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Experimental and Therapeutic Medicine logoLink to Experimental and Therapeutic Medicine
. 2016 Jul 27;12(3):1849–1856. doi: 10.3892/etm.2016.3554

IGF2BP2 rs11705701 polymorphisms are associated with prediabetes in a Chinese population: A population-based case-control study

Liyuan Han 1,*, Yuanyuan Li 2,*, Linlin Tang 1,*, Zhongwei Chen 3, Tao Zhang 3, Sihan Chen 3, Shengyuan Liu 3, Xiaolin Peng 3, Yifeng Mai 4, Renjie Zhuo 1, Changyi Wang 3,✉, Shiwei Duan 1,✉
PMCID: PMC4997985  PMID: 27588103

Abstract

Associations between insulin-like growth factor 2 mRNA-binding protein 2 (IGF2BP2) rs11705701, insulin receptor substrate 1 rs7578326, gastric inhibitory polypeptide receptor rs10423928 and transcription factor 7-like 2 rs12255372 gene polymorphisms with prediabetes and type 2 diabetes (T2D) have not been evaluated in the Han Chinese population. These four genetic variants were investigated for their associations with prediabetes and T2D among 490 unrelated patients with T2D, 471 patients with prediabetes and 575 healthy controls. Sequenom MassARRAY software was used to genotype the patients for these variants. The Generalized Multifactor Dimensionality Reduction method was used to analyze the gene-gene and gene-environment interactions. A breakdown analysis by gender revealed a significant association of IGF2BP2 rs11705701 with prediabetes under the dominant genetic model in females following application of the Bonferroni correction (odds ratio = 0.26; 95% confidence interval = 0.10–0.67; P=0.005). However, no significant associations were reported between any of the other three polymorphisms and T2D under any genetic models. Furthermore, there were no statistically significant gene-gene or gene-environment interactions when evaluated with the above association tests. The present case-control study reveals a significant association between IGF2BP2 rs11705701 and prediabetes in female patients.

Keywords: type 2 diabetes, prediabetes, insulin-like growth factor 2 mRNA-binding protein 2, insulin receptor substrate 1, gastric inhibitory polypeptide receptor, transcription factor 7-like 2

Introduction

Type 2 diabetes (T2D) is a complex disease that is affected by genetic and environmental factors, and their interactions. The prevalence of diabetes has increased substantially in China due to the changes of lifestyle (mainly overnutrition and lack of physical activity) (1). In a representative sample of Chinese adults in 2013, the incidence of diabetes and prediabetes was reported to be 11.6% and 50.1% respectively, which accounted for 113.9 million patients with diabetes and 493.4 million patients with prediabetes (2).

Insulin-like growth factor 2 mRNA-binding protein 2 (IGF2BP2) is a member of the IGF2 mRNA-binding protein family (2). IGF2BP2 is located on chromosome 3q27 and is involved in embryogenesis and pancreatic development (3). Furthermore, IGF2BP2 can adjust transcription of IGF2, which in turn is involved in the development of insulin function (4). Insulin receptor substrate 1 (IRS1) is an early mediator in the insulin-stimulated signal transduction pathway (5). Experiments in IRS1 knockout mice have demonstrated that IRS1 is an essential contributor to insulin activity in skeletal muscle, adipose tissue and pancreatic β-cells (6), and IRS1 has therefore been hypothesized to be a diabetes susceptibility gene (7). GIPR encodes gastric inhibitory polypeptide receptor (GIPR), which is capable of inducing insulin response following an oral glucose challenge (8). GIPR is expressed in the pancreas and in adipocytes (9), and it is important in the regulation of insulin secretion. The transcription factor 7-like 2 (TCF7L2) gene spans 215.9 kb on chromosome 10q25, and is the most recognized T2D susceptibility gene. TCF7L2 encodes a transcription factor implicated in Wnt signaling and proglucagon transcription (10). A previous study reported that TCF7L2 increases susceptibility to T2D through a reduction in blood glucose induced by insulin secretion (11).

