Next Article in Journal
Safety and Effectiveness of Abatacept in a Prospective Cohort of Patients with Rheumatoid Arthritis–Associated Interstitial Lung Disease
Next Article in Special Issue
Designing Effective Multi-Target Drugs and Identifying Biomarkers in Recurrent Pregnancy Loss (RPL) Using In Vivo, In Vitro, and In Silico Approaches
Previous Article in Journal
Gender Differences in Non-Persistence with Angiotensin-Converting Enzyme Inhibitors and Angiotensin Receptor Blockers among Older Hypertensive Patients with Peripheral Arterial Disease
 
 
Search for Articles:
Title / Keyword
Author / Affiliation / Email
Journal
Article Type
 
 
Advanced Search
 
Section
Special Issue
Volume
Issue
Number
Page
 
Journals
Biomedicines
Volume 10
Issue 7
10.3390/biomedicines10071481
biomedicines-logo
Submit to this Journal Review for this Journal Propose a Special Issue
► Article Menu

Article Menu

share Share announcement Help format_quote Cite question_answer Discuss in SciProfiles thumb_up
...
Endorse textsms
...
Comment
first_page
settings
Order Article Reprints
Font Type:
Arial Georgia Verdana
Font Size:
Aa Aa Aa
Line Spacing:
Column Width:
Background:
Article

Genetic Polymorphisms in the 3′-Untranslated Regions of SMAD5, FN3KRP, and RUNX-1 Are Associated with Recurrent Pregnancy Loss

by
Min-Jung Kwon
1,
Ji-Hyang Kim
2,
Jeong-Yong Lee
1,
Eun-Ju Ko
1,
Hyeon-Woo Park
1,
Ji-Eun Shin
2,
Eun-Hee Ahn
2,* and
Nam-Keun Kim
1,*
1
Department of Biomedical Science, College of Life Science, CHA University, Seongnam 13488, Korea
2
Department of Obstetrics and Gynecology, CHA Bundang Medical Center, School of Medicine, CHA University, Seongnam 13496, Korea
*
Authors to whom correspondence should be addressed.
Biomedicines 2022, 10(7), 1481; https://doi.org/10.3390/biomedicines10071481
Submission received: 28 April 2022 / Revised: 27 May 2022 / Accepted: 20 June 2022 / Published: 22 June 2022
(This article belongs to the Special Issue Advanced Research in Early Pregnancy Loss)
Browse Figure
Review Reports Versions Notes

Abstract

:
Recurrent pregnancy loss (RPL) is typically defined as two or more consecutive pregnancy losses prior to 20 weeks of gestation. Although the causes of idiopathic RPL are not completely understood, vascular development and glucose concentration were reported to correlate with the pregnancy loss. The TGF-β signaling pathway which plays a significant role in pregnancy is activated by the interaction between high glucose and SMAD signaling and affects the vascular cells. SMAD5 and RUNX-1 are involved in the TGF-β signaling pathway and contribute to advanced glycation end products (AGEs) production and vascular development. FN3KRP, a newly described gene, is also associated with vascular diseases and suggested to relate to AGEs. Therefore, in the present study, we investigated associations between RPL risk and genetic polymorphisms of SMAD5, FN3KRP, and RUNX-1 in 388 women with RPL and 280 healthy control women of Korean ethnicity. Participants were genotyped using real-time polymerase chain reaction and restriction fragment length polymorphism assay to determine the frequency of SMAD5 rs10515478 C>G, FN3KRP rs1046875 G>A, and RUNX-1 rs15285 G>A polymorphisms. We found that women with RPL had lower likelihoods of the FN3KRP rs1046875 AA genotype (adjusted odds ratio (AOR), 0.553; p = 0.010) and recessive model (AOR, 0.631; p = 0.017). Furthermore, combination analysis showed that SMAD5 rs10515478 C>G and FN3KRP rs1046875 G>A mutant alleles were together associated with reduced RPL risk. These findings suggest that the FN3KRP rs1046875 G>A polymorphism has a significant role on the prevalence of RPL in Korean women. Considering that it is the first study indicating a significant association between FN3KRP and pregnancy disease, RPL, our results suggest the need for further investigation of the role of FN3KRP in pregnancy loss.

1. Introduction

Recurrent pregnancy loss (RPL) is a relatively common condition defined as more than two clinical pregnancy losses prior to 20 gestational weeks [1]. RPL occurs in 1–5% of reproductive-aged women worldwide, but its exact cause is unknown in approximately half of cases [2,3]. While possible causes are suggested, RPL has been understudied, with multiple factors potentially involved, such as genetic factors, environmental factors and certain organic diseases [4]. Gestational diabetes, which is a common complication of pregnancy, is one factor that increases the risk of spontaneous abortion. Its main phenotype, high blood sugar, can induce widespread damage to the body including blood vessel dysfunction, which can contribute to infertility [5]. Adequate vascular function for carrying nutrients is essential for uterine and embryo development and successful birth [6,7]; when the vascular complications of diabetes occur, several problems such as preeclampsia, early birth and miscarriage can result [8]. In addition, high blood sugar contributes to the formation of advanced glycation end-products (AGEs), which are toxic metabolic products of lipids, nucleic acids, and proteins formed by non-enzymatic reactions with sugars [9]. Elevated levels of AGEs are associated with severe health issues such as cancer and vascular diseases and may also play a role in infertility [4].
The transforming growth factor-beta (TGF-β) signaling pathway, which is important for embryo development, is initiated by high glucose interacting with SMAD signaling [10] and stimulates AGE activation by interconnection of SMAD5 and TGF-β1 [11]. Runt-related transcription factors (RUNXs) are also involved in the TGF-β signaling pathway, working synergistically with SMADs [12,13]. In particular, RUNX-1 plays a similar role as SMAD5 in mouse embryo, contributing to vascular system development [12,14,15,16], and was found to promote the expression of endothelial-specific molecules for vascular formation in the mouse yolk sac [17]. Moreover, RUNX-1 is known as a glucose-related gene and suggested to mediate glucose-related disease [18,19,20]. Additionally, SMAD5 and RUNX-1 are target genes of miR-27a, a promoter of angiogenesis and glucose metabolism [21,22,23]. However, genetic variants of SMAD5 and RUNX-1 were not yet reported in pregnancy loss while there are reports about the significant polymorphisms in vascular disease and platelet dysfunction [24,25].
Fructosamine-3 kinase-related protein (FN3KRP) is a newly described gene that shares 65% similarity in structure with fructosamine-3-kinase (FN3K), which contributes to glucose metabolism by preventing the formation of AGEs [26,27,28,29]. Although it is not clear whether FN3KRP functions similarly to FN3K, a recent study indicates that a genetic polymorphism of FN3KRP is associated with glucose products [30]. In particular, FN3KRP rs1046875 G>A protects against cardiovascular diseases through HbA1c, glycated hemoglobin, and affects the binding affinity of miR-34a [31] which acts as a suppressor of angiogenesis and glucose metabolism [32,33]. Although the mechanism of HbA1c has to be elucidated, its increased level has been reported in patients with cardiometabolic diseases and it has positive correlation with the platelet dysfunction in diabetic mellitus [34,35,36]. As a precursor of hemoglobin-AGE, higher HbA1c concentration is also associated with various diseases such as vascular diseases, diabetes and pregnancy disorders [34,35,36,37]. Thus, FN3KRP rs1046875 G>A has a potential probability to play a significant role in pregnancy in relation to HbA1c.
Recent studies showed that either vascular function or glucose-related genes influence RPL risk [38,39,40]. In addition, we previously found that pregnancy disorders are associated with genetic polymorphisms in 3′-untranslated regions (3′-UTRs) [41,42]. 3′-UTR regulates gene expression by binding with microRNA (miRNA), thereby modifying the stability and function of mRNA; the single nucleotide polymorphisms (SNPs) which compose the miRNA binding site in 3′-UTRs affect disease occurrence by regulating the affinity with miRNA [43,44,45]. In the present study, we investigated whether RPL risk is related to specific genetic polymorphisms in SMAD5, RUNX-1, and FN3KRP in Korean women. Specifically, we evaluated potential associations between RPL and SMAD5 rs10515478 C>G, FN3KRP rs1046875 G>A, and RUNX-1 rs15285 G>A, which are located in miRNA binding site-containing 3′-UTRs and have >5% minor allele frequencies in the East Asian population.

