Journal of Diabetes 9 (2017), 994 1002 ORIGINAL ARTICLE Association of type 2 diabetes mellitus with the interaction between low-density lipoprotein receptorrelated protein 5 (LRP5) polymorphisms and overweight and obesity in rural Chinese adults Highlights This study provides important evidence that the interaction between LRP5 and environmental factors may affect the risk of type 2 diabetes mellitus (T2DM). The risk of T2DM may be increased with by an interaction between LRP5 and overweight and obesity. Furthermore, LRP5 polymorphisms are related β-cell function and lipid metabolism. Lu ZHANG, 1* Jinjin WANG, 2* Ming ZHANG, 4 Guo an WANG, 4 Yanxia SHEN, 4 Dongting WU, 3 Chongjian WANG, 1 Linlin LI, 1 Yongcheng REN, 1 Bingyuan WANG, 1,4 Hongyan ZHANG, 1 Xiangyu YANG, 1 Yang ZHAO, 1 Chengyi HAN, 1 Junmei ZHOU, 4 Chao PANG, 3 Lei YIN, 3 Jingzhi ZHAO, 3 Xinping LUO 4 and Dongsheng HU 1,4 1 Department of Epidemiology and Biostatistics, College of Public Health, Zhengzhou University, 2 Discipline of Public Health and Preventive Medicine, Center of Preventive Medicine Research and Assessment, Henan University of Traditional Chinese Medicine 3 Department of Prevention and Health Care, Military Hospital of Henan Province, Zhengzhou, and 4 Department of Prevention Medicine, Shenzhen University School of Medicine, Shenzhen, China Correspondence Dongsheng Hu, Department of Prevention Medicine, Shenzhen University School of Medicine, Nanhai Avenue, Shenzhen 518060, China. Tel: +86 755 8667 1951 Fax: +86 755 8667 1906 Email: hud@szu.edu.cn * These authors contributed equally to this work. Received 5 September 2016; revised 28 November 2016; accepted 3 January 2017. doi: 10.1111/1753-0407.12522 Abstract Background: Low-density lipoprotein receptor-related protein 5 (LRP5) plays an important role in glucose and cholesterol metabolism. The present cohort study evaluated associations of LRP5 variants with the incidence of type 2 diabetes mellitus (T2DM) in a rural adult Chinese population. Methods: In all, 7751 subjects aged 18 years without T2DM underwent genotyping at baseline; 6326 subjects (81.62%) were followed-up, and 5511 with a clear disease outcome were eligible for analysis. The same questionnaire was administered and the same anthropometric and blood biochemical examinations were performed at baseline and follow-up. Association analysis was performed for five single nucleotide polymorphisms and haplotypes of LRP5. Results: Cox proportional hazards testing of three different genetic models found no significant association between T2DM and LRP5 after adjusting for potential risk factors (P > 0.05). However, the incidence of T2DM in subjects with LRP5 mutational genotypes was higher in the overweight/obese than normal weight population. Under the dominant model, the risk of T2DM was increased with an interaction between rs11228303 and the waist-to-height ratio adjusted for baseline age, sex, and family history of T2DM (synergy index [SI] = 4.172; 95% confidence interval [CI] 1.014 17.166)], and body mass index (SI = 3.237; 95% CI 1.102 9.509). Furthermore, the A allele of rs3758644 was related to decreased fasting plasma insulin and homeostatic model assessment of β-cell function levels, whereas the T allele of rs12363572 was related to increased high-density lipoprotein cholesterol levels in new-onset diabetes patients (P < 0.05). 994 2017 Ruijin Hospital, Shanghai Jiaotong University School of Medicine and John Wiley & Sons Australia, Ltd
L. ZHANG et al. Interactions of LRP5 and obesity on T2DM Conclusions: The risk of T2DM may be associated with interactions between the LRP5 gene and overweight and obesity. Polymorphisms of LRP5 are related to β-cell function and lipid metabolism. Keywords: Chinese, cohort study, interaction, low-density lipoprotein receptorrelated protein 5 (LRP5), type 2 diabetes mellitus. Introduction Type 2 diabetes mellitus (T2DM) is a common disease caused by a complex interplay between many genetic and environmental factors that threatens nearly all nations. 1 Knowledge of the genetics of T2DM has evolved tremendously over recent years. Following advances in technology and analytical approaches, genome-wide association studies (GWAS) have revealed up to 65 loci credibly associated with T2DM. 2 It has been demonstrated that Wnt/β-catenin signaling is a key regulator of tissue development and homeostasis. 