Associations Of Serum OPG Levels And Gene Polymorphisms In RANKL/RANK/OPG System With Type 2 Diabetes Mellitus

BackgroundType 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 decade, the prevalence of T2DM has rapidly increased throughout the world. The prevalence of T2DM in the Chinese adult population was 11.6%, according to data obtained from a recent national survey; and 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. Modern twin studies have shown that diabetes is highly heritable, with the heritability of T2DM being 64%. 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, and some diabetes-associated genetic loci have been reported. However, its specific genetic mechanism remains largely unknown.Osteoprotegerin (OPG), Receptor activator of nuclear factor-kappa B (RANK) and its RANK ligand (RANKL) are three major proteins of the RANKL/RANK/OPG signaling pathway encoded by genes TNFSF11, TNFRSF11A and TNFRSF11B, respectively. RANKL/RANK/OPG pathway is critical for the regulation of bone metabolism and has an essential role in the formation, function and survival of osteoclasts. In physiological situations, 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. It has been well recognized that RANKL/RANK/OPG pathway plays an important role in regulation of bone metabolism. Moreover, the RANKL/RANK/OPG pathway is also involved in cell death and proliferation, vascular calcification and atherosclerosis, and inflammation and immunity. 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. In addition, there is evidence revealing that OPG is expressed in the pancreas and may protect pancreatic cells from further damage. Recently, a number of studies reported the associations of gene polymorphisms in the RANKL/RANK/OPG pathway with bone mineral density owing to the important role of RANKL/RANK/OPG pathway in regulation of bone metabolism. Our previous study have indicated that the relationships of polymorphisms in the genes in the RANKL/RANK/OPG pathway with bone mineral density in a cohort of Chinese post-menopausal women. RANKL/RANK/OPG pathway is involved in the development of diabetes, therefore the gene polymorphisms in TNFSF11, TNFRSF11A and TNFRSF11B, which encode RANKL, RANK and OPG, may be associaed with type 2 diabetes mellitus. However, by now, no relative study has been reported.Osteoprotegerin (OPG) is a secreted glycoprotein that belongs to the tumor necrosis factor receptor super-family. Elevated concentrations of OPG have been reported in diabetic individuals, and were independently associated with the diabetic microvascular complications. OPG may be a effective early biomarkers in diabetes. Therefore, we conducted the study to investigate the changes in serum OPG levels in patients with diabetes and pre-diabetes, analyze possible impact factors, and explore the associatons between gene polymorphisms in the RANKL/RANK/OPG pathway and T2DM in Han Chinese Women.PART 1 Associations of Gene Polymorphisms in RANKL/RANK/ OPG System with Type 2 Diabetes MellitusObjectiveThe aim of this part was to investigate the relationship between gene polymorphisms in the RANKL/RANK/OPG pathway and T2DM in Southern Han Chinese women in a case control study, and explore the potential genetic mechaisms of the RANKL/RANK/OPG pathway in T2DM.MethodsA total of 1,233 participants were enrolled in the case control study from January 2013 to December 2013 in The Third Hospital of Nanchang, which included 514 T2DM patients (case group) and 719 healthy control subjects (control group). All participants were unrelated post-menopause Han Chinese women living in Nanchang area for more than 5 years. Among these participants,719 healthy females with normal glucose tolerance were recruited from the database of our previous post-menopause women osteoporosis study, while 514 females with T2DM were enrolled from the department of endocrinology and metabolism of The Third Hospital of Nanchang. The written informed consent was obtained from each subject.2. Age, income, education, work, smoking history, drinking intake, physical exercise, menstruation, amenorrhea, marriage and birth history, a detailed medical history and family history were collected through a self-made questionnaire.3. Height, weight, waist circumference and hip circumference were measured to the nearest 0.5 cm and 0.5 kg, respectively. Body mass index (BMI) was calculated [formula:BMI= body weight (kg)/height2 (m2)]. Waist circumference and hip circumference (to the nearest 1cm) were measured, and waist-to-hip ratio (WHR) was calculated [formula:WHR= waist circumference (cm)/hip circumference (cm)].4. Body fat were detected using body fat measuring instrument Omron, HBF-375. Blood pressure and pulse rates were measured in the sitting position using electronic sphygmomanometer Omron, HEM-7200.5. Blood samples were taken after an overnight fast. Measurements of serum biochemical parameters, such as fasting plasma glucose (FPG),2 h