Association of vitamin D receptor genetic polymorphisms with the risk of infertility: a systematic review and meta-analysis.

This systematic review and meta-analysis were conducted based on the preferred reporting items for systematic reviews and meta-analyses (PRISMA) guidelines (Fig. 1 ) [ 33 ]. The PROSPERO registration number and the published protocol were CRD42023416535. Fig. 1 Flow diagram of study selection process Fig. 2 Forest plot of different genetic models in infertile women in FokI (rs2228570) SNP A; Allele contrast (F vs. f), B; Recessive model, C; Dominant model, D; Over dominant model, E; Homozygote model, F; FF vs. Ff model, G; Ff vs. ff model Fig. 3 Forest plot of different genetic models in infertile women in BsmI (rs1544410) SNP A; Allele contrast (B vs. b), B; Recessive model, C; Dominant model, D; Over dominant model, E; Homozygote model, F; BB vs. Bb model, G; Bb vs. bb model Fig. 4 Forest plot of different genetic models in infertile women in TaqI (rs731236) SNP A; Allele contrast (T vs. t), B; Recessive model, C; Dominant model, D; Over dominant model, E; Homozygote model, F; TT vs. Tt model, G; Tt vs. tt model Fig. 5 Forest plot of different genetic models in infertile women in ApaI (rs7975232) SNP A; Allele contrast (A vs. a), B; Recessive model, C; Dominant model, D; Over dominant model, E; Homozygote model, F; AA vs. Aa model, G; Aa vs. aa model Fig. 6 Forest plot of different genetic models in infertile men in FokI (rs2228570) SNP A; Allele contrast (F vs. f), B; Recessive model, C; Dominant model, D; Over dominant model, E; Homozygote model, F; FF vs. Ff model, G; Ff vs. ff model Fig. 7 Funnel plot of different genetic models in infertile women in FokI (rs2228570) SNP A; Allele contrast (F vs. f), B; Recessive model, C; Dominant model, D; Over dominant model, E; Homozygote model, F; FF vs. Ff model, G; Ff vs. ff model Fig. 8 Funnel plot of different genetic models in infertile women in BsmI (rs1544410) SNP A; Allele contrast (B vs. b), B; Recessive model, C; Dominant model, D; Over dominant model, E; Homozygote model, F; BB vs. Bb model, G; Bb vs. bb model Fig. 9 Funnel plot of different genetic models in infertile women in TaqI (rs731236) SNP A; Allele contrast (T vs. t), B; Recessive model, C; Dominant model, D; Over dominant model, E; Homozygote model, F; TT vs. Tt model, G; Tt vs. tt model Fig. 10 Funnel plot of different genetic models in infertile women in ApaI (rs7975232) SNP A; Allele contrast (A vs. a), B; Recessive model, C; Dominant model, D; Over dominant model, E; Homozygote model, F; AA vs. Aa model, G; Aa vs. aa model Fig. 11 Funnel plot of different genetic models in infertile men in FokI (rs2228570) SNP A; Allele contrast (F vs. f), B; Recessive model, C; Dominant model, D; Over dominant model, E; Homozygote model, F; FF vs. Ff model, G; Ff vs. ff model Fig. 12 Sensitivity plot of different genetic models in infertile women in FokI (rs2228570) SNP A; Allele contrast (F vs. f), B; Recessive model, C; Dominant model, D; Over dominant model, E; Homozygote model, F; FF vs. Ff model, G; Ff vs. ff model Fig. 13 Sensitivity plot of different genetic models in infertile women in BsmI (rs1544410) SNP A; Allele contrast (B vs. b), B; Recessive model, C; Dominant model, D; Over dominant model, E; Homozygote model, F; BB vs. Bb model, G; Bb vs. bb model Fig. 14 Sensitivity plot of different genetic models in infertile women in TaqI (rs731236) SNP A; Allele contrast (T vs. t), B; Recessive model, C; Dominant model, D; Over dominant model, E; Homozygote model, F; TT vs. Tt model, G; Tt vs. tt model Fig. 15 Sensitivity plot of different genetic models in infertile women in ApaI (rs7975232) SNP A; Allele contrast (A vs. a), B; Recessive model, C; Dominant model, D; Over dominant model, E; Homozygote model, F; AA vs. Aa model, G; Aa vs. aa model Fig. 16 Sensitivity plot of different genetic models in infertile men in FokI (rs2228570) SNP A; Allele contrast (F vs. f), B; Recessive model, C; Dominant model, D; Over dominant model, E; Homozygote model, F; FF vs. Ff model, G; Ff vs. ff model Flow diagram of study selection process Forest plot of different genetic models in infertile women in FokI (rs2228570) SNP A; Allele contrast (F vs. f), B; Recessive model, C; Dominant model, D; Over dominant model, E; Homozygote model, F; FF vs. Ff model, G; Ff vs. ff model Forest plot of different genetic models in infertile women in BsmI (rs1544410) SNP A; Allele contrast (B vs. b), B; Recessive model, C; Dominant model, D; Over dominant model, E; Homozygote model, F; BB vs. Bb model, G; Bb vs. bb model Forest plot of different genetic models in infertile women in TaqI (rs731236) SNP A; Allele contrast (T vs. t), B; Recessive model, C; Dominant model, D; Over dominant model, E; Homozygote model, F; TT vs. Tt model, G; Tt vs. tt model Forest plot of different genetic models in infertile women in ApaI (rs7975232) SNP A; Allele contrast (A vs. a), B; Recessive model, C; Dominant model, D; Over dominant model, E; Homozygote model, F; AA vs. Aa model, G; Aa vs. aa model Forest plot of different genetic models in infertile men in FokI (rs2228570) SNP A; Allele contrast (F vs. f), B; Recessive model, C; Dominant model, D; Over dominant model, E; Homozygote model, F; FF vs. Ff model, G; Ff vs. ff model Funnel plot of different genetic models in infertile women in FokI (rs2228570) SNP A; Allele contrast (F vs. f), B; Recessive model, C; Dominant model, D; Over dominant model, E; Homozygote model, F; FF vs. Ff model, G; Ff vs. ff model Funnel plot of different genetic models in infertile women in BsmI (rs1544410) SNP A; Allele contrast (B vs. b), B; Recessive model, C; Dominant model, D; Over dominant model, E; Homozygote model, F; BB vs. Bb model, G; Bb vs. bb model Funnel plot of different genetic models in infertile women in TaqI (rs731236) SNP A; Allele contrast (T vs. t), B; Recessive model, C; Dominant model, D; Over dominant model, E; Homozygote model, F; TT vs. Tt model, G; Tt vs. tt model Funnel plot of different genetic