Vasc. Biol. 21 (9), 1458–1463. Bruunsgaard, H., Pedersen, A.N., Schroll, M., Skinhoj, P., Pedersen, B.K., 1999. Impaired production of proinflammatory cytokines in response to lipopolysaccharide (LPS) stimulation in elderly humans. Clin. Exp. Immunol. 118, 235–241. Bruunsgaard, H., Pedersen, M., Pedersen, B.K., 2001. Aging and proinflammatory cytokines. Curr. Opin. Hematol. 8 (3), 131–136. Chaves, F.J., Corella, D., Sorli, J.V., Marin-Garcia, P., Guillen, M., Redon, J., 2004. Polymorphisms of the renin–angiotensin system influence height in normotensive women in a Spanish population. J. Clin. Endocrinol. Metab. 89 (5), 2301–2305. d’Alesio, A., Garabedian, M., Sabatier, J.P., Guaydier-Souquieres, G., Marcelli, C., Lemacon, A., Walrant-Debray, O., Jehan, F., 2005. Two single-nucleotide polymorphisms in the human vitamin D receptor promoter change protein–DNA complex formation and are associated with height and vitamin D status in adolescent girls. Hum. Mol. Genet. 14 (22), 3539–3548. Dandona, P., Aljada, A., Bandyopadhyay, A., 2004. Inflammation: the link between insulin resistance, obesity and diabetes. Trends Immunol. 25 (1), 4–7.
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IL-6 promoter polymorphisms and quantitative traits related to the metabolic syndrome in KORA S4 Harald Grallert a, Cornelia Huth a,b, Melanie Kolz a, Christa Meisinger a, Christian Herder c, Klaus Strassburger d, Guido Giani d, H.-Erich Wichmann a,b, Jerzy Adamski e, Thomas Illig a,*, Wolfgang Rathmann d a
e
GSF National Research Center for Environment and Health, Institute of Epidemiology, Germany b IBE, Chair of Epidemiology, University of Munich, Germany c German Diabetes Clinic, German Diabetes Center, Leibniz Institute at the Heinrich-Heine-University, Du¨sseldorf, Germany d Institute of Biometrics and Epidemiology, German Diabetes Center, Leibniz Institute at the Heinrich-Heine-University, Du¨sseldorf, Germany GSF National Research Center for Environment and Health, Institute of Experimental Genetics, Genome Analysis Center, Neuherberg, Germany Received 3 March 2006; received in revised form 28 April 2006; accepted 2 May 2006 Available online 23 June 2006
Abstract Interleukin-6 (IL-6) is a pleiotropic cytokine which has been proposed as ‘‘cytokine for gerontologists’’ and linked to age-related metabolic disturbances such as the metabolic syndrome or type 2 diabetes. Polymorphisms located in the promoter region of IL-6 have been reported to be involved in the regulation of IL-6 transcription. This study investigates whether IL-6 promoter variants 174 G/C and 573 G/C are associated with quantitative traits related to the metabolic syndrome (International Diabetes Federation criteria) in a population of normoglycemic subjects (n = 878) from the latest KORA survey (KORA S4). Genotyping was performed using MALDI-TOF MS. Besides lower height (p = 0.01) the 174 CC genotype was independently associated with lower waist (p = 0.002) and hip (p = 0.01) circumferences in men. Furthermore, the 174 CC genotype was associated with BMI (p = 0.004) when adjusted for waist and hip circumference. The present study does not suggest associations with further components of the metabolic syndrome. The association with height seems to be the central factor indicating an influence of IL-6 on growth through impaired bone metabolism. However, the complex relationships need further investigation. 2006 Elsevier Inc. All rights reserved. Keywords: IL-6; Single nucleotide polymorphism; KORA S4; Metabolic syndrome; Height
1. Introduction The metabolic syndrome (MS) is a common metabolic disorder. The rising number of persons suffering from MS mainly results from the increasing prevalence of obesity. Besides central obesity, additional risk factors like insulin resistance, glucose intolerance, hypertension and dyslipidemia are characteristic for the MS (Isomma, 2003). Although the heritability of the MS components is high, the genetic mechanisms responsible for this clustering *
Corresponding author. Tel.: +49 89 3187 4249; fax: +49 89 3187 4567. E-mail address: [email protected] (T. Illig).