2. Materials and Methods

2.1. Study Population

We obtained blood samples from women with RPL seen at the CHA Bundang Medical Center’s Fertility Center (Seongnam, Korea) between March 1999 and February 2012. RPL was diagnosed based on human chorionic gonadotropin levels, ultrasonography, and physical examination prior to 20 weeks of gestation. Women were excluded if they had pregnancy loss caused by anatomic, hormonal, autoimmune, or thrombotic factors. Fetal anatomic abnormalities were identified by hysterosalpingogram, sonography, computerized tomography, hysteroscopy, or magnetic resonance imaging. Hormonal causes of RPL, such as luteal insufficiency, hyperprolactinemia, or thyroid disease, were evaluated by measuring levels of thyroid-stimulating hormone, prolactin, follicle-stimulating hormone, free T4, luteinizing hormone, and progesterone in peripheral blood. Autoimmune causes of RPL, such as lupus and antiphospholipid syndrome, were evaluated using lupus anticoagulant and anticardiolipin antibodies. Thrombotic causes of RPL were evaluated by deficiencies in protein C and protein S and the presence of anti-β2 glycoprotein antibodies. Control blood samples were obtained from healthy women seen at the CHA Bundang Medical Center who were confirmed to have a normal 46 XX karyotype, regular menstrual cycle, history of at least one naturally conceived pregnancy, and no history of pregnancy loss. In total, we analyzed 388 samples from women with RPL and 280 samples from healthy control women. No participants had a history of smoking or alcohol use, and all participants were Korean. This study was approved by the Institutional Review Board of CHA Bundang Medical Center (IRB number: BD2010-123D, 21 June 2011), and all participants provided written informed consent.

2.2. Genotype Analysis

Genomic DNA was extracted from whole blood by obtaining the buffy coat after centrifugation and employing the G DEX II Genomic DNA Extraction Kit (Intron Biotechnology Inc., Seongnam, Korea). Genetic polymorphisms were determined by genotype analysis. Real-time polymerase chain reaction (PCR) was performed for SMAD5 rs10515478 C>G and FN3KRP rs1046875 G>A using the TaqMan SNP Genotyping Assay Kit (Applied Biosystems, Foster City, CA, USA). RUNX-1 rs8134179 G>A was analyzed by PCR-restriction fragment length polymorphism analysis with digestion by the MnlI restriction enzyme using forward primer 5′- GGC ACA GAG AAG GAG ATA TAG ACT -3′ and reverse primer 5′- ATA GTA TGC CAG GGC TCA GG -3′.

2.3. Assessment of Homocysteine, Folate, Total Cholesterol, and Uric Acid Concentrations and Blood Coagulation Status

Homocysteine levels were measured using a fluorescence polarization immunoassay and the Abbott IMx Analyzer (Abbott Laboratories, Abbott Park, IL, USA). Folate levels were measured using a radio-assay kit (ACS:180; Bayer, Tarrytown, NY, USA). Total cholesterol and uric acid levels were determined using commercially available enzymatic colorimetric tests (Roche Diagnostics, Mannheim, Germany). Platelets were measured using the Sysmex XE 2100 Automated Hematology System (Sysmex Corporation, Kobe, Japan). Prothrombin time and activated partial thromboplastin time were analyzed using an ACL TOP automated photo-optical coagulometer (Mitsubishi Chemical Medience, Tokyo, Japan).

2.4. Statistical Analysis

For data on participant characteristics, categorical variables were analyzed using Chi-square tests, and continuous variables were analyzed using Student’s t-tests. Multivariate logistic regression and Fisher’s exact tests were used to compare haplotype and genotype frequencies between RPL and healthy control women. Adjusted odds ratios (AORs) and 95% confidence intervals (CIs) were used to evaluate associations between genetic polymorphisms and RPL occurrence [46]. Model-based multifactor dimensionality reduction (MDR) was used to evaluate relationships between genotypes and participant characteristics [47,48,49]. Kruskal–Wallis tests were used for small sample sizes when the p-value of Levene’s test was less than 0.05. Statistical analysis was performed using GraphPad Prism 4.0 (GraphPad Software Inc., San Diego, CA, USA) and Medcalc version 12.7.1.0 (Medcalc Software, Mariakerke, Belgium). The HAPSTAT program (version3.0, www.bios.unc.edu/~lin/hapstat/, accessed on 17 March 2022) was used to evaluate the synergistic effects of polymorphic haplotypes. False discovery rate (FDR)-corrected p-values < 0.05 were considered to be statistically significant [50]. Data are reported as mean and standard deviation for continuous variables and frequencies and percentages for categorical variables.

3. Results

3.1. Participant Characteristics

We performed the age-matching of patients and controls and then undertook the comparison of clinical characteristics between two subjects. Compared with healthy control women, women with RPL had significantly shorter mean gestational durations and altered levels of pregnancy-related hormones (Table 1). Women with RPL also had significantly higher total cholesterol, blood urea nitrogen, platelets, prothrombin time, activated partial thromboplastin time, and hematocrit compared with healthy control women.

3.2. FN3KRP rs1046875 G>A Polymorphism Has Protective Role against RPL

Frequencies of SMAD5 rs10515478 C>G, FN3KRP rs1046875 G>A, and RUNX-1 rs15285 G>A polymorphisms were amplified and satisfied the Hardy–Weinberg principle (p > 0.05) on both control and patient groups (Table 2). We evaluated the association between each gene polymorphism and RPL and these results were adjusted by participant age. Among the three polymorphisms, only the FN3KRP rs1046875 AA genotype and recessive model differed significantly between women with RPL and healthy control women (Table 2). Specifically, women with RPL had a lower likelihood of the FN3KRP rs1046875 AA genotype and recessive model than healthy control women, and the protective role of rs1046875 AA genotype and recessive model became more pronounced when larger numbers of pregnancy losses (PL) had occurred (PL ≥ 2; AA genotype; AOR, 0.553; p = 0.010; recessive model; AOR, 0.631; p = 0.020; PL ≥ 3; AA genotype; AOR, 0.516; p = 0.016; recessive model; AOR, 0.594; p = 0.027); PL ≥ 4; AA genotype; AOR, 0.311; p = 0.009; recessive model; AOR, 0.333; p = 0.006). After FDR correction, the association between the FN3KRP rs1046875 AA genotype and RPL risk remained significant with all cases of PL but the recessive model remained significant only when PL ≥ 4.

