http://informahealthcare.com/rnf ISSN: 0886-022X (print), 1525-6049 (electronic) Ren Fail, 2014; 36(3): 478–487 ! 2014 Informa Healthcare USA, Inc. DOI: 10.3109/0886022X.2013.868319
STATE-OF-THE-ART REVIEW
Adiponectin gene polymorphisms and susceptibility to diabetic nephropathy: a meta-analysis Zi Lin1, Guoliang Huang1, Jingwen Zhang2, and Xiaoyu Lin3 Department of Endocrinology, Fujian Institute of Endocrinology, Union Hospital of Fujian Medical University, Fuzhou, China, 2Provincial Hospital of Fujian Medical University, Fuzhou, China, and 3School of Basic Medical Sciences of Fujian Medical University, Fuzhou, China
Abstract
Keywords
Adiponectin (ADIPOQ) plays an important role in the pathogenesis of diabetic nephropathy (DN) and previous studies regarding the association between ADIPOQ polymorphisms and DN risk reported conflicting results. To derive a more precise estimation of this association, we performed a meta-analysis to assess the association between four ADIPOQ polymorphisms [11391G4A (rs17300539), 11377C4G (rs266729), þ45T4G (rs2241766), and þ276G4T (rs1501299)] and risk for DN. Odds ratios (ORs) with corresponding 95% confidence intervals (95% CIs) were pooled to assess the association between four aforementioned polymorphisms and susceptibility to DN. Based on the included criteria, we selected 13 articles, among which 7 studies (cases/controls: 2749/7585) for 11391G4A, 8 studies for 11377C4G (3074/3842), 9 studies for þ45T4G (2654/7710), and 10 studies for þ276G4T (2812/7821), respectively. Our meta-analysis indicated no evidence heterogeneity among the included studies; thus, the fixed-effects model was used. Overall, there was an association between ADIPOQ 11391A allele with increased DN risk (OR ¼ 1.186, 95% CI: 1.051–1.338, p ¼ 0.006). Subgroup by ethnicity suggested significant association between þ45T4G polymorphism and DN risk among Caucasians (OR ¼ 1.122, 95% CI: 1.007–1.250, p ¼ 0.038). Sensitivity analysis suggested exclusion of any single study did not materially alter the overall pooled ORs above. Future studies are needed to validate these findings.
Adiponectin, diabetes, gene polymorphisms, meta-analysis, nephropathy
Introduction Diabetic nephropathy (DN) is a common microvascular complication of type 1 and type 2 diabetes (T1DM and T2DM), and it is the primary cause of end-stage renal disease (ESRD) worldwide.1–3 DN results from various causes including genetic and environmental factors, and major genes that contribute to the etiology of DN have yet to be identified.4–8 The adiponectin gene (gene ID 9370) is encoded as ADIPOQ (adipocyte C1q and collagen domain containing); and GBP28, ACRP30, APM1, and ACDC are this gene’s alternative names.9 The ADIPOQ gene is located in chromosome 3q27 and consists of three exons and two introns.9–11 ADIPOQ is an adipokine, which not only performs an important role in the regulation of insulin action, glucose, and lipid metabolism but also exerts anti-inflammatory and antiatherogenic effects, with a low circulating concentration in cases of insulin resistance, T2DM, and coronary heart disease.12–15 In contrast, ADIPOQ levels are high in cases
Address correspondence to G. Huang, Department of Endocrinology, Fujian Institute of Endocrinology, Union Hospital of Fujian Medical University, 29 Xinqu and Road, Fuzhou, Fujian 350001, China. Tel/Fax: +86 83357896/8423; E-mail: [email protected]
History Received 4 October 2013 Accepted 10 November 2013 Published online 17 December 2013
of DN, but it is still unclear whether these high levels are a cause or a consequence of the disease.16 The influence of ADIPOQ genetic polymorphisms in the development of DN is, nonetheless, not fully understood. Therefore, it is urgent to elucidate the association of ADIPOQ genetic polymorphisms and susceptibility to DN. Several polymorphisms in ADIPOQ gene have been identified, with 11391G4A (rs17300539), 11377C4G (rs266729), þ45T4G (rs2241766), and þ276G4T (rs1501299) and DN risk being extensively evaluated.8–10,16–33 Evidences have suggested that genetic variations of 11391G4A and 11377C4G in the promoter region of this gene can play a vital role in the increased risk of developing nephropathy partly through affecting the plasma levels of ADIPOQ.8,16 In addition, genetic association studies have demonstrated that single nucleotide polymorphisms (SNPs) þ45T4G in exon2 and þ276G4T in intron 2 of this gene confer the risk susceptibility to the development of DN.8,16,21 However, results are inconsistent and firm association has not yet been established. Considering the important roles of the ADIPOQ gene to the risk of developing DN and the insufficient power of a single study to provide responsible conclusion, we conducted a meta-analysis of all available studies relating four aforementioned polymorphisms in ADIPOQ gene to shed some light on these controversial issues.
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DOI: 10.3109/0886022X.2013.868319
Methods Search strategy A comprehensive search strategy was conducted towards the electronic databases including PubMed, Embase, Medline and CNKI databases. The following search terms were used: (1) adiponectin, ADIPOQ, APM1, and ACDC; (2) diabetic nephropathy and diabetes nephropathy; and (3) polymorphism, variation, variant, and mutation. An upper date limit of August 2013 was applied and we used no lower date limit. The language was restricted to English and Chinese. In addition, we reviewed the reference lists of retrieved papers and recent reviews.
