Beyond hormones: 3PM approach to vaginal microbiota dynamics in postmenopausal women

Menopause marks a critical transition characterized by ceased ovarian function and declining estrogen levels, affecting multiple systems with vasomotor symptoms and genitourinary syndrome of menopause (GSM). Recent evidence shows vaginal microbiota undergoes significant alterations during menopause, influencing GSM severity. This comprehensive review examined vaginal microbiota dynamics in postmenopausal women through Predictive, Preventive, and Personalized Medicine (3PM/PPPM), revealing characteristic shifts—increased alpha diversity, reduced Lactobacillus dominance, and transitions toward non-Lactobacillus species—that serve as potential predictive biomarkers for the menopausal state, premature ovarian insufficiency, and GSM symptoms. The analysis evaluated microbiota-based risk stratification strategies for vaginal dysbiosis and demonstrated the effectiveness of both hormonal interventions (systemic/local estrogen, tibolone, ospemifene) and non-hormonal alternatives (probiotics, energy-based devices, pessary) in normalizing microbiota composition and improving vaginal health. The application of PPPM/3PM transformed menopausal healthcare from reactive to proactive precision-based care by establishing microbiota-based biomarkers that predict health risks, enable early targeted interventions tailored to specific microbiota profiles, and guide personalized treatment approaches based on individual microbial compositions. While this paradigm shift significantly advances gynecological medicine, research gaps remain in validating baseline microbiota signatures as predictive biomarkers and establishing standardized screening protocols. Further studies are needed to validate interventions such as probiotics and prebiotics, optimizing strain selection for personalized, evidence-based preventive and therapeutic strategies. Developing standardized yet personalized protocols to restore a balanced vaginal microbiome could help alleviate menopause-related symptoms. Advancing microbiota-based personalized therapeutic approaches is crucial to enhancing the quality of life for postmenopausal women through targeted and individualized vaginal health management.

Predictive preventive personalized medicine (PPPM / 3PM), Primary ovarian insufficiency, Genitourinary syndrome of menopause, Quality of life, Postmenopausal women, Hormonal and non-hormonal therapy, Vaginal microbiota, Probiotics, Vaginal energy devices

Menopause is a defining transition in a woman’s life, characterized by the cessation of ovarian follicular activity and the significant shifts in endocrine function, including the reduction in estrogen levels and an increase in gonadotropins: follicle-stimulating hormone (FSH) and luteinizing hormone (LH). These hormonal changes impact multiple organs, including the cardiovascular, musculoskeletal, and neurological systems, leading to a variety of systemic changes [1, 2]. When menopause occurs before the age of 40, it is termed primary ovarian insufficiency (POI), also known as premature ovarian failure. One of the most significant clinical manifestations of menopause is the development of vasomotor symptoms (VSM) and the genitourinary syndrome of menopause (GSM). Defined by the North American Menopause Society (NAMS), GSM has a spectrum of symptoms affecting the genitourinary tract, mainly attributed to estrogen deficiency. These symptoms include vulvovaginal atrophy, dryness, urethral caruncle, urinary incontinence, overactive bladder symptoms, and sexual dysfunction [3, 4]. Additionally, changes in the vaginal mucosa, such as thinning of the superficial cell layer and reduction in the vaginal maturation index (VMI) and vaginal health index (VHI), are physical indicators of these transformations [5]. Microbiome profiles were highly relevant to digestive and urogenital health, skin integrity, wound healing processes, and mood regulation. Changes in microbial composition contributed to various conditions including metabolic disorders, inflammatory responses, immune system function, and mental health challenges. Research suggested that vaginal dryness associated with microbiome alterations appeared more prevalent in younger individuals with lower body mass indices. These connections highlighted the complex relationship between human microbiota and overall health across multiple body systems [6]. Recent scientific advances have highlighted the significant roles of vaginal microbiome in menopausal health. The postmenopausal period is marked by substantial alterations in vaginal microbiota composition, which may influence the presentation and severity of GSM symptoms [7]. Current research is exploring whether these microbiota changes might serve as early indicators or even contributory factors in the menopausal transition, particularly in cases of idiopathic POI. Understanding the complex interrelationships between vaginal microbiota, GSM manifestations, and menopausal progression could potentially reveal novel predictive biomarkers for declining ovarian function and GSM development. The application of Predictive, Preventive, and Personalized Medicine (PPPM/3PM) [8, 9] in the study of vaginal microbiota dynamics in postmenopausal women represented a transformative approach to optimizing menopausal health. By integrating microbiome-based diagnostics and interventions, this paradigm shift moved from a reactive healthcare model to a predictive, preventive, and personalized strategy tailored to individual patient needs. This comprehensive review examined the complex dynamics of vaginal microbiota changes in postmenopausal women through the PPPM framework, revealing characteristic shifts that served as potential predictive biomarkers for the menopausal state, premature ovarian insufficiency (POI), and genitourinary syndrome of menopause (GSM) symptoms. The analysis encompassed microbiota-based risk stratification strategies and demonstrated the effectiveness of both hormonal interventions and non-hormonal alternatives in normalizing microbiota composition and improving vaginal health indices. By advancing diagnostic approaches, preventive strategies, and therapeutic interventions, this paradigm shift facilitated the transition from reactive to proactive, personalized healthcare in gynecology, showing promise for optimizing long-term health outcomes in postmenopausal women through precisely tailored interventions based on individual microbiota profiles and clinical presentations. Search strategy and selection criteria A search from the PubMed database was conducted for original articles published in English, using the keywords “vaginal microbiota,” “menopause,” “primary ovarian failure,” “vulvovaginal atrophy,” and “genitourinary syndrome of menopause” from September 1993 until March 2024. Predictive value of vaginal microbiota for menopause and primary ovarian insufficiency (POI) Microbiota signatures for menopause Previous research investigating vaginal microbiota across various reproductive stages discovered significant shifts, particularly during the transition to the postmenopausal stage. The diversity of vaginal microbiota in postmenopausal women is summarized in Table 1. In postmenopausal women, alpha diversity, which encompasses species richness and evenness indices such as the Shannon diversity index and Simpson index, showed an increasing trend when compared to that of the premenopausal stage [10–15]. However, observations regarding species richness alone were mixed, with reports of both increase [12, 14] and decrease [10, 16]. These mixed findings might be attributed to variations in microbiome analysis methods, sample sizes, and population ethnicity among the studies. One study found no significant difference in alpha diversity, potentially due to the higher median age of the premenopausal control group and a limited sample size [17]. Table 1. | Participant | Bacterial identification technique | Vaginal microbiota diversity | Other findings | Interpretation | Reference | ||||| |---|---|---|---|---|---|---|---|---|---|---| | Study group (n) [mean/median age (years)] | Control group (n) [mean/ median age (years)] | α-Diversity | β-Diversity | ||||||| | Shannon | Simpson | Observed OTU | ACE/Choa-1 | ||||||| | 1. Postmenopausal women | |||||||||| | Korean postmenopausal women (19) [58.5] | Korean premenopausal women (11) [39.4] | V4 region 16S rRNA Gene | ↑ | ↓ | ↓ | ↓ | Difference | ↓ Lactate | Postmenopausal women showed increased alpha diversity and reduced species richness, with significant beta diversity variations compared to premenopausal individuals | [10] | | Postmenopausal women (375) [53] | Premenopausal women (270) [43] | V3–V4 region of 16S rRNA gene | ↑ | - | - | - | - | - | Alpha diversity significantly increased in postmenopausal women, while perimenopausal women showed alpha diversity levels similar to premenopausal individuals | [11] | | Perimenopausal women (310) [46] | ↔ | - | - | - | - | - | |||| | Algerian postmenopausal women (50) [57.7] | Algerian premenopausal women (50) [35.1] | V1–V3 region of 16S rRNA gene | ↑ | - | ↑ | - | Difference | ↓ Estrogen level | Postmenopausal women exhibit a significant increase in both alpha diversity and species richness, potentially due to reduced estrogen levels, and display distinct beta diversity compared to the premenopausal phase | [12] | | Postmenopausal women (35) | Premenopausal women (35) | V1–V2 regions of the 16S rRNA gene | ↑ | - | - | - | - | - | Alpha diversity significantly increased in postmenopausal women | [13] | | Postmenopausal women (20) [62.9] | Premenopausal women (18) [23.3] | V3–V4 region of the 16S rRNA gene | ↑ | - | - | ↑ | - | - | Postmenopausal women exhibited a significant increase in both alpha diversity and species richness compared to the premenopausal phase | [14] | | Japanese Postmenopausal women (35) [59.86] | Japanese Premenopausal women (35) [37.26] | V1–V2 of 16S rRNA gene | ↑ | - | - | - | ↑ vaginal pH ↑ OABSS | Postmenopausal women had a significant increase in alpha diversity, accompanied by an increase in vaginal pH and the overactive bladder symptom score | [15] | | | Postmenopausal women (15) [60.7] | Premenopausal women (15) [33] | V1–V3 region of 16S rRNA gene | ↓ Bacterial abundance (quantity of 16S rRNA gene copies) | - | - | Postmenopausal women exhibited a reduction in bacterial abundance (species richness) compared to women in their premenopausal phase | [16] | ||| | Postmenopausal