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A meta-analysis on associations of FTO, MTHFR and TCF7L2 polymorphisms with polycystic ovary syndrome
Journal Pre-proof A meta-analysis on associations of FTO, MTHFR and TCF7L2 polymorphisms with polycystic ovary syndrome
Xiye Wang, Kaiping Wang, Juanying Yan, Minyun Wu PII:
S0888-7543(19)30473-2
DOI:
https://doi.org/10.1016/j.ygeno.2019.08.023
Reference:
YGENO 9347
To appear in:
Genomics
Received date:
22 July 2019
Revised date:
31 July 2019
Accepted date:
26 August 2019
Please cite this article as: X. Wang, K. Wang, J. Yan, et al., A meta-analysis on associations of FTO, MTHFR and TCF7L2 polymorphisms with polycystic ovary syndrome, Genomics (2018), https://doi.org/10.1016/j.ygeno.2019.08.023
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© 2018 Published by Elsevier.
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A meta-analysis on associations of FTO, MTHFR and TCF7L2 polymorphisms with polycystic ovary syndrome
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Running head: FTO/MTHFR/TCF7L2 polymorphis ms and PCOS
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Xiye Wang, M.D. 1 , Kaiping Wang, M.D. 1 , Juanying Yan, M.D. 1 , and Minyun Wu, M.D.
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Address:
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1. Department of Gynaecology, People’s Hospital of Shengzhou City, Shengzhou, Zhejiang, China 2. Department of Obstetrics, Quzhou People's Hospital, Quzhou, Zhejiang, China
Correspondence to: Dr. Minyun Wu, Department of Obstetrics, Quzhou People's Hospital, No. 2 Bell Tower of Kecheng district, Quzhou 324000,
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Zhejiang, China. E-mail address: [email protected]; Phone number: 0086-0570-8895120; Fax number: 0086-0570-8895120
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Abstract
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Background: We aimed to better clarify the relationship between FTO/MTHFR/TCF7L2 polymorphisms and PCOS in a larger combined
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population by performing a meta-analysis.
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Methods: Eligible articles were retrieved from Pubmed, Embase, Web of Science and CNKI. Review Manager Version was used to perform statistical analyses.
Results: Forty-six studies were included for this meta-analysis. FTO rs9939609 polymorphism was found to be significantly associated with PCOS under dominant, recessive, over-dominant and allele comparisons, MTHFR rs1801131 polymorphism was found to be significantly
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associated with PCOS under recessive and allele comparisons, and MTHFR rs1801133 polymorphism was also found to be significantly associated with PCOS under dominant, recessive and allele comparisons in general population. In subgroup analyses, we found that positive
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results were mainly driven by the Asians.
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Conclusions: Collectively, this meta-analysis proved that FTO rs9939609, MTHFR rs1801131 and MTHFR rs1801133 polymorphisms could be used to identity individual with elevated susceptibility to may serve as predisposing factors of PCOS, especially for Asians.
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Keywords: Fat mass and obesity associated protein (FTO); Methylenetetrahydrofolate reductase (MTHFR); Transcription factor 7 Like 2 (TCF7L2); Polycystic ovary syndrome (PCOS); Meta-analysis
Introduction
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Polycystic ovary syndrome (PCOS), a common reproductive endocrine disorder of women at childbearing age, is characterized by polycystic ovaries, chronic anovulation, hyperandrogenism and an increased risk of multiple metabolic abnormalities such as insulin resistance, type II
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diabetes and obesity [1-3]. Although the precise pathogenesis mechanism of PCOS is still unrevealed, it was thought that genetic factors may contribute a lot to its development since numerous genetic variations were already found to be associated with a higher susceptibility to PCOS
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by previous genetic association studies [4-8].
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Fat mass and obesity associated protein (FTO), methylenetetrahydrofolate reductase (MTHFR) and transcription factor 7 like 2 (TCF7L2) were all demonstrated to be associated with various metabolic disorders such as type II diabetes and obesity by past clinical investigations [9-11].
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Given the metabolic nature of PCOS and the close relationship between above mentioned metabolic disorders and PCOS, it is possible that FTO,
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MTHFR and TCF7L2 polymorphisms may also affect individual susceptibility to PCOS. In recent years, some investigations already studied potential associations of FTO, MTHFR and TCF7L2 polymorphisms with PCOS.
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Nevertheless, the findings of these studies were not always consistent and the sample size of each study was also statistically insufficient. In this
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meta-analysis, we aimed to better clarify the relationship between FTO/MTHFR/TCF7L2 polymorphisms and PCOS in a larger combined population.
Materials and methods
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This meta-analysis was written in accordance with PRISMA checklist [12]. We also created an Open Science Framework (osf.io) account to
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make this meta-analysis more publicly available.
Literature search and inclusion criteria Eligible articles were
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retrieved from Pubmed, Web of Science, Embase and CNKI by using the following key words:
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(“methylenetetrahydrofolate reductase” OR “MTHFR” OR “Transcription Factor 7 Like 2” OR “TCF7L2” OR “fat mass and obesity associated protein” OR “FTO”) AND (“polymorphism” OR “variant” OR “variation” OR “mutation” OR “SNP” OR “genotype” OR “allele” OR “genetic
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association study” OR “genome-wide association study”) AND (“polycystic ovary syndrome” OR “PCOS”). Additionally, we also checked the reference lists of all retrieved articles.
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Inclusion criteria for this meta-analysis were as follows: (1) genetic association study about MTHFR/TCFL2/FTO polymorphisms and PCOS in human beings; (2) providing distributions of genotypes or alleles in cases and controls; (3) available full text in English or Chinese. We excluded studies when more than one of the following conditions was met: (1) studies that were not about MTHFR/TCFL2/FTO polymorphisms
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and PCOS; (2) reviews/comments/letters; (3) case reports or case series. If we found repeated publications by the same authors, only the most comprehensive study was included for this meta-analysis.
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Data extraction and quality assessment
Following information was extracted by two authors: the last name of the first author and publication year, country of the principal investigator
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and ethnicity of study participants, type of disease, total sample size of each study and the distribution of MTHFR/TCFL2/FTO polymorphisms
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in cases and controls. We also calculated the probability value (p value) of Hardy-Weinberg equilibrium (HWE). Newcastle-Ottawa scale (NOS) was used to evaluate the methodology quality of eligible studies [13]. The score of this scale ranged
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between zero and nine, if a study scored seven or more, we thought that the quality of this study was acceptable.
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Data extraction and quality assessment were conducted by two authors independently. We wrote to the corresponding authors for extra information when we thought that important information was missed.
Statistical analyses
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Review Manager Version 5.3.3 was used in this meta-analysis to perform statistical analyses. We used the Z test to assess whether MTHFR/TCFL2/FTO polymorphisms were significantly associated with PCOS, with the statistical significance p level set at 0.05. I2 statistics were used to evaluate between-study heterogeneities. Random-effect models (DerSimonian-Laird method) were used if I2 exceeded 50%.
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Otherwise, meta-analyses were conducted with fixed-effect models (Mantel-Haenszel method). We also conducted subgroup analyses by ethnicity of participants. We tested the robustness of synthetic results in sensitivity analyses. We evaluated publication biases by funnel plots.
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Results
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Characteristics of included studies
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One hundred and sixty-one articles were identified by our comprehensive literature searching. Sixty-seven articles were retrieved for eligibility assessment after exclusion of irrelevant and duplicate articles. Another fourteen reviews and three case series were subsequently excluded, and four other studies were excluded due to lack of essential data. Totally forty-six eligible studies were ultimately included for this meta-analysis (Fig. 1). Table 1 presented essential data extracted from included studies.