models in infertile women in ApaI (rs7975232) SNP A; Allele contrast (A vs. a), B; Recessive model, C; Dominant model, D; Over dominant model, E; Homozygote model, F; AA vs. Aa model, G; Aa vs. aa model Funnel plot of different genetic models in infertile men in FokI (rs2228570) SNP A; Allele contrast (F vs. f), B; Recessive model, C; Dominant model, D; Over dominant model, E; Homozygote model, F; FF vs. Ff model, G; Ff vs. ff model Sensitivity plot of different genetic models in infertile women in FokI (rs2228570) SNP A; Allele contrast (F vs. f), B; Recessive model, C; Dominant model, D; Over dominant model, E; Homozygote model, F; FF vs. Ff model, G; Ff vs. ff model Sensitivity plot of different genetic models in infertile women in BsmI (rs1544410) SNP A; Allele contrast (B vs. b), B; Recessive model, C; Dominant model, D; Over dominant model, E; Homozygote model, F; BB vs. Bb model, G; Bb vs. bb model Sensitivity plot of different genetic models in infertile women in TaqI (rs731236) SNP A; Allele contrast (T vs. t), B; Recessive model, C; Dominant model, D; Over dominant model, E; Homozygote model, F; TT vs. Tt model, G; Tt vs. tt model Sensitivity plot of different genetic models in infertile women in ApaI (rs7975232) SNP A; Allele contrast (A vs. a), B; Recessive model, C; Dominant model, D; Over dominant model, E; Homozygote model, F; AA vs. Aa model, G; Aa vs. aa model Sensitivity plot of different genetic models in infertile men in FokI (rs2228570) SNP A; Allele contrast (F vs. f), B; Recessive model, C; Dominant model, D; Over dominant model, E; Homozygote model, F; FF vs. Ff model, G; Ff vs. ff model For this meta-analysis, we accessed several international databases, including PubMed (Medline), Web of Science, and Scopus. These databases were searched for literature published up to January 2023, using specific search terms and their synonyms: "Infertility," "miscarriage," "VDR" or "vitamin D receptor," and "Polymorphism." Furthermore, we conducted a manual search within these databases, carefully examining the references of relevant studies, and also looked through grey literature to identify any additional related studies that might not have been captured through the initial database search. To ensure thoroughness and accuracy, the screening process was carried out independently by two authors (AM, MA). In cases where there were disagreements, they were resolved through discussion and consensus with a third author (YM). This rigorous approach helped maintain the integrity of the study selection process and ensured that all relevant studies were included for analysis. Eligible studies were limited to those [ 1 ] case–control studies whose main purpose was to determine the association between VDR gene polymorphisms ApaI, BsmI, TaqI, and FokI and the risk of infertility and miscarriage, (2) studies that have reported the frequencies of genotypes or alleles by comparing at least two groups, a group including Infertility or miscarriage against healthy groups, (3) and, studies report odds ratio (ORs) and 95% confidence interval (CIs). Exclusion criteria included (1) all other types of studies including cohorts, cross-sectional, case reports, case series, letters to the editor, reports, clinical trial studies, and review studies, (2) Case–control studies without reporting inclusion criteria, (3) Repetitive and non-English language studies. The process of data extraction involved utilizing a structured form designed to gather essential information from each included study. This information encompassed various key aspects, such as the author's name, study location, publication date, ethnicity of the participants, mean age of the study population, sample size, genotyping methods employed, as well as the number of cases and controls pertaining to VDR gene polymorphisms. The Newcastle–Ottawa Quality Assessment Scale (NOS) checklist was used with the purpose of methodological quality and risk evaluation of non-randomized studies in Meta-Analysis. The NOS consists of eight items divided into three categories: Selection of cases and controls, comparability of them, and Ascertainment of exposure. Items can be awarded a maximum of one star for each one within the two first categories and a maximum of two stars for Comparability. The scoring ranged from zero to nine. A score of 6 and above indicates the high quality of the study[ 34 ]. The control genotype distribution was assessed by the Hardy–Weinberg equilibrium (HWE) ( p  < 0.05 was considered meaningful). Calculation of ORs and 95% confidence intervals (CIs) in seven different genetic models was used to estimate its effect on a forest plot and the strength of the relationship between VDR polymorphisms and the risk of infertility and miscarriage. genetic models of polymorphisms are discussed in the following: TaqI —allele contrast (t vs. T), recessive model (tt vs. Tt + TT), dominant model (tt + Tt vs. TT), over dominant (Tt vs. TT + tt), tt vs. TT model, TT vs. Tt model, Tt vs. tt model; FokI — allele contrast (f vs. F), recessive model (ff vs. Ff + FF), dominant model (ff + Ff vs. FF), over-dominant model (Ff vs. FF + ff), ff vs. FF model, FF vs. Ff model, Ff vs. ff model; ApaI —allele contrast (a vs. A), recessive model (aa vs. Aa + AA), dominant model (aa + Aa vs. AA), over dominant model (Aa vs. AA + aa), aa vs. AA model, AA vs. Aa model, Aa vs. aa model; BsmI —allele contrast (b vs. B), recessive model (bb vs. Bb + BB), dominant model (bb + Bb vs. BB), over dominant model (Bb vs. BB + bb), bb vs. BB model, BB vs. Bb model, Bb vs. bb model. Also, heterogeneity between studies was assessed by Q Cochrane tests and I 2 . To test Publication biases, funnel plots and Egger’s test ( p  < 0.05) were used. All statistical analyses were done using MetaGenyo; a web tool to conduct a meta-analysis of genetic association studies [ 35 ]. The forest plot and funnel plot pertaining to all examined polymorphisms are depicted in (Figs. 2 ,  3 ,  4 ,  5 , 6 ,  7 , 8 , 9 , 10 , 11 , 12 , 13 , 14 , 15 and 16 ).

