Abstract
Background
Adiponectin (AdipoQ) is an adipose‐derived plasma protein that plays an important role in hepatic lipoprotein–lipid metabolism. Emerging evidence have shown that two common polymorphisms (T45 G and G276 T) in the AdipoQ gene may contribute to increasing susceptibility to nonalcoholic fatty liver disease (NAFLD); however individually published studies show inconclusive results. This meta‐analysis aimed to derive a more precise estimation of the association of AdipoQ T45 G (rs2241766 T>G) and G276 T (rs1501299 G>T) polymorphisms with NAFLD risk.
Method
Potential relevant studies were identified covering the following databases: PubMed, Embase, Web of Science, the Cochrane Library, Chinese National Knowledge Infrastructure (CNKI), Chinese Bio‐medicine Database (CBM), and Chinese Sci‐tech Journals databases. Statistical analyses were calculated using the version 12.0 STATA software (Stata Corp, College Station, TX, USA). Odds ratios (ORs) and its corresponding 95% confidence interval (CI) were calculated.
Result
Ten case–control studies were included with a total of 2,672 subjects, of these 1,117 being NAFLD patients and 1,555 being healthy controls. Our meta‐analysis results revealed that the T variant of AdipoQ rs2241766 T>G polymorphism may be associated with an increased risk of NAFLD. There was also a significant association between the G variant of AdipoQ rs1501299 G>T polymorphism and an increased risk of NAFLD. Country‐stratified analysis indicated that a higher AdipoQ rs2241766 T>G polymorphism was closely related with an increased risk of NAFLD in Chinese and Indian populations (all Ps < 0.05); a similar result was observed in Chinese populations between AdipoQ rs2241766 T>G polymorphism and an increased risk of NAFLD (P < 0.05).
Conclusion
In conclusion, the current meta‐analysis indicates that AdipoQ rs2241766 T>G and rs1501299 G>T polymorphisms may contribute to an increasing susceptibility to NAFLD. Moreover, this meta‐analysis also suggests for future larger studies with stratified case–control population, and greater focus on the gene–environment interactions regarding NAFLD susceptibility for valid studies.
Keywords: adiponectin, single‐nucleotide polymorphism, nonalcoholic fatty liver disease, meta‐analysis, meta‐regression
INTRODUCTION
Nonalcoholic fatty liver disease (NAFLD) is one of the most common causes of chronic liver disease, and its prevalence, worldwide, continues to increase with the growing obesity epidemic 1. Due to the dramatic increase in the prevalence of obesity, NAFLD is now receiving greater attention and is regarded as a major public health issue 2. The estimated prevalence of NAFLD in the obese population is more than 75%, while in the general population it is only 10–20% 3. NAFLD may be considered an additional feature of the Metabolic Syndrome with specific hepatic insulin resistance; it is closely related to obesity, hypoadiponectinemia, hyperinsulinemia, and type 2 diabetes (4, 5). Although it is generally agreed that NAFLD is a multifactorial disease induced by complex interactions between environmental and genetic factors, the exact cellular and molecular mechanisms leading to the development of NAFLD remain unclear (6, 7). In recent years, many studies have found that genetic influences play an important part in NAFLD etiology by altering hepatic lipid metabolism in a variety of different human tissues (8, 9).
Adiponectin (AdipoQ) is an adipose tissue specific plasma protein that plays a critical role in the regulation of glucose homeostasis and insulin sensitivity 10. It is now well understood that decreased plasma AdipoQ levels (hypoadiponectinemia) can be independently associated with the prevalence of NAFLD 11. Genetic and epigenetic changes in AdipoQ gene may reduce plasma AdipoQ levels and lead to dysregulation of hepatic lipid metabolism, thereby possibly explaining interindividual differences in NAFLD risk (12, 13). Therefore, it was hypothesized that polymorphisms in the AdipoQ gene could modulate individual differences in lipid metabolism and may contribute to an increasing susceptibility to NAFLD.