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Meta analyses results
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FTO rs9939609 polymorphism was found to be significantly associated with PCOS under dominant (p<0.0001, OR=0.72, 95%CI 0.66-0.78),
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recessive (p<0.0001, OR=1.74, 95%CI 1.42-2.13), over-dominant (p<0.0001, OR=1.28, 95%CI 1.17-1.40) and allele (p=0.0002, OR=0.79, 95%CI 0.70-0.89) comparisons, MTHFR rs1801131 polymorphism was found to be significantly associated with PCOS under recessive
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(p<0.0001, OR=3.43, 95%CI 1.97-5.96) and allele (p=0.04, OR=0.52, 95%CI 0.28-0.96) comparisons, and MTHFR rs1801133 polymorphism
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was also found to be significantly associated with PCOS under dominant (p=0.008, OR=0.69, 95%CI 0.53-0.91), recessive (p=0.0002, OR=1.43, 95%CI 1.19-1.73) and allele (p=0.003, OR=0.78, 95%CI 0.66-0.92) comparisons in general population. In subgroup analyses, we found that
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positive results were mainly driven by the Asian subgroup (Table 2).
Sensitivity analyses
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We tested the effects of each study on meta-analysis results in sensitivity analyses. The meta-analysis results remained unchanged in sensitivity analyses, suggesting that our findings were statistically robust.
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Publication biases
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We evaluated publication biases by using funnel plots. We did not observe dissymmetry in any funnel plots, which indicated that the possibility
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that our meta-analysis results were affected by overt publication biases was low.
Discussion
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In this meta-analysis, the combined results revealed that FTO rs9939609, MTHFR rs1801131 and MTHFR rs1801133 polymorphisms were all
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significantly associated with individual susceptibility to PCOS, especially in Asians. The meta-analysis results remained unchanged in sensitivity
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analyses, suggesting that our combined results were statistically robust. There are some points that should be considered when interpreting our meta-analysis results. Firstly, we did not impose any restrictions on investigated polymorphisms, meaning that as long as there were at least two eligible studies about one specific FTO/MTHFR/TCF7L2 polymorphisms, we would explore their potential associations with PCOS in this meta-analysis. However, considering that many combine
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analyses were still based on limited number of studies, maybe this meta-analysis was still not statistically sufficient to detect the actual genetic relationship between some of investigated polymorphisms and PCOS, thus further studies are still warranted to confirm genetic associations
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between FTO/MTHFR/TCF7L2 polymorphisms and PCOS. Secondly, the trends of associations for investigated polymorphisms in different
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ethnic subgroups were consistent, which suggested that combine studies of different ethnic origins in a meta-analysis was feasible, and the effects of FTO/MTHFR/TCF7L2 polymorphisms on susceptibility to PCOS in different ethnicities may be somehow similar. Thirdly, the
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aetiology of PCOS is very complicated, consequently, we strongly recommend future studies to conduct haplotype analyses and investigate
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potential gene-gene interactions to more comprehensively explore the effects of genetics on disease susceptibility [14]. Fourthly, according to our searching strategy, most of relevant studies were from Asian countries, while studies in other ethnicities were scarce, so future studies should
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continue to explore associations between FTO/MTHFR/TCF7L2 polymorphisms and PCOS, especially in Caucasians or Africans.
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This meta-analysis has some limitations. First, our meta-analysis results were derived from unadjusted combined analyses, and failure to adjust for some crucial baseline variables may impact the precision of our findings [15]. Second, environmental factors may also affect relationship between FTO/MTHFR/TCF7L2 polymorphisms and PCOS. Regrettably, most of included studies only focus on genetic associations, so we could not conduct analyses regarding genetic-environmental interactions [16]. Thirdly, we did not search for grey literatures. So although
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we did not observe dissymmetry in any funnel plots, there is still possibility that publication biases may influence our meta-analysis results [17].
Conclusions
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In summary, this meta-analysis proved that FTO rs9939609, MTHFR rs1801131 and MTHFR rs1801133 polymorphisms could be used to
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identity individual with elevated susceptibility to may serve as predisposing factors of PCOS, especially for Asians. However, further studies
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with larger sample sizes still need to verify our findings. Besides, given that the pathogenesis of PCOS is extremely complex, the probability that a specific genetic polymorphism could significantly contribute to its development is low, and we strongly recommend further studies to
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comprehensively explore potential roles of gene-gene interactions and gene-environmental interactions in the development of PCOS.
Authors' contributions
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Xiye Wang and Minyun Wu conceived of the study, participated in its design. Xiye Wang and Kaiping Wang conducted the systematic literature
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review. Juanying Yan performed data analyses. Xiye Wang and Minyun Wu drafted the manuscript. All authors have read and approved the final manuscript.
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Acknowledgments
None.
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Funding
None.
Conflict of interest
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The authors declare that they have no conflict of interest.
Ethical approval
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This article does not contain any studies with human participants or animals performed by any of the authors.
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References
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2. Pasquali R, Gambineri A. Polycystic ovary syndrome: a multifaceted disease from adolescence to adult age. Ann NY Acad Sci. 2006; 1092: 158-174.
3. Norman RJ, Dewailly D, Legro RS, Hickey TE. Polycystic ovary syndrome. Lancet. 2007; 370: 685-697. 4. Amato P, Simpson JL. The genetics of polycystic ovary syndrome. Best Pract Res Clin Obstet Gynaecol. 2004; 18: 707-718.
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5. Govind A, Obhrai MS, Clayton RN. Polycystic ovaries are inherited as an autosomal dominant trait: analysis of 29 polycystic ovary syndrome and 10 control families. J Clin Endocrinol Metab. 1999; 84: 38-43.
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6. Ioannidis A, Ikonomi E, Dimou NL, Douma L, Bagos PG. Polymorphisms of the insulin receptor and the insulin receptor substrates genes in
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polycystic ovary syndrome: A Mendelian randomization meta-analysis. Mol Genet Metable. 2010; 99: 174-183. 7. Urbanek M. The genetics of the polycystic ovary syndrome. Nat Clin Pract Endocrinol Metab. 2007; 3: 103-111.
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8. Luque-Ramirez M, San Millan JL, Escobar-Morreale HF. Genomic variants in polycystic ovary syndrome. Clin Chim Acta. 2006; 366:
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9. Gaulton KJ. Mechanisms of Type 2 Diabetes Risk Loci. Curr Diab Rep. 2017; 17: 72.
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10. Hara K, Kadowaki T, Odawara M. Genes associated with diabetes: potential for novel therapeutic targets? Expert Opin Ther Targets. 2016; 20: 255-267.
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11. Zdrojowy-Wełna A, Tupikowska M, Kolackov K, Bednarek-Tupikowska G. The role of fat mass and obesity-associated gene (FTO) in obesity - an overview. Endokrynol Pol. 2014; 65: 224-231. 12. Moher D, Liberati A, Tetzlaff J, Altman DG, PRISMA group. Preferred reporting items for systematic reviews and meta-analyses: the
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PRISMA statement. Ann Intern Med. 2009; 151: 264-269. 13. Stang A. Critical evaluation of the Newcastle-Ottawa scale for the assessment of the quality of nonrandomized studies in meta-analyses. Eur
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14. Nishi A, Milner DA Jr, Giovannucci EL, Nishihara R, Tan AS, Kawachi I, Ogino S. Integration of molecular pathology, epidemiology and social science for global precision medicine. Expert Rev Mol Diagn. 2016; 16: 11-23.
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15. Park JH, Li L, Choi JW, Baek KH. The Association of -429T>C and -374T>A Polymorphisms in the RAGE Gene with Polycystic Ovary
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Syndrome. Int J Med Sci. 2016; 13: 451-456.
