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Erschienen in: BMC Endocrine Disorders 1/2021

Open Access 01.12.2021 | Research article

Association between PNPLA3 rs738409 polymorphism and nonalcoholic fatty liver disease: a systematic review and meta-analysis

verfasst von: Nader Salari, Niloufar Darvishi, Kamran Mansouri, Hooman Ghasemi, Melika Hosseinian-Far, Fateme Darvishi, Masoud Mohammadi

Erschienen in: BMC Endocrine Disorders | Ausgabe 1/2021

Abstract

Background

Non-alcoholic fatty liver disease (NAFLD) is a common disorder that is known to be the leading cause of chronic liver disease worldwide. This study aims to systematically review and meta-analyze the association between PNPLA3 rs738409 polymorphism and non-alcoholic fatty liver.

Methods

Following a systematic review and meta-analysis method, articles without any time limitation, were extracted from SID, MagIran, IranDoc, Scopus, Embase, Web of Science (WoS), PubMed and ScienceDirect international databases. Random effects model was used for analysis, and heterogeneity of studies was investigated considering the I2 index and using Comprehensive Meta-Analysis software.

Results

The odds ratio of CC genotype in patients with non-alcoholic fatty liver demonstrates the protective effect of CC genotype with the ratio of 0.52, whereas CG genotype presents an increasing effect of CG genotype with the ratio of 0.19, and GG genotype also showed an increasing effect of GG genotype with the ratio of 1.05. Moreover, CG + GG genotypes as a single group demostrated an odds rartio of 0.88.

Conclusion

This meta-analysis highlights that people with CC genotype has 52% lower chance of developing non-alcoholic fatty liver disease, and those with CG genotype had 19% higher risk of developing non-alcoholic fatty liver. Those with GG genotype were 105% more likely to develop non-alcoholic fatty liver than others. Moreover, those present in a population with CG + GG genotypes were 88% more likely to have non-alcoholic fatty liver disease.
Hinweise

Publisher’s Note

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Abkürzungen
NAFLD
Non-alcoholic fatty liver disease
SNP
Single Nucleotide Polymorphism
TG
Tricylglycerol
WoS
Web of Science
PRISMA
Preferred Reporting Items for Systematic Reviews and Meta-Analysis.
STROBE
Strengthening the Reporting of Observational Studies in Epidemiology for cross- sectional Study

