Disease Markers
Volume 2019 (2019), Article ID 2970401, 10 pages
https://doi.org/10.1155/2019/2970401

Review Article

Systematic Meta-analysis Revealed an Association of PGC-1α rs8192678 Polymorphism in Type 2 Diabetes Mellitus

Wanning Xia,¹ Nanxing Chen,¹ Wenjia Peng,¹ Xianjie Jia,¹ Ying Yu,² Xuesen Wu,¹ and Huaiquan Gao¹

¹School of Public Health, Bengbu Medical University, Bengbu, Anhui 233030, China
²Department of Physiology, Bengbu Medical University, Bengbu, Anhui 233030, China

Correspondence should be addressed to Xuesen Wu; xuesenwu@163.com and Huaiquan Gao; ghq6502@163.com

Received 17 October 2018; Revised 4 January 2019; Accepted 22 January 2019; Published 3 March 2019

Academic Editor: Roberta Rizzo

Copyright © 2019 Wanning Xia et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Abstract.

Background. Genome-wide association study (GWAS) provides an unprecedented opportunity to reveal substantial genetic contribution to type 2 diabetes mellitus (T2DM) and glycemic identification of allelic heterogeneity and population-specific genetic variants, yet it also faces difficulty due to the vast amount of potential confounding factors and limited availability of clinical data. To identify responsible susceptibility loci and genomic polymorphism for T2DM and glycemic traits, we have systematically investigated a genome-wide association study related to T2DM. Although GWAS has captured many common genetic variations, which are related to T2DM, each risk allele (RA) of single-nucleotide polymorphisms (SNPs) at these loci is not conclusive. Therefore, it is common to present a combination of several SNPs to infer T2DM risk, yet it is still insufficient to be deterministic. To streamline the identification of a deterministic genetic variation in T2DM, we developed this meta-analysis as a showcase to comprehensively identify the association between cumulative RAs and T2DM risk by combining different studies in reported literature and databases. After all, we identified that PGC-1α rs8192678 polymorphism can be considered as a potentially deterministic biomarker in T2DM risk. Previous studies have potentially linked PGC-1α rs8192678 polymorphism to type 2 diabetes mellitus (T2DM) risk, but the results remain inconsistent in different populations and are not conclusive. We developed a new meta-analysis approach to systematically identify the association between PGC-1α rs8192678 polymorphism and T2DM, and we have comprehensively assessed different ethnic groups to validate our findings. Methods. We performed comprehensive information retrieval and knowledge discovery meta-analysis by searching extensively published literature and different electronic databases to acquire eligible studies for the above association study. We developed a method to use pooled odds ratios (ORs) and their corresponding 95% confidence intervals (CIs) in five genetic models (allelic, dominant, recessive, homozygous, and heterozygous genetic models) to identify the relationship among ethnicity subgroup analyses comprehensively. Results. We identified 20 eligible studies consisting of 16,182 subjects (8,038 cases and 8,144 controls) in our meta-analysis. PGC-1α rs8192678 polymorphisms of all subjects showed a significant association with T2DM susceptibility under all genetic models: allelic (OR: 1.24, 95% CI: 1.13-1.35), dominant (OR: 1.27, 95% CI: 1.14-1.42), recessive (OR: 1.24, 95% CI: 1.14-1.36), homozygous (OR: 1.40, 95% CI: 1.20-1.64), and heterozygous (OR: 1.20, 95% CI: 1.06-1.35). In the subgroup analysis, we identified a significant association between PGC-1α rs8192678 polymorphism and T2DM in the Caucasian and Indian populations under all genetic models we investigated. This is the most comprehensive study of the subject to date. Conclusion. Our development of meta-analysis revealed that the minor allele (A) carriers, especially AA genotype carriers, can lead to risk of T2DM in the Caucasian and Indian populations. This is the first report that such risk has been confirmed. Our finding shed new light into the genetic alteration in T2DM.