3 Furthermore, the Wnt signaling pathway plays a pivotal role in regulating pancreatic β-cell function and mass. 4 The molecular structure of low-density lipoprotein receptor-related protein (LRP) 5 is analogous to that of LRP6 and it functions similarly as a coreceptor for Wnt/β-catenin signaling. The LRP5 gene encodes a protein that is a receptor for low-density lipoprotein (LDL), with the protein playing an important role in Wnt signaling. 5 7 In addition, LRP5 is involved in glucose and cholesterol metabolism. 8 Several studies have found that LRP5 polymorphisms are associated with obesity and obesity-related metabolic factors, such as dyslipidemia and body mass index (BMI). 9 11 Obesity is associated with the development of insulin resistance, linked to the pathogenesis of T2DM. 12 The LRP5 gene is a novel member of the LDL receptor family associated with type 1 diabetes mellitus, 13 so it may be a potential T2DM susceptibility gene, but the association between LRP5 and T2DM is unclear. In the present study we investigated risk of T2DM associated with the LRP5 tag single nucleotide polymorphisms (SNPs) rs3758644, rs7102273, rs12363572, rs11228303, and rs4930588 in non-diabetic rural Chinese adults. Methods Study population The study was approved by the Ethics Committee of Shenzhen University. From July to August 2007 and from July to August 2008, 7751 participants were selected using a cluster sampling method from a rural area of Henan Province. All subjects signed informed consent forms before entering the study. Subjects were 18 years old and of Northern Chinese ancestry. Exclusion criteria were fasting plasma glucose (FPG) 7.0 mmol/l or self-reported diabetes diagnosis, pregnancy, physical disability, mental illness, obesity caused by disease or certain drugs, and cancer. In all, 6326 participants (315 subjects had died and 1110 were lost to follow-up) completed the follow-up survey in July August 2013 and July October 2014. The response rate was 81.62%. Individuals with missing biochemical data during the follow-up period (n = 815) were excluded, leaving 5511 participants (227 new-onset diabetes patients and 5284 non-t2dm participants) eligible for inclusion in the present study. Baseline examinations A standard questionnaire was used to assess demographic characteristics, medical history, smoking and alcohol status, physical activity level, family history of T2DM, and other risk factors. Height, weight, and waist circumference (WC) were measured in duplicate. Systolic and diastolic blood pressure (SBP and DBP, respectively) were measured in triplicate, and the results were averaged. The BMI was calculated as weight in kilograms divided by height in meters squared. Blood samples were collected from subjects after an overnight fast for the measurement of plasma glucose, total cholesterol (TC), triglycerides (TG), and highdensity lipoprotein cholesterol (HDL-C), as well as genotyping. Plasma glucose was measured using an oxidase enzymatic method, whereas TC, TG, and HDL-C were measured using an automatic biochemical analyzer (Hitachi, Tokyo, Japan). Concentrations of LDL-C were calculated using the Friedewald formula. 14 Current smokers were defined as those who had smoked at least 100 cigarettes in their lifetime and were still smoking during the follow-up period. 15 Alcohol drinkers were defined those consuming any drink containing alcohol (white spirits, beer, grape wine or rice wine) more than 12 times in the past 12 months. The physical activity level for each individual was classified as low, moderate or high based on the International Physical activity Questionnaire (IPAQ; http://www. ipaq.ki.se, accessed 1 July 2016). Normal weight was defined as a BMI between 18.5 and <24 kg/m 2, 2017 Ruijin Hospital, Shanghai Jiaotong University School of Medicine and John Wiley & Sons Australia, Ltd 995