3.3. Synergistic Effects of SMAD5, FN3KRP, and RUNX-1 Polymorphisms on RPL

Considering that RPL etiology has polygenic traits, combination analyses were performed to confirm associations with each allele or genotype combination and RPL. The likelihood of all combinations containing the A allele of FN3KRP rs1046875 G>A compared with the reference haplotype was lower among women with RPL than among healthy control women even though some were not statistically significant (Table 3). In particular, the combination of G, A, and G alleles in SMAD5 rs10515478 C>G, FN3KRP rs1046875 G>A, and RUNX-1 rs15285 G>A and of G and A alleles in SMAD5 rs10515478 C>G and FN3KRP rs1046875 G>A were significantly less in women with RPL. After FDR correction, the G-A combination but not the G-A-G combination remained significantly associated with RPL. In addition, the likelihood of the combination of SMAD5 rs10515478 CG and FN3KRP rs1046875 AA genotypes was significantly lower among women with RPL than among healthy control women (Table 4) holding the significance of allele combination.

3.4. Associations between Participant Characteristics and Genetic Polymorphisms

As women with RPL and healthy control women showed differences in clinicopathological factors (Table 1), we examined relationships between participant characteristics and each genetic polymorphism using MDR analysis. Both FN3KRP rs1046875 G>A (p = 0.046) and RUNX-1 rs15285 G>A (p = 0.038) were significantly associated with platelet count, with the lowest level for hetero genotype (Table 5). In addition, SMAD5 rs10515478 C>G was significantly associated with gestational duration with a longer duration for the GG genotype than for the CG or CC genotypes (CC, 18.45 ± 15.62; CG, 20.29 ± 15.64; GG, 25.82 ± 16.18).

4. Discussion

AGEs are products of the glycation of proteins or lipids resulting from exposure to sugars. High levels of AGEs are implicated in aging and the development or worsening of degenerative diseases including diabetes, atherosclerosis, chronic kidney disease, and Alzheimer’s disease [51]. The pathological contribution of AGEs to various diseases is under active investigation [4,52,53]. In particular, the accumulation of AGEs was found to contribute to sterility via damage to ovarian cells [31]. However, the role of AGEs in pregnancy loss has not previously been investigated. Therefore, we examined three genes previously linked to AGE formation and successful birth to determine their potential association with RPL. Furthermore, based on the contribution of non-coding regions to gene expression, regarding miRNA, we identified SNPs located in miRNA binding sites-containing 3′UTRs.
AGEs, which are produced by non-enzymatic glycation, have a deleterious impact on biological macromolecules via the formation of toxic compounds. However, the FN3KRP isoform of FN3K may protect proteins from non-enzymatic glycation [54]. Recent researches suggest that FN3KRP polymorphisms have protective effects against diabetes and cardiovascular diseases [26,28], which share common risk factors with pregnancy disorders such as vascular dysfunction and high glucose levels. Furthermore, a previous study reports increased FN3KRP expression when FN3KRP rs1046875 contains mutant alleles that alter its binding affinity with miR-34a [31]. As miR-34a is a negative regulator of angiogenesis and glucose metabolism [55], we hypothesize that FN3KRP rs1046875 mutations reduce levels of AGEs that can increase the risk of pregnancy loss (Figure 1). Consistent with previous studies, we found that women with RPL had a lower likelihood of a FN3KRP rs1046875 mutant genotype and recessive model, with stronger associations observed among women who experienced a larger number of pregnancy losses. These findings are the first to suggest the involvement of FN3KRP in RPL and possibly other pregnancy disorders.
Regarding allele combinations, we found that the G-A-G combination of FN3KRP rs1046875 G>A, SMAD5 rs10515478 C>G, and RUNX-1 rs15285 G>A was less likely in women with RPL than in healthy control women. SMAD5 and RUNX-1 are components of the TGF-β signaling pathway, which influences AGE formation (Figure 1). Specifically, SMAD5 and RUNX-1 appear to play similar roles in embryonic vascular development. A meshwork of blood vessels acts as an indispensable bridge for nutrient transmission from the mother to the fetus and is crucial for successful birth [7]. The importance of SMAD5 and RUNX-1 in pregnancy is confirmed by our finding of their interconnection with FN3KRP. Interestingly, the synergistic effect between SMAD5 rs10515478 C>G and FN3KRP rs1046875 G>A maintained a significant association with decreased RPL occurrence in both allele and genotype combination analysis. Therefore we infer that an interaction between FN3KRP rs1046875 G>A and SMAD5 rs10515478 C>G may reduce RPL risk and this interpretation is supported by previous reports that SMAD5 but not RUNX-1 influences AGEs directly through binding with TGF-β1.
Through additional analysis, we found association between RUNX-1 rs15285 G>A and platelet count. Platelet formation is affected by high glucose levels, and platelet aggregation is reduced by insulin [56,57]. As a hematopoiesis gene, RUNX-1 is believed to be closely linked to platelet count, with one study demonstrating that conditional knockout of RUNX-1 in mice results in an 80% reduction in platelets [58]. Thus, RUNX-1 rs15285 G>A may be the functional polymorphism underlying the involvement of RUNX-1 in aspects of hematopoiesis for platelet formation, although this possibility requires further study. Unlike RUNX-1, there are no previous reports of a relationship between FN3KRP and platelets. However, we found that FN3KRP rs1046875 G>A was also associated with platelet count in Table 4. Based on the link between platelet formation and glucose concentration, our results suggest that FN3KRP rs1046875 G>A is involved in glucose metabolism and AGE formation, which should be confirmed by future research. In addition, SMAD5 rs10515478 C>G was associated with gestational duration, consistent with our observation that mutant alleles of SMAD5 rs10515478 C>G had protective effect against RPL when they occurred in combination with mutant alleles of FN3KRP rs1046875 G>A.
In summary, our results suggest that a FN3KRP rs1046875 G>A mutant genotype is associated with RPL risk. However, some limitations of our study should be considered. First, the mechanism by which each gene affects the development of RPL remains unclear; thus, future functional studies are required to unravel these processes. Second, the investigation of additional participant characteristics is needed to clarify the nature of the associations between genetic polymorphisms and RPL. Third, the population of our study was restricted to Korean women. To ensure that the investigated genetic polymorphisms can serve as biomarkers of RPL, studies involving more diverse ethnic populations are needed. Finally, larger sample sizes are required to more robustly support our conclusions.

5. Conclusions

In conclusion, we evaluated associations between SMAD5 rs10515478 G>A, FN3KRP rs1046875 G>A, and RUNX-1 rs15285 G>A polymorphisms and idiopathic RPL. To the best of our knowledge, this is the first study investigating the involvement of these SNPs with RPL risk. In particular, the role of FN3KRP in pregnancy disorders has not previously been explored. Here, we indicated the probability that the mutant genotype and recessive model of FN3KRP rs1046875 G>A may reduce the pregnancy loss, and its mutant allele may act synergistically with mutant allele of SMAD5 rs10515478 G>A to protect against RPL. Furthermore, FN3KRP rs1046875 G>A and RUNX-1 rs15285 G>A were associated with platelet count, which is linked to glucose concentration. Therefore, our study provides a valuable cornerstone for further FN3KRP research offering the hypothesis that FN3KRP may be associated with pregnancy loss regarding AGEs. The FN3KRP rs1046875 G>A polymorphism could serve as a therapeutic target for RPL by reducing HbA1c levels and thereby contribute to the lower level of AGEs, preventing pregnancy loss.

Author Contributions

Formal analysis, M.-J.K.; Methodology, J.-H.K., J.-E.S. and E.-H.A.; Validation, M.-J.K., J.-Y.L., E.-J.K., H.-W.P., E.-H.A. and N.-K.K.; Writing—original draft, M.-J.K.; Writing—Review and editing, E.-H.A. and N.-K.K. All authors have read and agreed to the published version of the manuscript.