Human AdipoQ gene is located on chromosome 3q27 and consists of three exons and two introns, spanning approximately 17 kb 14. Although a large number of single‐nucleotide polymorphisms (SNPs) have been identified in the AdipoQ gene, T45 G (rs2241766 T>G) and G276 T (rs1501299 G>T) are the two most common polymorphisms that have been widely investigated 15, 16, 17. The AdipoQ T45 G polymorphism is a silent T to G substitution at codon 15 in intron 2, and G276 T results from a substitution of G to T in intron 2. Numerous studies have indicated that AdipoQ T45 G and G276 T polymorphisms may play critical roles in the pathogenesis of NAFLD 18, 19, 20, 21; however, there is still no direct evidence pointing to the possibility of these polymorphisms in increasing the risks of NAFLD 22. The inconsistent conclusions to link AdipoQ gene mutations with the risk of NAFLD may be due to the limitations in sample size in the corresponding investigations, creating inadequate statistical power in genetic studies of complex traits such as age, ethnicity, gender, the histological type, differentiation on tumor stage, and research methodology. Therefore, we performed a meta‐analysis of all eligible case–control studies to reveal a more precise relationship between AdipoQ T45 G and G276 T polymorphisms, and susceptibility to NAFLD.
MATERIALS AND METHODS
Literature Search Strategy
A comprehensive search for relevant studies was conducted on PubMed, Embase, Web of Science, the Cochrane Library, Chinese National Knowledge Infrastructure (CNKI), Chinese Bio‐medicine Database (CBM), and Chinese Sci‐tech Journals Database (VIP) databases from their inception through March 1, 2013, using the following terms: “genetic polymorphism” or “single‐nucleotide polymorphism” or “polymorphism” or “SNP” or “mutation” or “variation” or “variant”, “non‐alcoholic fatty liver disease” or “nonalcoholic steatohepatitis" or “NAFLD” or “fatty liver”, and “adiponectin” or “AdipoQ” or “apM‐1” or “adipose most abundant gene transcript 1.” There were no language restrictions. The references used in eligible articles or textbooks were also reviewed to find other potential studies. Any disagreements were resolved by discussions and consensus.
Inclusion and Exclusion Criteria
Studies included in our meta‐analysis have to meet the following criteria: 1 case–control studies focused on the association between AdipoQ T45 G and G276 T polymorphisms and NAFLD risk, 2 all patients should meet the diagnostic criteria for NAFLD, 3 the minimum number of cases in included studies should be greater than 30, 4 the genotype distribution of the controls should conform to the Hardy–Weinberg equilibrium (HWE), 5 published data about the allele and genotype frequencies of SNPs must be sufficient. Studies were excluded if they were 1 not a case–control study; 2 case–control studies with a small sample size (<30) or without healthy controls; 3 duplicate publications of data from the same study; 3 based on incomplete data; 4 a deviation from HWE in the genotype frequencies of the controls; 5 meta‐analyses, letters, reviews, or editorial articles. If more than one study by the same author using the same case series was published, either the study with the largest sample size or the most recent publication was included.
Data Extraction
Data from the published studies were extracted independently by two authors into a standardized form. For each study, the following characteristics and numbers were collected: the first author, year of publication, country, language, study design, number of subjects, source of cases and controls, detecting sample, genotype method, allele and genotype frequencies, and evidence of HWE in controls. In cases of conflicting evaluations, disagreements on inconsistent data from the eligible studies were resolved through discussions and careful reexamination of the full text by the authors.
Quality Assessment
Two authors independently assessed the quality of the included studies according to the modified Strengthening the Reporting of Observational Studies in Epidemiology (STROBE) quality score systems 23. Forty assessment items related to quality appraisal were used in this meta‐analysis with scores ranging from 0 to 40. On the basis of their scores, the included studies were classified into three levels: low quality (0–19), moderate quality 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, and high quality (30–40). Disagreements on STROBE scores of the included studies were resolved through a comprehensive reassessment by the authors.