Background

Non-alcoholic Fatty Liver Disease (NAFLD) is a common disorder that is known to be the leading cause of chronic liver disease worldwide. This disorder is caused by abnormal accumulation of fat in liver tissue cells and can eventually lead to liver cirrhosis [1, 2]. The disease was first diagnosed in 1980 and is subsequently being recognised as one of the major contributors to mortality from liver disorders [3].
In people with NAFLD, deaths from liver disease were reported to be 0.77 per thousand people per year, and cardiovascular deaths were 4.79 per thousand people per year [4]. The prevalence of NAFLD in East Asian countries is 15–45% and in the developed Western countries is found to be 20–30% [5]. According to a study in 2010, the prevalence of this disorder in the general population was 35% and in another study in 2019 the prevalence of NAFLD was 25% [6, 7].
The prevalence of NAFLD is associated with disorders such as obesity and insulin-resistant diabetes and metabolic syndrome [4, 8]. In the study of Yamamoto et al., it has been estimated that the number of obese and overweight people will rise to more than 2 billion by the year 2030, so the occurance of NAFLD is expected to increase with the rise in obesity levels in the general population. Moreover, with the prevalence of obesity in children in recent years, NAFLD has been recognised as the most common liver disorder in children [9, 10]. Other causes of non-alcoholic fatty liver disease include sedentary lifestyle, poor diet and genetic polymorphism of different genes [11].
Different types of genes may be involved in the pathogenesis of NAFLD. Genetic factors cause NAFLD in 27 to 39% of cases. One of the most important genetic factors for NAFLD is Single Nucleotide Polymorphism (SNP) (rs738409) in the patatin-like phospholipase domain containing protein 3 (PNPLA3). This SNP was first identified in 2008 by two independent studies on the independent genome.
The PNPLA3 rs738409 C > G SNP is a type of Missense that results in the replacement of cytosine with guanosine and, ultimately, the incorrect coding of methionine rather than isoleucine at position 148. This single nucleotide polymorphism is located in the third exon of the pnpla3 gene. The PNPLA3 gene is located on human chromosome 22 (chr22q13.31).
PNPLA3 encodes a protein known as adiponutrin (ADPN). This protein is expressed in adipocytes and hepatocytes. Moreover, this protein has lipolytic and lipogenic properties, however the exact function of adiponutrin is still unclear. PNPLA has also been reported to be highly expressed on human stellate cells. The encoded protein has retinol esterase activity and allows retinol secretion from hepatocytes while the mutation induces intracellular retention of this compound, therefore, PNPLA3 rs738409 is susceptible to NAFLD.
The function of PNPLA3 rs738409 is still unknown, however in vitro studies have shown that PNPLA3 protein has tricylglycerol (TG) hydrolase and lysophosphatidyl acyltransferase (LPAAT) and calcium independent phospholipase A2 activities. PNPLA3 also plays a critical role in homestasis of lipid metabolism. PNPLA3 eventually causes glycerolipid hydrolasis in the liver and inhibits lipid outflow into peripheral adipose tissue, thus contributing to hepatic steatosis and related disorders. NAFLD is characterised by the accumulation of lipids in hepatic steatosis.
The PNPLA3 gene is associated not only with liver fat content, but also with hepatic inflammation, hepatic steatohepatitis, fibrosis and cirrhosis, indicating that it plays a key role in the development of NAFLD. Inflammatory infiltration and liver damage are greater in patients carrying PNPLA3 I148M than in wild-type genotype individuals; this gene is thought to be closely linked to liver inflammation. Compared to non-carriers, homozygous carriers has 73% higher liver fat content, 3.2 times higher risk in high necroinflammatory scores and 3.2 times higher risk of developing fibrosis [4, 5, 7, 1119]. rs738409 of the patatin-like phospholipase domain containing gene 3 (PNPLA3) is known to be the most common and most potent gene in the development of NAFLD [4].
Furthermore, the association of PNPLA3 gene polymorphisms with other liver disorders such as alcoholic fatty liver (ALD) has also been observed [20]..
Since non-alcoholic fatty liver disease is very common and can have adverse side effects, understanding the factors affecting its occurrence can play a key role in the prevention and control of this disease and the treatment of those affected. This study aims to systematically review and meta-analyse the association between PNPLA3 rs738409 polymorphism and non-alcoholic fatty liver.

Methods

Search method

This study was performed to determine the association between PNPLA3 rs73409 C > G polymorphism using a systematic review and meta-analysis. Data were collected from Iranian and international databases of Web of Science (WoS), Embase, Scopus, PubMed, science direct, ProQuest, Google Scholar, SID, Irandoc and other international databases. International databses were searched using the keywords (PNPLA3 gene or PNPLA3 polymorphism OR patatin-like phospholipase domain-containing protein3) and (Non-alcoholic Fatty Liver Disease or NAFLD or Nonalcoholic Steatohepatitis) and their possible combination; Persian equivalent of keywords were used for searches within the Persian databases. The Google Scholar search engine was also used with both English and Persian keywords. In order to assess gray literature review, sites related to the subject, as well as the references within the found sources were analysed.