Interactions of LRP5 and obesity on T2DM overweight was defined as BMI between 24 and <28 kg/m 2, and general obesity was defined as BMI 28 kg/m 2. 16 Abdominal obesity was defined as a waist circumference (WC) 90 cm in men or 80 cm in women. 17 Dyslipidemia was defined as TC levels 5.17 mmol/l and/or TG levels 1.7 mmol/l and/or HDL-C levels <1.04 mmol/l and/or LDL-C levels 3.37 mmol/l. 18 Abdominal obesity was also defined as a waist-to-height ratio (WHtR; the ratio between WC and height) 0.5. 19 Follow-up examinations and ascertainment of outcomes Subjects were followed-up from July to August 2013 and from July to October 2014. The same baseline questionnaire and biochemical examinations were used to assess lifestyle and outcomes at the follow-up examinations. Furthermore, fasting plasma insulin levels was measured using a radioimmunoassay kit (Biotechnology Research Institute of North China, Beijing, China). Insulin resistance and β-cell function were calculated from FPG and insulin using the homeostasis model assessment formula. 20 Type 2 diabetes mellitus was defined as FPG 7.0 mmol/l or the use of insulin or oral hypoglycemic agents, and/or a self-reported history of T2DM. 21 Selection and genotyping of SNPs The tag SNPs rs3758644, rs7102273, rs12363572, rs11228303, and rs4930588 for LRP5 were selected for the Chinese population from the HapMap database with the screening criteria of a minor allele frequency (MAF) > 0.01 and r 2 0.8. Genomic DNA was extracted using a DNA purification kit (Yaneng BIO, Shenzhen, China). Polymerase chain reaction restriction fragment length polymorphism (PCR-RFLP) was used for genotyping, as described previously. 22 All SNPs passed genotype quality control analyses. To verify reproducibility, genotyping was repeated on a randomly selected 2% of samples, and the concordance rate was 100%. All subjects underwent genotyping at baseline. Statistical analysis Baseline data are summarized as the median with interquartile range (IQR) for quantitative variables. The Mann Whitney Wilcoxon test was used to assess the significance of differences in quantitative variables. Categorical variables are presented as numbers with percentages and were analyzed by the χ 2 test or Fisher s exact test. Person-years of follow-up were calculated from the date of the start of the baseline investigation to the date of outcome events or the end of follow-up. Hardy Weinberg equilibrium for each SNP was calculated using HAPLOVIEW v4.2 (http://www.broad.mit.edu/ haploview, accessed 1 July 2016). Cox proportional hazards regression models were used to estimate the effects of different genotype models on the incidence of diabetes, with baseline age, sex, family history of T2DM, smoking, alcohol intake, physical activity level, BMI, WC, and biochemical characteristics as covariates. Bonferroni correction was used for multiple testing due to the increased risk of Type I error (0.05 divided by 5; P < 0.01). Statistical analyses were performed using SAS version 9.1.3 (SAS Institute, Cary, NC, USA). Linkage disequilibrium block structure and haplotype frequencies were estimated using HAPLOVIEW v4.2. To correct for multiple testing bias, empirical global P- values for multiple haplotypes in each block were obtained with 50 000 permutations. 23 Under the dominant genotype model, the interaction was tested by calculating the relative excess risk due to interaction (RERI), the attributable proportion due to interaction (AP), and the synergy index (SI) based on the methods proposed by Andersson et al. 24 Two-sided P <0.05was considered statistically significant. Results L. ZHANG et al. In all, 5511 participants completed the entire study at the end of the follow-up period (227 new-onset diabetes patients and 5284 non-t2dm participants). The median duration of follow-up was 6 years, with a total of 33 932. Table 1 summarizes the general characteristics of the subjects at baseline. All SNPs were in Hardy Weinberg equilibrium (P > 0.05). Genotypic and allelic distributions of SNPs for LRP5 at baseline are given in Table 2. There was no difference in the distribution of SNPs of LRP5 (P > 0.05). Cox proportional hazards testing of the three different genetic models found no significant association between T2DM and LRP5 after adjusting for baseline age, sex, family history of T2DM, smoking, alcohol intake, physical activity level, BMI, WC, FPG, TC, TG, HDL-C, and LDL-C (Table 3). The AT haplotype in Block 1 (cases: 2.2%; controls: 1.1%) showed a significant association with T2DM (asymptotic P-value [P asym ] = 0.0235), but multiple comparisons (50 000 permutations) revealed no difference in the distribution between cases and controls (empirical P-value for 50 000 permutations [P perm ] = 0.1351). Haplotype analysis of Block 2 did not reveal any significant association with T2DM (Table 4). The incidence of T2DM in subjects with mutational genotypes of the LRP5 SNPs was analyzed in normal 996 2017 Ruijin Hospital, Shanghai Jiaotong University School of Medicine and John Wiley & Sons Australia, Ltd