women (17) [60.6] | Premenopausal women (20) [35.2] | Semiquantitative vaginal cultures | - | - | - | - | Difference | - | Postmenopausal women exhibited significant differences in beta diversity compared to premenopausal women | [18] | | Postmenopausal women (19) [55.6] | Premenopausal women (20) [44.7] | V4 region of 16S rRNA gene | ↔ alpha diversity | No difference | - | There was no significant difference in alpha and beta diversity between postmenopausal and premenopausal women | [17] | ||| | 2. Primary ovarian insufficiency women (POI) | |||||||||| | Primary ovarian failure/POF* (22) [30.5] | Healthy control (29) [29.79] | V3–V4 hypervariable region of 16S rRNA gene | ↑ | ↑ | ↑ | ↑ | Difference | - | Primary ovarian insufficiency exhibited distinct beta diversity and increased alpha diversity and species richness compared to premenopausal women, but lower alpha diversity relative to postmenopausal women | [24] | | Postmenopausal women (50) [57.96] | Primary ovarian failure/POF (22) [30.5] | ↑ alpha diversity | - | - | |||||| | *POF: FSH > 40 IU/L All POF received hormone replacement therapy | |||||||||| | Premature ovarian insufficiency: FSH > 40 IU/L (28) [34.61] | Healthy control (12) [32.5] | V3–V4 region of 16S rRNA gene | ↑ | - | - | - | No difference | ↑ FSH, LH ↑ Testosterone ↓ Estradiol, AMH ↔ Progesterone ↔ Prolactin | Premature ovarian insufficiency exhibited increased alpha diversity without beta diversity changes compared to premenopausal women, possibly due to altered levels of gonadotropins, androgens, estradiol, and AMH | [25] | | Premature ovarian insufficiency: FSH > 25 IU/L (30) [35.5]; three POI patients received hormone replacement therapy | Healthy control (26) [33] | V3–V4 hypervariable regions 16S rRNA gene | ↔ | ↔ | ↔ | ↔ | Difference | - | Premature ovarian insufficiency exhibited no difference in alpha diversity, species richness, and beta diversity compared to premenopausal women | [26] | The arrow symbols ↑, ↓, and ↔ denote a significant increase, decrease, and no significant change. The hyphen symbol (-) indicates that no information is available, mentioned, or described. Abbreviations: OABSS, overactive bladder symptom score; FSH, follicle-stimulating hormone; LH, luteinizing hormone; AMH, anti-Müllerian hormone Most studies revealed significant alterations in the composition of vaginal microbiota in postmenopausal women, as evidenced by significant changes in beta diversity [10, 12, 18]. Nonetheless, some studies did not find significant differences in beta diversity [14, 17], possibly due to the higher median age of the premenopausal control group and the limited sample size. Notably, the perimenopausal transition did not show significant changes in vaginal microbiota diversity [11]. Vaginal microbiota compositions, based on community state type, in postmenopausal women are summarized in Table 2. The vaginal microbiota is categorized into five community state types (CSTs) based on the VALENCIA nearest centroid-based classifier, as shown in Table 3 [11, 19]. CST I, II, III, and V are dominated by Lactobacillus species, in which each CST is characterized by different dominant Lactobacillus species. In contrast, CST IV is characterized by diverse anaerobes. Table 2. | Participant | Bacterial identification technique | Vaginal microbiota taxonomic classification | Microbial biomarker and co-occurrence analysis | Interpretation | Reference | ||||||| |---|---|---|---|---|---|---|---|---|---|---|---|---| | Study group (n) [mean/median age (years)] | Control group (n) [mean/median age (years)] | Genus and Species level | |||||||||| | Lactobacillus dominated | Non-Lactobacillus dominated | ||||||||||| | CST I | CST II | CST III | CST V | CST IV-A | CST IV-B | CST IV-C | |||||| | 1. Postmenopausal women | |||||||||||| | Postmenopausal women (28) [NA] | Premenopausal women (29) [NA] | V1–V2 region of 16S rRNA Gene | ↑ Odds of being classified CST IV-A or IV-B versus CST I | - | Postmenopausal women predominantly exhibited CST IV-A compared to CST I, while perimenopausal women more frequently showed CST II/V relative to CST I than premenopausal women | [21] | |||||| | ② | ⑤ | ③ | ④ | ① | ③ | - | |||||| | Perimenopausal women (30) [NA] | ↑ Odds of being classified CST II/V versus CST I | ||||||||||| | ② | ② | ① | ⑤ | ④ | ③ | - | |||||| | Postmenopausal women (375) [53] | Premenopausal women (270) [43] | V3–V4 region of 16S rRNA gene | ↓Lactobacillus | ↑ Anaerococcus ↑ Peptoniphilus ↑ Finegoldia | - | Postmenopausal women exhibited lower Lactobacillus spp. and higher anaerobic bacteria, particularly CST IV-C, with perimenopausal women also showing reduced Lactobacillus compared to premenopausal individuals | [11] | ||||| | ② | ⑤ | ③ | ⑥ | ⑦ | ④ | ① | |||||| | Perimenopausal women (310) [46] | ↓ Lactobacillus | ||||||||||| | ① | ④ | ② | ⑥ | ⑦ | ③ | ⑤ | |||||| | Postmenopausal women (15) [60.7] | Premenopausal women (15) [33] | V1–V3 region of 16S rRNA gene | ↓ Lactobacillus | ↑ various strictly anaerobic bacteria ↑ Gardnerella | - | Postmenopausal women exhibited lower levels of Lactobacillus spp. and higher levels of anaerobic bacteria, especially CST IV-C | [16] | ||||| | ③ | No | ③ | - | ② | ② | ① | |||||| | Algerian postmenopausal women (50) [57.7] | Algerian premenopausal women (50) [35.1] | V1–V3 region of 16S rRNA gene | ↓ | ↔ (↓) | ↔ (↓) | ↔ (↓) trend | ↑ CST IV | - | Postmenopausal women exhibited lower levels of Lactobacillus spp. Especially CST I and higher levels of anaerobic bacteria | [12] | || | ↓ Lactobacillus | ↑ Bifidobacterium ↑ Gradnerella ↑ Corynebacterium ↑ Mycoplasma ↑ Ureplasma ↑ Pseudomonas | ||||||||||| | ③ | ④ | ② | - | ① | |||||||| | Postmenopausal women (35) | Premenopausal women (35) | V1–V2 regions of the 16S rRNA gene | ↓ | ↓ | ↓ | ↔ | ↔ CST IV (↑ trend) ↑other bacteria | Positive correlation: among Lactobacillus spp except between L. iners and L. crispatus Negative correlation: L. iners and L. crispatus | Postmenopausal women showed reduced levels of Lactobacillus spp. and increased nonoptimal bacteria. A positive correlation existed among Lactobacillus spp., except between L. iners and L. crispatus | [13] | || | ↓ Lactobacillus | |||||||||||| | ④ | ③ | ② | No | ① | |||||||| | Chinese Postmenopausal women (45) [54] | Chinese Premenopausal women (1103) from 2 cohort - Initial cohort (516) [30] - Second cohort (587) [32] | Metagenomic shotgun sequencing | ↓ Lactobacillus | ↔ | Positive correlation A. vaginae and G. vaginalis | Postmenopausal women had lower Lactobacillus spp. and higher anaerobic bacteria, particularly CST IV-A and IV-B, with a positive correlation between A. vaginae and G. vaginalis | [22] | ||||| | ② | - | ③ | - | ① | ① | ② | |||||| | ↑ Viral sequences [Human endogenous retrovirus > Cyprinid herpes virus > others (HPV, herpes simplex virus, phages, torque teno virus)] ↔ Fungus: Candida albicans | |||||||||||| | Postmenopausal women (19) [55.6] | Premenopausal women (20) [44.7] | V4 region of 16S rRNA gene | ↔ * | - | ↓ | ↔ | ↔ | ↔ | ↔ | - | Postmenopausal women exhibited a higher level of L. crispatus, a lower level of L. iners, and a similar level of anaerobic bacteria compared to premenopausal women | [17] | | * L. crispatus exhibits comparable gene copy levels per swab in post and premenopausal women but has higher detection rates in postmenopausal women (18/19 vs. 12/20, p = 0.019) | |||||||||||| | Japanese Postmenopausal women (35) [59.86] | Japanese Premenopausal women (35) [37.26] | V1–V2 of 16S rRNA gene | ↓ | ↓ | ↓ | ↔ | ↔ CST IV ↑ other CST | - | Postmenopausal women showed a reduction in Lactobacillus spp. Levels and an increase in other community state types | [15] | || | ↓ Lactobacillus | |||||||||||| | ④ | ③ | ② | ⑥ | ⑤ CST IV ① other CST | |||||||| | Korean postmenopausal women (19) [58.5] | Korean premenopausal women (11) [39.4] | V4 region 16S rRNA Gene | ↓ Lactobacillus | ↑ Pseudomonas ↑ Proteus ↑ Finegoldia ↑ Atopobium ↑ Prevotella ↑ Unclassifies Lactobacillaceace ↑ Escherichia | LDA > 4 (Genus) Lactobacillus, Atopobium, Pseudomonas, Escherichia, Proteus, Prevotella, Lactobacillaceae_uc | Postmenopausal women showed a reduction in Lactobacillus spp. Levels and an increase in pathogenic bacteria | [10] | ||||| | Postmenopausal women (190) [NA] | Premenopausal women (341) [NA] | V4 region of 16S rRNA genes | ↓ Lactobacillus | ↑ Prevotella ↑ Gardnerella ↑ Bacillus ↑ Streptococcus | LDA ≥ 4 - Prevotella - Bacterial Vaginosis - HPV Positive correlation Prevotella with -Peptoniphilus, -Porphyromonas -Campylobacter -Anaerococcus Negative correlation Prevotella with Lactobacillus | Postmenopausal women had lower Lactobacillus spp. and higher pathogenic bacteria, with Prevotella spp. positively correlating with anaerobes but negatively with Lactobacillus spp. | [20] | ||||| | Lactobacillus > Prevotella > Gardnerella | |||||||||||| | Postmenopausal women (20) [62.9] | Premenopausal women (18) [23.3] | V3–V4 region of the 16S rRNA gene | ↓ Lactobacillus | ↑ All other bacteria groups | - | Postmenopausal women had lower Lactobacillus spp. and higher anaerobic bacteria | [14] | ||||| | Other BV-anaerobe > G. vaginalis > Prevotella > Steptococcus > other lactobacilli > L. iners > other Gram-positive uropathobionts > A. vaginae (rare L. crispatus, L. jensenii) | |||||||||||| | Postmenopausal women (17) [35.2] | Premenopausal women (20) [60.6] | Semiquantitative vaginal cultures | ↓ Lactobacillus | ↔ Gardnerella | - | Postmenopausal women had lower Lactobacillus spp. | [18] | ||||| | Postmenopausal women (1320) [65.5] | V3–V4 regions of the 16S rRNA gene | ③ | ④ | ② | ⑤ | ① CST IV [CSI IV-C > IV-A = IV-B] | - | Anaerobic bacteria predominated in postmenopausal women, with CST IV-C more common in Chinese women and CST I prevalent in Black women | [19] | ||| | - Chinese women | The others | - | - | - | - | - | - | ↑ | |||| | - Black women | The others | ↑ | - | - | - | - | - | - | |||| | Black Postmenopausal women (44) [55.9] | White Postmenopausal women (44) [56.1] | Short gun sequencing | ↑ | ↑ | ↔ | - | - | - | Among postmenopausal women, Black and White individuals had similar overall levels of Lactobacillus spp., with Black women showing higher levels of L. crispatus and L. gasseri | [23] | || | ↔ Lactobacillus | |||||||||||| | 1. Primary ovarian insufficiency women | |||||||||||| | Primary ovarian failure (POF)* (22) [30.5] | Healthy control (29) [29.79] | V3–V4 hypervariable region of 16S rRNA gene | - | - | ↑ | ↓ | ↑ Gradnerella ↑ Prevotella ↑ Bacteroides ↑ Sneathia ↑ Dialister ↑ Anaerococcus | LDA > 3.6 (Genus) ↑ Abundance - Prevotella - Gardnerella - Bifidobacterium - Snethia - Porphyromonas - Escherichia - Bacteroides - Dilister, Veillonella - Camphylobacter ↓ Abundance (LDA > 4.8) Lactobacillus Negative correlation Lactobacillus with all other genera | Patients with POF exhibited a lower relative abundance of Lactobacillus, particularly L. gallinarum, which has protective effects against POF, and higher levels of CST IV. Postmenopausal women also showed a significant reduction in Lactobacillus spp. compared to those with POF | [24] | || | *POF: FSH > 40 IU/L All POF received hormone replacement therapy | Others: ↓ L. gallinarum Overall: ↓ Lactobacillus | ||||||||||| | Postmenopausal women (50) [57.96] | Primary ovarian failure/POF (22) [30.5] | ↓ Lactobacillus | ↔ | ||||||||| | Premature ovarian insufficiency: FSH > 40 IU/L (28) [34.61] | Healthy control (12) [32.5] | V3–V4 region of 16S rRNA gene | ↓ Lactobacillus | ↑Bifidobacterium ↑Anaerococcus ↑Atopobium ↑Enterococcus ↑Gardnerella ↑Peptoniphilus ↑Prevotella ↑Streptococcus ↑Veillonella | LDA > 3 (Genus) ↓ Abundance - Lactobacillus (LDA 5.16) - Odoribacter -Brevundimonas ↑ Abundance - Streptococcus | Premature ovarian insufficiency altered vaginal microbiota, leading to reduced levels of Lactobacillus and Bifidobacterium, and an increase in CST IV | [25] | ||||| | Premature ovarian insufficiency FSH > 25 IU/L (30) [35.5] Three POI patients received hormone replacement therapy | Healthy control (26) [33] | V3–V4 hypervariable regions 16S rRNA gene | Species level ↓ Lactobacillus jensenii ↓ unclassified Lactobacillus | ↓ Bifidobacterium breve ↑ Atopobium vaginae ↑ Gardnerella vaginalis ↑ Streptococcus anginosus | LDA > 4 (Genus) ↑ Abundance -Atopobium -Gardnerella ↓Abundance -Bifidobacterium Positive correlation Atopobium with Gardnerella Negative correlation - Lactobacillus with all other genera - Atopobium and Gardnerella with Bifidobacterium | Significant alterations in the vaginal microbiota of POI patients were observed, with Gardnerella and Atopobium identified as potential predictors of the condition | [26] | ||||| | Genus level ↔ Lactobacillus (trend ↓) | ↓ Bifidobacterium ↑ Atopobium ↑Gardnerella | The arrow symbols ↑, ↓, and ↔ denote a significant increase, decrease, and no significant change. The hyphen symbol (-) indicates that no information is available, mentioned, or described. Abbreviations: CST, community state type; spp., species; LDA, linear discriminant analysis; POI, primary ovarian insufficiency; POF, premature ovarian failure; FSH, follicle-stimulating hormone; E2, estradiol; AMH, anti-Müllerian hormone; PRL, prolactin. The symbols ① through ⑥ indicate the relative abundance of each microbiota in the samples, with ① representing the highest abundance and each subsequent number indicating a progressively lower abundance Table 3. | CSTs | Dominated by | |---|---| | CST I | Lactobacillus crispatus | | CST II | Lactobacillus gasseri | | CST III | Lactobacillus iners | | CST IV | Diverse array of strict and facultative anaerobes with low-Lactobacillus species | | CST IV-A | High relative abundance of Candidatus Lachnocurva vaginae and moderate relative abundance of Gardnerella vaginalis | | CST IV-B | High relative abundance of Gardnerella vaginalis and low relative abundance of Candidatus Lachnocurva vaginae | | CST IV-C | Diverse array of strict and facultative anaerobes | | CST IV-C0 | Moderate relative abundance of Prevotella | | CST IV-C1 | Streptococcus species | | CST IV-C2 | Bifidobacterium species | | CST IV-C3 | Enterococcus species | | CST IV-C4 | Staphylococcus species | | CST V | Lactobacillus jensenii | Significant alterations in the vaginal microbiota in postmenopausal women were observed, including a decrease in Lactobacillus and an increase in non-Lactobacillus bacteria, particularly pathogenic bacteria [10–12, 14, 16, 20, 21], when compared to those of premenopausal women [11–16, 18, 20, 22]. Several studies reported changes in CSTs, with a decrease in CST I (dominated by Lactobacillus crispatus) [12, 13, 15], CST II (dominated by Lactobacillus gasseri) [13, 15], and/or CST III (dominated by Lactobacillus iners) [15, 17], along with an increase in CST IV [10–12, 14, 16, 20] in postmenopausal women. However, some studies reported a lower abundance of Lactobacillus species without a significant increase in pathogenic bacteria [13, 15, 17, 18, 22]. These discrepancies might be attributed to variations in bacterial identification techniques, population characteristics, and limited sample sizes. Interestingly, postmenopausal samples exhibited the highest proportion of bacteria in non-Lactobacillus-dominated clusters, particularly in CST IV [10–14, 16, 19–22]. Some of these studies further subclassified CST IV into CST IV-A, IV-B, and IV-C, identifying the highest proportions in CST IV-A [21, 22] or CST IV-C [11, 16]. There were also significant differences in vaginal microbiota across ethnicities among postmenopausal women [19, 23], in which those of Chinese women were more frequently classified as CST IV-C, while those of Black women were more frequently classified as CST I [19] and CST II [23] when compared to other ethnicities. These findings suggest that vaginal microbiome composition varies by ethnicity, highlighting the need for personalized interventions tailored to individual microbiome profiles. Perimenopausal women exhibited a reduction in Lactobacillus [11] and higher odds of being classified as CST II or V versus CST I, when compared to premenopausal women [21]. These findings suggested that perimenopausal women were more prone to have a shift in their vaginal microbiota from the Lactobacillus crispatus-dominated state to the state dominated by other Lactobacillus species, such as Lactobacillus gasseri or Lactobacillus jensenii. In other words, the menopausal transition resulted in altered composition of the vaginal microbiota. Microbiota signatures for primary ovarian insufficiency (POI) A parallel trend in alterations of vaginal diversity was observed in women with POI as summarized in Table 1. These women exhibited a significant increase in alpha diversity [24, 25], although their diversity remained lower than that of postmenopausal women [24]. On the other hand, one study did not observe these changes [26]. These contradictory findings might be attributed to differences in the FSH cutoff level for diagnosing POI and the impact of hormonal treatment therapy on vaginal microbiome alterations among studies. Vaginal microbiota compositions, based on community state type, in POI, are summarized in Table 2. Similar trends of lower Lactobacillus were revealed in women with POI [24–26], although one study reported a non-significant decrease in these bacteria [26], which could result from a lower threshold of FSH level for diagnosing POI. Bifidobacterium were also decreased in POI cases [25, 26]. Interestingly, one study where all POI patients received hormone replacement therapy found an increased abundance of Bifidobacterium, suggesting the influence of hormonal therapy on the vaginal microbiota and its potential to modulate the vaginal microbiome [24]. Conversely, non-Lactobacillus species such as Gardnerella, Prevotella, Bacteroides, Sneathia, Dialister, Anaerococcus, Enterococcus, Peptoniphilus, Streptococcus, and Veillonella were increased [24–26]. In particular, Atopobium and Gardnerella showed potential as key bacterial taxa in predicting POI, with ROC curve analyses demonstrating acceptable accuracy: Gardnerella (AUC = 0.744, P = 0.002) and Atopobium (AUC = 0.787, P < 0.001) [26]. Microbial biomarkers and co-occurrence analysis Linear discriminant analysis (LDA) is a powerful statistical approach for identifying key microbial taxa that significantly differentiate between groups, offering valuable insights into potential biomarkers for postmenopausal status and POI. In postmenopausal women, significant microbial biomarkers identified by a linear discriminant analysis (LDA) effect size (P < 0.05 and LDA effect size ≥ 4) included Lactobacillus, Atopobium, Pseudomonas, Escherichia, Proteus, and uncultured Lactobacillaceae [10], as well as Prevotella [10, 20]. Additionally, associations were found with bacterial vaginosis and HPV [20]. Positive correlations were observed between Prevotella and Peptoniphilus, Porphyromonas, Campylobacter, and Anaerococcus. Conversely, a negative correlation was identified between Prevotella and Lactobacillus [20]. In women with POI, significant LDA effect sizes > 3 were associated with higher abundances of Streptococcus, Atopobium, and Gardnerella [25, 26]. One study reported an LDA > 3.6 with increased abundances of Prevotella, Gardnerella, Bifidobacterium, Snethia, Porphyromonas, Escherichia, Bacteroides, Dialister, Veillonella, and Campylobacter [24]. However, significant LDA effect sizes > 3 indicating lower abundances were found for Lactobacillus [24, 25], Odoribacter, and Brevundimonas [25]. Bifidobacterium showed significant LDA effect sizes > 3 in both increased [24] and decreased abundances [25, 26], depending on each study. In other words, it was shown that POI was associated with either increased Bifidobacterium or decreased Bifidobacterium, indicating inconsistent findings. This variation might be attributed to differences in the use of hormonal replacement therapy. Positive correlations were observed between Gardnerella and Atopobium, while negative correlations were noted between Lactobacillus and all other genera [24, 26], as well as between Atopobium and Gardnerella with Bifidobacterium [26]. The ability of LDA to distinguish microbial signatures between reproductive aging states underscored its potential as a screening tool for postmenopausal transitions and POI. However, the inconsistencies in Bifidobacterium abundance highlighted the need to control for confounders such as hormonal therapy before clinical application. These microbial markers could facilitate the development of non-invasive diagnostic strategies for early detection of reproductive aging and POI, allowing for timely interventions. Future clinical implementation could involve vaginal microbiome screening as a routine part of women’s health assessments, focusing on the balance between protective (Lactobacillus) and potentially dysbiotic bacteria. Such an approach could enable personalized risk stratification for premature ovarian aging, guiding targeted therapeutic strategies. Large-scale longitudinal studies are warranted to validate these findings and establish standardized microbial biomarkers for reproductive health monitoring in precision medicine. Relationship between vaginal microbiota and clinical indicators of ovarian function Studies on vaginal microbiota in POI demonstrated the significant associations between ovarian function clinical indicators, such as FSH, LH, estradiol, progesterone, anti-Müllerian hormone (AMH), and vaginal microbiota, as summarized in Table 4 [24–26]. The specific patterns of microbial abundance and their correlations with hormonal levels highlighted the crucial role of microbiota in ovarian health. These findings suggested that changes in vaginal microbiota could be potential predictive biomarkers for ovarian insufficiency and menopausal status, offering opportunities for targeted therapeutic interventions [26]. Table 4. | Ovarian function clinical indicators | Correlation | Vaginal microbiota (genus/species) | |---|---|---| | Follicle-stimulating hormone (FSH) | Positive | Gardnerella [24, 26], Prevotella [24], L. iners [24], Streptococcus [25], Atopobium [25, 26], Villonella [25], Actinobacteria [26] | | Negative | Lactobacillus [24–26], L. gallinarum [24], Odoribacter [25], Bifidobacterium [26] | | | Luteinizing hormone (LH) | Positive | Gardnerella [24, 26], Prevotella [24], L. iners [24], Streptococcus [25], Anaerococcus [25], Atopobium [25, 26], Villonella [25], Actinobacteria [26] | | Negative | Lactobacillus [24], L. gallinarum [24], Odoribacter [25], Bifidobacterium [26] | | | Estradiol | Positive | Lactobacillus [24], L. gallinarum [24], Odoribacter [25], Bifidobacterium [26] | | Negative | Gardnerella [24], Prevotella [24], Atopobium [26] | | | Anti-Müllerian hormone (AMH) | Positive | Lactobacillus [24], Brevundimonas [25], Odoribacter [25], Bifidobacterium [26] | | Negative | Gardnerella [24, 26], Prevotella [24], Actinobacteria [26], Atopobium [26] | | | Inhibin B | Positive | Bifidobacterium [26] | | Negative | Actinobacteria [26], Gardnerella [26], Atopobium [26] | | | Prolactin | Positive | Anaerococcus [25], Atopobium [25] | | Progesterone | Positive | Prevotella [25], Anaerococcus [25], Atopobium [25] | The correlations between specific vaginal microbiota and clinical indicators present promising opportunities for developing novel diagnostic tools and predictive biomarkers. Particularly noteworthy is the relationship between AMH, a well-established marker of ovarian reserve [27], and its associated microbiota. The positive correlations of AMH with Lactobacillus [24], Brevundimonas [25], Odoribacter [25], and Bifidobacterium [26], coupled with its negative correlations with Gardnerella [24, 26], Prevotella [24], Actinobacteria [26], and Atopobium [26], suggested that these microbial patterns could potentially serve as non-invasive indicators of ovarian reserve status. For FSH and LH assessment, their consistent positive correlations with Gardnerella, Prevotella, Atopobium, L. iners, Streptococcus, and Actinobacteria, alongside negative correlations with Lactobacillus, L. gallinarum, Odoribacter, and Bifidobacterium [24–26], could provide complementary diagnostic information about gonadotropin levels. Estradiol’s positive correlation with Lactobacillus and negative correlation with Gardnerella and Prevotella [24–26] might offer insights into estrogen status, with these correlations showing patterns opposite to those observed with gonadotropins. These microbial signatures could be developed into comprehensive screening panels for assessing ovarian function through vaginal microbiome analysis. Future directions should focus on longitudinal studies to validate these microbial markers’ predictive value, standardizing sampling and analysis protocols and developing point-of-care testing methods. Integration of microbiome data with traditional clinical indicators could enhance the accuracy of ovarian function assessment and enable earlier interventions for reproductive health conditions. Additionally, understanding these correlations might lead to therapeutic approaches targeting specific microbial communities to support hormonal balance and ovarian function. Predictive assessment of ovarian function through vaginal microbiota analysis: current insights and future perspectives Previous research showed that vaginal health was closely associated with the equilibrium of the vaginal ecosystem, primarily maintained by vaginal microbiota [10, 24]. This equilibrium protects reproductive-age women from pathological conditions such as bacterial vaginosis and vaginal candidiasis, which are believed to result from disruption of the vaginal microbiota composition, especially Lactobacillus species [28]. This body of evidence supports the hypothesis that a balanced vaginal microbiota, predominantly consisting of Lactobacillus species, contributes to lower diversity during the premenopausal stage [29]. The menopausal transition, however, may disrupt this balance, leading to increased vaginal microbiota diversity and potentially contributing to abnormal vaginal symptoms such as those observed in the GSM. Despite these associations, recent research has yet to elucidate the underlying mechanisms and the causal relationships. Regarding microbiota as potential predictive indicators for menopausal state and POI, increased Shannon alpha diversity emerged as a significant marker [10–15, 24, 25]. This marker showed a stronger association with complete ovarian cessation, such as complete menopause or POF with FSH > 40, rather than conditions like perimenopause [11] or early POI with FSH > 25 [26]. Additional research is needed to establish precise cutoff levels for Shannon diversity and its relationship with FSH levels or ovarian function for a more accurate prediction of ovarian function decline. The composition of vaginal microbiota served as another significant indicator. Researches showed that menopause and POI were characterized by a decline in Lactobacillus-dominated communities and an increase in non-Lactobacillus species [10–16, 18, 20–22]. This shift was particularly evident in postmenopausal women with the prevalence of CST IV [10–14, 16, 19–22], while in POI, Atopobium and Gardnerella showed promise as predictive bacterial taxa, supported by ROC curve analyses [26]. Linear discriminant analysis (LDA) identified several significant microbial biomarkers. For postmenopausal women, these included Lactobacillus, Atopobium, Pseudomonas, Escherichia, Proteus, uncultured Lactobacillaceae, and Prevotella [10, 20]. For POI, key taxa included Streptococcus, Atopobium, Gardnerella, Prevotella, Bifidobacterium, Snethia, Porphyromonas, Escherichia, Bacteroides, Dialister, Veillonella, Campylobacter, and Lactobacillus [24–26]. In examining the relationship between ovarian function and vaginal microbiota in POI, elevated FSH and reduced estradiol levels—key indicators of declining ovarian function—showed positive correlations with Gardnerella, Prevotella, L. iners, Streptococcus, Atopobium, Villonella, and Actinobacteria [24–26]. Negative correlations were observed with Lactobacillus, L. gallinarum, Odoribacter, and Bifidobacterium [24–26]. These findings underscored the potential use of vaginal microbiota changes as predictive biomarkers for ovarian insufficiency and menopausal status. However, further research is essential to evaluate the predictive ability of these microbiota and to develop robust predictive models for ovarian function. Such models could facilitate early diagnosis and personalized interventions. Microbiota-based risk stratification for genitourinary syndrome of menopause (GSM) Microbial dysbiosis as a predictor of GSM development The vaginal microbiota plays a crucial role in maintaining the balance of vaginal ecosystems and ensuring good urogenital health by modulating innate immunity and serving as a first-line defense against infections, including HPV-induced cervical carcinogenesis. Lactobacillus spp. produce lactic acid, which helps maintain an acidic vaginal pH (~ 4.0), inhibiting the growth of pathogenic bacteria and viruses [30]. Additionally, the production of hydrogen peroxide (H2O2) and bacteriocins directly suppresses pathogen colonization [30]. Significant alterations in the vaginal microbiota occur during the menopausal transition. However, the mechanisms behind why some menopausal women develop more severe urogenital symptoms, defined as genitourinary syndrome of menopause (GSM), remain unclear and multifactorial [31, 32]. These multifactorial mechanisms include hormonal regulation changes, vascular dysfunction and vasoconstriction (as seen in the Flammer Syndrome phenotype), dehydration and altered sensory regulation, psychological and stress factors, metabolic, and lifestyle as well as vaginal microbiome alteration [31, 32]. GSM affects between 27 and 84% of postmenopausal women, significantly impacting their health, sexual function, and quality of life [3, 31, 32]. It is often underdiagnosed and undertreated [4, 31, 32]. Several studies suggest that the vaginal microbiome plays an important role in the development of GSM due to its close relationship with vaginal health, as summarized in Table 5. Table 5. | Participant | Bacterial identification technique | Vaginal microbiome diversity | Vaginal microbiota taxonomic classification | Other findings | Interpretation | Reference | |||||||||||| |---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---| | Study group (n) [mean/ median age (years)] | Control group (n) [mean/ median age (years)] | Genus and Species level | |||||||||||||||| | Lactobacillus dominated | Non-Lactobacillus dominated | ||||||||||||||||| | CST I | CST II | CST III | CST V | CST IV-A | CST IV-B | CST IV-C | |||||||||||| | Postmenopausal women (32) [44–77] | V6 region of 16S rRNA gene | ↑ α-diversity | ↓ Lactobacilli | ↑ Prevotella timonensis ↑ Porphyromonas ↑ Peptoniphilus ↑ Bacillus | Observed vaginal dryness correlated with observed vaginal atrophy Inverse correlation between Lactobacillus ratio and dryness | Postmenopausal women with moderate to severe vaginal dryness showed higher alpha diversity, lower Lactobacillus levels, and more anaerobic bacteria, with an inverse correlation between Lactobacillus ratio and dryness severity | [34] | ||||||||||| | Moderate to severe dryness group - Moderate vaginal dryness (10) - Severe vaginal dryness (1) | Least dryness group - No dryness (6) - Mild vaginal dryness (15) | ||||||||||||||||| | Postmenopausal women (1320) [65.5] | V3–V4 regions of the 16S rRNA gene | - | Sexual pain | Sexual pain was the only GSM symptom associated with a specific CST | Postmenopausal women experiencing sexual pain showed increased levels of anaerobic bacteria, specifically CST IV-C1 with lower all other CSTs | [19] | |||||||||||| | with GSM (925) | without GSM (395) | ↓ | ↓ | ↓ | ↓ | ↓ | ↓ | ↑ IV-C1 | |||||||||| | Postmenopausal women with atrophic vaginitis (30) [55.8] | Healthy postmenopausal women (29) [55.6] | V1–V3 regions of the 16S rRNA gene | ↔ α-diversity Difference in β-diversity | ↓ Lactobacillus | ↑ Gardnerella ↔ Anaerococcus ↔ Atopobium ↔ Prevotella | - | Postmenopausal women with atrophic vaginitis showed altered beta diversity, stable alpha diversity, and decreased Lactobacillus spp., and increased Gardnerella compared to asymptomatic women | [33] | |||||||||| | Postmenopausal women with GSM - Natural menopause (30) - Surgical menopause (37) - Premature ovarian failure (4) | - | Real-time PCR | - | ↓ Lactobacillus | ↑Opportunistic microflora: - Escherichia coli - Staphylococcus aureus - Streptococcus faecalis - Candida spp. - Corynebacterium spp. | - | In postmenopausal women with GSM, there was an increased abundance of opportunistic and facultative microflora, which correlated with a decrease in the number of Lactobacilli. These dysbiotic changes were accompanied by atrophy of the vaginal mucosa | [35] | |||||||||| | ↑ facultative microflora associated with ↓ Lactobacillus and ↑ atrophy of the vaginal mucosa | |||||||||||||||||| | Postmenopausal women (375) [53] Vaginal atrophy (191) | - | V3–V4 region of 16S rRNA gene | ↔ | ↓ Adjusted odds of vaginal atrophy: CST I* | ↑↑ Proportions of CST IV-A/C, CST IV-B | Lactobacillus dominance: ↓ Adjusted odds of GSM endpoint onset ↑ Adjusted odd of GSM remission Compositional principal component analysis: no taxon related to any GSM endpoint after multiple comparisons adjustment | GSM symptoms were generally correlated with lower levels of Lactobacillus species and higher levels of anaerobic bacteria. The association between GSM and the microbiota was observed in the form of community state types rather than individual taxa | [11] | |||||||||| | Vaginal dryness (218) | ↑ Shannon’s diversity associated with ↓ odds of vaginal dryness up to a threshold; then ↑ odds beyond the threshold | ↓↓ Adjusted odds of vaginal dryness: CST II/V* | ↑↑ Proportions of CST IV-A/C, CST IV-B | ||||||||||||||| | Urinary incontinence (124) | ↔ | ↑↑ Proportion of CST III | - | ||||||||||||||| | Low libido (172) | ↔ | ↓ Adjusted odds of low libido: CST II/V* and CST III * (*relative to CST IV-B) | ↑↑ Proportions of CST IV-B | ||||||||||||||| | Women with mild to moderate VVA Postmenopausal women (14) | Women without VVA Postmenopausal women (14) | V1–V2 region of 16S rRNA Gene | - | - | - | - | - | ↑ | - | - | ↑ 25-fold odds of being CST IV-A in women with mild-moderate signs of VVA | Women with mild to moderate vulvovaginal atrophy exhibited higher levels of CST IV-A | [21] | ||||| | Perimenopausal women (3) | Perimenopausal women (27) | ||||||||||||||||| | Premenopausal women (2) | Perimenopausal women (27) | The arrow symbols ↑, ↓, and ↔ denote a significant increase, decrease, and no significant change. The arrows ↑↑ ↓↓ denote the highest and lowest findings, respectively. The hyphen symbol (-) indicates that no information is available, mentioned, or described. Abbreviations: GSM, genitourinary syndrome of menopause; VVA, vulvovaginal atrophy; CST, community state type; spp., species The composition of the vaginal microbiota (beta diversity) in postmenopausal women experiencing GSM significantly differed from that in asymptomatic postmenopausal women [33]. One study found that patients with moderate to severe symptoms of GSM exhibited higher alpha diversity compared to those with mild or no symptoms, indicating an increase in both species’ richness and evenness [34]. However, other studies found no significant change in alpha diversity [11, 33], which might be due to limited sample sizes and differences in participant characteristics, as these studies included a range of symptoms from mild to severe compared to no symptoms. Interestingly, one study found a significant trend in the association between the odds of vaginal dryness (a dimension of GSM) and alpha diversity. It showed that higher Shannon diversity was linked to lower odds of vaginal dryness up to a certain threshold, beyond which higher odds were observed [11]. Studies consistently showed significant differences in the vaginal microbiota between postmenopausal women with GSM and those who were healthy or had milder symptoms. Indeed, Lactobacillus species were found to be lower in postmenopausal women experiencing GSM [19, 33, 34], and even lower among those with more severe symptoms [34]. Interestingly, the notable trend was not only the lower abundance of Lactobacillus but also the increase in anaerobic bacteria, specifically CST IV, in women with GSM [11, 19, 21, 33–35]. The evidence demonstrates that vaginal microbial dysbiosis could serve as a valuable predictive marker for GSM development and severity. The consistent pattern of reduced Lactobacillus abundance and increased anaerobic bacteria, particularly the shift toward CST IV, offers promising opportunities for early risk identification. While alpha diversity findings showed some variability, beta diversity consistently distinguished GSM patients from healthy controls. Community state types (CSTs) and their role in GSM progression When examining specific dimensions of GSM symptoms, vaginal atrophy and dryness were associated with the increase in CST IV types [11, 21, 33, 34]. Particularly, CST IV-A, dominated by Candidatus Lachnocurva vaginae, has been linked to vulvovaginal atrophy in one study [21], while another study reported the highest proportions of CST IV-A/C and CST IV-B among women with vaginal atrophy and dryness [11]. Additionally, there was a reduction in Lactobacillus-dominated CST (CST I, II, III, V) in the presence of these symptoms [11, 33, 34]. However, one study did not observe changes in Lactobacillus, which might be due to the inclusion of premenopausal and perimenopausal women in this study [21]. Considering CST, a study showed that CST I, dominated by Lactobacillus crispatus, was associated with lower odds of vulvovaginal atrophy, while CST II, dominated by Lactobacillus gasseri, was associated with the lowest odds of vaginal dryness [11]. For sexual-related symptoms, CST IV-B, dominated by Gardnerella vaginalis, was most associated with low libido [11], whereas CST IV-C1, dominated by Streptococcus species, was most associated with sexual pain [19]. These two studies also demonstrated a reduction in Lactobacillus-dominated CSTs [11, 19]. In contrast, lower odds of low libido were related to CST II/V and CST III [11]. Interestingly, for urinary symptoms such as urinary incontinence, CST III, dominated by Lactobacillus iners, was found to be more prevalent [11]. Although Lactobacillus iners was a species within the Lactobacillus genus, its benefits or adverse effects on vaginal health remained unclear [36–38]. It seemed to be a potential factor in transitioning to a dysbiotic state and provided less protection against pathogens because it did not produce D-lactic acid to protect against certain infections [36–38]. All of these findings suggest that the association between GSM and the microbiota is observed in the form of CST rather than individual taxa, possibly because these bacteria function as part of networks [11]. The distinct associations between specific CSTs and GSM symptoms present valuable opportunities for implementing EPMA approaches in managing menopausal vaginal health. For predictive purposes, monitoring shifts in vaginal microbiota composition, particularly the transition from Lactobacillus-dominated CSTs to CST IV subtypes, could serve as an early warning system for developing GSM symptoms. Specifically, the detection of CST IV-A, CST IV-A/C, and CST IV-B patterns could predict the onset of vaginal atrophy and dryness, while an increase in CST IV-B might signal potential libido issues. The presence of CST IV-C1 could alert clinicians to potential sexual pain development. This microbiome-based predictive approach enables preventive interventions before symptom manifestation becomes severe, supporting a precision-medicine approach to GSM management. Stratification of at-risk populations for GSM Estrogen deficiency during menopause caused significant changes in the vaginal microbiota. Lack of estrogen led to increased growth of anaerobes [21] and made the vaginal epithelium more susceptible to pathogens [34]. These alterations resulted in a reduction of Lactobacillus species, which were crucial for (1) protecting epithelial cells from oxidative stress [39]; (2) producing lactic acid to sustain an acidic, antimicrobial, and anti-inflammatory environment [11, 40–42]; and (3) supporting the structural integrity of the cervicovaginal epithelium, as demonstrated in in vitro studies [43]. In low-Lactobacillus CST IV communities, the absence of these protective mechanisms predisposed women to GSM symptoms. Anaerobic bacteria, especially Gardnerella vaginalis, and their metabolic by-products in CST IV-B communities were cytotoxic to vaginal epithelial cells, further contributing to vaginal atrophy [11, 44]. Then, a decrease in beneficial Lactobacillus species and the rise of harmful anaerobes appeared to play a critical role in GSM development. However, further research is necessary to explore whether the association between GSM and these microbiota is independent of estrogen levels. From the perspective of using vaginal microbiota as predictive factors for GSM risk stratification, research revealed several promising microbial markers. Higher alpha diversity emerged as a potential predictor for vaginal dryness symptoms [11, 34]. The microbial landscape in GSM was characterized by an overall increase in non-Lactobacillus species (CST IV) and a corresponding decrease in Lactobacillus species among postmenopausal individuals [11, 19, 33–35]. At the taxonomic level, several bacterial species showed significant associations with GSM. These included changes in Lactobacillus populations [11, 19, 33–35], as well as increases in Gardnerella [33], Prevotella timonensis, Porphyromonas, Peptoniphilus, and Bacillus [34]. These distinct microbial signatures could serve as valuable indicators for identifying at-risk populations and implementing timely interventions. Further research is needed to determine the predictive value of these microbiota-based markers and assess their robustness in guiding preventive strategies and personalized treatment approaches. In line with the principles for Predictive, Preventive, and Personalized Medicine (3PM/PPPM), implementing these findings would require establishing systematic screening protocols for vaginal microbiota composition during perimenopause and postmenopause. This screening would enable early identification of women at risk of dysbiosis before symptom onset, allowing for timely interventions. The ultimate goal is to develop evidence-based, personalized treatment approaches that can be tailored to individual risk profiles based on microbial indicators, thereby optimizing therapeutic outcomes through targeted interventions. Interventions for modulating vaginal microbiota during menopause as preventive strategies Hormonal therapy Hormone therapy is recognized as the most effective treatment for VMS and GSM. Systemic hormone therapy is FDA-approved as the primary treatment for moderate to severe VMS associated with menopause and is widely considered the gold standard (Level 1 evidence) [4]. Low-dose vaginal estrogen therapy, a form of local hormonal treatment, is effective and safe. It is recommended for treating the GSM due to its minimal systemic absorption [3, 4]. Ospemifene and intravaginal dehydroepiandrosterone (DHEA) are among the non-estrogen FDA-approved therapies available for treating vulvovaginal atrophy in postmenopausal women (Level 1 evidence) [3, 4]. Previous evidence showed that the vaginal microbiota underwent significant changes in postmenopausal women, and these changes were associated with ovarian function indicators. Additionally, the alterations of vaginal microbiota were even more pronounced in postmenopausal women experiencing the GSM, when compared to asymptomatic postmenopausal women, potentially playing a role in the development of GSM. For these reasons, hormonal treatments for relieving VSM and GSM appeared to impact the vaginal microbiota [33, 45–54], which might help normalize the microbial communities in atrophic vaginitis, making them resemble those of healthy controls [33]. Studies investigating the impact of hormonal therapy on the vaginal microbiota during postmenopause are comprehensively summarized in Table 6. Recent advances in understanding the intricate relationship between hormonal interventions and vaginal microbiome composition revealed promising opportunities for developing preventive and personalized therapeutic approaches for menopausal women. The modulation of vaginal microbiota through hormonal interventions represented a crucial intersection of endocrine and microbial mechanisms, where targeted treatments can potentially restore and maintain optimal vaginal health. Table 6. | Participant (n), [mean/ median age (years)] | Bacterial identification technique | Vaginal microbiome diversity | Vaginal microbiota taxonomic classification | Other findings | Interpretation | Reference | || |---|---|---|---|---|---|---|---|---| | Intervention group (n), [Age], [duration of treatment (week)] | Genus and Species level | ||||||| | Control group (n), [Age], [duration of treatment (week)] | Lactobacillus dominated | Non-Lactobacillus dominated | |||||| | 1. Local hormonal treatment | |||||||| | Postmenopausal women with moderate to severe GSM (144), [61] | Pos hoc analysis [For each intervention, the ratio of responders (individuals with a decrease in MBS of 2 or more points) to non-responders was equal.] | Vaginal estrogen tablets and moisturizing gels decreased alpha diversity and pathogenic bacteria, increasing Lactobacillus. Only the estrogen group significantly affected beta diversity, reduced vaginal pH, and raised lactate metabolites | [45] | ||||| | Vaginal estradiol tablet 0.01 mg with placebo gel (45), [12, 61] | 16S rRNA gene | ↓ Shannon diversity β-diversity change after treatment | ↑ Lactobacillus | ↑ Bifidobacterium ↓ Streptococcus mitis ↓ Peptoniphilus spp. ↓ Anaerococcus vaginalis ↓ Finegoldia magna | ↓ Vaginal pH ↑ Lactate ↑ Indole-3-lactate ↑phenyl-lactate metabolites | ||| | Vaginal moisturizing gel with placebo tablet (48), [12, 60] | ↓ Shannon diversity no β-diversity change after treatment | ↑ Lactobacillus acidophilus ↑ Lactobacillus kitasatonis | ↓ S mitis ↓ Prevotella timonensis ↓Peptoniphilus coxii ↓A.vaginalis | ↔ Vaginal pH ↔ Metabolites | |||| | Dual placebo (51), [12, 61] | As control group | ||||||| | Postmenopausal women with genitourinary syndrome of menopause and VMI of ≤ 40% (87) | Semiquantitative under microscope | - | Day 5–7 ↔ Lactobacillus Day 14–16 ↑ Lactobacillus | - | Day 5–7 ↔ VMI ↔ Total symptom score Day 14–16 ↑ VMI ↓ Total symptom score ↓ Vaginal pH | The estriol-Lactobacilli combination increased Lactobacillus levels and VMI, and reduced symptoms and pH of vaginal atrophy, with initial improvements evident after 12 days of therapy. Long-term use of two vaginal tablets effectively sustained these benefits | [46] | | | Gynoflor® vaginal tablet (44), [60.93] (Medinova AG, Switzerland) containing 0.03 mg estriol and 108 colony-forming units of Lactobacillus acidophilus KS400 | |||||||| | Administered daily for 12 days then one tablet on 2 consecutive says weekly for 12 weeks (maintenance phase) | 2 weeks of maintenance ↑ Lactobacillus 12 weeks of maintenance ↑ Lactobacillus | - | 2 weeks of maintenance ↑ VMI ↓↓ total symptom score 12 weeks of maintenance ↑ VMI ↓↓ total symptom score | ||||| | Placebo vaginal tablets (43), [62.34] | As control group | ||||||| | Postmenopausal women with urinary tract infection during previous year 0.5 mg of estriol in vaginal cream Once each night for 14 days followed by twice weekly application for 8 months (36), [64.7] | Vaginal culture | - | ↑ Lactobacillus | ↓ Enterobacteriaceae | ↓ Vaginal pH Relationship ↑Lactobacillus ↓ UTI ↑Enterobacteriaceae ↑UTI | Vaginal estriol cream lowered vaginal pH, increased Lactobacillus, and decreased Enterobacteriaceae, both of which were correlated with UTI prevention | [47] | | | Placebo cream (24), [65.4] | |||||||| | Postmenopausal women Estring 2 mg vaginal estradiol ring (19), [62.6], [12] | V4 region of 16S rRNA gene | ↔ α-diversity Secondary analysis + ↔ α-diversity | ↔ Lactobacillus Secondary analysis + ↑ Lactobacillus | ↑ VMI ↔ vaginal pH ↔ validated questionnaire score (PFDI-20, FSFI, VSQ) | The vaginal estrogen ring did not affect alpha diversity but may enhance Lactobacillus levels, potentially improving the Vaginal Maturation Index | [48] | || | + exclude participant who removed their ring before the end of the study or had urinary tract infection | |||||||| | Placebo vaginal ring (20), [60.7], [12] | As control group | ||||||| | Postmenopausal women received 0.01% vaginal estradiol cream (17), [60.6], [4] | Semiquantitative vaginal cultures | - | ↔ Lactobacillus | ↔ Gardnerella | ↓ vaginal pH ↔ Nugent score | Vaginal estradiol cream did not affect Lactobacillus, Gardnerella levels, or the Nugent score, but could lower vaginal pH | [18] | | | 2. Systemic hormonal treatment, Hormone mimicking compounds, and Selective estrogen receptor modulator | |||||||| | Postmenopausal women with overactive bladder symptoms Conjugated estrogen cream (0.625 mg/gram of cream) (Premarin, Pfizer, New York City, NY) 0.5 g twice weekly (41), [12, 68] | Expanded quantitative culture | ↓ Shannon index ↓ Species richness | ↔ Lactobacillus | - | ↓ overactive bladder severity symptom score | Estrogen cream reduced alpha diversity and species richness, potentially improving Overactive Bladder symptoms, without altering Lactobacillus levels | [49] | | | Postmenopausal women with atrophic vaginitis Low dose (0.3 mg) conjugated estrogen (Premarin, Wyeth) orally once per day (30), [55.8], [4] | 16S rRNA gene | ↓ Shannon diversity β-diversity change after treatment | ↑ Lactobacillus | ↓ Gardnerella ↓ Prevotella ↓ Atopobium | ↑ VMI ↓ Vaginal pH ↓ GSM symptom scores | Low-dose oral estrogen modified microbial diversity by enhancing Lactobacillus and reducing anaerobic bacteria, thereby improving the VMI, reducing GSM symptoms, lowering pH, and normalizing microbial communities in atrophic vaginitis to resemble those of healthy controls | [33] | | | Postmenopausal women with Oral Premarin (conjugated equine estrogen in combination with progesterone) (41), [21–82], [average using time of 13 years (from 2–30 years)] | PCR-DGGE | - | ↑ Lactobacillus L.crispatus > L.iners > L.gasseri > L. jensenii (Recovery of Lactobacillus species at each sampling time in every HRT subject) | ↓ Bacteroides ↓Prevotella ↓Gardnerella ↓Escherichia coli ↓Mobiluncus | ↓ Nugent score ↓ BV incidence | Oral estrogen increased Lactobacillus levels and reduced pathogenic bacteria, potentially decreasing the incidence of bacterial vaginosis | [51] | | | Age-matched control group (20), [NA], [-] | As control group | ||||||| | Postmenopausal women on oral Premarin estrogen replacement therapy (oral conjugated estrogen) for ≥ 2 months (10) | PCR-DGGE analysis *PCR species-specific primers | - | ↑ Lactobacillus (L.iners > L.crispatus > L. jensenii > L.gasseri) | ↓ Gardnerella vaginalis ↓ Streptococus agalactiae ↓ Escherichia coli | ↓ Nugent score ↓ BV | Oral conjugated estrogen therapy increased Lactobacillus levels, decreased pathogenic microflora associated with BV, and reduced symptoms of BV | [52] | | | *PCR species-specific primers detected more G. vaginalis compared to PCR-DGGE, likely due to the selection of eubacteria primers | |||||||| | Postmenopausal women without hormonal replacement therapy (10) | As control group | ||||||| | Postmenopausal women with Regular tibolone, a synthetic steroid, 2.5 mg orally per day at least in the past year (50), [54.76], [NA] | V3-V4 hypervariable region of 16S rRNA gene | ↓ α-diversity ↓ Shannon index ↓ Ach richness ↓ Chao ↑ Simpson diversity index Difference β -diversity | ↑ Lactobacillus | ↓ Gardnerella ↓ Prevotella ↓ Streptococcus ↓Escherichia-Shigella ↓ Atopobium ↓ Sneathia ↓ Bifidobacterium ↓ Aerococcus ↓ Anaerotruncus ↓ Anaerococcus | ↓GSM symptoms | Tibolone reduced alpha diversity and significantly altered beta diversity, increased Lactobacillus, and decreased pathogenic bacteria, potentially improving GSM symptoms | [50] | | | Postmenopausal women with no treatment (50), [53.34], [NA] | As control group | ||||||| | Postmenopausal women (58) Isoflavone (19), [16, 53] 150 mg of isoflavone 1 capsule once daily | Gram-stained and microscopy analysis | - | Compare to baseline ↔ Lactobacillus | - | ↔ Total urogenital symptoms ↑ VHI ↔ Vaginal pH ↔ FSH Estradiol | Isoflavone alone or with probiotics did not impact vaginal Lactobacillus, pH, urogenital symptoms, or serum FSH and estradiol levels, though it improved the VHI. Conversely, hormone therapy increased Lactobacillus, improved urogenital symptoms and VHI, altered vaginal pH, and affected serum FSH and estradiol levels | [53] | | | Isoflavone plus probiotic (20), [16, 52] 150 mg of isoflavone plus 1 pack of probiotics containing 109 CFU of L.acidophilus, L.casei, L.lactis, Bifidobacterium bifidum, and Bifidobacterium lactis once daily | Compare to baseline ↔ Lactobacillus | - | ↔ Total urogenital symptoms ↔ VHI ↔ Vaginal pH ↔ FSH Estradiol | ||||| | Hormone therapy (19), [52.2], [16] One table of 1 mg estradiol and 0.5 mf of norethisterone acetate once daily | Compare to baseline ↑ Lactobacillus | - | ↓ Total urogenital symptoms ↑ VHI ↓ Vaginal pH ↓ FSH ↑ Estradiol | ||||| | Compare among treatments ↔ mean change of MRS symptoms | |||||||| | Postmenopausal women with VVA (39), [53] | V3-V4 hypervariable region of 16S rRNA gene | Compared to the control group ↔ α-diversity ↔ β-diversity | - | - | - | Ospemifene and hormonal therapy similarly reduced alpha and beta diversity and decreased certain anaerobic bacteria levels, potentially enhancing the VHI and VMI | [54] | | | Compare posttreatment to baseline | |||||||| | ↓α-diversity ↔ β-diversity | ↔ Lactobacillus | ↓ Staphylococcus ↓ Clostridium ↔ Corynebacterium (↓ trend) ↔ Alloscardovia (↑ trend) ↔ Bifidobacterium (↑ trend) | ↑VHI ↑VMI | ||||| | Hormonal therapy group (HT) as control group (19), [12, 51] - Estradiol 1 mg with drosperinone 2 mg - Transdermal estradiol 50 µg/24 h combined with oral progesterone 200 mg | ↔ α-diversity (↓ trend) ↔ β-diversity | ↔ Lactobacillus (↑ trend) | Genus level: ↓Lachnospira ↓Acinetobacter ↓Achromobacter ↔ Bifidobacterium (↑ trend) ↔ Streptococcus (↓ trend) ↔ Prevotella (↓ trend) Species level: ↓ Prevotella disiens ↓ Ureaplasma parvum | ↑VHI ↑VMI | |||| | 3. Vaginal Microbiota Based on Symptom Improvement (Responder Vs Nonresponder) After Receiving Hormonal Therapy | |||||||| | Postmenopausal women with moderate to severe GSM (120), [NA] | Responders showed higher alpha diversity and vaginal pH at the start, with no significant differences in beta diversity and Lactobacillus levels compared to non-responders | [61] | |||||| | Responder ≥ 2-point decrease in symptom severity (60), [NA], [12] - Treatment 1: 0.01 mg Vaginal estradiol tablet for 2 weeks then 2 times/week (20), [NA] [12], - Treatment 2: Moisturizer 3 times/week (20), [NA] [12], - Placebo 12 weeks (20), [NA] [12], | V3-V4 region of 16S rRNA gene | At Baseline ↑ Shannon index ↔ β –diversity After treatment ↔ α-diversity ↔ β -diversity | At Baseline ↔ Lactobacillus After treatment ↔ Lactobacillus | - | At Baseline ↑ Vaginal pH ↔ VMI After treatment ↔ Vaginal pH ↔ VMI | ||| | Nonresponder ≤ 1 point decrease in symptom severity (60,) [NA], [12] - Treatment 1 (20), Treatment 2 (20), and Placebo (20) | As control group | ||||||| | Postmenopausal women with GSM+ (30), [NA] | Responders did not differ in alpha diversity, beta diversity, and Lactobacillus levels from non-responders, but exhibited increased vaginal glycogen and serum estradiol levels after treatment | [63] | |||||| | Responder: improvement of symptom ≥ 1 point decrease in MBS severity score (21), [10, 53] - Oral estradiol 17-beta-estradiol 0.5 mg/day for 8 weeks with medroxyprogesterone 10 mg/day orally for 14 days (7), [NA], [10] - Venlafaxine 37.5 mg/day for one week then 75 mg/day for 7 weeks tapered to 37.5 mg/day for another 14 days (7), [NA], [10] - Placebo (7), [NA], [10] | V3-V4 hypervariable region of 16S rRNA gene | At baseline ↔ α-diversity ↔ β-diversity At 4 weeks after treatment ↔ α-diversity ↔ β-diversity At 8 weeks after treatment ↔ α-diversity ↔ β-diversity | At baseline (pretreatment) ↔ Lactobacillus ↔ L.crispatus ↔ L.iners At 4 weeks after treatment ↔ Lactobacillus ↔ L.crispatus ↔ L.iners At 8 weeks after treatment ↔ Lactobacillus ↔ L.crispatus ↔ L.iners | - | At 8 weeks after Treatment ↑ Vaginal glycogen ↑ Serum unconjugated & total estradiol | ||| | Nonresponder: no improvement of symptoms < 1 point decrease in MBS severity score (9), [10, 54] - Oral estradiol (1), [NA], [10] - Treatment 2 (2), [NA], [10] - Placebo (6), [NA], [10] | As control group | At 8 weeks after Treatment ↔ Vaginal glycogen ↔ serum estradiol | The arrow symbols ↑, ↓, and ↔ denote a significant increase, decrease, and no significant change. The arrows ↑↑ ↓↓ denote the highest and lowest findings, respectively. The hyphen symbol (-) indicates that no information is available, mentioned, or described. Abbreviations: GSM, genitourinary syndrome of menopause; VVA, vulvovaginal atrophy; MBS, most bothersome symptom; VMI, vaginal maturation index; PFDI-20, Pelvic Floor Distress Inventory-20; FSFI, Female Sexual Function Index; VSQ, Vaginal Symptom Questionnaire; VHI, Vaginal Health Index; BV, bacterial vaginosis; UTI, urinary tract infection; PCR-DGGE, polymerase chain reaction-denaturing gradient electrophoresis; SERMs, selective estrogen receptor modulators, MRS, menopause rating scale Local hormonal therapy Vaginal estrogen therapy was found to be able to decrease alpha diversity [45, 55] and significantly altered the beta diversity of the vaginal microbiota [45]. However, one study did not observe changes in alpha diversity [48]. These contradictory findings might be due to differences in participant characteristics. The latter study included postmenopausal women regardless of whether they had GSM [48], while the others included only postmenopausal women with GSM symptoms [45, 55]. As previously discussed, a higher abundance of CST IV and a lower abundance of Lactobacillus-dominated CSTs were associated with more severe symptoms of the GSM. Hence, local estrogen therapy appeared to be beneficial by increasing Lactobacillus [45–48, 55, 56] and decreasing CST IV [45, 47, 55]. Interestingly, these microbial changes were accompanied by the improvement of vaginal symptoms [46] and vaginal health markers, including decreased vaginal pH, increased VMI, and increased VHI [18, 45–48]. One study observed the improvement of VMI without corresponding changes in GSM symptoms, potentially due to the inclusion of both postmenopausal women with and without GSM symptoms [48]. Some evidence also demonstrated that vaginal estrogen therapy increased Lactobacillus and decreased Enterobacteriaceae, and these findings were associated with the prevention of urinary tract infection [47]. In contrast, one study did not find significant changes in the vaginal microbiota except for a decrease in vaginal pH following local estrogen therapy [18]. These discrepancies might be due to the inclusion of postmenopausal participants without abnormal symptoms, the limited sample size, and the bacterial identification technique used in this study: semiquantitative vaginal culture [18]. This technique has low sensitivity for microbial detection, and thus may not be able to detect non-culturable and fastidious organisms present in low abundances [57–60]. The evidence suggested that local hormonal therapy had served as a promising intervention for modulating vaginal microbiota during menopause, demonstrating its ability to normalize microbiome composition through increasing Lactobacillus dominance and reducing dysbiotic patterns associated with CST IV. While the therapy had effectively improved both microbiome composition and clinical symptoms, the varying responses between studies that included only GSM women and those that included both women with and without GSM symptoms highlighted the importance of personalized therapeutic approaches, as baseline patient characteristics might influence treatment outcomes. This differential response pattern also underscored the potential value of vaginal microbiome profiling not only as a diagnostic tool but also as a precision medicine marker for monitoring treatment efficacy and guiding individualized therapy decisions. The dynamic relationship between microbiome changes and clinical improvements suggested that regular microbiome assessment could serve as a valuable biomarker for tracking therapeutic response and optimizing treatment strategies, particularly in cases where traditional clinical markers might not fully capture the biological improvements occurring at the microbial level. When comparing the effect of local vaginal hormonal therapy between treatment responders (those with improved GSM) and non-responders, it was shown that the responders had higher baseline alpha diversity than that of the non-responders [61]. This finding further underscored that baseline vaginal microbiome diversity could serve as a predictive biomarker for treatment response, offering valuable insights for personalized medicine approaches. Healthcare providers could potentially utilize initial microbiome diversity assessments to stratify patients and customize treatment strategies, ultimately optimizing therapeutic outcomes through more targeted interventions. Systemic hormonal therapy Systemic hormone therapy is a widely used treatment for managing menopausal symptoms and preventing long-term health consequences associated with estrogen deficiency in postmenopausal women. The primary component of systemic hormone therapy is estrogen, which is available in various formulations for individual needs and preferences. Conjugated equine estrogens (CEE) are one of the most common estrogen preparations used for hormone therapy. In addition to CEE, synthetic conjugated estrogens are another option for hormone therapy. 