Genetic variants in central metabolic genes influence some but not all relations of inflammatory...

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Introduction

The major health risk in polycystic ovary syndrome (PCOS) is caused by the metabolic syndrome. PCOS affects 5–10% of women and depending on the diagno-sis criteria resembles the complex interplay of altered hormonal conditions like hyper-androgenism, poly-cystic ovaries, ovulatory dysfunction and the metabolic syndrome (Azziz et al., 2006; Goodarzi et al., 2011a; Rotterdam ESHRE/ASRM-Sponsored PCOS Consensus Workshop Group, 2004; Toulis et al., 2011; Wild et al., 2010; Zawadski et al., 1992).

The metabolic syndrome in PCOS patients includes cardiovascular complications, insulin resistance

with increased prevalence for Type 2 diabetes and increased susceptibility for obesity, especially visceral obesity independent to BMI (Alemzadeh et al., 2010; Wild et al., 2010). PCOS patients also show increased serum triglyceride, LDL-cholesterol and reduced HDL-cholesterol profiles indicating disturbed lipid metabolism (Wild et al., 2011). The vast increase of adipose tissue in syndromes associated to obesity can result in an increase of inflammation and pro-inflammatory cytokine parameters detectable in serum (Gregor and Hotamisligil, 2011). In various studies an increase of inflammatory markers has been described in obese and non-obese PCOS patients (Escobar-Morreale

ORIGINAL ARTICLE

Genetic variants in central metabolic genes influence some but not all relations of inflammatory markers in a collective with polycystic ovary syndrome

Birgit Knebel1, Stefan Lehr1, Onno E. Janssen2, Susanne Hahn3, Ulrike Nitzgen1, Sylvia Jacob1, Jutta Haas4, Dirk Muller-Wieland4, and Jorg Kotzka1

1Institute of Clinical Biochemistry and Pathobiochemistry, German Diabetes Center at the Heinrich-Heine-University Duesseldorf, Leibniz Center for Diabetes Research, 40225 Düsseldorf, Germany, 2Endokrinologikum Hamburg, 22767 Hamburg, Germany, 3Medical Practice for Endocrinology, 42103 Wuppertal, Germany, and 4Institute for Diabetes Research, Department of General Internal Medicine, Asklepios Klinik St. Georg, Medical Faculty of Semmelweis University, 20099 Hamburg, Germany

AbstractPatients with polycystic ovary syndrome (PCOS) suffer, in addition to reproductive disturbances, from symptoms of the metabolic syndrome like insulin resistance, elevated coronary risk and visceral obesity. Genes with confirmed associations to the metabolic syndrome are also candidate genes for a relationship to metabolic parameters of the PCOS syndrome. The study presented indicates that genetic variants of the transcription factors LXRα or PPARγ and the PON-1 or the IGF-2 cluster are associated with altered metabolic phenotypes in PCOS patients. Next to this the absolute cytokine levels and the relation of certain cytokines to IL2, IL12 or INFγ are depending on the genotype. These observations support the hypothesis that various genetic variants in metabolic relevant genes might not only alter the metabolic characteristics within a cohort of PCOS patients but might also influence the cytokine level and the overall pattern of secreted cytokines.Keywords: PCOS, inflammation, cytokine relation, cytokine signature, obesity, gene variants in genes associated with metabolism

Address for Correspondence: Jorg Kotzka, Institute of Clinical Biochemistry and Pathobiochemistry, German Diabetes Center at the Heinrich-Heine-University Duesseldorf, Leibniz Center for Diabetes research, 40225 Düsseldorf, Germany. Tel: +49–211-3382 531. E-mail: [email protected]

(Received 25 January 2012; revised 07 May 2012; accepted 23 May 2012)

Archives of Physiology and Biochemistry, 2012; 118(4): 219–229© 2012 Informa UK, Ltd.ISSN 1381-3455 print/ISSN 1744-4160 onlineDOI: 10.3109/13813455.2012.697903

Archives of Physiology and Biochemistry

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10.3109/13813455.2012.697903

2012

Correlation of cytokine relations to gene variants in PCO syndrome

B. Knebel et al.

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220 B. Knebel et al.


et al., 2010; Wild et al., 2011). Other studies indicate that increased inflammatory markers are rather increased due to obesity than on the presence of the PCOS syndrome (Azziz et al., 2010; Deligeoroglou et al., 2001; Mukherjee and Maitra, 2010). We have previously reported a PCOS specific but obesity independent increase of cytokines. This was accompanied in PCOS patients compared to obese controls with an unaltered relation of th1 to th2 cytokines (Knebel et al., 2008, 2011).

The PCOS syndrome clusters in families and there-fore clearly has a genetic impact. Although several studies address this question, a distinct gene locus still remains to be identified (Azziz et al., 2010; Chen et al., 2011; Deligeoroglou et al., 2009; Goodarzi et al., 2011b; Mukherjee and Maitra, 2010). The pathogenesis of PCOS is regarded to be rather oligo- or polygenic, and therefore the syndrome might also be affected by genetic variants in several genes. Also the concept still is in focus, that variants in a gene even if not directly associated to the pathology can influence various physiological param-eters of a syndrome. As insulin resistance, disturbed lipid metabolism and serum lipid parameters are a charac-teristic of PCOS, genes and genetic variants associated to central aspects of the metabolic syndrome in healthy populations or populations with increased metabolic risk, might also resemble plausible candidates to modify the PCOS metabolic phenotype.