All the aforementioned genes have significant roles in insulin function and signaling. However, little is known with regard to the associations between the IGF2BP2 rs11705701, IRS1 rs7578326, GIPR rs10423928 and TCF7L2 rs12255372 polymorphisms and T2D or prediabetes in the Chinese population. In the present study, the potential associations of IGF2BP2 rs11705701, IRS1 rs7578326, GIPR rs10423928 and TCF7L2 rs12255372 with prediabetes and T2D were investigated in a Chinese population. Furthermore, a gene-gene and gene-environmental interaction analysis was conducted, as prediabetes and T2D are complex disorders affected by genetic and environmental factors and their interactions.

Materials and methods

Subjects

The present study included 490 unrelated patients with T2D [242 males and 248 females; mean age, 62.76±11.14 years; mean body mass index (BMI), 24.95±3.46 kg/m2], 471 patients with prediabetes (230 males and 241 females; mean age, 61.39±11.43 years; mean BMI, 25.28±3.82 kg/m2) and 575 healthy control patients (286 males and 289 females; mean age, 57.94±10.81 years; mean BMI, 23.52±3.17 kg/m2). T2D and prediabetes were diagnosed in accordance with the criteria of the American Diabetes Association guidelines, 2010 (12). All the participants were Han Chinese patients of 16 community health service centers in the Nanshan district of Shenzhen (China). A two-stage sampling method (involving, the use of computer-generated sampling to select 16 communities in the initial stage, followed by convenience sampling was used to determine eligible subjects in the second stage) and a procedure utilizing computer-generated random numbers were used. The inclusion criteria was as follows: i) Local residents who had lived in Shenzhen for ≥6 months; ii) standard clinical criteria (American Diabetes Association guidelines, 2010 (12) were applied with regard to prediabetes and T2D diagnosis, while the healthy controls were selected based on fasting blood glucose levels <6.1 mmol/l. Exclusion criteria included patients with hypertension, cancer, severe liver and kidney disease, or pregnancy. Written informed consent was obtained from all subjects. The present study was approved by the Ethical Committee of the Shenzhen Nanshan Center for Chronic Disease Control (Shenzhen, China).

Genotyping

Blood samples (5 ml) were collected imediately the morning following an overnight fast in ethylenediaminetetraacetic acid-containing collection tubes. DNA was isolated from peripheral blood lymphocytes using a Lab-Aid 820 Automated Blood DNA Extraction system (Zeesan Biotech, Xiamen, China). Genotyping was performed using the MassARRAY iPLEX system (Sequenom, San Diego, CA, USA) according to the manufacturers' protocol. Primers for the polymerase chain reaction (PCR) and single base extension were designed using Sequenom software (PyroMark Assay Design software; version 2.0.1.15; Qiagen GmbH, Hilden, Germany). In the present study, primer extension for genotyping was performed on the Sequenom MassARRAY iPLEX platform. In the primer extension, ddH2O, 10X Buffer, 25 mM dNTP, 25 mM MgCl2, 0.5 µM Primer, PCR enzyme and DNA template were used in the PCR reaction system. After purifying the products and transferring to SpectroCHIP, MALDI-TOF mass spectrometry was used for SNP genotyping. Thermocycling was carried out under the following conditions: Initial denaturation, 94°C for 15 sec followed by 45 cycles at 94°C for 20 sec (denaturation), 56°C for 30 sec (annealing), and 72°C for 1 min (extension), with a final extension step at 72°C for 3 min. The primer sequences are reported in Table I, and the characteristics of the study subjects are reported in Table II.

Table I.

Primer sequences used for genotyping.