0531-5565/$ - see front matter 2006 Elsevier Inc. All rights reserved. doi:10.1016/j.exger.2006.05.002
are still unclear (Lin et al., 2005). The risk of developing type 2 diabetes (T2DM) or the MS increases with age. Likewise, the production of cytokines from monocytes and macrophages and levels of circulating acute-phase proteins increases with age (Bruunsgaard et al., 2001; Fagiolo et al., 1993). Aging was associated with low-grade elevations in levels of circulating inflammatory mediators like interleukin-6 (IL-6) acting as predictors of mortality independent of pre-existing morbidity (Krabbe et al., 2004). IL-6 is a multifunctional cytokine which is produced not only in monocytes and macrophages but in several cell types including most cells of the immune system, chondrocytes, osteoblasts, skeletal and smooth muscle cells,
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hepatocytes, islet b cells and adipocytes (Kamimura et al., 2003). This variety of cell types secreting IL-6 indicates the complexity of IL-6 function and regulation. IL-6 plays a central role in host defence as it functions as a main regulator of the acute phase response (Fey and Gauldie, 1990). Besides further functions in the immune, nervous and endocrine systems, IL-6 is involved in bone metabolism, hematopoiesis and cancer (Kamimura et al., 2003). As the receptor subunit gp130 is widely expressed, deregulated high-level production of IL-6 combined with its agonistic soluble receptor sIL-6R may induce an undesired inflammatory state in many organs and could thus cause various diseases. The association of MS with low-grade inflammation is well documented (Sutherland et al., 2004). Raised IL-6 levels associated with expanded adipose tissue mass have been demonstrated in obese subjects as well as in diabetic patients, particularly in subjects also having features of the metabolic syndrome (Pickup et al., 1997). In the pathophysiology of the MS raised IL-6 levels were hypothesized to act on several key parameters. IL-6 was postulated to contribute to insulin resistance, increased glucose production in the liver, inhibition of the insulinmediated glucose uptake in skeletal muscle and to be involved in the development of hypertension (Eckel et al., 2005). Several polymorphisms in the IL-6 promoter region were analyzed for association with T2DM. Numerous studies on the promoter polymorphism 174 G/C have yielded controversial results (Kristiansen and MandrupPoulsen, 2005). Functional analyses have indicated a role of this polymorphism in IL-6 expression (Fishman et al., 1998; Terry et al., 2000). Recent studies on an age-related association of the 174 G/C polymorphism on IL-6 expression with controversial results have not contributed to clarity (Krabbe et al., 2004). This analysis investigates whether the 174 G/C and 573 G/C IL-6 promoter polymorphisms are associated with quantitative traits related to the International Diabetes Federation (IDF)-defined metabolic syndrome in fasting non-diabetic elderly participants of the large population-based study KORA S4. 2. Materials and methods
excluded in the present analysis. Oral glucose tolerance tests (OGTT) were performed in 1353 participants (Rathmann et al., 2003). After the exclusion of all subjects with impaired fasting glucose (IFG; fasting glucose levels of 100–125 mg/dl), impaired glucose tolerance (IGT; fasting glucose levels below 140 mg/dl and 2 h glucose levels of 140–199 mg/dl) and newly diagnosed diabetes, 878 normoglycemic (fasting/2 h OGTT serum glucose <110 mg/dl/ <140 mg/dl) individuals (417 male, 461 female) were included in the present analysis. Body weight was measured in light clothing to the nearest 0.1 kg and height to the nearest 0.1 cm. Waist circumference was measured at the maximum abdominal girth to the nearest 0.1 cm. Blood pressure in a sitting position was measured at the right arm three times, after 15 min rest, using an automatic device (OMRON HEM 705-CP). The mean of the second and third measurement was used for analysis. Blood glucose was assessed using a hexokinase method (Gluco-quant, Roche Diagnostics, Mannheim, Germany). HDL cholesterol was measured using the phosphotungstic acid method (Boehringer–Mannheim, Germany). Triglycerides were assessed with the Boehringer GPO-PAP assay. Serum IL-6 levels were measured as described in Muller et al. (2002) and in Herder et al. (2005a). In a genomic control approach, 210 SNPs were tested in KORA S4. No major population stratification within the population could be detected. 2.2. Definition of the metabolic syndrome The metabolic syndrome was defined according to the International Diabetes Federation (IDF) for Europid persons by presence of central obesity (waist circumference >94 cm in men, >80 cm in women) and two of four further factors (IDF, 2005). These are (1) raised triglyceride levels (P150 mg/dl) or specific treatment for this lipid abnormality, (2) reduced HDL cholesterol (<40 mg/dl in men, <50 mg/dl in women) or treatment for this abnormality, (3) raised blood pressure (BP) (systolic BP P 130 mmHg or diastolic BP P 85 mmHg) or treatment of previously diagnosed hypertension, (4) raised fasting plasma glucose (P100 mg/dl) or previously diagnosed T2DM (which is not relevant for the present study as subjects with known T2DM were excluded).