The transcription factors LXRα or PPARγ have been shown to be genetically linked to lipid metabolism but also β-cell function or insulin sensitivity (Curti et al., 2011; Zhao and Dahlman-Wright, 2010). Genetic variants in paraoxogenase (PON-1) or within the insulin/ IGF-2 cluster were predictive to cardiovascular risk and insu-lin resistance (Brophy et al., 2001; Gaidukov et al., 2006; Mackness et al., 1998; Rodríguez et al., 2004). Formerly we have shown that there is no direct genetic correlation to the pathogenesis of PCOS of neither distinct gene vari-ants in the complete coding sequences of these genes nor specific SNPs. But certain gene variants account for vari-ations in the metabolic specifics within the PCOS group investigated (Hahn et al., 2005; Knebel et al., 2008, 2009, 2011). As genetic variations influence serum parameters they also might account for variability in the secreted cytokine pattern of an individual.

In the present study we raise the hypothesis that genetic variants in these genes influence certain aspects of the metabolic risk within a PCOS cohort. Also the influence on cytokine secretion within the PCOS cohort is questioned.

Material and methods

PatientsAs previously reported PCOS patients (n = 129) were recruited by the Clinic of Endocrinology of the University of Essen based on the NIH criteria of 1990. The PCOS diagnosis was established if either oligo- or amenor-rhea were found in combination with clinical signs of

hyperandrogenism but exclusion of pituitary, adrenal, or ovarian origin (Hahn et al., 2005). PCOS probands received no medication influencing glucose metabolism or endocrine parameters for at least 3 month in advance of the study as reported (Hahn et al., 2005).

The Ethics Committee at the University of Essen approved the study protocol and written informed con-sent was obtained from all participants.

Molecular analysesGenomic DNA was extracted from whole blood using the blood extraction kit (Qiagen, Hilden, Germany). The targeted re-sequencing approach of PPARγ2, includ-ing all exons for the coding region of PPARγ1, 3 and 4 and their promoter regions and the coding region of LXRα, have been reported (Knebel et al., 2008, 2012). Generally exons and at least 50 nt 5′- and 3′-prime flank-ing intron regions and 3′UTR was investigated for at least 100 nt extending the stop codon. Data were analysed with SeqScape v2.1.1 (Applied Biosystems, Darmstadt, Germany) using reference sequences from various data-bases (NCBI). Genotyping of the paraoxogenase (PON)-1 promoter polymorphisms (rs705379 (NIH)) and the ApaI polymorphism in the IGF-2 Cluster (rs680 (NIH)) have been reporter (Knebel et al., 2009).

Determination of serum cytokine profilesCytokine concentrations (INFγ, TNFα, MCP-1, MIP-1β, G-CSF, IL1β, IL2, IL4, IL5, IL6, IL7, IL8, IL10, IL12, IL13, IL17) in serum of patients were determined with the Multiplex Immunoassay Bioplex System (BioRad, Munich) according to the supplier’s instructions. Data were collected on a Bio-Plex™ Protein Array System and analysed by Bio-Plex Manager™ (Version 3) software (BioRad) (Song et al., 2000). Data were normalized to background fluorescence and samples with an intra-assay variation in replicates >10% were excluded from analyses (Kellar and Iannone, 2002).

Determination of clinical parametersBlood and serum samples were collected according to standard protocols. Automated standard systems for clinical parameters, including insulin, glucose, HbA1c and hsCRP (Hitachi 912, Roche, Mannheim, Germany) and for cholesterol, triglyceride, HDL-cholesterol and LDL-cholesterol (Immulite 2000, Nichols Advantage, Bad Vilbach, Germany), were used. Surrogate indexes were calculated from fasting blood glucose and plasma insulin concentrations as follows: QUICKI = 1/(log(I0) + log(G0)), where I0 is fasting insulin (µU/ml) and G0 is fasting glucose (mg/dl); and HOMA-IR = (G0 * I0)/22.5, with fasting glucose expressed as mmol/l and fasting insulin expressed as µU/ml.

Statistical analysisGenotype data were combined for each gene sepa-rately according to major allelic frequency and the minor allele was grouped to the heterozygous genotype

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Correlation of cytokine relations to gene variants in PCO syndrome 221


as homozygous carriers of the minor allele were low in numbers. Data are shown as mean ± SD. For PPARγ and LXRα both analyzed genotypes were compared separately to the values determined in probands that carry neither of the investigated gene variants. Statistical significance of clinical parameters between genotypes observed was performed with Mann-Whitney U tests (XLSTAT, Addinsoft) or unpaired t-test with the assump-tion of normal distributed parameters. P-values < 0.05 were accepted as significant.

Results

The clinical and biochemical details of the PCOS study group have been described in detail (Hahn et al., 2005). In principle the investigated PCOS cohort was borderline obese but triglyceride, HDL-cholesterol, insulin, blood glucose or HbA1c were still in normal range despite elevated BMI (Table 1).