Statistical Analysis
Crude odds ratios (ORs) with 95% confidence intervals (CIs) were calculated under five genetic models: the allele model (mutant [M] allele vs. wild [W] allele), dominant model (WM + MM vs. WW), recessive model (MM vs. WW + WM), homozygous model (MM vs. WW), and heterozygous model (MM vs. WM). Genotype frequencies of controls were tested for HWE using the χ2 test for each study included in the meta‐analysis. The statistical significance of the pooled OR was examined using the Z‐test. Between‐study heterogeneity was estimated using Cochran's Q‐statistic, whereas a P < 0.05 was set to identify heterogeneity in the associations 24. We also quantified the effects of heterogeneity by using the I2 test (ranges from 0 to 100%), which represents the proportion of interstudy variability that can be contributed to heterogeneity rather than to chance 25. When a significant Q‐test with P < 0.05 or I 2 > 50% indicated existence of heterogeneity among studies existed, the random effects model (DerSimonian–Laird method) was conducted for the meta‐analysis; otherwise, the fixed effects model (Mantel–Haenszel method) was used. To explore potential sources of heterogeneity, subgroup analysis was performed by country, source of controls, and genotype method. Univariate and multivariable regression analyses were also performed to identify variable explained heterogeneity of the associations 26. Sensitivity analysis was performed by omitting each study in turn to assess the quality and consistency of the results. Begger's funnel plots and Egger's linear regression test were used to evaluate the publication bias 27. Two‐sided P < 0.05 was considered to be statistically significant. All calculations were performed using the STATA version 12.0 software (STATA Corporation, College Station, TX).
RESULTS
Characteristics of Included Studies
In accordance with the inclusion criteria, ten case–control studies were included in this meta‐analysis and 33 were excluded (18–22, 28–32). After screening the title and key words, 19 of these articles were excluded. Abstracts and full text from 24 articles were reviewed and an additional 12 trials were excluded. Of these, two were further excluded for incomplete data reported. The flow chart of the study selection process is shown in Fig. 1. A total of 2,672 subjects were involved in this meta‐analysis, of which 1,117 were NAFLD patients and 1,555 were healthy controls. The publication years of the involved studies ranged from 2005 to 2012 (Fig. 2). Seven studies were conducted in China, one in Japan, one in Italy, and the other in India. Overall, except for one study performed among Caucasians, the remaining nine studies were all among Asian populations. Five studies used population‐based (community populations) controls, while the other five studies used hospital‐based controls. The classical polymerase chain reaction‐restriction fragment length polymorphism (PCR‐RFLP) method was performed in seven studies, one study used the TaqMan method, one allele‐specific PCR (AS‐PCR), and the other direct sequencing method. The HWE test was conducted to evaluate the genotype distribution of the controls in all included studies. Each study did not deviate from the HWE (all Ps > 0.05). All quality scores of included studies were higher than 20 (moderate–high quality). The characteristics and methodological quality of the included studies are summarized in Table 1.
Figure 1.
Flow chart shows study‐selection procedure. Ten case–control studies were included.
Figure 2.
The distribution of the number of topic‐related literature in the electronic database over the last decade.
Table 1.