plasma glucose (2hPG), alanine aminotransferase (ALT), aspartate aminotransferase (AST), alkaline phosphatase (ALP), blood urea nitrogen (BUN), creatinine (Cr), high density lipoprotein cholesterol (HDL-C), low density lipoprotein cholesterol (LDL-C), total cholesterol (TC), triglycerides (TG), and blood uric acid (URCA) were performed using commercially available kits (Siemens Inc., Erlangen, Germany) on the day of the collection. Glycated hemoglobin (HbAlc) was measured by high-performance liquid chromatography (Bio-Rad Inc., CA, USA), while insulin levels were measured using an electrochemiluminescence immunoassay (Siemens Inc., Erlangen, Germany),6. A total of 21 tag SNPs in TNFSF11, TNFRSF11A and TNFRSF11B in RANKL/RANK/OPG system were included in the study,6 in TNFSF11 (rs9525641, rs2277439、rs2324851、rs2875459、rs2200287、rs9533166),9 in TNFRSF11A (rs9962159、rs4603673、rs7239261、rs4500848、rs6567270、rs1805034、rs4303637、 rs4941131、rs9646629), and 6 in TNFRSF11B (rsl 485286、rsl 1573869、rs3102728、 rs11573819、rs2073618、rs2073617). Genotyping was performed using an improved multiplex ligation detection reaction (iMLDR) technique developed by Genesky Biotechnologies Inc. (Shanghai, China).7. Data were analysed using SPSS version 19.0 for Windows. Quantitative data were reported as means ± standard deviation and compared between cases and controls using the unpaired Student’s t-test and categorical data were compared using the Chi-square test. The gene counting method was used to calculate the gene frequency and allele frequency. Genotypic frequencies were tested for conformity to Hardy-Weinberg equilibrium using the χ2 test, SNPs with,P>0.05 were conformed to Hardy-Weinberg equilibrium. Linkage disequilibrium coefficients were conducted using Haploview version 4.2. Haplotype block structure was determined by Haploview software. Odds ratios (ORs) and 95% confidence intervals (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. A P-value<0.05 was considered statistically significant.Results1. Patients with T2DM and controls had similar age distributions. As expected, these characteristics were associated with T2DM such as FPG,2hPG, HbAlc, weight and BMI; which were higher in T2DM patients than in controls (all P<0.05). Additionally, 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(t= 13.641, P<0.001).2. One SNP (rs9533166 in the TNFSF11 gene) was excluded from further analysis based on the Hardy-Weinberg equilibrium test (P=0.20), the other twenty SNPs were conformed to Hardy-Weinberg equilibrium (all P>0.05)..3. The distributions of allele frequencies in rs11573819 and rs2073618 in TNFRSF11B gene between case and controls were significantly different. The allele A (OR=0.83,95%CI=0.69-0.99, P=0.026) in rs11573819 site and allele C (OR=0.78, 95%CI=0.63-0.97, P=0.043) in rs2073618 site could decrease the risk of T2DM. The distributions of genotype frequencies in rs1485286, rs11573819, rs2073618 and s2073617 in TNFRSF11B gene between case and controls were significantly different (P=0.003, P=0.006, P=0.001 and P=0.014, respectively).4. Multivariable logistic regression analysis with diabetes as the dependent variable after adjustments for age, BMI, smoking history, alcohol intake and physical activity revealed that rs11573819 of the TNFRSF11B gene was significantly associated with T2DM (OR=0.78,95%CI=0.62-0.98, P=0.036).5. The allele frequencies of A and G in rs11573819 were 14.9% and 85.1% in T2DM group, and 18.3% and 81.7% in control group. The allele A of rs11573819 was identified as protective against the development of T2DM (OR=0.78,95%CI=0.62-0.98, P=0.036) after adjustments for age, BMI, smoking history, alcohol intake and physical activity. The genotype frequencies of AA, GA and GG in rs11573819 site in T2DM group were 73.3% ,23.6% and 3.1%, respectively, and 65.8%,31.8% and 2.4% in control group. Subjects with the GA genotype of rs11573819 had a lower risk of T2DM (OR=0.66,95%CI=0.50-0.87, P=0.003), compared with the GG genotype.6. Linkage disequilibrium blocks were subsequently generated using Haploview 4.2 software, and haplotype analysis was performed with PLINK software. Four regions of strong linkage disequilibrium (blocks 1-4) were found among all 20 SNPs. Two haplotypes (TATGG and CATAC) of block rs1485286-rs11573869-rs3102728-rs11573819-rs2073618 of TNFRSF11B were significantly associated with T2DM. Subjects with the TATGG haplotype may increase the risk of T2DM (OR=1.21, 95%CI=1.01-1.46, P=0.042), and the CATAC haplotype may decrease the risk of T2DM(OR=0.79,95%CI=0.64-0.99, P=0.037).ConclusionsOne SNP (rs11573819) of TNFRSF11B gene in RANKL/RANK/OPG system was significantly associated with the susceptibility of T2DM in Chinese Han women.PART 2 Serum OPG levels in subjects with different glucose regulation status and analysis of relevant factorsObjectiveThe aim of this study was to investigate the circulating OPG levels in post-menopausal women with diabetes and pre-diabetes