Common variants affecting metabolic parametersThe initial investigations of the collective indicated that there was no evidence for a direct genetic association of genetic variants in the complete coding regions of PPARγ (transcript variants 1–4, including promoters) and LXRα. Nevertheless common single nucleotide variants as rs1805192, rs3856806, and 5′UTR c.1-39T/C in PPARγ2, and rs2279238 or rs11039155 in LXRα were determined in the PCOS group (Knebel et al., 2008, 2012). Two further distinct informative SNPs, i.e., rs608 in the IGF-2 INS1/VTR IGF cluster or the rs705379 promoter polymorphism in PON-1 have been determined in the PCOS cohort, although a direct genetic association to the syndrome

has been excluded (Hahn et al., 2005; Knebel et al., 2008, 2009, 2012).

Analysing the various identified genetic alterations in detail revealed new insights on effects on metabolic characteristics within the PCOS cohort (Table 2). For the PPARγ variant rs1805192 (rs1805192CC// rs1805192GX; P12A) we observed that for PCOS patients the presence of rs1805192G/X was beneficial in respect to fasting insulin levels, insulin sensitivity and resistance compared to PCOS patients that were non-carriers of any genetic variant in PPARγ analysed here. Carriers of the c. 1-39T/C variant showed reduced fasting insulin and glucose levels, reduced insulin resistance and insulin sensitivity, a beneficial effect on BMI but significantly increased HDL-cholesterol values. The presence of rs3856806 (H478H in PPARγ2) did not alter metabolic parameters at all (data not shown). Compared with PCOS patients that were non-carriers of any genetic variant in LXRα analysed in this study the pres-ence of LXRα rs2279238AX in PCOS patients was associ-ated to lower BMI and weight but higher HDL-cholesterol. In this genotype fasting insulin, HbA1c and HOMA were significantly lower and differences determined in insulin sensitivity reached significance. In PCOS patients of the LXRα rs11039155AX genotype triglyceride levels, fasting glucose, HbA1c, insulin resistance were increased and insulin sensitivity diminished. The rs705379CX variant in PON-1 had no effect on lipid parameters in PCOS patients, but affected fasting insulin and glucose levels, insulin sen-sitivity and resistance.

Common variants affecting oGTTIn oral glucose tolerance tests carriers of either PPARγ variant rs1805192GX or c. 1-39CX insulin concentra-tions were significantly lower than PCOS patients of rs1805192CC and c. 1-39TT genotype (no SNP in Figure 1A). In regard to glucose concentration under these conditions no SNP carriers (rs1805192CC / c. 1-39TT) and c. 1-39CX carriers were essentially identi-cal but rs1805192GX carriers showed significant lower maximal concentrations (Figure 1A). Of genetic variants in LXRα, the insulin values of rs2279238AX carriers were already initially lower and the complete curves were parallel shifted downwards compared to rs2279238GG/ rs11039155 carriers (no SNP in Figure 1B). The pres-ence of rs11039155AX in PCOS patients’ resulted in a higher-angled insulin increase but decrease falls back to the initial values. The glucose concentration curve for rs2279238GG/rs11039155GG and rs2279238AX were fairly identical, but rs11039155AX carriers again had a higher-angled increase to a higher 60 min value, the decrease was slower and also did not reach the initial glucose concentrations within in the observation period (Figure 1B). The IGF-2 sequence variant rs608 did not influence the glucose or insulin concentrations dur-ing oral glucose tolerance testing at all (Figure 1C). The rs705379CX genotype variant in PON-1 showed lower increases of insulin and glucose concentrations com-pared to rs705379TT carriers (Figure 1D).

Table 1. Clinical characteristics of the PCOS cohort.Mean ± SD

n 129Age (years) 26.68 ± 5.68BMI (kg/m2) 29.68 ± 7.50Body weight (kg) 83.23 ± 23.73Cholesterol (mg/dl) 196.85 ± 38.64LDL-C (mg/dl) 131.61 ± 40.65HDL-C (mg/dl) 54.26 ± 16.37Triglyceride (mg/dl) 116.46 ± 66.09Fasting insulin (pmol/l) 262.18 ± 142.39Fasting glucose (mmol/l) 4.94 ± 0.67HbA1c (%) 5.14 ± 0.61QUICKI 0.30 ± 0.03HOMA IR 7.72 ± 5.02Fasting glucose (mmol/l) 4.88 ± 0.6960 min glucose (mmol/l) 7.42 ± 2.28120 min glucose (mmol/l) 5.72 ± 1.75Fasting insulin (pmol/l) 274.53 ± 164.0460 min insulin (pmol/l) 445.06 ± 317.91120 min insulin (pmol/l) 313.13 ± 243.54Notes: Data are given as mean ± SD (standard deviation). n gives the number of PCOS patients with all physiological parameters available, others were completely excluded from the analyses.

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Genotype specific cytokine levelsWe determined the cytokine concentrations in serum of the PCOS patients and related this separately to the observed genotypes of each gene investigated (Table 3). Gene variants investigated here affected the secreted cytokine pattern in PCOS patients. The most signifi-cant variations of cytokine levels were determined for the PPARγ variants as PCOS patients’ differed in IL4, IL5, IL13, IL17, IL1β INFγ, TNFα, MCP1, MIP-1β or G-CSF but also hsCRP levels according to the observed genotype. LXRα variants affected cytokine levels of IL4, IL5, IL7, IL10 or IL12 in PCOS patients. The IGF-2 rs608 SNP affects IL-13, IL-17, IL-1β and TNFα levels and PON-1 gene variant rs705379 had no significant influence on either tested cytokine or chemokine in PCOS patients.