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Study selection We first performed an initial screening of titles or abstracts. A second screening was based on full-text review. The inclusion criteria were: (1) case–control or cohort studies which evaluated the association between ADIPOQ 11391G4A (rs17300539), 11377C4G (rs266729), þ45T4G (rs2241766), and þ276G4T (rs1501299) polymorphisms and DN risk in type 1 or type 2 diabetic patients; (2) the controls were diabetic individuals without DN; (3) used an unrelated case–control or cohort design and had available genotype/allele counts for estimating an OR with its 95% CI. If two or more studies described outcomes among the same or overlapping groups of cases or controls, only the one with the largest available data was included in the meta analysis. Data extraction All data were reviewed and extracted independently by two investigators. From each study the following information was extracted: ADIPOQ polymorphisms, first author, publication year, ethnicity of study populations, study design, types of diabetes, genotyping methods, the number of cases and controls, minor allele frequency (MAF) in cases and controls. Any discrepancies in the extracted data were settled by discussion and, when necessary, adjudicated by a third reviewer. Statistical analysis In view of articles providing data only on allele counts accounted for the majority of the included ones, and to enhance study power to detect an association, we exclusively took account of allelic model in this meta-analysis. To test the population stratification in the controls, the departure of frequencies of ADIPOQ polymorphisms using the chi-square test from expectation under Hardy–Weinberg equilibrium (HWE) was assessed in controls. Pooled ORs (95% CI) for DN risk associated with ADIPOQ gene 11391A, 11377G, þ45G, and þ276T alleles compared with the alternative alleles were calculated, respectively. In this analysis, both the chi-square based Q statistic test (Cochran’s Q statistic) and the I2 statistic were applied to assess the between-study heterogeneity more precisely.35,36 Heterogeneity was considered significant for P Cochran’s Q statistic 50.10. With evidence heterogeneity among studies, the random-effects model
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(the DerSimonian and Laird method) was used to assess the summary ORs of each study;37 Otherwise, the fixed-effects model (the Mantel–Haenszel method) was used to pool the results.38 For exploring between-study heterogeneity or indicating that our results were statistically robust, subgroup analysis was also performed by ethnicity for ADIPOQ 11391G4A, 11377C4G, þ45T4G, and þ276G4T polymorphisms. Furthermore, we conducted a sensitivity analysis to examine the influence of a single study on the overall risk estimate by omitting one study in each turn.39 Eventually, potential publication bias was tested by Begg’s funnel plot and Egger’s test. Possible publication bias was assessed by visual inspection of the funnel plots in which the log ORs were plotted against their SEs.40 Significance was judged at the p50.1 level of Egger’s test.41 All analyses were performed using STATA version 12.0 (StataCorp LP, College Station, TX). p50.05 should be considered statistical significance, except where otherwise specified.
Results Characteristics of included studies A flow diagram schematizing the study selection process was shown in Figure 1. Following our search strategy 397 individual abstracts were retrieved originally, and 23 full-text articles were preliminarily identified for further detailed evaluation.8–10,16–34,42 On the basis of the exclusion criteria, 10 articles were excluded including one study containing overlapping data,31 one review article,34 one article with nondiabetic controls,27 one article with cross-sectional in design,28 one article with no included polymorphisms,32 and five articles with missing relevant data.10,29,30,33,42 Since the article by Vionnet et al.8 categorized data in Denmark, Finland, and France populations, we extracted them as three individual case–control studies. Finally, we included 13 articles, among which 7 studies (cases/controls: 2749/7585) for 11391G4A, 8 studies for 11377C4G (3074/3842), 9 studies for þ45T4G (2654/7710), and 10 studies for þ276G4T (2812/7821), respectively. The characteristics of included studies are summarized in Table 1. In the included studies, the distribution of genotypes in the controls was all in agreement with HWE, except for the þ45T4G polymorphism in one study17 and the þ276G4T polymorphism in one study.26 Meta-analysis results In Figure 2, our meta-analysis indicated no obvious heterogeneity among the included studies of the ADIPOQ 11391G4A polymorphism (Pheterogeneity ¼ 0.218, I2 ¼ 27.6%), 11377C4G polymorphism (Pheterogeneity ¼ 0.304, I2 ¼ 16.1%), þ45T4G polymorphism (Pheterogeneity ¼ 0.482, I2 ¼ 0.0%), and þ276G4T polymorphism (Pheterogeneity ¼ 0.430, I2 ¼ 0.9%), thus the fixed-effects model was used. ADIPOQ 11391A allele was associated with increased DN risk (OR ¼ 1.186, 95% CI: 1.051–1.338, p ¼ 0.006). However, no significant association between the 11377C4G, þ45T4G or þ276G4T polymorphism and DN risk was found in our present study (OR ¼ 0.966, 95% CI: 0.894–1.044, p ¼ 0.387; OR ¼ 1.100, 95% CI: 0.997–1.213,
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Pumbed:
Embase:
Medline:
CNKI:
N=16
N=52
N=18
N=341
N= 30 excluded for duplication
397 abstracts reviewed 374 excluded based on title and abstract
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23 full-text articles reviewed 10 excluded after full text review 1 review article 1 article with duplicated sample 1 article with non-diabetic controls 1 article with cross-sectional study 1 article with no included polymorphisms 5 articles with missing relevant data
13 quafilied articles involving subjects: 5 articles (7 studies) for adiponectin gene -11391G>A (rs17300539) 6 articles (8 studies) for adiponectin gene -11377C>G (rs266729) 7 articles (9 studies) for adiponectin gene +45T>G (rs2241766) 8 articles (10 studies) for adiponectin gene +276G>T (rs1501299)
Figure 1. Search flow diagram for studies included in the meta analysis.
p ¼ 0.057; OR ¼ 1.051, 95% CI: 0.976–1.133, p ¼ 0.189; respectively, Figure 2). MAF of the three ADIPOQ polymorphisms among the controls across the different ethnicities (Asians and Caucasians) was also assessed (Table 2), except for 11391G4A polymorphism because all ethnicities of the included studies of this polymorphism were Caucasians. For the 11377C4G and þ276G4T polymorphisms, there were similar results in the MAF among the controls across Asians and Caucasians. However, we found that the þ45G allele frequency in Asian populations was 29.5% (95% CI ¼ 23.1– 35.9%), which was significantly higher than that in Caucasian populations (11.9%, 95% CI ¼ 7.0–16.7%, p ¼ 0.003; Table 2). Subgroup analyses The subgroup analysis was undertaken according to ethnicity. we found that þ45T4G polymorphism was significantly associated with DN in Caucasian populations (G versus T: Pheterogeneity ¼ 0.442, I2 ¼ 0.0%, OR ¼ 1.122, 95%
CI: 1.007–1.250, p ¼ 0.038); for Asian populations, we did not find the similar result. However, no significant association between the 11377C4G or þ276G4T polymorphism and risk of DN was found among Caucasian populations and Asian populations (Table 3). Sensitivity analysis In this meta-analysis, we also performed sensitivity analyses to test the stability of the overall results. By exclusion of the study violating HWE or altering the statistic models, no material alternation was detected (Figure 3). Publication bias Funnel plot and Egger’s test were conducted to evaluate the publication bias in this meta-analysis. In Figure 4, the shape of the funnel plot did not reveal obvious asymmetry. Then, Egger’s linear regression test was used to provide statistical evidence of funnel plots asymmetry. For the ADIPOQ polymorphisms 11391G4A, 11377C4G, þ45T4G, and þ276G4T, the p values of Egger’s linear regression
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Table 1. Characteristics of studies included in the meta-analysis. Cases Polymorphisms
Name and Year
Ethnicity
Design
11391G4A
Jaziri (2010) Zhang (2009) Vionnet (2006a) Vionnet (2006b) Vionnet (2006c) Blech (2011) Ranjbar (2011) Wu (2009) Zhang (2009) Vionnet (2006a) Vionnet (2006b) Vionnet (2006c) Blech (2011) Min (2011) Lin (2010) Choe (2013) Jaziri (2010) Vionnet (2006a) Vionnet (2006b) Vionnet (2006c) Ma (2007) Blech (2011) Ranjbar (2011) Peng (2012) Choe (2013) Jaziri (2010) Vionnet (2006a) Vionnet (2006b) Vionnet (2006c) Ma (2007) Yoshioka (2004) Blech (2011) Guo (2008) Peng (2012)
Caucasian Caucasian Caucasian Caucasian Caucasian Caucasian Caucasian Asian Caucasian Caucasian Caucasian Caucasian Caucasian Asian Asian Asian Caucasian Caucasian Caucasian Caucasian Caucasian Caucasian Caucasian Asian Asian Caucasian Caucasian Caucasian Caucasian Caucasian Asian Caucasian Asian Asian
Pros. Pros. Retros. Retros. Retros. Retros. Retros. Retros. Pros. Retros. Retros. Retros. Retros. Retros. Retros. Pros. Pros. Retros. Retros. Retros. Retros. Retros. Retros. Retros. Pros. Pros. Retros. Retros. Retros. Retros. Retros. Retros. Retros. Retros.
