{"paper_id":"417f646e-dfd5-4af9-bc61-ad6e700bc2f4","body_text":"The implication of the vaginal microbiome in female infertility and assisted conception outcomes | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article The implication of the vaginal microbiome in female infertility and assisted conception outcomes xiuju chen, yanyu sui, jiayi gu, liang wang, Ningxia Sun This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-4194198/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Background : Infertility rates are on the rise, presenting a complex array of causative factors. Recent advancements in human microbiome and associated techniques have shed light on the potential impact of vaginal microbiota disruptions on female fertility. Our study aims to investigate differences in vaginal microbiome between fertile women and those experiencing infertility. Additionally, we aim to investigate how microbial composition in infertile population may affect the success of assisted reproduction technology (ART). Methods: We enrolled 194 women diagnosed with infertility at the Reproductive Medicine Center of Shanghai Changzheng Hospital between November 2018 and November 2021, along with 102 healthy women undergoing routine physical examinations at the hospital’s Physical Examination Center. Vaginal secretions were collected from both groups, and polymerase chain reaction (PCR) was used to amplify the bacterial 16S rRNA V4-V6 conserved region for microbial analysis. A machine learning model was built based on the genus abundances to predict infertility. Additionally, we employed the PICRUSt algorithm to predict the metabolic pathway activities, providing insights into potential molecular mechanisms underlying female infertility and ART outcomes. Results: Women with infertility exhibited a significantly different vaginal microbial composition compared to healthy women, with the infertility group showing higher microbial diversity. Burkholderia, Pseudomonas, and Prevotella levels were significantly elevated in the vaginal microbiota of the infertility group, while Bifidobacterium and Lactobacillus abundances were reduced. Recurrent implantation failure (RIF) within the infertile population showed even higher diversity of vaginal microbiota, with specific genera such as Mobiluncus, Peptoniphilus, Prevotella, and Varibaculum being more abundant. Overgrowth of Mobiluncus and Varibaculum emerged as independent risk factors affecting ART outcomes. Eleven metabolic pathways were associated with both RIF and infertility, with Prevotella demonstrating stronger correlations. Conclusions: The present study provides insights into the differences in vaginal mircobiome between healthy and infertile women, offering a new understanding of how vaginal microbiota may impact infertility and ART outcomes. Our findings underscore the significance of specific microbial taxa in women with recurrent implantation failure, suggesting avenues for targeted interventions to enhance embryo transplantation success rates. Vaginal microbiome Infertile Pregnancy outcome Assisted reproductive technology Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Introduction Infertility, defined as the inability to achieve a clinical pregnancy despite 12 months or longer of unprotected sexual intercourse, is a common challenge affecting approximately 15% of couples worldwide, with this proportion on the rise [ 1 , 2 ] . The etiology of infertility is often complex and diverse, encompassing female, male, and unexplained factors. Female infertility alone accounts for 40% of these cases, with ovulation disorders, uterine or cervical issues, tubal alterations, endometriosis, immune factors, and pelvic infections being the primary causes [ 3 ] . Notably, approximately 30% of female infertility cases remain unexplained, labeled as \"unexplained infertility\" (UI) [ 4 ] . Recent advancements in vaginal microecological group and microbial detection technology have revealed a potential link between vaginal microecological imbalance and infertility, providing new insights into this complex condition. The Human Microbiome Project has identified that approximately 9% of the human microbiome resides in the female genital tract (FGT) [ 5 ] . This microbiome plays a pivotal role in maintaining homeostasis, defending against pathogens, and potentially influencing fertility [ 6 ] . The vaginal microbiota, in particular, is typically dominated by Lactobacillus species [ 7 ] , promoting a low-diversity environment. Conversely, a more diverse vaginal microbiome, characterized by the presence of various strict and facultative anaerobes, is described as vaginal dysbiosis (VD) [ 8 ] . VD may increase the risk of infections, diseases, reproductive issues, and adverse pregnancy outcomes [ 9 – 11 ] Assisted reproductive technology (ART) is a set of procedures designed to overcome infertility and achieve successful pregnancy [ 12 ] . Despite its widespread use for almost 40 years [ 13 ] , the clinical pregnancy rate following ART remains stagnant, ranging between 30% and 40% [ 14 ] .Given the significant role of the vaginal microbiome in maintaining homeostasis and affecting fertility, there has been increasing interest in exploring its relationship with ART outcomes. Multiple studies have provided evidence indicating that vaginal dysbiosis, characterized by alterations in the vaginal microbiota, is significantly linked to reduced success rates in IVF, increased susceptibility to aneuploid pregnancy loss, and obstetric complications such as preterm rupture of membranes (PPROM) and preterm delivery [ 15 – 17 ] .The mechanisms underlying the regulation of the vaginal microbiome in infertility and ART outcomes remain enigmatic. Although research has indicated that alterations in the vaginal microbiome can influence pregnancy, its precise role in infertility and ART success remains unclear. In this study, we aim to elucidate the disparities in vaginal microflora distribution between fertile women of childbearing age and those experiencing infertility. We seek to investigate the microbial composition of the infertile population and its association with ART outcomes. Furthermore, given that recurrent implantation failure (RIF) is a significant factor limiting ART success rates, and its etiology remains enigmatic, with limited reports exploring the link between reproductive tract microorganisms and embryo implantation, we categorize infertile patients into RIF and non-RIF groups. This classification allows us to explore microbial composition differences between the two groups and to assess whether specific microbial genera may serve as potential therapeutic targets for addressing ART failures. By delving into this research, we hope to gain deeper insights into the vaginal microflora's potential role in fertility-related issues. Ultimately, this understanding could lead to more effective infertility treatments and improved reproductive outcomes, paving the way for more targeted therapeutic interventions in the future. Materials and Methods Sample Collection The study enrolled a total of 194 women diagnosed with infertility at the Reproductive Medicine Center of Shanghai Changzheng Hospital between November 2018 and November 2021, comprising the infertility group. Additionally, 102 healthy women who underwent routine physical examinations at the Physical Examination Center of the same hospital were recruited as the healthy control group. Inclusion criteria for the infertility group were: regular unprotected sexual activity for at least one year without achieving pregnancy; Inclusion criteria for the healthy group were: women with a history of childbirth and no history of infertility and other gynecological diseases. Exclusion criteria for both groups included the presence of an IUD, vaginal inflammation, any acute inflammatory condition, suspicion of cervical or endometrial neoplasia, and endocrine or autoimmune disorders. Subjects were also excluded if they had recently used hormones, antibiotics, or vaginal medications; undergone cervical treatment, endometrial biopsy, IUD removal, or hysteroscopy within the past week; performed douching within five days; or had sexual activity within 48 hours prior to sampling. Additionally, none of the participants were pregnant, lactating, or menstruating at the time of sampling. Age, current residence, menstrual history, and fertility history were collected from all participants. For nulliparous subjects, they were asked about their pregnancy plans. All participants provided written informed consent, and the study received approval from the Ethics Committee of Shanghai Changzheng Hospital(2023SL051).Vaginal secretions were obtained from the posterior fornix region of both groups. The samples were then rapidly frozen using liquid nitrogen, stored at − 80°C, and transported in dry ice to BGI-Shenzhen for further analysis. DNA Extraction and 16S rRNA Amplicon Sequencing DNA was extracted from vaginal secretions and amplified by PCR using high quality DNA. More accurate distance-based clustering of reads was achieved by using V4-V6 primers and PCR premixes with targeted primers for these regions. Subsequently, these clusters were categorized as amplicon sequence variants (ASVs) at the species level [ 18 ] . Finally, PCR products were purified using Agencourt AMPure XP magnetic beads and dissolved in elution buffer. The fragment ranges in the libraries were assessed by an Agilent 2100 Bioanalyzer and quality control libraries were sequenced on the HiSeq 2000 platform).The primers for the V4–V6 regions were listed as follows: 8F-‘AGAGTTTGAT[YM]TGGCTCAG’, 518R-‘ATTACCGCGGCTGCTGG’. Y and M represent bases C/T and C/G, respectively. Quantitative Real-Time Polymerase Chain Reaction Real-time PCR detection was carried out by using primers for amplifying 16s rRNA gene and beta-actin. The primers were listed as follow [ 19 ] : Lactobacillus crispatus : forward primer 5′-AGCGAGCGGAACTAACAGATTTAC-3′, reverse primer 5′-AGCTGATCATGCGATCTGCTT-3′; Lactobacillus iners : forward primer 5′-AGTCTGCCTTGAAGATCGG-3′, reverse primer 5′-CTTTTAAACAGTTGATAGGCATCATC-3′; beta-actin: forward primer 5′-AAAAGCCACCCCACTTCTCT-3′, reverse primer 5′-CTCAAGTTGGGGGACAAAAA-3′. The 20-µl PCR mixture contained 1 µl of DNA sample, 1 µl of each primer, 6 µl of ultra-pure water and 12 µl of 2*SYBR Green Mix. The Eppendorf realplex system (Eppendorf, USA) was used with the thermal cycling profile of 95°C for 5 min, and 40 cycles of 95°C for 30 s, 56°C for 30 s, and 72°C for 30 s. Each sample has three technical duplicates. The bacterial abundance was calculated by dividing the average CT value of 16s rRNA gene by the average CT value of beta- actin. Cleaning the Raw Sequencing Data The steps to process the raw data are as follows: 1) discard readings with low base quality: set 30 bp as the window length, truncate the end sequence of readings from the window if the average quality of the window is less than 17, and delete readings whose final reading length is less than 75% of the original reading length; 2) discard readings contaminated by adapters: the default adapter sequence overlaps with the read sequence at 15 bp, sets it to 15 bp, and allows mismatch of 3. 3) eliminate readings with Ns; 4) eliminate readings with low complexity: the length of continuous occurrence of bases in the readings is ≥ 10. The resulting data in fastq format were termed as clean data. Amplicon Sequence Variants and Taxonomy Analysis The 16S rRNA clean data were processed by DADA2 package in R [ 20 ] . DADA2 provides a sensitive and specific workflow in amplicon sequencing. The DADA2 pipeline proceeds as follows: 1) filter and trim clean data: discarding reads at the first instance of a quality score less than or equal to 2; 2) remove duplicated sequence entries in fastq files; 3) merge paired reads; 4) learn the error rates and infer the sample composition using the error rates; 5) construct a sequence table; 6) remove chimeras; and 7) assign taxonomy using naive Bayesian classifier method and the RDP_16sRNA reference databases. Normalizing the Relative Abundance of Bacteria For each sample, we divide the original count of bacteria by the total reading count, thus normalizing the abundance of each bacteria at the genus or species level. The proportion of each type of bacteria is used as the normalized abundance. In addition, bacteria that account for less than 0.5% are merged into \"other\". Bacteria that account for more than 0.5% of at least two samples will be retained for further data analysis. Diversity Analysis The α-diversity analysis was performed with the R software package. Based on the OUT table, we calculated the observed species, Shannon index, chao 1 index and ACE value to estimate the α-diversity of the colony and performed principal component analysis (PCA) based on the sequencing data. The Chao index and observed species reflect the abundance of OTUs in the samples, while the Shannon index and Simpson index reflect the diversity of OTUs in the samples. PCOA belongs to a type of beta diversity, which is usually used to represent the material differences between different environmental communities. The Functional Prediction of Microbiota Tax4fun [ 21 ] package in R was used to estimate Kyoto Encyclopedia of Genes and Genomes (KEGG) Ortholog (KO) scores for each sample, and further used the scores to predict the relative activity of the KEGG pathway. The Differential Abundance Analysis Wilcoxon rank-sum test was used to compare the abundance of microflora between the two groups. The p value was adjusted by Benjamini and Hochberg methods to avoid multiple tests, and Kruskal-Wallis test was used to make multiple comparisons between groups. Results Baseline characteristics of the female infertility patients and healthy controls The study compared the baseline characteristics of two groups: the healthy group and the infertile group. No significant differences were observed in age and body mass index (BMI) between the two groups (Wilcoxon test, P > 0.05). Specifically, the mean age in the infertile group was 31.7 ± 4.8, compared to 32.3 ± 4.52 in the healthy group(Fig. 1 A). Regarding infertility status, primary infertility was diagnosed in 50% of the women in the infertile group, while the remaining 50% were diagnosed with secondary infertility. Clinically detected pregnancy was achieved in 94 women (48.4%) among the 194 women with infertility. To further understand the differences within the infertile group, the study compared the baseline characteristics of women who became pregnant (n = 94) with those who did not (n = 100) (Table 1 ). Additionally, the study focused on the relationship between the vaginal microbiome and RIF. Therefore, a comparison of baseline characteristics was also conducted between the RIF(32)and non-RIF groups༈83༉ (Table 2 ). Table 1 Infertility group baseline characteristics Characteristic Group p-value 2 Overall, N = 194 1 Pregnant, N = 94 1 Not pregnant, N = 100 1 Age 31.7 (4.8) 30.8 (4.3) 32.6 (5.1) 0.007 BMI 22.2 (3.3) 21.9 (3.0) 22.4 (3.6) 0.24 Diagnose 0.15 Primary infertility 97 (50%) 52 (55%) 45 (45%) secondary infertility 97 (50%) 42 (45%) 55 (55%) ART style 0.34 ICSI 54 (28%) 22 (23%) 32 (32%) IVF 114 (59%) 60 (64%) 54 (54%) PGT 26 (13%) 12 (13%) 14 (14%) The reason for ART 0.49 tubal disease 71 (37%) 37 (39%) 34 (34%) unknow factor infertility 49 (25%) 24 (26%) 25 (25%) multi-factor infertility 15 (7.8%) 4 (4.3%) 11 (11%) genetic factor 4 (2.1%) 3 (3.2%) 1 (1.0%) male factor infertility 21 (11%) 11 (12%) 10 (10%) ovulatory dysfunction 33 (17%) 15 (16%) 18 (18%) Unknown 1 0 1 Embryo quality 0.036 good 180 (93%) 91 (97%) 89 (89%) bad 14 (7.2%) 3 (3.2%) 11 (11%) 1 Mean (SD); n (%) 2 Welch Two Sample t-test; Pearson's Chi-squared test; Fisher's exact test Table 2 RIF and not RIF groups baseline characteristics Characteristic RIF OR NOT p-value 2 Overall, N = 115 1 No, N = 83 1 Yes, N = 32 1 Age 31.5 (4.7) 30.7 (4.3) 33.5 (5.4) 0.011 BMI 22.2 (3.3) 21.7 (3.1) 23.4 (3.6) 0.030 The reason for ART 0.30 ovulatory dysfunction 22 (19%) 15 (18%) 7 (22%) tubal disease 41 (36%) 31 (37%) 10 (31%) multi-factor infertility 6 (5.2%) 2 (2.4%) 4 (12%) unknow factor infertility 31 (27%) 22 (27%) 9 (28%) male factor infertility 12 (10%) 10 (12%) 2 (6.2%) genetic factor 3 (2.6%) 3 (3.6%) 0 (0%) Diagnose 0.066 secondary infertility 56 (49%) 36 (43%) 20 (62%) Primary infertility 59 (51%) 47 (57%) 12 (38%) Embryo quality 0.093 bad 7 (6.1%) 3 (3.6%) 4 (12%) good 108 (94%) 80 (96%) 28 (88%) ART style 0.74 IVF 74 (64%) 55 (66%) 19 (59%) ICSI 27 (23%) 18 (22%) 9 (28%) PGT 14 (12%) 10 (12%) 4 (12%) 1 Mean (SD); n (%) 2 Welch Two Sample t-test; Fisher's exact test; Pearson's Chi-squared test The alterations of the vaginal microbiota in female patients with infertility. The study conducted 16S rRNA sequencing on swab samples from the vagina of 102 healthy controls and 194 patients with infertility to explore alterations in the reproductive tract microbiota. The sequencing data revealed the presence of ten major bacterial genera in the vagina, including Lactobacillus, Gardnerella, Atopobium, Prevotella,Streptococcus,Bifidobacterium,Ureaplasma,Anaerococcus,Peptostreptococcus , and Mycoplasma . Notably, Lactobacillus, Gardnerella , and Atopobium were more abundant in healthy samples, while the remaining genera such as Prevotella , Streptococcus , and Bifidobacterium were more abundant in infertility samples. This suggests a potential association between the genus abundant in infertility samples and the onset of infertility(Fig. 2A). Additionally, the study found higher microbial diversity indices (Observed, Chao1, ACE, and Shannon) in infertility samples compared to healthy samples. This indicates that the reproductive tract microbiota in female patients with infertility is more diverse than in healthy controls(Fig. 2B). We also examined the correlation between microbial diversity and BMI/age in both healthy and infertile groups. Age was not correlated with microbial diversity in either group. However, microbial diversity was insignificantly correlated with BMI in the infertility group, while all indices except the Shannon index were increased with BMI in the healthy group. This suggests that microbial community composition might vary with BMI in healthy individuals but might be influenced by other factors in the infertility group(Fig. 2C). Principal Coordinate Analysis (PCoA) based on microbiota abundance further distinguished infertility samples from healthy controls, indicating significant changes in the reproductive tract microbiota in patients with infertility(Fig. 2D). Infertile women harbor an altered vaginal microbiome compared with healthy controls. To further explore the vaginal microbiota associated with infertility, a differential abundance analysis was conducted by comparing the microbiota composition of infertility patients with that of healthy controls at the genus level. This analysis revealed 45 genera with increased abundance and 43 genera with decreased abundance in the infertility group compared to the healthy controls (Fig. 3 A; P < 0.05 and log2 fold change > 1). These findings indicate significant alterations in the vaginal microbiota of infertility patients. To assess the predictive capability of the genera with significant differential abundance in infertility, a machine learning model was built. Specifically, 296 samples were randomly divided into a training set (n = 147) and a test set (n = 149). Using the abundance data of different bacteria, a naive Bayesian model was constructed in the training set. The performance of the model was evaluated based on its ability to predict infertility. Encouragingly, the model exhibited an Area Under the Curve (AUC) of over 90% (Fig. 3 B) and a sensitivity and specificity of over 85% (Fig. 3 C) in both the training set and the test set. These results suggest that the vaginal microbiota has a strong predictive power for infertility, with high accuracy. Within the vaginal flora of infertility patients, several genera exhibited elevated levels with statistically significant differences compared to healthy controls. Specifically, Burkholderia, Pseudomonas , and Prevotella were found to be elevated in the infertility group (Fig. 3 D; P < 0.05). These findings suggest that these bacteria may play a pathogenic role in female infertility. In contrast, the abundance of Bifidobacterium and Lactobacillus was significantly reduced in the vaginal flora of infertility patients (Fig. 3 D; P < 0.05). Given their known protective roles in the vaginal microbiota, this reduction may contribute to the development of infertility. Vaginal microbiome is associated with recurrent implantation failure, tubal disease and assisted reproductive technology outcome To further investigate the association between the vaginal microbiome and specific etiologies of infertility, as well as outcomes of ART and baseline characteristics like age and body mass index (BMI), we employed canonical correlation analysis. The results revealed strong correlations between the vaginal microbiome and tubal disease, ovulatory dysfunction, RIF, and ART outcome (Fig. 4 A; P < 0.05). Notably, patients with tubal disease, those without ovulatory dysfunction, and those experiencing RIF or ART failure exhibited significantly higher Shannon diversity indices (Fig. 4 B). Particularly, RIF was found to be significantly associated with Shannon diversity (Wilcoxon test, P < 0.05). Differential abundance analysis at the genus level further identified specific bacterial genera that were altered in the RIF and ovulatory dysfunction groups compared to their respective control groups. By intersecting these differences with those observed between infertile patients and healthy controls, we identified four genera ( Mobiluncus, Peptoniphilus, Prevotella , and Varibaculum ) that were abundantly present in the RIF group. Similarly, two genera ( Porphyromonas and Prevotella ) were found to be abundant in the tubal disease group, while another two genera ( Mobiluncus and Varibaculum ) were abundant in the ART failure group (Fig. 4 C). These findings suggest that the vaginal microbiome is more closely associated with RIF, tubal disease, and ART outcome, providing further evidence for the role of vaginal microbiota in infertility etiologies and treatment outcomes. Pathway analysis interprets association of vaginal microbiome with recurrent implantation failure, tubal disease and assisted reproductive technology outcome To further explore the underlying molecular mechanisms that might be associated with female infertility and outcomes of ART, we employed the PICRUSt algorithm to predict the activities of metabolic pathways based on the microbiome data obtained from different samples. By comparing the pathway activities among different groups, we identified 11 metabolic pathways that were simultaneously associated with RIF and infertility. These pathways included colanic acid building blocks biosynthesis, acetyl-CoA fermentation to butanoate II, mixed acid fermentation, superpathway of N-acetylneuraminate degradation, superpathway of hexitol degradation (bacteria), L-histidine degradation I, hexitol fermentation to lactate, formate, ethanol and acetate, superpathway of GDP-mannose-derived O-antigen building blocks biosynthesis, superpathway of glycolysis and Entner-Doudoroff, L-valine degradation I, and pyruvate fermentation to acetone. Notably, one additional pathway, involving the degradation of N-acetylglucosamine, N-acetylmannosamine, and N-acetylneuraminate, was found to be simultaneously associated with RIF, ART failure, and infertility (Fig. 5 A; P < 0.05). The activities of these pathways were found to be elevated in both the RIF and infertility groups (Fig. 5 B; P < 0.05). Furthermore, these pathways were closely linked to the regulation of vaginal pH, which is a critical factor in maintaining the balance of the vaginal microbiome. Correlation analysis revealed a positive association between the genera that were associated with infertility, RIF, and ART outcomes, and the activities of these pathways (Fig. 5 C). Notably, Prevotella , one of the genera identified in our previous analysis, showed a stronger correlation with multiple pathways compared to other genera. This suggests that Prevotella may play a crucial role in processes related to infertility, RIF, and ART outcomes through these metabolic pathways. structural unit biosynthesis superpathway, glycolysis and Entner-Doudoroff superpathway, L- valine degradation I, pyruvate fermentation to acetone) have been associated with both RIF and infertility. In addition, one pathway (N-acetylglucosamine, N-acetylmannosamine, and N-acetylneuraminic acid degradation superpathway) was associated with both RIF,ART failure, and infertility (P < 0.05)(A).The activity of these pathways in Fig. 5Awas elevated inboth the RIF and infertility groups(B).Correlation analysis showed that the genera related to the results of sterility, RIF and ART were positively correlated with the activities of these pathways(C). Discussion Infertility has been recognized as a global public health problem by the World Health Organization (WHO). It has indeed been a longstanding yet overlooked health concern in China. In 1990, around 9% of Chinese couples faced infertility issues [ 22 ] . Alarmingly, by 2020, this proportion had surged to 18%, indicating that approximately one out of every five childbearing-age couples in China is at risk of infertility [ 23 ] . Notably, China leads the world in the number of assisted reproduction cycles, having conducted over 1.3 million such treatment cycles by 2020. Despite the remarkable advancements in ART, the implantation rates of transferred embryos remain unsatisfactory. Global live birth/assisted reproduction fresh cycle ratio only 5%-29% from 2004–2013 [ 24 ] .There is a growing interest in exploring the reproductive tract flora as a potential novel strategy to enhance ART outcomes among patients with unexplained infertility, particularly those experiencing RIF. However, the current understanding of the impact of reproductive tract flora on ART outcomes is still limited by a scarcity of evidence. The dynamic interaction between the human host and vaginal microbiota is influenced by various endogenous and exogenous factors, leading to alterations in the relative abundances of Lactobacillus and other vaginal microbial components. The vaginal microbiota of reproductive-aged women typically encompasses at least five distinct community state types, four of which are primarily dominated by Lactobacillus spp [ 25 ] . Lactobacillus spp play a crucial role in maintaining the health of the female reproductive tract, inhibiting the adhesion of other bacteria to epithelial cells and producing lactic acid that kills or suppresses the growth of numerous other bacteria [ 26 , 27 ] . Conversely, the presence of non-Lactobacillus vaginal microbiota species may enhance susceptibility to infections and contribute to adverse reproductive outcomes, including infertility and preterm birth. Previous studies have demonstrated a correlation between low Lactobacillus abundance and infertility, findings that align with the results of our study [ 27 , 28 ] . Utilizing 16S rRNA gene sequencing, our previous study has revealed for the first time the distribution characteristics of reproductive tract microbiota in healthy Chinese women, as well as their functional roles in female infertility of the Chinese population [ 29 ] .In this study we aimed to further expand our knowledge on vaginal microecosystems in the context of female infertility and ART outcomes. By comparing the vaginal microbiomes of infertile women with those of healthy women, we elucidated the microbial composition of the infertile population and its association with the outcome of ART in assisted conception of Chinese population. First of all, our findings revealed a notable difference in vaginal diversity, characterized by a decrease in Lactobacillus dominance and an elevation in Burkholderia, Pseudomonas , and Prevotella levels within the infertility cohort. Previous studies have indicated that the non-Lactobacillus-dominant group exhibited a lower live birth rate and a higher preterm birth rate [ 30 – 32 ] . The identification of specific genera with altered abundances, especially along with the predictive capability of the machine learning model, offers new insights into the potential role of the vaginal microbiota in female infertility. These observations further highlighting the significance of vaginal microbiota in infertility status and female reproductive health. Secondly, we also observed a significant increase in the presence of Mobiluncus and Varibaculum in the failure group compared to individuals who had a successful pregnancy by assisted reproductive technologies. Mobiluncus is a specialized anaerobic, Gram-unstable or Gram-negative campylobacterium of the vaginal flora that has been highly associated with bacterial vaginosis (VC) [ 33 – 35 ] . Previous research has demonstrated a correlation between VC and infertility [ 36 , 37 ] , and our findings align with this evidence as we observed a high prevalence of Mobiluncus in infertile populations who have experienced unsuccessful ART outcomes. Varibaculum has indeed been reported to be associated with both bladder cancer and prostate cancer [ 38 , 39 ] , providing further evidence of its potential role in various health conditions. In addition, there is also evidence suggesting an association between Varibaculum and male infertility [ 40 ] . This association is particularly interesting given that male infertility is a significant factor contributing to overall infertility issues. In our study, we identified Varibaculum overgrowth as an independent risk factor affecting ART outcomes. This finding aligns with previous reports of Varibaculum's association with infertility and suggests that it may play a role in the success or failure of ART procedures. However, the exact mechanism by which Varibaculum affects infertility and ART outcomes remains unclear. Besides, RIF, which affects 15–20% of individuals undergoing in vitro fertilization and embryo transfer (IVF-ET) programs [ 41 ] , is indeed a complex issue that requires careful consideration. Kitaya et al. reported elevated levels of Gardnerella and Burkholderia in the reproductive tracts of women experiencing RIF compared to those without [ 42 ] . Our observation regarding the association between RIF and an overgrowth of Mobiluncus, Peptoniphilus, Prevotella and Varibaculum is intriguing. Among them, Prevotella is associated with a variety of pathways and has attracted much attention from scholars. Prevotella is a Gram-negative, anaerobic, and immobile bacillus, and recent studies have shown that its high concentration is associated with infertility [ 43 ] . Additionally, in many patients with IUA, a significant reduction in Lactobacillus in the vagina has been observed, along with excessive growth of Gardnerella and Prevotella . IUA can lead to abnormal menstruation, infertility, or recurrent miscarriage [ 44 – 46 ] . Furthermore, the relative abundance of Prevotella is higher in pregnant women with PPROM compared to those who deliver at term [ 47 ] . Finally, to gain deeper insights into the potential molecular mechanisms underlying female infertility and ART outcomes, we conducted molecular pathway prediction in this study. Our findings reveal a strong association between Prevotella spp . and various pathways, suggesting that Prevotella spp. may play a crucial role in processes such as infertility, RIF, and ART outcomes through multiple metabolic pathways. One limitation of this study is the small sample size available for analysis, which may have affected the ability to detect significant differences in vaginal microbial diversity among patients with different causes of infertility in terms of pregnancy outcomes after ART. It is possible that with a larger sample size, more subtle differences in microbial diversity may have been identified that could have provided additional insights into the relationship between vaginal microbiota and ART success. Future studies with larger sample sizes are needed to further explore this association and to validate the findings of this study. Given the potential influence of the FGT microbiome on embryo implantation and pregnancy outcomes, it is crucial to consider its role in ART success. Modifying the FGT microbiome may be a promising approach to improving ART outcomes in specific cases. Therefore, it is advisable for infertile women to undergo reproductive tract microorganism screening prior to receiving ART [ 48 , 49 ] . This approach may help to increase the chances of successful embryo implantation and pregnancy, ultimately leading to more successful ART outcomes. Conclusions The present study offers valuable insights into the variations in vaginal microbiota between healthy and infertile women, shedding light on the potential influence of vaginal microbiota on infertility and outcomes related to assisted reproductive technology. This study identified specific genera associated with women experiencing repeated implantation failure (RIF) within the infertile population. Future