Next, we evaluated the cytokine ratios of th1 and th2 specific cytokines in the PCOS cohort in order to deter-mine whether beside a direct effect on a certain cytokine concentration the general chemokine and cytokine pattern or signature is affected by the various genetic

variants. We used the th1 cytokines IL2, IL12 and INFγ levels as basis for these examinations and compared the relations determined for each cytokine depending on the genetic variant observed in the PCOS cohort (Figure 2). Variants altering distinct relation for at least 1.5-fold were considered. These investigations revealed that any ratio of IL2, IL12 and INFγ to MCP-1 was altered by at least one variant of each gene anal-ysed. The ratio of IL2 to the various cytokines deter-mined reveals that the ratio to IL13 and hsCRP was altered according to the specific PPARγ genotype in PCOS. Furthermore the IL2 ratio to IL5, IL7, Il10, IL12, TNFα and hsCRP was increased if PCOS patients carry the LXRα variant rs2279238AX compared to rs2279238GG, whereas rs11039155 did not show a sig-nificant effect. The IL2 ratio to IL1ß was decreased due to the presence of rs608AX in IGF-2 whereas PON-1 rs705379CX increases the IL2 ratio to IL1β compared with rs705379TT (Figure 2A). The cytokine pattern in relation to IL12 is affected by PPARγ gene variants rs1805192 and c. 1-39T/C as observed for IL2 ratios.

Table 2. Comparison of clinical chemical characterizations with genotype variants of corresponding genes determined in the PCOS study group.Gene PPARγ PPARγ PPARγ IGF-2 IGF-2

rsID rs1805192 CC/ rs1805192 GX c. 1-39C/T CX rs608 GG rs608 AXc. 1-39C/T TT

n 80 8 17 50 71BMI (kg/m2) 30.25 ± 7.75 28.29 ± 7.23 26.76 ± 6.24‡ 28.78 ± 6.86 30.39 ± 7.79Body weight (kg) 81.83 ± 29.34 77.29 ± 20.83 75.24 ± 19.25 81.02 ± 22.31 84.29 ± 24.29Cholesterol (mg/dl) 190.98 ± 39.11 190.83 ± 28.64 201.50 ± 39.46 199.53 ± 38.65 193.75 ± 39.67LDL-C (mg/dl) 128.90 ± 37.23 137.33 ± 56.98 128.87 ± 47.48 131.17 ± 40.79 132,19 ± 42.64HDL-C (mg/dl) 52.61 ± 16.26 49.67 ± 17.36 64.00 ± 15.48‡ 58.80 ± 16.75 50.74 ± 15.28‡Triglyceride (mg/dl) 119.89 ± 70.89 89.17 ± 51.02 108.00 ± 76.61 108.16 ± 77.46 121.57 ± 56.94Fasting insulin (pmol/l) 289.63 ± 171.90 163.78 ± 36.59‡ 199.74 ± 102.61‡ 256.69 ± 130.61 287.40 ± 188.36Fasting glucose (mmol/l) 4.98 ± 0.77 4.65 ± 0.37 4.69 ± 0.46 4.94 ± 0.64 4.87 ± 0.69HbA1c (%) 5.06 ± 0.87 5.14 ± 0.63 5.10 ± 0.44 5.15 ± 0.64 4.89 ± 1.28QUICKI 0.29 ± 0.03 0.31 ± 0.01‡ 0.31 ± 0.03‡ 0.30 ± 0.03 0.30 ± 0.03HOMA IR 8.33 ± 5.40 4.31 ± 1.23‡ 5.36 ± 3.03‡ 7.20 ± 3.99 8.13 ± 5.75Gene LXRα LXRα LXRα Pon-1 Pon-1