11377C4G
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þ45T4G
þ276G4T
Type T2DM T1DM T1DM T1DM T1DM T2DM T2DM T2DM T1DM T1DM T1DM T1DM T2DM T2DM T2DM T2DM T2DM T1DM T1DM T1DM T1DM T2DM T2DM T2DM T2DM T2DM T1DM T1DM T1DM T1DM T2DM T2DM T2DM T2DM
or T1DM
or T1DM
or T1DM
or T1DM
Controls
Genotyping
N
MAF
N
MAF
TaqMan DASH TaqMan TaqMan TaqMan NA PCR-RFLP TaqMan DASH Ampli-Fluor Ampli-Fluor Ampli-Fluor NA PCR-RFLP PCR-RFLP SNaPShot PCR-MB Ampli-Fluor Ampli-Fluor Ampli-Fluor PCR-DASH NA PCR-RFLP DS SNaPShot TaqMan TaqMan TaqMan TaqMan PCR-DASH FAS-PCR NA PCR-RFLP DS
115 578 489 387 300 852 28 216 578 489 387 300 852 50 202 245 115 489 387 300 196 852 28 42 245 115 489 387 300 196 108 852 78 42
0.14 0.11 0.09 0.03 0.10 0.12 0.07 0.22 0.26 0.24 0.33 0.23 0.25 0.24 0.21 0.30 0.19 0.10 0.05 0.14 0.04 0.21 0.14 0.24 0.29 0.31 0.31 0.29 0.28 0.21 0.29 0.32 0.22 0.35
3975 599 463 469 391 1483 205 178 599 463 469 391 1483 58 201 448 3975 463 469 391 236 1483 205 40 448 3975 463 469 391 236 208 1483 108 40
0.09 0.10 0.07 0.03 0.07 0.11 0.03 0.28 0.24 0.26 0.34 0.26 0.25 0.30 0.19 0.29 0.14 0.10 0.06 0.12 0.05 0.19 0.17 0.30 0.30 0.28 0.28 0.32 0.24 0.18 0.31 0.31 0.21 0.29
Notes: Cases, individuals with diabetic nephropathy (DN); controls, diabetic individuals without DN; N, total number of cases or controls; MAF, minor allele frequency; Pros., prospective design; Retros., retrospective design; T2DM, type 2 diabetes mellitus; T1DM, type1 diabetes mellitus; DASH, dynamic allele-specific hybridisation; NA, not available; PCR-RFLP, polymerase chain reaction restriction fragment length polymorphism; MB, molecular beacon; DS, direct sequencing; FAS, afluorescent allele-specific.
test were 0.197, 0.182, 0.171, and 0.599, respectively. Hence, the above results indicated that publication bias was not evident in this meta-analysis.
Discussion Diabetes mellitus affects the kidney in stages, and 30–40% of the patients with T1DM and T2DM suffer from DN.1,2,43 It is well recognized that DN has genetic components and individual susceptibility, while the pathophysiology of DN remains unclear and needs to be fully investigated.6,44–47 Recently, more attention has been paid to the identification of the genetic variations of nephropathy in T1DM and T2DM. Thus, identification of susceptibility or resistance genes in DN will provide better knowledge of pathomechanism, genetic biomarkers and future therapies.48–50 Considerable studies have indicated that the levels of ADIPOQ are influenced by genetic polymorphisms in the ADIPOQ gene51–54 and these polymorphisms have been implicated in DN.8–10,16–32 Nephropathy has been associated with high ADIPOQ levels in T1DM and T2DM.16,31 Thus, the genetic studies of DN and ADIPOQ gene will give new
insight into the underlying etiology and lead to new therapies for treatment and prevention in this disease. Previous studies regarding the association between SNPs within ADIPOQ and DN risk reported conflicting results. Studies proposed that these SNPs were determinants of the DN risk.9,16,21 However, several studies also showed that these SNPs were not associated with susceptibility to DN.17,22,23 Among these SNPs, 11391G/A and 11377C/G are located in the ADIPOQ gene promoter region, whereas þ45T4G and þ276G4T are located in the exon2 and intron 2 region.9–11 Evidences suggested that genetic variation in the promoter region of this gene could play an important role in the risk of DN partly through affecting the plasma levels of ADIPOQ.8,16 Moreover, studies demonstrated that 11377C4G polymorphism could alter the sequence in one of four SP1 binding sites in this promoter region and reduce ADIPOQ promoter transcription activity.9,55 Jaziri et al.16 reported high ADIPOQ concentrations associated with the 11391A, þ45G alleles might be the cause, rather than a consequence of DN. Several studies indicated that SNP þ276G4T was associated with lower plasma ADIPOQ levels, while no significant association between þ276G4T and the prevalence of DN
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Figure 2. Forest plot for meta-analysis association between the ADIPOQ polymorphisms with DN risk (allele model). (a) 11391G4A, (b) 11377C4G, (c) þ45T4G, (d) þ276G4T.
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Figure 2. Continued.
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Table 2. MAF among controls stratified by ethnicity. Variables
Allele (major/minor)
MAF of Asians (%)
MAF of Caucasians (%)
pa
11377 þ45 þ276
C4G T4G G4T
25.7 (95% CI ¼ 11.1–40.2) 29.5 (95% CI ¼ 23.1–35.9) 27.8 (95% CI ¼ 20.5–35.0)
27.0 (95% CI ¼ 22.0–32.0) 11.9 (95% CI ¼ 7.0–16.7) 26.8 (95% CI ¼ 21.4–32.2)
0.711 0.003 0.781
Note: aStudent’s t test for MAF between Asians and Caucasians.