Funding

This research was funded by National Research Foundation (NRF), grant number (2018R1D1A1A09082764 and 2020R1F1A107452511).

Institutional Review Board Statement

The study was conducted in accordance with the Declaration of Helsinki and approved by the Institutional Review Board of CHA Bundang Medical Center (IRB number: BD2010-123D).

Informed Consent Statement

Informed consent was obtained from all subjects involved in the study. Written informed consent has been obtained from the patients to publish this paper.

Data Availability Statement

Publicly available datasets were analyzed in this study. This data can be found here: https://figshare.com/articles/dataset/Genetic_Polymorphisms_in_the_3_-Untranslated_Regions_of_SMAD5_FN3KRP_and_RUNX-1_Are_Associated_with_Recurrent_Pregnancy_Loss/20115293 (accessed on 27 April 2022).

Conflicts of Interest

The authors declare no conflict of interest.

References

  1. Arias-Sosa, L.A.; Acosta, I.D.; Lucena-Quevedo, E.; Moreno-Ortiz, H.; Esteban-Pérez, C.; Forero-Castro, M. Genetic and epigenetic variations associated with idiopathic recurrent pregnancy loss. J. Assist. Reprod. Genet. 2018, 35, 355–366. [Google Scholar] [CrossRef] [PubMed]
  2. Pillarisetty, L.S.; Mahdy, H. Recurrent Pregnancy Loss; StatPearls Publishing LLC: Treasure Island, FL, USA, 2021. [Google Scholar]
  3. Jung, Y.W.; Jeon, Y.J.; Rah, H.; Kim, J.H.; Shin, J.E.; Choi, D.H.; Cha, S.H.; Kim, N.K. Genetic variants in microRNA machinery genes are associated with idiopathic recurrent pregnancy loss risk. PLoS ONE 2014, 9, e95803. [Google Scholar]
  4. Zhu, J.-L.; Cai, Y.Q.; Long, S.L.; Chen, Z.; Mo, Z.C. The role of advanced glycation end products in human infertility. Life Sci. 2020, 255, 117830. [Google Scholar] [CrossRef] [PubMed]
  5. Stepan, H.; Hund, M.; Andraczek, T. Combining biomarkers to predict pregnancy complications and redefine preeclampsia: The angiogenic-placental syndrome. Hypertension 2020, 75, 918–926. [Google Scholar] [CrossRef]
  6. Zygmunt, M.; Herr, F.; Münstedt, K.; Lang, U.; Liang, O.D. Angiogenesis and vasculogenesis in pregnancy. Eur. J. Obstet. Gynecol. Reprod. Biol. 2003, 110 (Suppl. S1), S10–S18. [Google Scholar] [CrossRef]
  7. Boeldt, D.S.; Bird, I.M. Vascular adaptation in pregnancy and endothelial dysfunction in preeclampsia. J. Endocrinol. 2017, 232, R27–R44. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  8. Leguizamón, G.; Trigubo, D.; Pereira, J.; Vera, M.; Fernández, J. Vascular complications in the diabetic pregnancy. Curr. Diab. Rep. 2015, 15, 22. [Google Scholar] [CrossRef]
  9. Pertynska-Marczewska, M.; Cypryk, K. The possible impact of advanced glycation end products on pregnancy outcome in women with diabetes mellitus type 1. Minerva Endocrinol. 2017, 42, 271–279. [Google Scholar] [CrossRef]
  10. Li, J.H.; Huang, X.R.; Zhu, H.J.; Johnson, R.; Lan, H.Y. Role of TGF-β signaling in extracellular matrix production under high glucose conditions. Kidney Int. 2003, 63, 2010–2019. [Google Scholar] [CrossRef] [Green Version]
  11. Li, J.H.; Huang, X.R.; Zhu, H.J.; Oldfield, M.; Cooper, M.; Truong, L.D.; Johnson, R.J.; Lan, H.Y. Advanced glycation end products activate Smad signaling via TGF-beta-dependent and independent mechanisms: Implications for diabetic renal and vascular disease. FASEB J. 2004, 18, 176–178. [Google Scholar] [CrossRef]
  12. Ito, Y.; Miyazono, K. RUNX transcription factors as key targets of TGF-beta superfamily signaling. Curr. Opin. Genet. Dev. 2003, 13, 43–47. [Google Scholar] [CrossRef]
  13. Miyazawa, K.; Miyazono, K. Regulation of TGF-β family signaling by inhibitory Smads. Cold Spring Harb. Perspect. Biol. 2017, 9, a022095. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  14. Monsivais, D.; Nagashima, T.; Prunskaite-Hyyryläinen, R.; Nozawa, K.; Shimada, K.; Tang, S.; Hamor, C.; Agno, J.E.; Chen, F.; Masand, R.P.; et al. Endometrial receptivity and implantation require uterine BMP signaling through an ACVR2A-SMAD1/SMAD5 axis. Nat. Commun. 2021, 12, 3386. [Google Scholar] [CrossRef] [PubMed]
  15. Iwatsuki, K.; Tanaka, K.; Kaneko, T.; Kazama, R.; Okamoto, S.; Nakayama, Y.; Ito, Y.; Satake, M.; Takahashi, S.; Miyajima, A.; et al. Runx1 promotes angiogenesis by downregulation of insulin-like growth factor-binding protein-3. Oncogene 2005, 24, 1129–1137. [Google Scholar] [CrossRef] [Green Version]
  16. Takakura, N.; Watanabe, T.; Suenobu, S.; Yamada, Y.; Noda, T.; Ito, Y.; Satake, M.; Suda, T. A role for hematopoietic stem cells in promoting angiogenesis. Cell 2000, 102, 199–209. [Google Scholar] [CrossRef] [Green Version]
  17. Gao, L.J.; Gao, P.; Chen, C.; Tan, K.; Speck, N.A. RUNX1 and the endothelial origin of blood. Exp. Hematol. 2018, 68, 2–9. [Google Scholar] [CrossRef] [Green Version]
  18. Lam, J.D.; Oh, D.J.; Wong, L.L.; Amarnani, D.; Park-Windhol, C.; Sanchez, A.V.; Cardona-Velez, J.; McGuone, D.; Stemmer-Rachamimov, A.O.; Eliott, D.; et al. Identification of RUNX1 as a mediator of aberrant retinal angiogenesis. Diabetes 2017, 66, 1950–1956. [Google Scholar] [CrossRef] [Green Version]
  19. Whitmore, H.; Amarnani, D.; Delgado-Tirado, S.; Arboleda-Velasquez, J.; Kim, L.A. In vitro analysis of RUNX1 and VEGF pathway interactions. Investig. Ophthalmol. Vis. Sci. 2019, 60, 2687. [Google Scholar]
  20. Xing, X.; Wang, H.; Niu, T.; Jiang, Y.; Shi, X.; Liu, K. RUNX1 can mediate the glucose and O-GlcNAc-driven proliferation and migration of human retinal microvascular endothelial cells. BMJ Open Diabetes Res. Care 2021, 9, e001898. [Google Scholar] [CrossRef]
  21. Deng, Y.; Bai, H.; Hu, H. rs11671784 G/A variation in miR-27a decreases chemo-sensitivity of bladder cancer by decreasing miR-27a and increasing the target RUNX-1 expression. Biochem. Biophys. Res. Commun. 2015, 458, 321–327. [Google Scholar] [CrossRef]
  22. Nie, M.; Yu, S.; Peng, S.; Fang, Y.; Wang, H.; Yang, X. miR-23a and miR-27a promote human granulosa cell apoptosis by targeting SMAD5. Biol. Reprod. 2015, 93, 98. [Google Scholar] [CrossRef] [PubMed]
  23. Li, X.; Xu, M.; Ding, L.; Tang, J. MiR-27a: A novel biomarker and potential therapeutic target in tumors. J. Cancer 2019, 10, 2836–2848. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  24. Cho, J.H.; Han, M.Y.; Cha, S.H.; Jung, J.H.; Yoon, K.L. Genetic polymorphism of SMAD5 is associated with Kawasaki disease. Pediatric Cardiol. 2014, 35, 601–607. [Google Scholar] [CrossRef] [PubMed]