L. ZHANG et al. Interactions of LRP5 and obesity on T2DM Table 1 Clinical and biochemical characteristics of the study population (n = 5511) Variable T2DM (n = 227) Non-T2DM (n = 5284) χ 2 /-value P-value Age (years) 52 [44 60] 49 [41 58] 3.1696 0.0015 No. men 97 (42.73) 2088 (39.52) 1.1444 0.2847 Current smokers 69 (30.40) 1450 (27.44) 0.9520 0.3292 Family history of T2DM 17 (9.19) 280 (6.25)* 2.2129 0.1369 Alcohol intake 26 (11.45) 630 (11.92) 0.0457 0.8308 Physical activity level Low 128 (56.39) 3107 (58.80) 2.1202 0.3464 Moderate 40 (17.62) 1017 (19.25) High 59 (25.99) 1160 (21.95) BMI (kg/m 2 ) 25.73 [23.04 28.12] 23.89 [21.69 26.22] 7.0489 <0.0001 WC (cm) 88.80 [80.15 95.00] 81.50 [75.00 88.50] 8.4016 <0.0001 SBP (mmhg) 130.33 [118.33 141.33] 121.00 [110.67 134.33] 6.4633 <0.0001 DBP (mmhg) 82.00 [74.67 89.00] 77.00 [70.67 85.33] 5.8533 <0.0001 FPG (mmol/l) 5.51 [5.13 5.84] 5.19 [4.88 5.49] 8.4145 <0.0001 TC (mmol/l) 4.45 [3.93 5.07] 4.28 [3.75 4.90] 2.8161 0.0049 TG (mmol/l) 1.65 [1.13 2.36] 1.32 [0.95 1.89] 5.9001 <0.0001 HDL-C (mmol/l) 1.10 [0.95 1.25] 1.14 [0.99 1.32] 2.6806 0.0073 LDL-C (mmol/l) 2.50 [2.05 2.90] 2.40 [2.00 2.90] 0.7814 0.4346 Data are given as the median [interquartile range] or n (%). *The family history of type 2 diabetes mellitus (T2DM) is unknown for 804 subjects. BMI, body mass index; WC, waist circumference; SBP, systolic blood pressure; DBP, diastolic blood pressure; FPG, fasting plasma glucose; TC, total cholesterol; TG, triglyceride; HDL-C, high-density lipoprotein cholesterol; LDL-C, low-density lipoprotein cholesterol. Table 2 Genotypic and allelic distributions of single nucleotide polymorphisms (SNPs) of the low-density lipoprotein receptor-related protein 5 (LRP5) gene among rural Chinese adults at baseline SNP Genotype/allele T2DM (n = 227) Non-T2DM (n = 5284) χ 2 P-value* rs3758644 GG 216 (95.15) 5149 (97.45) 0.0514 AG 11 (4.85) 134 (2.54) AA 0 (0.00) 1 (0.02) G 443 (97.58) 10 432(98.71) 4.2690 0.0388 A 11 (2.42) 136 (1.29) rs7102273 TT 127 (55.95) 3083(58.35) 1.1705 0.5570 CT 83 (36.56) 1889 (35.75) CC 17 (7.49) 312 (5.90) T 337(74.23) 8055 (76.22) 0.9504 0.3296 C 117 (25.77) 2513 (23.78) rs12363572 CC 194(85.46) 4500 (85.16) 0.0924 0.9548 CT 31 (13.66) 745 (14.10) TT 2 (0.88) 39 (0.74) C 419 (92.29) 9745 (92.21) 0.0037 0.9513 T 35 (7.71) 823(7.79) rs11228303 CC 192 (86.88) 4539 (87.66) 0.1422 0.9314 CT 28 (12.67) 620 (11.97) TT 1 (0.45) 19 (0.37) C 412 (93.21) 9698(93.65) 0.1336 0.7148 T 30 (6.79) 658 (6.35) rs4930588 TT 164 (72.25) 3929 (74.36) 0.5557 0.7574 GT 58 (25.55) 1237 (23.41) GG 5 (2.20) 118 (2.23) T 386 (85.02) 9095 (86.06) 0.3912 0.5316 G 68 (14.98) 1473 (13.94) Data are given as n (%). *Fisher s exact probabilities. Genotyping failed in 112 participants. 2017 Ruijin Hospital, Shanghai Jiaotong University School of Medicine and John Wiley & Sons Australia, Ltd 997
Interactions of LRP5 and obesity on T2DM L. ZHANG et al. Table 3 Cox proportional hazards analysis of risk of type 2 diabetes mellitus (T2DM) with low-density lipoprotein receptor-related protein 5(LRP5) single nucleotide polymorphisms (SNPs) during follow-up SNP Genotype Crude HR (95%CI) P-value Adjusted HR* (95%CI) P-value rs3758644 GG Reference Reference AG 1.227 (0.648 2.322) 0.5300 1.132 (0.553 2.315) 0.7349 AA NA 0.9810 NA 0.9817 AA + AG vs GG 1.218 (0.644 2.305) 0.5438 1.130 (0.553 2.313) 0.7372 AA vs AG + GG NA 0.9810 NA 0.9817 rs7102273 TT Reference Reference CT 1.122 (0.848 1.484) 0.4195 1.266 (0.909 1.763) 0.1629 CC 1.298 (0.781 2.157) 0.3137 1.278 (0.695 2.350) 0.4293 CC + CT vs TT 1.149 (0.881 1.497) 0.3050 1.268 (0.926 1.736) 0.1388 CC vs CT + TT 1.241 (0.757 2.036) 0.3919 1.169 (0.645 2.118) 0.6074 rs12363572 CC Reference Reference CT 1.104 (0.754 1.615) 0.6119 1.205 (0.772 1.880) 0.4126 TT 1.563 (0.388 6.300) 0.5310 1.414 (0.197 10.158) 0.7305 TT + CT vs CC 1.124 (0.775 1.628) 0.5382 1.211 (0.782 1.876) 0.3902 TT vs CT + CC 1.541 (0.333 6.205) 0.5428 1.201 (0.797 1.812) 0.3812 rs11228303 CC Reference Reference CT 1.205 (0.809 1.795) 0.3590 1.300 (0.817 2.068) 0.2678 TT 1.293 (0.181 9.232) 0.7975 1.826 (0.253 13.157) 0.5502 TT + CT vs CC 1.208 (0.816 1.788) 0.3457 1.322 (0.838 2.086) 0.2302 TT vs CT + CC 1.263 (0.177 9.010) 0.8156 1.761 (0.245 12.677) 0.5740 rs4930588 TT Reference Reference GT 1.112 (0.823 1.502) 0.4891 1.225 (0.852 1.762) 0.2729 GG 0.922 (0.378 