16. Xue H, Zhao H, Liu X, Zhao YR, Chen ZJ, Ma J. Association of single-nucleotide polymorphisms rs2197076 and rs2241883 of
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FABP1 gene with polycystic ovary syndrome. J Assist Reprod Genet. 2016; 33: 75-83.
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17. Liu Z, Wang Z, Hao C, Tian Y, Fu J. Effects of ADIPOQ polymorphisms on PCOS risk: a meta-analysis. Reprod Biol Endocrinol. 2018; 16: 120.
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Figure legends Fig. 1. Flowchart of study selection for the present study.
Table 1. The characteristics of included studies for this meta-analysis.
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Genotypes (wtwt/wtmt/mtmt) First author, year
Country
Ethnicity
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Sample size
Cases FTO rs1421085 Attaoua 2008
France
Caucasian
207/100
Gu 2012
China
Asian
28/27
Kim 2012
Korea
Asian
377/386
Song 2014
Korea
Asian
432/927
Xue 2015
China
Asian
Ben Salem 2015
Tunisia
Hatziagelaki 2012
Greece
Liu 2018
China
Ramos 2015
Brazil
Song 2014 Xue 2015
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Alleles (wt) %
Controls
Cases/Controls
P-value for HWE
NOS score
NA
NA
51.2%/54/0%
NA
7
19/7/2
26/1/0
80.4%/98.1%
0.922
8
270/96/11
281/93/12
84.1%/84.8%
0.215
8
333/87/12
710/207/10
87.2%/87.8%
0.234
8
212/198
165/42/5
157/39/2
87.7%/89.1%
0.805
8
Mixed
128/150
NA
NA
59.4%/61.3%
NA
7
Caucasian
52/105
NA
NA
NA
NA
7
Asian
147/120
119/25/3
111/8/1
89.5%/95.8%
0.070
8
Mixed
199/99
NA
NA
NA
NA
7
Korea
Asian
432/927
334/86/12
710/207/10
87.2%/87.8%
0.234
8
China
Asian
212/198
162/44/6
160/32/6
86.8%/88.9%
0.011
8
Barber 2008
UK
Caucasian
463/1336
133/231/99
480/644/212
53.7%/60.0%
0.870
8
Ben Salem 2015
Tunisia
Mixed
128/150
NA
NA
60.2%/60.0%
N
7
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FTO rs8050136
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FTO rs9939609
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Branavan 2018
Sri Lanka
Caucasian
55/110
20/13/22
72/23/15
48.2%/75.9%
0.944
8
Kim 2014
South Korea
Asian
552/559
427/118/7
445/106/8
88.0%/89.1%
0.559
8
Li 2013
China
Asian
3599/3082
2665/867/67
2490/563/29
86.1%/89.9%
0.717
8
Louwers 2014
UK
Caucasian
563/791
NA
NA
54.0%/58.0%
NA
7
Louwers 2014
The Netherlands
Caucasian
510/2720
NA
NA
62.0%/63.0%
NA
7
Ramos 2015
Brazil
Mixed
199/99
NA
NA
NA
NA
7
Saxena 2013
USA
Mixed
510/448
NA
NA
56.9%//60.4%
NA
7
Xie 2018
China
Asian
102/97
79/22/1
74/22/1
88.2%//87.6%
0.650
8
Yan 2009
China
Asian
215/227
155/55/5
183/43/1
84.9%/90.1%
0.963
8
Yuan 2015
China
Asian
733/892
564/153/16
717/168/7
87.4%//89.8%
0.404
8
Idali 2012
Iran
Mixed
71/100
24/34/13
94/6/0
57.7%/94.0%
0.757
8
Jiang 2015
China
Asian
90/122
66/24/0
98/23/1
86.7%/89.8%
0.782
8
Palep-Singh 2007
UK
Caucasian
25/16
14/10/1
10/5/1
76.0%/78.1%
0.732
7
Palep-Singh 2007
UK
Asian
9/9/3
2/7/0
64.2%/61.1%
0.057
7
Qi 2015
China
Asian
115/58
71/39/5
43/14/1
78.7%/86.2%
0.903
8
Szafarowska 2016
Poland
Caucasian
76/56
40/26/10
34/17/5
69.7%/75.9%
0.202
8
Wu 2016
China
Asian
244/257
143/77/24
166/84/7
74.4%/80.9%
0.344
8
Mixed
261/256
209/49/3
209/45/2
89.5%/90.4%
0.803
8
Mixed
227/115
67/125/35
33/67/15
57.0%/57.8%
0.037
7
MTHFR rs1801131
MTHFR rs1801133
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21/9
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Carlus 2016
India
Choi 2009
USA
Idali 2012
Iran
Mixed
71/100
36/31/4
66/25/9
72.5%/78.5%
0.009
8
Jain 2012
India
Mixed
92/95
76/16/0
82/13/0
91.3%/93.2%
0.474
8
Jiang 2015
China
Asian
90/122
13/37/40
13/56/53
35.0%/33.6%
0.752
8
Jiao 2018
China
Asian
336/307
52/162/122
96/139/72
39.5%/53.9%
0.119
8
Karadeniz 2010
Turkey
Caucasian
86/70
15/65/6
35/28/7
55.2%/70.0%
0.690
8
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Naghavi 2015
Iran
Mixed
112/196
61/38/13
136/51/9
71.4%/82.4%
0.149
7
Orio 2003
Italy
Caucasian
70/70
16/41/13
17/38/15
52.1%/51.4%
0.469
8
Ożegowska 2016
Poland
Caucasian
168/99
87/52/29
53/37/9
67.3%/72.2%
0.495
7
Palep-Singh 2007
UK
Caucasian
25/16
11/12/2
10/5/1
68.0%/78.1%
0.732
7
Palep-Singh 2007
UK
Asian
21/9
14/7/0
9/0/0
83.3%//100.0%
NA
7
Qi 2015
China
Asian
115/58
14/60/41
21/23/14
38.3%/56.0%
0.137
8
Sills 2001
USA
Mixed
36/18
25/9/2
8/9/1
81.9%/69.4%
0.571
7
Szafarowska 2016
Poland
Caucasian
76/56
33/39/4
19/30/7
69.1%/60.7%
0.357
8
Tsanadis 2002
Greece
Caucasian
30/45
12/14/4
20/19/6
63.3%/65.6%
0.663
7
Wu 2016
China
Asian
244/257
94/106/44
122/104/31
60.2%/67.7%
0.231
8
Jin 2010
China
Asian
169/95
NA
NA
57.4%/67.9%
NA
7
Liu 2010
China
Asian
826/620
331/380/115
250/292/78
63.1%/63.9%
0.610
8
Liu 2012
China
Asian
145/192/69
147/154/40
59.4%/65.7%
0.972
8
Kim 2012
Korea
Asian
377/386
352/25/0
358/28/0
96.7%/96.4%
0.460
8
Včelák 2012
Czech Republic
Caucasian
329/376
169/135/25
199/151/26
72.0%/73.0%
0.714
7
Caucasian
358/2476
177/151/30
1175/1084/217
70.5%/69.3%
0.139
8
Caucasian
476/936
301/156/19
620/278/38
79.6%/81.1%
0.334
8
Mixed
118/147
37/51/30
46/69/32
53.0%/54.8%
0.523
8
TCF7L2 r290487
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TCF7L2 rs7901695
TCF7L2 rs7903146
ur
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406/341
o r p
e
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f o
Barber 2007
UK
Barber 2007
Finland
Ben-Salem 2014
Tunisia
Christopoulos 2008
Greece
Caucasian
183/148
43/108/32
52/76/20
53.0%/60.8%
0.346
8
Gammoh 2015
Bahrain
Mixed
242/236
NA
NA
66.1%/61.9%
NA
7
Kim 2012
Korea
Asian
377/386
350/27/0
357/29/0
96.4%/96.2%
0.443
8
Ramos 2013
Brazil
Mixed
200/102
109/57/34
52/38/12
68.8%/69.6%
0.228
8
Reddy 2016
India
Mixed
248/210
127/99/22
105/88/17
71.2%/71.0%
0.810
8
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Včelák 2012
Czech Republic
Caucasian
329/376
178/127/24
205/147/24
73.4%/74.1%
0.837
7
Xu 2010
China
Asian
326/290
261/61/4
232/56/2
89.4%/89.7%
0.485
8
Ramos 2013
Brazil
Mixed
200/102
124/67/9
63/36/3
78.8%/79.4%
0.423
8
Reddy 2016
India
Mixed
241/205
89/113/39
82/100/23
60.4%/64.4%
0.362
8
Ben-Salem 2014
Tunisia
Mixed
118/138
45/42/31
44/60/34
55.9%/53.6%
0.139
8
Biyasheva 2009
USA
Mixed
624/553
NA
NA
72.7%/70.5%
NA
7
Gammoh 2015
Bahrain
Mixed
242/236
NA
NA
66.1%//67.8%
NA
7
Kim 2012
Korea
Asian
377/386
372/5/0
383/3/0
99.3%/99.6%
0.939
8
Reddy 2016
India
Mixed
248/209
146/92/10
132/69/8
77.4%/79.7%
0.784
8
Včelák 2012
Czech Republic
Caucasian
329/376
206/147/23
75.7%/74.3%
0.633
7
TCF7L2 rs11196205
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TCF7L2 rs12255372
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189/120/20
Abbreviations: wt, Wild type; mt, Mutant type; HWE, Hardy-Weinberg equilibrium; NOS, Newcastle-ottawa scale; NA, Not available.