2.1. Study population 2.3. Genotyping The KORA (Cooperative Health Research in the Region of Augsburg) Survey S4 (formerly known as S2000) is a population-based study of adults performed in Southern Germany (Wichmann et al., 2005). This survey was conducted under the same conditions as the previous three surveys within the WHO MONICA Augsburg project (Holle et al., 2005). One thousand six hundred and fifty-three subjects were included in the 55- to 74-year age group. One hundred and thirty-one individuals with known diabetes and 169 subjects who were not fasting were
Genomic DNA of KORA participants was extracted from blood leukocytes using the Puregene DNA Isolation Kit (Gentra Systems, MN 55441, USA) according to the manufacturer’s recommendation. Genotyping for the 174 G/C (rs1800795) and 573 G/C (rs1800796, denoted as 572 in previous studies) IL-6 promoter variants was carried out by means of matrix-assisted laser desorption ionization-time of flight (MALDI-TOF) analysis of allele-dependent primer extension products as described
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elsewhere (Weidinger et al., 2004). Control genotyping of 384 subjects was performed with the TaqMan method.
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3. Results 3.1. Characteristics of the study participants
2.4. Statistical analysis Violation of the Hardy–Weinberg equilibrium (HWE) was tested by Fisher’s exact test. All statistical analyses were carried out for the whole population and stratified by sex. Quantitative traits approximating normal distribution on the original scale are presented as means ± standard deviation whereas traits not approximately normally distributed are given as median and 25th/75th percentiles. Traits that did not accomplish normal distribution after logarithmic transformation were analyzed by Kruskal–Wallis tests. Quantitative traits which were normally distributed were analyzed by linear regression. For the 174 G/C polymorphism dominant, recessive and separate pairwise comparison (nominal) models were used. According to the low minor allele frequency the 573 G/C polymorphism was analyzed only by a dominant model. For analysis of blood pressure subjects with hypertension or medication against hypertension were generally excluded. Association of the genotypes with the IDF-defined MS was assessed by logistic regression. Multiple regression models were also fitted to adjust for potential confounders. Correlations were calculated by Pearson’s or Spearman’s correlation coefficient r2 for traits which were normally or not normally distributed. p-values of p < 0.01 were considered statistically significant. All analyses were carried out using SAS (V. 9.1 Cary, NC, USA).
Characteristics of the normoglycemic elderly KORA S4 participants stratified by sex are shown in Table 1. Significant sex differences were observed for waist circumference, waist-to-hip ratio, height, weight, fasting plasma glucose, triglycerides, uric acid and blood pressure, which were all higher in men, and for hip circumference, percent body fat, HDL cholesterol levels and HbA1c, which were higher in women (p < 0.01). No significant sex differences were observed for BMI, 2 h glucose, LDL cholesterol levels, fasting insulin, HOMA-IR, leukocyte count and IL-6. Except for IL-6 levels, HOMA IR, fasting insulin and triglycerides, all quantitative traits were normally distributed. Comparing age categories (55–64 and 65–74 years) there was a trend towards higher IL-6 levels in the elderly (men p = 0.03; women p = 0.02). 3.2. Genotyping results Genotype distributions of both polymorphisms were in HWE (p > 0.05). Genotyping success rates were >98%, with an error rate <1% in 210 routine duplicates. No discordant genotypes were found in quality control genotyping with the TaqMan method for both polymorphisms (data not shown). Major allele frequencies of 57% G for the 174 and 95% G for the 573 polymorphism corresponded to allele frequencies in dbSNP from the National Center for Biotechnology Information (NCBI).
Table 1 Characteristics of normoglycemic survey participants aged 55–74 years by sex: KORA Survey 4, Augsburg, Germany
Age (years) Body mass index (kg/m2) Height (cm)a Weight (kg)a Body fat (%)a WHRa Waist circumference (cm)a Hip circumference (cm)a Serum uric acid (mg/dl)a HDL cholesterol (mmol/L)a LDL cholesterol (mmol/L) Fasting plasma glucose (mg/dl)a Two-hour plasma glucose (mg/dl) Systolic blood pressure (mmHg)a Diastolic blood pressure (mmHg)a Leukocytes (·10 3/ll) HbA1c (%)a HOMA-IR Fasting insulin (mU/L) Triglycerides (mg/dl)a Interleukin-6 (pg/ml)
N (m/w)
Men
Women
(417/460) (414/460) (416/461) (414/461) (412/457) (416/460) (416/460) (416/460) (417/460) (416/459) (415/460) (417/460) (417/460) (290/337) (290/337) (417/460) (417/460) (416/460) (416/460) (412/456) (413/460)
63.6 ± 5.6 27.4 ± 3.3 172.3 ± 6.3 81.4 ± 10.9 32.18 ± 4.09 0.95 ± 0.05 98.6 ± 8.9 103.7 ± 6.2 6.13 ± 1.26 1.40 ± 0.34 3.97 ± 0.10 97.1 ± 6.8 100.4 ± 21.3 134.3 ± 17.6 80.1 ± 9.6 6.07 ± 1.56 5.52 ± 0.33 2.06 (1.49/2.98) 8.70 (6.15/12.15) 113.0 (79.5/155.5) 1.88 (0.95/3.24)
63.4 ± 5.4 27.9 ± 4.6 159.3 ± 6.1 70.8 ± 12.1 39.67 ± 5.06 0.83 ± 0.06 88.3 ± 10.8 106.6 ± 9.4 4.78 ± 1.07 1.71 ± 0.44 4.02 ± 1.07 94.2 ± 7.0 103.3 ± 20.6 125.9 ± 18.1 76.7 ± 9.4 5.79 ± 1.31 5.60 ± 0.33 2.05 (1.48/3.01) 8.70 (6.60/12.90) 101.0 (78.0/137.0) 1.62 (0.94/2.61)
a p < 0.01 for sex differences. Data are presented as means ± standard deviations or medians (25th/75th percentiles) for traits that were not normally distributed (insulin, HOMA-IR, triglycerides, and IL-6).