2.249) 0.8583 1.357 (0.548 3.361) 0.5092 GG + GT vs TT 1.094 (0.817 1.465) 0.5461 1.239 (0.875 1.754) 0.2273 GG vs GT + TT 0.898 (0.369 2.183) 0.8120 1.296 (0.526 3.194) 0.5731 *Adjusted for baseline age, sex, family history of T2DM, smoking, alcohol intake, physical activity level, body mass index, waist circumference, fasting plasma glucose, total cholesterol, triglyceride, high-density lipoprotein cholesterol, and low-density lipoprotein cholesterol. The analysis involved 5284 normoglycemic individuals and 227 patients identified as having new-onset diabetes during the follow-up period. HR, hazard ratio; CI, confidence interval; NA, not available. weight and overweight/obese groups. Regardless of general obesity or abdominal obesity, the incidence of T2DM in subjects with LRP5 mutational genotypes was higher in the overweight/obese than normal weight population (Table 5). The association of the interaction between LRP5 and baseline environmental risk factors (including smoking, alcohol intake, physical activity level, general obesity, abdominal obesity, and dyslipidemia) with the risk of T2DM was analyzed. As indicated in Table 6, under dominant genetic models, T2DM risk was increased with the interaction of LRP5 rs11228303 and WHtR adjusted for baseline age, sex and family history of T2DM (RERI = 2.186 [95% confidence interval {CI} 0.423 3.949]; AP = 0.564 [95% CI 0.300 0.829]; SI = 4.172 [95% CI 1.014 17.166]) or BMI (RERI = 2.107 [95% CI 0.250 3.964]; AP = 0.520 [95% CI 0.243 0.798]; SI = 3.237 [95% CI 1.102 9.509]). The effects of the risk allele of LRP5 SNPs on biochemical characteristics of new-onset diabetes were analyzed. As indicated in Table 7, the A allele of rs3758644 was related to decreased fasting plasma insulin and Table 4 Results of haplotype analysis of the low-density lipoprotein receptor-related protein 5 (LRP5) gene Haplotype Frequency Case/control frequencies χ 2 P asym P perm Block 1 GT 0.750 0.719/0.751 2.313 0.1283 0.5518 GC 0.237 0.256/0.236 0.984 0.3211 0.8958 AT 0.012 0.023/0.011 5.129 0.0235 0.1351 Block 2 CCT 0.738 0.731/0.739 0.130 0.7186 0.9996 CCG 0.126 0.127/0.126 0.010 0.9195 1.0000 TCT 0.067 0.061/0.067 0.222 0.6377 0.9980 CTT 0.049 0.054/0.049 0.274 0.6004 0.9961 Block 1 include rs3758644 and rs7102273; Block 2 include rs12363572, rs11228303, and rs4930588. P asym, asymptotic P-value; P perm, empirical P-value for 50 000 permutations. 998 2017 Ruijin Hospital, Shanghai Jiaotong University School of Medicine and John Wiley & Sons Australia, Ltd
L. ZHANG et al. Interactions of LRP5 and obesity on T2DM Table 5 Incidence of type 2 diabetes mellitus (T2DM) in subjects with mutational genotypes of low-density lipoprotein receptor-related protein 5 (LRP5) single nucleotide polymorphisms (SNPs) in the normal weight and overweight/obese populations SNP (genotype) Normal BMI (n = 2791) Overweight/general obesity (n = 2720) Normal WHtR (n = 2277) Abdominal obesity (n = 3234) rs3758644 (AG + AA) No. subjects with T2DM 4 7 3 8 Person-years of follow-up 424.38 476.74 351.16 549.96 9.43 14.68 8.54 14.55 rs7102273 (CT + CC) No. subjects with T2DM 31 68 23 77 Person-years of follow-up 7155.36 7027.17 5859.43 8329.10 4.33 9.68 3.93 9.24 rs12363572 (CT + TT) No. subjects with T2DM 11 22 7 26 Person-years of follow-up 2557.66 2464.45 2079.61 2942.50 4.30 8.93 3.37 8.84 rs11228303 (CT + TT) No. subjects with T2DM 3 26 2 27 Person-years of follow-up 2013.09 2087.53 1711.86 2388.76 1.49 12.45 1.17 11.30 rs4930588 (GT + GG) No. subjects with T2DM 17 45 15 48 Person-years of follow-up 4363.18 4390.44 3598.43 5161.19 3.90 10.25 4.17 9.30 Overweight was defined as a body mass index (BMI) between 24 and <28 kg/m 2 ; general obesity was defined as a BMI 28 kg/m 2. Abdominal obesity was defined as a waist-to-height ratio (WHtR) 0.5. homeostatic model assessment of β-cell function (HOMA-β) levels, whereas the T allele of rs12363572 was related to increased HDL-C levels (P < 0.05). Discussion In the present study we found that the risk of T2DM may be related to an interaction between the LRP5 gene and overweight/obesity. In addition, we found that LRP5 polymorphisms are related to β-cell function and lipid metabolism. The association of LRP5 with T2DM epidemiology has been studied in Japan and China, with no direct association found. 