Background Although sexually transmitted infections are regarded as the main cause of tubal infertility, the association between the common vaginal microbiome and female fecundability has yet to be determined. The objective of this study was to find convincing evidence relating to the impact of the vaginal bacterial structure on the fecundability of women planning pregnancy. Methods We recruited women who took part in the Free Pre-pregnancy Health Examination Project from 13 June 2018 to 31 October 2018 (n = 89, phase I) and from 1 November 2018 to 30 May 2020 (n = 389, phase II). We collected pre-pregnancy vaginal swabs from each subject; then, we followed up each subject to acquire the pregnancy-planning outcome in 1 year. In phase I, 16S rRNA gene sequencing was performed to investigate the vaginal bacterial content between the pregnancy and non-pregnancy groups. These findings were verified in phase II by applying a quantitative real-time polymerase chain reaction for the measurement of the absolute abundance of specific species. Cox models were used to estimate fecundability ratios (FR) for each vaginal microbiome type. Results In phase I, 59.6% (53/89) of women became pregnant within 1 year. The principal coordinate analysis showed that the pre-pregnancy vaginal microbial community structures of the pregnant and non-pregnant groups were significantly different (PERMANOVA test, R2 = 0.025, P = 0.049). The abundance of the genus Lactobacillus in the pregnancy group was higher than that of the non-pregnant group (linear discriminant analysis effect size (LDA) > 4.0). The abundance of the genus Gardnerella in the non-pregnant group was higher than those in the pregnant group (LDA > 4.0). In phase II, female fecundability increased with higher absolute loads of Lactobacillus gasseri (quartile Q4 vs Q1, FR = 1.71, 95%CI 1.02–2.87) but decreased with higher absolute loads of Fannyhessea vaginae (Q4 vs Q1, FR = 0.62, 95%CI 0.38–1.00). Clustering analysis showed that the vaginal microbiome of type D (characterized by a higher abundance of Lactobacillus iners, a lower abundance of Lactobacillus crispatus and Lactobacillus gassri) was associated with a 55% reduction of fecundability (FR = 0.45, 95%CI 0.26–0.76) compared with type A (featuring three Lactobacillus species, low Gardnerella vaginalis and Fannyhessea vaginae abundance). Conclusions This cohort study demonstrated an association between the pre-pregnancy vaginal microbiome and female fecundability. A vaginal microbiome characterized by a higher abundance of L. iners and lower abundances of L. crispatus and L. gasseri appeared to be associated with a lower fecundability. Further research now needs to confirm whether manipulation of the vaginal microenvironment might improve human fecundability.

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Background: Although observational studies have demonstrated that blood lipids are associated with female infertility, the causality of this association remains unclear. We performed a univariable and multivariable Mendelian randomization (MR) analysis to evaluate the causal relationship between blood lipids and female infertility. Methods: Single-nucleotide polymorphisms associated with lipid traits in univariate analysis were obtained from the Million Veteran Program (MVP) and Global Lipids Genetics Consortium (GLGC), involving up to 215,551 and 188,577 European individuals, respectively. Blood lipids in multivariate analysis were obtained from the latest genome-wide association study meta-analysis with lipid levels in 73 studies encompassing >300,000 participants. Data on female infertility were obtained from the FinnGen Consortium R6 release, which included 6481 samples and 75,450 controls. Subsequently, MR analysis was performed using inverse variance-weighted (IVW), weighted median, weighted-mode, simple-mode and MR-Egger regression to demonstrate the causal relationship between lipids and female infertility. Results: After controlling confounding factors including body mass index and age at menarche, two-sample MR demonstrated that genetically predicted LDL-C and TC were causally associated with the risk of female infertility (When the genetic instruments come from the MVP database, LDL-C and female infertility, IVW OR: 1.13, 95% CI: 1.001–1.269, p = 0.047; TC and female infertility, IVW OR: 1.16, 95% CI: 1.018–1.317, p = 0.025, and when the genetic instruments came from the GLGC database, LDL-C and female infertility, IVW OR: 1.10, 95% CI: 1.008–1.210, p = 0.033; TC and female infertility, IVW OR: 1.14, 95% CI: 1.024–1.258, p = 0.015). However, the IVW estimate showed that HDL-C was not significantly associated with the risk of female infertility (when the genetic instruments came from the MVP database, IVW OR: 1.00, 95% CI: 0.887–1.128, p = 0.999; when the genetic instruments came from the GLGC database, IVW OR: 1.00, 95% CI: 0.896–1.111, p = 0.968). The multivariable MR analysis also provided evidence that LDL-C (OR: 1.12, 95% CI: 1.006–1.243, p = 0.042) was significantly associated with the risk of female infertility after considering the correlation of all lipid-related traits. Conclusion: These findings support a causal relationship between increased LDL-cholesterol and increased female infertility risk. Furthermore, the association between lipid-related traits and female infertility risk merits more studies.