Out of a total of 6060 and 2194 relevant citations on infertility and miscarriage, respectively, 5930 references remained after eliminating duplicates. Following title and abstract screening, and full-text review, 24 studies meeting the search criteria were identified, comprising 3424 cases and 3697 controls (refer to Tables 1 , 2 and 3 ). Notably, the total cases and controls were categorized based on the methodology of the studies rather than individual SNP analysis. Among these studies, 18 investigated the association of VDR genetic polymorphisms with female infertility, 2 with male infertility, and 4 with miscarriage. Tables 4 , 5 , and 6 summarizes the characteristics and genotype frequencies of the included studies. Table 1 Characteristics of studies included in the meta-analysis (infertile women) Study author Year Country Continent Ethnicity Disease Case/control in each SNP Age case/control (Mean) Genotyping Method NOS score FokI (rs2228570)  E. Wehr et al 2011 Austria Europe PCOS 538/135 23–31/ 26–36 NucleoSpin Blood method 8  E. Isbilen et al 2020 Turkey Asia Idiopathic infertility 101/99 27.45 ± 5.75/ 29.91 ± 4.98 PCR–RFLP 7  J. Djurovic et al 2018 Sebria Europe Idiopathic infertility 114/130 PCR–RFLP 6  M. Szczepańska et al 2015 Poland Europe Caucasian Endometriosis-associated infertility 154/346 20–42/ 19–39 PCR–RFLP 8  S. Dasgupta et al 2015 India Asia PCOS 252/252 PCR–RFLP 9  Mila W. Reginatto et al 2018 Brazil America Idiopathic infertility 49/57 35 ± 0.5/ 44 ± 0.9 TaqMan qPCR and Sanger sequencing 8  M Jafari et al 2021 Iran Asia Endometriosis-associated infertility 116/113 32 ± 12/ 31 ± 14 ARMS-PCR and PCR–RFLP 9  M. Bagheri et al 2012 Iran Asia PCOS 46/46 26.58 ± 3.33/ 28.24 ± 5.25 PCR–RFLP 8  T. Mahmoudi et al 2009 Iran Asia PCOS 162/162 28.92 ± 0.41/ 29.91 ± 0.58 PCR 9  D. Jedrzejuk et al 2015 Poland Europe Caucasian PCOS 90/98 PCR and mini-sequencing 9  T. Mahmoudi et al 2015 Iran Asia PCOS 35/35 19–42/ 19–44 PCR–RFLP 8  D. K. Song et al 2019 Korea Asia PCOS 432/927 24 ± 5/ 27 ± 5 8  F. L. Vilarino et al 2011 Brazil America Endometriosis-associated infertility 132/133 35.1 ± 3.9/ 39.7 ± 3.2 PCR–RFLP 7  F. L. Vilarino et al 2011 Brazil America Idiopathic infertility 62/133 35.7 ± 5.0/ 39.7 ± 3.2 PCR–RFLP 7  F. L. Vilarino et al 2011 Brazil America Endometriosis-associated infertility 147/154 PCR–RFLP 6 TaqI (rs731236)  E. Wehr et al 2011 Austria Europe PCOS 536/137 23–31/ 26–36 NucleoSpin Blood method 8  M. Bagheri et al 2013 Iran Asia PCOS 38/38 26.03 ± 4.98/ 27.18 ± 4.95 PCR–RFLP 8  A. S. El-Shal et al 2013 Egypt Africa Caucasian PCOS 150/150 29.8 ± 5.6/ 29.3 ± 6.2 PCR–RFLP 9  T. Mahmoudi et al 2009 Iran Asia PCOS 162/162 28.92 ± 0.41/ 29.91 ± 0.58 PCR 9  D. Jedrzejuk et al 2015 Poland Europe Caucasian PCOS 90/98 PCR and mini-sequencing 9  T. Mahmoudi et al 2015 Iran Asia _ PCOS 35/35 19–42/19–44 PCR–RFLP 8  E. Isbilen et al 2020 Turkey Asia Idiopathic infertility 101/99 27.45 ± 5.75/ 29.91 ± 4.98 PCR–RFLP 7  J. Djurovic et al 2018 Sebria Europe Idiopathic infertility 114/128 PCR–RFLP 6  S. Dasgupta et al 2015 India Asia PCOS 251/252 PCR–RFLP 9  F. L. Vilarino et al 2011 Brazil America Endometriosis-associated infertility 132/133 35.1 ± 3.9/ 39.7 ± 3.2 PCR–RFLP 7  F. L. Vilarino et al 2011 Brazil America Idiopathic infertility 62/133 35.7 ± 5.0/ 39.7 ± 3.2 PCR–RFLP 7  M Jafari et al 2021 Iran Asia Endometriosis-associated infertility 116/114 32 ± 12/ 31 ± 14 ARMS-PCR and PCR–RFLP 9 BsmI (rs1544410)  E. Wehr et al 2011 Austria Europe PCOS 537/137 23–31/ 26–36 NucleoSpin Blood method 8  M. Bagheri et al 2012 Iran Asia PCOS 46/46 26.58 ± 3.33/ 28.24 ± 5.25 PCR–RFLP 8  T. Mahmoudi et al 2009 Iran Asia PCOS 162/162 28.92 ± 0.41/ 29.91 ± 0.58 PCR 9  D. Jedrzejuk et al 2015 Poland Europe Caucasian PCOS 90/98 PCR and mini-sequencing 9  T. Mahmoudi et al 2015 Iran Asia PCOS 35/35 19–42/ 19–44 PCR–RFLP 8  D. K. Song et al 2019 Korea Asia PCOS 430/923 24 ± 5/ 27 ± 5 - 8  N. Ramezani et al 2020 Iran Asia PCOS 38/40 28.58 ± 5.83/ 31.34 ± 5.5 PCR–RFLP 8  E. Isbilen et al 2020 Turkey Asia Idiopathic infertility 101/100 27.45 ± 5.75/ 29.91 ± 4.98 PCR–RFLP 7  J. Djurovic et al 2018 Serbia Europe Idiopathic infertility 106/119 PCR–RFLP 6  M. Szczepańska et al 2015 Poland Europe Caucasian Endometriosis-associated infertility 154/346 20–42/ 19–39 PCR–RFLP 8  F. L. Vilarino et al 2011 Brazil America Endometriosis-associated infertility 132/133 35.1 ± 3.9/ 39.7 ± 3.2 PCR–RFLP 7  F. L. Vilarino et al 2011 Brazil America Idiopathic infertility 62/133 35.7 ± 5.0/ 39.7 ± 3.2 PCR–RFLP 7  Mila W. Reginatto et al 2018 Brazil America Idiopathic infertility 54/86 35 ± 0.5/ 44 ± 0.9 TaqMan qPCR and Sanger sequencing 8  M Jafari et al 2021 Iran Asia Endometriosis-associated infertility 116/112 32 ± 12/ 31 ± 14 ARMS-PCR and PCR–RFLP 9 ApaI (rs7975232)  E. Wehr et al 2011 Austria Europe PCOS 543/145 23–31/ 26–36 NucleoSpin Blood method 8  A. S. El-Shal et al 2013 Egypt Africa Caucasian PCOS 150/150 29.8 ± 5.6/ 29.3 ± 6.2 PCR–RFLP 9  T. Mahmoudi et al 2009 Iran Asia PCOS 162/162 28.92 ± 0.41/ 29.91 ± 0.58 PCR 9  D. Jedrzejuk et al 2015 Poland Europe Caucasian PCOS 90/98 PCR and mini-sequencing-RFLP 9  T. Mahmoudi et al 2015 Iran Asia PCOS 35/35 19–42/ 19–44 PCR–RFLP 8  E. Isbilen et al 2020 Turkey Asia Idiopathic infertility 101/100 27.45 ± 5.75/ 29.91 ± 4.98 PCR–RFLP 7  J. Djurovic et al 2018 Serbia Europe Idiopathic infertility 114/129 PCR–RFLP 6  S. Dasgupta et al 2015 India Asia PCOS 249/251 PCR–RFLP 9  S. Siddamalla et al 2018 India Asia PCOS 95/130 PCR–RFLP 9  M Jafari et al 2021 Iran Asia Endometriosis-associated infertility 116/114 32 ± 12/ 31 ± 14 ARMS-PCR and PCR–RFLP 9  D. K. Song et al 2019 Korea Asia PCOS 432/927 24 ± 5/ 27 ± 5 - 8  F. L. Vilarino et al 2011 Brazil America Endometriosis-associated infertility 132/133 35.1 ± 3.9/ 39.7 ± 3.2 PCR–RFLP 7  F. L. Vilarino et al 2011 Brazil America Idiopathic infertility 72/133 35.7 ± 5.0/ 39.7 ± 3.2 PCR–RFLP 7 NR not reported, PCR Polymerase chain reaction, RFLP restriction fragment length polymorphism Table 2 Characteristics of studies included in the meta-analysis (infertile men) Study author Year Country Continent Ethnicity Disease Total cases/controls Case/control in each SNP Age case/control (Mean) Genotyping Method NOS score FokI (rs2228570)  M. Mohebi et al 2016 Iran Asia infertile men 100/100 100/100 39.1 ± 4.7/ 39.4 ± 5.07 PCR–RFLP 8  M. Mohebi et al 2016 Iran Asia infertile men 100/100 100/100 39.3 ± 4.8/ 39.4 ± 5.07 PCR–RFLP 8  R. Bhakat et al 2017 India Asia infertile men 50/54 50/54 28.78 ± 4.92/- PCR–RFLP 7 Table 3 Characteristics of studies (miscarriage) Study author Year Country Continent Ethnicity Disease Total cases/controls Case/control in each SNP Age