25,26 However, in the present study we found a substantial effect of the interaction between LRP5 SNPs and environmental risk factors on T2DM risk in a rural adult Chinese population. With regard to genetic factors for diseases, genetic polymorphisms individually result in the occurrence Table 6 Risk of type 2 diabetes mellitus (T2DM) with interactions between low-density lipoprotein receptor-related protein 5 (LRP5) single nucleotide polymorphisms (SNPs) and environmental factors SNP Variable OR (95%CI) P value RERI (95%CI) AP (95%CI) SI (95%CI) rs11228303 Gene 0.400 (0.096 1.661) 0.2072 2.186 (0.423 3.949) 0.564 (0.300 0.829) 4.172 (1.014 17.166) WHtR 2.289 (1.568 3.341) <0.0001 Gene and WHtR 3.876 (2.294 6.548) <0.0001 rs11228303 Gene 0.506 (0.158 1.625) 0.2572 2.107 (0.250 3.964) 0.520 (0.243 0.798) 3.237 (1.102 9.509) BMI 2.436 (1.732 3.425) <0.0001 Gene and BMI 4.049 (2.449 6.694) <0.0001 *Adjusted for baseline age, sex, and family history of T2DM. For the SNP rs11228303 category, CC genotype = 0, CT/TT genotype = 1; for the body mass index (BMI) category, normal weight = 0, overweight/obesity = 1; for the waist-to-height ratio (WHtR) category, normal weight = 0, obesity = 1. OR, odds ratio; CI, confidence interval; RERI, relative excess risk due to interaction; AP, attributable proportion due to interaction; SI, synergy index. 2017 Ruijin Hospital, Shanghai Jiaotong University School of Medicine and John Wiley & Sons Australia, Ltd 999
Interactions of LRP5 and obesity on T2DM L. ZHANG et al. Table 7 Association of low-density lipoprotein receptor-related protein 5 (LRP5) single nucleotide polymorphisms (SNPs) with biochemical characteristics in subjects with new-onset diabetes SNP Genotype FPG FPI HOMA-IR HOMA-β TC TG HDL-C LDL-C rs3758644 GG 7.51 (6.24 8.72) 13.55 (9.09 20.65) 4.58 (2.94 7.55) 73.47 (47.58 114.57) 4.79 (4.19 5.50) 1.89 (1.33 2.73) 1.08 (0.92 1.27) 2.69 (2.24 3.24) AA + AG 8.85 (7.07 9.35) 5.82 (5.54 10.38) 2.18 (1.96 5.05) 20.03 (17.13 34.40) 4.73 (4.48 5.40) 1.86 (1.52 2.23) 1.00 (0.88 1.15) 3.06 (2.65 3.46) -value 1.0487 7.3796 2.1579 8.2042 0.1148 0.0961 0.8752 1.5085 P-value* 0.3058 0.0066 0.1418 0.0042 0.7347 0.7566 0.3495 0.2194 rs12363572 CC 7.51 (6.29 8.85) 13.80 (8.78 20.83) 4.58 (2.72 7.80) 73.76 (46.18 116.98) 4.82 (4.15 5.47) 1.90 (1.36 2.88) 1.07 (0.91 1.23) 2.74 (2.20 3.25) TT + CT 7.53 (6.31 8.87) 11.57 (8.00 16.58) 4.13 (3.11 6.12) 69.12 (42.89 86.28) 4.74 (4.34 5.41) 1.78 (1.28 2.01) 1.12 (0.97 1.43) 2.75 (2.36 3.21) -value 0.4050 0.9770 0.5690 0.9574 0.4280 1.4656 2.2372 0.6450 P-value* 0.6854 0.3286 0.5693 0.3383 0.6725 0.1427 0.0253 0.5189 *Mann Whitney Wilcoxon test. Data show the median with the interquartile range in parentheses. FPG, fasting plasma glucose; FPI, fasting plasma insulin; HOMA-IR, homeostasis model assessment of insulin resistance; HOMA-β, homeostasis model assessment of β-cell function; TC, total cholesterol; TG, triglyceride; HDL-C, high-density lipoprotein cholesterol; LDL-C, low-density lipoprotein cholesterol. and development of diseases, and certain interactions among them affect disease. Because haplotypes precisely reflect genetic inter-relationships, investigations of haplotypes are helpful in the discovery of multiple genetic polymorphisms involved in diseases. 27 Haplotype analysis in the present study revealed that the AT haplotype of rs3758644 and rs7102273 was associated with T2DM risk, but this association disappeared after 50 000 permutations. This effect may be more apparent if more tag SNPs and samples are investigated. The pathogenesis of diabetes results from complex interactions between genetic backgrounds and the environment. To verify this hypothesis, we analyzed the incidence of T2DM in subjects with mutational genotypes of LRP5 SNPs in the normal weight and overweight/obese populations. Regardless of general obesity or abdominal obesity, the incidence of T2DM in subjects with LRP5 mutational genotypes was higher in the overweight/obese than normal weight population. In addition, we used dominant genetic models to analyze the additive interactions between LRP5 SNPs and environmental risk factors, including smoking status, alcohol intake, physical activity level, dyslipidemia, abdominal obesity, and general obesity. The risk of T2DM was increased with the interaction of rs11228303 and abdominal obesity as well as general obesity. The results of the present study suggest that there would be a 2.186 relative excess risk due to the additive interaction of rs11228303 and abdominal