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Table 2. Meta-analysis results. Variables
Sample size
Dominant comparison
Recessive comparison
Over-dominant comparison
Allele comparison
(MM vs. Mm + mm)
(mm vs. MM + Mm)
(Mm vs. MM + mm)
(M vs. m)
p value
p value
p value
p value
OR (95%CI)
0.99 (0.82-1.20)
0.19
0.91 (0.78-1.05)
0.99 (0.82-1.20)
0.26
0.91 (0.77-1.07)
1.35 (0.74-2.45)
0.29
0.85 (0.63-1.15)
OR (95%CI)
OR (95%CI)
FTO rs1421085
OR (95%CI)
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Overall
1256/1638
0.55
0.95 (0.78-1.14)
0.06
1.69 (0.99-2.91)
0.94
Asian
1049/1538
0.55
0.95 (0.78-1.14)
0.06
1.69 (0.99-2.91)
0.94
Overall
1170/1599
0.08
0.63 (0.37-1.05)
0.33
1.22 (0.82-1.81)
0.33
Asian
791/1245
0.24
0.73 (0.43-1.23)
0.06
1.84 (0.96-3.53)
0.33
1.35 (0.74-2.45)
0.18
0.75 (0.49-1.14)
1.74 (1.42-2.13)
<0.0001
1.28 (1.17-1.40)
0.0002
0.79 (0.70-0.89)
2.32 (0.81-6.59)
0.48
1.08 (0.88-1.32)
0.02
0.73 (0.57-0.94)
1.97 (1.38-2.80)
<0.0001
1.33 (1.21-1.47)
<0.000
0.73 (0.67-0.80)
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FTO rs8050136
FTO rs9939609 Overall
7629/10511
<0.0001
0.72 (0.66-0.78)
<0.0001
Caucasian
1591/4957
0.10
0.49 (0.21-1.15)
0.12
Asian
5201/4857
<0.0001
0.72 (0.66-0.79)
0.0002
ur
MTHFR rs1801131 Overall
642/618
0.07
Caucasian
101/72
0.31
Asian
470/446
0.04
2060/1889
0.008
MTHFR rs1801133 Overall
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0.51 (0.24-1.07)
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1
<0.0001
3.43 (1.97-5.96)
0.19
1.58 (0.80-3.12)
0.04
0.52 (0.28-0.96)
0.73 (0.39-1.35)
0.55
1.37 (0.48-3.90)
0.49
1.25 (0.66-2.39)
0.37
0.80 (0.48-1.31)
0.74 (0.56-0.98)
0.002
3.23 (1.55-6.70)
0.54
1.10 (0.82-1.46)
0.003
0.69 (0.54-0.88)
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0.69 (0.53-0.91)
0.0002
1.43 (1.19-1.73)
0.07
1.23 (0.98-1.53)
0.003
0.78 (0.66-0.92)
Caucasian
455/356
0.24
0.71 (0.40-1.26)
0.87
1.04 (0.68-1.59)
0.26
1.40 (0.78-2.54)
0.13
0.85 (0.69-1.05)
Asian
806/753
0.02
0.52 (0.30-0.90)
<0.0001
1.60 (1.27-2.03)
0.19
1.15 (0.94-1.40)
0.005
0.66 (0.50-0.88)
Overall
1401/1056
0.34
0.87 (0.65-1.16)
0.07
1.26 (0.98-1.61)
0.99
1.00 (0.84-1.19)
0.07
0.81 (0.65-1.02)
Asian
1401/1056
0.34
0.87 (0.65-1.16)
0.07
1.26 (0.98-1.61)
0.99
1.00 (0.84-1.19)
0.07
0.81 (0.65-1.02)
TCF7L2 r290487
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TCF7L2 rs7901695 Overall
706/762
0.84
0.97 (0.75-1.26)
0.73
1.11 (0.63-1.96)
0.96
1.01 (0.77-1.31)
0.76
0.97 (0.78-1.20)
Overall
2857/5307
0.61
0.97 (0.87-1.09)
0.24
1.13 (0.92-1.39)
0.90
0.99 (0.89-1.11)
0.67
0.98 (0.90-1.07)
Caucasian
1346/3936
0.35
0.94 (0.82-1.07)
0.65
1.06 (0.82-1.37)
0.48
1.05 (0.92-1.20)
0.36
0.95 (0.86-1.06)
703/676
0.90
0.98 (0.71-1.35)
0.50
1.79 (0.33-9.84)
0.79
0.96 (0.69-1.32)
0.16
0.86 (0.69-1.06)
441/307
0.62
0.93 (0.68-1.25)
0.10
1.53 (0.92-2.55)
0.93 (0.69-1.25)
0.26
0.88 (0.70-1.10)
1938/1898
0.77
1.03 (0.84-1.27)
0.81
1.05 (0.71-1.53)
0.95 (0.77-1.18)
0.56
1.03 (0.92-1.15)
TCF7L2 rs7903146
Asian TCF7L2 rs11196205 Overall Overall
Abbreviations: OR, Odds ratio; CI, Confidence interval; NA, Not available.
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TCF7L2 rs12255372
0.61 0.67
All investigated polymorphisms contain a majo r allele (M) and a minor allele (m), the do minant co mparison is defined as MM versus Mm + mm, recessive comparison is defined as mm vs. MM + Mm, over-dominant comparison is defined as Mm versus MM + mm, and the allele comparison is defined as M versus m.
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The values in bold represent there is statistically significant differences between cases and controls.
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Highlights
This is so far the most comprehensive evidence-based meta-analysis on FTO/MTHFR/TCF7L2 polymorphisms and PCOS.
This meta-analysis proved that FTO rs9939609, MTHFR rs1801131 and MTHFR rs1801133 polymorphisms could be used to identity
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individual with higher susceptibility to PCOS, especially for Asians.