Criteria for selection and evaluation of articles

Following the search process, all articles were collected in the EndNote software, and all duplicates were removed. Inclusion criteria were: 1- Case control studies, 2- Cohort, and 3- Studies examining the relationship between pnpla3 gene and non-alcoholic fatty liver disease and Exclusion criteria were: 1- Cross-sectional studies, 2- Case reports, 3- Intervention studies, 4- Letters to editor, 5- Studies where the full-text was not available, and 6- Studies in which individuals in the population under study have underlying disease.
Then a list of titles and abstracts was prepared and after hiding the full text of the articles they were provided to the reviewers. Each article was independently reviewed by two reviewers, and in case of disagreement between the two reviewers, the third reviewer’s judgement was considered as the criterion for approval of articles.
During the qualitative evaluation phase, the STROBE checklist was used to evaluate the studies qualitatively. This checklist consists of 22 criteria, of which 18 are used to assess all research papers, and 4 are specific to the type study. The checklist is used to evaluate the study objectives, determination of appropriateness of the sample size, type of study, sampling method, research population, data collection method(s), definition of variables and method of sampling, study data collection tools, study objectives, the statistical test used to assess the findings, and the maximum score derived from this checklist is 32. The articles with a score below 14 were excluded. Studies were then reviewed according to the PRISMA 2009 four-step process, including article identification, screening, eligibility criteria and finally meta-analysis.

Statistical analysis

In this study, heterogeneity of studies was investigated using I2 test, data were analyzed using Comprehensive Meta-analysis software (Biostat, Englewood, NJ, USA version 3), probability of publication bias results were evaluated using both funnel diagrams and Egger test; please note that the significance level was set at 0.05.