  25. Yu, K.; Merguerian, M.; Deuitch, N.; Bresciani, E.; Davis, J.; Craft, K.; Cunningham, L.C.; Liu, P.P. Genomic landscape of RUNX1-familial platelet disorder with myeloid malignancies reveals rising clonal hematopoiesis. Blood 2021, 138 (Suppl. S1), 1090. [Google Scholar] [CrossRef]
  26. Alderawi, A.; Caramori, G.; Baker, E.H.; Hitchings, A.W.; Rahman, I.; Rossios, C.; Adcock, I.; Cassolari, P.; Papi, A.; Ortega, V.E.; et al. FN3K expression in COPD: A potential comorbidity factor for cardiovascular disease. BMJ Open Respir. Res. 2020, 7, e000714. [Google Scholar] [CrossRef]
  27. Van Schaftingen, E.; Collard, F.; Wiame, E.; Veiga-da-Cunha, M. Enzymatic repair of Amadori products. Amino Acids 2012, 42, 1143–1150. [Google Scholar] [CrossRef]
  28. Szwergold, B. A Hypothesis: Fructosamine-3-Kinase-Related-Protein (FN3KRP) catalyzes deglycation of maillard intermediates directly downstream from fructosamines. Rejuvenation Res. 2021, 24, 310–318. [Google Scholar] [CrossRef]
  29. Szwergold, B.S.; Bunker, R.D.; Loomes, K.M. The physiological substrates of fructosamine-3-kinase-related-protein (FN3KRP) are intermediates of nonenzymatic reactions between biological amines and ketose sugars (fructation products). Med. Hypotheses 2011, 77, 739–744. [Google Scholar] [CrossRef]
  30. Sajuthi, S.P.; Sharma, N.K.; Chou, J.W.; Palmer, N.D.; McWilliams, D.R.; Beal, J.; Comeau, M.E.; Ma, L.; Calles-Escandon, J.; Demons, J.; et al. Mapping adipose and muscle tissue expression quantitative trait loci in African Americans to identify genes for type 2 diabetes and obesity. Hum. Genet. 2016, 135, 869–880. [Google Scholar] [CrossRef] [Green Version]
  31. Ghanbari, M.; Franco, O.H.; de Looper, H.W.; Hofman, A.; Erkeland, S.J.; Dehghan, A. Genetic variations in microRNA-binding sites affect microRNA-mediated regulation of several genes associated with cardio-metabolic phenotypes. Circ. Cardiovasc. Genet. 2015, 8, 473–486. [Google Scholar] [CrossRef] [Green Version]
  32. Kim, H.R.; Roe, J.S.; Lee, J.E.; Cho, E.J.; Youn, H.D. p53 regulates glucose metabolism by miR-34a. Biochem. Biophys. Res. Commun. 2013, 437, 225–231. [Google Scholar] [CrossRef] [PubMed]
  33. Yu, G.; Yao, W.; Xiao, W.; Li, H.; Xu, H.; Lang, B. MicroRNA-34a functions as an anti-metastatic microRNA and suppresses angiogenesis in bladder cancer by directly targeting CD44. J. Exp. Clin. Cancer Res. 2014, 33, 779. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  34. Yeung, A.; Lun, S.; Luo, S.; Schooling, C.M. The Impact of Glycated Hemoglobin (HbA1c) on Cardiovascular Disease Risk: A Mendelian Randomization Study Using UK Biobank. Diabetes Care 2018, 41, 1991–1997. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  35. Cai, X.; Liu, X.; Sun, L.; He, Y.; Zheng, S.; Zhang, Y.; Huang, Y. Prediabetes and the risk of heart failure: A meta-analysis. Diabetes Obes. Metab. 2021, 23, 1746–1753. [Google Scholar] [CrossRef] [PubMed]
  36. Walinjkar, R.S.; Khadse, S.; Kumar, S.; Bawankule, S.; Acharya, S. Platelet indices as a predictor of microvascular complications in type 2 diabetes. Indian J. Endocrinol. Metab. 2019, 23, 206–210. [Google Scholar]
  37. Hughes, R.C.; Rowan, J.; Florkowski, C.M. Is there a role for HbA1c in pregnancy? Curr. Diab. Rep. 2016, 16, 5. [Google Scholar] [CrossRef]
  38. Amin, I.; Pandith, A.A.; Manzoor, U.; Mir, S.H.; Afroze, D.; Koul, A.M.; Wani, S.; Ahmad, A.; Qasim, I.; Rashid, M.; et al. Implications of VEGF gene sequence variations and its expression in recurrent pregnancy loss. Reprod. Biomed. Online 2021, 43, 1035–1044. [Google Scholar] [CrossRef]
  39. An, H.J.; Kim, J.H.; Ahn, E.H.; Kim, Y.R.; Kim, J.O.; Park, H.S.; Ryu, C.S.; Kim, E.G.; Cho, S.H.; Lee, W.S.; et al. 3′-UTR polymorphisms in the vascular endothelial growth factor gene (VEGF) contribute to susceptibility to recurrent pregnancy loss (RPL). Int. J. Mol. Sci. 2019, 20, 3319. [Google Scholar] [CrossRef] [Green Version]
  40. Bahia, W.; Soltani, I.; Haddad, A.; Radhouani, A.; Mahdhi, A.; Ferchichi, S.; Almawi, W.Y. Contribution of ADIPOQ variants to the genetic susceptibility of recurrent pregnancy loss. Reprod. Sci. 2021, 28, 263–270. [Google Scholar] [CrossRef]
  41. Cho, H.Y.; Park, H.S.; Ahn, E.H.; Ko, E.J.; Park, H.W.; Kim, Y.R.; Kim, J.H.; Lee, W.S.; Kim, N.K. Association of polymorphisms in plasminogen activator inhibitor-1 (PAI-1), tissue plasminogen activator (tPA), and renin (REN) with recurrent pregnancy loss in korean women. J. Pers. Med. 2021, 11, 1378. [Google Scholar] [CrossRef]
  42. Kim, J.O.; Ahn, E.H.; Sakong, J.H.; An, H.J.; Park, H.S.; Kim, Y.R.; Lee, J.R.; Lee, W.S.; Kim, N.K. Association of miR-27aA>G, miR-423C>a, miR-449bA>G, and miR-604A>G polymorphisms with risk of recurrent implantation failure. Reprod. Sci. 2020, 27, 29–38. [Google Scholar] [CrossRef] [PubMed]
  43. Mayr, C. Regulation by 3′-untranslated regions. Annu. Rev. Genet. 2017, 51, 171–194. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  44. Syeda, Z.A.; Langden, S.; Munkhzul, C.; Lee, M.; Song, S.J. Regulatory mechanism of microRNA expression in cancer. Int. J. Mol. Sci. 2020, 21, 1723. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  45. Abdi, A.; Zafarpiran, M.; Farsani, Z.S. The computational analysis conducted on miRNA target sites in association with SNPs at 3′UTR of ADHD-implicated Genes. Cent. Nerv. Syst. Agents Med. Chem. 2020, 20, 58–75. [Google Scholar] [CrossRef] [PubMed]
  46. Carp, H.; Toder, V.; Aviram, A.; Daniely, M.; Mashiach, S.; Barkai, G. Karyotype of the abortus in recurrent miscarriage. Fertil. Steril. 2001, 75, 678–682. [Google Scholar] [CrossRef]
  47. Ritchie, M.D.; Hahn, L.W.; Roodi, N.; Bailey, L.R.; Dupont, W.D.; Parl, F.F.; Moore, J.H. Multifactor-dimensionality reduction reveals high-order interactions among estrogen-metabolism genes in sporadic breast cancer. Am. J. Hum. Genet. 2001, 69, 138–147. [Google Scholar] [CrossRef] [Green Version]
  48. Lee, S.Y.; Chung, Y.; Elston, R.C.; Kim, Y.; Park, T. Log-linear model-based multifactor dimensionality reduction method to detect gene gene interactions. Bioinformatics 2007, 23, 2589–2595. [Google Scholar]
  49. Hahn, L.W.; Ritchie, M.D.; Moore, J.H. Multifactor dimensionality reduction software for detecting gene-gene and gene-environment interactions. Bioinformatics 2003, 19, 376–382. [Google Scholar] [CrossRef] [Green Version]
  50. Hochberg, Y.; Benjamini, Y. More powerful procedures for multiple significance testing. Stat. Med. 1990, 9, 811–818. [Google Scholar] [CrossRef]