Table 3. Stratified analyses of the ADIPOQ polymorphisms on DN risk. Odds ratio Subgroup by ethnicity
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11377C4G (G versus C) þ45T4G (G versus T) þ276G4T (T versus G)
Asians Caucasians Asians Caucasians Asians Caucasians
Studies (no. of cases/controls) 3 5 2 7 4 6
(468/437) (2606/3405) (287/488) (2367/7222) (473/804) (2339/7017)
OR[95% CI] 0.852 0.984 1.008 1.122 0.978 1.068
[0.684–1.063] [0.905–1.069] [0.802–1.266] [1.007–1.250] [0.817–1.170] [0.983–1.159]
Heterogeneity 2
POR
I (%)
PH
0.156 0.696 0.947 0.038 0.806 0.119
39.5 0.0 0.0 0.0 0.0 31.5
0.192 0.461 0.332 0.442 0.798 0.199
Notes: CI, confidence interval; PH, the P value of heterogeneity test.
Figure 3. Sensitivity analyses. Horizontal line mean effect size (a) 11391G4A, (b) 11377C4G, (c) þ45T4G, (d) þ276G4T.
was found in other studies.17,20,23 In view of inconsistent results, we performed a meta-analysis of these SNPs to derive a more precise estimation of the relationship between these SNPs and susceptibility to DN. In this meta-analysis, 14 articles (7 studies for 11391G4A, 8 studies for 11377C4G, 9 studies for
þ45T4G, and 10 studies for þ276G4T) were performed to provide the assessment of the relationship between ADIPOQ polymorphisms and DN. No obvious heterogeneity was found among the included studies. For ADIPOQ 11391G4A polymorphism, studies observed significant association with DN risk in the allele genetic comparison model. For the
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Figure 4. Funnel plot for assessing publication bias in this meta-analysis. (a) 11391G4A, (b) 11377C4G, (c) þ45T4G, (d) þ276G4T.
11377C4G, þ45T4G, or þ276G4T polymorphism, no significant association with DN was found. However, in the subgroup analysis of þ45T4G by ethnicity, the results demonstrated significant association was found among Caucasian populations, partly because there was evidence difference in the MAF of þ45T4G among the controls across Asians and Caucasians. Sensitivity analysis indicated that our results were statistically robust. Some possible limitations of this meta-analysis should be acknowledged in explaining the results. First, most included studies were retrospective in design. Second, publication bias might have occurred because only published studies were included and unpublished studies which had null results were missed, which might bias the results. Therefore, these biases could lead to an overestimation of the effects in this metaanalysis. Third, the studies involved were relatively less and small study samples. More and larger studies should be warranted in future to elucidate the role of the variant in DN susceptibility. Finally, DN is a multifactorial disease that results from complex interactions between many environmental and genetic factors. We only focused on ADIPOQ gene polymorphisms, and did not cover other DN susceptibility genes or polymorphisms. In conclusion, for the 11391G4A and þ45T4G polymorphisms, significant association with DN was found by the allele genetic comparison model in overall or subgroup analysis. However, our meta-analysis provided evidence that 11377C4G and þ276G4T polymorphisms were unlikely to be associated with genetic susceptibility of DN based on
the current published studies. To get a more exact conclusion on the effects of ADIPOQ polymorphisms on DN risk, future large and well-designed studies are necessary.
Declaration of interest The authors report no conflicts of interest. The authors alone are responsible for the content and writing of the paper.
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with type 2 diabetes mellitus. Chin J Diabetes Mellitus. 2010; 2(2):106–110. [in Chinese]. Peng C, Hong Y, Fu L. Association of adiponectin gene polymorphism in type 2 diabetes with nephropathy. J Med Res. 2012;41(4):156–159. [in Chinese]. Bostrom MA, Freedman BI, Langefeld CD, Liu L, Hicks PJ, Bowden DW. Association of adiponectin gene polymorphisms with type 2 diabetes in an African American population enriched for nephropathy. Diabetes. 2009;58(2):499–504. Prior SL, Javid J, Gill GV, Bain SC, Stephens JW. The adiponectin rs17300539 G4A variant and nephropathy risk. Kidney Int. 2008; 74(10):1361. Zhao H, Chen L, Zhang C, Han J. Polymorphism in the 50 promoter region of the APN gene contributes to development of type 2 diabetes nephropathy. Proc Clin Med. 2008;1(7):483–485. [in Chinese]. Jia M, Feng X, Zhang Z, Qiao B, Qin X, Sun Y. Correlation of polymorphism of adiponectin promoter with diabetes type 2 and its complications. Chin J Lab Med. 2008;31(2):163–169. [in Chinese]. Jorsal A, Tarnow L, Frystyk J, et al. Serum adiponectin predicts all-cause mortality and end stage renal disease in patients with type I diabetes and diabetic nephropathy. Kidney Int. 2008; 74(5):649–654. Rudofsky Jr G, Schlimme M, Schlotterer A, et al. No association of the 94T/G polymorphism in the adiponectin gene with diabetic complications. Diabetes Obes Metab. 2005;7(4):455–459. Gu HF, Alvarsson A, Efendic S, Brismar K. SOX2 has genderspecific genetic effects on diabetic nephropathy in samples from patients with type 1 diabetes mellitus in the GoKinD study. Gend Med. 2009;6(4):555–564. Gu HF. Biomarkers of adiponectin: plasma protein variation and genomic DNA polymorphisms. Biomark Insights. 2009;4:123–133. Higgins JPT, Thompson SG, Deeks JJ, et al. Measuring inconsistency in meta-analyses. BMJ. 2003;327(7414):557–560. Cochran WG. The combination of estimates from different experiments. Biometrics. 1954;10:101–129. DerSimonian R, Laird N. Meta-analysis in clinical trials. Control Clin Trials. 1986;7(3):177–188. Mantel N, Haenszel W. Statistical aspects of the analysis of data from retrospective studies of disease. J Natl Cancer Inst. 1959; 22(4):719–748. Tobias A. Assessing the influence of a single study in the metaanalysis estimate. Stata Tech Bull. 1999;8:15–17. Stuck AE, Rubenstein LZ, Wieland D. Bias in meta-analysis detected by a simple, graphical test. Asymmetry detected in funnel plot was probably due to true heterogeneity. BMJ. 1998; 316(7129):469–471. Bowden J, Tierney JF, Copas AJ, Burdett S. Quantifying, displaying and accounting for heterogeneity in the meta-analysis of RCTs using standard and generalised Q statistics. BMC Med Res Methodol. 2011;11:41. Chung HF, Chen PS, Long K, Hsu CC, Huang MC. The association of ADIPOQ gene polymorphisms and clinical risk factors with nephropathy progression in type 2 diabetes. Meeting Abstracts. Experimental Biology 2012, EB, San Diego, CA, Apr 21–25, 2012. Hasslacher C, Ritz E, Wahl P, Michael C. Similar risks of nephropathy in patients with type I or type II diabetes mellitus. Nephrol Dial Transplant. 1989;4:859–863. Makita Y, Moczulski DK, Bochenski J, Smiles AM, Warram JH, Krolewski AS. Methylenetetrahydrofolate reductase gene polymorphism and susceptibility to diabetic nephropathy in type 1 diabetes. Am J Kidney Dis. 2003;41(6):1189–1194. Wang L, Teng Z, Cai S, Wang D, Zhao X, Yu K. The association between the PPARg2 Pro12Ala polymorphism and nephropathy susceptibility in type 2 diabetes: a meta-analysis based on 9,176 subjects. Diagn Pathol. 2013;8(1):118 (1–7). doi: 10.1186/17461596-8-118. Freedman BI, Tuttle AB, Spray BJ. Familial predisposition to nephropathy in African-Americans with non-insulin-dependent diabetes mellitus. Am J Kidney Dis. 1995;25(5):710–713. ¨ sterholm AM, He B, Pitkaniemi J, et al. Genome-wide scan for O type 1 diabetic nephropathy in the Finnish population reveals suggestive linkage to a single locus on chromosome 3q. Kidney Int. 2007;71(2):140–145.