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Figure 1
Xiye Wang, Kaiping Wang, Juanying Yan, Minyun Wu PII:
S0888-7543(19)30473-2
DOI:
https://doi.org/10.1016/j.ygeno.2019.08.023
Reference:
YGENO 9347
To appear in:
Genomics
Received date:
22 July 2019
Revised date:
31 July 2019
Accepted date:
26 August 2019
Please cite this article as: X. Wang, K. Wang, J. Yan, et al., A meta-analysis on associations of FTO, MTHFR and TCF7L2 polymorphisms with polycystic ovary syndrome, Genomics (2018), https://doi.org/10.1016/j.ygeno.2019.08.023
This is a PDF file of an article that has undergone enhancements after acceptance, such as the addition of a cover page and metadata, and formatting for readability, but it is not yet the definitive version of record. This version will undergo additional copyediting, typesetting and review before it is published in its final form, but we are providing this version to give early visibility of the article. Please note that, during the production process, errors may be discovered which could affect the content, and all legal disclaimers that apply to the journal pertain.
© 2018 Published by Elsevier.
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A meta-analysis on associations of FTO, MTHFR and TCF7L2 polymorphisms with polycystic ovary syndrome
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Running head: FTO/MTHFR/TCF7L2 polymorphis ms and PCOS
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Xiye Wang, M.D. 1 , Kaiping Wang, M.D. 1 , Juanying Yan, M.D. 1 , and Minyun Wu, M.D.
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Address:
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1. Department of Gynaecology, People’s Hospital of Shengzhou City, Shengzhou, Zhejiang, China 2. Department of Obstetrics, Quzhou People's Hospital, Quzhou, Zhejiang, China
Correspondence to: Dr. Minyun Wu, Department of Obstetrics, Quzhou People's Hospital, No. 2 Bell Tower of Kecheng district, Quzhou 324000,
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Zhejiang, China. E-mail address: [email protected]; Phone number: 0086-0570-8895120; Fax number: 0086-0570-8895120
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Abstract
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Background: We aimed to better clarify the relationship between FTO/MTHFR/TCF7L2 polymorphisms and PCOS in a larger combined
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population by performing a meta-analysis.
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Methods: Eligible articles were retrieved from Pubmed, Embase, Web of Science and CNKI. Review Manager Version was used to perform statistical analyses.
Results: Forty-six studies were included for this meta-analysis. FTO rs9939609 polymorphism was found to be significantly associated with PCOS under dominant, recessive, over-dominant and allele comparisons, MTHFR rs1801131 polymorphism was found to be significantly
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associated with PCOS under recessive and allele comparisons, and MTHFR rs1801133 polymorphism was also found to be significantly associated with PCOS under dominant, recessive and allele comparisons in general population. In subgroup analyses, we found that positive
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results were mainly driven by the Asians.
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Conclusions: Collectively, this meta-analysis proved that FTO rs9939609, MTHFR rs1801131 and MTHFR rs1801133 polymorphisms could be used to identity individual with elevated susceptibility to may serve as predisposing factors of PCOS, especially for Asians.
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Keywords: Fat mass and obesity associated protein (FTO); Methylenetetrahydrofolate reductase (MTHFR); Transcription factor 7 Like 2 (TCF7L2); Polycystic ovary syndrome (PCOS); Meta-analysis
Introduction
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Polycystic ovary syndrome (PCOS), a common reproductive endocrine disorder of women at childbearing age, is characterized by polycystic ovaries, chronic anovulation, hyperandrogenism and an increased risk of multiple metabolic abnormalities such as insulin resistance, type II
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diabetes and obesity [1-3]. Although the precise pathogenesis mechanism of PCOS is still unrevealed, it was thought that genetic factors may contribute a lot to its development since numerous genetic variations were already found to be associated with a higher susceptibility to PCOS
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by previous genetic association studies [4-8].
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Fat mass and obesity associated protein (FTO), methylenetetrahydrofolate reductase (MTHFR) and transcription factor 7 like 2 (TCF7L2) were all demonstrated to be associated with various metabolic disorders such as type II diabetes and obesity by past clinical investigations [9-11].
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Given the metabolic nature of PCOS and the close relationship between above mentioned metabolic disorders and PCOS, it is possible that FTO,
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MTHFR and TCF7L2 polymorphisms may also affect individual susceptibility to PCOS. In recent years, some investigations already studied potential associations of FTO, MTHFR and TCF7L2 polymorphisms with PCOS.
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Nevertheless, the findings of these studies were not always consistent and the sample size of each study was also statistically insufficient. In this
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meta-analysis, we aimed to better clarify the relationship between FTO/MTHFR/TCF7L2 polymorphisms and PCOS in a larger combined population.
Materials and methods
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This meta-analysis was written in accordance with PRISMA checklist [12]. We also created an Open Science Framework (osf.io) account to
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make this meta-analysis more publicly available.
Literature search and inclusion criteria Eligible articles were
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retrieved from Pubmed, Web of Science, Embase and CNKI by using the following key words:
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(“methylenetetrahydrofolate reductase” OR “MTHFR” OR “Transcription Factor 7 Like 2” OR “TCF7L2” OR “fat mass and obesity associated protein” OR “FTO”) AND (“polymorphism” OR “variant” OR “variation” OR “mutation” OR “SNP” OR “genotype” OR “allele” OR “genetic
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association study” OR “genome-wide association study”) AND (“polycystic ovary syndrome” OR “PCOS”). Additionally, we also checked the reference lists of all retrieved articles.
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Inclusion criteria for this meta-analysis were as follows: (1) genetic association study about MTHFR/TCFL2/FTO polymorphisms and PCOS in human beings; (2) providing distributions of genotypes or alleles in cases and controls; (3) available full text in English or Chinese. We excluded studies when more than one of the following conditions was met: (1) studies that were not about MTHFR/TCFL2/FTO polymorphisms
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and PCOS; (2) reviews/comments/letters; (3) case reports or case series. If we found repeated publications by the same authors, only the most comprehensive study was included for this meta-analysis.
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Data extraction and quality assessment
Following information was extracted by two authors: the last name of the first author and publication year, country of the principal investigator
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and ethnicity of study participants, type of disease, total sample size of each study and the distribution of MTHFR/TCFL2/FTO polymorphisms
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in cases and controls. We also calculated the probability value (p value) of Hardy-Weinberg equilibrium (HWE). Newcastle-Ottawa scale (NOS) was used to evaluate the methodology quality of eligible studies [13]. The score of this scale ranged
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between zero and nine, if a study scored seven or more, we thought that the quality of this study was acceptable.
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Data extraction and quality assessment were conducted by two authors independently. We wrote to the corresponding authors for extra information when we thought that important information was missed.
Statistical analyses
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Review Manager Version 5.3.3 was used in this meta-analysis to perform statistical analyses. We used the Z test to assess whether MTHFR/TCFL2/FTO polymorphisms were significantly associated with PCOS, with the statistical significance p level set at 0.05. I2 statistics were used to evaluate between-study heterogeneities. Random-effect models (DerSimonian-Laird method) were used if I2 exceeded 50%.
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Otherwise, meta-analyses were conducted with fixed-effect models (Mantel-Haenszel method). We also conducted subgroup analyses by ethnicity of participants. We tested the robustness of synthetic results in sensitivity analyses. We evaluated publication biases by funnel plots.
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Results
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Characteristics of included studies
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One hundred and sixty-one articles were identified by our comprehensive literature searching. Sixty-seven articles were retrieved for eligibility assessment after exclusion of irrelevant and duplicate articles. Another fourteen reviews and three case series were subsequently excluded, and four other studies were excluded due to lack of essential data. Totally forty-six eligible studies were ultimately included for this meta-analysis (Fig. 1). Table 1 presented essential data extracted from included studies.