Results

This study investigated the association between PNPLA3 I148M rs738409 polymorphism and non-alcoholic fatty liver disease through systematic review and meta-analysis. Following searching various databases, a total of 1391 articles entered the study, of which 220 articles were from EMBASE database, 47 articles from ProQuest, 109 articles from PubMed, 84 articles from ScienceDirect, 243 articles from Scopus, 447 articles from Web of Science (WoS), 1 article from SID, 57 articles from Irandoc, 145 articles from Google Scholar, and 2 articles were selected following the reviews of other articles, and were found within the references.
Once the articles were collected, 360 duplicate articles were eliminated, and after reviewing the title and abstracts, 692 other articles were also removed and 339 articles were left subjected to secondary evaluation. After reviewing the full text of the articles in terms of thematic relevance as well as qualitative review of the articles, 308 additional articles were excluded and finally 31 articles entered the meta-analysis process (please see Tables 1 and 2).
Table 1
Characteristics of studies entered into the meta-analysis
Row
Author [References]
Publication year
Area
Age of case group
Age of control group
Case Group Size
Control group Size
1
Alam, S [21].
2017
Bangladesh
39.1 ± 8.6
29.64 ± 7.03
99
75
2
Baclig, M. O [22].
2014
Philippines
20–70
20–70
32
36
3
Bhatt, S. P [23].
2013
India
38.2 ± 7
37.1 ± 6.9
162
173
4
Chen, L. Z [24].
2019
China
26.71 ± 2.81
22.48 ± 3.12
512
451
5
Choobini, Neda [19]
2016
Iran
47.9 ± 12.3
40 ± 13.9
95
183
6
Di Costanzo, A [25].
2018
Italy
54
49.7
218
227
7
Gorden, A [26].
2013
America
47 ± 10.6
46 ± 11.8
748
344
8
Hotta, K [27].
2010
Japan
51.7 ± 15
47.2 ± 14.8
253
578
9
Hudert, C. A [28].
2019
Germany
14.11 ± 2.15
46.73 ± 16.3
70
200
10
Karoli, R [29].
2019
India
45 ± 8.2
46 ± 7
100
100
11
Kawaguchi, Takahisa [30]
2012
Japan
52.05 ± 14.85
48.8 ± 16.3
529
932
21
Krishnasamy, N [31].
2020
India
43.15 ± 9.245
41.99 ± 12.7
105
102
13
Lee, S. S [32].
2014
Korea
45.3 ± 15.5
45.3 ± 10.6
155
184
14
Li, Y. L. [33]
2012
China
46.7 ± 13.6
43.1 ± 13.4
203
202
15
Liu, W. Y [34].
2019
China
40.2 ± 12.5
46.6 ± 9.2
349
58
16
Niriella, M. A [35].
2017
Sri Lanka
42–71
(42–71)
1360
391
17
Niu, T. H [36].
2014
China
49.7 ± 16.7
47.69 ± 15.68
390
409
18
Oniki, Kentaro [37]
2015
Argentina
61.2 ± 10.5
67.5 ± 6
393
740
19
Park, J. H [38].
2015
South Korea
48.9 ± 7
49.1 ± 7.2
602
761
20
Peng, X. E [39].
2012
China
45.33 ± 12.48
43.87 ± 13
553
553
21
Rametta, R [40].
2014
Italy
49.7 ± 12.1
47.7 ± 12.1
137
260
22
Shang, X. R [41].
2015
China
11.81 ± 2.20
11.44 ± 2.99
162
865
23
Uygun, A [42].
2017
Turkey
42.1 ± 11.4
34.1 ± 12.8
216
150
24
Valenti, L. [43]
2012
Italy
49.5 ± 12
48(1 ± 2
144
257
25
Valenti, L. [44]
2010
Italy
46.4 ± 11
48.4 ± 13
253
179
26
Vespasiani-Gentilucci, U [45].
2016
Italy
51.5 ± 12.3
40.1 ± 13.1
60
125
27
Wang, C. W [18].
2011
Taiwan
48.11 ± 12.05
45.4 ± 15.93
156
723
28
Wang, X. L. [46]
2016
China
45 ± 13
45 ± 13
376
382
29
Xia, M. F [16].
2016
China
60
61
1385
2915
30
Yang, H. H [47].
2018
China
70.95 ± 4.73
72.53 ± 4.73
97
362
31
Zhang, R. N [48].
2016
China
38.2 ± 13.78
42.64 ± 10.58
59
72
Table 2
Overview of CC, CG, GG and CG + GG genotypes based on the obtained studies
Row
Author [References]
Genotype
Dominant
CC
CG
GG
CG + GG
Case
Control
OR
Case
Control
OR
Case
Control
OR
Case
Control
OR
1
Alam, S [21].
45
37
0.398
27
43
1.365
3
19
5.700
30
62
2.514
2
Baclig, M. O [22].
26
14
0.299
8
12
2.100
2
6
3.923
10
18
3.343
3
Bhatt, S. P [23].
149
112
0.361
16
35
2.704
8
15
2.105
24
50
2.772
4
Chen, L. Z [24].
196
114
0.373
194
236
1.133
61
162
2.959
255
398
2.683
5
Choobini, Neda [19]
15
13
1.776
14
17
2.631
154
65
0.408
168
82
0.563
6
Di Costanzo, A [25].
123
92
0.617
56
91
2.188
48
35
0.713
104
126
1.620
7
Gorden, A [26].
218
411
0.705
103
244
1.133
5
47
4.546
108
291
1.391
8
Hotta, K [27].
175
45
0.498
296
111
0.745
104
97
2.834
400
208
2.057
9
Hudert, C. A [28].
118
20
0.278
71
31
1.444
11
19
6.401
82
50
3.598
10
Karoli, R [29].
51
20
0.240
32
55
2.597
17
25
1.627
59
80
2.780
11
Kawaguchi, Takahisa [30]
247
88
0.553
468
236
0.799
217
203
2.052
685
439
1.759
12
Krishnasamy, N [31].
59
19
0.261
29
50
2.288
14
36
3.280
43
86
6.211
13
Lee, S. S [32].
55
31
0.586
92
75
0.938
37
49
1.837
129
124
1.705
14
Li, Y. L. [33]
94
70
0.605
90
84
0.878
18
49
3.253
108
133
1.654
15
Liu, W. Y [34].
24
94
0.522
24
152
1.093
8
85
2.012
32
237
1.719
16
Niriella, M. A [35].
25
54
0.605
134
464
0.993
232
842
1.114
366
1306
1.652
17
Niu, T. H [36].
183
48
0.173
176
153
0.855
50
189
6.751
226
342
5.769
18
Oniki, Kentaro [37]
223
38
0.248
394
111
0.346
121
45
0.662
515
156
0.288
19
Park, J. H [38].
280
172
0.678
364
293
1.034
117
137
1.622
481
430
1.455
20
Peng, X. E [39].
235
183
0.669
259
276
1.131
59
93
1.693
318
369
1.482
21
Rametta, R [40].
150
51
0.435
95
67
1.662
15
19
2.630
110
86
2.299
22
Shang, X. R [41].
338
60
0.917
418
74
0.899
109
28
1.449
527
102
1.090
23
Uygun, A [42].
85
64
0.322
50
90
1.429
15
62
3.623
65
152
3.106
24
Valenti, L.(2012) [43]
146
55
0.470
95
68
1.526
16
21
2.572
111
89
2.128
25
Valenti, L.(2010) [44]
118
103
0.355
56
114
1.801
5
36
5.773
61
150
2.817
26
Vespasiani-Gentilucci, U [45].
83
29
0.473
34
18
1.147
8
13
4.045
42
31
2.112
27
Wang, C. W [18].
269
40
0.582
335
80
1.219
119
36
1.523
454
116
1.718
28
Wang, X. L. [46]
169
122
0.605
174
191
1.234
39
63
1.770
213
254
1.652
29
Xia, M. F [16].
1200
486
0.773
1363
684
1.111
352
215
1.338
1715
899
1.294
30
Yang, H. H [47].
110
27
0.884
123
40
1.364
129
30
0.809
252
70
1.132
31
Zhang, R. N [48].
32
12
0.319
31
27
1.116
9
20
3.590
40
47
3.133
The PRISMA 4-step process highlighting the processes in obtaining the final articles for our meta-analysis is presented in Fig. 1.

