Skip to main content

IGF2BP2 and obesity interaction analysis for type 2 diabetes mellitus in Chinese Han population



The objective of this study was to systematically evaluate the contribution of the insulin-like growth factor 2 mRNA-binding protein 2 (IGF2BP2) to type 2 diabetes mellitus (T2DM) and its interaction with obesity to T2DM susceptibility.


To clarify whether IGF2BP2 is an independent risk factor for T2DM in Chinese population, we conducted a study with a total of 2,301 Chinese Han subjects, including 1,166 T2DM patients and 1,135 controls, for the genotype of a most common and widely studied polymorphism—rs4402960 of IGF2BP2. Genotyping was performed by iPLEX technology. Gene and environment interaction analysis was performed by using multiple logistic regression models.


The repeatedly confirmed association between IGF2BP2 (rs4402960) and T2DM had not been replicated in this cohort (P = 0.182). Interestingly, we found that obese subjects (body mass index (BMI) ≥ 28.0 kg/m2) bearing the minor A allele had an increased risk to develop T2DM (P = 0.008 for allele analysis and P < 0.001 for genotype analysis).


The present study provided data suggesting that the wild C allele of IGF2BP2 (rs4402960) had a protective effect against T2DM in obese subjects of Chinese Han population.


Type 2 diabetes mellitus (T2DM) is a complex metabolic disorder characterized by hyperglycemia as a result of pancreatic beta cell dysfunction and insulin resistance. Multiple environmental factors and genetic determinants are considered to be involved in the pathogenesis of the disease. Genome-wide association studies (GWAS) have emerged as a powerful approach for identifying novel genetic variants contributing to the risk of T2DM and further facilitate the elucidation of the genetic etiology of diabetes. Among the variants relating to glucose metabolism revealed by GWAS, insulin-like growth factor 2 mRNA-binding protein 2 (IGF2BP2) was the most extensively studied and supposed to be a T2DM susceptibility gene [13].

IGF2BP2 is encoded by the IGF2BP2 gene which is located on chromosome 3q27. Its strong association with β cell function is established by regulating IGF2 post-translation [4]. Several variants of the IGF2BP2 gene were investigated for relationship with T2DM, of which rs4402960 was the most extensively studied. However, due to the ethnic difference in risk allele frequency, the contribution of this common variant to T2DM appears to be race dependent which makes it a highly controversial candidate for T2DM [13, 5]. To get a thorough understanding of the effect of IGF2BP2 on the susceptibility of T2DM, we therefore genotyped for IGF2BP2 rs4402960 in a total of 2,301 Chinese Han individuals in our present study.

Given that environment/lifestyle changes can modify the risk of T2DM, likely contributing factors such as gender, body mass index (BMI), and smoking behavior of subjects to T2DM would also be interesting to investigate [6]. Obesity (BMI ≥ 28.0 kg/m2) is especially widely recognized as an independent risk factor for T2DM in Chinese population [7]. Therefore, it would be valuable to conduct a particular analysis for obesity in this cohort. Previous studies provided evidence for gene-obesity interaction in human complex disease [8, 9]. Studies also supported the assumption that IGF2 is strongly associated with obesity [10]. It is evident that obesity is associated with T2DM, and IGF2 levels in T2DM patients are associated with T2DM. We hypothesize that obesity may modify the association between IGF2BP2 and T2DM—also called the interaction of IGF2BP2 and obesity with T2DM. Since some variants are known to affect the risk of T2DM through obesity [11, 12], this work aimed to evaluate the interaction effect of IGF2BP2 and obesity on T2DM susceptibility.


Study population

Individuals enrolled in the cohort, including 1,166 T2DM patients and 1,135 controls, were of Southern Han Chinese ancestry residing in the Shanghai metropolitan area. T2DM patients registered in the analysis were recruited from the Endocrinology and Metabolism outpatient clinics at Fudan University Huashan Hospital in Shanghai. All subjects in the cohort were informed and consented to take part in the study. The protocol was approved by the Ethics Committee of Huashan Hospital affiliated to Fudan University.


The subjects were interviewed for the documentation of medical histories, medications, regular physical examinations, and laboratory assessment of T2DM risk factors. BMI was calculated as the weight in kilograms divided by the square of height in meters. Systolic and diastolic blood pressure (BP) values were the means of two physician-obtained measurements on the left arm of a seated participant.