case/control (Mean) Genotyping Method NOS score TaqI (rs731236)  A Barisic et al 2019 Slovenia and Croatia Europe Recurrent pregnancy loss (RPL) loss of two or more pregnancies before 24 weeks of gestation 160/160 160/160 PCR–RFLP 7 ApaI (rs7975232)  D. Liu et al 2021 China Asia Recurrent pregnancy loss (RPL) loss of two or more pregnancies before 24 weeks of gestation 75/83 71/49 20–45/ 20–45 sequencing PCR 8 FokI (rs2228570)  V. E. Radzinsky et al 2021 Russia Asia Recurrent pregnancy loss (RPL) loss of two or more pregnancies before 24 weeks of gestation 43/77 43/76 18–41/ 18–41 RT-PCR 9  A Barisic et al 2019 Slovenia and Croatia Europe Recurrent pregnancy loss (RPL) loss of two or more pregnancies before 24 weeks of gestation 160/160 160/160 PCR–RFLP 7 Table 4 Distribution of genotype and allele among cases and controls (infertile women) Study author Cases Controls P-HWE P-HWE adjusted FF Ff ff F f FF Ff ff F f FokI (rs2228570)   E. Wehr et al 82 241 215 405 671 22 60 53 104 166 0.473 0.683   E. Isbilen et al 19 17 65 55 147 17 15 67 49 149 0.00 0.551   J. Djurovic et al 21 59 34 101 127 12 64 54 88 172 0.257 0.325   M. Szczepańska et al 37 88 29 162 146 65 189 92 319 373 0.065 0.683   S. Dasgupta et al 10 87 155 107 397 15 85 152 115 389 0.501 0.583   Mila W. Reginatto et al 23 17 9 63 35 29 21 7 79 35 0.311 0.01   M Jafari et al 3 76 37 82 150 5 65 43 75 151 0.001 0.972   M. Bagheri et al 4 20 22 28 64 2 15 29 19 73 0.972 0.727   T. Mahmoudi et al 12 67 83 91 233 7 59 96 73 251 0.581 0.965   D. Jedrzejuk et al 11 51 28 73 107 25 50 23 100 96 0.836 0.972   T. Mahmoudi et al 2 17 16 21 49 1 10 24 12 58 0.972 0.768   D. K. Song et al 67 212 153 346 518 159 435 333 753 1101 0.407 0.678   F. L. Vilarino et al 60 61 11 181 83 59 64 10 182 84 0.190 0.475   F. L. Vilarino et al 31 28 3 90 34 59 64 10 182 84 0.190 0.475   F. L. Vilarino et al 65 69 13 199 95 64 77 13 205 103 0.126 0.473 Study author Cases Controls P-HWE P-HWE adjusted TT Tt tt T t TT Tt tt T t TaqI (rs731236)   E. Wehr et al 226 238 72 690 382 49 65 23 163 111 0.854 0.931   M. Bagheri et al 16 14 8 46 30 17 19 2 53 23 0.255 0.398   A. S. El-Shal et al 40 74 36 154 146 69 61 20 199 101 0.27 0.398   T. Mahmoudi et al 71 71 20 213 111 72 76 14 220 104 0.330 0.398   D. Jedrzejuk et al 37 45 8 119 61 49 37 12 135 61 0.237 0.398   T. Mahmoudi et al 15 14 6 44 26 15 16 4 46 24 0.931 0.931   E. Isbilen et al 44 37 20 125 77 76 15 8 167 31 0.000 0.000   J. Djurovic et al 47 46 21 140 88 54 54 20 162 94 0.296 0.398   S. Dasgupta et al 112 92 47 316 186 110 105 37 325 179 0.151 0.398   F. L. Vilarino et al 55 62 15 172 92 50 71 12 171 95 0.060 0.243   F. L. Vilarino et al 20 30 12 70 54 50 71 12 171 95 0.060 0.243   M Jafari et al 43 64 9 150 82 59 49 6 167 61 0.301 0.398 Study author Cases Controls P-HWE P-HWE adjusted BB Bb bb B b BB Bb bb B b BsmI (rs1544410)   E. Wehr et al 77 244 216 398 676 22 66 49 110 164 0.977 0.977   M. Bagheri et al 15 27 4 57 35 20 24 2 64 28 0.115 0.179   T. Mahmoudi et al 24 85 53 133 191 18 91 53 127 197 0.023 0.080   D. Jedrzejuk et al 14 45 31 73 107 13 42 43 68 128 0.591 0.747   T. Mahmoudi et al 10 12 13 32 38 5 23 7 33 37 0.059 0.119   D. K. Song et al 4 40 386 48 812 3 94 826 100 1746 0.851 0.917   N. Ramezani et al 25 10 3 60 16 23 16 1 62 18 0.352 0.493   E. Isbilen et al 39 49 13 127 75 19 59 22 97 103 0.070 0.122   J. Djurovic et al 29 50 27 108 104 36 72 11 144 94 0.003 0.017   M. Szczepańska et al 56 76 22 188 120 147 154 45 448 244 0.640 0.747   F. L. Vilarino et al 10 69 53 89 175 8 66 59 82 184 0.059 0.111   F. L. Vilarino et al 4 34 24 42 82 8 66 59 82 184 0.059 0.111   Mila W. Reginatto et al 23 8 23 54 54 31 8 47 70 102 0.000 0.000   M Jafari et al 17 65 34 99 133 9 71 32 89 135 0.006 0.004 Study author Cases Controls P-HWE P-HWE adjusted AA Aa aa A a AA Aa aa A a ApaI (rs7975232)   E. Wehr et al 127 274 142 528 558 37 60 48 134 156 0.043 0.113   A. S. El-Shal et al 22 65 63 109 191 18 64 68 100 200 0.624 0.737   T. Mahmoudi et al 36 68 58 140 184 23 90 49 136 188 0.073 0.137   D. Jedrzejuk et al 19 52 19 90 90 17 49 32 83 113 0.812 0.812   T. Mahmoudi et al 9 11 15 29 41 6 21 8 33 37 0.227 0.369   E. Isbilen et al 9 85 7 103 99 2 94 4 98 102 0.000 0.000   J. Djurovic et al 12 54 48 78 150 13 77 39 103 155 0.005 0.023   S. Dasgupta et al 12 120 117 144 354 12 117 122 141 361 0.014 0.048   S. Siddamalla et al 32 21 42 85 105 25 35 70 85 175 0.000 0.000   M Jafari et al 18 55 43 91 141 25 59 30 109 119 0.692 0.749   D. K. Song et al 28 164 240 220 644 46 367 514 459 1395 0.056 0.122   F. L. Vilarino et al 44 72 16 160 104 49 67 17 165 101 0.423 0.550   F. L. Vilarino et al 22 29 11 73 51 49 67 17 165 101 0.423 0.550 P-HWE P value for Hardy–Weinberg equilibrium Table 5 Distribution of genotype and allele among cases and controls (infertile men) Study author cases controls P-HWE P-HWE adjusted FF Ff ff F f FF Ff ff F f FokI (rs2228570)   M. Mohebi et al 1 21 78 23 177 0 18 82 18 182 0.322 0.322   M. Mohebi et al 3 23 74 29 171 0 18 82 18 182 0.322 0.322   R. Bhakat et al 8 13 29 29 71 3 6 45 12 96 0.001 0.003 Table 6 Distribution of genotype and allele among cases and controls (miscarriage) Study author C ases C ontrols P-HWE P-HWE adjusted FF Ff ff F f FF Ff ff F f FokI (rs2228570)   V. E. Radzinsky et al 16 21 6 53 33 21 37 18 79 73 0.828 0.832   A Barisic et al 17 75 68 109 211 26 87 47 139 181 0.177 0.355 Study author Cases Controls P-HWE P-HWE adjusted AA Aa aa A a AA Aa aa A a ApaI (rs7975232)   D. Liu et al 48 20 3 116 26 30 17 2 77 21 0.832 0.832 Study author Cases Controls P-HWE P-HWE adjusted TT Tt tt T t TT Tt tt T t TaqI (rs731236)   A Barisic et al 23 64 73 110 210 34 67 59 135 185 0.073 0.293 Characteristics of studies included in the meta-analysis (infertile women) NR not reported, PCR Polymerase chain reaction, RFLP restriction fragment length polymorphism Characteristics of studies included in the meta-analysis (infertile men) Characteristics of studies (miscarriage) Distribution of genotype and allele among cases and controls (infertile women) P-HWE P value for Hardy–Weinberg equilibrium Distribution of genotype and allele among cases and controls (infertile men) Distribution of genotype and allele among cases and controls (miscarriage) Fourteen studies involving 5,210 participants reported on the association between FokI SNP and female infertility. While a protective association was observed in the FF vs. Ff model (OR = 0.87, 95% CI = 0.76–1.00, P  = 0.05), no significant association was found in other genetic models. Thirteen studies examined the association between BsmI SNP and infertility, with no significant correlation found in any of the genetic models assessed. Eleven studies focused on TaqI