Gene/SNP PCR round Primer sequence (5′-3′)
IGF2BP2 First ACGTTGGATGTGATGGTTAGAGCCTGGTCC
rs11705701 Second ACGTTGGATGGCTTGGAATCTTCTTCTGCC
IRS1 First ACGTTGGATGGATTTCCGTTGGTGACACAG
rs7578326 Second ACGTTGGATGTCTGACATGTGGCACTTTAC
GIPR First ACGTTGGATGGGAAAATACTAGTCTCAGTGG
rs10423928 Second ACGTTGGATGCTTAGCATATACACATGCTC
TCF7L2 First ACGTTGGATGGAGTGTGCATTAAAGCTTGG
rs12255372 Second ACGTTGGATGAGGTATAGTTCTCCTGGTCC
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PCR, polymerase chain reaction; SNP, single nucleotide polymorphism; IGF2BP2, insulin-like growth factor 2 mRNA-binding protein 2; IRS1, insulin receptor substrate 1; GIPR, gastric inhibitory polypeptide receptor; TCF7L2, transcription factor 7-like 2.

Table II.

Characteristics of the subjects included in the present study.

Category Controls Prediabetes Type 2 diabetes P1 P2 P3
Total subjects (n) 575 471 490
  Female/male (n) 289/286 241/230 248/242 0.812 0.915 0.953
  Age (years) 57.94±10.81 61.39±11.43 62.76±11.14 0.001a 0.055 0.001a
  BMI (kg/m2) 23.52±3.17 25.28±3.82 24.95±3.46 0.001a 0.143 0.001a
Female
  Age (years) 58.50±10.00 61.66±10.43 63.99±10.28 0.001a 0.012a 0.001a
  BMI (kg/m2) 23.18±3.09 25.18±4.04 24.69±3.58 0.001a 0.123 0.001a
Male
  Age (years) 57.38±11.56 61.10±12.42 61.51±11.85 0.001a 0.708 0.001a
  BMI (kg/m2) 23.87±3.22 25.37±3.59 25.20±3.32 0.001a 0.595 0.001a
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Groups were compared using one-way analysis of variance. P-values: P1, P-value of the patients with prediabetes vs. the controls; P2, P-value of the patients with type 2 diabetes vs. those with prediabetes; P3, P-value of the patients with type 2 diabetes vs. the controls. BMI, body mass index.

a

Statistically significant difference. Data are presented as mean ± standard deviation.

Statistical analysis

Data are expressed as the mean ± standard deviation for continuous variables. Comparisons among the three groups for continuous variables were performed using one-way analyses of variance. Deviation from the Hardy-Weinberg equilibrium was assessed by χ2 test. Binary logistic regression analysis was performed to calculate odds ratios (ORs) and 95% confidence intervals (CIs) subsequent to adjusting for age and BMI. The genetic models tested in the present study included additive, dominant and recessive models. Bonferroni correction was applied to determine the significance thresholds. As a result, P<0.006 was adopted as the threshold of significance (Tables III and IV).

Table III.

Number of patients with the four polymorphisms and their association with prediabetes and type 2 diabetes in the whole sample set, based on three genetic models.

Additive model Dominant model Recessive model
Variable Wild-type homozygote Heterozygote Variant homozygote Major allele Minor allele OR (95%CI)b P OR (95% CI)b P OR (95% CI)b P HWE
IGF2BP2 rs11705701 0.17
  Controls 360 190 17 910 224 Reference Reference Reference
  Prediabetes 268 162 31 698 224 0.79 (0.63–0.98) 0.03a 0.44 (0.23–0.83) 0.01a 0.83 (0.63–1.08) 0.17
  Type 2 diabetes 280 162 19 722 200 0.70 (0.35–1.42) 0.32 0.70 (0.35–1.42) 0.32 0.94 (0.72–1.23) 0.67
IRS1 rs7578326
  Controls 401 134 15 936 164 Reference Reference Reference 0.35
  Prediabetes 349 111   8 809 127 0.90 (0.69–1.17) 0.44 0.91 (0.68–1.22) 0.55 0.67 (0.27–1.70) 0.41
  Type 2 diabetes 363 118   6 844 130 0.89 (0.69–1.16) 0.41 0.93 (0.69–1.24) 0.62 0.51 (0.19–1.39) 0.19
GIPR rs10423928
  Controls 362 171 20 895 211 Reference Reference Reference 0.97
  Prediabetes 309 146 14 764 174 0.98 (0.77–1.24) 0.86 0.99 (0.76–1.31) 0.99 0.83 (0.40–1.72) 0.63
  Type 2 diabetes 318 151 18 787 187 1.03 (0.81–1.30) 0.79 1.03 (0.78–1.35) 0.82 1.08 (0.54–2.15) 0.82
TCF7L2 rs12255372
  Controls 208 283 72 699 427 Reference Reference Reference 0.10
  Prediabetes 194 203 64 591 331 0.88 (0.73–1.07) 0.22 0.80 (0.61–1.04) 0.10 0.98 (0.67–1.44) 0.93
  Type 2 diabetes 195 194 71 584 336 0.90 (0.74–1.09) 0.30 0.78 (0.60–1.01) 0.06 1.10 (0.76–1.60) 0.59
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a