Main Characteristics and Methodological Quality of All Eligible Studies
| Number | Gender (M/F) | Age (years) | Source | ||||||||||
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| First author | Year | Country | Case | Control | Case | Control | Case | Control | Case | Control | Genotype method | SNP ID | STROBE score |
| Shi et al. 19 | 2005 | China | 107 | 112 | 50/57 | 57/55 | 57.7 ± 5.3 | 57.7 ± 5.3 | PB | PB | PCR–RFLP | rs2241766 (T>G) | 23 |
| Huang et al. 24 | 2010 | China | 185 | 322 | 36/149 | 88/234 | 50.6 ± 12.2 | 46.2 ± 17.6 | PB | PB | PCR–RFLP | rs2241766 (T>G) | 29 |
| Fan et al. 17 | 2011 | China | 100 | 100 | – | – | – | – | HB | HB | PCR–RFLP | rs1501299 (G>T) | 26 |
| Zhang et al. 22 | 2012 | China | 119 | 350 | 42/77 | 112/238 | 59.4 ± 10.9 | 50.5 ± 17.3 | PB | PB | PCR–RFLP | rs1501299 (G>T) | 25 |
| Wang et al. 25 | 2008 | China | 165 | 160 | 80/85 | 77/83 | 46.4 ± 14.0 | 46.8 ± 15.4 | HB | HB | PCR–RFLP | rs2241766 (T>G) | 31 |
| rs1501299 (G>T) | |||||||||||||
| Wong et al. 21 | 2008 | China | 79 | 40 | 52/27 | 17/23 | 44.0 ± 7.0 | 44.0 ± 6.0 | HB | HB | TaqMan | rs2241766 (T>G) | 33 |
| rs1501299 (G>T) | |||||||||||||
| Tokushige et al. 20 | 2009 | Japan | 119 | 115 | 65/54 | 58/57 | 50.3 ± 17.8 | 47.5 ± 18.7 | PB | PB | AS–PCR | rs2241766 (T>G) | 29 |
| rs1501299 (G>T) | |||||||||||||
| Zhou et al. 23 | 2010 | China | 106 | 106 | 53/53 | 53/53 | 57.8 ± 12.3 | 54.9 ± 12.1 | PB | PB | PCR–RFLP | rs2241766 (T>G) | 30 |
| rs1501299 (G>T) | |||||||||||||
| Gupta et al. 18 | 2012 | India | 137 | 250 | – | – | – | – | HB | HB | Direct sequencing | rs2241766 (T>G) | 32 |
M, male; F, female; PB, population‐based; HB, hospital‐based; AS, allele‐specific; PCR, polymerase chain reaction; RFLP, restriction fragment length polymorphism; SNP, single nucleotide polymorphism.
Meta‐Analysis Results
The association between AdipoQ rs2241766 T>G and rs1501299 G>T polymorphisms and NAFLD risk were discussed in eight studies. The heterogeneity did not exist (P < 0.05), so the fixed effects model was conducted to pool these results. The meta‐analysis revealed that the rs2241766 T>G and rs1501299 G>T polymorphisms may increase the risk of NAFLD under both of the allele models—dominant and homozygous (all Ps < 0.05, Fig. 3). Country‐stratified analysis indicated that a higher AdipoQ rs2241766 T>G polymorphism was closely related to an increased risk of NAFLD in Chinese and Indian populations (all Ps < 0.05). A similar result was observed in Chinese populations for AdipoQ rs1501299 G>T polymorphism and an increased risk of NAFLD (P < 0.05). This, however, was not observed in Japanese populations (all Ps > 0.05). Subgroup analysis by the source of control indicated that there were significant associations between the rs2241766 T>G polymorphism and an increased risk of NAFLD in the population‐based subgroup and the hospital‐based subgroup in majority groups, and the rs1501299 G > T polymorphism may increase the risk of NAFLD in population‐based subgroups (Fig. 4). Further subgroup analyses based on genotyping method suggested that the rs2241766 T>G and rs1501299 G>T polymorphisms were associated with an increased risk of NAFLD in PCR‐RFLP subgroup in majority groups (Table 2).
Figure 3.
Forest plot of the association between AdipoQ T45 G and G276 T polymorphisms and susceptibility to NAFLD under the allele and dominant models.
Figure 4.
Subgroup analysis by source of controls and genotyping method for the association between AdipoQ T45 G and G276 T polymorphisms and susceptibility to NAFLD under the allele and dominant models.
Table 2.