Peripheral venous blood samples were collected in tubes in the fasting state and 2 h after diet in all subjects. The blood was centrifuged at 3,000 rpm for 10 min for plasma separation and immediately used to measure biomarkers. Fasting plasma glucose (FPG) and postprandial plasma glucose (PPG) were quantified by the glucose oxidase-peroxidase procedure. Serum total cholesterol (TC), triglyceride (TG), high-density lipoprotein (HDL) cholesterol, urea nitrogen (UN), uric acid (UA), and alanine transaminase (ALT) levels were measured by an enzymatic method with a chemical analyzer (Hitachi 7600-020, Tokyo, Japan). Low-density lipoprotein (LDL) cholesterol levels were calculated using the Friedewald formula. The day-to-day and inter-assay coefficients of variation at the central laboratory in our hospital for all analyses were between 1% and 3%.


Hypertension (HT) was defined as blood pressure ≥140/90 mmHg or a history of hypertension medication. BMI was classified based on the Chinese criteria: obese [13] (BMI ≥ 28.0 kg/m2). The definition of high serum TG was TG ≥ 150 mg/dl, and that of high serum TC was TC ≥ 200 ml/dl. Diabetes was defined according to the 1999 WHO criteria [14]: fasting plasma glucose ≥7.0 mmol/l and/or 2-h plasma glucose ≥11.1 mmol/l in the oral glucose tolerance test (OGTT). All diabetic patients were unrelated and diagnosed after the age of 27 years. Known subtypes of diabetes, such as type 1 diabetes, gestational diabetes, rare forms of T2DM, and secondary diabetes (pancreatitis, hemochromatosis), were excluded based on antibody measurements and inheritance. The nondiabetic unrelated individuals were identified as the control population based on the following criteria: (1) no family history of diabetes, (2) ≥45 years of age, and (3) normal glucose tolerance verified by OGTT. The clinical characteristics of the participants are summarized in Table 1.

Table 1 The clinical characteristics of the subjects

SNP genotyping

Peripheral venous blood samples were collected from all study subjects, and the genomic DNA was extracted from peripheral blood leukocytes by the conventional proteinase K-phenol-chloroform extraction method. A total of 2,301 Chinese Han individuals were genotyped for IGF2BP2 rs4402960 by using iPLEX (Sequenom, San Diego, CA, USA); the single nucleotide polymorphism (SNP) involved was detected by matrix-assisted laser desorption/ionization time-of-flight mass spectrometry. The genotype distribution was in Hardy-Weinberg equilibrium (P > 0.05), and there was a 99.9% genotype concordance rate when duplicated samples were compared across plates.

Statistical analysis

Continuous variables were detected when the variables followed normal distribution using the Kolmogorov-Smirnov test. Variables that were not normally distributed were log-transformed to approximate normal distribution for analysis. Results are described as mean ± SD or median unless stated otherwise. Differences in variables between T2DM and control were determined by unpaired t test. Between-group differences in properties were accessed by χ2 analysis. Univariate logistic regression was performed to determine variables associated with T2DM and to estimate confounding factors possibly disturbing the relation of BMI and/or IGF2BP2 to T2DM. Multivariable logistic regression (MLR) was carried out to control potential confounders for determining the independent contribution of variables to T2DM. For interaction analysis, MLR was conducted to include two main variables and their interaction item to determine the interaction effect. In order to better investigate the interaction between BMI and IGF2BP2 on T2DM, we performed two analyses according to the following variables: the allele and genotype of IGF2BP2. Odds ratios (OR) with 95% confidence intervals (CI) were calculated for the relative risk of BMI and/or IGF2BP2 with T2DM. Results were analyzed using the Statistical Package for the Social Sciences for Windows version 16.0 (SPSS, Chicago, IL, USA). Tests were two-sided, and a P value of <0.05 was considered significant.


Clinical characteristics of the subjects

The baseline clinical characteristics of the 2,301 subjects are listed in Table 1. There are 456 males and 710 females (mean age, 65.46 ± 10.56 years) in the cases and 352 males and 783 females (mean age, 59.09 ± 7.85 years) in the controls. Diabetic patients had more weight than controls. Systolic blood pressure (SBP), diastolic blood pressure (DBP), FPG, PPG, TC, and TG levels were higher in the cases than in the controls, while the HDL level was lower in the cases. Serum UA, UN, ALT, and LDL levels were similar between the two groups. The minor allele (A) frequency of rs4402960 was 25.27% and 27.03% in the cases and controls, respectively.