obesity, 56.4% of T2DM in subjects exposed to both risk factors was attributable to the additive interaction, and the risk of T2DM in subjects with abdominal obesity with the rs11228303 minor allele was 4.172-fold higher than in subjects exposed to a single risk factor alone. In addition, there would be a 2.107 relative excess risk due to the interaction of rs11228303 and general obesity, 52.0% of T2DM in subjects exposed to both risk factors was attributable to the interaction, and the risk of T2DM in subjects with general obesity with the rs11228303 minor allele was 3.237-fold higher than in subjects exposed to a single risk factor alone. Recently, several studies have shown an association of LRP5 rs4988300 with BMI in Caucasian diabetic patients on haplotype analysis, 9 an association of the rs682429 AA and rs312016 GG genotypes with increased TC and HDL-C levels, 28 and an association of the T allele of rs3736228 with increased TC levels in a general Han population. 29 These findings suggest that the LRP5 gene may be associated with obesity and blood lipids, which is consistent with the results of the present study. Furthermore, we found that LRP5 polymorphisms were related to declined β-cell function and fasting plasma 1000 2017 Ruijin Hospital, Shanghai Jiaotong University School of Medicine and John Wiley & Sons Australia, Ltd
L. ZHANG et al. Interactions of LRP5 and obesity on T2DM insulin levels, which were closely related to the risk of diabetes. The strengths of the present cohort study are its large sample size of a rural population and direct measurements of anthropometric and clinical parameters. Some limitations of the study need to be addressed. First, because oral glucose tolerance tests (OGTTs) were not performed, some T2DM patients may not have been detected, which may underestimate the experimental results; however, a recent study showed that the prevalence of T2DM estimated using FPG alone and FPG or 2-h OGTT was highly correlated (r = 0.98). 30 In addition, baseline fasting insulin levels were not determined, so the dynamic changes in fasting insulin, homeostasis model assessment of insulin resistance (HOMA-IR), and HOMA-β were not analyzed. In summary, LRP5 is a receptor for Wnt ligands, and Wnt signaling is necessary for embryogenesis, although it also plays important roles in postnatal development and tissue homeostasis. Thus, further molecular genetic studies will help our understanding of the association between LRP5 variants and T2DM. Acknowledgements This study was supported by grants from the National Natural Science Foundation of China (No. 81373074 and 81402752), Science and Technology Development Foundation of Shenzhen (No. JCYJ201404180914135-62), and the Natural Science Foundation of Shenzhen University (No. 201404). Disclosure None declared. References 1. Mihaescu R, Meigs J, Sijbrands E, Janssens AC. Genetic risk profiling for prediction of type 2 diabetes. PLoS Curr. 2011; 3: RRN1208. 2. Hivert MF, Vassy JL, Meigs JB. Susceptibility to type 2 diabetes mellitus: From genes to prevention. Nat Rev Endocrinol. 2014; 10: 198 205. 3. Joiner DM, Ke J, Zhong Z, Xu HE, Williams BO. Lrp5 and Lrp6 in development and disease. Trends Endocrinol Metab. 2013; 24: 31 9. 4. Schinner S. Wnt-signalling and the metabolic syndrome. Horm Metab Res. 2009; 41: 159 63. 5. Pinson KI, Brennan J, Monkley S, Avery BJ, Skarnes WC. An LDL-receptor-related protein mediates Wnt signaling in mice. Nature. 2000; 407: 535 8. 6. Wehrli M, Dougan ST, Caldwell K et al. Arrow encodes an LDL-receptor-related protein essential for Wingless signaling. Nature. 2000; 407: 527 30. 7. Mao J, Wang J, Liu B et al. Low-density lipoprotein receptor-related protein-5 binds to Axin and regulates the canonical Wnt signaling pathway. Mol Cell. 2001; 7: 801 9. 8. Fujino T, Asaba H, Kang MJ et al. Low-density lipoprotein receptor-related protein 5 (LRP5) is essential for normal cholesterol metabolism and glucose-induced insulin secretion. Proc Natl Acad Sci U S A. 2003; 100: 229 34. 9. Guo YF, Xiong DH, Shen H et al. Polymorphisms of the low-density lipoprotein receptor-related protein 5(LRP5) gene are associated with obesity phenotypes in a large family-based association study. J Med Genet. 2006; 43: 798 803. 10. Yang CW, Li CI, Liu CS et al. The joint effect of cigarette smoking and polymorphisms on LRP5, LEPR, near MC4R and SH2B1 genes on metabolic syndrome susceptibility in Taiwan. Mol Biol Rep. 2013; 40: 525 33. 