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48. Freedman BI, Bostrom M, Daeihagh P, Bowden DW. Genetic factors in diabetic nephropathy. Clin J Am Soc Nephrol. 2007; 2(6):1306–1316. 49. McKillop AM, Flatt PR. Recent developments in the search for biomarkers for the diagnosis and monitoring of diabetes. Recent Pat Endocr Metab Immune Drug Discov. 2008; 2(3):172–177. 50. Yamagishi SI, Fukami K, Ueda S, Okuda S. Molecular mechanisms of diabetic nephropathy and its therapeutic intervention. Curr Drug Targets. 2007;8(8):952–959. 51. Jee SH, Sull JW, Lee JE, et al. Adiponectin concentrations: a genome-wide association study. Am J Hum Genet. 2010; 87(4):545–552.
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52. Ling H, Waterworth DM, Stirnadel HA, et al. Genome-wide linkage and association analyses to identify genes influencing adiponectin levels: the GEMS Study. Obesity. 2009;17(4):737–744. 53. Richards JB, Waterworth D, O’Rahilly S, et al. A genome-wide association study reveals variants in ARL15 that influence adiponectin levels. PLoS Genet. 2009;5(12):e1000768. 54. Heid IM, Wagner SA, Gohlke H, et al. Genetic architecture of the APM1 gene and its influence on adiponectin plasma levels and parameters of the metabolic syndrome in 1,727 healthy Caucasians. Diabetes. 2006;55(2):375–384. 55. Laumen H, Saningong AD, Heid IM, et al. Functional characterization of promoter variants of the adiponectin gene complemented by epidemiological data. Diabetes. 2009;58(4):984–991.
STATE-OF-THE-ART REVIEW
Adiponectin gene polymorphisms and susceptibility to diabetic nephropathy: a meta-analysis Zi Lin1, Guoliang Huang1, Jingwen Zhang2, and Xiaoyu Lin3 Department of Endocrinology, Fujian Institute of Endocrinology, Union Hospital of Fujian Medical University, Fuzhou, China, 2Provincial Hospital of Fujian Medical University, Fuzhou, China, and 3School of Basic Medical Sciences of Fujian Medical University, Fuzhou, China
Abstract
Keywords
Adiponectin (ADIPOQ) plays an important role in the pathogenesis of diabetic nephropathy (DN) and previous studies regarding the association between ADIPOQ polymorphisms and DN risk reported conflicting results. To derive a more precise estimation of this association, we performed a meta-analysis to assess the association between four ADIPOQ polymorphisms [11391G4A (rs17300539), 11377C4G (rs266729), þ45T4G (rs2241766), and þ276G4T (rs1501299)] and risk for DN. Odds ratios (ORs) with corresponding 95% confidence intervals (95% CIs) were pooled to assess the association between four aforementioned polymorphisms and susceptibility to DN. Based on the included criteria, we selected 13 articles, among which 7 studies (cases/controls: 2749/7585) for 11391G4A, 8 studies for 11377C4G (3074/3842), 9 studies for þ45T4G (2654/7710), and 10 studies for þ276G4T (2812/7821), respectively. Our meta-analysis indicated no evidence heterogeneity among the included studies; thus, the fixed-effects model was used. Overall, there was an association between ADIPOQ 11391A allele with increased DN risk (OR ¼ 1.186, 95% CI: 1.051–1.338, p ¼ 0.006). Subgroup by ethnicity suggested significant association between þ45T4G polymorphism and DN risk among Caucasians (OR ¼ 1.122, 95% CI: 1.007–1.250, p ¼ 0.038). Sensitivity analysis suggested exclusion of any single study did not materially alter the overall pooled ORs above. Future studies are needed to validate these findings.
Adiponectin, diabetes, gene polymorphisms, meta-analysis, nephropathy
Introduction Diabetic nephropathy (DN) is a common microvascular complication of type 1 and type 2 diabetes (T1DM and T2DM), and it is the primary cause of end-stage renal disease (ESRD) worldwide.1–3 DN results from various causes including genetic and environmental factors, and major genes that contribute to the etiology of DN have yet to be identified.4–8 The adiponectin gene (gene ID 9370) is encoded as ADIPOQ (adipocyte C1q and collagen domain containing); and GBP28, ACRP30, APM1, and ACDC are this gene’s alternative names.9 The ADIPOQ gene is located in chromosome 3q27 and consists of three exons and two introns.9–11 ADIPOQ is an adipokine, which not only performs an important role in the regulation of insulin action, glucose, and lipid metabolism but also exerts anti-inflammatory and antiatherogenic effects, with a low circulating concentration in cases of insulin resistance, T2DM, and coronary heart disease.12–15 In contrast, ADIPOQ levels are high in cases
Address correspondence to G. Huang, Department of Endocrinology, Fujian Institute of Endocrinology, Union Hospital of Fujian Medical University, 29 Xinqu and Road, Fuzhou, Fujian 350001, China. Tel/Fax: +86 83357896/8423; E-mail: [email protected]
History Received 4 October 2013 Accepted 10 November 2013 Published online 17 December 2013
of DN, but it is still unclear whether these high levels are a cause or a consequence of the disease.16 The influence of ADIPOQ genetic polymorphisms in the development of DN is, nonetheless, not fully understood. Therefore, it is urgent to elucidate the association of ADIPOQ genetic polymorphisms and susceptibility to DN. Several polymorphisms in ADIPOQ gene have been identified, with 11391G4A (rs17300539), 11377C4G (rs266729), þ45T4G (rs2241766), and þ276G4T (rs1501299) and DN risk being extensively evaluated.8–10,16–33 Evidences have suggested that genetic variations of 11391G4A and 11377C4G in the promoter region of this gene can play a vital role in the increased risk of developing nephropathy partly through affecting the plasma levels of ADIPOQ.8,16 In addition, genetic association studies have demonstrated that single nucleotide polymorphisms (SNPs) þ45T4G in exon2 and þ276G4T in intron 2 of this gene confer the risk susceptibility to the development of DN.8,16,21 However, results are inconsistent and firm association has not yet been established. Considering the important roles of the ADIPOQ gene to the risk of developing DN and the insufficient power of a single study to provide responsible conclusion, we conducted a meta-analysis of all available studies relating four aforementioned polymorphisms in ADIPOQ gene to shed some light on these controversial issues.