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Meta analyses results
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FTO rs9939609 polymorphism was found to be significantly associated with PCOS under dominant (p<0.0001, OR=0.72, 95%CI 0.66-0.78),
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recessive (p<0.0001, OR=1.74, 95%CI 1.42-2.13), over-dominant (p<0.0001, OR=1.28, 95%CI 1.17-1.40) and allele (p=0.0002, OR=0.79, 95%CI 0.70-0.89) comparisons, MTHFR rs1801131 polymorphism was found to be significantly associated with PCOS under recessive
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(p<0.0001, OR=3.43, 95%CI 1.97-5.96) and allele (p=0.04, OR=0.52, 95%CI 0.28-0.96) comparisons, and MTHFR rs1801133 polymorphism
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was also found to be significantly associated with PCOS under dominant (p=0.008, OR=0.69, 95%CI 0.53-0.91), recessive (p=0.0002, OR=1.43, 95%CI 1.19-1.73) and allele (p=0.003, OR=0.78, 95%CI 0.66-0.92) comparisons in general population. In subgroup analyses, we found that
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positive results were mainly driven by the Asian subgroup (Table 2).
Sensitivity analyses
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We tested the effects of each study on meta-analysis results in sensitivity analyses. The meta-analysis results remained unchanged in sensitivity analyses, suggesting that our findings were statistically robust.
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Publication biases
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We evaluated publication biases by using funnel plots. We did not observe dissymmetry in any funnel plots, which indicated that the possibility
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that our meta-analysis results were affected by overt publication biases was low.
Discussion
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In this meta-analysis, the combined results revealed that FTO rs9939609, MTHFR rs1801131 and MTHFR rs1801133 polymorphisms were all
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significantly associated with individual susceptibility to PCOS, especially in Asians. The meta-analysis results remained unchanged in sensitivity
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analyses, suggesting that our combined results were statistically robust. There are some points that should be considered when interpreting our meta-analysis results. Firstly, we did not impose any restrictions on investigated polymorphisms, meaning that as long as there were at least two eligible studies about one specific FTO/MTHFR/TCF7L2 polymorphisms, we would explore their potential associations with PCOS in this meta-analysis. However, considering that many combine
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analyses were still based on limited number of studies, maybe this meta-analysis was still not statistically sufficient to detect the actual genetic relationship between some of investigated polymorphisms and PCOS, thus further studies are still warranted to confirm genetic associations
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between FTO/MTHFR/TCF7L2 polymorphisms and PCOS. Secondly, the trends of associations for investigated polymorphisms in different
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ethnic subgroups were consistent, which suggested that combine studies of different ethnic origins in a meta-analysis was feasible, and the effects of FTO/MTHFR/TCF7L2 polymorphisms on susceptibility to PCOS in different ethnicities may be somehow similar. Thirdly, the
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aetiology of PCOS is very complicated, consequently, we strongly recommend future studies to conduct haplotype analyses and investigate
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potential gene-gene interactions to more comprehensively explore the effects of genetics on disease susceptibility [14]. Fourthly, according to our searching strategy, most of relevant studies were from Asian countries, while studies in other ethnicities were scarce, so future studies should
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continue to explore associations between FTO/MTHFR/TCF7L2 polymorphisms and PCOS, especially in Caucasians or Africans.
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This meta-analysis has some limitations. First, our meta-analysis results were derived from unadjusted combined analyses, and failure to adjust for some crucial baseline variables may impact the precision of our findings [15]. Second, environmental factors may also affect relationship between FTO/MTHFR/TCF7L2 polymorphisms and PCOS. Regrettably, most of included studies only focus on genetic associations, so we could not conduct analyses regarding genetic-environmental interactions [16]. Thirdly, we did not search for grey literatures. So although
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we did not observe dissymmetry in any funnel plots, there is still possibility that publication biases may influence our meta-analysis results [17].
Conclusions
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In summary, this meta-analysis proved that FTO rs9939609, MTHFR rs1801131 and MTHFR rs1801133 polymorphisms could be used to
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identity individual with elevated susceptibility to may serve as predisposing factors of PCOS, especially for Asians. However, further studies
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with larger sample sizes still need to verify our findings. Besides, given that the pathogenesis of PCOS is extremely complex, the probability that a specific genetic polymorphism could significantly contribute to its development is low, and we strongly recommend further studies to
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comprehensively explore potential roles of gene-gene interactions and gene-environmental interactions in the development of PCOS.
Authors' contributions
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Xiye Wang and Minyun Wu conceived of the study, participated in its design. Xiye Wang and Kaiping Wang conducted the systematic literature
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review. Juanying Yan performed data analyses. Xiye Wang and Minyun Wu drafted the manuscript. All authors have read and approved the final manuscript.
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Acknowledgments
None.
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Funding
None.
Conflict of interest
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The authors declare that they have no conflict of interest.
Ethical approval
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This article does not contain any studies with human participants or animals performed by any of the authors.
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References
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2. Pasquali R, Gambineri A. Polycystic ovary syndrome: a multifaceted disease from adolescence to adult age. Ann NY Acad Sci. 2006; 1092: 158-174.
3. Norman RJ, Dewailly D, Legro RS, Hickey TE. Polycystic ovary syndrome. Lancet. 2007; 370: 685-697. 4. Amato P, Simpson JL. The genetics of polycystic ovary syndrome. Best Pract Res Clin Obstet Gynaecol. 2004; 18: 707-718.
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5. Govind A, Obhrai MS, Clayton RN. Polycystic ovaries are inherited as an autosomal dominant trait: analysis of 29 polycystic ovary syndrome and 10 control families. J Clin Endocrinol Metab. 1999; 84: 38-43.
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6. Ioannidis A, Ikonomi E, Dimou NL, Douma L, Bagos PG. Polymorphisms of the insulin receptor and the insulin receptor substrates genes in
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polycystic ovary syndrome: A Mendelian randomization meta-analysis. Mol Genet Metable. 2010; 99: 174-183. 7. Urbanek M. The genetics of the polycystic ovary syndrome. Nat Clin Pract Endocrinol Metab. 2007; 3: 103-111.
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8. Luque-Ramirez M, San Millan JL, Escobar-Morreale HF. Genomic variants in polycystic ovary syndrome. Clin Chim Acta. 2006; 366:
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9. Gaulton KJ. Mechanisms of Type 2 Diabetes Risk Loci. Curr Diab Rep. 2017; 17: 72.
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10. Hara K, Kadowaki T, Odawara M. Genes associated with diabetes: potential for novel therapeutic targets? Expert Opin Ther Targets. 2016; 20: 255-267.
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11. Zdrojowy-Wełna A, Tupikowska M, Kolackov K, Bednarek-Tupikowska G. The role of fat mass and obesity-associated gene (FTO) in obesity - an overview. Endokrynol Pol. 2014; 65: 224-231. 12. Moher D, Liberati A, Tetzlaff J, Altman DG, PRISMA group. Preferred reporting items for systematic reviews and meta-analyses: the
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PRISMA statement. Ann Intern Med. 2009; 151: 264-269. 13. Stang A. Critical evaluation of the Newcastle-Ottawa scale for the assessment of the quality of nonrandomized studies in meta-analyses. Eur
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14. Nishi A, Milner DA Jr, Giovannucci EL, Nishihara R, Tan AS, Kawachi I, Ogino S. Integration of molecular pathology, epidemiology and social science for global precision medicine. Expert Rev Mol Diagn. 2016; 16: 11-23.
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15. Park JH, Li L, Choi JW, Baek KH. The Association of -429T>C and -374T>A Polymorphisms in the RAGE Gene with Polycystic Ovary
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Syndrome. Int J Med Sci. 2016; 13: 451-456.