Investigation of heterogeneity and publication bias (CC genotype)

The heterogeneity of the studies was evaluated using the I2 test. Based on this test, I2 = 82.2% was obtained, which indicates high heterogeneity in the included studies. Moreover, the results of the publication bias study were compared with the Egger test (please see Fig. 2 A), which was not statistically significant (P = 0.052).
The total number of samples included in the case group and in the control group were 9973 and 13,048 respectively. The odds ratio of CC genotype in patients with non-alcoholic fatty liver was 0.48 based on meta-analysis (95% CI: 0.40–056), indicating an protective effect of CC genotype with 0.52, meaning that those with this genotype are 52% less likely to develop non-alcoholic fatty liver than others. In Fig. 2 B, the odds ratio based on the random effects model is shown where the black small rectanlges has the odds ratio and the rectangle length indicates the 95% confidence interval; the diamond shape represents the odds ratio for the entire study (Fig. 2 B).

Investigation of heterogeneity and publication bias (CG genotype)

The heterogeneity of the studies was evaluated using the I2 test. Based on this test, I2 = 80.3% was obtained, which indicates high heterogeneity in the included studies. Moreover, the results of the publication bias study were compared with the Egger test (please see Fig. 3 A), which was not statistically significant (P = 0.072).
The total number of samples included in the case group and in the control group were 9973 and 13,048 respectively. The odds ratio of CG genotype in patients with non-alcoholic fatty liver was 1.19 based on meta-analysis (95% CI: 1–1.33), indicating an increasing effect of CG genotype with 0.19, meaning that those with this genotype are 19% more likely to develop non-alcoholic fatty liver than others. In Fig. 3 B, the odds ratio based on the random effects model is shown where the black small rectanlges has the odds ratio and the rectangle length indicates the 95% confidence interval; the diamond shape represents the odds ratio for the entire study (Fig. 3 B).