rsID rs2279238GG/ rs2279238 AX rs11039155 AX rs705379 TT rs705379 CXrs11039155GG

n 78 15 13 31 95BMI (kg/m2) 30.05 ± 8.04 25.80 ± 5.56‡ 32.25 ± 7.82 31.26 ± 8.34 29.32 ± 7.22Body weight (kg) 83.81 ± 25.48 70.20 ± 13.77‡ 92.04 ± 22.01 87.45 ± 25.20 82.08 ± 23.37Cholesterol (mg/dl) 195.29 ± 37.71 194.54 ± 32.43 198.67 ± 35.75 194.08 ± 37.63 197.41 ± 39.43LDL-C (mg/dl) 132.57 ± 43.03 123.42 ± 39.14 138.00 ± 35.21 125.96 ± 38.91 132.78 ± 41.79HDL-C (mg/dl) 53.90 ± 15.78 62.33 ± 14.40‡ 48.33 ± 14.48 55.23 ± 17.24 54.21 ± 16.26Triglyceride (mg/dl) 108.01 ± 50.52 107.77 ± 56.51 144.00 ± 62.89‡ 114.88 ± 49.49 117.10 ± 70.71Fasting insulin (pmol/l) 269.98 ± 186.85 198.07 ± 85.19‡ 329.24 ± 162.11 311.02 ± 140.32 249.30 ± 140.40‡Fasting glucose (mmol/l) 4.85 ± 0.59 4.63 ± 0.72 5.18 ± 0.99‡ 5.07 ± 0.61 4.84 ± 0.69‡HbA1c (%) 5.06 ± 0.46 4.90 ± 0.31‡ 5.64 ± 0.86‡ 5.03 ± 0.37 5.17 ± 0.67QUICKI 0.30 ± 0.03 0.31 ± 0.02‡ 0.28 ± 0.02‡ 0.29 ± 0.02 0.30 ± 0.03‡HOMA IR 7.54 ± 5.35 4.88 ± 2.24‡ 10.96 ± 4.74‡ 5.85 ± 1.03 6.96 ± 4.35‡Notes: Data are given as mean ± SD (standard deviation). Values indicated with ‡ are significant differences (p < 0.05). dbSNP rsID: SNP polymorphism database (http://www.ncbi.nlm.gov/projects/SNP). n gives the number of PCOS carriers of the respective genetic variant as indicated. PPARγ rs1805192 CC/ c. 1-39C/T TT and LXRα rs2279238GG/ rs11039155GG carriers were “wildtype” for the both investigated genetic variants. Carriers of any variation in PPARγ or LXRα respectively were excluded from this group. PCOS patients that carried more than one genetic variant per gene or other rare genetic variants in the respective gene were excluded from the analyses.

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The cytokine relations of IL12 to IL2, IL4, IL1ß, INFγ and G-CSF were increased by LXRα gene variant rs2279238AX compared to rs2279238GG. IL13 was increased in relation to IL12 by the presence of IGF-2 rs608AX genotype whereas the IL12/TNFα ratio was decreased compared to rs608GG. No further specific alterations of cytokine levels in relation to IL12 can be observed for PON-1 rs705379 (Figure 2B). In relation to INFγ, the PPARγ genotype rs1805192 and c. -39T/C and LXRα rs2279238 again showed the identical pat-terns as observed for IL2 ratios. The INFγ to IL13 ratio was increased by IGF-2 rs608, whereas PON-1 gene variant rs705379 did not further alter the cytokine pat-tern specifically (Figure 2C). None of the gene variants investigated affected the relations of th1 cytokines to IL6, IL8 or MIP-1β in the PCOS group and rs11039155 had no significant influence on any cytokine relation investigated at all.

Discussion

PCOS accounts for a life-long risk of infertility, menstrual dysfunction and the metabolic syndrome including obe-sity, T2DM, and premature heart disease (Alemzadeh et al., 2010; Wild et al., 2010). Initially the presence of polycystic ovaries and the reproductive disturbances observed were in focus, but over the past few years more importance was attached to the metabolic abnormalities of the syndrome. Also a comparable male phenotype with hyperandrogenism and the typical metabolic com-plications observed as in PCOS women might exist but remains to be defined. The genetic basics of PCOS still remain to be identified, but multiple genetic variations have been described to influence the metabolic specifics of the syndrome (Ewens et al., 2008, 2011; Mukherjee and Maitra, 2010; San-Millán and Escobar-Morreale, 2010; Unluturk et al., 2007).

Figure 1. Plasma glucose and insulin concentrations during oral glucose tolerance test. Data (insulin (pmol/l); glucose (mmol/l)) are given as means ± SEM (p < 0.05) for (A) PPARγ variants rs1805192 and c. 1-39 (B) LXRα variants rs2279238 and rs11039155, (C) IGF-2 variant rs608, (D) PON-1 variant rs705379.

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We focused on variations in central transcription fac-tors of lipid metabolism sensitive to cellular cholesterol and insulin levels, i.e., PPARγ and LXRα, as well as vari-ants in genes associated to insulin levels and secretion or atherosclerosis risk, i.e., SNPs in PON-1 and IGF-2 (Curti et al., 2011; Gaidukov et al., 2006; Kettner et al., 2011; Legry et al., 2008, 2011; Mackness et al.,1998; Price et al., 2011; Rodríguez et al., 2004; Zhao and Dahlman-Wright, 2010). None of these variants is directly geneti-cally correlated to the pathogenesis of PCOS. In our analyses we compare carriers of one gene variant solely to probands without any further SNPs investigated in the gene of interest. In detail, for analyses of the rs185192GX genotype compared to the rs185192CC genotype, carri-ers of c. 1-39CX, or rs3856806TXwere excluded from the rs185192CC genotype group.

The best investigated genetic variant on metabolic features including PCOS patients is PPARγ2 Pro12Ala (rs185192). PPARγ2 Pro12Ala is not thought to be directly genetically associated to PCOS, but rather functions as a genetic modifier. A significant protective effect of Pro12Ala on insulin resistance or surrogate indexes of insulin resistance and sensitivity, i.e., HOMA-IR and QUICKI, suggested that this protective effect might be mediated by the reduced insulin resistance of carriers of this variant (San-Millan and Escobar-Morreale 2010). We also observed the protective effect of rs185192GX on insulin and glucose concentrations during oral glucose tolerance test and on HOMA-IR or QUICKI. The influ-ence on metabolic parameters of the other PPARγ2 c. 1-39T/C variant has not been described in detail before. Next to the effect on HOMA-IR and QUICKI this variant

Table 3. Comparison of serum cytokine levels with genotype variants of corresponding genes determined in the PCOS study group.