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DOI: 10.3109/0886022X.2013.868319
Methods Search strategy A comprehensive search strategy was conducted towards the electronic databases including PubMed, Embase, Medline and CNKI databases. The following search terms were used: (1) adiponectin, ADIPOQ, APM1, and ACDC; (2) diabetic nephropathy and diabetes nephropathy; and (3) polymorphism, variation, variant, and mutation. An upper date limit of August 2013 was applied and we used no lower date limit. The language was restricted to English and Chinese. In addition, we reviewed the reference lists of retrieved papers and recent reviews.
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Study selection We first performed an initial screening of titles or abstracts. A second screening was based on full-text review. The inclusion criteria were: (1) case–control or cohort studies which evaluated the association between ADIPOQ 11391G4A (rs17300539), 11377C4G (rs266729), þ45T4G (rs2241766), and þ276G4T (rs1501299) polymorphisms and DN risk in type 1 or type 2 diabetic patients; (2) the controls were diabetic individuals without DN; (3) used an unrelated case–control or cohort design and had available genotype/allele counts for estimating an OR with its 95% CI. If two or more studies described outcomes among the same or overlapping groups of cases or controls, only the one with the largest available data was included in the meta analysis. Data extraction All data were reviewed and extracted independently by two investigators. From each study the following information was extracted: ADIPOQ polymorphisms, first author, publication year, ethnicity of study populations, study design, types of diabetes, genotyping methods, the number of cases and controls, minor allele frequency (MAF) in cases and controls. Any discrepancies in the extracted data were settled by discussion and, when necessary, adjudicated by a third reviewer. Statistical analysis In view of articles providing data only on allele counts accounted for the majority of the included ones, and to enhance study power to detect an association, we exclusively took account of allelic model in this meta-analysis. To test the population stratification in the controls, the departure of frequencies of ADIPOQ polymorphisms using the chi-square test from expectation under Hardy–Weinberg equilibrium (HWE) was assessed in controls. Pooled ORs (95% CI) for DN risk associated with ADIPOQ gene 11391A, 11377G, þ45G, and þ276T alleles compared with the alternative alleles were calculated, respectively. In this analysis, both the chi-square based Q statistic test (Cochran’s Q statistic) and the I2 statistic were applied to assess the between-study heterogeneity more precisely.35,36 Heterogeneity was considered significant for P Cochran’s Q statistic 50.10. With evidence heterogeneity among studies, the random-effects model
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(the DerSimonian and Laird method) was used to assess the summary ORs of each study;37 Otherwise, the fixed-effects model (the Mantel–Haenszel method) was used to pool the results.38 For exploring between-study heterogeneity or indicating that our results were statistically robust, subgroup analysis was also performed by ethnicity for ADIPOQ 11391G4A, 11377C4G, þ45T4G, and þ276G4T polymorphisms. Furthermore, we conducted a sensitivity analysis to examine the influence of a single study on the overall risk estimate by omitting one study in each turn.39 Eventually, potential publication bias was tested by Begg’s funnel plot and Egger’s test. Possible publication bias was assessed by visual inspection of the funnel plots in which the log ORs were plotted against their SEs.40 Significance was judged at the p50.1 level of Egger’s test.41 All analyses were performed using STATA version 12.0 (StataCorp LP, College Station, TX). p50.05 should be considered statistical significance, except where otherwise specified.
Results Characteristics of included studies A flow diagram schematizing the study selection process was shown in Figure 1. Following our search strategy 397 individual abstracts were retrieved originally, and 23 full-text articles were preliminarily identified for further detailed evaluation.8–10,16–34,42 On the basis of the exclusion criteria, 10 articles were excluded including one study containing overlapping data,31 one review article,34 one article with nondiabetic controls,27 one article with cross-sectional in design,28 one article with no included polymorphisms,32 and five articles with missing relevant data.10,29,30,33,42 Since the article by Vionnet et al.8 categorized data in Denmark, Finland, and France populations, we extracted them as three individual case–control studies. Finally, we included 13 articles, among which 7 studies (cases/controls: 2749/7585) for 11391G4A, 8 studies for 11377C4G (3074/3842), 9 studies for þ45T4G (2654/7710), and 10 studies for þ276G4T (2812/7821), respectively. The characteristics of included studies are summarized in Table 1. In the included studies, the distribution of genotypes in the controls was all in agreement with HWE, except for the þ45T4G polymorphism in one study17 and the þ276G4T polymorphism in one study.26 Meta-analysis results In Figure 2, our meta-analysis indicated no obvious heterogeneity among the included studies of the ADIPOQ 11391G4A polymorphism (Pheterogeneity ¼ 0.218, I2 ¼ 27.6%), 11377C4G polymorphism (Pheterogeneity ¼ 0.304, I2 ¼ 16.1%), þ45T4G polymorphism (Pheterogeneity ¼ 0.482, I2 ¼ 0.0%), and þ276G4T polymorphism (Pheterogeneity ¼ 0.430, I2 ¼ 0.9%), thus the fixed-effects model was used. ADIPOQ 11391A allele was associated with increased DN risk (OR ¼ 1.186, 95% CI: 1.051–1.338, p ¼ 0.006). However, no significant association between the 11377C4G, þ45T4G or þ276G4T polymorphism and DN risk was found in our present study (OR ¼ 0.966, 95% CI: 0.894–1.044, p ¼ 0.387; OR ¼ 1.100, 95% CI: 0.997–1.213,
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Pumbed:
Embase:
Medline:
CNKI:
N=16
N=52
N=18
N=341
N= 30 excluded for duplication
397 abstracts reviewed 374 excluded based on title and abstract
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23 full-text articles reviewed 10 excluded after full text review 1 review article 1 article with duplicated sample 1 article with non-diabetic controls 1 article with cross-sectional study 1 article with no included polymorphisms 5 articles with missing relevant data
13 quafilied articles involving subjects: 5 articles (7 studies) for adiponectin gene -11391G>A (rs17300539) 6 articles (8 studies) for adiponectin gene -11377C>G (rs266729) 7 articles (9 studies) for adiponectin gene +45T>G (rs2241766) 8 articles (10 studies) for adiponectin gene +276G>T (rs1501299)
Figure 1. Search flow diagram for studies included in the meta analysis.