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3.3. IL-6 promoter polymorphisms and the metabolic syndrome Three hundred and thirty-two subjects (37.8%) of the analyzed population fulfilled the IDF criteria for the metabolic syndrome although no subjects with diabetes, IFG or IGT were included. Comparing this group with the remaining subjects, no statistically significant association between the 174 G/C and 573 G/C variants and the metabolic syndrome could be demonstrated ( 174 CC vs. GG OR (95% CI) = 1.22 (0.80–1.85), GC vs. GG OR (95% CI) = 1.08 (0.79–1.49); 573 GC/CC vs. GG OR (95% CI) = 0.87 (0.54–1.39)). 3.4. The
174 G/C polymorphism and quantitative traits
The results of the nominal linear regression for the 174 G/C polymorphism are displayed in Table 2. There was no significant association with age. The CC genotype was significantly associated with lower height in men (b = 2.23 cm, p = 0.01). A negative association between the C allele and waist circumference was also observed for men when adjusted for BMI and age. CC genotype carriers showed reduced waist circumference compared to GG genotype carriers in the nominal model (b = 1.91 cm, p = 0.002). This association resists additional adjustment for smoking, physical activity and alcohol intake (CC vs. GG: b = 1.90 cm, p = 0.003). The b-estimate of the recessive model shows a significant association (CC vs. GG/GC b = 1.47 cm, p = 0.008) but with a b-estimate of the GC heterozygotes of 0.74 cm the association could also be additive (b = 0.96 cm p = 0.003). A similar behavior was observable for hip circumference. The association results for waist and hip circumference are independent of BMI, although BMI and waist circumference (r2 = 0.87 within each gender group) and BMI and hip circumference (r2 = 0.81 men; r2 = 0.89 women) are highly correlated. After additional adjustment for waist and hip circumference, the C allele is associated with higher BMI in men (CC vs. GG: b = 0.65 kg/m2, p = 0.004; GC vs. GG: b = 0.41 kg/m2, p = 0.02) pointing to a dominant association (GC/CC vs. GG: b = 0.47 kg/m2, p = 0.004). For all other traits analyzed by linear regression modeling, there was no significant association for the 174 G/C polymorphism. The Kruskal–Wallis test did not show any significant association for IL-6 levels, fasting insulin levels or HOMA-IR with the 174 G/C polymorphism. 3.5. The
573 G/C polymorphism and quantitative traits
The results for the 573 G/C polymorphism are presented in Table 3. The C allele carriers of the 573 G/C polymorphism showed a trend toward association with younger age (b = 1.37 years, p = 0.03). In addition, carriers of a C allele without medication against hypertension
(n = 609) also showed a trend toward higher diastolic blood pressure (b = 3.00, p = 0.03). After additional adjustment for smoking, physical activity and alcohol intake no essential change was observed (data not shown). None of the other traits analyzed by linear regression, nor IL-6 levels (all subjects: p = 0.60; men: p = 0.13; women: p = 0.59), insulin levels (all subjects: p = 0.28; men: p = 0.77; women: p = 0.19) or HOMA-IR (all subjects: p = 0.45; men: p = 0.63; women: p = 0.22) analyzed by the Kruskal–Wallis test, were significantly associated with the 573 G/C polymorphism. 