Statistically significant difference.

b

Adjusted for age and body mass index. OR, odds ratio; CI, confidence interval; HWE, Hardy-Weinberg equilibrium; IGF2BP2, insulin-like growth factor 2 mRNA-binding protein 2; IRS1, insulin receptor substrate 1; GIPR, gastric inhibitory polypeptide receptor; TCF7L2, transcription factor 7-like 2.

Table IV.

Number of patients with the four polymorphisms and their association with prediabetes and type 2 diabetes, based on three genetic models, and stratified by gender.

Additive model Dominant model Recessive model
Variable Wild-type homozygote Heterozygote Variant homozygote Major allele Minor allele OR (95% CI)b P OR (95% CI)b P OR (95% CI)b P HWE
IGF2BP2 rs11705701
Female
  Controls 173 104   7 450 118 Reference Reference Reference 0.058
  Prediabetes 137   80 20 354 120 0.79 (0.58–1.07) 0.146 0.26 (0.10–0.67) 0.005a 0.91 (0.63–1.33) 0.656
  Type 2 diabetes 141   80   9 362 98 1.01 (0.72–1.41) 0.947 0.62 (0.21–1.81) 0.383 1.07 (0.73–1.57) 0.705
Male
  Controls 187   86 10 460 106 Reference Reference Reference 0.972
  Prediabetes 131   82 11 344 104 0.79 (0.57–1.09) 0.158 0.77 (0.31–1.93) 0.585 0.75 (0.51–1.10) 0.148
  Type 2 diabetes 139   82 10 360 102 0.83 (0.60–1.14) 0.265 0.72 (0.28–1.84) 0.506 0.82 (0.56–1.19) 0.305
IRS1 rs7578326
Female
  Controls 202   67 10 471 87 Reference Reference Reference 0.142
  Prediabetes 185   48   6 418 60 0.78 (0.54–1.12) 0.184 0.74 (0.48–1.14) 0.174 0.74 (0.24–2.27) 0.602
  Type 2 diabetes 182   58   5 422 68 0.93 (0.65–1.33) 0.719 0.96 (0.63–1.46) 0.875 0.66 (0.20–2.10) 0.487
Male
  Controls 199   67   5 465 77 Reference Reference Reference 0.817
  Prediabetes 164   63   2 391 67 1.06 (0.72–1.54) 0.765 1.06 (0.72–1.54) 0.766 0.53 (0.10–2.85) 0.466
  Type 2 diabetes 181   60   1 422 62 0.86 (0.59–1.27) 0.474 0.91 (0.60–1.37) 0.654 0.24 (0.02–2.21) 0.214
GIPR rs10423928
Female
  Controls 186   88   6 460 100 Reference Reference Reference 0.238
  Prediabetes 169   63   7 401 77 0.96 (0.68–1.36) 0.821 0.91 (0.61–1.35) 0.653 1.40 (0.44–4.44) 0.565
  Type 2 diabetes 166   69 10 401 89 1.02 (0.73–1.43) 0.883 0.96 (0.65–1.41) 0.846 1.75 (0.58–5.30) 0.316
Male
  Controls 176   83 14 435 111 Reference Reference Reference 0.205
  Prediabetes 140   83   7 363 97 1.00 (0.73–1.38) 0.962 1.09 (0.75–1.60) 0.628 0.60 (.23–1.56) 0.304
  Type 2 diabetes 152   82   8 386 98 1.01 (0.73–1.39) 0.925 1.07 (0.73–1.56) 0.703 0.73 (0.29–1.86) 0.514
TCF7L2 rs12255372
Female
  Controls 97 148 37 342 222 Reference Reference Reference 0.116
  Prediabetes 99 107 31 305 169 0.83 (0.63–1.10) 0.209 0.74 (0.51–1.08) 0.114 0.93 (0.53–1.60) 0.797
  Type 2 diabetes 90 102 38 282 178 0.95 (0.72–1.25) 0.736 0.80 (0.54–1.18) 0.276 1.25 (0.74–2.12) 0.395
Male
  Controls 111 135 35 357 205 Reference Reference Reference 0.35
  Prediabetes   95   96 33 286 162 0.93 (0.71–1.22) 0.645 0.86 (0.59–1.25) 0.445 1.04 (0.61–1.78) 0.87
  Type 2 diabetes 105   92 33 302 158 0.86 (0.66–1.12) 0.266 0.75 (0.52–1.09) 0.142 0.97 (0.56–1.65) 0.919
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a