p ¼ 0.057; OR ¼ 1.051, 95% CI: 0.976–1.133, p ¼ 0.189; respectively, Figure 2). MAF of the three ADIPOQ polymorphisms among the controls across the different ethnicities (Asians and Caucasians) was also assessed (Table 2), except for 11391G4A polymorphism because all ethnicities of the included studies of this polymorphism were Caucasians. For the 11377C4G and þ276G4T polymorphisms, there were similar results in the MAF among the controls across Asians and Caucasians. However, we found that the þ45G allele frequency in Asian populations was 29.5% (95% CI ¼ 23.1– 35.9%), which was significantly higher than that in Caucasian populations (11.9%, 95% CI ¼ 7.0–16.7%, p ¼ 0.003; Table 2). Subgroup analyses The subgroup analysis was undertaken according to ethnicity. we found that þ45T4G polymorphism was significantly associated with DN in Caucasian populations (G versus T: Pheterogeneity ¼ 0.442, I2 ¼ 0.0%, OR ¼ 1.122, 95%
CI: 1.007–1.250, p ¼ 0.038); for Asian populations, we did not find the similar result. However, no significant association between the 11377C4G or þ276G4T polymorphism and risk of DN was found among Caucasian populations and Asian populations (Table 3). Sensitivity analysis In this meta-analysis, we also performed sensitivity analyses to test the stability of the overall results. By exclusion of the study violating HWE or altering the statistic models, no material alternation was detected (Figure 3). Publication bias Funnel plot and Egger’s test were conducted to evaluate the publication bias in this meta-analysis. In Figure 4, the shape of the funnel plot did not reveal obvious asymmetry. Then, Egger’s linear regression test was used to provide statistical evidence of funnel plots asymmetry. For the ADIPOQ polymorphisms 11391G4A, 11377C4G, þ45T4G, and þ276G4T, the p values of Egger’s linear regression
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Table 1. Characteristics of studies included in the meta-analysis. Cases Polymorphisms
Name and Year
Ethnicity
Design
11391G4A
Jaziri (2010) Zhang (2009) Vionnet (2006a) Vionnet (2006b) Vionnet (2006c) Blech (2011) Ranjbar (2011) Wu (2009) Zhang (2009) Vionnet (2006a) Vionnet (2006b) Vionnet (2006c) Blech (2011) Min (2011) Lin (2010) Choe (2013) Jaziri (2010) Vionnet (2006a) Vionnet (2006b) Vionnet (2006c) Ma (2007) Blech (2011) Ranjbar (2011) Peng (2012) Choe (2013) Jaziri (2010) Vionnet (2006a) Vionnet (2006b) Vionnet (2006c) Ma (2007) Yoshioka (2004) Blech (2011) Guo (2008) Peng (2012)
Caucasian Caucasian Caucasian Caucasian Caucasian Caucasian Caucasian Asian Caucasian Caucasian Caucasian Caucasian Caucasian Asian Asian Asian Caucasian Caucasian Caucasian Caucasian Caucasian Caucasian Caucasian Asian Asian Caucasian Caucasian Caucasian Caucasian Caucasian Asian Caucasian Asian Asian
Pros. Pros. Retros. Retros. Retros. Retros. Retros. Retros. Pros. Retros. Retros. Retros. Retros. Retros. Retros. Pros. Pros. Retros. Retros. Retros. Retros. Retros. Retros. Retros. Pros. Pros. Retros. Retros. Retros. Retros. Retros. Retros. Retros. Retros.
11377C4G
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þ45T4G
þ276G4T
Type T2DM T1DM T1DM T1DM T1DM T2DM T2DM T2DM T1DM T1DM T1DM T1DM T2DM T2DM T2DM T2DM T2DM T1DM T1DM T1DM T1DM T2DM T2DM T2DM T2DM T2DM T1DM T1DM T1DM T1DM T2DM T2DM T2DM T2DM
or T1DM
or T1DM
or T1DM
or T1DM
Controls
Genotyping
N
MAF
N
MAF
TaqMan DASH TaqMan TaqMan TaqMan NA PCR-RFLP TaqMan DASH Ampli-Fluor Ampli-Fluor Ampli-Fluor NA PCR-RFLP PCR-RFLP SNaPShot PCR-MB Ampli-Fluor Ampli-Fluor Ampli-Fluor PCR-DASH NA PCR-RFLP DS SNaPShot TaqMan TaqMan TaqMan TaqMan PCR-DASH FAS-PCR NA PCR-RFLP DS
115 578 489 387 300 852 28 216 578 489 387 300 852 50 202 245 115 489 387 300 196 852 28 42 245 115 489 387 300 196 108 852 78 42
0.14 0.11 0.09 0.03 0.10 0.12 0.07 0.22 0.26 0.24 0.33 0.23 0.25 0.24 0.21 0.30 0.19 0.10 0.05 0.14 0.04 0.21 0.14 0.24 0.29 0.31 0.31 0.29 0.28 0.21 0.29 0.32 0.22 0.35
3975 599 463 469 391 1483 205 178 599 463 469 391 1483 58 201 448 3975 463 469 391 236 1483 205 40 448 3975 463 469 391 236 208 1483 108 40
0.09 0.10 0.07 0.03 0.07 0.11 0.03 0.28 0.24 0.26 0.34 0.26 0.25 0.30 0.19 0.29 0.14 0.10 0.06 0.12 0.05 0.19 0.17 0.30 0.30 0.28 0.28 0.32 0.24 0.18 0.31 0.31 0.21 0.29
Notes: Cases, individuals with diabetic nephropathy (DN); controls, diabetic individuals without DN; N, total number of cases or controls; MAF, minor allele frequency; Pros., prospective design; Retros., retrospective design; T2DM, type 2 diabetes mellitus; T1DM, type1 diabetes mellitus; DASH, dynamic allele-specific hybridisation; NA, not available; PCR-RFLP, polymerase chain reaction restriction fragment length polymorphism; MB, molecular beacon; DS, direct sequencing; FAS, afluorescent allele-specific.
test were 0.197, 0.182, 0.171, and 0.599, respectively. Hence, the above results indicated that publication bias was not evident in this meta-analysis.