1. Introduction

T2DM is a significant global health issue with potential life-threatening complications if not controlled well. Over 451 million people worldwide were estimated to live with diabetes according to the International Diabetes Federation [1]. T2DM continues to rise worldwide and is projected to rise to 693 million by 2045. T2DM is the most prevalent type of diabetes, accounting for around 90% of all types of diabetes. T2DM is characterized by hyperglycemia caused by impaired insulin secretion and insulin resistance. Hyperglycemia, if not controlled well, can eventually cause severe damages to various human organs, leading to serious complications, such as cardiovascular dysfunction, neuropathological damages, kidney failure, retinopathological changes, and even blindness. Although the etiology for diabetes remains unclear, both environmental and genetic factors are known to be related to the development of T2DM [2, 3]. A large number of susceptibility genes have been reported via a genome-wide association study (GWAS). Due to differences in race and region, most susceptible genes only have a weak effect on the risk of T2DM and have not been replicated by a large population.

After comprehensive investigations on T2DM, we found that peroxisome proliferator-activated receptor γ coactivator-1α (PGC-1α) is crucially important to T2DM. PGC-1α is a multifunctional regulatory factor originally identified as a coactivator of PPARγ in 1998 [4]. PGC-1α, located on chromosome 4p15.1, is highly expressed in the liver and skeletal muscle and is involved in maintaining glucose, lipid, and energy homeostasis. PGC-1α, as a crucial gene regulatory element in various metabolic processes, has been shown to play pivotal roles in the development of obesity, insulin resistance, and T2DM [5–7]. In the previous studies from different countries, the association of several single-nucleotide polymorphisms (SNPs) of PGC-1α, such as rs8192678G>A, rs2970847C>T, rs3736265G>A, and rs3755863 C>T, with risk of T2DM were studied [8–11], while an A allele (mutant type) of the rs8192678 G>A polymorphism in PGC-1α gene has been speculated in T2DM risk in some studies [12–14]; such possible link was not identified from other studies [15]. This discrepancy may be due to the relatively small sample size and differences among ethnicities. It is important that we can develop a meta-analysis approach to overcome limitations and disadvantages of previous studies by combining various studies and including ethnicity for reaching a conclusive validation.

In this study, the association of PGC-1α rs8192678 (G>A) polymorphism and T2DM was extensively investigated more comprehensively by combining relevant studies. In addition, we combined ethnicity in all eligible studies to elucidate the explicit association of PGC-1α rs8192678 (G>A) polymorphism and T2DM.

2. Methods

2.1. Strategy of Literature Search and Database Utilization

The electronic databases, both in English and Chinese, including PubMed, Springer, Chinese National Knowledge Infrastructure, SinoMed, and Wanfang were searched for collecting relevant literatures published from January 2001 to May 2018. For all databases, we expanded broad search terms such as “PPARGC1A,” “PGC-1alpha”, “T2DM,” “diabetic,” “insulin,” “hyperglycemia,” “polymorphism,” and “mutation” for more comprehensive information harvest. We do not set up any other filter to ensure all relevant information will be included in our investigations.

2.1.1. Selection Criteria

To ensure the comprehensiveness and integration of information retrieval, we imposed the following criteria for all relevant literatures:

(1)Studies published in peer-reviewed journals(2)Case-control studies referring to the association between PGC-1α polymorphism and T2DM risk(3)Cases and controls with allele and genotype frequencies(4)Genetic variants of controls that met the Hardy-Weinberg equilibrium (HWE, ).

The following studies were excluded:

(1)Studies without sufficient data(2)Non-case-control studies(3)Studies based on family(4)Redundant studies of duplicated data

2.2. Data Extraction

Two researchers (Wan-ning Xia, Nan-xing Chen) independently reviewed full texts to carefully select eligible papers and extracted all information, including the first author, year of publication, ethnicity of subjects, age, gender, BMI, diagnostic criteria, sample size, molecular methods for genotype testing, and allele and genotype distribution in cases and controls. The third researcher (Wen-jia Peng) reviewed, cross-verified, and then finalized the carefully selected literature. Any disagreements were resolved by all 3 researchers through careful reexaminations altogether. If similar data were reported more than two times in different papers, only one was adopted in order to eliminate any bias. The genotype frequencies of controls in all included literatures were verified by HWE.