Statistically significant results.

b

Adjusted for age and body mass index. OR, odds ratio; CI, confidence interval; HWE, Hardy-Weinberg equilibrium; IGF2BP2, insulin-like growth factor 2 mRNA-binding protein 2; IRS1, insulin receptor substrate 1; GIPR, gastric inhibitory polypeptide receptor; TCF7L2, transcription factor 7-like 2.

All data were analyzed using SPSS version 17.0 software (SPSS, Inc., Chicago, IL, USA). A power analysis was performed with the Power and Sample Size Calculation software (version 3.0.43) (13). Generalized multifactor dimensionality reduction (GMDR; www.ssg.uab.edu/gmdr) was applied to analyze the potential gene-gene and gene-environment interactions. A number of parameters, including testing balance accuracy (TBA), cross-validation consistency (CVC) and sign test P-value were obtained. The model with the maximum TBA, the maximum CVC and a sign test P-value of <0.05 were considered to represent the best model.

Results

Characteristics of subjects

The characteristics of the study subjects are reported in Table II. There were significant differences in the age and BMI of the control patients and patients with prediabetes or T2D (P=0.001), although differences between these two groups were not significant (P=0.055 and 0.143, respectively).

Association between the four SNPs and prediabetes and T2D

The allele and genotype frequencies of the four SNPs in the whole study cohort are summarized in Table III. Association of these genotypic variants with prediabetes or T2D was performed using logistic regression, subsequent to adjusting for age and BMI, in association with additive, dominant and recessive genetic models. The genotype distribution of rs11705701 in the IGF2B15P2 gene, rs7578326 in the IRS1 gene, rs10423928 in the GIPR gene and rs12255372 in the TCF7L2 gene among the three groups corresponded with the HWE. None of these SNPs had a significant allelic or genotypic association with T2D. The ORs and 95% CIs for IGF2BP2 rs11705701 with prediabetes in the additive and dominant models were (OR=0.79; 95% CI=0.63–0.98; P=0.03) and (OR=0.44; 95% CI=0.23–0.83; P=0.01) respectively (Table III). However, these were not concluded to be significant following application of the Bonferroni correction.

The allele and genotype frequencies of the four SNPs separated by gender are reported in Table IV. IGF2BP2 rs11705701 was associated with female patients with pre-diabetes to a greater degree than female T2D and control patients, under the dominant model following application of the Bonferroni correction (OR=0.26, 95% CI=0.10–0.67, P=0.005). No significant association was observed between the other three SNPs and prediabetes in the present study. In addition, associations between the four SNPs and prediabetes and T2D in females and males. According to power calculations, the present sample size provided 52.3% power (∂=0.05) to detect a significant association of rs11705701 with prediabetes.

