Type 2 diabetes mellitus (T2DM) is a complicated

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1 GENETIC TESTING AND MOLECULAR BIOMARKERS Volume 20, Number 6, 2016 ª Mary Ann Liebert, Inc. Pp DOI: /gtmb Gene Polymorphisms in the RANKL/RANK/OPG Pathway Are Associated with Type 2 Diabetes Mellitus in Southern Han Chinese Women Peng Duan, 1,2, * Ping Tu, 2, * Lian Si, 2 Wan Hu, 2 Meng Liu, 2 Jia Liu, 2 and Yaoming Xue 1 Aims: Receptor activator of nuclear factor-kappa B ligand (RANKL), its receptor activator of nuclear factorkappa B (RANK), and decoy receptor osteoprotegerin (OPG) are three major proteins of the RANKL/RANK/ OPG signaling pathway encoded by TNFSF11, TNFRSF11A, and TNFRSF11B, respectively. This pathway plays a critical role in bone remodeling and may have a role in the pathogenesis of type 2 diabetes mellitus (T2DM). The aim of this study was to investigate the relationship between gene polymorphisms in the RANKL/ RANK/OPG pathway and T2DM in Southern Han Chinese women. Methods: A total of 1233 participants, including 514 T2DM patients and 719 healthy control subjects, were enrolled in this case control study. Twenty-one single-nucleotide polymorphisms (SNPs) of TNFSF11, TNFRSF11A, and TNFRSF11B were genotyped using an improved multiplex ligation detection reaction technique. Results: Two SNPs of TNFRSF11B (rs and rs ) were significantly associated with T2DM ( p = 0.04 and p = 0.009, respectively). Subjects with the GA genotype of rs had a lower risk of T2DM (odds ratio [OR] = 0.67, 95% confidence interval [CI] = , p = 0.005) compared with subjects with the GG genotype. The GG genotype of rs was associated with increased risk for T2DM (OR = 1.94, 95% CI = , p = 0.01) compared with the CC genotype. Conclusions: This study suggests that TNFRSF11B but not TNFSF11 and TNFRSF11A genetic polymorphisms are associated with T2DM in Southern Han Chinese women. These findings provide preliminary support for the potential role of the RANKL/RANK/OPG pathway in T2DM. Introduction Type 2 diabetes mellitus (T2DM) is a complicated metabolic disorder characterized by the presence of hyperglycemia caused by defects in insulin action and/or insulin secretion. For the past few decades, the prevalence of T2DM has rapidly increased throughout the world (Frier, 2014). The prevalence of T2DM in the Chinese adult population was 11.6%, according to data obtained from a recent national survey (Xu et al., 2013). Consequently, diabetes has become a major public health problem in China. As with many common disorders, diabetes has a strong genetic component, and genetic susceptibility apparently plays an important role in both the etiology and manifestation of diabetes (Li et al., 2015). Modern twin studies have shown that diabetes is highly heritable, with the heritability of T2DM being 64% (van Dongen et al.,2012). Extensive research has been carried out in the field of genetics of diabetes, but the search for the genes responsible has progressed slowly. Recently, genome-wide studies have identified several novel risk genes for diabetes (Hanson et al., 2014; Anderson et al., 2015), and some diabetes-associated genetic loci have been reported (Karadeniz et al., 2013; Yang and Du, 2014). However, its specific genetic mechanism remains largely unknown (Sun et al., 2014). Receptor activator of nuclear factor-kappa B (RANK) and its RANK ligand (RANKL) play a critical role in the regulation of bone remodeling. RANKL stimulates osteoclastogenesis and bone resorption by binding to its receptor, RANK, whereas osteoprotegerin (OPG) acts as a decoy receptor that inhibits binding between RANKL and RANK and restrains bone resorption. RANKL, its receptor RANK, and decoy receptor OPG are three major proteins of the RANKL/ RANK/OPG signaling pathway encoded by genes TNFSF11, TNFRSF11A, and TNFRSF11B, respectively (Sigl and Penninger, 2014). The RANKL/RANK/OPG pathway is well recognized to be critical for the regulation of bone metabolism and has an essential role in the formation, function, and survival of osteoclasts (Ndip et al., 2014). Moreover, the RANKL/RANK/OPG pathway is also involved in cell death and proliferation, vascular calcification and atherosclerosis, 1 Department of Endocrinology and Metabolism, Nanfang Hospital, Southern Medical University, Guangzhou, China. 2 Department of Endocrinology and Metabolism, The Third Hospital of Nanchang, Nanchang, China. *These authors contributed equally to this work. 285