11. Saarinen A, Saukkonen T, Kivelä T et al. Low density lipoprotein receptor-related protein 5 (LRP5) mutations and osteoporosis, impaired glucose metabolism and hypercholesterolaemia. Clin Endocrinol. 2010; 72: 481 8. 12. Loh NY, Neville MJ, Marinou K et al. LRP5 regulates human body fat distribution by modulating adipose progenitor biology in dose- and depot-specific fashion. Cell Metab. 2015; 21: 262 72. 13. Hey PJ, Twells RC, Phillips MS et al. Cloning of a novel member of the low-density lipoprotein receptor family. Gene. 1998; 216: 103 11. 14. Friedewald WT, Levy RI, Fredrickson DS. Estimation of the concentration of low-density lipoprotein cholesterol in plasma, without use of the preparative ultracentrifuge. Clin Chem. 1972; 18: 499 502. 15. Tomar SL, Asma S. Smoking-attributable periodontitis in the United States: Findings from NHANES III. National Health and Nutrition Examination Survey. J Periodontol. 2000; 71: 743 51. 16. Zhou BF. Predictive value of body mass index and waist circumference for risk factors of certain related diseases in Chinese adults: Study on optimal cut-off points of body mass index and waist circumference in Chinese adults. Biomed Environ Sci. 2002; 15: 83 96. 17. Alberti KG, Zimmet P, Shaw J, IDF Epidemiology Task Force Consensus Group. The metabolic syndrome: A new worldwide definition. Lancet. 2005; 366: 1059 62. 18. Expert Panel on Detection, Evaluation, and Treatment of High Blood Cholesterol in Adults. Executive summary of the Third Report of the National Cholesterol Education Program (NCEP) expert panel on detection, evaluation, and treatment of high blood cholesterol in adults (Adult Treatment Panel III). JAMA. 2001; 285: 2486 97. 19. Browning LM, Hsieh SD, Ashwell M. A systematic review of waist-to-height ratio as a screening tool for the prediction of cardiovascular disease and diabetes: 0.5 could be a suitable global boundary value. Nutr Res Rev. 2010; 23: 247 9. 2017 Ruijin Hospital, Shanghai Jiaotong University School of Medicine and John Wiley & Sons Australia, Ltd 1001
Interactions of LRP5 and obesity on T2DM 20. Matthews DR, Hosker JP, Rudenski AS, Naylor BA, Treacher DF, Turner RC. Homeostasis model assessment: Insulin resistance and beta-cell function from fasting plasma glucose and insulin concentrations in man. Diabetologia. 1985; 28: 412 9. 21. American Diabetes Association. Standards of medical care in diabetes. [erratum published in Diabetes Care. 2005; 28: 990]. Diabetes Care. 2005; 28 (Suppl. 1): S4 36. 22. Wang J, Yan G, Zhang J et al. Association of LRP5, TCF7L2, and GCG variants and type 2 diabetes mellitus as well as fasting plasma glucose and lipid metabolism indexes. Hum Immunol. 2015; 76: 339 43. 23. Xun PC, Zhao Y, Yi HG, Bai J, Yu H, Chen F. The application of permutation test in the hypothesis test. Appl Stat Manag. 2006; 25: 616 21. 24. Andersson T, Alfredsson L, Kallberg H, Zdravkovic S, Ahlbom A. Calculating measures of biological interaction. Eur J Epidemiol. 2005; 20: 575 9. 25. Zenibayashi M, Miyake K, Horikawa Y et al. Lack of association of LRP5 and LRP6 polymorphisms with type 2 diabetes mellitus in the Japanese population. Endocr J. 2008; 55: 699 707. L. ZHANG et al. 26. Xuan M, Wang Y, Wang W, Yang J, Li Y, Zhang X. Association of LRP5 gene polymorphism with type 2 diabetes mellitus and osteoporosis in postmenopausal women. Int J Clin Exp Med. 2014; 7: 247 54. 27. Tang K, Wang W, Wang Q et al. Haplotypes of RHO polymorphisms and susceptibility to age-related macular degeneration. Int J Clin Exp Pathol. 2015; 8: 3174 9. 28. Gao H, Tian Y, Meng H et al. Associations of apolipoprotein E and low-density lipoprotein receptor-related protein 5 polymorphisms with dyslipidemia and generalized aggressive periodontitis in a Chinese population. J Periodontal Res. 2015; 50: 509 18. 29. Jiang XY, Chen Y, Xu L et al. Association of LPR5 polymorphism with bone mass density and cholesterol level in population of Chinese Han. Exp Clin Endocrinol Diabetes. 2010; 118: 388 91. 30. NCD Risk Factor Collaboration (NCD-RisC). Effects of diabetes definition on global surveillance of diabetes prevalence and diagnosis: A pooled analysis of 96 population-based studies with 331,288 participants. Lancet Diabetes Endocrinol. 2015; 3: 624 37. 1002 2017 Ruijin Hospital, Shanghai Jiaotong University School of Medicine and John Wiley & Sons Australia, Ltd