GenersID

PPARγ rs1805192 PPARγ c.1-39T/C IGF-2 rs608

GX CC CX TT GG AXIL-2 50.99 ± 26.63 44.82 ± 20.60 50.46 ± 13.72 44.58 ± 23.51 43.24 ± 19.39 52.00 ± 23.41IL-4 55.63 ± 12.49 52.37 ± 13.69 61.70 ± 5.42 48.89 ± 13.50 51.16 ± 15.76 57.16 ± 11.27IL-5 14.53 ± 5.07 12.59 ± 5.30 15.21 ± 3.09 10.29 ± 4.35 11.89 ± 4.72 14.92 ± 5.60IL-6 162.14 ± 74.67 131.07 ± 52.81 156.55 ± 42.25 130.86 ± 60.96 142.70 ± 53.13 155.23 ± 59.65IL-7 8.80 ± 3.58 8.91 ± 4.34 9.50 ± 3.12 8.33 ± 4.22 9.99 ± 4.60 9.84 ± 3.88IL-8 4.41 ± 2.55 4.27 ± 2.20 4.64 ± 1.47 4.17 ± 2.45 4.37 ± 2.02 4.79 ± 2.34IL-10 11.53 ± 5.51 10.37 ± 4.64 11.44 ± 3.45 10.32 ± 5.12 11.08 ± 4.52 12.28 ± 4.90IL-12 24.05 ± 10.63 21.80 ± 8.99 24.82 ± 6.49 21.39 ± 9.83 23.99 ± 9.02 24.59 ± 9.49IL-13 112.71 ± 77.04 51.19 ± 24.59 60.92 ± 34.09 48.06 ± 20.03 45.86 ± 40.90 73.44 ± 50.03IL-17 28.69 ± 12.12 17.54 ± 6.69 25.15 ± 11.86 17.98 ± 7.34 20.60 ± 6.91 22.92 ± 10.33IL-1ß 7.78 ± 2.53 5.35 ± 1.97 6.63 ± 1.48 4.77 ± 1.84 7.02 ± 1.72 5.33 ± 1.60IFN-γ 130.09 ± 22.03 95.08 ± 32.40 128.29 ± 29.42 94.16 ± 33.11 110.28 ± 32.20 123.13 ± 33.89

TNF-α 117.25 ± 54.76 73.59 ± 39.11 84.03 ± 47.80 74.86 ± 37.15 64.77 ± 21.93 96.17 ± 34.33MCP-1 204.96 ± 193.84 68.08 ± 75.92 30.57 ± 10.12 38.22 ± 19.01 61.76 ± 121.59 37.10 ± 129.20

MIP-1β 106.93 ± 18.57 105.78 ± 52.99 145.91 ± 63.69 92.47 ± 34.27 80.72 ± 47.87 114.49 ± 49.28

G-CSF 84.83 ± 19.70 65.32 ± 16.75 79.81 ± 15.12 68.88 ± 21.96 61.13 ± 22.65 79.11 ± 19.10hsCRP 0.53 ± 0.30 0.79 ± 0.51 1.11 ± 0.75 0.54 ± 0.18 0.53 ± 0.15 0.52 ± 0.35

GenersID

LXRα rs2279238 LXRα rs11039155 Pon-1 rs705379

AX GG AX GG CX TTIL-2 48.54 ± 19.83 47.61 ± 21.14 43.90 ± 22.58 54.28 ± 18.99 41.90 ± 17.41 48.37 ± 21.23IL-4 51.09 ± 13.13 53.85 ± 13.64 54.38 ± 11.82 60.85 ± 6.82 55.42 ± 8.47 53.82 ± 13.30IL-5 8.73 ± 6.07 14.06 ± 4.45 16.15 ± 6.01 14.72 ± 4.00 13.88 ± 4.39 13.30 ± 4.91IL-6 105.47 ± 52.48 137.25 ± 60.68 135.54 ± 51.42 138.49 ± 38.96 151.79 ± 51.57 139.54 ± 59.41IL-7 5.67 ± 4.30 9.66 ± 3.99 10.13 ± 4.43 10.25 ± 3.57 9.52 ± 3.58 9.56 ± 4.01

IL-8 3.03 ± 2.56 4.12 ± 2.14 4.02 ± 2.45 4.79 ± 1.97 4.09 ± 1.86 4.67 ± 2.31IL-10 7.33 ± 5.13 11.79 ± 4.21 12.27 ± 4.85 12.12 ± 4.13 11.55 ± 2.87 10.78 ± 4.87IL-12 14.99 ± 9.77 24.03 ± 8.54 23.57 ± 9.37 25.80 ± 8.30 23.10 ± 7.80 22.99 ± 8.93IL-13 41.83 ± 30.87 50.03 ± 20.05 46.05 ± 15.80 57.19 ± 17.41 40.92 ± 65.99 54.76 ± 33.91IL-17 14.46 ± 8.00 20.99 ± 9.28 19.00 ± 7.36 23.04 ± 8.66 15.73 ± 11.73 20.56 ± 6.84