16. Xue H, Zhao H, Liu X, Zhao YR, Chen ZJ, Ma J. Association of single-nucleotide polymorphisms rs2197076 and rs2241883 of
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FABP1 gene with polycystic ovary syndrome. J Assist Reprod Genet. 2016; 33: 75-83.
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17. Liu Z, Wang Z, Hao C, Tian Y, Fu J. Effects of ADIPOQ polymorphisms on PCOS risk: a meta-analysis. Reprod Biol Endocrinol. 2018; 16: 120.
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Figure legends Fig. 1. Flowchart of study selection for the present study.
Table 1. The characteristics of included studies for this meta-analysis.
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Genotypes (wtwt/wtmt/mtmt) First author, year
Country
Ethnicity
p e
Sample size
Cases FTO rs1421085 Attaoua 2008
France
Caucasian
207/100
Gu 2012
China
Asian
28/27
Kim 2012
Korea
Asian
377/386
Song 2014
Korea
Asian
432/927
Xue 2015
China
Asian
Ben Salem 2015
Tunisia
Hatziagelaki 2012
Greece
Liu 2018
China
Ramos 2015
Brazil
Song 2014 Xue 2015
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Alleles (wt) %
Controls
Cases/Controls
P-value for HWE
NOS score
NA
NA
51.2%/54/0%
NA
7
19/7/2
26/1/0
80.4%/98.1%
0.922
8
270/96/11
281/93/12
84.1%/84.8%
0.215
8
333/87/12
710/207/10
87.2%/87.8%
0.234
8
212/198
165/42/5
157/39/2
87.7%/89.1%
0.805
8
Mixed
128/150
NA
NA
59.4%/61.3%
NA
7
Caucasian
52/105
NA
NA
NA
NA
7
Asian
147/120
119/25/3
111/8/1
89.5%/95.8%
0.070
8
Mixed
199/99
NA
NA
NA
NA
7
Korea
Asian
432/927
334/86/12
710/207/10
87.2%/87.8%
0.234
8
China
Asian
212/198
162/44/6
160/32/6
86.8%/88.9%
0.011
8
Barber 2008
UK
Caucasian
463/1336
133/231/99
480/644/212
53.7%/60.0%
0.870
8
Ben Salem 2015
Tunisia
Mixed
128/150
NA
NA
60.2%/60.0%
N
7
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FTO rs8050136
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FTO rs9939609
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Branavan 2018
Sri Lanka
Caucasian
55/110
20/13/22
72/23/15
48.2%/75.9%
0.944
8
Kim 2014
South Korea
Asian
552/559
427/118/7
445/106/8
88.0%/89.1%
0.559
8
Li 2013
China
Asian
3599/3082
2665/867/67
2490/563/29
86.1%/89.9%
0.717
8
Louwers 2014
UK
Caucasian
563/791
NA
NA
54.0%/58.0%
NA
7
Louwers 2014
The Netherlands
Caucasian
510/2720
NA
NA
62.0%/63.0%
NA
7
Ramos 2015
Brazil
Mixed
199/99
NA
NA
NA
NA
7
Saxena 2013
USA
Mixed
510/448
NA
NA
56.9%//60.4%
NA
7
Xie 2018
China
Asian
102/97
79/22/1
74/22/1
88.2%//87.6%
0.650
8
Yan 2009
China
Asian
215/227
155/55/5
183/43/1
84.9%/90.1%
0.963
8
Yuan 2015
China
Asian
733/892
564/153/16
717/168/7
87.4%//89.8%
0.404
8
Idali 2012
Iran
Mixed
71/100
24/34/13
94/6/0
57.7%/94.0%
0.757
8
Jiang 2015
China
Asian
90/122
66/24/0
98/23/1
86.7%/89.8%
0.782
8
Palep-Singh 2007
UK
Caucasian
25/16
14/10/1
10/5/1
76.0%/78.1%
0.732
7
Palep-Singh 2007
UK
Asian
9/9/3
2/7/0
64.2%/61.1%
0.057
7
Qi 2015
China
Asian
115/58
71/39/5
43/14/1
78.7%/86.2%
0.903
8
Szafarowska 2016
Poland
Caucasian
76/56
40/26/10
34/17/5
69.7%/75.9%
0.202
8
Wu 2016
China
Asian
244/257
143/77/24
166/84/7
74.4%/80.9%
0.344
8
Mixed
261/256
209/49/3
209/45/2
89.5%/90.4%
0.803
8
Mixed
227/115
67/125/35
33/67/15
57.0%/57.8%
0.037
7
MTHFR rs1801131
MTHFR rs1801133
al
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21/9
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Carlus 2016
India
Choi 2009
USA
Idali 2012
Iran
Mixed
71/100
36/31/4
66/25/9
72.5%/78.5%
0.009
8
Jain 2012
India
Mixed
92/95
76/16/0
82/13/0
91.3%/93.2%
0.474
8
Jiang 2015
China
Asian
90/122
13/37/40
13/56/53
35.0%/33.6%
0.752
8
Jiao 2018
China
Asian
336/307
52/162/122
96/139/72
39.5%/53.9%
0.119
8
Karadeniz 2010
Turkey
Caucasian
86/70
15/65/6
35/28/7
55.2%/70.0%
0.690
8
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Naghavi 2015
Iran
Mixed
112/196
61/38/13
136/51/9
71.4%/82.4%
0.149
7
Orio 2003
Italy
Caucasian
70/70
16/41/13
17/38/15
52.1%/51.4%
0.469
8
Ożegowska 2016
Poland
Caucasian
168/99
87/52/29
53/37/9
67.3%/72.2%
0.495
7
Palep-Singh 2007
UK
Caucasian
25/16
11/12/2
10/5/1
68.0%/78.1%
0.732
7
Palep-Singh 2007
UK
Asian
21/9
14/7/0
9/0/0
83.3%//100.0%
NA
7
Qi 2015
China
Asian
115/58
14/60/41
21/23/14
38.3%/56.0%
0.137
8
Sills 2001
USA
Mixed
36/18
25/9/2
8/9/1
81.9%/69.4%
0.571
7
Szafarowska 2016
Poland
Caucasian
76/56
33/39/4
19/30/7
69.1%/60.7%
0.357
8
Tsanadis 2002
Greece
Caucasian
30/45
12/14/4
20/19/6
63.3%/65.6%
0.663
7
Wu 2016
China
Asian
244/257
94/106/44
122/104/31
60.2%/67.7%
0.231
8
Jin 2010
China
Asian
169/95
NA
NA
57.4%/67.9%
NA
7
Liu 2010
China
Asian
826/620
331/380/115
250/292/78
63.1%/63.9%
0.610
8
Liu 2012
China
Asian
145/192/69
147/154/40
59.4%/65.7%
0.972
8
Kim 2012
Korea
Asian
377/386
352/25/0
358/28/0
96.7%/96.4%
0.460
8
Včelák 2012
Czech Republic
Caucasian
329/376
169/135/25
199/151/26
72.0%/73.0%
0.714
7
Caucasian
358/2476
177/151/30
1175/1084/217
70.5%/69.3%
0.139
8
Caucasian
476/936
301/156/19
620/278/38
79.6%/81.1%
0.334
8
Mixed
118/147
37/51/30
46/69/32
53.0%/54.8%
0.523
8
TCF7L2 r290487
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TCF7L2 rs7901695
TCF7L2 rs7903146
ur
o J
406/341
o r p
e
r P
f o
Barber 2007
UK
Barber 2007
Finland
Ben-Salem 2014
Tunisia
Christopoulos 2008
Greece
Caucasian
183/148
43/108/32
52/76/20
53.0%/60.8%
0.346
8
Gammoh 2015
Bahrain
Mixed
242/236
NA
NA
66.1%/61.9%
NA
7
Kim 2012
Korea
Asian
377/386
350/27/0
357/29/0
96.4%/96.2%
0.443
8
Ramos 2013
Brazil
Mixed
200/102
109/57/34
52/38/12
68.8%/69.6%
0.228
8
Reddy 2016
India
Mixed
248/210
127/99/22
105/88/17
71.2%/71.0%
0.810
8
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Včelák 2012
Czech Republic
Caucasian
329/376
178/127/24
205/147/24
73.4%/74.1%
0.837
7
Xu 2010
China
Asian
326/290
261/61/4
232/56/2
89.4%/89.7%
0.485
8
Ramos 2013
Brazil
Mixed
200/102
124/67/9
63/36/3
78.8%/79.4%
0.423
8
Reddy 2016
India
Mixed
241/205
89/113/39
82/100/23
60.4%/64.4%
0.362
8
Ben-Salem 2014
Tunisia
Mixed
118/138
45/42/31
44/60/34
55.9%/53.6%
0.139
8
Biyasheva 2009
USA
Mixed
624/553
NA
NA
72.7%/70.5%
NA
7
Gammoh 2015
Bahrain
Mixed
242/236
NA
NA
66.1%//67.8%
NA
7
Kim 2012
Korea
Asian
377/386
372/5/0
383/3/0
99.3%/99.6%
0.939
8
Reddy 2016
India
Mixed
248/209
146/92/10
132/69/8
77.4%/79.7%
0.784
8
Včelák 2012
Czech Republic
Caucasian
329/376
206/147/23
75.7%/74.3%
0.633
7
TCF7L2 rs11196205
f o
TCF7L2 rs12255372
r P
e
o r p
189/120/20
Abbreviations: wt, Wild type; mt, Mutant type; HWE, Hardy-Weinberg equilibrium; NOS, Newcastle-ottawa scale; NA, Not available.