4. Discussion This study investigated two IL-6 promoter polymorphisms ( 174 G/C and 573 G/C) for association with quantitative traits related to the IDF-defined metabolic syndrome. The results of this study do not support the hypothesis that the 174 G/C polymorphism is associated with age. For the 573 G/C polymorphism there was a tendency for the C allele to be associated with lower age. Our data suggest a negative association of the 174 C allele with height in men. Moreover, for men lower waist and hip circumferences as well as higher BMI were associated with the 174 C allele. For the 573 C allele the data showed a further tendency to be associated with higher diastolic blood pressure. Low-grade inflammation has been postulated as the link between insulin resistance, obesity and diabetes (Dandona et al., 2004). As IL-6 is one of the major cytokines in low-grade inflammation, the physiological and pathophysiological effects of IL-6 have been examined extensively. Elevated circulating levels of IL-6 have been suggested to cause insulin resistance and T2DM (Herder et al., 2005b; Hu et al., 2004; Pickup et al., 2000; Pradhan et al., 2001; Spranger et al., 2003). In vitro there is evidence for a role of IL-6 in causing impaired insulin signaling in adipocytes (Kristiansen and Mandrup-Poulsen, 2005; and references therein). High IL-6 levels have been one of the most powerful predictors of morbidity and mortality in the elderly (Ferrucci et al., 1999; Harris et al., 1999). However, IL-6 has also been proposed to have an enhancing effect on glucose and lipid metabolism (Steensberg et al., 2000; Van Hall et al., 2003; Wallenius et al., 2003). There is increasing evidence for an age-related effect of the 174 G/C polymorphism (Bonafe et al., 2001; Fishman et al., 1998; Olivieri et al., 2003). Men with 174 GG genotype were proposed to be disadvantaged for longevity (Bonafe et al., 2001; Ross et al., 2003). This was proposed to be due to higher IL-6 expression (Olivieri et al., 2003) but another study observed lower IL-6 expression in this context (Rea et al., 2003). According to its influence on diseases of aging, IL-6 was proposed to be ‘‘a cytokine for gerontologists’’ (Ershler, 1993). In contrast, some studies propose no influence of the IL-6 variant in aging (Wang et al., 2001). Likewise, the present study reveals no significant impact of the 174 IL-6 promoter polymorphism on
H. Grallert et al. / Experimental Gerontology 41 (2006) 737–745 Table 2 Estimated associations in the nominal linear regression model and scores of the Kruskal–Wallis test for the IL-6 traits related to the metabolic syndrome GC vs. GG Total (A) Linear regression Age (years) n = 857 (408/449) BMI (kg/m2) n = 857 (406/451) BMI (kg/m2)a n = 857 (406/451) Waist circumference (cm)b n = 857 (406/451) Waist circumference (cm) n = 857 (406/451) Hip circumference (cm)b n = 857 (406/450) Hip circumference (cm) n = 857 (406/450) Height (cm) n = 859 (408/451) Weight (kg) n = 857 (406/451) Body fat (%) n = 851 (404/447) WHR n = 857 (406/451) Serum uric acid (mg/dl) n = 857 (406/451) HDL cholesterol (mmol/L) n = 855 (405/450) LDL cholesterol (mmol/L) n = 854 (404/450) Triglycerides (log) n = 849 (401/448) Fasting glucose (mg/dl) n = 857 (406/451) Two-hour glucose (mg/dl) n = 857 (406/451) Systolic blood pressure (mmHg) n = 611 (283/328) Diastolic blood pressure (mmHg) n = 611 (283/328) Leukocytes (·10 3/ll) n = 857 (406/451) HbA1c (%) n = 857 (406/451)
0.03 0.39 0.33 0.41
174 G/C polymorphism on quantitative
CC vs. GG Men
(0.94) (0.22) (0.01) (0.59)
741
0.08 0.64 0.41 0.76
Women (0.90) (0.08) (0.02) (0.44)
0.16 0.16 0.26 0.10
(0.98) (0.74) (0.17) (0.93)
Total 0.09 0.06 0.26 0.77
Men (0.88) (0.89) (0.13) (0.44)
0.50 0.03 0.65 2.02
Women (0.55) (0.95) (0.004) (0.11)
0.30 0.10 0.14 0.33
(0.70) (0.88) (0.56) (0.83)
0.40 (0.30)
0.74 (0.14)
0.22 (0.70)
0.61 (0.22)
1.91 (0.002)
0.56 (0.45)
0.23 (0.73)
0.28 (0.68)
0.70 (0-53)
0.87 (0.31)
1.46 (0.10)
0.38 (0.79)
0.70 (0.02)
0.68 (0.11)
1.03 (0.04)
0.55 (0.16)
1.39 (0.01)
0.19 (0.77)
0.74 (0.11)
1.33 (0.05)
0.19 (0.76)
0.75 (0.21)
2.23 (0.01)
0.58 (0.48)
0.48 (0.59)
0.71 (0.56)
0.27 (0.84)
0.75 (0.52)
2.10 (0.19)
0.42 (0.84)
0.07 (0.83)
0.41 (0.31)
0.25 (0.65)
0.33 (0.47)
0.11 (0.83)
0.59 (0.40)
0.0034 (0.36) 0.09 (0.29)
0.001 (0.83) 0.11 (0.43)
0.006 (0.30) 0.08 (0.45)
0.0006 (0.89) 0.13 (0.26)