Univariate and multiple logistic regression analyses for diabetes

To estimate the association of various clinical factors and T2DM, univariate logistic regression models were developed to include age, sex, BMI, hypertension, lipid profiles, UA, and SNP (rs4402960) (Table 2). The univariate logistic analyses indicated that age, sex, BMI, hypertension, and TC were significantly associated with T2DM (P < 0.05 for all), except for UA and SNP (rs4402960) (P = 0.744 and 0.182, respectively). The proportion of T2DM was 48.17% and 62.75% in the low-BMI group and high-BMI group, respectively. In subjects with high BMI (more than 28.0 kg/m2), the OR for T2DM was 1.813 (95% CI 1.538–2.136, P < 0.001). MLR demonstrated that BMI remained significantly different between the cases and controls after adjustment for potential confounders (P < 0.05, data not shown).

Table 2 Univariate logistic regression analysis for diabetes

BMI by IGF2BP2 interaction analysis for diabetes

In the model of rs440296 with the allele variable, MLR models were developed to include two main effect variables: BMI and rs4402960. The interaction item among them was detected in the MLR model after adjustment for relevant potential confounders (P = 0.023, Table 3 and Figure 1), and the interaction effect was also estimated (ORInt = 1.603, 95% CI 1.068–2.405). In another model of rs440296 with the genotype variable, the interaction term of BMI and rs4402960 was detected by the same method (P = 0.001, Table 4 and Figure 2), as well as the interaction effect (ORInt = 1.661, 95% CI 1.235–2.233). In these two models, MLR models signified that BMI was a significant and independent risk factor for T2DM, while rs4402960 alone was not significantly associated with T2DM. The interaction terms of BMI and SNP rs4402960 which were detected in the two models only confirmed a significant association between this SNP and T2DM in the high-BMI group (P = 0.008 for allele analysis and P < 0.001 for genotype analysis, data not shown).

Table 3 The interaction effect analysis of BMI and allele of IGF2BP2 (rs4402690) for diabetes
Figure 1
figure 1

The interaction analysis of BMI and SNP (rs4402960 with the C/A allele) for diabetes.

Table 4 The interaction effect analysis of BMI and genotype of IGF2BP2 (rs4402690) for diabetes
Figure 2
figure 2

The interaction analysis of BMI and SNP (rs4402960 with the CC/CA/AA genotype) for diabetes.


We conducted a study to evaluate the interaction effect of IGF2BP2 and obesity on T2DM in a large case-control sample of Chinese population. To our knowledge, this is the first investigation of the evaluation of interaction effect on T2DM based on variables of obesity and IGF2BP2.

Genotyping was performed for IGF2BP2 (rs4402960) in a T2DM case-control cohort comprising of 2,301 Chinese Han individuals. However, the documented association of IGF2BP2 (rs4402960) with T2DM in various ethnic populations [1518] had not been replicated. In line with our finding, subsequent large-scale population studies also reported a lack of association in Caucasians [1921]. Such conflicting results of various association studies may be attributed to the diverse ethnic/regional backgrounds, as well as the limited number of participants which had insufficient statistical power to detect a slight effect of the common polymorphism IGF2BP2 (rs4402960) on T2DM susceptibility. Therefore, a larger sample size is necessary to detect the association between this IGF2BP2 genetic variant and T2DM.

IGF2BP2 in pancreatic and adipose tissues can downregulate the expression of IGF2, a growth factor that plays a pivotal role in controlling adipogenesis [22] and pancreatic development [23]. Therefore, IGF2BP2 may contribute to T2DM through impaired β cell function or alterations in adipose tissue as well. Consistent with the hypothesis, a study observed a more than twofold increase in IGF2BP2 expression level in the adipose tissue of diabetic patients compared to controls [20]. Similarly, an altered expression of IGF2BP2 in adipocytes of T2DM subjects compared with healthy people was also detected [24]. Unfortunately, the current study did not find a significant association between IGF2PB2 (rs4402960) and T2DM, as a consequence of the insufficient statistical power in the present sample size or the different impacts of various IGF2BP2 genetic variants. Further studies are required to elucidate the association of other IGF2BP2 variants with the risk of T2DM.

Considering that lifestyle changes can modify the risk of T2DM, the likely effect of metabolic quantitative traits on T2DM susceptibility needs to be addressed, as well as their interaction with various gene variants. Here, MLR models were developed to demonstrate that obesity (BMI ≥28.0 kg/m2) remained an independently risk factor for T2DM after potential confounder adjustment (P < 0.05, data not shown). Although it had been known for decades that both T2DM and obesity have a genetic basis [25], the mechanism by which obesity relates to T2DM in humans is still unclear. Since some genetic variants are known to affect the risk of T2DM through obesity, a hypothesis comes up that IGF2BP2 may have a relationship with obesity. However, no significant difference in IGF2BP2 expression in obese patients carrying different rs4402960 genotypes has been found [20].