2.3. Quality Assessment

The Newcastle-Ottawa Scale (NOS) [16] tool was used to appraise the quality of studies by at least two researchers independently to cross-validate the result. This scale consists of three categories (Selection, Comparability, and Exposure) with eight items in total. A study can be awarded a maximum of one star for each numbered item within the Selection and Exposure categories. A maximum of two stars can be given for Comparability. Item scores range from 0 to 9 stars, and an overall score of more than 6 stars was regarded as high quality [17] .

2.4. Statistical Analyses

All statistical analyses were performed using STATA version 12.0 and RevMan v5.3 (the Cochrane Collaboration) software. The G allele is wild, and the A allele is mutated for the rs8192678 G>A polymorphism in the PGC-1α gene. The pooled odds ratio (OR) with a 95% confidence interval (95% CI) was calculated in different genetic models, allelic (A vs. G), dominant (AG + AA vs. GG), recessive (AA vs. AG + GG), homozygous (AA vs. GG), and heterozygous (AG vs. GG) genetic models. Subgroup analysis by ethnicity was also conducted. Heterogeneity across studies was assessed via a chi-square test and . The fixed effects model was used for no heterogeneity (, ), whereas the random effects model was selected. The HWE was examined via the chi-square test. A funnel plot was used to detect potential publication bias, and the funnel plot symmetry was evaluated using Egger’s linear regression testing on the OR. If publication bias was indicated, we further evaluated the number of missing studies by the Trim and Fill method and recalculated the pooled risk estimation with the addition of those missing studies [18].

3. Results

3.1. Characteristics of the Studies

After duplicates were removed, 68 papers of potentially relevant studies were included initially. Then, 32 papers were excluded by reading abstracts carefully. Among those remaining relevant studies, 16 papers (3 studies based on family, 3 non-case-control studies, 5 studies with insufficient data, 1 duplicate data study, and 4 studies not meting HWE) were also excluded after reading the full texts (Figure 1). Thus, 20 papers consisting of 16,182 subjects (8,038 cases and 8,144 controls) were finally included in this meta-analysis. The characteristics of included studies are listed in Table 1. 10 studies were undertaken in an East Asian population, 7 studies were in a Caucasian population, 2 studies were in an Indian population, and 1 study was in African people. Quality scores ranged from 4 to 8 stars with a median value of 6.48 (Table 2).

Figure 1: The flow chart of literature search and selection.

Table 1: Characteristics of the included studies about the association between PGC-1αrs8192678 polymorphism and T2DM.

Author
Year
Ethnicity
T2DM
Control
Matching variables
Chi-square values of HWE
value

GG
GA
AA
Total
GG
GA
AA
Total

Chen et al. [26]
2004
Chinese
155
255
84
494
185
264
106
555
Age, gender
0.466
0.495

De-yao et al. [27]
2014
Chinese
165
175
90
430
190
181
60
431
Age, gender
2.492
0.115

Hui et al. [28]
2009
Chinese
71
28
41
140
47
30
11
88
Gender, BMI
1.276
0.259

Kim et al. [14]
2005
Korean
251
355
152
762
88
163
51
303
Gender
2.779
0.096

Lu et al. [29]
2006
Chinese
44
55
21
120
54
41
11
106
—
0.581
0.446

Sun et al. [30]
2006
Chinese
122
190
78
390
181
256
88
525
Age, gender, BMI
0.025
0.876

Weng et al. [13]
2010
Chinese
91
129
56
276
340
502
207
1049
Age, gender, BMI
0.78
0.377

Zhang et al. [31]
2007
Chinese
97
121
45
263
144
111
27
282
Age, BMI
0.679
0.41

Zhu et al. [8]
2017
Chinese
138
251
108
502
250
382
150
784
Age, gender
0.036
0.85

Zhu et al. [32]
2009
Chinese
181
303
111
595
143
240
112
495
Gender, BMI
0.347
0.556