0.006 (0.30) 0.18 (0.33)
0.007 (0.34) 0.07 (0.63)
0.019 (0.51)
0.035 (0.36)
0.002 (0.97)
0.012 (0.76)
0.005 (0.92)
0.020 (0.72)
0.10 (0.22)
0.19 (0.09)
0.01 (0.93)
0.04 (0.73)
0.18 (0.22)
0.11 (0.46)
0.016 (0.65)
0.016 (0.78)
0.018 (0.68)
0.002 (0.96)
0.034 (0.64)
0.048 (0.40)
0.82 (0.12)
0.88 (0.25)
0.74 (0.32)
1.11 (0.11)
1.07 (0.28)
1.20 (0.22)
0.83 (0.60)
2.60 (0.28)
0.83 (0.70)
0.92 (0.66)
2.80 (0.37)
0.71 (0.80)
3.04 (0.06)
1.15 (0.62)
4.25 (0.06)
3.22 (0.13)
3.95 (0.19)
2.28 (0.44)
0.08 (0.92)
1.27 (0.41)
0.91 (0.45)
0.29 (0.80)
0.46 (0.79)
0.13 (0.75)
0.02 (0.88)
0.25 (0.17)
0.19 (0.18)
0.12 (0.40)
0.11 (0.63)
0.14 (0.44)
0.005 (0.85)
0.05 (0.16)
0.04 (0.28)
0.003 (0.93)
0.03 (0.56)
0.03 (0.50)
Total
Men p-value
Medians
Women
N = 858 (408/450)
Medians
(B) Kruskal–Wallis test IL6 levels GG GC CC
p-value
1.67 (0.91/2.52) 1.74 (1.02/3.07) 1.68 (0.89/3.02)
0.20
1.73 (0.89/2.55) 2.02 (1.20/3.55) 1.78 (0.81/3.42)
HOMA-IR GG GC CC
2.04 (1.47/2.92) 2.05 (1.50/3.04) 2.13 (1.47/3.00)
81
2.02 (1.44/2.90) 2.05 (1.51/3.08) 2.22 (1.47/2.97)
76
2.09 (1.48/2.93) 2.04 (1.47/3.02) 2.08 (1.47/3.02)
95
Fasting insulin GG GC CC
8.70 (6.30/12.60) 8.70 (6.60/12.45) 9.15 (6.30/12.60)
90
8.70 (6.00/12.00) 8.70 (6.30/12.30) 9.30 (6.15/11.70)
87
8.70 (6.53/12.90) 8.70 (6.60/12.53) 9.08 (6.60/13.50)
96
0.07
Medians
1.66 (0.91/2.49) 1.60 (0.95/2.72) 1.64 (0.89/2.72)
p-value
0.95
(A) Linear regression: b-values (p-values) are given after adjustment for BMI and age. The traits BMI, height, weight and body fat were only adjusted for age. The trait age was only adjusted for gender in the whole group; aadditionally adjusted for waist and hip circumference. bAdjustment without BMI. All analyses for the total group were additionally adjusted for sex. p-value <0.01 was regarded as significant. Statistical significant values are shown in bold. (B) Kruskal–Wallis test: medians (25th/75th percentiles).
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Table 3 Estimated effects of the IL-6 573 G/C polymorphism on quantitative traits related to the metabolic syndrome – comparing genotype effects of the minor allele in a dominant model (CC/GC vs. GG) N (m/w) Age (years) BMI (kg/m2) Height (cm) Weight (kg) Body fat (%) WHR Waist circumference (cm) Hip circumference (cm) Serum uric acid (mg/dl) HDL cholesterol (mmol/L) LDL cholesterol (mmol/L) Triglycerides (log) Fasting glucose (mg/dl) Two-hour glucose (mg/dl) Systolic blood pressure (mmHg) Diastolic blood pressure (mmHg) Leukocytes (·10 3/ll) HbA1c (%)
858 858 860 858 853 858 858 857 858 856 855 848 858 858 609 609 858 858
(411/447) (411/447) (413/447) (411/447) (409/444) (411/447) (411/447) (411/446) (411/447) (410/446) (409/446) (406/442) (411/447) (411/447) (285/324) (285/324) (411/447) (411/447)
All
Men
1.37 (0.03) 0.21 (0.65) 1.43 (0.04) 0.78 (0.57) 0.30 (0.56) 0.004 (0.45) 0.70 (0.23) 0.25 (0.58) 0.18 (0.16) 0.017 (0.70) 0.01 (0.90) 0.014 (0.79) 0.183 (0.82) 0.74 (0.76) 1.91 (0.45) 3.00 (0.026) 0.12 (0.49) 0.011 (0.77)
Women
1.27 (0.15) 0.08 (0.87) 1.75 (0.07) 1.35 (0.43) 0.27 (0.64) 0.004 (0.54) 0.92 (0.18) 0.53 (0.36) 0.22 (0.26) 0.053 (0.31) 0.06 (0.70) 0.028 (0.72) 0.210 (0.84) 1.48 (0.66) 5.46 (0.09) 4.19 (0.022) 0.04 (0.86) 0.047 (0.37)
1.47 (0.12) 0.37 (0.65) 1.05 (0.30) 0.09 (0.96) 0.34 (0.69) 0.004 (0.62) 0.41 (0.66) 0.09 (0.89) 0.14 (0.41) 0.026 (0.71) 0.10 (0.57) 0.005 (0.94) 0.160 (0.89) 0.13 (0.97) 2.21 (0.57) 1.49 (0.46) 0.20 (0.38) 0.082 (0.16)
b-values (p-values) are given after adjustment for BMI and age. The traits BMI, height, weight and body fat were only adjusted for age. The trait age was only adjusted for gender in the whole group; aadditionally adjusted for waist and hip circumference. bAdjustment without BMI. All analyses for the total group were additionally adjusted for sex. p-value <0.01 was regarded as significant.