IL-1β 6.42 ± 2.15 5.93 ± 2.37 6.25 ± 2.10 5.85 ± 1.03 7.19 ± 2.11 5.53 ± 1.84

IFN-γ 110.76 ± 38.69 110.20 ± 32.28 108.45 ± 34.65 124.07 ± 20.01 110.89 ± 27.94 110.10 ± 31.16

TNF-α 52.14 ± 43.75 80.29 ± 36.93 77.37 ± 40.05 87.15 ± 28.99 60.14 ± 52.78 86.95 ± 37.23

MCP-1 27.39 ± 11.96 55.78 ± 36.99 32.23 ± 5.94 46.03 ± 28.13 26.48 ± 145.44 59.44 ± 35.39

MIP-1β 92.72 ± 54.86 103.40 ± 46.23 79.29 ± 52.21 114.59 ± 44.71 90.32 ± 36.17 112.63 ± 51.49

G-CSF 68.48 ± 17.13 72.96 ± 22.47 67.48 ± 17.11 74.47 ± 13.44 74.56 ± 18.42 70.63 ± 19.60hsCRP 0.46 ± 0.52 0.77 ± 0.49 0.53 ± 0.49 0.65 ± 0.34 0.53 ± 0.14 0.68 ± 0.49Notes: Data are given as mean (pg/ml) ± SD (standard deviation). Values shown in bold indicate significant differences (p < 0.05). dbSNP rsID: SNP polymorphism database (http://www.ncbi.nlm.gov/projects/SNP/).

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also has a significant impact on fasting insulin and HDL-cholesterol levels, but not on glucose concentration during challenging. So there is an overlap to the effect of rs185192GX but also specific action of this variation. This

gets obvious best in oral glucose tolerance tests where the effect on insulin concentration is like the rs185192GX but glucose concentration are more like that of probands without any genetic variation.

Figure 2. Relation of th1 specific cytokines to th2 specific cytokines in the PCOS cohort compared to controls. Cytokine levels determined for th1 cytokines of IL2 (A), IL12 (B) und INFγ (C) are given in relation to the determined th2 cytokines and chemokines (IL4, IL5, IL7, IL10, IL1β IL6, IL7, IL13,IL17, IL1β, INFg, TNFa, MCP-1 MIP-1β, G-CSF and hsCRP).

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LXRα polymorphisms influence metabolic serum parameters (Legry et al., 2011; Price et al., 2011). Former studies investigated rather one or the other variant sepa-rately, thus neglecting putative private effects of either SNP. The rs11039155 has been found to be associated with increased HDL-cholesterol and a general protec-tion of metabolic risk (Legry et al., 2008, 2011). Although located in the promoter, an effect of this SNP on LXRα expression was formerly excluded (Legry et al., 2008). The rs2279238 has been shown to be associated to increased cardiovascular risk, insulin sensitivity in clamp analyses but no association to fasting glucose or glucose or insulin in clamp analyses (Kettner et al., 2011).

Our investigations now indicate that the two genetic LXRα variants independently influence the susceptibility to metabolic risk factors and the genetic variants inter-act in the determination of the individual risk. In PCOS patients’ rs11039155 alone increases the overall risk whereas rs2279238 alone is more protective as it reduces the metabolic risk, and therefore confirms our former observation (Knebel et al., 2012).

The IGF-2 variant is informative for the whole insulin/IGF cluster and therefore resembles a marker for insulin secretion. In PCOS patients the rs608 transition at IGF2 3′-UTR region has been reported to stimulate ovarian androgen secretion (San-Millan et al., 2004). Another study did not confirm further associations to meta-bolic parameters in PCOS patients (Ramos Cirilo et al., 2011). This is in accordance to our previous observations (Knebel et al., 2009) and the study presented here.

PON1 gene polymorphisms have been associated with various human diseases, including diabetes and coronary heart disease. The variation in the promoter region (rs705379) has been shown to influence PON-1 serum concentration and activity (Brophy et al., 2001; Deakin et al., 2003; San-Millan et al., 2006). Interestingly the rs705379TT genotype accounts for lower PON-1 expression associated with higher glucose concentra-tions in patients with abnormal glucose levels (Leviev et al., 2001), an observation we can confirm here for PCOS patients. Furthermore in our PCOS cohort higher fasting insulin levels can be observed.

The patterns of cytokine secretion in PCOS patients were suggested as disease markers in various studies (Luque-Ramírez and Escobar-Morreale, 2010; Samy et al., 2009). An increase in inflammatory markers has been described in adipose and non-adipose PCOS patients, whereas other studies indicate that increased inflammatory markers are further increased due to obesity rather than the presence of the PCOS syndrome (Escobar-Morreale et al., 2003; Gonzales et al., 1999, Kelly et al., 2001; Knebel et al., 2008; Lindholm et al., 2011; Luque-Ramírez and Escobar-Morreale, 2011; Mohlig et al., 2004; Puder et al., 2005).

However, a clear correlation to a distinct PCOS spe-cific cytokine profile has not yet been identified. The cor-relation of specific cytokine levels to the PCOS syndrome is difficult as correlations to the metabolic features of

PCOS patients seem to interact. In the case of such a multi-factorial situation one effect might mask the effect of another component completely. The source of pro-inflammatory cytokines derived by increased WAT mass in states of obesity (Bastard et al., 2006) is a closer link and might mask the PCOS specific effect on cytokine secretion.