Ek et al. (a) [19]
2001
Caucasian
186
200
68
454
97
80
21
198
—
0.541
0.462

Ek et al. (b) [19]
2001
Caucasian
76
97
28
201
146
116
31
293
—
1.2
0.273

Fanelli et al. [33]
2005
Caucasian
51
56
18
125
86
96
41
223
Gender
2.431
0.126

Jemaa et al. [34]
2015
Caucasian
166
231
90
478
176
170
56
402
Age, gender
2.055
0.152

Kruzliak et al. [12]
2015
Caucasian
80
334
467
881
40
147
161
348
Age, gender
0.529
0.467

Kunej et al. [35]
2004
Caucasian
141
129
35
305
114
111
15
240
Age, gender, BMI
3.156
0.076

Lacquemant et al. [15]
2002
Caucasian
129
137
44
310
163
159
47
369
—
0.705
0.401

Shokouhi et al. [9]
2015
Caucasian
127
43
3
173
159
13
1
173
Age
1.531
0.216

Bhat et al. (a) [11]
2007
Indian
68
103
28
199
112
80
21
213
BMI
1.401
0.237

Bhat et al. (b) [11]
2007
Indian
69
70
13
152
143
96
19
258
BMI
0.269
0.604

Sharma et al. [25]
2018
Indian
220
254
80
554
258
249
64
571
Age, sex
0.112
0.737

Cheema et al. (a) [36]
2015
African
92
4
14
110
102
14
0
116
Gender, BMI
0.478
0.489

Cheema et al. (b) [36]
2015
African
103
19
2
124
104
18
0
122
Gender
0.774
0.379

HWE: Hardy-Weinberg equilibrium; value: value of HWE.

Table 2: Results of quality assessment by NOS.

Study ID
Year
Selection
Comparability
Exposure
Score

Adequate definition of cases
Representativeness of cases
Selection of controls
Definition of controls
Control for important factors
Ascertainment of exposure
Same method of ascertainment for cases and controls
Non-response rate

Bhat et al. (a)
2007
☆
☆
—
☆
☆
☆
☆
☆
7

Bhat et al. (b)
2007
☆
☆
—
☆
☆
☆
☆
☆
7

Cheema et al. (a)
2015
☆
☆
—
☆
☆
☆
☆
☆
7

Cheema et al. (b)
2015
☆
☆
—
☆
—
☆
☆
☆
6

Chen et al.
2004
☆
☆
☆
☆
☆
☆
☆
☆
8

De-yao et al.
2014
—
☆
☆
☆
☆
☆
☆
☆
7

Ek et al. (a)
2001
☆
—
☆
☆
—
☆
☆
☆
6

Ek et al. (b)
2001
☆
—
☆
☆
—
☆
☆
☆
6

Fanelli et al.
2005
☆
☆
—
☆
—
—
☆
—
4

Hui et al.
2009
—
☆
—
☆
☆
☆
☆
☆
6

Jemaa et al.
2015
—
☆
☆
☆
☆
☆
☆
☆
7

Kim et al.
2005
☆
☆
—
☆
—
☆
☆
☆
6

Kruzliak et al.
2015
—
☆
☆
☆
☆
☆
☆
☆
7

Kunej et al.
2004
—
☆
—
—
☆☆
—
☆
☆
5

Lacquemant et al.
2002
—
—
☆
☆
—
☆
☆
☆
5

Lu et al.
2006
—
☆
☆
☆
—
☆
☆
—
5

Sharma et al.
2018
☆
—
☆
☆
☆
☆
☆
—
6

Shokouhi et al.
2015
☆
☆
☆
☆
☆
☆
☆
☆
8

Sun et al.
2006
—
☆
☆
☆
☆☆
☆
☆
☆
8

Weng et al.
2010
—
☆
—
☆
☆☆
☆
☆
☆
7

Zhang et al.
2007
—
☆
☆
☆
☆
☆
☆
☆
7

Zhu et al.
2017
—
☆
—
☆
☆
☆
☆
☆
6

Zhu et al.
2009
☆
☆
☆
☆
☆
☆
☆
☆
8

Note: a study can be awarded a maximum of one star for each numbered item within the Selection and Exposure categories. A maximum of two stars can be given for Comparability. A maximum of 2 stars can be allotted in this category, one for age, the other for other controlled factors (gender, BMI, WHR, and so on).