SNP, reporting a positive association in some genetic models, including Allele contrast and Recessive model, but not in others. Twelve case–control studies evaluated the ApaI SNP, showing a protective association in the Dominant model and Aa vs. aa model, but no significant association in other genetic models. Two studies involving 404 participants investigated the association between FokI SNP and male infertility, revealing a protective association in the Recessive model. Four studies examined the association between VDR genetic polymorphisms and miscarriage, evaluating TaqI, ApaI, FokI, and BsmI SNPs. Characteristics of these studies are summarized in Tables 7 , 8 and 9 . Table 7 Main results of pooled ORs in the meta-analysis of VDR gene polymorphisms (infertile women) Comparisons Number of studies Test of association Test of heterogeneity Publication bias OR 95% CI p -value Model p -value I^2 p -value (Egger's test) FokI (rs2228570)   f vs. F Allele contrast 14 0.939 [0.861; 1.023] 0.151 Fixed 0.083 0.356 0.220   ff vs. Ff + FF Recessive model 14 0.887 [0.780; 1.008] 0.067 Fixed 0.336 0.104 0.354   ff + Ff vs. FF Dominant model 14 0.971 [0.831; 1.134] 0.712 Fixed 0.270 0.163 0.650   Ff vs. FF + ff Over dominant 14 1.090 [0.970; 1.225] 0.145 Fixed 0.948 0.000 0.213   ff vs. FF Homozygote 14 0.944 [0.777; 1.147] 0.564 Fixed 0.094 0.341 0.516   FF vs. Ff 14 0.876 [0.765; 1.003] 0.056 Fixed 0.776 0.000 0.439   Ff vs. ff 14 1.001 [0.849; 1.180] 0.985 Fixed 0.650 0.000 0.961 TaqI (rs731236)   t vs. T Allele contrast 11 1.206 [1.084; 1.342] 0.000 Fixed 0.000 0.704 0.148   tt vs. Tt + TT Recessive model 11 1.383 [1.119; 1.709] 0.002 Fixed 0.165 0.285 0.160   tt + Tt vs. TT Dominant model 11 1.214 [0.048; 1.407] 0.009 Fixed 0.000 0.698 0.270   Tt vs. TT + tt Over dominant 11 1.0325 [0.892; 1.195] 0.668 Fixed 0.009 0.558 0.489   tt vs. TT Homozygote 11 1.459 [1.162; 1.832] 0.001 Fixed 0.019 0.514 0.169   TT vs. Tt 11 1.285 [1.024; 1.611] 0.029 Fixed 0.479 0.000 0.350   Tt vs. tt 11 1.134 [0.969; 1.327] 0.115 Fixed 0.001 0.649 0.325 BsmI (rs1544410)   b vs. B Allele contrast 13 1.030 [0.929; 1.141] 0.570 Fixed 0.076 0.375 0.448   bb vs. Bb + BB Recessive model 13 1.131 [0.938; 1.365] 0.195 Fixed 0.103 0.340 0.044   bb + Bb vs. BB Dominant model 13 0.986 [0.844; 1.151] 0.859 Fixed 0.046 0.424 0.367   Bb vs. BB + bb Over dominant 13 0.915 [0.795; 1.053] 0.216 Fixed 0.114 0.325 0.445   bb vs. BB Homozygote 13 1.095 [0.864; 1.388] 0.449 Fixed 0.038 0.442 0.805   BB vs. Bb 13 1.161 [0.950; 1.419] 0.142 Fixed 0.116 0.323 0.085   Bb vs. bb 13 0.936 [0.794; 1.103] 0.434 Fixed 0.055 0.408 0.342 ApaI (rs7975232)   a vs. A Allele contrast 12 0.953 [0.872; 1.042] 0.299 Fixed 0.212 0.229 0.898   aa vs. Aa + AA Recessive model 12 1.000 [0.879; 1.138] 0.992 Fixed 0.060 0.411 0.353   aa + Aa vs. AA Dominant model 12 0.837 [0.703; 0.997] 0.046 Fixed 0.197 0.243 0.168   Aa vs. AA + aa Over dominant 12 0.913 [0.807; 1.033] 0.149 Fixed 0.008 0.553 0.079   aa vs. AA Homozygote 12 0.842 [0.688; 1.031] 0.097 Fixed 0.458 0.000 0.704   AA vs. Aa 12 1.056 [0.921; 1.210] 0.432 Fixed 0.031 0.468 0.260   Aa vs. aa 12 0.833 [0.691; 1.004] 0.055 Fixed 0.090 0.365 0.042 Table 8 Main results of pooled ORs in the meta-analysis of VDR gene polymorphisms (infertile men) Comparisons Number of studies Test of association Test of heterogeneity Publication bias OR 95% CI p -value Model p -value I^2 p -value (Egger's test) FokI (rs2228570)   f vs. F Allele contrast 2 0.980 [0.667; 1.439] 0.918 Fixed 0.001 0.850 0.053   ff vs. Ff + FF Recessive model 2 0.421 [0.182; 0.976] 0.043 Fixed 0.055 0.653 0.140   ff + Ff vs. FF Dominant model 2 1.216 [0.768; 1.923] 0.402 Fixed 0.110 0.545 0.111   Ff vs. FF + ff Over dominant 2 1.476 [0.945; 2.307] 0.086 Fixed 0.410 0.000 0.133   ff vs. FF Homozygote 2 0.590 [0.181; 1.928] 0.383 Fixed 0.065 0.633 0.162   FF vs. Ff 2 0.484 [0.184; 1.273] 0.141 Fixed 0.117 0.533 0.143   Ff vs. ff 2 1.267 [0.790; 2.031] 0.325 Fixed 0.823 0.000 0.291 Table 9 Main results of pooled ORs in the meta-analysis of VDR gene polymorphisms (miscarriage) Comparisons Number of studies Test of association Test of heterogeneity Publication bias OR 95% CI p -value Model p -value I^2 p -value (Egger's test) FokI (rs2228570)  f vs. F Allele contrast 4 1.238 [1.016; 1.508] 0.034 Fixed 0.187 0.373 0.934  ff vs. Ff + FF Recessive model 4 1.476 [1.096; 1.986] 0.010 Fixed 0.626 0.000 0.657  ff + Ff vs. FF Dominant model 4 1.139 [0.792; 1.638] 0.481 Fixed 0.164 0.411 0.418  Ff vs. FF + ff Over dominant 4 0.774 [0.587; 1.020] 0.068 Fixed 0.886 0.000 0.144  ff vs. FF Homozygote 4 1.462 [0.966; 2.212] 0.072 Fixed 0.188 0.372 0.693  FF vs. Ff 4 1.469 [1.074; 2.011] 0.016 Fixed 0.898 0.000 0.328  Ff vs. ff 4 0.992 [0.675; 1.456] 0.968 Fixed 0.294 0.190 0.379 Main results of pooled ORs in the meta-analysis of VDR gene polymorphisms (infertile women) Main results of pooled ORs in the meta-analysis of VDR gene polymorphisms (infertile men) Main results of pooled ORs in the meta-analysis of VDR gene polymorphisms (miscarriage) Heterogeneity was observed in certain genetic models, particularly in the TaqI SNP. Egger's tests revealed no publication bias. The results of sensitivity analysis are presented in relevant charts.

Some comparisons revealed heterogeneity, but it was somewhat addressed by ethnicity-based subgroup analysis. According to our findings, VDR ApaI and FokI can have a role in infertility/recurrent miscarriage. These SNPs might be utilized to assess the risk of infertility/recurrent miscarriage. The observed relationships should be replicated in a bigger meta-analysis. Furthermore, expression studies are essential for fully comprehending the function of VDR polymorphisms in the etiology of infertility/recurrent miscarriage. Finally, investigations should be conducted to determine whether nutritional therapies such as vitamin D can provide a possible response to the hereditary propensity. Finally, our findings imply that VDR FokI and ApaI polymorphisms may be linked to infertility/recurrent miscarriage. However, more research with a larger sample size and considering other confounding factors is required in the future to reach a conclusive conclusion.