and analyze the relevant factors and its associations with insulin resistance.Methods1. A total of 271 unrelated Chinese Han post-menopausal women were recruited from the database of our previous post-menopause women osteoporosis study from January 2013 to December 2013. The subjects were divided into type 2 diabetes group (n=93), impaired glucose regulation (IGR) group also termed as pre-diabetes (n=90) and normal glucose tolerance group (n=88), according to different glucose tolerance categories. The written informed consent was obtained from each subject.2. Height, weight, waist circumference and hip circumference were measured to the nearest 0.5 cm and 0.5 kg, respectively. Body mass index (BMI) was calculated [formula:BMI= body weight (kg)/height2 (m2)]. Waist circumference and hip circumference (to the nearest 1cm) were measured, and waist-to-hip ratio (WHR) was calculated [formula:WHR= waist circumference (cm)/hip circumference (cm)].3. Body fat were detected using body fat measuring instrument Omron, HBF-375. Blood pressure and pulse rates were measured in the sitting position using electronic sphygmomanometer Omron, HEM-7200.4. Blood samples were taken after an overnight fast. Measurements of serum biochemical parameters, such as fasting plasma glucose (FPG),2 h plasma glucose (2hPG), alanine aminotransferase (ALT), aspartate aminotransferase (AST), alkaline phosphatase (ALP), blood urea nitrogen (BUN), creatinine (Cr), high density lipoprotein cholesterol (HDL-C), low density lipoprotein cholesterol (LDL-C), total cholesterol (TC), triglycerides (TG), and blood uric acid (URCA) were performed using commercially available kits (Siemens Inc., Erlangen, Germany) on the day of the collection. Glycated hemoglobin (HbAlc) was measured by high-performance liquid chromatography (Bio-Rad Inc., CA, USA), serum OPG levels were measured by enzyme-linked immunosorbent assay kit (RayBiotech, Norcross, GA, USA), while insulin levels were measured using an electrochemiluminescence immunoassay (Siemens Inc., Erlangen, Germany). Insulin resistance was determined by homeostasis model assessment (HOMA-IR), HOMA-IR index was calculated [formula:fasting plasma glucose (mmol/L) X fasting serum insulin (mU/mL)/22.5].5. SPSS 19.0 was used to perform the statistical analysis. Data are reported as means+standard deviation or median (25th-75th percentile). Normality was tested for via the Kolmogorov-Smirnov test. Statistical comparisons between groups were made using one-way ANOVA with post-hoc LSD t tests for normally distributed variables.Categorical data were compared using the Chi-square test. For non-normally distributed variables, non-parametric comparisons were made using Kruskal-Wallis test. The bivariate correlations between OPG and other parameters were determined by Spearman’s correlation analysis. Stepwise multilinear regression analysis was performed in order to study the independent variables that may affect OPG values. Statistical significance is defined as a.P-value<0.05 on two-tailed testing.Results1. There was no significant difference in age, BMI, systolic blood pressure (DBP), heart rates, height, body fat, AST, BNU, Cr and LDL-C between these groups (all i>>0.05). The serum OPG concentration in NGT group was (151.00+45.72) pg/ml, which was significantly lower than that in pre-diabetes group (169.28+64.91) pg/ml and diabetes group (183.20+56.53 pg/ml), the difference was statistically significant (P=0.031 and P<0.001). The serum OPG level in pre-diabetes group was lower than diabetes group, but there was no statistically significant difference (P=0.096). The serum OPG levels in pre-diabetes group and diabetes group are still higher than the control group after correction of related factors in different models, such as age and body mass index. The waist-to-hip ratio, ALT, URCA, TG, FPG, 2hPG, FINS, HbAlc and HOMA-IR in pre-diabetes and diabetes group were significantly higher than that in NGT group (all P<0.05).2. OPG showed a significant positive correlation with HOMA-IR (r=0.134, P=0.027), age (r=0.453, P<0.001), waist-to-hip ratio(r=0.125, P=0.039), body fat(r=0.213, P<0.001), ALP(r=0.175, P=0.004), Cr(r=0.193, P=0.001),2hPG(r= 0.270, P<0.001), and HbAlc (r=0.214, P<0.001).3. In stepwise multiple linear regression analysis with OPG as a dependent variable, age, body fat, waist-to-hip ratio, BMI, SBP, DBP, ALT, AST, ALP, BUN, Cr, TC, TG, HDL-C, LDL-C,2hPG, HbAlc and HOMA-IR were added to the model. Finally, HOMR-IR (P=0.019), age (P<0.001),2hPG (P=0.002), AST (P<0.001), ALP (P=0.003), Cr (P<0.001), and URCA(P=0.006) were found to be independent predictors of OPG.Conclusions1. Serum OPG levels in patients with diabetes and pre-diabetes was higher than the subjects with normal glucose tolerance in Chinese post-menopausal women.2. The serum OPG levels in postmenopausal women were positively correlated with insulin resistance.3. OPG may be a effective early biomarkers in patient with diabetes.