Gene-gene and gene-environmental interactions

The potential gene-gene and gene-environmental interactions were then investigated. As shown in Table V, the interaction model between IGF2BP2 rs11705701, IRS1 rs7578326, GIPR rs10423928 and TCF7L2 rs12255372 was the best model to detect gene-gene interactions between patients with T2D and control patients, with a maximum TBA of 54.5%. However, the value of TBA was not higher than 60%, despite the sign test P-value being <0.05; this gene-gene interaction model was therefore not considered to be robust and reliable. No significant gene-environment interactions were identified when comparing patients with prediabetes or T2D and control patients.

Table V.

Gene-gene and gene-environment interaction analysis by generalized multi-factor dimensionality reduction.

Model Testing balance accuracy Sign test Cross-validation consistency
Prediabetes vs. controls
Gene-environment interactions
  BMI 0.602 0.001 10/10
  BMI, rs7578326 0.608 0.001 6/10
  BMI, rs11705701, rs7578326 0.596 0.001 6/10
  Age, BMI, rs11705701, rs7578326 0.605 0.001 9/10
  Gender, BMI, rs11705701, rs12255372, rs7578326 0.559 0.001 10/10
  Gender, age, BMI, rs11705701, rs12255372, rs7578326 0.603 0.001 10/10
  Gender, age, BMI, rs11705701, rs12255372, rs7578326, rs10423928 0.564 0.015 10/10
Gene-gene interactions
  rs12255372 0.509 0.178 7/10
  rs11705701, rs12255372 0.494 0.623 6/10
  rs11705701, rs12255372, rs7578326 0.515 0.175 8/10
  rs11705701, rs12255372, rs7578326, rs10423928 0.523 0.054 10/10
Type 2 diabetes vs. controls
Gene-environment interactions
  Age 0.591 0.001 10/10
  Age, rs11705701 0.595 0.001 5/10
  Age, BMI, rs12255372 0.613 0.001 9/10
  Age, BMI, rs12255372, rs10423928 0.604 0.001 9/10
  Age, BMI, rs11705701, rs12255372, rs10423928 0.582 0.001 8/10
  Gender, age, BMI, rs11705701, rs12255372, rs10423928 0.583 0.001 7/10
  Gender, age, BMI, rs11705701, rs12255372, rs7578326, rs10423928 0.554 0.001 10/10
Gene-gene interactions
  rs12255372 0.532 0.054 10/10
  rs12255372, rs10423928 0.543 0.015 9/10
  rs12255372, rs7578326, rs10423928 0.515 0.054 4/10
  rs11705701, rs12255372, rs7578326, rs10423928 0.548 0.001 10/10
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BMI, body mass index.

Discussion

In the present study, the association between IGF2BP2 rs11705701, IRS1 rs7578326, GIPR rs10423928 and TCF7L2 rs12255372 polymorphisms were analyzed in an independent case-control sample. The results suggested a significant association between IGF2BP2 rs11705701 and prediabetes in females. No statistically significant gene-gene and gene-environment interactions were observed. To the best of our knowledge, this is the first study to report an association between IGF2BP2 rs11705701 and prediabetes in the Han Chinese population.

No statistically significant associations were identified between rs11705701 and T2D susceptibility in the whole and subgroup analyses. However, Chistiakov et al (14) reported that the rs11705701-A allele was associated with higher T2D risk in a Russian population. Similarly, Li et al (15) suggested that rs11705701 had secondary effects on insulin resistance and β-cell function, thereby contributing to T2D risk in Mexican Americans. The allele frequencies of rs11705701 therefore differed among populations. For example, the minor allele frequency was 0.38 in a Russian population and 0.33 in a sample of Mexican Americans, but was 0.21 in the current study. The discrepancies among studies may be due to the variation of minor allele frequencies in the different ethnic populations.