age. However, the scope of the present study for investigating associations on longevity is limited as the highest investigated age of 74 years is rather moderate. The data for the 573 G/C variant which was not investigated for longevity in previous studies point to the direction that the C allele carriers might be disadvantaged in this context. This effect might correspond to the previously reported association with higher IL-6 levels in elderly people for the 573 C allele (Brull et al., 2001). Although an impact of IL-6 promoter variants on IL-6 levels has been proposed (Ferna´ndez-Real et al., 2000), the present study gives no evidence of an association with IL-6 levels for any variant in the population-based elderly cohort. In the present study, the 174 C allele is associated with reduced waist circumference in men. The fact that this association only occurs when BMI is included in the adjustment is a consequence of the opposite estimated BMI effect. The BMI-adjusted association of the 174 C allele with hip circumference was in the same direction in men as with waist circumference. Estimated effects of waist and hip circumferences, independent of BMI, in the same direction are unexpected, because larger waist circumference indicates increased cardiovascular risk whereas a larger hip circumference has a protective influence. However, risk estimates of waist and hip circumferences have been proposed to be independent of each other and of BMI (Lissner et al., 2001). The associations with waist and hip circumference and BMI seem to be due to lower height which is associated with the C allele in men, and suggest an association with a particular stature (Fig. 1). Thus, men carrying a 174 C allele may be smaller but not lighter than men carrying no C allele, as weight and body fat mass do not differ significantly between these
IL-6 levels inflammation bone metabolism height
GG
weight waist
CC GC
hip BMI Fig. 1. Hypothetical model for the association of the 174 C allele with height in men. C allele carriers might develop enhanced IL-6 expression levels. These would induce inflammatory processes which may inhibit bone metabolism. Thus, men with a 174 C allele would show impaired growth. Lower height may negatively influence waist and hip circumference. As weight of C allele carriers did not differ from the GG homozygotes, the C allele carriers have increased BMI.
genotype groups. Hence, the distribution of fat mass over the body might differ. IL-6 and related cytokines have profound effects on bone metabolism by regulating osteoclast and osteoblast development and function (Manolagas, 1998). Men who carry a C allele might have impaired regulation of bone metabolism, inhibiting growth. An explanation of the gender-specific association might be hormonal protection from bone mineral loss by estrogens in women. As estrogens can inhibit IL-6 expression (Bruunsgaard et al., 1999) men could also be more affected by deregulated IL-6 expression. In this context, several other variants showed gender-specific associations with height.
H. Grallert et al. / Experimental Gerontology 41 (2006) 737–745
Polymorphisms in the parathyroid hormone type 1 receptor gene (PTHR1) (Scillitani et al., 2006), the vitamin D receptor (d’Alesio et al., 2005), and the estrogen receptor alpha (ESR1) were associated with height in women (Schuit et al., 2004) as well as variants in the renin–angiotensin system (Chaves et al., 2004). For men a polymorphic CA repeat in the insulin-like growth factor I (IGF-I) (Rietveld et al., 2004) was associated with height. Furthermore, variants in the aromatase gene (CYP19) were associated with height, which is even sustained considering interactions with variants in a locus on the Y chromosome (Ellis et al., 2001). If height was reduced more considerably than weight, BMI would increase. This phenomenon would explain the higher BMI in the present study, which is significantly associated with the C allele when adjustment is made for the controversially estimated effects of waist and hip circumference. In a previous study, the 174 CC genotype was associated with higher BMI in T2DM subjects but not in healthy subjects, indicating a higher risk of developing T2DM (Stephens et al., 2004). Controversially, Hamid et al. (2005) have reported an association of the G allele carriers with higher BMI in a large study of glucose-tolerant subjects. In 2003 Sesso et al. found hypertension to be associated with low-grade inflammation. In the present study, a trend was observed towards higher diastolic blood pressure for the 573 C allele carriers, especially in men. Blood pressure might be influenced by IL-6 expression but to the authors’ knowledge no other study has yet observed an association of this polymorphism with hypertension. Thus, the