3.2. Meta-analysis Results

For the study of the whole population included in our investigation, PGC-1α rs8192678 polymorphisms have shown a significant association with T2DM risk under allelic (OR: 1.24, 95% CI: 1.13-1.35), dominant (OR: 1.27, 95% CI: 1.14-1.42), recessive (OR: 1.24, 95% CI: 1.14-1.36), homozygous (OR: 1.40, 95% CI: 1.20-1.64), and heterozygous (OR: 1.20, 95% CI: 1.06-1.35) genetic models (Table 3). A Fixed Effect model was only used to the recessive genetic model (Figure 2).

Table 3: Summary of meta-analysis of association between PGC-1αrs8192678 polymorphism and T2DM.

Genetic model
Pooled OR (95% CI)
value
value
Study number
Cases
Controls
I² (%)

Allelic genetic model
1.24 (1.13-1.35)
4.76
<0.05
20
8038
8144
66.4
<0.05

East Asian subgroup
1.15 (1.02-1.29)
2.34
<0.05
10
3972
4618
67.9
<0.05

Caucasian subgroup
1.28 (1.09-1.49)
3.07
<0.05
7
2927
2246
65.4
<0.05

Indian subgroup
1.35 (1.12-1.62)
3.13
<0.05
2
905
1042
39.8
0.19

African subgroup
1.84 (0.90-3.74)
1.68
0.09
1
234
238
58.1
0.12

Dominant genetic model
1.27 (1.14-1.42)
4.27
<0.05
20
8038
8144
57.5
<0.05

East Asian subgroup
1.15 (1.00-1.32)
1.91
0.06
10
3972
4618
51.1
<0.05

Caucasian subgroup
1.37 (1.10-1.71)
2.84
<0.05
7
2927
2246
64.8
<0.05

Indian subgroup
1.54 (1.12-2.11)
2.68
<0.05
2
905
1042
61.2
0.08

African subgroup
1.28 (0.77-2.13)
0.97
0.33
1
234
238
0
0.71

Recessive genetic model
1.24 (1.14-1.36)
4.95
<0.05
20
8038
8144
45.5
<0.05

East Asian subgroup
1.17 (1.04-1.31)
2.63
<0.05
10
3972
4618
64.9
<0.05

Caucasian subgroup
1.31 (1.12-1.53)
3.45
<0.05
7
2927
2246
0
0.52

Indian subgroup
1.35 (1.02-1.78)
2.08
<0.05
2
905
1042
0
0.88

African subgroup
18.85 (2.55-139.61)
2.87
<0.05
1
234
238
0
0.34

Homozygous genetic model
1.40 (1.20-1.64)
4.34
<0.05
20
8038
8144
50
<0.05

East Asian subgroup
1.31 (1.04-1.64)
2.3
<0.05
10
3972
4618
64.8
<0.05

Caucasian subgroup
1.47 (1.21-1.79)
3.84
<0.05
7
2927
2246
4.9
0.39

Indian subgroup
1.59 (1.18-2.14)
3.06
<0.05
2
905
1042
0
0.54

African subgroup
13.63 (1.71-108.58)
2.47
<0.05
1
234
238
0
0.36

Heterozygous genetic model
1.20 (1.06-1.35)
2.87
0.004
20
8038
8144
59.5
<0.05

East Asian subgroup
1.08 (0.94-1.24)
1.07
0.29
10
3972
4618
45.4
0.06

Caucasian subgroup
1.32 (1.05-1.66)
2.37
0.02
7
2927
2246
63.9
<0.05

Indian subgroup
1.52 (1.08-2.13)
2.40
0.02
2
905
1042
62.9
0.07

African subgroup
0.63 (0.20-2.07)
0.76
0.45
1
234
238
68.3
0.08

A, mutant type; genetic model: allelic (A vs. G), dominant (AG + AA vs. GG), recessive (AA vs. AG + GG), homozygous (AA vs. GG), and heterozygous (AG vs. GG).