Investigation of heterogeneity and publication bias (GG genotype)

The heterogeneity of the studies was evaluated using the I2 test. Based on this test, I2 = 86.3% was obtained, which indicates heterogeneity in the included studies. Moreover, the results of the publication bias study were compared with the Egger test (please see Fig. 4 A), which was not statistically significant (P = 0.064).
The total number of samples included in the case group and in the control group were 9973 and 13,048 respectively. The odds ratio of GG genotype in patients with non-alcoholic fatty liver was 2.05 based on meta-analysis (95% CI: 1.64–2.56), indicating an increasing effect of GG genotype with 1.05, meaning that those with this genotype are 105% more likely to develop non-alcoholic fatty liver than others. In Fig. 4 B, the odds ratio based on the random effects model is shown where the black small rectanlges has the odds ratio and the rectangle length indicates the 95% confidence interval; the diamond shape represents the odds ratio for the entire study (Fig. 4 B).

Investigation of heterogeneity and publication bias (CG + GG genotype)

The heterogeneity of the studies was evaluated using the I2 test. Based on this test, I2 = 90.7% was obtained, which indicates heterogeneity in the included studies. Moreover, the results of the publication bias study were compared with the Egger test (please see Fig. 5 A), which was not statistically significant (P = 0.054).
The total number of samples included in the case group and in the control group were 9973 and 13,048 respectively. The odds ratio of CG + GG genotype in patients with non-alcoholic fatty liver was 1.88 based on meta-analysis (95% CI: 1.5–2.3), indicating an increasing effect of CG + GG genotype with 0.88, meaning that those with this genotype are 88% more likely to develop non-alcoholic fatty liver than others. In Fig. 5 B, the odds ratio based on the random effects model is shown where the black small rectanlges has the odds ratio and the rectangle length indicates the 95% confidence interval; the diamond shape represents the odds ratio for the entire study (Fig. 5 B).