2 286 DUAN ET AL. and inflammation and immunity (Hanada et al., 2011; Kukita and Kukita, 2013). Recently, evidence has demonstrated that the RANKL/RANK/OPG pathway may have a potential role in the pathogenesis of diabetes; blocking this pathway improved hepatic insulin resistance and prevented the development of diabetes mellitus (Kiechl et al., 2013). In addition, there is evidence revealing that OPG is expressed in the pancreas and may protect pancreatic b-cells from further damage (Schrader et al., 2007). Therefore, we hypothesized that genetic variations in genes in the RANKL/RANK/OPG pathway are associated with diabetes mellitus. In the present study, we conducted a case control study in a cohort of 1233 Southern Han Chinese women to investigate the association between single-nucleotide polymorphisms (SNPs) and haplotypes in the TNFSF11, TNFRSF11A, and TNFRSF11B genes in the RANKL/RANK/OPG pathway with T2DM. Materials and Methods Study population A total of 1233 participants were enrolled in this case control study from February 2012 to December 2014 in The Third Hospital of Nanchang, which included 514 T2DM patients (case group) and 719 healthy control subjects (control group). All participants were unrelated Southern Han Chinese women. Among these participants, 719 healthy females with normal glucose tolerance were recruited from the database of our previous female osteoporosis study (Tu et al., 2015), while 514 age- and gender-matched females with T2DM were enrolled from the Department of Endocrinology and Metabolism of The Third Hospital of Nanchang. T2DM patients were diagnosed and confirmed by doctors according to the World Health Organization (WHO, 1999) diagnostic criteria for diabetes as follows: fasting plasma glucose (FPG) 7.0 mmol/l or 2-h postprandial plasma glucose (2hPG) 11.1 mmol/l during a 75-g oral glucose tolerance test. Exclusion criteria for the case group were as follows: (1) subjects who received any medication containing glucocorticoids during the past 3 months; (2) subjects who suffered from diseases deemed to affect plasma glucose such as adrenal diseases or chronic liver disease; and (3) subjects with type 1 diabetes, maturity onset diabetes of the young, and other types of diabetes. Inclusion criteria for the control group were as follows: (1) FPG <6.1 mmol/l; (2) 2hPG <7.8 mmol/l during an oral glucose tolerance test; and (3) no history of diabetes. Demographic data were collected by interview through a questionnaire, which included information such as age, alcohol intake, smoking history, and history of diabetes. This study was approved by the Ethics Committee of The Third Hospital of Nanchang, and written informed consent was obtained from each subject. The entire study was performed based on the principles of the Declaration of Helsinki. Anthropometric measurements and biochemical assays Height and weight were measured to the nearest 1 cm and 0.1 kg, respectively. Body mass index (BMI) was calculated by the following formula: BMI ¼ body weight (kg)=height 2 (m 2 ): FPG, 2hPG, triglycerides (TG), total cholesterol (TC), low-density lipoprotein cholesterol (LDL-C), high-density lipoprotein cholesterol (HDL-C), aspartate aminotransferase (AST), alanine aminotransferase (ALT), blood uric acid (URCA), creatinine (Cr), and blood urea nitrogen (BUN) levels were measured using commercially available kits (Siemens Medical Solutions, Erlangen, Germany) with an automatic biochemistry analyzer (ADVIA 2400; Siemens, Inc., Erlangen, Germany). Glycated hemoglobin (HbA1c) was estimated