Figure 2: Forest plot of T2DM associated with PGC-1α rs8192678 polymorphism under recessive genetic model (AA vs. AG + GG).

In a subgroup analysis, a significant association between PGC-1α rs8192678 polymorphisms and T2DM was only found in the East Asian population under allelic (OR: 1.15, 95% CI: 1.02-1.29), recessive (OR: 1.17, 95% CI: 1.04-1.31), and homozygous (OR: 1.31, 95% CI: 1.04-1.64) genetic models (Table 3).

A significant association was observed in the Caucasian population under allelic (OR: 1.28, 95% CI: 1.09-1.49), dominant (OR: 1.37, 95% CI: 1.10-1.71), recessive (OR: 1.31, 95% CI: 1.12-1.53), homozygous (OR: 1.47, 95% CI: 1.21-1.79), and heterozygous (OR: 1.32, 95% CI: 1.05-1.66) genetic models (Table 3).

This significant association was also found in the Indian population under allelic (OR: 1.35, 95% CI: 1.12-1.62), dominant (OR: 1.54, 95% CI: 1.12-2.11), recessive (OR: 1.35, 95% CI: 1.02-1.78), homozygous (OR: 1.59, 95% CI: 1.18-2.14), and heterozygous (OR: 1.52, 95% CI: 1.08-2.13) genetic models (Table 3).

No AA genotype carrier was found in the African population, nor was there any significant association detected in this study.

3.3. Publication Bias

The publication bias of these studies was assessed under a recessive genetic model by using the funnel plot (Figure 3). Egger’s linear regression test indicated possible publication bias for the association (, ) under the recessive genetic model. The Trim and Fill method was used to recalculate the pooled risk estimate. There was some indication of asymmetry (seven studies trimmed), but the results were still stable before and after this analysis (Figure 4).

Figure 3: Funnel plot of T2DM associated with PGC-1α rs8192678 polymorphism under recessive genetic model (AA vs. AG + GG).

Figure 4: Filled funnel plot of T2DM associated with PGC-1α rs8192678 polymorphism under recessive genetic model (AA vs. AG + GG).

3.4. Sensitivity Analysis

We used the leave-one-out method in the sensitivity analysis, whereas only one article was excluded each time. The removal of any one study from this meta-analysis did not change the association between PGC-1α rs8192678 polymorphisms and T2DM susceptibility under the recessive genetic model. This suggests that our results are stable, reliable, and robust (Figure 5).

Figure 5: Sensitivity analysis under recessive genetic model (AA vs. AG + GG).

4. Discussion

Many epidemiological studies have investigated the genetic risk of developing T2DM using multiple single-nucleotide polymorphism- (SNP-) based GWAS approaches. However, up to today, the quantitative association of cumulative risk alleles (RAs) of such SNPs with T2DM risk has not been deterministic, limiting the potential applications of this type of research. Hence, the aim of this study was to identify deterministic genetic alteration associated with T2DM. The comprehensive meta-analysis of cross-ethnicity studies revealed T2DM risk in relation to PGC-1α gene rs8192678 polymorphism. We firstly report this current meta-analysis that carrying one RA in T2DM-associated SNPs can be associated with a clear risk of prevalent or incident T2DM, and we have validated our finding statistically.

In this meta-analysis, the association between PGC-1α gene rs8192678 polymorphism and T2DM susceptibility was detected under different genetic models. Individuals with A allele carriers can have an increased risk of T2DM, especially among the Caucasian and Indian populations. After subgroup analysis, a significant association was detected in the Caucasian population under all genetic models, and also found in the Indian population. Furthermore, people with AA genotype carriers can have a higher risk of T2DM compared to people with other genotype carriers. The association was detected in the East Asian population under recessive and homozygous genetic models. No significant association was found among the Africans because of the only one study with small sample size and without AA genotype carriers in control. Heterogeneity showed reduction after ethnicity subgroup analysis. This infers that ethnicity can be a main reason for heterogeneity. The possible publication bias, as detected by Funnel plot and Egger’s linear regression test, suggests that further studies are still needed to validate the conclusions, while the sensitivity analysis supports that our results are stable, robust, and reliable.