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Table 2. Meta-analysis results. Variables
Sample size
Dominant comparison
Recessive comparison
Over-dominant comparison
Allele comparison
(MM vs. Mm + mm)
(mm vs. MM + Mm)
(Mm vs. MM + mm)
(M vs. m)
p value
p value
p value
p value
OR (95%CI)
0.99 (0.82-1.20)
0.19
0.91 (0.78-1.05)
0.99 (0.82-1.20)
0.26
0.91 (0.77-1.07)
1.35 (0.74-2.45)
0.29
0.85 (0.63-1.15)
OR (95%CI)
OR (95%CI)
FTO rs1421085
OR (95%CI)
f o
Overall
1256/1638
0.55
0.95 (0.78-1.14)
0.06
1.69 (0.99-2.91)
0.94
Asian
1049/1538
0.55
0.95 (0.78-1.14)
0.06
1.69 (0.99-2.91)
0.94
Overall
1170/1599
0.08
0.63 (0.37-1.05)
0.33
1.22 (0.82-1.81)
0.33
Asian
791/1245
0.24
0.73 (0.43-1.23)
0.06
1.84 (0.96-3.53)
0.33
1.35 (0.74-2.45)
0.18
0.75 (0.49-1.14)
1.74 (1.42-2.13)
<0.0001
1.28 (1.17-1.40)
0.0002
0.79 (0.70-0.89)
2.32 (0.81-6.59)
0.48
1.08 (0.88-1.32)
0.02
0.73 (0.57-0.94)
1.97 (1.38-2.80)
<0.0001
1.33 (1.21-1.47)
<0.000
0.73 (0.67-0.80)
o r p
FTO rs8050136
FTO rs9939609 Overall
7629/10511
<0.0001
0.72 (0.66-0.78)
<0.0001
Caucasian
1591/4957
0.10
0.49 (0.21-1.15)
0.12
Asian
5201/4857
<0.0001
0.72 (0.66-0.79)
0.0002
ur
MTHFR rs1801131 Overall
642/618
0.07
Caucasian
101/72
0.31
Asian
470/446
0.04
2060/1889
0.008
MTHFR rs1801133 Overall
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0.51 (0.24-1.07)
e
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1
<0.0001
3.43 (1.97-5.96)
0.19
1.58 (0.80-3.12)
0.04
0.52 (0.28-0.96)
0.73 (0.39-1.35)
0.55
1.37 (0.48-3.90)
0.49
1.25 (0.66-2.39)
0.37
0.80 (0.48-1.31)
0.74 (0.56-0.98)
0.002
3.23 (1.55-6.70)
0.54
1.10 (0.82-1.46)
0.003
0.69 (0.54-0.88)
o J
0.69 (0.53-0.91)
0.0002
1.43 (1.19-1.73)
0.07
1.23 (0.98-1.53)
0.003
0.78 (0.66-0.92)
Caucasian
455/356
0.24
0.71 (0.40-1.26)
0.87
1.04 (0.68-1.59)
0.26
1.40 (0.78-2.54)
0.13
0.85 (0.69-1.05)
Asian
806/753
0.02
0.52 (0.30-0.90)
<0.0001
1.60 (1.27-2.03)
0.19
1.15 (0.94-1.40)
0.005
0.66 (0.50-0.88)
Overall
1401/1056
0.34
0.87 (0.65-1.16)
0.07
1.26 (0.98-1.61)
0.99
1.00 (0.84-1.19)
0.07
0.81 (0.65-1.02)
Asian
1401/1056
0.34
0.87 (0.65-1.16)
0.07
1.26 (0.98-1.61)
0.99
1.00 (0.84-1.19)
0.07
0.81 (0.65-1.02)
TCF7L2 r290487
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TCF7L2 rs7901695 Overall
706/762
0.84
0.97 (0.75-1.26)
0.73
1.11 (0.63-1.96)
0.96
1.01 (0.77-1.31)
0.76
0.97 (0.78-1.20)
Overall
2857/5307
0.61
0.97 (0.87-1.09)
0.24
1.13 (0.92-1.39)
0.90
0.99 (0.89-1.11)
0.67
0.98 (0.90-1.07)
Caucasian
1346/3936
0.35
0.94 (0.82-1.07)
0.65
1.06 (0.82-1.37)
0.48
1.05 (0.92-1.20)
0.36
0.95 (0.86-1.06)
703/676
0.90
0.98 (0.71-1.35)
0.50
1.79 (0.33-9.84)
0.79
0.96 (0.69-1.32)
0.16
0.86 (0.69-1.06)
441/307
0.62
0.93 (0.68-1.25)
0.10
1.53 (0.92-2.55)
0.93 (0.69-1.25)
0.26
0.88 (0.70-1.10)
1938/1898
0.77
1.03 (0.84-1.27)
0.81
1.05 (0.71-1.53)
0.95 (0.77-1.18)
0.56
1.03 (0.92-1.15)
TCF7L2 rs7903146
Asian TCF7L2 rs11196205 Overall Overall
Abbreviations: OR, Odds ratio; CI, Confidence interval; NA, Not available.
e
r P
f o
o r p
TCF7L2 rs12255372
0.61 0.67
All investigated polymorphisms contain a majo r allele (M) and a minor allele (m), the do minant co mparison is defined as MM versus Mm + mm, recessive comparison is defined as mm vs. MM + Mm, over-dominant comparison is defined as Mm versus MM + mm, and the allele comparison is defined as M versus m.
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The values in bold represent there is statistically significant differences between cases and controls.
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Highlights
This is so far the most comprehensive evidence-based meta-analysis on FTO/MTHFR/TCF7L2 polymorphisms and PCOS.
This meta-analysis proved that FTO rs9939609, MTHFR rs1801131 and MTHFR rs1801133 polymorphisms could be used to identity
f o
individual with higher susceptibility to PCOS, especially for Asians.
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Figure 1