research should continue to expand upon these findings, aiming to uncover additional microbial markers and develop innovative therapeutic interventions to effectively address the challenges posed by infertility. Declarations Acknowledgements We thank all donors and colleagues of the Reproductive Medicine Center of Shanghai Changzheng Hospital for their participation and cooperation. Authors’ contributions N.S. and L.W. designed the study. X.C., Y.S., J.G. and L.W. performed the majority of experiments. X.C. and J.G. prepared the human samples. X.C., Y.S, L.W, and N.S. analyzed the data and wrote the paper. All authors discussed and approved the data and reviewed the manuscript. Funding This research was funded by the National Natural Science Foundation of China [82271662] and the Military innovation project special project (21JSZ06);Shanghai Shen-Kang hospital development center special medical enterprise integration innovation achievements[SHDC2022CRD007], All the authors have no conflict of interest to declare. Availability of data and materials The data presented in the study are deposited in the The National Omics Data Encyclopedia (NODE) repository, accession number OED 911162. Declarations Ethics approval and consent to participate the study received approval from the Ethics Committee of Shanghai Changzheng Hospital(2023SL051). Furthermore, we informed all patients of the study’s purpose, and they provided written informed consent. Consent for publication Not applicable. Competing interests The authors declare that they have no competing interests References ZEGERS-HOCHSCHILD F, ADAMSON G D, DE MOUZON J, et al. The International Committee for Monitoring Assisted Reproductive Technology (ICMART) and the World Health Organization (WHO) Revised Glossary on ART Terminology, 2009 [J]. Human reproduction (Oxford, England), 2009, 24(11): 2683–7. BOIVIN J, BUNTING L, COLLINS J A, et al. International estimates of infertility prevalence and treatment-seeking: potential need and demand for infertility medical care [J]. Human reproduction (Oxford, England), 2007, 22(6): 1506–12. KOROMA L, STEWART L. Infertility: evaluation and initial management [J]. Journal of midwifery & women's health, 2012, 57(6): 614–21. AZPIROZ M A, ORGUILIA L, PALACIO M I, et al. Potential biomarkers of infertility associated with microbiome imbalances [J]. American journal of reproductive immunology (New York, NY: 1989), 2021, 86(4): e13438. PETERSON J, GARGES S, GIOVANNI M, et al. The NIH Human Microbiome Project [J]. Genome research, 2009, 19(12): 2317–23. KåHRSTRöM C T, PARIENTE N, WEISS U. Intestinal microbiota in health and disease [J]. Nature, 2016, 535(7610): 47. ANAHTAR M N, BYRNE E H, DOHERTY K E, et al. Cervicovaginal bacteria are a major modulator of host inflammatory responses in the female genital tract [J]. Immunity, 2015, 42(5): 965–76. SARAF V S, SHEIKH S A, AHMAD A, et al. Vaginal microbiome: normalcy vs dysbiosis [J]. Archives of microbiology, 2021, 203(7): 3793–802. BLOSTEIN F, GELAYE B, SANCHEZ S E, et al. Vaginal microbiome diversity and preterm birth: results of a nested case-control study in Peru [J]. Annals of epidemiology, 2020, 41: 28–34. PETRICEVIC L, DOMIG K J, NIERSCHER F J, et al. Characterisation of the vaginal Lactobacillus microbiota associated with preterm delivery [J]. Scientific reports, 2014, 4: 5136. LEITICH H, KISS H. Asymptomatic bacterial vaginosis and intermediate flora as risk factors for adverse pregnancy outcome [J]. Best practice & research Clinical obstetrics & gynaecology, 2007, 21(3): 375–90. DE GEYTER C. Assisted reproductive technology: Impact on society and need for surveillance [J]. Best practice & research Clinical endocrinology & metabolism, 2019, 33(1): 3–8. KOEDOODER R, SINGER M, SCHOENMAKERS S, et al. The ReceptIVFity cohort study protocol to validate the urogenital microbiome as predictor for IVF or IVF/ICSI outcome [J]. Reproductive health, 2018, 15(1): 202. ADRIAENSSENS T, VAN VAERENBERGH I, COUCKE W, et al. Cumulus-corona gene expression analysis combined with morphological embryo scoring in single embryo transfer cycles increases live birth after fresh transfer and decreases time to pregnancy [J]. Journal of assisted reproduction and genetics, 2019, 36(3): 433–43. JULIANA N C A, SUITERS M J M, AL-NASIRY S, et al. The Association Between Vaginal Microbiota Dysbiosis, Bacterial Vaginosis, and Aerobic Vaginitis, and Adverse Pregnancy Outcomes of Women Living in Sub-Saharan Africa: A Systematic Review [J]. Frontiers in public health, 2020, 8: 567885. HAAHR T, ZACHO J, BRäUNER M, et al. Reproductive outcome of patients undergoing in vitro fertilisation treatment and diagnosed with bacterial vaginosis or abnormal vaginal microbiota: a systematic PRISMA review and meta-analysis [J]. BJOG: an international journal of obstetrics and gynaecology, 2019, 126(2): 200–7. GREWAL K, LEE Y S, SMITH A, et al. Chromosomally normal miscarriage is associated with vaginal dysbiosis and local inflammation [J]. BMC medicine, 2022, 20(1): 38. BUKIN Y S, GALACHYANTS Y P, MOROZOV I V, et al. The effect of 16S rRNA region choice on bacterial community metabarcoding results [J]. Scientific data, 2019, 6: 190007. MA L, LV Z, SU J, et al. Consistent condom use increases the colonization of Lactobacillus crispatus in the vagina [J]. PloS one, 2013, 8(7): e70716. CALLAHAN B J, MCMURDIE P J, ROSEN M J, et al. DADA2: High-resolution sample inference from Illumina amplicon data [J]. Nature methods, 2016, 13(7): 581–3. AßHAUER K P, WEMHEUER B, DANIEL R, et al. Tax4Fun: predicting functional profiles from metagenomic 16S rRNA data [J]. Bioinformatics (Oxford, England), 2015, 31(17): 2882–4. CHE Y, CLELAND J. Infertility in Shanghai: prevalence, treatment seeking and impact [J]. Journal of obstetrics and gynaecology: the journal of the Institute of Obstetrics and Gynaecology, 2002, 22(6): 643–8. QIAO J, WANG Y, LI X, et al. A Lancet Commission on 70 years of women's reproductive, maternal, newborn, child, and adolescent health in China [J]. Lancet (London, England), 2021, 397(10293): 2497–536. KUSHNIR V A, BARAD D H, ALBERTINI D F, et al. Systematic review of worldwide trends in assisted reproductive technology 2004–2013 [J]. Reproductive biology and endocrinology: RB&E, 2017, 15(1): 6. CHEN C, SONG X, WEI W, et al. The microbiota continuum along the female reproductive tract and its relation to uterine-related diseases [J]. Nature communications, 2017, 8(1): 875. KYONO K, HASHIMOTO T, KIKUCHI S, et al. A pilot study and case reports on endometrial microbiota and pregnancy outcome: An analysis using 16S rRNA gene sequencing among IVF patients, and trial therapeutic intervention for dysbiotic endometrium [J]. Reproductive medicine and biology, 2019, 18(1): 72–82. BERNABEU A, LLEDO B, DíAZ M C, et al. Effect of the vaginal microbiome on the pregnancy rate in women receiving assisted reproductive treatment [J]. Journal of assisted reproduction and genetics, 2019, 36(10): 2111–9. KOEDOODER R, SINGER M, SCHOENMAKERS S, et al. The vaginal microbiome as a predictor for outcome of in vitro fertilization with or without intracytoplasmic sperm injection: a prospective study [J]. Human reproduction (Oxford, England), 2019, 34(6): 1042–54. SUN N, DING H, YU H, et al. Comprehensive Characterization of Microbial Community in the Female Genital Tract of Reproductive-Aged Women in China [J]. Frontiers in cellular and infection microbiology, 2021, 11: 649067. PUNZóN-JIMéNEZ P, LABARTA E. The impact of the female genital tract microbiome in women health and reproduction: a review [J]. Journal of assisted reproduction and genetics, 2021, 38(10): 2519–41. ZENG H, HE D, HU L, et al. Non-Lactobacillus dominance of the vagina is associated with reduced live birth rate following IVF/ICSI: a propensity score-matched cohort study [J]. Archives of gynecology and obstetrics, 2022, 305(2): 519–28. AL-MEMAR M, BOBDIWALA S, FOURIE H, et al. The association between vaginal bacterial composition and miscarriage: a nested case-control study [J]. BJOG: an international journal of obstetrics and gynaecology, 2020, 127(2): 264–74. ZENG W, MA H, FAN W, et al. Structure determination of CAMP factor of Mobiluncus curtisii and insights into structural dynamics [J]. International journal of biological macromolecules, 2020, 150: 1027–36. TAYLOR-ROBINSON A W, BORRIELLO S P, TAYLOR-ROBINSON D. Identification and preliminary characterization of a cytotoxin isolated from Mobiluncus spp [J]. International journal of experimental pathology, 1993, 74(4): 357–66. SCHWEBKE J R, DESMOND R A. A randomized trial of the duration of therapy with metronidazole plus or minus azithromycin for treatment of symptomatic bacterial vaginosis [J]. Clinical infectious diseases: an official publication of the Infectious Diseases Society of America, 2007, 44(2): 213–9. RAVEL J, MORENO I, SIMóN C. Bacterial vaginosis and its association with infertility, endometritis, and pelvic inflammatory disease [J]. American journal of obstetrics and gynecology, 2021, 224(3): 251–7. SWIDSINSKI S, MOLL W M, SWIDSINSKI A. Bacterial Vaginosis-Vaginal Polymicrobial Biofilms and Dysbiosis [J]. Deutsches Arzteblatt international, 2023, 120(20): 347–54. HRBáČEK J, TLáSKAL V, ČERMáK P, et al. Bladder cancer is associated with decreased urinary microbiota diversity and alterations in microbial community composition [J]. Urologic oncology, 2023, 41(2): 107.e15-.e22. HURST R, MEADER E, GIHAWI A, et al. Microbiomes of Urine and the Prostate Are Linked to Human Prostate Cancer Risk Groups [J]. European urology oncology, 2022, 5(4): 412–9. MäNDAR R, PUNAB M, BOROVKOVA N, et al. Complementary seminovaginal microbiome in couples [J]. Research in microbiology, 2015, 166(5): 440–7. COUGHLAN C, LEDGER W, WANG Q, et al. Recurrent implantation failure: definition and management [J]. Reproductive biomedicine online, 2014, 28(1): 14–38. KITAYA K, NAGAI Y, ARAI W, et al. Characterization of Microbiota in Endometrial Fluid and Vaginal Secretions in Infertile Women with Repeated Implantation Failure [J]. Mediators of inflammation, 2019, 2019: 4893437. SHAH H N, COLLINS D M. Prevotella, a new genus to include Bacteroides melaninogenicus and related species formerly classified in the genus Bacteroides [J]. International journal of systematic bacteriology, 1990, 40(2): 205–8. GACHET C, PRAT M, BURUCOA C, et al. Spermatic Microbiome Characteristics in Infertile Patients: Impact on Sperm Count, Mobility, and Morphology [J]. Journal of clinical medicine, 2022, 11(6). GOMES I A, MONTEIRO P B, MOURA G A, et al. Microbiota and seminal quality: A systematic review [J]. JBRA assisted reproduction, 2023. ZHAO Z, MAO X, ZHENG Y, et al. Research progress in the correlation between reproductive tract microbiota and intrauterine adhesion [J]. Zhong nan da xue xue bao Yi xue ban = Journal of Central South University Medical sciences, 2022, 47(11): 1495–503. YAN C, HONG F, XIN G, et al. Alterations in the vaginal microbiota of patients with preterm premature rupture of membranes [J]. Frontiers in cellular and infection microbiology, 2022, 12: 858732. GARCíA-VELASCO J A, BUDDING D, CAMPE H, et al. The reproductive microbiome - clinical practice recommendations for fertility specialists [J]. Reproductive biomedicine online, 2020, 41(3): 443–53. MOLINA N M, SOLA-LEYVA A, SAEZ-LARA M J, et al. New Opportunities for Endometrial Health by Modifying Uterine Microbial Composition: Present or Future? [J]. Biomolecules, 2020, 10(4). Additional Declarations No competing interests reported. Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {\"props\":{\"pageProps\":{\"initialData\":{\"identity\":\"rs-4194198\",\"acceptedTermsAndConditions\":true,\"allowDirectSubmit\":true,\"archivedVersions\":[],\"articleType\":\"Research Article\",\"associatedPublications\":[],\"authors\":[{\"id\":287392555,\"identity\":\"bf6c3c33-7afc-4b37-85f2-2a6bf6b2953a\",\"order_by\":0,\"name\":\"xiuju chen\",\"email\":\"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA1ElEQVRIiWNgGAWjYBACNvbmA8Z/DGrk+OUPHyBOCx/PsYQCnopjxpIz2BKI0yIn4WPwgecMc+KGGzwGRDpMgsdwg2QbG2PD7Z6PN94w2MnpNhDSIt1WbGDYJsPMOOfsZss5DMnGZgcIaZE5vM0gsY2NjZkhd5s0D8OBxG0EtUgkmP842MbMw8aQ84xYLSkGhg1nmCV4JHLYiNQCDGRjhopjBhI8x4wt5xgQ4Rf5dmBUMhjU1O8/3vzwxpsKOzmCWlCABLFRg6yFVB2jYBSMglEwIgAAORo/7DHh7IQAAAAASUVORK5CYII=\",\"orcid\":\"\",\"institution\":\"Department of Reproductive Medicine, Second Affiliated Hospital of Naval Medical University Shanghai\",\"correspondingAuthor\":true,\"prefix\":\"\",\"firstName\":\"xiuju\",\"middleName\":\"\",\"lastName\":\"chen\",\"suffix\":\"\"},{\"id\":287392557,\"identity\":\"1c8ead44-09f0-4ab9-a4ad-327275afe7ff\",\"order_by\":1,\"name\":\"yanyu sui\",\"email\":\"\",\"orcid\":\"\",\"institution\":\"Department of Reproductive Medicine, Second Affiliated Hospital of Naval Medical University Shanghai\",\"correspondingAuthor\":false,\"prefix\":\"\",\"firstName\":\"yanyu\",\"middleName\":\"\",\"lastName\":\"sui\",\"suffix\":\"\"},{\"id\":287392560,\"identity\":\"97611d0d-1852-426a-88b8-2074c0c1cf74\",\"order_by\":2,\"name\":\"jiayi gu\",\"email\":\"\",\"orcid\":\"\",\"institution\":\"Department of Reproductive Medicine, Second Affiliated Hospital of Naval Medical University Shanghai\",\"correspondingAuthor\":false,\"prefix\":\"\",\"firstName\":\"jiayi\",\"middleName\":\"\",\"lastName\":\"gu\",\"suffix\":\"\"},{\"id\":287392561,\"identity\":\"d1bfc686-4aa9-4825-9ed3-8074e9498cb7\",\"order_by\":3,\"name\":\"liang wang\",\"email\":\"\",\"orcid\":\"\",\"institution\":\"Department of Reproductive Medicine, Second Affiliated Hospital of Naval Medical University Shanghai\",\"correspondingAuthor\":false,\"prefix\":\"\",\"firstName\":\"liang\",\"middleName\":\"\",\"lastName\":\"wang\",\"suffix\":\"\"},{\"id\":287392563,\"identity\":\"fefc253c-aace-4777-99fe-b085d8e3d10f\",\"order_by\":4,\"name\":\"Ningxia Sun\",\"email\":\"\",\"orcid\":\"\",\"institution\":\"Department of Reproductive Medicine, Second Affiliated Hospital of Naval Medical University Shanghai\",\"correspondingAuthor\":false,\"prefix\":\"\",\"firstName\":\"Ningxia\",\"middleName\":\"\",\"lastName\":\"Sun\",\"suffix\":\"\"}],\"badges\":[],\"createdAt\":\"2024-03-31 04:29:17\",\"currentVersionCode\":1,\"declarations\":\"\",\"doi\":\"10.21203/rs.3.rs-4194198/v1\",\"doiUrl\":\"https://doi.org/10.21203/rs.3.rs-4194198/v1\",\"draftVersion\":[],\"editorialEvents\":[],\"editorialNote\":\"\",\"failedWorkflow\":false,\"files\":[{\"id\":54365556,\"identity\":\"f6cfde55-80cd-4b48-b6f8-5bf8ae4761c7\",\"added_by\":\"auto\",\"created_at\":\"2024-04-09 12:26:50\",\"extension\":\"png\",\"order_by\":1,\"title\":\"Figure 1\",\"display\":\"\",\"copyAsset\":false,\"role\":\"figure\",\"size\":32520,\"visible\":true,\"origin\":\"\",\"legend\":\"\\u003cp\\u003eComparison of healthy and infertility groups on age and BMI(A).Canonical correlation analysis indicated that age and BMI correlated with flora in both groups ,*=p \\u0026lt; 0.05 (B).\\u003c/p\\u003e\",\"description\":\"\",\"filename\":\"Figure1.png\",\"url\":\"https://assets-eu.researchsquare.com/files/rs-4194198/v1/ef0f08adcd0c24f678870403.png\"},{\"id\":54365558,\"identity\":\"ca28cad8-5de1-4209-b71d-27cad17bbe29\",\"added_by\":\"auto\",\"created_at\":\"2024-04-09 12:26:50\",\"extension\":\"png\",\"order_by\":2,\"title\":\"Figure 2\",\"display\":\"\",\"copyAsset\":false,\"role\":\"figure\",\"size\":108772,\"visible\":true,\"origin\":\"\",\"legend\":\"\\u003cp\\u003ethe top 10 bacterial genus in vagina of both groups included\\u003cem\\u003eLactobacillus\\u003c/em\\u003e, \\u003cem\\u003eGardnerella\\u003c/em\\u003e,\\u003cem\\u003eAtopobium\\u003c/em\\u003e, \\u003cem\\u003ePrevotella\\u003c/em\\u003e, \\u003cem\\u003eStreptococcus\\u003c/em\\u003e, \\u003cem\\u003eBifidobacterium\\u003c/em\\u003e, \\u003cem\\u003eUreaplasma\\u003c/em\\u003e, \\u003cem\\u003eAnaerococcus\\u003c/em\\u003e, \\u003cem\\u003ePeptostreptococcus\\u003c/em\\u003e and \\u003cem\\u003eMycoplasma\\u003c/em\\u003e(A).Analysis of alpha diversity in the healthy and infertile groups, including observed,chao 1,ACE and shannon index(B). Correlation analysis of BMI/age with alpha diversity of species in both groups(C).Bray- Curtis distance principal coordinate analysis (PCoA) was performed at the genus level for all taxonomic features of the two groupsof vaginal flora(D).\\u003c/p\\u003e\",\"description\":\"\",\"filename\":\"Figure2.png\",\"url\":\"https://assets-eu.researchsquare.com/files/rs-4194198/v1/57a8cd6a6f0ffc724a78cf08.png\"},{\"id\":54365560,\"identity\":\"ef42156b-f3ac-46c5-8828-a74fde1aa00c\",\"added_by\":\"auto\",\"created_at\":\"2024-04-09 12:26:55\",\"extension\":\"png\",\"order_by\":3,\"title\":\"Figure 3\",\"display\":\"\",\"copyAsset\":false,\"role\":\"figure\",\"size\":48945,\"visible\":true,\"origin\":\"\",\"legend\":\"\\u003cp\\u003eAnalysis of the difference in abundance at the genus level between the infertility and healthy groups revealed that 45 genera increased in abundance and 43 genera decreased in abundance in the infertility group , P \\u0026lt; 0.05 and log2 fold change \\u0026gt;1(A).This is a machine learning model for predicting infertility in which 296 samples were randomly divided into a training set (147) and a test set (149). A machine learning model (naive Bayesian model) was constructed in the training set using the abundance of different bacteria. The training and prediction results of this model showed an AUC of 95% for the training set and 90% for the test set(B).The results of this machine learning model training and prediction showed a sensitivity and specificity of more than85% for both groups (C).In the vaginal flora of the infertility group,\\u003cem\\u003eBurkholderia\\u003c/em\\u003e, \\u003cem\\u003ePseudomonas\\u003c/em\\u003e, and \\u003cem\\u003ePrevotella \\u003c/em\\u003ewere elevated, whereas \\u003cem\\u003eBifidobacterium\\u003c/em\\u003e and \\u003cem\\u003eLactobacillus \\u003c/em\\u003ewere significantly reduced, and the differences were statistically significant, P \\u0026lt; 0.05(D).\\u003c/p\\u003e\",\"description\":\"\",\"filename\":\"Figure3.png\",\"url\":\"https://assets-eu.researchsquare.com/files/rs-4194198/v1/34df1eac07f697195072f78e.png\"},{\"id\":54365559,\"identity\":\"0a59e049-ec9c-41ea-904d-5e26cd313253\",\"added_by\":\"auto\",\"created_at\":\"2024-04-09 12:26:50\",\"extension\":\"png\",\"order_by\":4,\"title\":\"Figure 4\",\"display\":\"\",\"copyAsset\":false,\"role\":\"figure\",\"size\":65704,\"visible\":true,\"origin\":\"\",\"legend\":\"\\u003cp\\u003eTypical correlation analysis suggested that tubal disease, ovulatorydysfunction, RIF, andART outcome were strongly associated with the vaginal microbiome (p \\u0026lt; 0.05), while age, BMI, genetic factors, male factors and multi-factors appeared to be less associated with vaginal flora(p \\u0026gt; 0.05)(A).In terms of Shannon Index diversity, diversity was higher inpatients with tubal disease, without ovulatory dysfunction, with RIF, or withART failure,among them, RIF was strongly associated with Shannondiversity (Wilcoxon test, P \\u0026lt; 0.05)(B).According to the difference of bacterial abundance between infertility patients and healthy people, 4 genera (\\u003cem\\u003eMobiluncus\\u003c/em\\u003e, \\u003cem\\u003ePeptoniphilus\\u003c/em\\u003e, \\u003cem\\u003ePrevotella\\u003c/em\\u003e, and \\u003cem\\u003eVaribaculum\\u003c/em\\u003e) were found in RIF group and infertility group, 2 genera (\\u003cem\\u003ePorphyromonas\\u003c/em\\u003e and \\u003cem\\u003ePrevotella\\u003c/em\\u003e) were found in tubal disease group and infertility group, and 2 genera(\\u003cem\\u003eMobiluncus\\u003c/em\\u003eand \\u003cem\\u003eVaribaculum\\u003c/em\\u003e) were found in failed antiretroviral therapy group and infertility group (P \\u0026lt; 0.05)(C).\\u003c/p\\u003e\",\"description\":\"\",\"filename\":\"Figure4.png\",\"url\":\"https://assets-eu.researchsquare.com/files/rs-4194198/v1/3491ec02da9089a69226ef7b.png\"},{\"id\":54365555,\"identity\":\"6e0e80a4-e528-4aff-96be-6075025b9464\",\"added_by\":\"auto\",\"created_at\":\"2024-04-09 12:26:48\",\"extension\":\"png\",\"order_by\":5,\"title\":\"Figure 5\",\"display\":\"\",\"copyAsset\":false,\"role\":\"figure\",\"size\":79697,\"visible\":true,\"origin\":\"\",\"legend\":\"\\u003cp\\u003eUsing the PICRUSt algorithm and predicting pathway activitiesbased on microbiome data from different samples, 11 pathways were identified by comparing the differences in pathway activities between different groups (coranate building block biosynthesis, acetyl-CoA fermentation to butyric acid II, mixed acid fermentation, N-acetylneuraminic acid degradation superpathway, hexitol degradationsuperpathway (bacterial), L-histidine degradation I, hexitol fermentation to lactate, formate, ethanol and acetate, GDP-mannose-derived O-antigenic\\u003c/p\\u003e\\n\\u003cp\\u003estructural unit biosynthesis superpathway, glycolysis and Entner-Doudoroff superpathway, L- valine degradation I, pyruvate fermentation to acetone) have been associated with both RIF and infertility. In addition, one pathway (N-acetylglucosamine, N-acetylmannosamine, and N-acetylneuraminic acid degradation superpathway) was associated with both RIF,ART failure, and infertility (P \\u0026lt; 0.05)(A).The activity of these pathways in Figure 5Awas elevated inboth the RIF and infertility groups(B).Correlation analysis showed that the genera related to the\\u003c/p\\u003e\\n\\u003cp\\u003eresults of sterility, RIF and ART were positively correlated with the activities of these \\u0026nbsp;pathways(C).\\u003c/p\\u003e\",\"description\":\"\",\"filename\":\"Figure5.png\",\"url\":\"https://assets-eu.researchsquare.com/files/rs-4194198/v1/3eb19e234a6bf46f7e97a93e.png\"},{\"id\":56207810,\"identity\":\"8dfa50a8-8841-4e28-a492-40ac6a4570b7\",\"added_by\":\"auto\",\"created_at\":\"2024-05-09 23:18:48\",\"extension\":\"pdf\",\"order_by\":0,\"title\":\"\",\"display\":\"\",\"copyAsset\":false,\"role\":\"manuscript-pdf\",\"size\":2009509,\"visible\":true,\"origin\":\"\",\"legend\":\"\",\"description\":\"\",\"filename\":\"manuscript.pdf\",\"url\":\"https://assets-eu.researchsquare.com/files/rs-4194198/v1/78caed8e-5db4-462b-9cc6-023f01a11a84.pdf\"}],\"financialInterests\":\"No competing interests reported.\",\"formattedTitle\":\"The implication of the vaginal microbiome in female infertility and assisted conception outcomes\",\"fulltext\":[{\"header\":\"Introduction\",\"content\":\"\\u003cp\\u003eInfertility, defined as the inability to achieve a clinical pregnancy despite 12 months or longer of unprotected sexual intercourse, is a common challenge affecting approximately 15% of couples worldwide, with this proportion on the rise\\u003csup\\u003e[\\u003cspan citationid=\\\"CR1\\\" class=\\\"CitationRef\\\"\\u003e1\\u003c/span\\u003e, \\u003cspan citationid=\\\"CR2\\\" class=\\\"CitationRef\\\"\\u003e2\\u003c/span\\u003e]\\u003c/sup\\u003e. The etiology of infertility is often complex and diverse, encompassing female, male, and unexplained factors. Female infertility alone accounts for 40% of these cases, with ovulation disorders, uterine or cervical issues, tubal alterations, endometriosis, immune factors, and pelvic infections being the primary causes \\u003csup\\u003e[\\u003cspan citationid=\\\"CR3\\\" class=\\\"CitationRef\\\"\\u003e3\\u003c/span\\u003e]\\u003c/sup\\u003e. Notably, approximately 30% of female infertility cases remain unexplained, labeled as \\\"unexplained infertility\\\" (UI) \\u003csup\\u003e[\\u003cspan citationid=\\\"CR4\\\" class=\\\"CitationRef\\\"\\u003e4\\u003c/span\\u003e]\\u003c/sup\\u003e. Recent advancements in vaginal microecological group and microbial detection technology have revealed a potential link between vaginal microecological imbalance and infertility, providing new insights into this complex condition.\\u003c/p\\u003e \\u003cp\\u003eThe Human Microbiome Project has identified that approximately 9% of the human microbiome resides in the female genital tract (FGT) \\u003csup\\u003e[\\u003cspan citationid=\\\"CR5\\\" class=\\\"CitationRef\\\"\\u003e5\\u003c/span\\u003e]\\u003c/sup\\u003e. This microbiome plays a pivotal role in maintaining homeostasis, defending against pathogens, and potentially influencing fertility\\u003csup\\u003e[\\u003cspan citationid=\\\"CR6\\\" class=\\\"CitationRef\\\"\\u003e6\\u003c/span\\u003e]\\u003c/sup\\u003e. The vaginal microbiota, in particular, is typically dominated by Lactobacillus species\\u003csup\\u003e[\\u003cspan citationid=\\\"CR7\\\" class=\\\"CitationRef\\\"\\u003e7\\u003c/span\\u003e]\\u003c/sup\\u003e, promoting a low-diversity environment. Conversely, a more diverse vaginal microbiome, characterized by the presence of various strict and facultative anaerobes, is described as vaginal dysbiosis (VD) \\u003csup\\u003e[\\u003cspan citationid=\\\"CR8\\\" class=\\\"CitationRef\\\"\\u003e8\\u003c/span\\u003e]\\u003c/sup\\u003e. VD may increase the risk of infections, diseases, reproductive issues, and adverse pregnancy outcomes\\u003csup\\u003e[\\u003cspan additionalcitationids=\\\"CR10\\\" citationid=\\\"CR9\\\" class=\\\"CitationRef\\\"\\u003e9\\u003c/span\\u003e\\u0026ndash;\\u003cspan citationid=\\\"CR11\\\" class=\\\"CitationRef\\\"\\u003e11\\u003c/span\\u003e]\\u003c/sup\\u003e\\u003c/p\\u003e \\u003cp\\u003eAssisted reproductive technology (ART) is a set of procedures designed to overcome infertility and achieve successful pregnancy\\u003csup\\u003e[\\u003cspan citationid=\\\"CR12\\\" class=\\\"CitationRef\\\"\\u003e12\\u003c/span\\u003e]\\u003c/sup\\u003e. Despite its widespread use for almost 40 years\\u003csup\\u003e[\\u003cspan citationid=\\\"CR13\\\" class=\\\"CitationRef\\\"\\u003e13\\u003c/span\\u003e]\\u003c/sup\\u003e, the clinical pregnancy rate following ART remains stagnant, ranging between 30% and 40%\\u003csup\\u003e[\\u003cspan citationid=\\\"CR14\\\" class=\\\"CitationRef\\\"\\u003e14\\u003c/span\\u003e]\\u003c/sup\\u003e.Given the significant role of the vaginal microbiome in maintaining homeostasis and affecting fertility, there has been increasing interest in exploring its relationship with ART outcomes. Multiple studies have provided evidence indicating that vaginal dysbiosis, characterized by alterations in the vaginal microbiota, is significantly linked to reduced success rates in IVF, increased susceptibility to aneuploid pregnancy loss, and obstetric complications such as preterm rupture of membranes (PPROM) and preterm delivery\\u003csup\\u003e[\\u003cspan additionalcitationids=\\\"CR16\\\" citationid=\\\"CR15\\\" class=\\\"CitationRef\\\"\\u003e15\\u003c/span\\u003e\\u0026ndash;\\u003cspan citationid=\\\"CR17\\\" class=\\\"CitationRef\\\"\\u003e17\\u003c/span\\u003e]\\u003c/sup\\u003e.The mechanisms underlying the regulation of the vaginal microbiome in infertility and ART outcomes remain enigmatic. Although research has indicated that alterations in the vaginal microbiome can influence pregnancy, its precise role in infertility and ART success remains unclear.\\u003c/p\\u003e \\u003cp\\u003eIn this study, we aim to elucidate the disparities in vaginal microflora distribution between fertile women of childbearing age and those experiencing infertility. We seek to investigate the microbial composition of the infertile population and its association with ART outcomes. Furthermore, given that recurrent implantation failure (RIF) is a significant factor limiting ART success rates, and its etiology remains enigmatic, with limited reports exploring the link between reproductive tract microorganisms and embryo implantation, we categorize infertile patients into RIF and non-RIF groups. This classification allows us to explore microbial composition differences between the two groups and to assess whether specific microbial genera may serve as potential therapeutic targets for addressing ART failures.\\u003c/p\\u003e \\u003cp\\u003eBy delving into this research, we hope to gain deeper insights into the vaginal microflora's potential role in fertility-related issues. Ultimately, this understanding could lead to more effective infertility treatments and improved reproductive outcomes, paving the way for more targeted therapeutic interventions in the future.\\u003c/p\\u003e\"},{\"header\":\"Materials and Methods\",\"content\":\"\\u003cdiv id=\\\"Sec3\\\" class=\\\"Section2\\\"\\u003e \\u003ch2\\u003eSample Collection\\u003c/h2\\u003e \\u003cp\\u003eThe study enrolled a total of 194 women diagnosed with infertility at the Reproductive Medicine Center of Shanghai Changzheng Hospital between November 2018 and November 2021, comprising the infertility group. Additionally, 102 healthy women who underwent routine physical examinations at the Physical Examination Center of the same hospital were recruited as the healthy control group.\\u003c/p\\u003e \\u003cp\\u003eInclusion criteria for the infertility group were: regular unprotected sexual activity for at least one year without achieving pregnancy; Inclusion criteria for the healthy group were: women with a history of childbirth and no history of infertility and other gynecological diseases.\\u003c/p\\u003e \\u003cp\\u003eExclusion criteria for both groups included the presence of an IUD, vaginal inflammation, any acute inflammatory condition, suspicion of cervical or endometrial neoplasia, and endocrine or autoimmune disorders. Subjects were also excluded if they had recently used hormones, antibiotics, or vaginal medications; undergone cervical treatment, endometrial biopsy, IUD removal, or hysteroscopy within the past week; performed douching within five days; or had sexual activity within 48 hours prior to sampling. Additionally, none of the participants were pregnant, lactating, or menstruating at the time of sampling.\\u003c/p\\u003e \\u003cp\\u003eAge, current residence, menstrual history, and fertility history were collected from all participants. For nulliparous subjects, they were asked about their pregnancy plans. All participants provided written informed consent, and the study received approval from the Ethics Committee of Shanghai Changzheng Hospital(2023SL051).Vaginal secretions were obtained from the posterior fornix region of both groups. The samples were then rapidly frozen using liquid nitrogen, stored at \\u0026minus;\\u0026thinsp;80\\u0026deg;C, and transported in dry ice to BGI-Shenzhen for further analysis.\\u003c/p\\u003e \\u003cp\\u003eDNA Extraction and 16S rRNA Amplicon Sequencing\\u003c/p\\u003e \\u003cp\\u003eDNA was extracted from vaginal secretions and amplified by PCR using high quality DNA. More accurate distance-based clustering of reads was achieved by using V4-V6 primers and PCR premixes with targeted primers for these regions. Subsequently, these clusters were categorized as amplicon sequence variants (ASVs) at the species level\\u003csup\\u003e[\\u003cspan citationid=\\\"CR18\\\" class=\\\"CitationRef\\\"\\u003e18\\u003c/span\\u003e]\\u003c/sup\\u003e. Finally, PCR products were purified using Agencourt AMPure XP magnetic beads and dissolved in elution buffer. The fragment ranges in the libraries were assessed by an Agilent 2100 Bioanalyzer and quality control libraries were sequenced on the HiSeq 2000 platform).The primers for the V4\\u0026ndash;V6 regions were listed as follows: 8F-\\u0026lsquo;AGAGTTTGAT[YM]TGGCTCAG\\u0026rsquo;, 518R-\\u0026lsquo;ATTACCGCGGCTGCTGG\\u0026rsquo;. Y and M represent bases C/T and C/G, respectively.\\u003c/p\\u003e \\u003cp\\u003eQuantitative Real-Time Polymerase Chain Reaction\\u003c/p\\u003e \\u003cp\\u003eReal-time PCR detection was carried out by using primers for amplifying 16s rRNA gene and beta-actin. The primers were listed as follow\\u003csup\\u003e[\\u003cspan citationid=\\\"CR19\\\" class=\\\"CitationRef\\\"\\u003e19\\u003c/span\\u003e]\\u003c/sup\\u003e : \\u003cem\\u003eLactobacillus crispatus\\u003c/em\\u003e: forward primer 5\\u0026prime;-AGCGAGCGGAACTAACAGATTTAC-3\\u0026prime;, reverse primer 5\\u0026prime;-AGCTGATCATGCGATCTGCTT-3\\u0026prime;; \\u003cem\\u003eLactobacillus iners\\u003c/em\\u003e: forward primer 5\\u0026prime;-AGTCTGCCTTGAAGATCGG-3\\u0026prime;, reverse primer 5\\u0026prime;-CTTTTAAACAGTTGATAGGCATCATC-3\\u0026prime;; beta-actin: forward primer 5\\u0026prime;-AAAAGCCACCCCACTTCTCT-3\\u0026prime;, reverse primer 5\\u0026prime;-CTCAAGTTGGGGGACAAAAA-3\\u0026prime;. The 20-\\u0026micro;l PCR mixture contained 1 \\u0026micro;l of DNA sample, 1 \\u0026micro;l of each primer, 6 \\u0026micro;l of ultra-pure water and 12 \\u0026micro;l of 2*SYBR Green Mix. The Eppendorf realplex system (Eppendorf, USA) was used with the thermal cycling profile of 95\\u0026deg;C for 5 min, and 40 cycles of 95\\u0026deg;C for 30 s, 56\\u0026deg;C for 30 s, and 72\\u0026deg;C for 30 s. Each sample has three technical duplicates. The bacterial abundance was calculated by dividing the average CT value of 16s rRNA gene by the average CT value of beta- actin.\\u003c/p\\u003e \\u003cp\\u003eCleaning the Raw Sequencing Data\\u003c/p\\u003e \\u003cp\\u003eThe steps to process the raw data are as follows: 1) discard readings with low base quality: set 30 bp as the window length, truncate the end sequence of readings from the window if the average quality of the window is less than 17, and delete readings whose final reading length is less than 75% of the original reading length; 2) discard readings contaminated by adapters: the default adapter sequence overlaps with the read sequence at 15 bp, sets it to 15 bp, and allows mismatch of 3. 3) eliminate readings with Ns; 4) eliminate readings with low complexity: the length of continuous occurrence of bases in the readings is \\u0026ge;\\u0026thinsp;10. The resulting data in fastq format were termed as clean data.\\u003c/p\\u003e \\u003cp\\u003eAmplicon Sequence Variants and Taxonomy Analysis\\u003c/p\\u003e \\u003cp\\u003eThe 16S rRNA clean data were processed by DADA2 package in R\\u003csup\\u003e[\\u003cspan citationid=\\\"CR20\\\" class=\\\"CitationRef\\\"\\u003e20\\u003c/span\\u003e]\\u003c/sup\\u003e. DADA2 provides a sensitive and specific workflow in amplicon sequencing. The DADA2 pipeline proceeds as follows: 1) filter and trim clean data: discarding reads at the first instance of a quality score less than or equal to 2; 2) remove duplicated sequence entries in fastq files; 3) merge paired reads; 4) learn the error rates and infer the sample composition using the error rates; 5) construct a sequence table; 6) remove chimeras; and 7) assign taxonomy using naive Bayesian classifier method and the RDP_16sRNA reference databases.\\u003c/p\\u003e \\u003cp\\u003eNormalizing the Relative Abundance of Bacteria\\u003c/p\\u003e \\u003cp\\u003eFor each sample, we divide the original count of bacteria by the total reading count, thus normalizing the abundance of each bacteria at the genus or species level. The proportion of each type of bacteria is used as the normalized abundance. In addition, bacteria that account for less than 0.5% are merged into \\\"other\\\". Bacteria that account for more than 0.5% of at least two samples will be retained for further data analysis.\\u003c/p\\u003e \\u003cp\\u003eDiversity Analysis\\u003c/p\\u003e \\u003cp\\u003eThe α-diversity analysis was performed with the R software package. Based on the OUT table, we calculated the observed species, Shannon index, chao 1 index and ACE value to estimate the α-diversity of the colony and performed principal component analysis (PCA) based on the sequencing data. The Chao index and observed species reflect the abundance of OTUs in the samples, while the Shannon index and Simpson index reflect the diversity of OTUs in the samples. PCOA belongs to a type of beta diversity, which is usually used to represent the material differences between different environmental communities.\\u003c/p\\u003e \\u003cp\\u003eThe Functional Prediction of Microbiota\\u003c/p\\u003e \\u003cp\\u003eTax4fun\\u003csup\\u003e[\\u003cspan citationid=\\\"CR21\\\" class=\\\"CitationRef\\\"\\u003e21\\u003c/span\\u003e]\\u003c/sup\\u003e package in R was used to estimate Kyoto Encyclopedia of Genes and Genomes (KEGG) Ortholog (KO) scores for each sample, and further used the scores to predict the relative activity of the KEGG pathway.\\u003c/p\\u003e \\u003cp\\u003eThe Differential Abundance Analysis\\u003c/p\\u003e \\u003cp\\u003eWilcoxon rank-sum test was used to compare the abundance of microflora between the two groups. The p value was adjusted by Benjamini and Hochberg methods to avoid multiple tests, and Kruskal-Wallis test was used to make multiple comparisons between groups.\\u003c/p\\u003e \\u003c/div\\u003e\"},{\"header\":\"Results\",\"content\":\"\\u003cdiv id=\\\"Sec5\\\" class=\\\"Section2\\\"\\u003e \\u003ch2\\u003eBaseline characteristics of the female infertility patients and healthy controls\\u003c/h2\\u003e \\u003cp\\u003eThe study compared the baseline characteristics of two groups: the healthy group and the infertile group. No significant differences were observed in age and body mass index (BMI) between the two groups (Wilcoxon test, P\\u0026thinsp;\\u0026gt;\\u0026thinsp;0.05). Specifically, the mean age in the infertile group was 31.7\\u0026thinsp;\\u0026plusmn;\\u0026thinsp;4.8, compared to 32.3\\u0026thinsp;\\u0026plusmn;\\u0026thinsp;4.52 in the healthy group(Fig.\\u0026nbsp;\\u003cspan refid=\\\"Fig1\\\" class=\\\"InternalRef\\\"\\u003e1\\u003c/span\\u003eA). Regarding infertility status, primary infertility was diagnosed in 50% of the women in the infertile group, while the remaining 50% were diagnosed with secondary infertility. Clinically detected pregnancy was achieved in 94 women (48.4%) among the 194 women with infertility. To further understand the differences within the infertile group, the study compared the baseline characteristics of women who became pregnant (n\\u0026thinsp;=\\u0026thinsp;94) with those who did not (n\\u0026thinsp;=\\u0026thinsp;100) (Table\\u0026nbsp;\\u003cspan refid=\\\"Tab1\\\" class=\\\"InternalRef\\\"\\u003e1\\u003c/span\\u003e). Additionally, the study focused on the relationship between the vaginal microbiome and RIF. Therefore, a comparison of baseline characteristics was also conducted between the RIF(32)and non-RIF groups༈83༉ (Table\\u0026nbsp;\\u003cspan refid=\\\"Tab2\\\" class=\\\"InternalRef\\\"\\u003e2\\u003c/span\\u003e).\\u003c/p\\u003e \\u003cp\\u003e \\u003cdiv class=\\\"gridtable\\\"\\u003e\\u003ctable float=\\\"Yes\\\" id=\\\"Tab1\\\" border=\\\"1\\\"\\u003e \\u003ccaption language=\\\"En\\\"\\u003e \\u003cdiv class=\\\"CaptionNumber\\\"\\u003eTable 1\\u003c/div\\u003e \\u003cdiv class=\\\"CaptionContent\\\"\\u003e \\u003cp\\u003eInfertility group baseline characteristics\\u003c/p\\u003e \\u003c/div\\u003e \\u003c/caption\\u003e \\u003ccolgroup cols=\\\"5\\\"\\u003e \\u003cdiv align=\\\"left\\\" class=\\\"colspec\\\" colname=\\\"c1\\\" colnum=\\\"1\\\"\\u003e\\u003c/div\\u003e \\u003cdiv align=\\\"left\\\" class=\\\"colspec\\\" colname=\\\"c2\\\" colnum=\\\"2\\\"\\u003e\\u003c/div\\u003e \\u003cdiv align=\\\"left\\\" class=\\\"colspec\\\" colname=\\\"c3\\\" colnum=\\\"3\\\"\\u003e\\u003c/div\\u003e \\u003cdiv align=\\\"left\\\" class=\\\"colspec\\\" colname=\\\"c4\\\" colnum=\\\"4\\\"\\u003e\\u003c/div\\u003e \\u003cdiv align=\\\"left\\\" class=\\\"colspec\\\" colname=\\\"c5\\\" colnum=\\\"5\\\"\\u003e\\u003c/div\\u003e \\u003cthead\\u003e \\u003ctr\\u003e \\u003cth align=\\\"left\\\" colname=\\\"c1\\\" morerows=\\\"1\\\" rowspan=\\\"2\\\"\\u003e \\u003cp\\u003eCharacteristic\\u003c/p\\u003e \\u003c/th\\u003e \\u003cth align=\\\"left\\\" colspan=\\\"3\\\" nameend=\\\"c4\\\" namest=\\\"c2\\\"\\u003e \\u003cp\\u003eGroup\\u003c/p\\u003e \\u003c/th\\u003e \\u003cth align=\\\"left\\\" colname=\\\"c5\\\" morerows=\\\"1\\\" rowspan=\\\"2\\\"\\u003e \\u003cp\\u003ep-value\\u003csup\\u003e2\\u003c/sup\\u003e\\u003c/p\\u003e \\u003c/th\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003cth align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003eOverall, N\\u0026thinsp;=\\u0026thinsp;194\\u003csup\\u003e1\\u003c/sup\\u003e\\u003c/p\\u003e \\u003c/th\\u003e \\u003cth align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003ePregnant, N\\u0026thinsp;=\\u0026thinsp;94\\u003csup\\u003e1\\u003c/sup\\u003e\\u003c/p\\u003e \\u003c/th\\u003e \\u003cth align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003eNot pregnant, N\\u0026thinsp;=\\u0026thinsp;100\\u003csup\\u003e1\\u003c/sup\\u003e\\u003c/p\\u003e \\u003c/th\\u003e \\u003c/tr\\u003e \\u003c/thead\\u003e \\u003ctbody\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003e\\u003cb\\u003eAge\\u003c/b\\u003e\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e31.7 (4.8)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e30.8 (4.3)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e32.6 (5.1)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e0.007\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003e\\u003cb\\u003eBMI\\u003c/b\\u003e\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e22.2 (3.3)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e21.9 (3.0)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e22.4 (3.6)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e0.24\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003e\\u003cb\\u003eDiagnose\\u003c/b\\u003e\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e0.15\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003ePrimary infertility\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e97 (50%)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e52 (55%)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e45 (45%)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003esecondary infertility\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e97 (50%)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e42 (45%)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e55 (55%)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003e\\u003cb\\u003eART style\\u003c/b\\u003e\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e0.34\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eICSI\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e54 (28%)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e22 (23%)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e32 (32%)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eIVF\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e114 (59%)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e60 (64%)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e54 (54%)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003ePGT\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e26 (13%)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e12 (13%)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e14 (14%)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003e\\u003cb\\u003eThe reason for ART\\u003c/b\\u003e\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e0.49\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003etubal disease\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e71 (37%)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e37 (39%)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e34 (34%)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eunknow factor infertility\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e49 (25%)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e24 (26%)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e25 (25%)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003emulti-factor infertility\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e15 (7.8%)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e4 (4.3%)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e11 (11%)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003egenetic factor\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e4 (2.1%)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e3 (3.2%)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e1 (1.0%)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003emale factor infertility\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e21 (11%)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e11 (12%)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e10 (10%)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eovulatory dysfunction\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e33 (17%)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e15 (16%)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e18 (18%)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eUnknown\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e1\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e0\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e1\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003e\\u003cb\\u003eEmbryo quality\\u003c/b\\u003e\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e0.036\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003egood\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e180 (93%)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e91 (97%)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e89 (89%)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003ebad\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e14 (7.2%)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e3 (3.2%)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e11 (11%)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colspan=\\\"5\\\" nameend=\\\"c5\\\" namest=\\\"c1\\\"\\u003e \\u003cp\\u003e\\u003csup\\u003e1\\u003c/sup\\u003eMean (SD); n (%)\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colspan=\\\"5\\\" nameend=\\\"c5\\\" namest=\\\"c1\\\"\\u003e \\u003cp\\u003e\\u003csup\\u003e2\\u003c/sup\\u003eWelch Two Sample t-test; Pearson's Chi-squared test; Fisher's exact test\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003c/tbody\\u003e \\u003c/colgroup\\u003e \\u003c/table\\u003e\\u003c/div\\u003e \\u003c/p\\u003e \\u003cp\\u003e \\u003cdiv class=\\\"gridtable\\\"\\u003e\\u003ctable float=\\\"Yes\\\" id=\\\"Tab2\\\" border=\\\"1\\\"\\u003e \\u003ccaption language=\\\"En\\\"\\u003e \\u003cdiv class=\\\"CaptionNumber\\\"\\u003eTable 2\\u003c/div\\u003e \\u003cdiv class=\\\"CaptionContent\\\"\\u003e \\u003cp\\u003eRIF and not RIF groups baseline characteristics\\u003c/p\\u003e \\u003c/div\\u003e \\u003c/caption\\u003e \\u003ccolgroup cols=\\\"5\\\"\\u003e \\u003cdiv align=\\\"left\\\" class=\\\"colspec\\\" colname=\\\"c1\\\" colnum=\\\"1\\\"\\u003e\\u003c/div\\u003e \\u003cdiv align=\\\"left\\\" class=\\\"colspec\\\" colname=\\\"c2\\\" colnum=\\\"2\\\"\\u003e\\u003c/div\\u003e \\u003cdiv align=\\\"left\\\" class=\\\"colspec\\\" colname=\\\"c3\\\" colnum=\\\"3\\\"\\u003e\\u003c/div\\u003e \\u003cdiv align=\\\"left\\\" class=\\\"colspec\\\" colname=\\\"c4\\\" colnum=\\\"4\\\"\\u003e\\u003c/div\\u003e \\u003cdiv align=\\\"left\\\" class=\\\"colspec\\\" colname=\\\"c5\\\" colnum=\\\"5\\\"\\u003e\\u003c/div\\u003e \\u003cthead\\u003e \\u003ctr\\u003e \\u003cth align=\\\"left\\\" colname=\\\"c1\\\" morerows=\\\"1\\\" rowspan=\\\"2\\\"\\u003e \\u003cp\\u003eCharacteristic\\u003c/p\\u003e \\u003c/th\\u003e \\u003cth align=\\\"left\\\" colspan=\\\"3\\\" nameend=\\\"c4\\\" namest=\\\"c2\\\"\\u003e \\u003cp\\u003eRIF OR NOT\\u003c/p\\u003e \\u003c/th\\u003e \\u003cth align=\\\"left\\\" colname=\\\"c5\\\" morerows=\\\"1\\\" rowspan=\\\"2\\\"\\u003e \\u003cp\\u003ep-value\\u003csup\\u003e2\\u003c/sup\\u003e\\u003c/p\\u003e \\u003c/th\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003cth align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003eOverall, N\\u0026thinsp;=\\u0026thinsp;115\\u003csup\\u003e1\\u003c/sup\\u003e\\u003c/p\\u003e \\u003c/th\\u003e \\u003cth align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003eNo, N\\u0026thinsp;=\\u0026thinsp;83\\u003csup\\u003e1\\u003c/sup\\u003e\\u003c/p\\u003e \\u003c/th\\u003e \\u003cth align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003eYes, N\\u0026thinsp;=\\u0026thinsp;32\\u003csup\\u003e1\\u003c/sup\\u003e\\u003c/p\\u003e \\u003c/th\\u003e \\u003c/tr\\u003e \\u003c/thead\\u003e \\u003ctbody\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003e\\u003cb\\u003eAge\\u003c/b\\u003e\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e31.5 (4.7)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e30.7 (4.3)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e33.5 (5.4)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e0.011\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003e\\u003cb\\u003eBMI\\u003c/b\\u003e\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e22.2 (3.3)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e21.7 (3.1)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e23.4 (3.6)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e0.030\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003e\\u003cb\\u003eThe reason for ART\\u003c/b\\u003e\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e0.30\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eovulatory dysfunction\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e22 (19%)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e15 (18%)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e7 (22%)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003etubal disease\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e41 (36%)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e31 (37%)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e10 (31%)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003emulti-factor infertility\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e6 (5.2%)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e2 (2.4%)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e4 (12%)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eunknow factor infertility\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e31 (27%)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e22 (27%)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e9 (28%)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003emale factor infertility\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e12 (10%)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e10 (12%)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e2 (6.2%)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003egenetic factor\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e3 (2.6%)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e3 (3.6%)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e0 (0%)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003e\\u003cb\\u003eDiagnose\\u003c/b\\u003e\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e0.066\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003esecondary infertility\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e56 (49%)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e36 (43%)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e20 (62%)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003ePrimary infertility\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e59 (51%)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e47 (57%)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e12 (38%)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003e\\u003cb\\u003eEmbryo quality\\u003c/b\\u003e\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e0.093\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003ebad\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e7 (6.1%)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e3 (3.6%)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e4 (12%)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003egood\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e108 (94%)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e80 (96%)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e28 (88%)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003e\\u003cb\\u003eART style\\u003c/b\\u003e\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e0.74\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eIVF\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e74 (64%)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e55 (66%)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e19 (59%)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eICSI\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e27 (23%)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e18 (22%)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e9 (28%)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003ePGT\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e14 (12%)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e10 (12%)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e4 (12%)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colspan=\\\"5\\\" nameend=\\\"c5\\\" namest=\\\"c1\\\"\\u003e \\u003cp\\u003e\\u003csup\\u003e1\\u003c/sup\\u003eMean (SD); n (%)\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colspan=\\\"5\\\" nameend=\\\"c5\\\" namest=\\\"c1\\\"\\u003e \\u003cp\\u003e\\u003csup\\u003e2\\u003c/sup\\u003eWelch Two Sample t-test; Fisher's exact test; Pearson's Chi-squared test\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003c/tbody\\u003e \\u003c/colgroup\\u003e \\u003c/table\\u003e\\u003c/div\\u003e \\u003c/p\\u003e \\u003cp\\u003e \\u003c/p\\u003e \\u003cp\\u003e \\u003cb\\u003eThe alterations of the vaginal microbiota in female patients with infertility.\\u003c/b\\u003e \\u003c/p\\u003e \\u003cp\\u003eThe study conducted 16S rRNA sequencing on swab samples from the vagina of 102 healthy controls and 194 patients with infertility to explore alterations in the reproductive tract microbiota. The sequencing data revealed the presence of ten major bacterial genera in the vagina, including \\u003cem\\u003eLactobacillus, Gardnerella, Atopobium, Prevotella,Streptococcus,Bifidobacterium,Ureaplasma,Anaerococcus,Peptostreptococcus\\u003c/em\\u003e, and \\u003cem\\u003eMycoplasma\\u003c/em\\u003e. Notably, \\u003cem\\u003eLactobacillus, Gardnerella\\u003c/em\\u003e, and \\u003cem\\u003eAtopobium\\u003c/em\\u003e were more abundant in healthy samples, while the remaining genera such as \\u003cem\\u003ePrevotella\\u003c/em\\u003e, \\u003cem\\u003eStreptococcus\\u003c/em\\u003e, and \\u003cem\\u003eBifidobacterium\\u003c/em\\u003e were more abundant in infertility samples. This suggests a potential association between the genus abundant in infertility samples and the onset of infertility(Fig.\\u0026nbsp;2A). Additionally, the study found higher microbial diversity indices (Observed, Chao1, ACE, and Shannon) in infertility samples compared to healthy samples. This indicates that the reproductive tract microbiota in female patients with infertility is more diverse than in healthy controls(Fig.\\u0026nbsp;2B).\\u003c/p\\u003e \\u003cp\\u003eWe also examined the correlation between microbial diversity and BMI/age in both healthy and infertile groups. Age was not correlated with microbial diversity in either group. However, microbial diversity was insignificantly correlated with BMI in the infertility group, while all indices except the Shannon index were increased with BMI in the healthy group. This suggests that microbial community composition might vary with BMI in healthy individuals but might be influenced by other factors in the infertility group(Fig.\\u0026nbsp;2C). Principal Coordinate Analysis (PCoA) based on microbiota abundance further distinguished infertility samples from healthy controls, indicating significant changes in the reproductive tract microbiota in patients with infertility(Fig.\\u0026nbsp;2D).\\u003c/p\\u003e \\u003cp\\u003e \\u003cb\\u003eInfertile women harbor an altered vaginal microbiome compared with healthy controls.\\u003c/b\\u003e \\u003c/p\\u003e \\u003cp\\u003eTo further explore the vaginal microbiota associated with infertility, a differential abundance analysis was conducted by comparing the microbiota composition of infertility patients with that of healthy controls at the genus level. This analysis revealed 45 genera with increased abundance and 43 genera with decreased abundance in the infertility group compared to the healthy controls (Fig.\\u0026nbsp;\\u003cspan refid=\\\"Fig2\\\" class=\\\"InternalRef\\\"\\u003e3\\u003c/span\\u003eA; \\u003cem\\u003eP\\u003c/em\\u003e\\u0026thinsp;\\u0026lt;\\u0026thinsp;0.05 and log2 fold change\\u0026thinsp;\\u0026gt;\\u0026thinsp;1). These findings indicate significant alterations in the vaginal microbiota of infertility patients.\\u003c/p\\u003e \\u003cp\\u003eTo assess the predictive capability of the genera with significant differential abundance in infertility, a machine learning model was built. Specifically, 296 samples were randomly divided into a training set (n\\u0026thinsp;=\\u0026thinsp;147) and a test set (n\\u0026thinsp;=\\u0026thinsp;149). Using the abundance data of different bacteria, a naive Bayesian model was constructed in the training set. The performance of the model was evaluated based on its ability to predict infertility. Encouragingly, the model exhibited an Area Under the Curve (AUC) of over 90% (Fig.\\u0026nbsp;\\u003cspan refid=\\\"Fig2\\\" class=\\\"InternalRef\\\"\\u003e3\\u003c/span\\u003eB) and a sensitivity and specificity of over 85% (Fig.\\u0026nbsp;\\u003cspan refid=\\\"Fig2\\\" class=\\\"InternalRef\\\"\\u003e3\\u003c/span\\u003eC) in both the training set and the test set. These results suggest that the vaginal microbiota has a strong predictive power for infertility, with high accuracy.\\u003c/p\\u003e \\u003cp\\u003eWithin the vaginal flora of infertility patients, several genera exhibited elevated levels with statistically significant differences compared to healthy controls. Specifically, \\u003cem\\u003eBurkholderia, Pseudomonas\\u003c/em\\u003e, and \\u003cem\\u003ePrevotella\\u003c/em\\u003e were found to be elevated in the infertility group (Fig.\\u0026nbsp;\\u003cspan refid=\\\"Fig2\\\" class=\\\"InternalRef\\\"\\u003e3\\u003c/span\\u003eD; \\u003cem\\u003eP\\u003c/em\\u003e\\u0026thinsp;\\u0026lt;\\u0026thinsp;0.05). These findings suggest that these bacteria may play a pathogenic role in female infertility. In contrast, the abundance of \\u003cem\\u003eBifidobacterium\\u003c/em\\u003e and \\u003cem\\u003eLactobacillus\\u003c/em\\u003e was significantly reduced in the vaginal flora of infertility patients (Fig.\\u0026nbsp;\\u003cspan refid=\\\"Fig2\\\" class=\\\"InternalRef\\\"\\u003e3\\u003c/span\\u003eD; \\u003cem\\u003eP\\u003c/em\\u003e\\u0026thinsp;\\u0026lt;\\u0026thinsp;0.05). Given their known protective roles in the vaginal microbiota, this reduction may contribute to the development of infertility.\\u003c/p\\u003e \\u003cp\\u003e \\u003c/p\\u003e \\u003c/div\\u003e \\u003cdiv id=\\\"Sec6\\\" class=\\\"Section2\\\"\\u003e \\u003ch2\\u003eVaginal microbiome is associated with recurrent implantation failure, tubal disease and assisted reproductive technology outcome\\u003c/h2\\u003e \\u003cp\\u003eTo further investigate the association between the vaginal microbiome and specific etiologies of infertility, as well as outcomes of ART and baseline characteristics like age and body mass index (BMI), we employed canonical correlation analysis. The results revealed strong correlations between the vaginal microbiome and tubal disease, ovulatory dysfunction, RIF, and ART outcome (Fig.\\u0026nbsp;\\u003cspan refid=\\\"Fig3\\\" class=\\\"InternalRef\\\"\\u003e4\\u003c/span\\u003eA; \\u003cem\\u003eP\\u003c/em\\u003e\\u0026thinsp;\\u0026lt;\\u0026thinsp;0.05). Notably, patients with tubal disease, those without ovulatory dysfunction, and those experiencing RIF or ART failure exhibited significantly higher Shannon diversity indices (Fig.\\u0026nbsp;\\u003cspan refid=\\\"Fig3\\\" class=\\\"InternalRef\\\"\\u003e4\\u003c/span\\u003eB). Particularly, RIF was found to be significantly associated with Shannon diversity (Wilcoxon test, \\u003cem\\u003eP\\u003c/em\\u003e\\u0026thinsp;\\u0026lt;\\u0026thinsp;0.05).\\u003c/p\\u003e \\u003cp\\u003eDifferential abundance analysis at the genus level further identified specific bacterial genera that were altered in the RIF and ovulatory dysfunction groups compared to their respective control groups. By intersecting these differences with those observed between infertile patients and healthy controls, we identified four genera (\\u003cem\\u003eMobiluncus, Peptoniphilus, Prevotella\\u003c/em\\u003e, and \\u003cem\\u003eVaribaculum\\u003c/em\\u003e) that were abundantly present in the RIF group. Similarly, two genera (\\u003cem\\u003ePorphyromonas\\u003c/em\\u003e and \\u003cem\\u003ePrevotella\\u003c/em\\u003e) were found to be abundant in the tubal disease group, while another two genera (\\u003cem\\u003eMobiluncus\\u003c/em\\u003e and \\u003cem\\u003eVaribaculum\\u003c/em\\u003e) were abundant in the ART failure group (Fig.\\u0026nbsp;\\u003cspan refid=\\\"Fig3\\\" class=\\\"InternalRef\\\"\\u003e4\\u003c/span\\u003eC). These findings suggest that the vaginal microbiome is more closely associated with RIF, tubal disease, and ART outcome, providing further evidence for the role of vaginal microbiota in infertility etiologies and treatment outcomes.\\u003c/p\\u003e \\u003cp\\u003e \\u003c/p\\u003e \\u003cp\\u003e \\u003cb\\u003ePathway analysis interprets association of vaginal microbiome with recurrent implantation failure, tubal disease and assisted reproductive technology outcome\\u003c/b\\u003e \\u003c/p\\u003e \\u003cp\\u003eTo further explore the underlying molecular mechanisms that might be associated with female infertility and outcomes of ART, we employed the PICRUSt algorithm to predict the activities of metabolic pathways based on the microbiome data obtained from different samples. By comparing the pathway activities among different groups, we identified 11 metabolic pathways that were simultaneously associated with RIF and infertility. These pathways included colanic acid building blocks biosynthesis, acetyl-CoA fermentation to butanoate II, mixed acid fermentation, superpathway of N-acetylneuraminate degradation, superpathway of hexitol degradation (bacteria), L-histidine degradation I, hexitol fermentation to lactate, formate, ethanol and acetate, superpathway of GDP-mannose-derived O-antigen building blocks biosynthesis, superpathway of glycolysis and Entner-Doudoroff, L-valine degradation I, and pyruvate fermentation to acetone. Notably, one additional pathway, involving the degradation of N-acetylglucosamine, N-acetylmannosamine, and N-acetylneuraminate, was found to be simultaneously associated with RIF, ART failure, and infertility (Fig.\\u0026nbsp;\\u003cspan refid=\\\"Fig4\\\" class=\\\"InternalRef\\\"\\u003e5\\u003c/span\\u003eA; \\u003cem\\u003eP\\u003c/em\\u003e\\u0026thinsp;\\u0026lt;\\u0026thinsp;0.05).\\u003c/p\\u003e \\u003cp\\u003eThe activities of these pathways were found to be elevated in both the RIF and infertility groups (Fig.\\u0026nbsp;\\u003cspan refid=\\\"Fig4\\\" class=\\\"InternalRef\\\"\\u003e5\\u003c/span\\u003eB; \\u003cem\\u003eP\\u003c/em\\u003e\\u0026thinsp;\\u0026lt;\\u0026thinsp;0.05). Furthermore, these pathways were closely linked to the regulation of vaginal pH, which is a critical factor in maintaining the balance of the vaginal microbiome. Correlation analysis revealed a positive association between the genera that were associated with infertility, RIF, and ART outcomes, and the activities of these pathways (Fig.\\u0026nbsp;\\u003cspan refid=\\\"Fig4\\\" class=\\\"InternalRef\\\"\\u003e5\\u003c/span\\u003eC). Notably, \\u003cem\\u003ePrevotella\\u003c/em\\u003e, one of the genera identified in our previous analysis, showed a stronger correlation with multiple pathways compared to other genera. This suggests that \\u003cem\\u003ePrevotella\\u003c/em\\u003e may play a crucial role in processes related to infertility, RIF, and ART outcomes through these metabolic pathways.\\u003c/p\\u003e \\u003cp\\u003e \\u003c/p\\u003e \\u003cp\\u003estructural unit biosynthesis superpathway, glycolysis and Entner-Doudoroff superpathway, L- valine degradation I, pyruvate fermentation to acetone) have been associated with both RIF and infertility. In addition, one pathway (N-acetylglucosamine, N-acetylmannosamine, and N-acetylneuraminic acid degradation superpathway) was associated with both RIF,ART failure, and infertility (P\\u0026thinsp;\\u0026lt;\\u0026thinsp;0.05)(A).The activity of these pathways in Fig.\\u0026nbsp;5Awas elevated inboth the RIF and infertility groups(B).Correlation analysis showed that the genera related to the\\u003c/p\\u003e \\u003cp\\u003eresults of sterility, RIF and ART were positively correlated with the activities of these pathways(C).\\u003c/p\\u003e \\u003c/div\\u003e\"},{\"header\":\"Discussion\",\"content\":\"\\u003cp\\u003eInfertility has been recognized as a global public health problem by the World Health Organization (WHO). It has indeed been a longstanding yet overlooked health concern in China. In 1990, around 9% of Chinese couples faced infertility issues \\u003csup\\u003e[\\u003cspan citationid=\\\"CR22\\\" class=\\\"CitationRef\\\"\\u003e22\\u003c/span\\u003e]\\u003c/sup\\u003e. Alarmingly, by 2020, this proportion had surged to 18%, indicating that approximately one out of every five childbearing-age couples in China is at risk of infertility \\u003csup\\u003e[\\u003cspan citationid=\\\"CR23\\\" class=\\\"CitationRef\\\"\\u003e23\\u003c/span\\u003e]\\u003c/sup\\u003e. Notably, China leads the world in the number of assisted reproduction cycles, having conducted over 1.3\\u0026nbsp;million such treatment cycles by 2020. Despite the remarkable advancements in ART, the implantation rates of transferred embryos remain unsatisfactory. Global live birth/assisted reproduction fresh cycle ratio only 5%-29% from 2004\\u0026ndash;2013\\u003csup\\u003e[\\u003cspan citationid=\\\"CR24\\\" class=\\\"CitationRef\\\"\\u003e24\\u003c/span\\u003e]\\u003c/sup\\u003e.There is a growing interest in exploring the reproductive tract flora as a potential novel strategy to enhance ART outcomes among patients with unexplained infertility, particularly those experiencing RIF. However, the current understanding of the impact of reproductive tract flora on ART outcomes is still limited by a scarcity of evidence.\\u003c/p\\u003e \\u003cp\\u003eThe dynamic interaction between the human host and vaginal microbiota is influenced by various endogenous and exogenous factors, leading to alterations in the relative abundances of \\u003cem\\u003eLactobacillus\\u003c/em\\u003e and other vaginal microbial components. The vaginal microbiota of reproductive-aged women typically encompasses at least five distinct community state types, four of which are primarily dominated by \\u003cem\\u003eLactobacillus spp\\u003c/em\\u003e\\u003csup\\u003e[\\u003cspan citationid=\\\"CR25\\\" class=\\\"CitationRef\\\"\\u003e25\\u003c/span\\u003e]\\u003c/sup\\u003e. \\u003cem\\u003eLactobacillus spp\\u003c/em\\u003e play a crucial role in maintaining the health of the female reproductive tract, inhibiting the adhesion of other bacteria to epithelial cells and producing lactic acid that kills or suppresses the growth of numerous other bacteria\\u003csup\\u003e[\\u003cspan citationid=\\\"CR26\\\" class=\\\"CitationRef\\\"\\u003e26\\u003c/span\\u003e, \\u003cspan citationid=\\\"CR27\\\" class=\\\"CitationRef\\\"\\u003e27\\u003c/span\\u003e]\\u003c/sup\\u003e. Conversely, the presence of non-Lactobacillus vaginal microbiota species may enhance susceptibility to infections and contribute to adverse reproductive outcomes, including infertility and preterm birth. Previous studies have demonstrated a correlation between low Lactobacillus abundance and infertility, findings that align with the results of our study\\u003csup\\u003e[\\u003cspan citationid=\\\"CR27\\\" class=\\\"CitationRef\\\"\\u003e27\\u003c/span\\u003e, \\u003cspan citationid=\\\"CR28\\\" class=\\\"CitationRef\\\"\\u003e28\\u003c/span\\u003e]\\u003c/sup\\u003e. Utilizing 16S rRNA gene sequencing, our previous study has revealed for the first time the distribution characteristics of reproductive tract microbiota in healthy Chinese women, as well as their functional roles in female infertility of the Chinese population\\u003csup\\u003e[\\u003cspan citationid=\\\"CR29\\\" class=\\\"CitationRef\\\"\\u003e29\\u003c/span\\u003e]\\u003c/sup\\u003e.In this study we \\u003cem\\u003eaimed to further expand our knowledge on vaginal microecosystems in the context of female\\u003c/em\\u003e infertility and ART outcomes. By comparing the vaginal microbiomes of infertile women with those of healthy women, we elucidated the microbial composition of the infertile population and its association with the outcome of ART in assisted conception of Chinese population.\\u003c/p\\u003e \\u003cp\\u003eFirst of all, our findings revealed a notable difference in vaginal diversity, characterized by a decrease in \\u003cem\\u003eLactobacillus\\u003c/em\\u003e dominance and an elevation in \\u003cem\\u003eBurkholderia, Pseudomonas\\u003c/em\\u003e, and \\u003cem\\u003ePrevotella\\u003c/em\\u003e levels within the infertility cohort. Previous studies have indicated that the non-Lactobacillus-dominant group exhibited a lower live birth rate and a higher preterm birth rate\\u003csup\\u003e[\\u003cspan additionalcitationids=\\\"CR31\\\" citationid=\\\"CR30\\\" class=\\\"CitationRef\\\"\\u003e30\\u003c/span\\u003e\\u0026ndash;\\u003cspan citationid=\\\"CR32\\\" class=\\\"CitationRef\\\"\\u003e32\\u003c/span\\u003e]\\u003c/sup\\u003e. The identification of specific genera with altered abundances, especially along with the predictive capability of the machine learning model, offers new insights into the potential role of the vaginal microbiota in female infertility. These observations further highlighting the significance of vaginal microbiota in infertility status and female reproductive health.\\u003c/p\\u003e \\u003cp\\u003eSecondly, we also observed a significant increase in the presence of \\u003cem\\u003eMobiluncus\\u003c/em\\u003e and \\u003cem\\u003eVaribaculum\\u003c/em\\u003e in the failure group compared to individuals who had a successful pregnancy by assisted reproductive technologies. \\u003cem\\u003eMobiluncus\\u003c/em\\u003e is a specialized anaerobic, Gram-unstable or Gram-negative campylobacterium of the vaginal flora that has been highly associated with bacterial vaginosis (VC) \\u003csup\\u003e[\\u003cspan additionalcitationids=\\\"CR34\\\" citationid=\\\"CR33\\\" class=\\\"CitationRef\\\"\\u003e33\\u003c/span\\u003e\\u0026ndash;\\u003cspan citationid=\\\"CR35\\\" class=\\\"CitationRef\\\"\\u003e35\\u003c/span\\u003e]\\u003c/sup\\u003e. Previous research has demonstrated a correlation between VC and infertility\\u003csup\\u003e[\\u003cspan citationid=\\\"CR36\\\" class=\\\"CitationRef\\\"\\u003e36\\u003c/span\\u003e, \\u003cspan citationid=\\\"CR37\\\" class=\\\"CitationRef\\\"\\u003e37\\u003c/span\\u003e]\\u003c/sup\\u003e, and our findings align with this evidence as we observed a high prevalence of \\u003cem\\u003eMobiluncus\\u003c/em\\u003e in infertile populations who have experienced unsuccessful ART outcomes. Varibaculum has indeed been reported to be associated with both bladder cancer and prostate cancer\\u003csup\\u003e[\\u003cspan citationid=\\\"CR38\\\" class=\\\"CitationRef\\\"\\u003e38\\u003c/span\\u003e, \\u003cspan citationid=\\\"CR39\\\" class=\\\"CitationRef\\\"\\u003e39\\u003c/span\\u003e]\\u003c/sup\\u003e, providing further evidence of its potential role in various health conditions. In addition, there is also evidence suggesting an association between \\u003cem\\u003eVaribaculum\\u003c/em\\u003e and male infertility\\u003csup\\u003e[\\u003cspan citationid=\\\"CR40\\\" class=\\\"CitationRef\\\"\\u003e40\\u003c/span\\u003e]\\u003c/sup\\u003e. This association is particularly interesting given that male infertility is a significant factor contributing to overall infertility issues. In our study, we identified \\u003cem\\u003eVaribaculum\\u003c/em\\u003e overgrowth as an independent risk factor affecting ART outcomes. This finding aligns with previous reports of \\u003cem\\u003eVaribaculum's\\u003c/em\\u003e association with infertility and suggests that it may play a role in the success or failure of ART procedures. However, the exact mechanism by which \\u003cem\\u003eVaribaculum\\u003c/em\\u003e affects infertility and ART outcomes remains unclear.\\u003c/p\\u003e \\u003cp\\u003eBesides, RIF, which affects 15\\u0026ndash;20% of individuals undergoing in vitro fertilization and embryo transfer (IVF-ET) programs\\u003csup\\u003e[\\u003cspan citationid=\\\"CR41\\\" class=\\\"CitationRef\\\"\\u003e41\\u003c/span\\u003e]\\u003c/sup\\u003e, is indeed a complex issue that requires careful consideration. Kitaya et al. reported elevated levels of \\u003cem\\u003eGardnerella\\u003c/em\\u003e and \\u003cem\\u003eBurkholderia\\u003c/em\\u003e in the reproductive tracts of women experiencing RIF compared to those without\\u003csup\\u003e[\\u003cspan citationid=\\\"CR42\\\" class=\\\"CitationRef\\\"\\u003e42\\u003c/span\\u003e]\\u003c/sup\\u003e. Our observation regarding the association between RIF and an overgrowth of \\u003cem\\u003eMobiluncus, Peptoniphilus, Prevotella\\u003c/em\\u003e and \\u003cem\\u003eVaribaculum\\u003c/em\\u003e is intriguing. Among them, \\u003cem\\u003ePrevotella\\u003c/em\\u003e is associated with a variety of pathways and has attracted much attention from scholars. \\u003cem\\u003ePrevotella\\u003c/em\\u003e is a Gram-negative, anaerobic, and immobile bacillus, and recent studies have shown that its high concentration is associated with infertility\\u003csup\\u003e[\\u003cspan citationid=\\\"CR43\\\" class=\\\"CitationRef\\\"\\u003e43\\u003c/span\\u003e]\\u003c/sup\\u003e. Additionally, in many patients with IUA, a significant reduction in \\u003cem\\u003eLactobacillus\\u003c/em\\u003e in the vagina has been observed, along with excessive growth of \\u003cem\\u003eGardnerella\\u003c/em\\u003e and \\u003cem\\u003ePrevotella\\u003c/em\\u003e. IUA can lead to abnormal menstruation, infertility, or recurrent miscarriage\\u003csup\\u003e[\\u003cspan additionalcitationids=\\\"CR45\\\" citationid=\\\"CR44\\\" class=\\\"CitationRef\\\"\\u003e44\\u003c/span\\u003e\\u0026ndash;\\u003cspan citationid=\\\"CR46\\\" class=\\\"CitationRef\\\"\\u003e46\\u003c/span\\u003e]\\u003c/sup\\u003e. Furthermore, the relative abundance of \\u003cem\\u003ePrevotella\\u003c/em\\u003e is higher in pregnant women with PPROM compared to those who deliver at term\\u003csup\\u003e[\\u003cspan citationid=\\\"CR47\\\" class=\\\"CitationRef\\\"\\u003e47\\u003c/span\\u003e]\\u003c/sup\\u003e .\\u003c/p\\u003e \\u003cp\\u003eFinally, to gain deeper insights into the potential molecular mechanisms underlying female infertility and ART outcomes, we conducted molecular pathway prediction in this study. Our findings reveal a strong association between \\u003cem\\u003ePrevotella spp\\u003c/em\\u003e. and various pathways, suggesting that \\u003cem\\u003ePrevotella spp.\\u003c/em\\u003e may play a crucial role in processes such as infertility, RIF, and ART outcomes through multiple metabolic pathways.\\u003c/p\\u003e \\u003cp\\u003eOne limitation of this study is the small sample size available for analysis, which may have affected the ability to detect significant differences in vaginal microbial diversity among patients with different causes of infertility in terms of pregnancy outcomes after ART. It is possible that with a larger sample size, more subtle differences in microbial diversity may have been identified that could have provided additional insights into the relationship between vaginal microbiota and ART success. Future studies with larger sample sizes are needed to further explore this association and to validate the findings of this study.\\u003c/p\\u003e \\u003cp\\u003eGiven the potential influence of the FGT microbiome on embryo implantation and pregnancy outcomes, it is crucial to consider its role in ART success. Modifying the FGT microbiome may be a promising approach to improving ART outcomes in specific cases. Therefore, it is advisable for infertile women to undergo reproductive tract microorganism screening prior to receiving ART\\u003csup\\u003e[\\u003cspan citationid=\\\"CR48\\\" class=\\\"CitationRef\\\"\\u003e48\\u003c/span\\u003e, \\u003cspan citationid=\\\"CR49\\\" class=\\\"CitationRef\\\"\\u003e49\\u003c/span\\u003e]\\u003c/sup\\u003e. This approach may help to increase the chances of successful embryo implantation and pregnancy, ultimately leading to more successful ART outcomes.\\u003c/p\\u003e\"},{\"header\":\"Conclusions\",\"content\":\"\\u003cp\\u003eThe present study offers valuable insights into the variations in vaginal microbiota between healthy and infertile women, shedding light on the potential influence of vaginal microbiota on infertility and outcomes related to assisted reproductive technology. This study identified specific genera associated with women experiencing repeated implantation failure (RIF) within the infertile population. Future research should continue to expand upon these findings, aiming to uncover additional microbial markers and develop innovative therapeutic interventions to effectively address the challenges posed by infertility.\\u003c/p\\u003e\"},{\"header\":\"Declarations\",\"content\":\"\\u003cp\\u003e\\u003cstrong\\u003eAcknowledgements\\u0026nbsp;\\u003c/strong\\u003e\\u003c/p\\u003e\\n\\u003cp\\u003eWe thank all donors and colleagues of the Reproductive Medicine Center of Shanghai Changzheng Hospital for their participation and cooperation.\\u003c/p\\u003e\\n\\u003cp\\u003e\\u003cstrong\\u003eAuthors\\u0026rsquo; contributions\\u003c/strong\\u003e\\u003c/p\\u003e\\n\\u003cp\\u003eN.S.\\u0026nbsp;and \\u0026nbsp; L.W.\\u0026nbsp;designed the study.\\u0026nbsp;X.C.,\\u0026nbsp;Y.S.,\\u0026nbsp;J.G. and\\u0026nbsp;L.W. performed the majority of experiments.\\u0026nbsp;X.C. and\\u0026nbsp;J.G. prepared the human samples.\\u0026nbsp;X.C.,\\u0026nbsp;Y.S,\\u0026nbsp;L.W, and\\u0026nbsp;N.S. analyzed the data and wrote the paper. All authors discussed and approved the data and reviewed the manuscript.\\u003c/p\\u003e\\n\\u003cp\\u003e\\u003cstrong\\u003eFunding\\u003c/strong\\u003e\\u003c/p\\u003e\\n\\u003cp\\u003eThis research was funded by the National Natural Science Foundation of China [82271662] and the Military innovation project special project (21JSZ06);Shanghai Shen-Kang hospital development center special medical enterprise integration innovation achievements[SHDC2022CRD007], All the authors have no conflict of interest to declare.\\u003c/p\\u003e\\n\\u003cp\\u003e\\u003cstrong\\u003eAvailability of data and materials\\u003c/strong\\u003e\\u003c/p\\u003e\\n\\u003cp\\u003eThe data presented in the study are deposited in the The National Omics Data Encyclopedia (NODE) repository, accession number OED 911162.\\u003c/p\\u003e\\n\\u003cp\\u003e\\u003cstrong\\u003eDeclarations\\u003c/strong\\u003e\\u003c/p\\u003e\\n\\u003cp\\u003e\\u003cstrong\\u003eEthics approval and consent to participate\\u003c/strong\\u003e\\u003c/p\\u003e\\n\\u003cp\\u003e\\u0026nbsp;the study received approval from the Ethics Committee of Shanghai Changzheng Hospital(2023SL051). Furthermore, we informed all patients of the study\\u0026rsquo;s purpose, and they provided written informed consent.\\u0026nbsp;\\u003c/p\\u003e\\n\\u003cp\\u003e\\u003cstrong\\u003eConsent for publication\\u003c/strong\\u003e\\u003c/p\\u003e\\n\\u003cp\\u003e\\u0026nbsp;Not applicable.\\u003c/p\\u003e\\n\\u003cp\\u003e\\u0026nbsp;\\u003cstrong\\u003eCompeting interests\\u003c/strong\\u003e\\u003c/p\\u003e\\n\\u003cp\\u003e\\u0026nbsp;The authors declare that they have no competing interests\\u003c/p\\u003e\"},{\"header\":\"References\",\"content\":\"\\u003col\\u003e\\u003cli\\u003e\\u003cspan\\u003eZEGERS-HOCHSCHILD F, ADAMSON G D, DE MOUZON J, et al. The International Committee for Monitoring Assisted Reproductive Technology (ICMART) and the World Health Organization (WHO) Revised Glossary on ART Terminology, 2009 [J]. Human reproduction (Oxford, England), 2009, 24(11): 2683\\u0026ndash;7.\\u003c/span\\u003e\\u003c/li\\u003e \\u003cli\\u003e\\u003cspan\\u003eBOIVIN J, BUNTING L, COLLINS J A, et al. International estimates of infertility prevalence and treatment-seeking: potential need and demand for infertility medical care [J]. Human reproduction (Oxford, England), 2007, 22(6): 1506\\u0026ndash;12.\\u003c/span\\u003e\\u003c/li\\u003e \\u003cli\\u003e\\u003cspan\\u003eKOROMA L, STEWART L. Infertility: evaluation and initial management [J]. Journal of midwifery \\u0026amp; women's health, 2012, 57(6): 614\\u0026ndash;21.\\u003c/span\\u003e\\u003c/li\\u003e \\u003cli\\u003e\\u003cspan\\u003eAZPIROZ M A, ORGUILIA L, PALACIO M I, et al. Potential biomarkers of infertility associated with microbiome imbalances [J]. American journal of reproductive immunology (New York, NY: 1989), 2021, 86(4): e13438.\\u003c/span\\u003e\\u003c/li\\u003e \\u003cli\\u003e\\u003cspan\\u003ePETERSON J, GARGES S, GIOVANNI M, et al. The NIH Human Microbiome Project [J]. Genome research, 2009, 19(12): 2317\\u0026ndash;23.\\u003c/span\\u003e\\u003c/li\\u003e \\u003cli\\u003e\\u003cspan\\u003eK\\u0026aring;HRSTR\\u0026ouml;M C T, PARIENTE N, WEISS U. Intestinal microbiota in health and disease [J]. Nature, 2016, 535(7610): 47.\\u003c/span\\u003e\\u003c/li\\u003e \\u003cli\\u003e\\u003cspan\\u003eANAHTAR M N, BYRNE E H, DOHERTY K E, et al. Cervicovaginal bacteria are a major modulator of host inflammatory responses in the female genital tract [J]. Immunity, 2015, 42(5): 965\\u0026ndash;76.\\u003c/span\\u003e\\u003c/li\\u003e \\u003cli\\u003e\\u003cspan\\u003eSARAF V S, SHEIKH S A, AHMAD A, et al. Vaginal microbiome: normalcy vs dysbiosis [J]. Archives of microbiology, 2021, 203(7): 3793\\u0026ndash;802.\\u003c/span\\u003e\\u003c/li\\u003e \\u003cli\\u003e\\u003cspan\\u003eBLOSTEIN F, GELAYE B, SANCHEZ S E, et al. Vaginal microbiome diversity and preterm birth: results of a nested case-control study in Peru [J]. Annals of epidemiology, 2020, 41: 28\\u0026ndash;34.\\u003c/span\\u003e\\u003c/li\\u003e \\u003cli\\u003e\\u003cspan\\u003ePETRICEVIC L, DOMIG K J, NIERSCHER F J, et al. Characterisation of the vaginal Lactobacillus microbiota associated with preterm delivery [J]. Scientific reports, 2014, 4: 5136.\\u003c/span\\u003e\\u003c/li\\u003e \\u003cli\\u003e\\u003cspan\\u003eLEITICH H, KISS H. Asymptomatic bacterial vaginosis and intermediate flora as risk factors for adverse pregnancy outcome [J]. Best practice \\u0026amp; research Clinical obstetrics \\u0026amp; gynaecology, 2007, 21(3): 375\\u0026ndash;90.\\u003c/span\\u003e\\u003c/li\\u003e \\u003cli\\u003e\\u003cspan\\u003eDE GEYTER C. Assisted reproductive technology: Impact on society and need for surveillance [J]. Best practice \\u0026amp; research Clinical endocrinology \\u0026amp; metabolism, 2019, 33(1): 3\\u0026ndash;8.\\u003c/span\\u003e\\u003c/li\\u003e \\u003cli\\u003e\\u003cspan\\u003eKOEDOODER R, SINGER M, SCHOENMAKERS S, et al. The ReceptIVFity cohort study protocol to validate the urogenital microbiome as predictor for IVF or IVF/ICSI outcome [J]. Reproductive health, 2018, 15(1): 202.\\u003c/span\\u003e\\u003c/li\\u003e \\u003cli\\u003e\\u003cspan\\u003eADRIAENSSENS T, VAN VAERENBERGH I, COUCKE W, et al. Cumulus-corona gene expression analysis combined with morphological embryo scoring in single embryo transfer cycles increases live birth after fresh transfer and decreases time to pregnancy [J]. Journal of assisted reproduction and genetics, 2019, 36(3): 433\\u0026ndash;43.\\u003c/span\\u003e\\u003c/li\\u003e \\u003cli\\u003e\\u003cspan\\u003eJULIANA N C A, SUITERS M J M, AL-NASIRY S, et al. The Association Between Vaginal Microbiota Dysbiosis, Bacterial Vaginosis, and Aerobic Vaginitis, and Adverse Pregnancy Outcomes of Women Living in Sub-Saharan Africa: A Systematic Review [J]. Frontiers in public health, 2020, 8: 567885.\\u003c/span\\u003e\\u003c/li\\u003e \\u003cli\\u003e\\u003cspan\\u003eHAAHR T, ZACHO J, BR\\u0026auml;UNER M, et al. Reproductive outcome of patients undergoing in vitro fertilisation treatment and diagnosed with bacterial vaginosis or abnormal vaginal microbiota: a systematic PRISMA review and meta-analysis [J]. BJOG: an international journal of obstetrics and gynaecology, 2019, 126(2): 200\\u0026ndash;7.\\u003c/span\\u003e\\u003c/li\\u003e \\u003cli\\u003e\\u003cspan\\u003eGREWAL K, LEE Y S, SMITH A, et al. Chromosomally normal miscarriage is associated with vaginal dysbiosis and local inflammation [J]. BMC medicine, 2022, 20(1): 38.\\u003c/span\\u003e\\u003c/li\\u003e \\u003cli\\u003e\\u003cspan\\u003eBUKIN Y S, GALACHYANTS Y P, MOROZOV I V, et al. The effect of 16S rRNA region choice on bacterial community metabarcoding results [J]. Scientific data, 2019, 6: 190007.\\u003c/span\\u003e\\u003c/li\\u003e \\u003cli\\u003e\\u003cspan\\u003eMA L, LV Z, SU J, et al. Consistent condom use increases the colonization of Lactobacillus crispatus in the vagina [J]. PloS one, 2013, 8(7): e70716.\\u003c/span\\u003e\\u003c/li\\u003e \\u003cli\\u003e\\u003cspan\\u003eCALLAHAN B J, MCMURDIE P J, ROSEN M J, et al. DADA2: High-resolution sample inference from Illumina amplicon data [J]. Nature methods, 2016, 13(7): 581\\u0026ndash;3.\\u003c/span\\u003e\\u003c/li\\u003e \\u003cli\\u003e\\u003cspan\\u003eA\\u0026szlig;HAUER K P, WEMHEUER B, DANIEL R, et al. Tax4Fun: predicting functional profiles from metagenomic 16S rRNA data [J]. Bioinformatics (Oxford, England), 2015, 31(17): 2882\\u0026ndash;4.\\u003c/span\\u003e\\u003c/li\\u003e \\u003cli\\u003e\\u003cspan\\u003eCHE Y, CLELAND J. Infertility in Shanghai: prevalence, treatment seeking and impact [J]. Journal of obstetrics and gynaecology: the journal of the Institute of Obstetrics and Gynaecology, 2002, 22(6): 643\\u0026ndash;8.\\u003c/span\\u003e\\u003c/li\\u003e \\u003cli\\u003e\\u003cspan\\u003eQIAO J, WANG Y, LI X, et al. A Lancet Commission on 70 years of women's reproductive, maternal, newborn, child, and adolescent health in China [J]. Lancet (London, England), 2021, 397(10293): 2497\\u0026ndash;536.\\u003c/span\\u003e\\u003c/li\\u003e \\u003cli\\u003e\\u003cspan\\u003eKUSHNIR V A, BARAD D H, ALBERTINI D F, et al. Systematic review of worldwide trends in assisted reproductive technology 2004\\u0026ndash;2013 [J]. Reproductive biology and endocrinology: RB\\u0026amp;E, 2017, 15(1): 6.\\u003c/span\\u003e\\u003c/li\\u003e \\u003cli\\u003e\\u003cspan\\u003eCHEN C, SONG X, WEI W, et al. The microbiota continuum along the female reproductive tract and its relation to uterine-related diseases [J]. Nature communications, 2017, 8(1): 875.\\u003c/span\\u003e\\u003c/li\\u003e \\u003cli\\u003e\\u003cspan\\u003eKYONO K, HASHIMOTO T, KIKUCHI S, et al. A pilot study and case reports on endometrial microbiota and pregnancy outcome: An analysis using 16S rRNA gene sequencing among IVF patients, and trial therapeutic intervention for dysbiotic endometrium [J]. Reproductive medicine and biology, 2019, 18(1): 72\\u0026ndash;82.\\u003c/span\\u003e\\u003c/li\\u003e \\u003cli\\u003e\\u003cspan\\u003eBERNABEU A, LLEDO B, D\\u0026iacute;AZ M C, et al. Effect of the vaginal microbiome on the pregnancy rate in women receiving assisted reproductive treatment [J]. Journal of assisted reproduction and genetics, 2019, 36(10): 2111\\u0026ndash;9.\\u003c/span\\u003e\\u003c/li\\u003e \\u003cli\\u003e\\u003cspan\\u003eKOEDOODER R, SINGER M, SCHOENMAKERS S, et al. The vaginal microbiome as a predictor for outcome of in vitro fertilization with or without intracytoplasmic sperm injection: a prospective study [J]. Human reproduction (Oxford, England), 2019, 34(6): 1042\\u0026ndash;54.\\u003c/span\\u003e\\u003c/li\\u003e \\u003cli\\u003e\\u003cspan\\u003eSUN N, DING H, YU H, et al. Comprehensive Characterization of Microbial Community in the Female Genital Tract of Reproductive-Aged Women in China [J]. Frontiers in cellular and infection microbiology, 2021, 11: 649067.\\u003c/span\\u003e\\u003c/li\\u003e \\u003cli\\u003e\\u003cspan\\u003ePUNZ\\u0026oacute;N-JIM\\u0026eacute;NEZ P, LABARTA E. The impact of the female genital tract microbiome in women health and reproduction: a review [J]. Journal of assisted reproduction and genetics, 2021, 38(10): 2519\\u0026ndash;41.\\u003c/span\\u003e\\u003c/li\\u003e \\u003cli\\u003e\\u003cspan\\u003eZENG H, HE D, HU L, et al. Non-Lactobacillus dominance of the vagina is associated with reduced live birth rate following IVF/ICSI: a propensity score-matched cohort study [J]. Archives of gynecology and obstetrics, 2022, 305(2): 519\\u0026ndash;28.\\u003c/span\\u003e\\u003c/li\\u003e \\u003cli\\u003e\\u003cspan\\u003eAL-MEMAR M, BOBDIWALA S, FOURIE H, et al. The association between vaginal bacterial composition and miscarriage: a nested case-control study [J]. BJOG: an international journal of obstetrics and gynaecology, 2020, 127(2): 264\\u0026ndash;74.\\u003c/span\\u003e\\u003c/li\\u003e \\u003cli\\u003e\\u003cspan\\u003eZENG W, MA H, FAN W, et al. Structure determination of CAMP factor of Mobiluncus curtisii and insights into structural dynamics [J]. International journal of biological macromolecules, 2020, 150: 1027\\u0026ndash;36.\\u003c/span\\u003e\\u003c/li\\u003e \\u003cli\\u003e\\u003cspan\\u003eTAYLOR-ROBINSON A W, BORRIELLO S P, TAYLOR-ROBINSON D. Identification and preliminary characterization of a cytotoxin isolated from Mobiluncus spp [J]. International journal of experimental pathology, 1993, 74(4): 357\\u0026ndash;66.\\u003c/span\\u003e\\u003c/li\\u003e \\u003cli\\u003e\\u003cspan\\u003eSCHWEBKE J R, DESMOND R A. A randomized trial of the duration of therapy with metronidazole plus or minus azithromycin for treatment of symptomatic bacterial vaginosis [J]. Clinical infectious diseases: an official publication of the Infectious Diseases Society of America, 2007, 44(2): 213\\u0026ndash;9.\\u003c/span\\u003e\\u003c/li\\u003e \\u003cli\\u003e\\u003cspan\\u003eRAVEL J, MORENO I, SIM\\u0026oacute;N C. Bacterial vaginosis and its association with infertility, endometritis, and pelvic inflammatory disease [J]. American journal of obstetrics and gynecology, 2021, 224(3): 251\\u0026ndash;7.\\u003c/span\\u003e\\u003c/li\\u003e \\u003cli\\u003e\\u003cspan\\u003eSWIDSINSKI S, MOLL W M, SWIDSINSKI A. Bacterial Vaginosis-Vaginal Polymicrobial Biofilms and Dysbiosis [J]. Deutsches Arzteblatt international, 2023, 120(20): 347\\u0026ndash;54.\\u003c/span\\u003e\\u003c/li\\u003e \\u003cli\\u003e\\u003cspan\\u003eHRB\\u0026aacute;ČEK J, TL\\u0026aacute;SKAL V, ČERM\\u0026aacute;K P, et al. Bladder cancer is associated with decreased urinary microbiota diversity and alterations in microbial community composition [J]. Urologic oncology, 2023, 41(2): 107.e15-.e22.\\u003c/span\\u003e\\u003c/li\\u003e \\u003cli\\u003e\\u003cspan\\u003eHURST R, MEADER E, GIHAWI A, et al. Microbiomes of Urine and the Prostate Are Linked to Human Prostate Cancer Risk Groups [J]. European urology oncology, 2022, 5(4): 412\\u0026ndash;9.\\u003c/span\\u003e\\u003c/li\\u003e \\u003cli\\u003e\\u003cspan\\u003eM\\u0026auml;NDAR R, PUNAB M, BOROVKOVA N, et al. Complementary seminovaginal microbiome in couples [J]. Research in microbiology, 2015, 166(5): 440\\u0026ndash;7.\\u003c/span\\u003e\\u003c/li\\u003e \\u003cli\\u003e\\u003cspan\\u003eCOUGHLAN C, LEDGER W, WANG Q, et al. Recurrent implantation failure: definition and management [J]. Reproductive biomedicine online, 2014, 28(1): 14\\u0026ndash;38.\\u003c/span\\u003e\\u003c/li\\u003e \\u003cli\\u003e\\u003cspan\\u003eKITAYA K, NAGAI Y, ARAI W, et al. Characterization of Microbiota in Endometrial Fluid and Vaginal Secretions in Infertile Women with Repeated Implantation Failure [J]. Mediators of inflammation, 2019, 2019: 4893437.\\u003c/span\\u003e\\u003c/li\\u003e \\u003cli\\u003e\\u003cspan\\u003eSHAH H N, COLLINS D M. Prevotella, a new genus to include Bacteroides melaninogenicus and related species formerly classified in the genus Bacteroides [J]. International journal of systematic bacteriology, 1990, 40(2): 205\\u0026ndash;8.\\u003c/span\\u003e\\u003c/li\\u003e \\u003cli\\u003e\\u003cspan\\u003eGACHET C, PRAT M, BURUCOA C, et al. Spermatic Microbiome Characteristics in Infertile Patients: Impact on Sperm Count, Mobility, and Morphology [J]. Journal of clinical medicine, 2022, 11(6).\\u003c/span\\u003e\\u003c/li\\u003e \\u003cli\\u003e\\u003cspan\\u003eGOMES I A, MONTEIRO P B, MOURA G A, et al. Microbiota and seminal quality: A systematic review [J]. JBRA assisted reproduction, 2023.\\u003c/span\\u003e\\u003c/li\\u003e \\u003cli\\u003e\\u003cspan\\u003eZHAO Z, MAO X, ZHENG Y, et al. Research progress in the correlation between reproductive tract microbiota and intrauterine adhesion [J]. Zhong nan da xue xue bao Yi xue ban\\u0026thinsp;=\\u0026thinsp;Journal of Central South University Medical sciences, 2022, 47(11): 1495\\u0026ndash;503.\\u003c/span\\u003e\\u003c/li\\u003e \\u003cli\\u003e\\u003cspan\\u003eYAN C, HONG F, XIN G, et al. Alterations in the vaginal microbiota of patients with preterm premature rupture of membranes [J]. Frontiers in cellular and infection microbiology, 2022, 12: 858732.\\u003c/span\\u003e\\u003c/li\\u003e \\u003cli\\u003e\\u003cspan\\u003eGARC\\u0026iacute;A-VELASCO J A, BUDDING D, CAMPE H, et al. The reproductive microbiome - clinical practice recommendations for fertility specialists [J]. Reproductive biomedicine online, 2020, 41(3): 443\\u0026ndash;53.\\u003c/span\\u003e\\u003c/li\\u003e \\u003cli\\u003e\\u003cspan\\u003eMOLINA N M, SOLA-LEYVA A, SAEZ-LARA M J, et al. New Opportunities for Endometrial Health by Modifying Uterine Microbial Composition: Present or Future? [J]. Biomolecules, 2020, 10(4).\\u003c/span\\u003e\\u003c/li\\u003e\\u003c/ol\\u003e\"}],\"fulltextSource\":\"\",\"fullText\":\"\",\"funders\":[],\"hasAdminPriorityOnWorkflow\":false,\"hasManuscriptDocX\":true,\"hasOptedInToPreprint\":true,\"hasPassedJournalQc\":\"\",\"hasAnyPriority\":false,\"hideJournal\":true,\"highlight\":\"\",\"institution\":\"\",\"isAcceptedByJournal\":false,\"isAuthorSuppliedPdf\":false,\"isDeskRejected\":\"\",\"isHiddenFromSearch\":false,\"isInQc\":false,\"isInWorkflow\":false,\"isPdf\":false,\"isPdfUpToDate\":true,\"isWithdrawnOrRetracted\":false,\"journal\":{\"display\":true,\"email\":\"info@researchsquare.com\",\"identity\":\"researchsquare\",\"isNatureJournal\":false,\"hasQc\":true,\"allowDirectSubmit\":true,\"externalIdentity\":\"\",\"sideBox\":\"\",\"snPcode\":\"\",\"submissionUrl\":\"/submission\",\"title\":\"Research Square\",\"twitterHandle\":\"researchsquare\",\"acdcEnabled\":true,\"dfaEnabled\":false,\"editorialSystem\":\"\",\"reportingPortfolio\":\"\",\"inReviewEnabled\":false,\"inReviewRevisionsEnabled\":true},\"keywords\":\"Vaginal microbiome, Infertile, Pregnancy outcome, Assisted reproductive technology\",\"lastPublishedDoi\":\"10.21203/rs.3.rs-4194198/v1\",\"lastPublishedDoiUrl\":\"https://doi.org/10.21203/rs.3.rs-4194198/v1\",\"license\":{\"name\":\"CC BY 4.0\",\"url\":\"https://creativecommons.org/licenses/by/4.0/\"},\"manuscriptAbstract\":\"\\u003cp\\u003e\\u003cstrong\\u003eBackground\\u003c/strong\\u003e: Infertility rates are on the rise, presenting a complex array of causative factors. Recent advancements in human microbiome and associated techniques have shed light on the potential impact of vaginal microbiota disruptions on female fertility. Our study aims to investigate differences in vaginal microbiome between fertile women and those experiencing infertility. Additionally, we aim to investigate how microbial composition in infertile population may affect the success of assisted reproduction technology (ART).\\u003c/p\\u003e\\n\\u003cp\\u003e\\u003cstrong\\u003eMethods: \\u003c/strong\\u003eWe enrolled 194 women diagnosed with infertility at the Reproductive Medicine Center of Shanghai Changzheng Hospital between November 2018 and November 2021, along with 102 healthy women undergoing routine physical examinations at the hospital’s Physical Examination Center. Vaginal secretions were collected from both groups, and polymerase chain reaction (PCR) was used to amplify the bacterial 16S rRNA V4-V6 conserved region for microbial analysis. A machine learning model was built based on the genus abundances to predict infertility. Additionally, we employed the PICRUSt algorithm to predict the metabolic pathway activities, providing insights into potential molecular mechanisms underlying female infertility and ART outcomes.\\u003c/p\\u003e\\n\\u003cp\\u003e\\u003cstrong\\u003eResults:\\u003c/strong\\u003e Women with infertility exhibited a significantly different vaginal microbial composition compared to healthy women, with the infertility group showing higher microbial diversity. \\u003cem\\u003eBurkholderia, Pseudomonas,\\u003c/em\\u003e and\\u003cem\\u003e Prevotella\\u003c/em\\u003e levels were significantly elevated in the vaginal microbiota of the infertility group, while \\u003cem\\u003eBifidobacterium\\u003c/em\\u003e and\\u003cem\\u003e Lactobacillus \\u003c/em\\u003eabundances were reduced. Recurrent implantation failure (RIF) within the infertile population showed even higher diversity of vaginal microbiota, with specific genera such as \\u003cem\\u003eMobiluncus, Peptoniphilus, Prevotella,\\u003c/em\\u003e and\\u003cem\\u003e Varibaculum \\u003c/em\\u003ebeing more abundant. Overgrowth of \\u003cem\\u003eMobiluncus\\u003c/em\\u003e and \\u003cem\\u003eVaribaculum\\u003c/em\\u003e emerged as independent risk factors affecting ART outcomes. Eleven metabolic pathways were associated with both RIF and infertility, with \\u003cem\\u003ePrevotella\\u003c/em\\u003e demonstrating stronger correlations.\\u003c/p\\u003e\\n\\u003cp\\u003e\\u003cstrong\\u003eConclusions:\\u003c/strong\\u003e The present study provides insights into the differences in vaginal mircobiome between healthy and infertile women, offering a new understanding of how vaginal microbiota may impact infertility and ART outcomes. Our findings underscore the significance of specific microbial taxa in women with recurrent implantation failure, suggesting avenues for targeted interventions to enhance embryo transplantation success rates.\\u003c/p\\u003e\",\"manuscriptTitle\":\"The implication of the vaginal microbiome in female infertility and assisted conception outcomes\",\"msid\":\"\",\"msnumber\":\"\",\"nonDraftVersions\":[{\"code\":1,\"date\":\"2024-04-09 12:26:42\",\"doi\":\"10.21203/rs.3.rs-4194198/v1\",\"editorialEvents\":[{\"type\":\"communityComments\",\"content\":0}],\"status\":\"published\",\"journal\":{\"display\":true,\"email\":\"info@researchsquare.com\",\"identity\":\"researchsquare\",\"isNatureJournal\":false,\"hasQc\":true,\"allowDirectSubmit\":true,\"externalIdentity\":\"\",\"sideBox\":\"\",\"snPcode\":\"\",\"submissionUrl\":\"/submission\",\"title\":\"Research Square\",\"twitterHandle\":\"researchsquare\",\"acdcEnabled\":true,\"dfaEnabled\":false,\"editorialSystem\":\"\",\"reportingPortfolio\":\"\",\"inReviewEnabled\":false,\"inReviewRevisionsEnabled\":true}}],\"origin\":\"\",\"ownerIdentity\":\"a0cb86f5-80a4-4a56-8af7-7ce0b70740e1\",\"owner\":[],\"postedDate\":\"April 9th, 2024\",\"published\":true,\"recentEditorialEvents\":[],\"rejectedJournal\":[],\"revision\":\"\",\"amendment\":\"\",\"status\":\"posted\",\"subjectAreas\":[],\"tags\":[],\"updatedAt\":\"2024-05-09T23:10:07+00:00\",\"versionOfRecord\":[],\"versionCreatedAt\":\"2024-04-09 12:26:42\",\"video\":\"\",\"vorDoi\":\"\",\"vorDoiUrl\":\"\",\"workflowStages\":[]},\"version\":\"v1\",\"identity\":\"rs-4194198\",\"journalConfig\":\"researchsquare\"},\"__N_SSP\":true},\"page\":\"/article/[identity]/[[...version]]\",\"query\":{\"redirect\":\"/article/rs-4194198\",\"identity\":\"rs-4194198\",\"version\":[\"v1\"]},\"buildId\":\"qtupq5eGEP_6zYnWcrvyt\",\"isFallback\":false,\"isExperimentalCompile\":false,\"dynamicIds\":[84888],\"gssp\":true,\"scriptLoader\":[]}","source_license":"CC-BY-4.0","license_restricted":false}