This meta-analysis suggests that vitamin D receptor gene variations may play a role in infertility risk and outcome. The TaqI polymorphism may increase the susceptibility to infertility in women, possibly by affecting the implantation and placentation processes. The ApaI and FokI polymorphisms may have protective effects against infertility in women and men, respectively, possibly by modulating the immune system and the hormonal balance. These findings may have implications for the diagnosis, prevention, and treatment of infertility, as well as for the understanding of the molecular mechanisms of reproductive health. In the development of pregnancy problems, genetic variables have grown increasingly relevant. Previous research has linked VDR gene variations to infertility in women and men due to PCOS, endometriosis, preeclampsia, idiopathic infertility, and other causes [ 36 , 37 ]. Serum 25-hydroxyvitamin D [25 (OH) D] has been found to inhibit VDR-mediated pathogenesis by modulating target gene expression [ 38 ]. The VDR gene is a potential gene for infertility because it controls several genes that participate in diverse molecular and cellular processes [ 39 ]. Recurrent miscarriages (RM), which occur at a rate of 1 to 3% of female reproductive age, are another major medical, social, and psychological complication associated with pregnancy. Although numerous pathways for the development of RM have already been discovered, the underlying causes of around 50% of patients remain unexplained [ 13 , 14 ]. Nevertheless, the multifaceted etiology of this problem, including immune system irregularities and vitamin D inadequacy, has been recognized for some time. As a result, it seems that altered metabolism of the VD/VDR complex via immune response modulation might be significant in the pathogenesis of both spontaneous abortion and RM [ 40 ]. Furthermore, VDR is a receptor with a pleiotropic action on human cells. remarkably, the presence of polymorphic variations in the VDR gene may affect VDR activity [ 41 ]. Regarding the evaluation of the connection between VDR gene polymorphisms and infertility/recurrent miscarriage in numerous single studies in different populations, the findings are contradictory. Given the volume of data accumulated and the ambiguous role of VDR in the etiology of infertility/recurrent miscarriage in general, we decided to conduct a comprehensive meta-analysis of any published research on the association between the most studied VDR polymorphisms and any infertility/recurrent miscarriage. The present study included a total of 22 articles and showed that the VDR gene TaqI polymorphism was associated with infertility susceptibility in women. ApaI and FokI gene polymorphisms were found to be significantly protective SNPs against women's and men's infertility. The published studies related to the association of selected VDR SNPs and recurrent miscarriage were not enough for meta-analysis, therefore, a systematic review was alone performed. The findings were consistent with prior research and may give an entirely novel biomarker in infertility/recurrent miscarriage with diverse etiologies [ 20 , 42 – 45 ]. A subgroup analysis was also undertaken to investigate the possible significance of patient ethnicity or infertility etiology on the association between VDR polymorphisms and the risk of infertility. TaqI SNP was shown to be significantly connected with infertility in Africans, while BsmI was found to be associated with the disease mostly in Asians. This finding could be explained by genetic differences between ethnic groupings. Furthermore, due to the procedure of natural selection, functional variations in various groups may differ [ 26 ]. Furthermore, VDR ApaI (rs7975232) was found to be associated with infertility susceptibility in the PCOS subgroup, however, a protection association with idiopathic infertility was found. VDR gene polymorphism could contribute to the pathophysiology of infertility by influencing gene expression and mRNA stability, and hence the cellular and molecular processes associated with infertility etiology. Nevertheless, these polymorphisms are mostly nonfunctional, linkage disequilibrium with another undiscovered functional variant of the VDR gene appears to be the most likely explanation for the observed association. We meta-analyzed the VDR gene TaqI, BsmI, FokI, ApaI polymorphisms, and women/men infertility for the first time. The FokI SNP is the only VDR polymorphism leading to a VDR protein with a different structure. Furthermore, it is the only SNP that is not linked to any other VDR polymorphism, implying that it plays a distinct function [ 46 ]. The polymorphism, which is a C to T alteration, is located at the 5' end of the gene. This alteration results in a protein of a different size, a 424 amino acid (aa) variant encoded by the major allele form (ACG) and a 427 aa variant expressed by the minor allele form (ATG). The variations are thought to be functionally relevant, with the 424 aa VDR variant having higher transcriptional activity and being associated with lower circulating 25(OH) D levels than the 427 aa variant [ 46 , 47 ]. Moreover, Yan et al. showed that women with RPL have lower levels of VDR expression in chorionic villi, decidua, and serum compared with normal pregnant women [ 48 ]. It has previously been suggested that CC genotype / 424 aa VDR variant has a higher frequency in women with RPL, which leads to lower circulating 25(OH) D levels, respectively. Several studies have demonstrated that high Vitamin D levels might protect against a variety of illnesses, including infertility and recurrent miscarriage. The idea has been suggested in several research that greater pre-diagnosis plasma levels of 25-hydroxy vitamin D, the predominant circulating form of vitamin D, is related to a significant decrease in pregnancy problems such as PCOS, endometriosis, infertility, and recurrent pregnancy loss [ 49 – 52 ]. Furthermore, comprehensive reviews and meta-analyses revealed a substantial reduction in total pregnancy outcomes in Vitamin D-deficient patients [ 53 , 54 ]. Our finding revealed a marginally significant association of FokI SNP with infertility under the FF vs. Ff genetic model (OR = 0.8763, 95% CI [0.7651–1.0036], P  = 0.05). This indicates that the FokI f allele might be a risk factor for infertility, future studies with larger sample sizes and considering other confounder variables still need to confirm these findings though. The functional evaluation of the three significant non-coding VDR SNPs (Bsml, TaqI, and ApaI) examined in this meta-analysis revealed contradictory findings from prior studies regarding their biological implications. Even if these SNPs are nonfunctional, the impacts identified in this meta-analysis and other studies could be driven by other, actually important SNPs in significant LD located elsewhere in the VDR gene. Some studies aimed at characterizing differences in VDR expression for SNPs in the 3' end of the VDR gene found that the Bsml-ApaI-TaqI haplotype BAt (rs1544410-A/rs7975232-A/rs731236-C) had higher levels of VDR mRNA expression than the baT (rs1544410-G/rs7975232-C/rs731236-T). These SNPs could be implicated in