Meta‐Analysis of the Association Between AdipoQ T45G and G276T Polymorphisms With NonAFLD
| W allele vs. M allele | WW + WM vs. MM | WW vs. WM + MM | WW vs. MM | WW vs. WM | |||||||||||
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| (allele model) | (dominant model) | (recessive model) | (homozygous model) | (heterozygous model) | |||||||||||
| Subgroups | OR | [95%CI] | P | OR | [95%CI] | P | OR | [95%CI] | P | OR | [95%CI] | P | OR | [95%CI] | P |
| T45G (rs2241766 T>G) | |||||||||||||||
| Overall | 1.33 | [1.12, 1.58] | 0.001 | 1.84 | [1.22, 2.76] | 0.003 | 1.19 | [1.00, 1.43] | 0.057 | 1.71 | [1.24, 2.37] | 0.001 | 1.15 | [0.94, 1.40] | 0.170 |
| Country | |||||||||||||||
| China | 1.50 | [1.26, 1.78] | <0.001 | 2.01 | [1.40, 2.89] | <0.001 | 1.48 | [1.19, 1.84] | <0.001 | 2.45 | [1.67, 3.59] | <0.001 | 1.39 | [1.09, 1.77] | 0.008 |
| Japan | 0.86 | [0.58, 1.27] | 0.454 | 0.95 | [0.41, 2.18] | 0.904 | 0.79 | [0.47, 1.32] | 0.365 | 0.85 | [0.36, 2.01] | 0.703 | 0.77 | [0.45, 1.34] | 0.358 |
| India | 0.64 | [0.44, 0.94] | 0.022 | 0.20 | [0.05, 0.75] | 0.017 | 0.68 | [0.44, 1.05] | 0.083 | 0.18 | [0.05, 0.70] | 0.013 | 0.77 | [0.49, 1.21] | 0.255 |
| Source of controls | |||||||||||||||
| Population‐based | 1.36 | [1.04, 1.77] | 0.024 | 1.72 | [0.87, 3.41] | 0.120 | 1.58 | [1.25, 2.00] | <0.001 | 2.15 | [1.48, 3.13] | <0.001 | 1.47 | [1.14, 1.89] | 0.003 |
| Hospital‐based | 1.29 | [1.00, 1.67] | 0.001 | 1.95 | [1.18, 3.22] | 0.009 | 0.79 | [0.59, 1.06] | 0.110 | 0.75 | [0.37, 1.51] | 0.416 | 0.77 | [0.56, 1.06] | 0.112 |
| Genotype methods | |||||||||||||||
| PCR‐RFLP | 1.45 | [1.13, 1.87] | 0.003 | 1.71 | [0.91, 3.22] | 0.095 | 1.53 | [1.22, 1.92] | <0.001 | 2.45 | [1.67, 3.59] | <0.001 | 1.39 | [1.09, 1.77] | 0.008 |
| Others | 1.14 | [0.90, 1.44] | 0.282 | 2.03 | [1.16, 3.54] | 0.013 | 0.76 | [0.56, 1.04] | 0.084 | 0.52 | [0.26, 1.06] | 0.072 | 0.77 | [0.54, 1.09] | 0.144 |
| G276T (rs1501299 G>T) | |||||||||||||||
| Overall | 1.27 | [1.10, 1.48] | 0.002 | 1.76 | [1.18, 2.62] | 0.006 | 1.26 | [0.99, 1.61] | 0.061 | 2.01 | [1.26, 3.20] | 0.003 | 1.20 | [0.91, 1.57] | 0.194 |
| Country | |||||||||||||||
| China | 1.11 | [0.92, 1.33] | 0.269 | 1.65 | [1.04, 2.63] | 0.034 | 1.27 | [0.96, 1.67] | 0.093 | 1.92 | [1.16, 3.18] | 0.011 | 1.22 | [0.89, 1.67] | 0.218 |
| Japan | 1.30 | [0.86, 1.97] | 0.222 | 2.42 | [0.72, 8.09] | 0.151 | 1.25 | [0.74, 2.09] | 0.402 | 2.56 | [0.75, 8.73] | 0.135 | 1.14 | [0.67, 1.94] | 0.641 |
| Source of controls | |||||||||||||||
| Population‐based | 1.36 | [1.04, 1.79] | 0.024 | 1.75 | [1.01, 3.03] | 0.047 | 1.56 | [1.07, 2.27] | 0.021 | 2.56 | [1.25, 5.26] | 0.010 | 1.40 | [0.94, 2.09] | 0.098 |
| Hospital‐based | 1.21 | [0.99, 1.51] | 0.068 | 1.81 | [0.91, 3.57] | 0.090 | 1.08 | [0.78, 1.49] | 0.634 | 1.67 | [0.91, 3.09] | 0.101 | 1.05 | [0.72, 1.51] | 0.814 |
| Genotype methods | |||||||||||||||
| PCR‐RFLP | 1.28 | [1.06, 1.55] | 0.011 | 1.86 | [1.12, 3.09] | 0.016 | 1.33 | [0.99, 1.79] | 0.062 | 1.92 | [1.16, 3.18] | 0.011 | 1.22 | [0.89, 1.67] | 0.218 |
| Others | 1.30 | [0.93, 1.82] | 0.127 | 1.47 | [0.67, 3.23] | 0.032 | 1.14 | [0.74, 1.75] | 0.552 | 2.56 | [0.75, 8.73] | 0.135 | 1.14 | [0.67, 1.94] | 0.641 |
WW, wild homozygote; WM, heterozygote; MM, mutant homozygote; OR, odds ratios; 95% CI, 95% confidence interval; PCR, polymerase chain reaction; RFLP, restriction fragment length polymorphism.