ObjectiveSubfertility is a common problem for couples in modern society. Many studies have confirmed that lifestyle factors can affect fertility although there are conflicting conclusions relating to the effects of physical activity and sleep duration on fertility. In this study, we aimed to summarize and analyze the available evidence.MethodsPubMed, Web of Science, Cochrane, and Embase databases (as of October 14, 2022) were systematically searched for eligible prospective cohort studies. Data were extracted and effect values were combined. We also performed methodological quality and bias risk assessments for all the included studies.ResultsA total of 10 eligible articles were included in our analysis; seven investigated the relationship between physical activity and fertility, and three investigated the effect of sleep duration on fertility. Compared with the lowest level of physical activity, high intensity physical activity (the highest levels of physical activity) was negatively correlated with fertility [odds ratio (OR) = 0.84; 95% confidence interval (CI): 0.70, 1.00, I2 = 64%]. However, we did not find an association between moderate intensity physical activity and fertility (OR = 1.09; 95% CI: 0.98, 1.22, I2 = 60%). We observed an inverse association between limited sleep duration (≤ 7 h) and fertility (OR = 0.92; 95% CI: 0.84, 1.00, I2 = 0%) compared with 8 h of sleep. The relationship between long sleep duration (≥9 h) and fertility was not statistically significant (OR = 0.85; 95% CI: 0.60, 1.21, I2 = 83%). According to the Newcastle-Ottawa Scale score, the overall quality of the research articles included was ranked as medium to high (6–9). Through GRADE system, the quality of evidence for the impact of high intensity physical activity and limited sleep duration on fertility was moderate, while the quality of evidence for the impact of moderate intensity physical activity and long sleep duration on fertility was low.ConclusionThe current evidence shows that high intensity physical activity and limited sleep time are negatively related to fertility. But there was great heterogeneity among studies, and the quality of research evidence was low to median. Thus, further high-quality research is needed to confirm this conclusion.PROSPERO registration numberCRD42022298137.

Introduction As problems associated with infertility and population aging increase, there is a growing interest in the factors that cause a decline in human fertility. Time-to-pregnancy (TTP) is a good indicator with which to reflect human fecundability. Here, we present a comprehensive overview of this topic. Methods Relevant qualitative and quantitative studies were identified by searching the Web of science and PubMed electronic databases. We included all literature, written in English, from inception to the 10th April 2021 providing the focus was on TTP. We conducted a narrative synthesis using thematic analysis. Results Traditional TTP-related study protocols include prospective and retrospective cohorts that provide a wealth of data to reveal potential influences on TTP. Thus far, a variety of factors have been shown to be associated with TTP in couples preparing for pregnancy, including basic demographic characteristics, menstrual status, chronic disease status, environmental endocrine disruptor exposure, and lifestyles. However, there are inevitable epidemiological bias in the existing studies, including recall bias, selection bias and measurement bias. Some methodological advances have brought new opportunities to TTP research, which make it possible to develop precision interventions for population fertility. Future TTP studies should take advantage of artificial intelligence, machine learning, and high-throughput sequencing technologies, and apply medical big data to fully consider and avoid possible bias in the design. Conclusion There are many opportunities and future challenges for TTP related studies which would provide a scientific basis for the “precise health management” of the population preparing for pregnancy.