  51. Vistoli, G.; De Maddis, D.; Cipak, A.; Zarkovic, N.; Carini, M.; Aldini, G. Advanced glycoxidation and lipoxidation end products (AGEs and ALEs): An overview of their mechanisms of formation. Free. Radic. Res. 2013, 47 (Suppl. S1), 3–27. [Google Scholar] [CrossRef] [Green Version]
  52. Merhi, Z. Advanced glycation end products and their relevance in female reproduction. Hum. Reprod. 2014, 29, 135–145. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  53. Stensen, M.H.; Tanbo, T.; Storeng, R.; Fedorcsak, P. Advanced glycation end products and their receptor contribute to ovarian ageing. Hum. Reprod. 2014, 29, 125–134. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  54. Conner, J.R.; Beisswenger, P.J.; Szwergold, B.S. Some clues as to the regulation, expression, function, and distribution of fructosamine-3-kinase and fructosamine-3-kinase-related protein. Ann. N. Y. Acad. Sci. 2005, 1043, 824–836. [Google Scholar] [CrossRef]
  55. Zhang, H.F.; Wang, Y.C.; Han, Y.D. MicroRNA-34a inhibits liver cancer cell growth by reprogramming glucose metabolism. Mol. Med. Rep. 2018, 17, 4483–4489. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  56. Rodriguez, B.A.T.; Johnson, A.D. Platelet measurements and Type 2 Diabetes: Investigations in two population-based cohorts. Front. Cardiovasc. Med. 2020, 7, 118. [Google Scholar] [CrossRef]
  57. Sudic, D.; Razmara, M.; Forslund, M.; Ji, Q.; Hjemdahl, P.; Li, N. High glucose levels enhance platelet activation: Involvement of multiple mechanisms. Br. J. Haematol. 2006, 133, 315–322. [Google Scholar] [CrossRef]
  58. Tang, X.; Sun, L.; Wang, G.; Chen, B.; Luo, F. RUNX1: A regulator of NF-kB Signaling in pulmonary diseases. Curr. Protein Pept. Sci. 2018, 19, 172–178. [Google Scholar]
Biomedicines 10 01481 g001 550
Figure 1. Overview of the formation and impact of AGEs. Proteins in the blood, such as albumin and hemoglobin, react with plasma glucose and generate glycated proteins including fructosamine and HbA1c, leading to the formation of AGEs through non-enzymatic glycation. The AGE formation process is restricted by FN3K, which is similar in structure to FN3KRP. The high level of AGEs can induce multiple diseases such as cancer, diabetes mellitus, vascular system dysfunction and infertility and interacts with the TGF- β signaling pathway. In our study, we indicated the association between FN3KRP rs1046875 G>A and RPL. Therefore, we propose that FN3KRP may inhibit the formation of AGEs and affect the RPL, infertility diseases.
Figure 1. Overview of the formation and impact of AGEs. Proteins in the blood, such as albumin and hemoglobin, react with plasma glucose and generate glycated proteins including fructosamine and HbA1c, leading to the formation of AGEs through non-enzymatic glycation. The AGE formation process is restricted by FN3K, which is similar in structure to FN3KRP. The high level of AGEs can induce multiple diseases such as cancer, diabetes mellitus, vascular system dysfunction and infertility and interacts with the TGF- β signaling pathway. In our study, we indicated the association between FN3KRP rs1046875 G>A and RPL. Therefore, we propose that FN3KRP may inhibit the formation of AGEs and affect the RPL, infertility diseases.
Biomedicines 10 01481 g001
Table
Table 1. Clinical characteristics of control and RPL groups.
Table 1. Clinical characteristics of control and RPL groups.
CharacteristicControl (n = 280)RPL (n = 388)p *
Age (year, mean ± SD)33.02 ± 5.7433.21 ± 4.550.339 *
BMI (kg/m2, mean ± SD)21.58 ± 3.1821.49 ± 3.840.730 *
Previous pregnancy losses (n)N/A3.28 ± 1.84
Live births (n)1.72 ± 0.72N/A
Mean gestational age (weeks)39.28 ± 1.677.36 ± 1.93<0.0001 *
Homocysteine (µmol/L)7.28 ± 1.586.98 ± 2.100.402 *
Folate (mg/mL)13.71 ± 8.3714.21 ± 11.940.887 *
Total cholesterol (mg/dL)239.00 ± 85.19187.73 ± 49.420.0004
Uric acid (mg/dL)4.19 ± 1.443.80 ± 0.840.360 *
BUN (mg/dL)8.03 ± 2.019.99 ± 2.77<0.0001 *
Creatinine (mg/dL)0.69 ± 0.080.72 ± 0.120.050 *
PLT (103/μL)235.18 ± 63.60255.43 ± 59.220.0007
PT (s)11.53 ± 3.1011.58 ± 0.86<0.0001 *
aPTT (s)30.78 ± 4.6132.24 ± 4.330.006
Hct (%)35.35 ± 4.2637.31 ± 3.37<0.0001 *
TSH (uIU/mL)N/A2.18 ± 1.55
FSH (mIU/mL)8.12 ± 2.857.52 ± 10.52<0.0001 *
LH (mIU/mL)3.32 ± 1.746.30 ± 12.09<0.0001 *
E2 (Basal)26.00 ± 14.7535.71 ± 29.460.002 *
Prolactin (ng/mL)N/A15.68 ± 12.98
FBSN/A95.24 ± 16.97
Abbreviations: RPL, recurrent pregnancy loss; SD, standard deviation; N/A, not applicable; BMI, body mass index; BUN, blood urea nitrogen; PLT, platelets; PT, prothrombin time; aPTT, activated partial thromboplastin time; Hct, hematocrit; TSH, thyroid-stimulating hormone; FSH, follicle-stimulating hormone; LH, luteinizing hormone; E2, estradiol; FBS, fasting blood sugar. p *: p-values were calculated by chi-square test for categorical data and two-sided t-test for continuous data.
Table
Table 2. Genotype frequencies of gene polymorphisms in control and RPL groups.
Table 2. Genotype frequencies of gene polymorphisms in control and RPL groups.
GenotypeControls
(n = 280)		PL ≥ 2
(n = 388)		AOR (95% CI)cpFDR-pPL ≥ 3
(n = 206)		AOR (95% CI)pFDR-pPL ≥ 4
(n = 82)		AOR (95% CI)pFDR-p
SMAD5 rs10515478 C>G
CC92 (32.9)137 (35.3) 69 (33.5)1.000 (reference) 29 (35.4)1.000 (reference)
CG138 (49.3)194 (50.0)0.949 (0.949–1.339)0.7660.766100 (48.5)0.965 (0.641–1.451)0.8630.95435 (42.7)0.808 (0.462–1.414)0.4560.920
GG50 (17.9)57 (14.7)0.771 (0.771–1.225)0.2710.40737 (18.0)0.996 (0.587–1.691)0.9890.98918 (22.0)1.174 (0.589–2.340)0.6500.650
Dominant (CC vs. CG + GG) 0.894 (0.894–1.237)0.4990.499 0.965 (0.658–1.415)0.8540.903 0.890 (0.530–1.493)0.6580.954
Recessive (CC + CG vs. GG) 0.789 (0.789–1.197)0.2650.398 0.981 (0.612–1.572)0.9350.935 1.270 (0.687–2.348)0.4460.590
HWE-p0.8880.382
FN3KRP rs1046875 G>A
GG70 (25.0)122 (31.4) 65 (31.6)1.000 (reference) 25 (30.5)1.000 (reference)
GA141 (50.4)200 (51.5)0.820 (0.820–1.182)0.2880.687108 (52.4)0.819 (0.538–1.249)0.3540.95449 (59.8)0.972 (0.555–1.702)0.9200.920
AA69 (24.6)66 (17.0)0.553 (0.553–0.866)0.0100.03033 (16.0)0.516 (0.302–0.882)0.0160.0488 (9.8)0.311 (0.130–0.742)0.0090.027
Dominant (GG vs. AG + AA) 0.733 (0.733–1.035)0.0780.234 0.723 (0.484–1.078)0.1110.333 0.761 (0.442–1.310)0.3240.954
Recessive (GG + AG vs. AA) 0.631 (0.631–0.922)0.0200.051 0.594 (0.374–0.943)0.0270.081 0.333 (0.153–0.725)0.0060.018
HWE-p0.9050.298
RUNX-1 rs15285 G>A