Discussion Diabetes mellitus affects the kidney in stages, and 30–40% of the patients with T1DM and T2DM suffer from DN.1,2,43 It is well recognized that DN has genetic components and individual susceptibility, while the pathophysiology of DN remains unclear and needs to be fully investigated.6,44–47 Recently, more attention has been paid to the identification of the genetic variations of nephropathy in T1DM and T2DM. Thus, identification of susceptibility or resistance genes in DN will provide better knowledge of pathomechanism, genetic biomarkers and future therapies.48–50 Considerable studies have indicated that the levels of ADIPOQ are influenced by genetic polymorphisms in the ADIPOQ gene51–54 and these polymorphisms have been implicated in DN.8–10,16–32 Nephropathy has been associated with high ADIPOQ levels in T1DM and T2DM.16,31 Thus, the genetic studies of DN and ADIPOQ gene will give new
insight into the underlying etiology and lead to new therapies for treatment and prevention in this disease. Previous studies regarding the association between SNPs within ADIPOQ and DN risk reported conflicting results. Studies proposed that these SNPs were determinants of the DN risk.9,16,21 However, several studies also showed that these SNPs were not associated with susceptibility to DN.17,22,23 Among these SNPs, 11391G/A and 11377C/G are located in the ADIPOQ gene promoter region, whereas þ45T4G and þ276G4T are located in the exon2 and intron 2 region.9–11 Evidences suggested that genetic variation in the promoter region of this gene could play an important role in the risk of DN partly through affecting the plasma levels of ADIPOQ.8,16 Moreover, studies demonstrated that 11377C4G polymorphism could alter the sequence in one of four SP1 binding sites in this promoter region and reduce ADIPOQ promoter transcription activity.9,55 Jaziri et al.16 reported high ADIPOQ concentrations associated with the 11391A, þ45G alleles might be the cause, rather than a consequence of DN. Several studies indicated that SNP þ276G4T was associated with lower plasma ADIPOQ levels, while no significant association between þ276G4T and the prevalence of DN
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Figure 2. Forest plot for meta-analysis association between the ADIPOQ polymorphisms with DN risk (allele model). (a) 11391G4A, (b) 11377C4G, (c) þ45T4G, (d) þ276G4T.
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Figure 2. Continued.
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Table 2. MAF among controls stratified by ethnicity. Variables
Allele (major/minor)
MAF of Asians (%)
MAF of Caucasians (%)
pa
11377 þ45 þ276
C4G T4G G4T
25.7 (95% CI ¼ 11.1–40.2) 29.5 (95% CI ¼ 23.1–35.9) 27.8 (95% CI ¼ 20.5–35.0)
27.0 (95% CI ¼ 22.0–32.0) 11.9 (95% CI ¼ 7.0–16.7) 26.8 (95% CI ¼ 21.4–32.2)
0.711 0.003 0.781
Note: aStudent’s t test for MAF between Asians and Caucasians.
Table 3. Stratified analyses of the ADIPOQ polymorphisms on DN risk. Odds ratio Subgroup by ethnicity
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11377C4G (G versus C) þ45T4G (G versus T) þ276G4T (T versus G)
Asians Caucasians Asians Caucasians Asians Caucasians
Studies (no. of cases/controls) 3 5 2 7 4 6
(468/437) (2606/3405) (287/488) (2367/7222) (473/804) (2339/7017)
OR[95% CI] 0.852 0.984 1.008 1.122 0.978 1.068
[0.684–1.063] [0.905–1.069] [0.802–1.266] [1.007–1.250] [0.817–1.170] [0.983–1.159]
Heterogeneity 2
POR
I (%)
PH
0.156 0.696 0.947 0.038 0.806 0.119
39.5 0.0 0.0 0.0 0.0 31.5
0.192 0.461 0.332 0.442 0.798 0.199
Notes: CI, confidence interval; PH, the P value of heterogeneity test.
Figure 3. Sensitivity analyses. Horizontal line mean effect size (a) 11391G4A, (b) 11377C4G, (c) þ45T4G, (d) þ276G4T.
was found in other studies.17,20,23 In view of inconsistent results, we performed a meta-analysis of these SNPs to derive a more precise estimation of the relationship between these SNPs and susceptibility to DN. In this meta-analysis, 14 articles (7 studies for 11391G4A, 8 studies for 11377C4G, 9 studies for
þ45T4G, and 10 studies for þ276G4T) were performed to provide the assessment of the relationship between ADIPOQ polymorphisms and DN. No obvious heterogeneity was found among the included studies. For ADIPOQ 11391G4A polymorphism, studies observed significant association with DN risk in the allele genetic comparison model. For the
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DOI: 10.3109/0886022X.2013.868319
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Figure 4. Funnel plot for assessing publication bias in this meta-analysis. (a) 11391G4A, (b) 11377C4G, (c) þ45T4G, (d) þ276G4T.
11377C4G, þ45T4G, or þ276G4T polymorphism, no significant association with DN was found. However, in the subgroup analysis of þ45T4G by ethnicity, the results demonstrated significant association was found among Caucasian populations, partly because there was evidence difference in the MAF of þ45T4G among the controls across Asians and Caucasians. Sensitivity analysis indicated that our results were statistically robust. Some possible limitations of this meta-analysis should be acknowledged in explaining the results. First, most included studies were retrospective in design. Second, publication bias might have occurred because only published studies were included and unpublished studies which had null results were missed, which might bias the results. Therefore, these biases could lead to an overestimation of the effects in this metaanalysis. Third, the studies involved were relatively less and small study samples. More and larger studies should be warranted in future to elucidate the role of the variant in DN susceptibility. Finally, DN is a multifactorial disease that results from complex interactions between many environmental and genetic factors. We only focused on ADIPOQ gene polymorphisms, and did not cover other DN susceptibility genes or polymorphisms. In conclusion, for the 11391G4A and þ45T4G polymorphisms, significant association with DN was found by the allele genetic comparison model in overall or subgroup analysis. However, our meta-analysis provided evidence that 11377C4G and þ276G4T polymorphisms were unlikely to be associated with genetic susceptibility of DN based on
the current published studies. To get a more exact conclusion on the effects of ADIPOQ polymorphisms on DN risk, future large and well-designed studies are necessary.
Declaration of interest The authors report no conflicts of interest. The authors alone are responsible for the content and writing of the paper.
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