Gene variants with an impact on metabolic factors could also account for an alteration of the secreted cyto-kine pattern. Especially for the nuclear receptors there is a direct effect on the expression of inflammatory genes or the regulation of these genes due to altered presence of metabolic derivate (Bensinger et al., 2008; Hong et al., 2008). Here we show that the investigated gene variants can not only account for metabolic alterations within the PCOS group but also specifically interfere with the secreted cytokine pattern among PCOS patients. In this regard the PPARγ variants have the largest impact on the level of distinct cytokines within the PCOS cohort. LXRα and IGF-2 variants also influence the distinct cytokine concentration whereas PON-1 does not account for dis-tinct altered cytokine levels. We have formerly shown that the secretion pattern of pro-inflammatory cytokines IL4, IL5, IL6, IL10, IL1β, INFγ, TNFα, MCP-1 or hsCRP is increased, that of IL7 is reduced and no alteration of IL8 or IL17 was detectable in this cohort in direct compari-son to BMI matched controls (Knebel et al., 2008). Here we show that the alteration of IL4, IL5, IL7, IL10, IL12, IL13, IL17, IL1β, INFg, TNFa, MCP-1 MIP-1β, G-CSF and hsCRP within the PCOS cohort are directly influenced by the genetic variants investigated. Interestingly IL-6 levels were not affected by the variants investigated in our PCOS cohort. This fact further strengthens the for-mer observations that the alterations observed in IL6 levels were directly caused by the presence of PCOS and not accompanying factors such as obesity (Escobar-Morreale et al., 2003; Gonzales et al., 1999; Kelly et al., 2001; Knebel et al., 2008; Lindholm et al., 2011; Luque-Ramírez and Escobar-Morreale, 2011; Mohlig et al., 2004; Puder et al., 2005).

The determination of individual levels of a cytokine might not be as informative. The investigation of various cytokine relations will rather account for altered pattern of the complete set of cytokines investigated to follow the idea of an altered cytokine signature depending on the genetic setting. We chose the accepted model of th1/th2 relation (Kidd, 2003) to determine the overall shift in the cytokine levels investigated. Initially th1 was associ-ated to a fast activation of cellular immune response (“innate immunity”). Th2 cells were thought to modulate the chronic immune reactions by activation of B-cells and initiation of the specific humoral immune answer (“adaptive immunity”). In principle the cell populations differ in their specific cytokine profile with type “type 1 cytokines” INF-γ, IL2 and IL12 produced by th1 cells or “type 2 cytokines” like IL4, IL5, IL6, IL10 or IL13 produced by th2 cells. Although the individual cytokine levels show significant variations, the relation of th1 to th2 cytokines

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in this study group revealed that the ratio of certain cytokines differed significantly within the PCOS patients according to the genotype. Interestingly gene variants in one gene do not necessarily influence the cytokine rela-tion identically as seen in the case of MCP-1 and PPARγ variants. Gene variants that alter distinct cytokine levels like the promoter variant in LXRα have no impact on the overall cytokine pattern, whereas other like PON-1 might still account alteration in the ratios thus indicating com-plete shifted patterns. The most differences were caused by LXRα rs2279238 whereas rs11039155 has no impact reaching our criteria. This is interesting as both SNPs were thought to be in close linkage disequilibrium (Legry et al., 2008) and most studies investigated only one SNP (Dahlmann et al., 2006, 2010; Kettner et al., 2011; Legry et al., 2011; Price et al., 2011). We show that there are already differences on metabolic parameters and the effect of both variants on cytokine levels also clearly differed.

The sole cytokines not influenced at all were IL6 and IL8. HsCRP is affected by common PPARγ as well as LXRα variants. Furthermore the ratios to MCP-1 were altered by variants in any gene investigated. These findings shield a critical light on MCP-1 as a putative biomarker as the individual genetic setting can account for alterations of this marker.

Unfortunately our analyses do not account for a certain cytokine pattern which can serve as a valid biomarker for PCOS diagnosis. Our results indicate that caution should be given to use the levels of one or a few cytokines as bio-marker for PCOS. Next to direct effects that have a large impact on cytokine levels, like metabolic features and obesity, genetic variants in various genes can also influ-ence the individual cytokine pattern. One has to admit that due to the study design the interpretation of the data bares some limitations. Separating the PCOS patients investigated according to the genotype per gene reduces case number for comparing the effect of the genetic vari-ants on metabolic characteristics and cytokine signature within the PCOS group. Although observations reported here reached statistical significance, we cannot com-pletely exclude that there are unrevealed ethical or cohort specific effects influencing the outcome. Regarding these limitations it would be interesting to determine how the genetic setting influences metabolic parameters and cytokine signature in other PCOS cohorts. These findings would further support the hypothesis that the individual risk of a proband is determined as the sum of the indi-vidual genetic setting.

Acknowledgements

This study was supported by the German Diabetes Center, and the LiDia programme of City of Hamburg.

Declaration of interest

The authors report no conflict of interest.

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Genetic variants in central metabolic genes influence some but not all relations of inflammatory...

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