GG218 (77.9)292 (75.3) 159 (77.2)1.000 (reference) 64 (78.0)1.000 (reference)
GA55 (19.6)85 (21.9)1.156 (1.156–1.694)0.4580.68741 (19.9)1.013 (0.643–1.596)0.9540.95415 (18.3)0.927 (0.491–1.751)0.8150.920
AA7 (2.5)11 (2.8)1.167 (1.167–3.062)0.7530.7536 (2.9)1.136 (0.373–3.459)0.8230.9893 (3.7)1.423 (0.357–5.677)0.6180.650
Dominant (GG vs. AG + AA) 1.157 (1.157–1.666)0.4340.499 1.027 (0.667–1.582)0.9030.903 0.983 (0.542–1.782)0.9540.954
Recessive (GG + AG vs. AA) 1.135 (1.135–2.968)0.7960.796 1.140 (0.376–3.455)0.8160.935 1.460 (0.369–5.788)0.5900.590
HWE-p0.1280.122
Note: The odds ratio was adjusted by age of participants. RPL, recurrent pregnancy loss; AOR, adjusted odds ratio; CI, confidence interval; HWE, Hardy–Weinberg equilibrium; FDR, False discovery rate.
Table
Table 3. Haplotype analysis of gene polymorphisms in control and RPL groups.
Table 3. Haplotype analysis of gene polymorphisms in control and RPL groups.
Allele CombinationControls
(2n = 560)		RPL
(2n = 776)		OR (95% CI)pFDR-p
SMAD5 rs10515478 C>G/FN3KRP rs1046875 G>A/RUNX-1 rs15285 G>A
C-G-G138 (24.6)212 (27.4)1.000 (reference)
C-G-A20 (3.6)42 (5.4)1.367 (0.770–2.427)0.2850.55
C-A-G146 (26.0)192 (24.7)0.856 (0.632–1.160)0.3160.553
C-A-A18 (3.3)22 (2.9)0.796 (0.412–1.538)0.4960.578
G-G-G112 (20.0)167 (21.5)0.971 (0.704–1.339)0.8560.856
G-G-A11 (2.0)23 (3.0)1.361 (0.643–2.881)0.4190.578
G-A-G96 (17.1)98 (12.7)0.665 (0.467–0.947)0.0230.163
G-A-A19 (3.5)20 (2.5)0.685 (0.353–1.331)0.2620.553
SMAD5 rs10515478 C>G/FN3KRP rs1046875 G>A
C-G159 (28.3)253 (32.7)1.000 (reference)
C-A163 (29.2)215 (27.7)0.829 (0.624–1.102)0.1960.293
G-G122 (21.9)191 (24.6)0.984 (0.728–1.330)0.9160.916
G-A116 (20.6)117 (15.1)0.634 (0.458–0.877)0.0060.017
SMAD5 rs10515478 C>G/RUNX-1 rs15285 G>A
C-G284 (50.8)404 (52.0)1.000 (reference)
C-A38 (6.7)64 (8.3)1.184 (0.771–1.819)0.4400.660
G-G207 (36.9)265 (34.2)0.900 (0.710–1.140)0.3830.660
G-A31 (5.6)43 (5.5)0.975 (0.600–1.586)0.9190.919
FN3KRP rs1046875 G>A/RUNX-1 rs15285 G>A
G-G250 (44.7)379 (48.8)1.000 (reference)
G-A31 (5.5)65 (8.4)1.383 (0.876–2.184)0.1630.184
A-G241 (43.0)290 (37.4)0.794 (0.628–1.003)0.0530.158
A-A38 (6.8)42 (5.4)0.729 (0.457–1.163)0.1840.184
Note: RPL, recurrent pregnancy loss; OR, odds ratio; CI, confidence interval; FDR, False discovery rate.
Table
Table 4. Combination analysis of gene polymorphisms between control and RPL groups.
Table 4. Combination analysis of gene polymorphisms between control and RPL groups.
Genotype CombinationControls
(n = 280)		RPL
(n = 388)		AOR (95% CI)pFDR-p
SMAD5 rs10515478 C>G/FN3KRP rs1046875 G>A
CC/GG23 (8.2)42 (10.8)1.000 (reference)
CC/GA46 (16.4)68 (17.5)0.810 (0.431–1.523)0.5140.821
CC/AA23 (8.2)27 (7.0)0.640 (0.300–1.365)0.2480.497
CG/GG35 (12.5)58 (14.9)0.886 (0.457–1.716)0.7190.821
CG/GA66 (23.6)103 (26.5)0.865 (0.476–1.572)0.6350.821
CG/AA37 (13.2)33 (8.5)0.482 (0.241–0.967)0.0400.306
GG/GG12 (4.3)22 (5.7)0.934 (0.387–2.255)0.8790.879
GG/GA29 (10.4)29 (7.5)0.572 (0.275–1.190)0.1350.361
GG/AA9 (3.2)6 (1.5)0.345 (0.106–1.120)0.0770.306
SMAD5 rs10515478 C>G/RUNX-1 rs15285 G>A
CC/GG71 (25.4)103 (26.5)1.000 (reference)
CC/GA20 (7.1)30 (7.7)0.999 (0.522–1.912)0.9990.999
CC/AA1 (0.4)4 (1.0)3.196 (0.344–29.734)0.3070.859
CG/GG109 (38.9)147 (37.9)0.923 (0.624–1.366)0.6880.999
CG/GA25 (8.9)41 (10.6)1.162 (0.647–2.087)0.6160.999
CG/AA4 (1.4)6 (1.5)0.960 (0.259–3.560)0.9510.999
GG/GG38 (13.6)42 (10.8)0.763 (0.447–1.302)0.3220.859
GG/GA10 (3.6)14 (3.6)0.963 (0.405–2.291)0.9320.999
GG/AA2 (0.7)1 (0.3)0.291 (0.025–3.352)0.3220.859
FN3KRP rs1046875 G>A/RUNX-1 rs15285 G>A
GG/GG54 (19.3)95 (24.5)1.000 (reference)
GG/GA16 (5.7)24 (6.2)0.875 (0.427–1.795)0.7160.832
GG/AA0 (0.0)3 (0.8)NA0.9940.994
GA/GG111 (39.6)141 (36.3)0.729 (0.480–1.107)0.1380.275
GA/GA27 (9.6)52 (13.4)1.107 (0.624–1.964)0.7280.832
GA/AA3 (1.1)7 (1.8)1.343 (0.333–5.414)0.6780.832
AA/GG53 (18.9)56 (14.4)0.609 (0.368–1.008)0.0540.241
AA/GA12 (4.3)9 (2.3)0.434 (0.171–1.098)0.0780.241
AA/AA4 (1.4)1 (0.3)0.147 (0.016–1.352)0.0900.241
Note: The odds ratio was adjusted by age of participants. RPL, recurrent pregnancy loss; AOR, adjusted odds ratio; CI, confidence interval; N/A, not applicable; FDR, False discovery rate.
Table
Table 5. Association between various clinical parameters and gene polymorphisms.
Table 5. Association between various clinical parameters and gene polymorphisms.
GenotypeBMI
(kg/m2)		Previous Pregnancy Losses (n)Mean Gestational Age (Weeks)Hcy
(umol/L)		Folate
(ng/mL)		T.chol
(mg/dL)		BUN
(mg/dL)		Creatin
(mg/dL)		PLT
(103/uL)		PT
(s)		aPTT
(s)
Mean ± SDMean ± SDMean ± SDMean ± SDMean ± SDMean ± SDMean ± SDMean ± SDMean ± SDMean ± SDMean ± SD
SMAD5 rs10515478 C>G
CC21.45 ± 3.082.99 ± 1.5818.45 ± 15.627.12 ± 2.0713.77 ± 11.13194.84 ± 59.449.34 ± 2.780.71 ± 0.11240.17 ± 60.5811.70 ± 2.0831.12 ± 4.64
CG21.71 ± 4.272.94 ± 1.4720.29 ± 15.646.92 ± 2.0614.23 ± 12.51187.54 ± 52.189.83 ± 2.830.72 ± 0.12246.52 ± 59.8611.39 ± 1.0232.10 ± 4.44
GG21.06 ± 2.573.33 ± 1.4925.82 ± 16.186.90 ± 2.1714.98 ± 9.76198.36 ± 49.869.89 ± 2.370.73 ± 0.12250.13 ± 73.5511.90 ± 2.4132.02 ± 4.07
Pa0.3740.2220.0230.7220.8850.5460.3820.5120.4800.1550.184
FN3KRP rs1046875 G>A
GG21.76 ± 4.752.95 ± 1.2819.56 ± 15.747.08 ± 2.1314.22 ± 11.25198.73 ± 59.399.69 ± 2.290.71 ± 0.11256.76 ± 69.3511.82 ± 2.4232.13 ± 4.39
GA21.33 ± 3.123.17 ± 1.7820.86 ± 15.976.85 ± 1.8113.51 ± 8.02183.88 ± 42.749.45 ± 2.870.72 ± 0.12239.37 ± 61.5511.54 ± 1.3331.80 ± 4.44
AA21.67 ± 2.982.68 ± 0.8621.94 ± 15.967.22 ± 2.6316.29 ± 20.17201.78 ± 71.1210.11 ± 3.060.71 ± 0.13242.67 ± 52.1211.24 ± 0.9630.96 ± 4.64
Pa0.465b 0.3760.6750.462b 0.977b 0.4700.3730.9050.0460.1550.274
RUNX-1
rs15285 G>A
GG21.57 ± 3.863.05 ± 1.5120.12 ± 15.786.87 ± 1.9814.22 ± 12.16189.80 ± 54.179.68 ± 2.720.72 ± 0.12246.53 ± 61.8311.59 ± 1.8231.60 ± 4.51
GA21.35 ± 3.032.87 ± 1.5022.73 ± 16.257.30 ± 1.9613.91 ± 10.32202.42 ± 57.429.32 ± 2.670.70 ± 0.10233.34 ± 60.1711.54 ± 1.0432.29 ± 4.26
AA21.47 ± 2.653.27 ± 1.8521.46 ± 16.198.07 ± 3.9914.27 ± 7.36167.75 ± 16.6810.72 ± 4.580.70 ± 0.10281.60 ± 80.8111.30 ± 1.8132.16 ± 5.20
Pa0.8530.5410.507b 0.1660.9880.3090.5090.5920.0380.9090.540
Note: BUN, blood urea nitrogen; T.chol, total cholesterol; BMI, body mass index; Hcy, homocysteine; PLT, platelet count; PT, prothrombin time; aPTT, activated partial thromboplastin time. a One-way analysis of variance test. b Kruskal–Wallis test.
Publisher’s Note: MDPI stays neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Share and Cite