A genome-wide study reported that IGF2BP2 gene polymorphisms were associated with increased T2D risk (16). The most widely studied polymorphism of IGF2BP2 gene is rs4402960, which has previously been investigated in numerous populations with contradictory results (17–19). Wu et al (20) performed the most comprehensive meta-analysis of 35 studies with 175,965 subjects for the two widely studied IFG2BP2 polymorphisms, rs4402960 and rs1470579. This previous study demonstrated that IGF2BP2 polymorphisms were significantly associated with increased risk of T2D, particularly in East Asian and Caucasian populations (20). It has also been reported that the rs11705701 and rs4402960 polymorphisms were in marked linkage disequilibrium (LD) in Mexican Americans; however, only rs4402960 contributed to T2D risk, which indicated that rs11705701 may be in LD with a causal variant, with functional consequences for IGF2BP2 in Mexican Americans (15).

T2D and prediabetes are complex disorders that may have an etiology in the interactions between multiple genes or environmental factors. The present study used a gene-gene and gene-environmental analysis to determine the presence of interactions among the tested variants and clinical parameters. No statistically significant and robust interactions were observed between these polymorphisms and the analyzed clinical parameters, however, BMI and age were identified as the most significant factors for the development of prediabetes and T2D, respectively.

Notably, the present study failed to replicate the associations of IRS1 rs7578326, GIPR rs10423928 and TCF7L2 rs12255372 with prediabetes and T2D in the Chinese population. This is in contrast to the study by Sonestedt et al (21), which demonstrated that the GIPR rs10423928 modified T2D risk by affecting dietary composition. A meta-analysis of nine genome-wide association studies suggested that the rs10423928-A allele increased T2D risk in corroborating studies (35,689 cases and 89,798 control patients) (22). A previous genome-wide association study demonstrated that the IRS1 rs7578326 polymorphism contributed to T2D susceptibility in European populations (23). In addition, the TCF7L2 rs12255372 was identified to be significantly associated with T2D (24), but a meta-analysis revealed no significant effect of rs12255372 on T2D risk in a Han Chinese population (25). The contradictory results between studies may be attributed to multiple factors, including different sample size, diverse genetic backgrounds, differing environmental factors and inclusion criteria.

In the current study, strict inclusion and exclusion criteria of sample collection were applied in a homogeneous population in order to reduce sample bias, which strengthened the validity of the study. Furthermore, the potential gene-gene and gene-environmental interactions were evaluated using GMDR. GMDR is appropriate to both dichotomous and quantitative phenotypes that allow adjustment for covariates in population-based studies (26) However, there were also limitations to the present study: It is well-established that diabetes is affected by numerous factors, but the confounding factors included in the current study were limited.

In conclusion, the present results indicated that IGF2BP2 rs11705701 may have a significant association with prediabetes in females. However, additional studies with larger sample sizes are required to confirm these findings. Functional studies are essential to investigate whether the IGF2BP2 rs11705701 works independently or in combination with other genes.

Acknowledgements

The present study was supported by grants from the National Natural Science Foundation of China (grant nos. 81373094 and 81402745), the Natural Science Foundation of Ningbo City (grant no. 2011A610037), Ningbo Social Development Research Projects (grant nos. 2014C50051 and 2014A610268), the Key Program of Education Commission of Zhejiang Province (grant no. Z201017918), the Natural Science Foundation of Zhejiang Province (grant nos. LR13H020003 and LQ13H260002), Zhejiang Province Scientific Research Projects of Education (grant no. Y201326971), the Ministry of Education, Humanities and Social Sciences project (grant no. 14YJC630046), the Scientific Research Fund of Ningbo University (grant no. xkl1349) and the Ningbo University Talent Project (grant no. ZX2012000046). We would like to thank the participants, doctors and nurses in the community health centers for their involvement in the data and sample collection.

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