Discussion

In this study, after investigating the association between different genotypes of PNPLA3 rs738409 polymorphism and non-alcoholic fatty liver disease, we highlighted that people with CC genotype with the odds ratio of 0.48, have 52% lower risk of developing non-alcoholic fatty liver, while this ratio in CG and GG genotypes were 1.19 and 2.05 respectively, and therefore the probability of developing the disease in those with these genotypes were 19% (CG) and 105% (GG) higher. On the other hand, considering the CG + GG groups as a single population/group, and following a statistical analysis, it was concluded that the odds ratio of this group in relation to occurance of Non-alcoholic fatty liver was 1.88, meaning that this group were 88% more likely to develop the disorder than others. The effect of the G allele on non-alcoholic fatty liver disease can also be emphasized. A study in India in 2020 also found that the G allele plays a key role in the development of NAFLD [31].
NAFLD is recognised as one of the most common liver diseases in the world with unknown etiology and pathogenesis. However, several factors including genetics, diet and inactivity, have been presented as some of the key reasons for the development of the disease. It has also been found that a good diet and regular exercise can reduce the risk of developing insulin resistance and can boost glucose homeostasis. Other SNPs such as rs2896019 and rs3810322 have also been reported to increase the risk of non-alcoholic fatty liver disease [1]. Past genomic studies have identified two genes PNPLA3 I148M and TM6SF2 E167K as the most likely genetic factors in the development of NAFLD [49].
According to meta-analysis by Zhang et al. (2015) on some studies undertaken in Asian countries, when comparing people having the G allele with a population with the C allele, the probability of non-alcoholic fatty liver disease was reported to be 1.92, and therefore it was concluded that the G allele is likely to increase the development of non-alcoholic fatty liver to the liver in people with G allele by 92%; Moreover, it can increase the risk of renal fibrosis and ALT serum levels. Development of NAFLD in the dominant phenotype (CG + GG) was 110% higher than the recessive phenotype. On the other hand, comparing the CG + GG populations with the CG genotype, it was concluded that the risk of NAFLD was higher in the homozygous GG population than in other populations [5].
Another meta-analysis conducted in 2019 stated that this polymorphism had a major impact on the development of tissue damage in liver and that the G allele was considered as a risk factor for NAFLD in such a way that the ratio of development of the disease in those with one G allele to those without it was 1.88, and 4.01 in those where both alleles were G. It has also been suggested that this gene increases alanine aminotransferase levels in serum [50].
According to a meta-analysis by Jiaying et al. (2020), this gene is involved in the development of non-alcoholic osteopathy (NASH) in children and adolescents; it is also accosiated with factors such as serum alanine transaminase, aspartate transaminase, gamma glutamyl transferase, that are indicators of liver damage [51].
Another meta-analysis in 2015, it was reported that all genetic variations in the rs738409 polymorphism in the pnpla3 gene was strongly associated with the incidence of NAFLD and NASH, especially in Asian and Spanish populations. In this study, however, no association was found between rs738409 polymorphism and hepatic steatosis. It was also reported that the GG genotype had a high impact on the development of NAFLD as well as renal fibrosis. The ratio of this genotype over inflammation occurance was reported as 3.13 [14].
According to a study by Chobin et al., the CG genotype was identified as a predisposing genotype to a 2.63-fold increase in the likelihood of developing the disease. Moreover, it was reported that the GG genotypes possess a protective effect, meaning that existence of such gentype results in a 59% decrease in developing NAFLD. Furtheremore, the odds of developing the disease in the CC genotype was 1.78 [19].
According to another study by Sood et al. (2016) in Japan, the odds ratio of the GG genotype was 36.5% in obese people and 47.8% in the non-obese population who had a fatty liver. Moreover, the modified odds ratio of non-alcoholic fatty liver disease in GG genotype was reported to be 4.15 in non-obese individuals, and 2.76 in obese pupulation. This genotype also increases the chances of developing steatosis and liver fibrosis [52]. A family history of NAFLD may result in higher levels of ALT and cholesterol among children. Moreover, it was reported that for every 10 unit increase in ALT (in IU / L) there will be approximately 1.5 times and for every 20 unit (mg / L) increase in body cholesterol, there will be approximately 2 times the risk of developing NAFLD in children [53, 54].

Limitation

The limitation of this study was the lack of access to the full-text of some of the sources.

Conclusion

This meta-analysis study demonstrated that people with the CC genotype were 52% less likely to develop non-alcoholic fatty liver disease, and people with CG genotype were 19% more likely to develop non-alcoholic fatty liver. Moreover, population with the GG genotype, had 105% more chance of developing a non-alcoholic fatty liver. Moreover, population with CG + GG genetypes demonstrate 88% more chance of developing the disease, and this is suggesting the effect of G allele on non-alcoholic fatty liver disease. In future, the effects of genetic and environmental factors on the level fo tissue damage, and also the effect of this gene on fibrosis and liver cirrhosis can be studied.

Acknowledgements

We hereby express our gratitude and appreciation to the Student Research Committee of Kermanshah University of Medical Sciences.

Declarations

Ethics approval was received from the ethics committee of deputy of research and technology, Kermanshah University of Medical Sciences (IR.KUMS.REC.1399.205).
Not applicable.

Competing interests

The authors declare that they have no conflict of interest.
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Metadaten
Titel
Association between PNPLA3 rs738409 polymorphism and nonalcoholic fatty liver disease: a systematic review and meta-analysis
verfasst von
Nader Salari
Niloufar Darvishi
Kamran Mansouri
Hooman Ghasemi
Melika Hosseinian-Far
Fateme Darvishi
Masoud Mohammadi
Publikationsdatum
01.12.2021
Verlag
BioMed Central
Erschienen in
BMC Endocrine Disorders / Ausgabe 1/2021
Elektronische ISSN: 1472-6823
DOI
https://doi.org/10.1186/s12902-021-00789-4

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