Our study is the first analysis of the interaction of obesity and IGF2BP2 variant on T2DM susceptibility. It is noteworthy that the risk imparted by the minor A allele was higher than that by the C allele in obese subjects (P = 0.008 for allele analysis and P < 0.001 for genotype analysis), thus prompting the speculation of a possible interaction between IGF2BP2 (rs4402960) and obesity in determining overall T2DM risk. However, the underlying mechanism has been still unknown. The associations between variants of IGF2BP2 and abdominal/visceral total fat were evidenced in Canadian Caucasians [26] and Mexican Americans [12], suggesting a possible role of IGF2BP2 in insulin resistance. This finding implies that IGF2BP2 (rs4402960) may disturb T2DM susceptibility through its contribution to insulin resistance, which is experienced mainly by obese individuals.

Several limitations of the study deserve comment. Firstly, the subjects who participated in the study were recruited from Shanghai, so they may not have been representative of China as a whole. Secondly, it is important to mention that our study was performed on Chinese individuals, and our findings may not be relevant to people of other ethnicities.


The present study did not replicate the association of IGF2BP2 (rs4402960) and T2DM. Interestingly, it provided data suggesting that the wild C allele of IGF2BP2 (rs4402960) had a protective effect against T2DM in obese subjects of Chinese Han population. Further studies and a larger sample size are required to elucidate the relevant mechanisms, as well as the likely contribution of other IGF2BP2 variants to the risk of T2DM.


  1. Rong R, Hanson RL, Ortiz D, Wiedrich C, Kobes S, Knowler WC, Bogardus C, Baier LJ: Association analysis of variation in/near FTO, CDKAL1, SLC30A8, HHEX, EXT2, IGF2BP2, LOC387761, and CDKN2B with type 2 diabetes and related quantitative traits in Pima Indians. Diabetes 2009, 58(2):478–488.

    Article  PubMed  CAS  PubMed Central  Google Scholar 

  2. Han X, Luo Y, Ren Q, Zhang X, Wang F, Sun X, Zhou X, Ji L: Implication of genetic variants near SLC30A8, HHEX, CDKAL1, CDKN2A/B, IGF2BP2, FTO, TCF2, KCNQ1, and WFS1 in type 2 diabetes in a Chinese population. BMC Med Genet 2010, 11: 81.

    Article  PubMed  PubMed Central  Google Scholar 

  3. Stancakova A, Kuulasmaa T, Paananen J, Jackson AU, Bonnycastle LL, Collins FS, Boehnke M, Kuusisto J, Laakso M: Association of 18 confirmed susceptibility loci for type 2 diabetes with indices of insulin release, proinsulin conversion, and insulin sensitivity in 5,327 nondiabetic Finnish men. Diabetes 2009, 58(9):2129–2136. 10.2337/db09-0117

    Article  PubMed  CAS  PubMed Central  Google Scholar 

  4. Ruchat SM, Elks CE, Loos RJ, Vohl MC, Weisnagel SJ, Rankinen T, Bouchard C, Pérusse L: Association between insulin secretion, insulin sensitivity and type 2 diabetes susceptibility variants identified in genome-wide association studies. Acta Diabetol 2009, 46(3):217–226. 10.1007/s00592-008-0080-5

    Article  PubMed  CAS  Google Scholar 

  5. Lee YH, Kang ES, Kim SH, Han SJ, Kim CH, Kim HJ, Ahn CW, Cha BS, Nam M, Nam CM, Lee HC: Association between polymorphisms in SLC30A8, HHEX, CDKN2A/B, IGF2BP2, FTO, WFS1, CDKAL1, KCNQ1 and type 2 diabetes in the Korean population. J Hum Genet 2008, 53(11–12):991–998.

    Article  PubMed  CAS  Google Scholar 

  6. Kim WY, Kim JE, Choi YJ, Huh KB: Nutritional risk and metabolic syndrome in Korean type 2 diabetes mellitus. Asia Pac J Clin Nutr 2008, 17(Suppl 1):47–51.