Initially, Ek et al. [19] performed a case-control study about the relationship between PGC-1α rs8192678 polymorphism and T2DM. The association between PGC-1α polymorphism and T2DM, insulin secretion, and other related indicators was also explored in different countries, but with inconsistent and contradictory conclusions [20–23]. The discrepancy among original studies was common in any study of complex disease. The reasons for the discrepancy can be multifaceted, including sample size, ethnicity differences, study design, and inclusion criteria. In 2006, Barroso et al. [24] found a significant association between PGC-1α rs8192678 polymorphism and T2DM in the Caucasian population. Eight Caucasian studies from 2001 to 2005 were included in their meta-analysis, and only 2 genetic models (allelic, additive) were adopted. Because both unrelated and genealogical samples were included in their analysis, the comparability of studies might be decreased and their conclusion may be more limited, compared to the results from our study. In 2011, Yang et al. [10] also performed a similar meta-analysis, including studies from 2001 to 2010, and found an obvious association among Indians but not in Caucasians. However, only an allelic genetic model was adopted with subgroup analysis stratified by ethnicity. The studies included in the meta-analysis comprised family-based studies, redundantly repeated data studies, and studies that are not matched in HWE. Sharma et al. [25] investigated the possible association between PGC-1α rs8192678 polymorphism and T2DM but only included 8 studies. However, a high level of heterogeneity among these studies was identified with an value of 81.58%. Hence, the reliability of the previous results has been questionable. We performed a much more comprehensive investigation, and in our meta-analysis, all included studies were detected by HWE. The selection of literature by inclusion and exclusion criteria was strictly followed to reduce the potential selection bias. We performed more comprehensively five genetic models with subgroup analysis stratified by ethnicity and literatures (after 2010). Therefore, our much more comprehensive approach offers more reliable results with new updates.

Despite our promising results, several potential limitations should also be addressed. Firstly, T2DM is a complex multifactorial disease, and we only considered the individual polymorphism without taking into account the interaction with other polymorphisms or environmental factors (dietary pattern, lifestyle, behavioral habits, etc.). Secondly, the study did not consider other relevant factors (gender, age, etc.), due to the limitation of availability of original research data. Thirdly, obesity is an important intermediate factor in the development of T2DM, but the definitions of obesity were different or not available in our included studies. It would be valuable and interesting to include obesity in the association with subgroup analysis. Finally, sample sizes were insufficient in the Indian and African populations. However, even though those factors are obviously related, none of them is indispensible in T2DM development, since T2DM can occur without any of the above factors. Therefore, despite of those limitations, a reliable conclusion can be still made from our comprehensive study.

5. Conclusion

A significant association between PGC-1α rs8192678 polymorphism and T2DM susceptibility has been detected in this meta-analysis. Individuals with A allele, especially AA genotype carriers, can be more susceptible to T2DM in the Caucasian and Indian populations. Such association in East Asian and African populations needs to be further explored in larger sample studies. Further research about the interaction effect on PGC-1α rs8192678 polymorphism with other SNPs or environmental factors will be followed. Our finding revealed an explicit genetic variation in T2DM development. Our study provides further insight into the mechanisms by which genetic variation influences type 2 diabetes risk and glycemic traits, and it further supports that the genetic variant for type 2 diabetes risk can confer the risk diversity among different ethnicities.

Abbreviations

T2DM: | Type 2 diabetes mellitus
PGC-1α: | Peroxisome proliferator-activated receptor γ coactivator-1α
NOS: | Newcastle-Ottawa Scale
HWE: | Hardy-Weinberg equilibrium.

Conflicts of Interest

The authors declare that they have no conflicts of interest.

Authors’ Contributions

Xuesen Wu and Huaiquan Gao conceived and guided the project.

Acknowledgments

This study is supported in part by the National Natural Science Foundation of China (number: 81373100) and Bengbu Medical College internal grant. We are grateful to all faculty, students, and professionals who participated in this study at Bengbu Medical University.

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