Meta‐Regression and Sensitivity Analyses
Univariate and multivariate meta‐regression analyses were conducted for AdipoQ rs2241766 T>G and rs1501299 G>T polymorphisms to explore possible sources of heterogeneity among studies. The results showed that none of the potential factors may explain sources of heterogeneity (all Ps > 0.05) (Table 3). Sensitivity analysis findings suggested that country of origin could influence the pooled estimates of rs1501299 G>T polymorphisms (Fig. 5), indicating a statistically robust result. Funnel plot and Egger's linear regression test were performed to assess the publication biases of included studies. The shapes of the funnel plots did not reveal any evidence of obvious asymmetry under the allele and dominant models of AdipoQ rs2241766 T>G and rs1501299 G> T polymorphisms (Fig. 6). Egger's test also did not display strong statistical evidence of publication bias (all Ps > 0.05).
Table 3.
Univariate and Multivariate Meta‐Regression Analyses of Potential Source of Heterogeneity
| T (rs2241766 T>G) | G276T (rs1501299 G>T) | |||||||
|---|---|---|---|---|---|---|---|---|
| Heterogeneity factors | β [95%CI] | SE | Z | P | β [95%CI] | SE | Z | P |
| Publication year | ||||||||
| Univariate | −0.239 [‐.596, 0.116] | 0.182 | −1.32 | 0.188 | −0.030 [−0.395, 0.336] | 0.186 | −0.16 | 0.874 |
| Multivariate | −0.137 [−0.618, 0.343] | 0.245 | −0.56 | 0.575 | −0.207 [−0.527, 0.113] | 0.163 | −1.27 | 0.204 |
| Country | ||||||||
| Univariate | 1.221 [−0.308, 2.752] | 0.781 | 1.57 | 0.118 | −0.384 [−1.678, 0.909] | 0.660 | −0.58 | 0.560 |
| Multivariate | 0.872 [−1.310, 3.053] | 1.113 | 0.78 | 0.434 | −0.890 [−1.662, −0.118] | 0.394 | −2.26 | 0.024 |
| Source of control | ||||||||
| Univariate | 0.773 [−1.028, 2.574] | 0.919 | 0.84 | 0.400 | −0.370 [−1.262, 0.522] | 0.455 | −0.81 | 0.416 |
| Multivariate | 0.416 [−1.621, 2.453] | 1.040 | 0.40 | 0.689 | −0.642 [−1.424, 0.139] | 0.399 | −1.61 | 0.107 |
| Genotype method | ||||||||
| Univariate | 1.221 [−0.308, 2.752] | 0.781 | 1.57 | 0.118 | −0.384 [−1.678, 0.909] | 0.660 | −0.58 | 0.560 |
| Multivariate | 0.872 [−1.310, 3.053] | 1.113 | 0.78 | 0.434 | −0.367 [−1.150, 0.417] | 0.400 | 0.92 | 0.359 |
SE, standard error; 95% CI, 95% confidence interval.
Figure 5.
Sensitivity analysis for the associations between AdipoQ T45 G and G276 T polymorphisms and susceptibility to NAFLD under the dominant model. Results were computed by omitting each study in turn. Meta‐analysis random‐effects estimates (exponential form) were used. The two ends of the dotted lines represent the 95% CI.