    PubMed  CAS  Google Scholar 

  7. Xin Z, Liu C, Niu WY, Feng JP, Zhao L, Ma YH, Hua L, Yang JK: Identifying obesity indicators which best correlate with type 2 diabetes in a Chinese population. BMC Public Health 2012, 12: 732. 10.1186/1471-2458-12-732

    Article  PubMed  PubMed Central  Google Scholar 

  8. Wang X, Ding X, Su S, Spector TD, Mangino M, Iliadou A, Snieder H: Heritability of insulin sensitivity and lipid profile depend on BMI: evidence for gene-obesity interaction. Diabetologia 2009, 52(12):2578–2584. 10.1007/s00125-009-1524-3

    Article  PubMed  CAS  PubMed Central  Google Scholar 

  9. Lamina C, Forer L, Schönherr S, Kollerits B, Ried JS, Gieger C, Peters A, Wichmann HE, Kronenberg F: Evaluation of gene-obesity interaction effects on cholesterol levels: a genetic predisposition score on HDL-cholesterol is modified by obesity. Atherosclerosis 2012, 225(2):363–369. 10.1016/j.atherosclerosis.2012.09.016

    Article  PubMed  CAS  Google Scholar 

  10. Lasram K, Ben Halim N, Benrahma H, Mediene-Benchekor S, Arfa I, Hsouna S, Kefi R, Jamoussi H, Ben Ammar S, Bahri S, Abid A, Benhamamouch S, Barakat A, Abdelhak S: Contribution of CDKAL1 rs7756992 and IGF2BP2 rs4402960 polymorphisms in type 2 diabetes, diabetic complications, obesity risk and hypertension in the Tunisian population. J Diabetes 2014. doi:10.1111/1753–0407.12147

    Google Scholar 

  11. Ng MC, Park KS, Oh B, Tam CH, Cho YM, Shin HD, Lam VK, Ma RC, So WY, Cho YS, Kim HL, Lee HK, Chan JC, Cho NH: Implication of genetic variants near TCF7L2, SLC30A8, HHEX, CDKAL1, CDKN2A/B, IGF2BP2, and FTO in type 2 diabetes and obesity in 6,719 Asians. Diabetes 2008, 57(8):2226–2233. 10.2337/db07-1583

    Article  PubMed  CAS  PubMed Central  Google Scholar 

  12. Li X, Allayee H, Xiang AH, Trigo E, Hartiala J, Lawrence JM, Buchanan TA, Watanabe RM: Variation in IGF2BP2 interacts with adiposity to alter insulin sensitivity in Mexican Americans. Obesity (Silver Spring) 2009, 17(4):729–736. 10.1038/oby.2008.593

    Article  PubMed Central  Google Scholar 

  13. Bei-Fan Z: Predictive values 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. Asia Pac J Clin Nutr 2002, 11(Suppl 8):S685-S693.

    Article  Google Scholar 

  14. Alberti KG, Zimmet PZ: Definition, diagnosis and classification of diabetes mellitus and its complications. Part 1: diagnosis and classification of diabetes mellitus provisional report of a WHO consultation. Diabet Med 1998, 15(7):539–553. 10.1002/(SICI)1096-9136(199807)15:7<539::AID-DIA668>3.0.CO;2-S

    Article  PubMed  CAS  Google Scholar 

  15. Chauhan G, Spurgeon CJ, Tabassum R, Bhaskar S, Kulkarni SR, Mahajan A, Chavali S, Kumar MV, Prakash S, Dwivedi OP, Ghosh S, Yajnik CS, Tandon N, Bharadwaj D, Chandak GR: Impact of common variants of PPARG, KCNJ11, TCF7L2, SLC30A8, HHEX, CDKN2A, IGF2BP2, and CDKAL1 on the risk of type 2 diabetes in 5,164 Indians. Diabetes 2010, 59(8):2068–2074. 10.2337/db09-1386

    Article  PubMed  CAS  PubMed Central  Google Scholar 

  16. Gu T, Horová E, Möllsten A, Seman NA, Falhammar H, Prázný M, Brismar K, Gu HF: IGF2BP2 and IGF2 genetic effects in diabetes and diabetic nephropathy. J Diabetes Complications 2012, 26(5):393–398. 10.1016/j.jdiacomp.2012.05.012

    Article  PubMed  Google Scholar 

  17. Wu J, Wu J, Zhou Y, Zou H, Guo S, Liu J, Lu L, Xu H: Quantitative assessment of the variation in IGF2BP2 gene and type 2 diabetes risk. Acta Diabetol 2012, 49(Suppl 1):S87-S97.