by high-performance liquid chromatography using a Bio-Rad Variant II Dual Kit with the VariantÔ machine (Bio-Rad, Hercules, CA) (Younis et al., 2010). SNP selection and genotyping Twenty-one tagged SNPs were selected as described in our previous protocol (Tu et al., 2015). The six tagging SNPs of TNFSF11 gene could tag 92.8% of SNPs, the nine tagging SNPs of TNFSF11A gene tagged 46.3% of SNPs, and the six tagging SNPs of TNFSF11B gene tagged 50.0% of SNPs with Parameter Table 1. Demographic and Clinical Characteristics of Study Subjects Case (n = 514) (n = 719) p a Age (year) Height (m) Weight (kg) <0.001 BMI (kg/m 2 ) <0.001 ALT (U/L) <0.001 AST (U/L) BUN (mmol/l) Cr (mmol/l) URCA (mmol/l) TC (mmol/l) TG (mmol/l) <0.001 HDL-C (mmol/l) <0.001 LDL-C (mmol/l) FPG (mmol/l) < hPG (mmol/l) <0.001 HbA1c (%) <0.001 Smoking history Never (%) 494 (96.1) 701 (97.5) Past (%) 15 (2.9) 3 (0.4) Current (%) 5 (1.0) 15 (2.1) Alcohol intake (%) <0.001 Never (%) 489 (95.1) 661 (91.9) Past (%) 16 (3.1) 2 (0.3) Current (%) 9 (1.8) 56 (7.8) Physical activity 0.03 Low physical 319 (62.1) 397 (55.2) activity (%) Moderate physical 188 (36.6) 303 (42.1) activity (%) High physical activity (%) 7 (1.4) 19 (2.6) Data are presented as mean SD or n (%). a Two-sample t-test was used to compare the means among groups. Chi-square test was used for testing categorical data among groups. 2hPG, 2-h postprandial plasma glucose; ALT, alanine aminotransferase; AST, aspartate aminotransferase; BMI, body mass index; BUN, blood urea nitrogen; Cr, creatinine; FPG, fasting plasma glucose; HbA1c, glycated hemoglobin; HDL-C, high-density lipoprotein cholesterol; LDL-C, low-density lipoprotein cholesterol; TC, total cholesterol; TG, triglycerides; URCA, blood uric acid.

3 TNFRSF11B POLYMORPHISMS AND T2DM 287 Table 2. Associations of Tagging SNPs of TNFSF11, TNFRSF11A, and TNFRSF11B with Type 2 Diabetes Mellitus Gene SNP Allele HWE Risk allele a minor allele frequency (MAF) of >0.05 according to the HapMap Phase III (release 27) Han Chinese database. Genomic DNA from whole blood samples was extracted using the QIAamp DNA Mini Kit (Qiagen, Inc., Hilden, Germany). Genotyping was performed using an improved multiplex ligation detection reaction (imldr) technique developed by the Genesky Biotechnologies, Inc. (Shanghai, China). A multiplex PCR-ligase detection reaction assay was used in the imldr. Alleles for each SNP were distinguished by different fluorescent labels of allele-specific oligonucleotide probe pairs. The different SNPs were further distinguished by the different extended lengths at the 3 end (Yang et al., 2015). All individuals that performed the genotyping were blinded to the clinical data. For quality control, genotyping was repeated in 5% of the samples; and 1% was directly sequenced using an automated sequencer (ABI3730XL; Applied Biosystems, Inc., Pittsburgh, PA) to confirm the results of the imldr. Statistical analysis Quantitative data were compared between cases and controls using the unpaired Student s t-test, and categorical data were compared using the chi-square test. The Hardy Weinberg equilibrium (HWE) was considered as a quality control measure for genotyping. SNPs with p < 0.05 were excluded from further analysis. Linkage disequilibrium coefficients were conducted using Haploview version 4.2 (Barrett et al., 2005). Haplotype block structure was determined by Haploview software using a confidence interval (CI) algorithm (Gabriel et al., 2002), and the significances of each haplotype (with frequency >10%) within the defined Risk allele frequency Cases OR (95% CI) p a (empirical p-value) b TNFSF11 rs C/T 0.13 C ( ) 0.06 TNFSF11 rs G/A 0.08 G ( ) 0.06 TNFSF11 rs G/A 0.13 A ( ) 0.65 TNFSF11 rs C/T 0.09 T ( ) 0.59 TNFSF11 rs G/A 0.10 A ( ) 0.48 TNFSF11 rs c C/T 0.02 C TNFRSF11A rs G/A 0.12 G ( ) 0.08 TNFRSF11A rs G/C 0.67 G ( ) 0.98 TNFRSF11A rs C/A 0.84 C ( ) 0.72 TNFRSF11A rs C/T 0.85 T ( ) 0.31 TNFRSF11A rs A/T 0.07 T ( ) 0.58 TNFRSF11A rs C/T 0.27 C ( ) 0.95 TNFRSF11A rs C/T 0.88 T ( ) 0.46 TNFRSF11A rs C/T 0.18 T ( ) 0.32 TNFRSF11A rs G/C 0.83 G ( ) 0.90 TNFRSF11B rs C/T 0.78 T ( ) 0.19 TNFRSF11B rs G/A 0.15 G ( ) 0.67 TNFRSF11B rs C/T 0.78 C ( ) 0.33 TNFRSF11B rs G/A 0.77 G ( ) 0.04 (0.49) TNFRSF11B rs C/G 0.99 G ( ) (0.12) TNFRSF11B rs G/A 0.40 G ( ) 0.53 Bold indicates significant ( p < 0.05). a Logistic regression analysis after adjustments for age, BMI, smoking history, alcohol intake, and physical activity. b Empirical p-values are for the alleles based on 10,000 permutations. c rs was excluded from further analysis because it was not in Hardy Weinberg equilibrium ( p = 0.02). CI, confidence interval; HWE, p-values for Hardy Weinberg equilibrium; OR, odds ratio; SNP, single-nucleotide polymorphism. blocks were further analyzed by PLINK software (Purcell et al., 2007). Odds ratios (ORs) and 95% CIs were calculated to estimate the strength of the association between genotypes and risk of T2DM by logistic regression analysis, which was adjusted for age, BMI, smoking history, alcohol intake, and physical activity. Correction of multiple testing was performed using Haploview 4.2 software through 10,000 permutations that randomly permutated the case/ Genotype Table 3. Genotype and Allele Frequencies of Positive SNPs of TNFRSF11B in the Case and Control Groups Cases (n = 514), n (%) (n = 719), n (%) OR (95% CI) p rs GG 377 (73.3) 473 (65.8) 1.00 (Reference) GA 121 (23.6) 229 (31.8) 0.67 ( ) AA 16 (3.1) 17 (2.4) 1.13 ( ) 0.75 GA+AA 137 (26.7) 246 (34.2) 0.71 ( ) 0.01 G 875 (85.1) 1175 (81.7) 1.00 (Reference) A 153 (14.9) 263 (18.3) 0.75 ( ) 0.04 rs CC 288 (56.0) 418 (58.1) 1.00 (Reference) CG 181 (35.2) 274 (38.1) 0.94 ( ) 0.64 GG 45 (8.8) 27 (3.8) 1.94 ( ) 0.01 CG+GG 226 (44.0) 301 (41.9) 1.09 ( ) 0.46 C 757 (73.6) 1110 (77.2) 1.00 (Reference) G 271 (26.4) 328 (22.8) 2.01 ( ) Bold indicates significant ( p < 0.05).

4 288 DUAN ET AL. control status independently of genotypes. All statistical tests were two sided and determined with SPSS 19.0 for Windows (SPSS, Inc., Chicago, IL). A p-value <0.05 was considered statistically significant. The statistical power was calculated using an additive model based on the control allele frequencies obtained in our study. Using two-sided a of 0.05 for SNPs with the MAF over 0.2, our case control sample size had over 80% power to detect the minimum OR of Results General characteristics of the overall population Demographic and biochemical characteristics of subjects are summarized in Table 1. Patients with T2DM and controls had similar age distributions. As expected, these characteristics were associated with T2DM such as FPG, 2hPG, HbA1c, weight, and BMI, which were higher in T2DM patients than in controls (all p < 0.05). In addition, TG, LDL-C, and ALT levels were significantly higher in T2DM patients than in normal controls, while HDL-C levels were lower in T2DM patients (all p < 0.05). Association between polymorphisms and haplotypes in genes in the RANKL/RANK/OPG pathway and type 2 diabetes The basic characteristics and frequencies of genotypes of the SNPs are listed in Table 2. One SNP (rs in the TNFSF11gene) was excluded from further analysis based on the HWE test ( p < 0.05). As shown in Table 2, multivariable logistic regression analysis after adjustments for age, BMI, smoking history, alcohol intake, and physical activity revealed that rs and rs alleles of the TNFRSF11B gene were significantly associated with T2DM ( p = 0.04 and p = 0.009, respectively). However, the results were no longer significant after correction for multiple testing by 10,000 permutations (empirical p = 0.49 and 0.12, respectively). The genotypic and allelic frequencies of rs and rs in T2DM and control subjects are shown in Table 3. The allele A of rs was identified as protective against the development of T2DM (OR = 0.75, 95% CI = , p = 0.04). Subjects with the GA genotype of rs had a lower risk of T2DM (OR = 0.67, 95% CI = , p = 0.005), while the AA genotype was not associated with lower risk