gene expression control, specifically by mRNA stability modulation. To be more specific, the existence of the TaqI G allele improves VDR mRNA stability and half-life, leading to an increased VDR synthesis and therefore directly altering vitamin D levels and consequently subsequent effects of vitamin D [ 55 , 56 ]. A significant association was found between ApaI and infertility in the present meta-analysis. We observed a borderline and a significant protective association for the Aa vs. aa model (OR = 0.83, P  = 0.05) and the dominant model (OR = 0.84, P  < 0.05), however, no significant association was reported in other genetic model contrasts. These findings show that individuals who inherited ApaI SNP in a dominant form might be more protected against infertility. This polymorphism is in strong linkage disequilibrium with the poly(A) microsatellite located in the 3′ untranslated region [ 45 ] of the VDR gene, which appears to influence VDR messenger RNA stability and VDR translational activity (9). Sub-group analysis, however, showed a protective association against infertility in the PCOS subgroup under dominant (AA + Aa vs. aa), over-dominant, (Aa vs. AA + aa, AA vs. aa, and Aa vs. aa genetic models and a susceptibility association under the recessive genetic model in idiopathic infertility sub-group. In our study, we noted a higher frequency of the genotype containing a mutated t allele of TaqI polymorphism. Interestingly, TaqI polymorphism was the only SNP that showed significant association with infertility overall and based on the etiology, excluding Over dominant genetic model. Our results showed that TaqI polymorphism may increase susceptibility to infertility under the allele contrast, recessive, dominant, homozygote, TT vs. Tt, Tt vs. tt genetic models. This indicates the If Taq t allele is a protective factor for infertility, then the infertility chance of patients with Taq t allele will be lower than that of patients with Taq T allele (OR > 1, P  < 0.05). These data suggested the role of these genetic variants might be attributed with infertility due to the influence on the VDR function and consequently disturbed vitamin D metabolism or might be due to the LD with other VDR SNPs. These results suggest the special role of maternal setting genetic variants of the VDR gene in the etiology of this pregnancy complication. However, further research is required to determine what exactly FokI is acting as a marker for infertility. As ~ 50% of patients with recurrent pregnancy loss (RPL) do not have a definite etiology, we further aimed to perform the meta-analysis of the association between VDR polymorphisms and recurrent miscarriage. The potential association of VDR polymorphisms with the etiology of recurrent miscarriages has been indicated in several studies [ 31 , 45 , 57 ]. Although with conflicting results, most of them suggested VDR SNPs association with RM in women. A study reported lower expression of VDR in trophoblastic, decidua, and serum villi in the RM group compared to the control, suggesting that impaired VDR expression in the first trimester of pregnancy may be associated with the occurrence of RM [ 48 ]. Accordingly, it could be suggested that VDR SNPs might be involved in the susceptibility and protection against RPL through influence on the VDR mRNA expression level and stability or due to the LD with other SNPs. In our study, we only found the association of FokI, and RPL in more than two studies, therefore the meta-analysis was performed for FokI polymorphism. Our data showed that FokI is significantly associated with a lower risk of RPL in allele contrast 9OR = 1.23, P  = 0.034), recessive model (OR = 1.47, P  = 0.010), and FF vs. Ff (OR = 1.46, OR = 0.016) genetic models. This indicates that carriers of FokI SNP might be more protected against RPL, however, it is required to be studied in larger sample sizes and to examine the exact functional effect of this SNP on the RPL etiology. Because of racial differences, evidence of disease occurrence is not always accurate. This shows that various races have distinct effects on genetic background [ 58 ]. Therefore, based on subgroup analysis of different races, it can be found that the same polymorphisms in disease susceptibility in different populations play different roles. In our study, subgroup analysis suggested that the VDR gene BsmI polymorphism was significantly associated with susceptibility to infertility for the comparison of (AA vs. aa), (AA vs. Aa), and recessive model, and was protective SNP in the over-dominant genetic model in Asian population. For VDR gene TaqI polymorphism, it was significantly associated with susceptibility to infertility under the comparison of allele contrast (A vs. a), recessive model (AA vs. Aa + aa), dominant model (AA + Aa vs. aa), over-dominant (Aa vs. AA + aa), AA vs. aa, AA vs. Aa, Aa vs. aa genetic models in African and Asian population. However, for VDR gene ApaI polymorphism, it was protectively associated with infertility under dominant model (AA + Aa vs. aa), over-dominant (Aa vs. AA + aa), AA vs. aa, AA vs. Aa, Aa vs. aa genetic models and a susceptibility association was observed under recessive model (aa vs. Aa + AA) in Asian. FokI polymorphism was not significantly associated with infertility under any genetic models in any geographic population. The opposite association in different populations for an SNP in subgroup analysis might be due to ethnic differences. Of course, it also may be the difference in results caused by the insufficient number of studies included. We certainly need more and better research to get more reliable results. Our study contains certain characteristics linked to study design that can help to strengthen the conclusions. The criteria for study selection were stringent, and such an exact selection guaranteed the right degree of analysis. Both groups (patients and controls) had participants who were similar in terms of age, ethnic background, and area of residence, reducing the impact of known confounders. In the genetic models, statistical power was adequate for genotype and allele frequencies of reported gene polymorphisms, as well as relationships between individual VDR polymorphisms and the probability of infertility/RPL. A drawback of this research is that we did not have original data, so we were unable to control for other factors such as circulating vitamin D levels, sun exposure, aspirin/NSAID use, stage disease, calcium, and vitamin D intake. The main drawback of the current study is the relatively small sample size and a lack of enough publications on the association of VDR SNPs and RPL. Finally, only four single nucleotide variations of the VDR gene were studied in this study, while, there are several more genetic variations that influence VD metabolism. The limitation lies in the incapacity to explore diverse age groups through subgroup analyses, relying on the specified age range of 20 to 40 years in the primary studies. This constraint was underscored within the study's delineation of limitations regarding subgroup analyses grounded on age. Also, this study relied on secondary data sources, limiting our ability to control for potential confounding factors, including circulating vitamin D levels, sun exposure, aspirin/NSAID use, stage of disease, calcium, and vitamin D intake. The relatively small sample size in the current study reduced both statistical power and the generalizability of the results. Furthermore, insufficient publications on the association of VDR SNPs and RPL hindered comparison with previous studies. The examination in this study was confined to four single nucleotide variations of the VDR gene—FokI, BsmI, ApaI, and TaqI. However, numerous other genetic variations, such as CYP2R1, CYP27B1, CYP24A1, and GC, influence vitamin D metabolism. Consequently, our findings may not fully capture the genetic effects of vitamin D on RPL.

Infertility is a disease of the female or male reproductive system in which pregnancy does not occur after 12 months of regular unprotected sex [ 1 ]. This disease is a very common condition that affects between 48.5 and 186 million males and females worldwide, respectively. According to WHO, almost one out of six people of reproductive age experience infertility during their lifetime [ 2 , 3 ]. Genetic, environmental, and some idiopathic factors are among the effective causes of infertility[ 4 ]. Male infertility is usually due to problems in the semen existence, the absence or low levels of sperm, or the abnormal shape and movement of sperm, and infertility in women is also caused by a range of abnormalities of the ovaries, uterus, fallopian tubes, the endocrine system, etc. [ 5 ]. Furthermore, Miscarriage is generally defined as the loss of a pregnancy before viability, which is considered the other complication of successful pregnancy [ 6 ]. It is estimated that 23 million miscarriages occur worldwide each year [ 7 ]. The short-term national economic cost of miscarriage in the UK was estimated at 471 million pounds annually in 2005 [ 8 ]. Physical consequences of miscarriage include bleeding or infection and psychological consequences such as increased risk of anxiety, depression, post-traumatic stress disorder, and suicide [ 9 ]. Its determinants include fetal genetic and chromosomal abnormalities, genital anatomy, endometrial pathology, hereditary thrombophilia, antiphospholipid syndrome, etc. Most of these factors are difficult to correct, but there are also controllable ones whose negative effects can be completely reduced before conception. These include nutritional deficiencies, including vitamin D (Vit D) deficiency [ 10 , 11 ]. Vit D is a hormone that has a fundamental role in endocrine function, regulation of cell proliferation, and other metabolic pathways, such as pathways involved in the immune response [ 12 ]. Recent studies show the relationship between vitamin D deficiency and adverse pregnancy outcomes, including miscarriage [ 13 – 15 ]. Vit D is locally metabolized in the male reproductive system and the expression of Vitamin D receptor (VDR) has been shown in human testes and in ejaculated human sperms [ 16 ]. Studies have proven that Men who receive more diet and supplements produce sperm with less DNA damage [ 17 ]. Maternal Vit D deficiency is associated with many gynecological and obstetric diseases such as polycystic ovary syndrome, endometriosis, ovarian cancer, as well as gestational diabetes, which are all associated with reduced successful pregnancy. Preeclampsia and preterm labor are related, which can affect fertility [ 4 , 18 , 19 ]. Polycystic ovary syndrome (PCOS) is the most common endocrine metabolic disorder that affects 5 to 10% of women of reproductive age and is one of the common causes of ovulatory infertility [ 20 ]. The VDR gene is considered an important candidate gene for PCOS [ 21 ]. Since new research studies indicated the significance of vitamin D in the endocrine system and its relation not only with bone mineral density but also with certain cancers, autoimmune diseases, diabetes mellitus, depression, allergy, cardiovascular disease, pregnancy complications, infertility, and even frailty, vitamin D deficiency has just been identified as endemic to a variety of health consequences [ 22 , 23 ]. The results of various studies taken together have shown that a variety of environmental and genetic factors influence vitamin D status variations. Studying the genetic basis of vitamin D metabolism, however, has brought to light the significance of multiple genes, including CG, DHCR1, CYP2R1, CYP24A1, and VDR [ 24 ]. By interacting with the vitamin D receptor (VDR), a member of the superfamily of steroid/thyroid hormone receptors, 1,25(OH)2D3, the active form of vitamin D, affects the transcriptional activation and repression of several target genes [ 25 ]. The VDR gene has many single-nucleotide polymorphisms (SNPs), which have been linked to a variety of physiological and pathological characteristics including different pregnancy complications in numerous populations [ 26 – 28 ]. VDR gene polymorphisms most likely have an impact on the expression and function of VDR [ 29 ].VDRs are found in the endometrium, placenta, decidual cells, ovarian granulosa cells, fallopian tube epithelium, pituitary gland, and hypothalamus. [ 30 ] Expression of the VDR in the placenta and decidua, which probably has an active role in the local autocrine and paracrine response, suggests that the local synthesis of Vit D potentially modulates placental function and fetal growth. Therefore, VDR gene function could be influenced by several factors such as genetic polymorphism that might related to susceptibility to fertility problems [ 31 , 32 ]. The most intensively studied VDR polymorphisms are FokI (rs2228570), TaqI (rs731236), BsmI (rs1544410), and ApaI (rs7975232) variants. The association of these polymorphisms with different types of infertility complications including PCOS, endometriosis, miscarriage, etc. has been investigated in single studies, with conflicting results. Considering the above-mentioned observations, our meta-analysis study aimed to more powerfully and comprehensively assess the association between four VDR polymorphisms (rs2228570, rs1544410, rs7975232, rs731236) and infertility and miscarriage in different populations and geographical regions by conducting a systematic review.