    Article  PubMed  Google Scholar 

  18. Zhang SM, Xiao JZ, Ren Q, Han XY, Tang Y, Yang WY, Ji LN: Replication of association study between type 2 diabetes mellitus and IGF2BP2 in Han Chinese population. Chin Med J (Engl) 2013, 126(21):4013–4018.

    CAS  Google Scholar 

  19. Duesing K, Fatemifar G, Charpentier G, Marre M, Tichet J, Hercberg S, Balkau B, Froguel P, Gibson F: Evaluation of the association of IGF2BP2 variants with type 2 diabetes in French Caucasians. Diabetes 2008, 57(7):1992–1996. 10.2337/db07-1789

    Article  PubMed  CAS  PubMed Central  Google Scholar 

  20. Chistiakov DA, Nikitin AG, Smetanina SA, Bel'chikova LN, Suplotova LA, Shestakova MV, Nosikov VV: The rs11705701 G > A polymorphism of IGF2BP2 is associated with IGF2BP2 mRNA and protein levels in the visceral adipose tissue—a link to type 2 diabetes susceptibility. Rev Diabet Stud 2012, 9(2–3):112–122.

    Article  PubMed  PubMed Central  Google Scholar 

  21. Moore AF, Jablonski KA, McAteer JB, Saxena R, Pollin TI, Franks PW, Hanson RL, Shuldiner AR, Knowler WC, Altshuler D, Florez JC, Diabetes Prevention Program Research Group: Extension of type 2 diabetes genome-wide association scan results in the diabetes prevention program. Diabetes 2008, 57(9):2503–2510. 10.2337/db08-0284

    Article  PubMed  CAS  PubMed Central  Google Scholar 

  22. Louveau I, Gondret F: Regulation of development and metabolism of adipose tissue by growth hormone and the insulin-like growth factor system. Domest Anim Endocrinol 2004, 27(3):241–255. 10.1016/j.domaniend.2004.06.004

    Article  PubMed  CAS  Google Scholar 

  23. Miralles F, Portha B: Early development of beta-cells is impaired in the GK rat model of type 2 diabetes. Diabetes 2001, 50(Suppl 1):S84-S88.

    Article  PubMed  CAS  Google Scholar 

  24. Parikh H, Lyssenko V, Groop LC: Prioritizing genes for follow-up from genome wide association studies using information on gene expression in tissues relevant for type 2 diabetes mellitus. BMC Med Genomics 2009, 2: 72. 10.1186/1755-8794-2-72

    Article  PubMed  PubMed Central  Google Scholar 

  25. Baier LJ, Hanson RL: Genetic studies of the etiology of type 2 diabetes in Pima Indians: hunting for pieces to a complicated puzzle. Diabetes 2004, 53(5):1181–1186. 10.2337/diabetes.53.5.1181

    Article  PubMed  CAS  Google Scholar 

  26. Ruchat SM, Elks CE, Loos RJ, Vohl MC, Weisnagel SJ, Rankinen T, Bouchard C, Pérusse L: Evidence of interaction between type 2 diabetes susceptibility genes and dietary fat intake for adiposity and glucose homeostasis-related phenotypes. J Nutrigenet Nutrigenomics 2009, 2(4–5):225–234.

    Article  PubMed  CAS  Google Scholar 

Download references


The study was supported by a grant from The National Nature Science Foundation of China (No. 81270903).

Author information

Authors and Affiliations


Corresponding author

Correspondence to Jie Wen.

Additional information

Competing interests

The authors declare that they have no competing interests.

Authors' contributions

JW and R-MH conceived of the study, participated in its design and coordination, and helped to draft the manuscript. H-HW participated in the design of the study, performed the statistical analysis, and drafted the manuscript. N-JL, ZY, X-MT, Y-PD, X-CW, BL, and Z-YZ contributed the samples, reagents, and analysis tools. All authors read and approved the final manuscript.

Authors’ original submitted files for images

Below are the links to the authors’ original submitted files for images.

Authors’ original file for figure 1

Authors’ original file for figure 2

Rights and permissions

Open Access  This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made.

The images or other third party material in this article are included in the article’s Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder.

To view a copy of this licence, visit

The Creative Commons Public Domain Dedication waiver ( applies to the data made available in this article, unless otherwise stated in a credit line to the data.

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Wu, HH., Liu, NJ., Yang, Z. et al. IGF2BP2 and obesity interaction analysis for type 2 diabetes mellitus in Chinese Han population. Eur J Med Res 19, 40 (2014).

Download citation

  • Received:

  • Accepted:

  • Published:

  • DOI: