Characteristics of the vaginal microbiome in women with premature ovarian insufficiency

Multiomics insights into the female reproductive aging

The human female reproductive lifespan significantly diminishes with age, leading to decreased fertility, reduced fertility quality and endocrine function disorders. While many aspects of aging in general have been extensively documented, the precise mechanisms governing programmed aging in the female reproductive system remain elusive. Recent advancements in omics technologies and computational capabilities have facilitated the emergence of multiomics deep phenotyping. Through the application and refinement of various high-throughput omics methods, a substantial volume of omics data has been generated, deepening our comprehension of the pathogenesis and molecular underpinnings of reproductive aging. This review highlights current and emerging multiomics approaches for investigating female reproductive aging, encompassing genomics, epigenomics, transcriptomics, proteomics, metabolomics, and microbiomics. We elucidate their influence on fundamental cell biology and translational research in the context of reproductive aging, address the limitations and current challenges associated with multiomics studies, and offer a glimpse into future prospects.

Change of the vaginal microbiome with oral contraceptive therapy in women with polycystic ovary syndrome: a 6-month longitudinal cohort study

BACKGROUND

The association between the vaginal microbiome and polycystic ovary syndrome (PCOS) is reported, but the longitudinal changes in the vaginal microbiome that accompany oral contraceptive therapy have not been described.

METHODS

This cohort study included 50 PCOS patients who wanted to make their menstrual periods more regular and accepted only oral contraceptive therapy and lifestyle coaching, then they were successfully followed up for 6 months. Venous blood was collected, and follicle-stimulating hormone (FSH), luteinizing hormone (LH), total testosterone (T), anti-Müllerian hormone (AMH), and estradiol (E2) were assayed at baseline and at months 3 and 6. Vaginal swabs were collected at baseline and at months 3 and 6. 16S rRNA genes were sequenced to identify the microbiota structure. Latent class trajectory models were used to explore the trajectory of the changes in Lactobacillus abundance.

RESULTS

At 3 months, all patients reported regular periods, and the improvement lasted until 6 months. The body mass index and waist-to-hip ratio decreased with treatment (P < 0.01), and the AMH and T levels showed downward trends. We did not find a statistically significant relationship between hormone levels at the previous time point and the vaginal microbiota at subsequent time points (P > 0.05). The relative abundance of Lactobacillus increased with treatment, and trajectory analysis revealed five classes of Lactobacillus changes. Class 1, stable high level, accounted for 26%; class 2, decrease followed by increase, accounted for 18%; class 3, stable low level, accounted for 10%; class 4, increase, accounted for 20%; class 5, increase followed by decrease, accounted for 26%. Logistic models showed that compared to class 1, a higher baseline T level was associated with a reduced risk of class 2 change (odds ratio (OR) = 0.03, 95% confidence interval (CI):0.01-0.52) and class 4 change (OR = 0.10, 95% CI:0.01-0.93).

CONCLUSIONS

The abundance of Lactobacilli increased with PCOS treatment; however, the trajectory was inconsistent for each individual. Evidence of the effects of female hormone levels on the vaginal microbiome is insufficient.

Vaginal Microbiota Changes in Patients With Premature Ovarian Insufficiency and Its Correlation With Ovarian Function

OBJECTIVES

To reveal the characteristics of vaginal microbiota in premature ovarian insufficiency (POI) patients and their relationship with ovarian function.

MATERIALS AND METHODS

In this case-control study, the vaginal bacterial composition of 30 POI patients and 26 healthy women of comparable age was assessed by 16S rRNA gene sequencing targeting the V3-V4 hypervariable regions. The metabolic functions of vaginal microflora were preliminarily predicted through the PICRUSt2 analysis. Redundancy analysis and Spearman's correlation analyzed the relationships between vaginal microbiota and ovarian function indicators.

RESULTS

Actinobacteria, Atopobium, and Gardnerella were significantly increased in POI patients. Their increments were significantly negatively correlated with anti-müllerian hormone (AMH) and inhibin B, and positively correlated with follicle-stimulating hormone (FSH) and luteinizing hormone (LH). While Bifidobacterium was significantly decreased in POI patients. Its relative abundance was significantly positively correlated with AMH and negatively correlated with FSH and LH. Then, POI patients included in this study were divided into POI (25 < FSH ≤ 40) (n = 9) and premature ovarian failure (POF) (FSH > 40) (n = 21) subgroups according to serum FSH levels. Compared with the controls, Firmicutes and Lactobacillus were significantly decreased only in POF (FSH > 40) patients, while no difference was observed in POI (25 < FSH ≤ 40) patients. Lactobacillus was negatively correlated with FSH. Firmicutes was significantly reduced and Actinobacteria was significantly increased in POF (FSH > 40) patients compared with POI (25 < FSH ≤ 40) patients. The key bacterial taxa Gardnerella and Atopobium showed potency in predicting POI.

CONCLUSIONS

Here we demonstrated significant changes in the vaginal microbiota of POI patients, and these changes were significantly correlated with reduced ovarian reserve, endocrine disruption, and symptoms of perimenopausal syndrome. Differences in vaginal microbiota between POI (25 < FSH ≤ 40) and POF (FSH > 40) patients were also identified. These findings may provide new evidence for the relationship between vaginal microbiota and ovarian function.