of T2DM (OR = 1.13, 95% CI = , p = 0.75). Furthermore, the GG genotype of rs was associated with a higher risk of T2DM (OR = 1.94, 95% CI = , p = 0.01). Moreover, subjects with the G allele of rs have a significantly higher risk of T2DM compared with subjects carrying the C allele (OR = 2.01, 95% CI = , p = 0.009), suggesting that the G allele is the risk allele. Linkage disequilibrium blocks were subsequently generated using Haploview 4.2 software, and haplotype analysis was performed with the PLINK software. Four regions of strong linkage disequilibrium (blocks 1 4) were found among all 20 SNPs (Fig. 1). Two haplotypes (TATGG and CATAC) of block rs rs rs rs rs of TNFRSF11B were significantly associated with T2DM ( p = 0.04 and p = 0.04, respectively) (Table 4). Discussion T2DM is a genetically heterogeneous disorder for which pathogenesis is determined by both genetic and environmental factors. Therefore, smoking history, alcohol consumption, and physical activity were included in our case control study. Each of these factors was significantly different between the diabetic and control groups and was used as covariates in the subsequent correlation analysis. Because participants in this study are female, the percentage of current or previous cigarette smoking and alcohol drinking is quite small. It was reported that less than 3% of Chinese women have a history of smoking or drinking alcohol regularly (Nechuta et al., 2010). In the current study, we confirmed that two SNPs (rs and rs ) of TNFRSF11B were significantly associated with T2DM in Chinese women. Furthermore, FIG. 1. LD structure of single-nucleotide polymorphisms in TNFSF11, TNFRSF11A, and TNFRSF11B genes in the study. LD plots with r 2 values were generated by Haploview software. D values multiplied by 100 are shown as numbers in the diamonds. The r 2 values are indicated by the degree of darkness. LD, Linkage disequilibrium.

5 TNFRSF11B POLYMORPHISMS AND T2DM 289 Table 4. Associations of Haplotypes of TNFSF11, TNFRSF11A, and TNFRSF11B with Type 2 Diabetes Mellitus Gene Haplotype Cases Haplotype frequencies there was no association between TNFSF11 or TNFRSF11A and T2DM in our cohort. These findings are partially consistent with a previous report by Billings et al. (2012). They found that the rs allele was significantly associated with T2DM ( p = 0.01) in a cohort of European descent, but there was no evidence for the association of the rs allele and T2DM in their study ( p = 0.32). In contrast, another study revealed that SNP rs of the TNFRSF11B gene was significantly associated with diabetic retinopathy in Slovenian patients with T2DM (Mankoc Ramuš et al., 2013). In addition, the rs polymorphism has been reported to be associated with diabetic foot in a Polish population (Nehring et al., 2013). The associations of genetic variations in TNFRSF11B with T2DM found in this study can be partially explained by the possible roles of the RANKL/RANK/OPG pathway in diabetes. First, studies have identified that the RANKL/RANK/OPG pathway can activate nuclear factor-kappa B (NF-kB) and its downstream effectors (Raju et al., 2011), and the NF-kB signaling pathway plays an important role in insulin resistance and T2DM (Kim, 2012). A recent study revealed that blockade of RANKL/RANK/OPG signaling improved hepatic insulin resistance and prevented the development of diabetes mellitus (Wilson, 2013). Second, numerous previous studies have found that the expression of OPG, which is encoded by the TNFRSF11B gene, was elevated in subjects with diabetes compared with control subjects (Secchiero et al., 2006). Elevated OPG levels have been shown to be associated with insulin resistance and T2DM (Yaturu et al., 2008). In addition, TNFRSF11B can be expressed in the pancreas (Cardozo et al., 2001), where it is a cytokine-induced gene in pancreatic islet b-cells that may protect b-cells from further damage; this protective effect may be mediated through the inhibition of p38 MAPK phosphorylation (Saldeen et al., 