Figure 6.
Begger's funnel plot of the association between AdipoQ T45 G and G276 T polymorphisms and susceptibility to NAFLD under the allele model and dominant mode.
DISCUSSION
We performed this current meta‐analysis to evaluate whether AdipoQ T45 G and G276 T genetic polymorphisms contribute to susceptibility of NAFLD. The findings of our meta‐analysis indicated that genetic polymorphisms in the AdipoQ gene were significantly correlated with an increased risk of NAFLD under the allele and dominant models, especially for rs2241766 T>G and rs1501299 G>T genetic polymorphisms, revealing that AdipoQ genetic variants may serve as a crucial predictor in the development and progression of NAFLD. We hypothesized that AdipoQ T45 G and G276 T gene variants may be associated with changes in hepatic lipid metabolism and result in low plasma AdipoQ levels (17, 21). It has been reported that AdipoQ was identified as an adipose tissue specific plasma protein that is involved in hepatic lipid and glucose metabolism and plays an important role in the development of metabolic syndrome 33. As one of the most common forms of chronic liver disease, NAFLD is closely related to hereditary susceptibility, obesity, insulin resistance, and the metabolic syndrome 34. Due to its antiatherogenic, antiinflammatory, and antiinsulin resistance properties, AdipoQ has been demonstrated to have a protective effect against the development of various obesity‐related metabolic diseases 35. In addition, some documents have reported that low serum AdipoQ concentrations were associated with low insulin resistance and may be a risk factor for NAFLD, which has been implicated as a convenient biomarker for identifying the subjects with the metabolic syndrome (36, 37). Although the fundamental molecular pathways of NAFLD remained undefined, it was believed that genetic variations of the AdipoQ gene could contribute to an individual's susceptibility to NAFLD 19. Common functional polymorphisms in the human AdipoQ gene may result in a decrease of its protein level and dysregulation of hepatic lipid metabolism, thus contributing to the occurrence and development of NAFLD 20. Furthermore, Giovanni Musso et al. have also revealed that the AdipoQ T45 G and G276 T genetic polymorphisms may functionally transform its transcription and contribute to the increased risk of NAFLD, and may be closely associated with the severity of liver disease 30.
Furthermore, we conducted stratified analysis based on country to investigate the relationship between AdipoQ T45 G and G276 T genetic variations on the pathogenesis of NAFLD. The results of our meta‐analysis showed that AdipoQ rs2241766 T>G polymorphism were closely related to an increased risk of NAFLD in Chinese and Indian populations; similar results were observed in Chinese populations between AdipoQ rs2241766 T> G polymorphism and increased risk of NAFLD.
Some limitations of this meta‐analysis should be acknowledged. First, there were only ten articles included in the present meta‐analysis, so the sample size was relatively small and may not provide sufficient statistical power to estimate the correlation between AdipoQ T45 G and G276 T polymorphisms and susceptibility to NAFLD. Therefore, more studies with larger sample sizes are still needed to accurately provide a more representative statistical analysis. Second, as a type of a retrospective study, a meta‐analysis may encounter recall or selection bias and may possibly influence the reliability of our study results 38. Finally, our lack of access to the original data from the studies limited further evaluations of the potential interactions of other factors (insulin resistance and family history of type 2 diabetes) with susceptibility to NAFLD, such as gene–environment and gene–gene interactions.
In conclusion, our meta‐analysis suggests that AdipoQ T45 G and G276 T polymorphisms may contribute to increasing susceptibility to NAFLD, especially among the general population. These relationships have the potential to provide functional profiling of the AdipoQ gene involved in hepatic lipid metabolism and to help us understand the biological processes associated with the development of NAFLD. Considering the limitations mentioned above, detailed studies are needed to confirm our findings. Further studies investigating the effect of gene–environment interactions on NAFLD susceptibility are also essential.
ACKNOWLEDGMENTS
The authors acknowledge the reviewers for their helpful comments on this article.
Grant sponsor: Chinese Foundation for Hepatitis Prevention and Control “Tian Qing” Liver Research Fund; Grant number: 20120101.
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