MDPI and ACS Style

Kwon, M.-J.; Kim, J.-H.; Lee, J.-Y.; Ko, E.-J.; Park, H.-W.; Shin, J.-E.; Ahn, E.-H.; Kim, N.-K. Genetic Polymorphisms in the 3′-Untranslated Regions of SMAD5, FN3KRP, and RUNX-1 Are Associated with Recurrent Pregnancy Loss. Biomedicines 2022, 10, 1481. https://doi.org/10.3390/biomedicines10071481

AMA Style

Kwon M-J, Kim J-H, Lee J-Y, Ko E-J, Park H-W, Shin J-E, Ahn E-H, Kim N-K. Genetic Polymorphisms in the 3′-Untranslated Regions of SMAD5, FN3KRP, and RUNX-1 Are Associated with Recurrent Pregnancy Loss. Biomedicines. 2022; 10(7):1481. https://doi.org/10.3390/biomedicines10071481

Chicago/Turabian Style

Kwon, Min-Jung, Ji-Hyang Kim, Jeong-Yong Lee, Eun-Ju Ko, Hyeon-Woo Park, Ji-Eun Shin, Eun-Hee Ahn, and Nam-Keun Kim. 2022. "Genetic Polymorphisms in the 3′-Untranslated Regions of SMAD5, FN3KRP, and RUNX-1 Are Associated with Recurrent Pregnancy Loss" Biomedicines 10, no. 7: 1481. https://doi.org/10.3390/biomedicines10071481

APA Style

Kwon, M. -J., Kim, J. -H., Lee, J. -Y., Ko, E. -J., Park, H. -W., Shin, J. -E., Ahn, E. -H., & Kim, N. -K. (2022). Genetic Polymorphisms in the 3′-Untranslated Regions of SMAD5, FN3KRP, and RUNX-1 Are Associated with Recurrent Pregnancy Loss. Biomedicines, 10(7), 1481. https://doi.org/10.3390/biomedicines10071481

Note that from the first issue of 2016, this journal uses article numbers instead of page numbers. See further details here.

Article Metrics

Back to TopTop