observed trend could represent a chance finding. Many of the controversies that were found for the 174 G/C polymorphism might be due to insufficient statistical power or lack of correction for multiple testing. Ethnic and gender differences and genetic heterogeneity might bias association studies as well. For the present study, the significance level was lowered to 0.01. As traits and SNPs are correlated no consistent Bonferroni correction was performed. Furthermore, Barbieri et al. (2005) suggest additive effects of IL6 and PPARc variants. Thus, interactions with the effects of other variants should be investigated in further studies. The KORA population analyzed in this study is a population-based German adult study which benefits from the high-quality phenotyping of the internationally approved MONICA/KORA surveys. In particular, the subgroup analyzed comprised an OGTT and fasting measurement of parameters relevant for the metabolic syndrome. The advantages of the study are its size and the phenotyping in the field of metabolic parameters. To our knowledge it is the second largest association study of IL-6 variants on glucose-tolerant subjects besides a Danish study (Hamid et al., 2005). In conclusion, single nucleotide polymorphisms in the promoter region of IL-6 were associated with components of the metabolic syndrome in German Caucasians. In men, the 174 CC genotype seems to be associated with lower height, waist and hip circumference and higher BMI. As
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height seems to be the central factor causing these associations, a role of the 174 G/C polymorphism in bone metabolism is indicated. However, to improve the understanding of the complex relationships further studies are necessary. Acknowledgements The OGTT study was partly funded by the German Federal Ministry of Health, the Ministry of School, Science and Research of the State of North-Rhine-Westfalia, and the Anna Wunderlich-Ernst Ju¨hling Foundation (W.R., G.G.). Parts of this work were supported by the German Ministry of Education and Research (BMBF)/ National Genome Research Network (NGFN) and the Deutsche Forschungsgemeinschaft (Wi621/12-1). The KORA Survey 4 was financed by the GSF, which is funded by the German Federal Ministry of Education, Science, Research and Technology and the State of Bavaria. The authors are indebted to K. Papke (head of KORA Study Center) and B. Schwertner (survey organization) and their co-workers for organizing and conducting the data collection. We are grateful to the KORA Study Group (Head: Professor H.E. Wichmann) for initiating the KORA Survey 4. We also thank all participants of the OGTT study. References Barbieri, M., Rizzo, M.R., Papa, M., Acampora, R., De Angelis, L., Olivieri, F., Marchegiani, F., Franceschi, C., Paolisso, G., 2005. Role of interaction between variants in the PPARG and interleukin-6 genes on obesity related metabolic risk factors. Exp. Gerontol. 40 (7), 599–604. Bonafe, M., Olivieri, F., Cavallone, L., Giovagnetti, S., Mayegiani, F., Cardelli, M., Pieri, C., Marra, M., Antonicelli, R., Lisa, R., Rizzo, M.R., Paolisso, G., Monti, D., Franceschi, C., 2001. A genderdependent genetic predisposition to produce high levels of IL-6 is detrimental for longevity. Eur. J. Immunol. 31 (8), 2357–2361. Brull, D.J., Montgomery, H.E., Sanders, J., Dhamrait, S., Luong, L., Rumley, A., Lowe, G.D., Humphries, S.E., 2001. Interleukin-6 gene 174g > c and 572g > c promoter polymorphisms are strong predictors of plasma interleukin-6 levels after coronary artery bypass surgery. Arterioscler. Thromb. Vasc. Biol. 21 (9), 1458–1463. Bruunsgaard, H., Pedersen, A.N., Schroll, M., Skinhoj, P., Pedersen, B.K., 1999. Impaired production of proinflammatory cytokines in response to lipopolysaccharide (LPS) stimulation in elderly humans. Clin. Exp. Immunol. 118, 235–241. Bruunsgaard, H., Pedersen, M., Pedersen, B.K., 2001. Aging and proinflammatory cytokines. Curr. Opin. Hematol. 8 (3), 131–136. Chaves, F.J., Corella, D., Sorli, J.V., Marin-Garcia, P., Guillen, M., Redon, J., 2004. Polymorphisms of the renin–angiotensin system influence height in normotensive women in a Spanish population. J. Clin. Endocrinol. Metab. 89 (5), 2301–2305. d’Alesio, A., Garabedian, M., Sabatier, J.P., Guaydier-Souquieres, G., Marcelli, C., Lemacon, A., Walrant-Debray, O., Jehan, F., 2005. Two single-nucleotide polymorphisms in the human vitamin D receptor promoter change protein–DNA complex formation and are associated with height and vitamin D status in adolescent girls. Hum. Mol. Genet. 14 (22), 3539–3548. Dandona, P., Aljada, A., Bandyopadhyay, A., 2004. Inflammation: the link between insulin resistance, obesity and diabetes. Trends Immunol. 25 (1), 4–7.
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