2001; Blázquez-Medela et al., 2011). Thus, genetic variation in the TNFRSF11B gene may be associated with T2DM. The major limitation of this study is that only Chinese women were recruited. Chinese women usually practice healthier habits than men. For example, most Chinese women do not drink alcohol or smoke and choose a healthier diet than men. Owingtotheminimalimpactoflifestyleandsocialfactors,it might be more appropriate to recruit women as research participants than men for this study to investigate the association of genetic variations with diabetes. However, large-scale studies in different ethnic populations are needed to confirm and extend these current findings. Furthermore, because this was a hospital-based case control study, recruitment and selection bias are unavoidable. Moreover, although the results obtained in this study indicate a role for the TNFRSF11B gene in the development of diabetes, the exact mechanism underlying this observed association remains to be clarified. In conclusion, the current study demonstrates for the first time that two SNPs (rs and rs ) of TNFRSF11B were significantly associated with T2DM in a Chinese Han population. These findings provide preliminary support for the potential genetic role of the RANKL/RANK/ OPG pathway in T2DM. Further studies with larger sample sizes are required to establish the role of TNFRSF11B in the etiology of diabetes and determine the exact biological function and contribution of TNFRSF11B. Acknowledgments OR (95% CI) p (empirical p-value) a TNFSF11: rs rs rs rs rs TAGTA ( ) 0.91 TGACG ( ) 0.82 CAGCG ( ) 0.99 TAGCG ( ) 0.24 TNFRSF11A: rs rs CA ( ) 0.17 GC ( ) 0.40 CC ( ) 0.59 TNFRSF11A: rs rs TC ( ) 0.25 AT ( ) 0.29 TT ( ) 0.98 TNFRSF11B: rs rs rs rs rs TATGG ( ) 0.04 (0.45) CATAC ( ) 0.04 (0.41) CACGC ( ) 0.32 CGTGC ( ) 0.76 TATGC ( ) 0.33 CATGC ( ) 0.52 Bold indicates significant ( p < 0.05). a Empirical p-values are for the alleles based on 10,000 permutations. This work was supported by the National Natural Science Foundation of China (No ) and the Jiangxi Province Science Foundation for Youths (No BAB ).

6 290 DUAN ET AL. Author Disclosure Statement No competing financial interests exist. References Anderson D, Cordell HJ, Fakiola M, et al. (2015) First genomewide association study in an Australian aboriginal population provides insights into genetic risk factors for body mass index and type 2 diabetes. PLoS One10:e Barrett JC, Fry B, Maller J, et al. (2005) Haploview: analysis and visualization of LD and haplotype maps. Bioinformatics 21: Billings LK, Hsu YH, Ackerman RJ, et al. (2012) Impact of common variation in bone-related genes on type 2 diabetes and related traits. Diabetes 61: Blázquez-Medela AM, López-Novoa JM, Martínez-Salgado C (2011) Osteoprotegerin and diabetes-associated pathologies. Curr Mol Med 11: Cardozo AK, Kruhoffer M, Leeman R, et al. (2001) Identification of novel cytokine-induced genes in pancreatic beta-cells by high-density oligonucleotide arrays. Diabetes 50: Frier BM (2014) Hypoglycaemia in diabetes mellitus: epidemiology and clinical implications. Nat Rev Endocrinol 10: Gabriel SB, Schaffner SF, Nguyen H, et al. (2002) The structure of haplotype blocks in the human genome. Science 296: Hanada R, Hanada T, Sigl V, et al. (2011) RANKL/RANKbeyond bones. J Mol Med (Berl) 89: Hanson RL, Muller YL, Kobes S, et al. (2014) A genome-wide association study in American Indians implicates DNER as a susceptibility locus for type 2 diabetes. Diabetes 63: Karadeniz M, Erdogan M, Berdeli A, et al. (2013) Association of interleukin G>C promoter polymorphism with increased risk of type 2 diabetes mellitus patients with diabetic nephropathy in Turkey. Genet Test Mol Biomarkers 18: Kiechl S, Wittmann J, Giaccari A, et al. (2013) Blockade of receptor activator of nuclear factor-kb (RANKL) signaling improves hepatic insulin resistance and prevents development of diabetes mellitus. Nat Med 19: Kim JK (2012) Endothelial nuclear factor kb in obesity and aging: is endothelial nuclear factor kb a master regulator of inflammation and insulin resistance? Circulation 125: Kukita A, Kukita T (2013) Multifunctional properties of RANKL/RANK in cell differentiation, proliferation and metastasis. Future Oncol 9: Li CW, Concepcion E, Tomer Y (2015) Dissecting the role of the FOXP3 gene in the joint genetic susceptibility to autoimmune thyroiditis and diabetes: a genetic and functional analysis. Gene 556: Mankoc Ramuš S, Kumše T, Globocnik Petrovic M, et al. (2013) SNP rs of the osteoprotegerin gene is associated with diabetic retinopathy in Slovenian patients with type 2 diabetes. Biomed Res Int 2013: Ndip A, Wilkinson FL, Jude EB, et al. (2014) RANKL-OPG and RAGE modulation in vascular calcification and diabetes: novel targets for therapy. Diabetologia 57: Nechuta SJ, Shu XO, Li HL, et al. (2010) Combined impact of lifestyle-related factors on total and cause-specific mortality among Chinese women: prospective cohort study. PLoS Med 7:e Nehring P, Mrozikiewicz-Rakowska B, Sobczyk-Kopcio1A, et al. (2013) Osteoprotegerin gene rs and rs polymorphisms in type 2 diabetes patients and sex-specific rs polymorphism as a risk factor for diabetic foot. Pol Arch Med Wewn 123: Purcell S, Neale B, Todd-Brown K, et al. (2007) PLINK: a tool set for whole-genome association and population-based linkage analyses. Am J Hum Genet 81: Raju R, Balakrishnan L, Nanjappa V, et al. (2011) A comprehensive manually curated reaction map of RANKL/RANKsignaling pathway. Database (Oxford) 2011:bar021. Saldeen J, Lee JC, Welsh N (2001) Role of p38 mitogenactivated protein kinase (p38 MAPK) in cytokine-induced rat islet cell apoptosis. Biochem Pharmacol 61: Schrader J, Rennekamp W, Niebergall U, et al. (2007) Cytokineinduced osteoprotegerin expression protects pancreatic beta cells through p38 mitogen-activated protein kinase signalling against cell death. Diabetologia 50: Secchiero P, Corallini F, Pandolfi A, et al. (2006) An increased osteoprotegerin serum release characterizes the early onset of diabetes mellitus and may contribute to endothelial cell dysfunction. Am J Pathol 169: Sigl V, Penninger JM (2014) RANKL/RANK from bone physiology to breast cancer. Cytokine Growth Factor Rev 25: Sun X, Yu W, Hu C (2014) Genetics of type 2 diabetes: insights into the pathogenesis and its clinical application. Biomed Res Int 2014: Tu P, Duan P, Zhang RS, et al. (2015). Polymorphisms in genes in the RANKL/RANK/OPG pathway are associated with bone mineral density at different skeletal sites in postmenopausal women. Osteoporos Int 26: van Dongen J, Slagboom PE, Draisma HH, et al. (2012) The continuing value of twin studies in the omics era. Nat Rev Genet 13: Wilson C (2013) Diabetes: blocking RANKL signalling might prevent T2DM. Nat Rev Endocrinol 9:188. World Health Organization (1999) Definition, Diagnosis and Classification of Diabetes Mellitusand Its Complications: report of a WHO Consultation. World Health Organization, Geneva. Xu Y, Wang L, He J, et al. (2013) Prevalence and control of diabetes in Chinese adults. JAMA 310: Yang J, Zhang J, Wang X, et al. (2015) Identification of functional tag single nucleotide polmorphisms within the entire CAT gene and their clinical relevance in patients with noiseinduced hearing loss. Int J Clin Exp Pathol 8: Yang S, Du Q (2014) Association of GCK -30G> a polymorphism with gestational diabetes mellitus and type 2 diabetes mellitus risk: a meta-analysis involving 18 case-control studies. Genet Test Mol Biomarkers 18: Yaturu S, Rains J, Jain SK (2008) Relationship of elevated osteoprotegerin with insulin resistance, CRP, and TNF-alpha levels in men with type 2 diabetes. Cytokine 44: Younis NN, Soran H, Sharma R, et al. (2010) Small-dense LDL and LDL glycation in metabolic syndrome and in statintreated and non-statin-treated type 2 diabetes. Diab Vasc Dis Res 7: Address correspondence to: Yaoming Xue, PhD Department of Endocrinology and Metabolism Nanfang Hospital Southern Medical University Guangzhou China xueyaoming999@126.com