17β-Estradiol, a formulation that is chemically identical to the estradiol produced by the ovaries, is also used for hormone therapy. Ethinyl estradiol, a synthetic estrogen, is primarily found in hormonal contraceptives but may be used in certain hormone therapy regimens. To mitigate the risk of endometrial hyperplasia and cancer, progestogens are often combined with estrogen preparations as a common hormone therapy regimen in postmenopausal women with an intact uterus [4]. Systemic hormonal therapy was found to decrease alpha diversity [33, 49] and significantly changed the beta diversity of the vaginal microbiota [33]. Indeed, this therapy increased Lactobacillus species and decreased facultative anaerobes associated with CST IV [33, 51–53]. However, one study did not observe changes in bacterial taxa, which might be attributed to the bacterial identification technique used: expanded quantitative culture [49]. Interestingly, the microbial changes were accompanied by the improvement of GSM [33, 49, 53]. Additionally, systemic hormonal therapy could enhance vaginal health markers, including decreasing vaginal pH [33, 53], increasing VHI [53], and increasing VMI [33]. It also corrected an imbalanced vaginal ecosystem, as indicated by a decrease in the Nugent score [51, 52]. In detail, the Nugent score evaluates the presence and quantity of different bacterial morphotypes, including Lactobacillus species, Gardnerella vaginalis, and other anaerobes. The score ranges from 0 to 10, in which 0–3 is considered normal, 4–6 indicating intermediate flora, and 7–10, suggesting bacterial vaginosis [62]. Hence, by decreasing the Nugent score, systemic hormonal therapy reduced the incidence of bacterial vaginosis [51, 52]. Overall, this therapy supported the recovery of Lactobacillus species at each sampling time [51] and normalized microbial communities in atrophic vaginitis to be comparable to those of healthy controls [33]. These findings demonstrated the significant preventive and therapeutic potential of systemic hormone therapy in modulating vaginal microbiota during menopause through its ability to normalize microbial communities and restore vaginal health to a state comparable to healthy controls. The parallel improvements observed between vaginal microbiome restoration (characterized by increased Lactobacillus dominance, decreased CST IV patterns, and reduced Nugent scores) and clinical parameters (including GSM symptoms, vaginal pH, VHI, and VMI) reinforced the intricate relationship between vaginal microbiota composition and genitourinary health. This correlation not only supported the role of vaginal microbiome assessment as a valuable biological marker for monitoring therapeutic response but also highlighted its potential utility in clinical practice for tracking treatment efficacy. When comparing the effects of systemic hormonal therapy between treatment responders (those with improved GSM) and non-responders, it was observed that there were no significant differences in the vaginal microbiota composition between these two groups, both before and after treatment [63]. However, the responders exhibited higher levels of serum estradiol and vaginal glycogen [63]. Vaginal glycogen, produced by the epithelial cells under the influence of estrogen, is critical for maintaining vaginal health. It serves as a primary nutrient source for Lactobacillus species. These beneficial bacteria convert glycogen into lactic acid, which helps maintain a low vaginal pH and inhibits the growth of pathogenic bacteria and fungi. Consequently, the presence of glycogen and its conversion into lactic acid by Lactobacilli are vital for maintaining a healthy vaginal environment after treatment [12, 64]. These findings offered crucial insights for personalized therapeutic approaches by revealing that serum estradiol and vaginal glycogen levels, rather than baseline microbiota composition, differentiated between treatment responders and non-responders. This understanding suggested that pretreatment hormonal and metabolic markers could serve as predictive indicators for treatment response, enabling more targeted therapeutic strategies and optimized patient care through evidence-based patient stratification. Hormone-mimicking compounds Tibolone Tibolone, a synthetic steroid used mainly in postmenopausal hormone therapy, exhibits estrogenic, progestogenic, and androgenic activities, mimicking natural hormones like estrogen, progesterone, and testosterone. Classified as a selective tissue estrogenic activity regulator, it affects the brain, vagina, and bones but does not impact the endometrium and breast. Tibolone is metabolized in the liver and intestines into 3 alpha-hydroxy (3α-OH), 3 beta-hydroxy (3β-OH), and a Δ4-isomer. The 3α-OH and 3β-OH metabolites exert estrogenic effects, whereas the Δ4-isomer shows progestogenic and androgenic properties, with variations in activity due to tissue-specific metabolism. Tibolone efficiently alleviates menopausal symptoms, offering benefits for mood, sleep, and concentration, as well as improving bone density. Unlike traditional therapies, it has a lower incidence of abnormal vaginal bleeding and breast pain. It also enhances skeletal muscle mass without affecting venous thromboembolism or coronary artery disease [65, 66]. Tibolone positively influenced vaginal microbiota by modifying its composition, as evidenced by changes in beta diversity, particularly an increased abundance of Lactobacillus and a reduction in pathogenic anaerobes. It also decreased alpha diversity, indicating a shift toward a more stable and less complex microbial community. Interestingly, these microbiological changes were associated with the improvement of GSM [50]. Tibolone effectively normalized the vaginal microbiome, with these microbial changes correlating directly with symptom improvement. This dual action highlighted two significant clinical implications. First, it suggested potential use as a preventive intervention. Second, the positive shifts in vaginal microbiome composition toward a more premenopausal-like state, along with corresponding improvements in vaginal health indices, further emphasized that microbiome analysis could serve as a novel biomarker for monitoring treatment response and therapeutic efficacy. This molecular-level indicator complemented traditional clinical assessments and provided an objective measure of therapeutic success. Isoflavones Isoflavones are a type of phytoestrogen. These plant-derived compounds are predominantly found in soybeans and other legumes. Isoflavones can interact with estrogen receptors, mimicking the effects of estrogen. In detail, they have an affinity for estrogen receptors alpha (ERα) and beta (ERβ) and exhibit both agonistic and antagonistic activities toward them [53]. Isoflavones showed no beneficial effect on vaginal microbiota, total urogenital symptoms, vaginal pH, FSH level, and estradiol levels. However, they did increase VHI. Nevertheless, the improvement of VHI was not observed when isoflavones were combined with probiotics. This lack of observed changes might be due to the bacterial identification techniques used: gram staining and microscopy analysis [53]. These results were also consistent with those from a systematic review, in which they suggested that isoflavones exerted no benefit in the improvement of urogenital symptoms [67]. Current evidence did not support the role of isoflavones in normalizing vaginal microbiome or improving vaginal health indices. Future investigations with larger sample sizes and more advanced bacterial identification techniques are warranted to fully elucidate their potential therapeutic benefits in postmenopausal women. Selective estrogen receptor modulator (SERM) Ospemifene Ospemifene is a SERM that acts as both an estrogen agonist and antagonist, depending on the tissue [54]. It exhibits estrogenic effects on vaginal tissue and bone, has a neutral to weak estrogenic effect on endometrial tissue, and exerts an anti-estrogenic effect on breast tissue [68]. Ospemifene is one of the therapies approved by the FDA for the treatment of vulvovaginal atrophy in postmenopausal women [3, 4]. The impact of ospemifene on vaginal microbiota was comparable to that of systemic hormonal therapy, in which no significant differences in microbiota diversity and composition between the two treatment groups [54]. Following ospemifene treatment, lower alpha diversity and reduced abundance of non-Lactobacillus facultative anaerobic bacteria were demonstrated. These were accompanied by the improvement of VHI and VMI. Although significant alterations of Lactobacillus species were not observed, both ospemifene and systemic hormonal treatment showed a non-significant increase in Lactobacillus in the study [54]. These findings demonstrated that ospemifene provided an additional preventive and therapeutic option for normalizing vaginal microbiome in postmenopausal women. Non-hormonal interventions The modulation of vaginal microbiota during menopause emerged as a potential preventive strategy for maintaining urogenital health. While hormonal interventions directly addressed estrogen deficiency, non-hormonal approaches offered alternative or complementary options for microbiome modulation. Several interventions were believed to impact the vaginal microbiota, either beneficially or detrimentally. Beneficial interventions, such as probiotics and vaginal energy-based therapies, were used to alleviate the GSM. Conversely, intravaginal practices and vaginal pessaries were thought to disturb the balance of the vaginal ecosystem by introducing foreign substances, posing potential risks for abnormal vaginal symptoms, including discharge, bacterial vaginosis, or ulcers. The relevant studies on the impact of non-hormonal interventions during postmenopause are summarized in Table 7. Understanding these interventions’ impacts on vaginal microbiota provided opportunities for targeted preventive strategies and personalized therapeutic approaches. Table 7. | Participant (n), [mean/ median age (years)] | Bacterial identification technique | Vaginal microbiome diversity | Vaginal microbiota taxonomic classification | Other findings | Interpretation | Reference | |||||| |---|---|---|---|---|---|---|---|---|---|---|---|---| | Intervention group (n),[Age],[duration of treatment (week)] | Genus and Species level | ||||||||||| | Control group (n), [Age], [duration of treatment (week)] | Lactobacillus dominated | Non-Lactobacillus dominated | |||||||||| | 1. Probiotic | |||||||||||| | Postmenopausal Japanese women (35), [NA] | V1 and V2 region of 16S rRNA gene | Lactobacillus-containing vaginal gel reduced pathogenic microflora and lowered vaginal pH, with a non-significant trend toward lowering alpha diversity and GSM symptoms. In contrast, externally applied Lactobacillus-containing wash and cream lessened GSM symptoms but seemed to disturb alpha diversity by increasing it, without notably affecting pathogenic microflora or pH | [15] | ||||||||| | Apply Delicate Softwash®+ then Delicate Softgelcream®+ at external genitalia once a day (16), [59.88], [4] | ↑ α-diversity | ↔ | ↔ Pathogenic flora | ↔ Vaginal pH ↓ GSM | |||||||| | Apply Inner gel®+ intravaginally once every three days before bedtime in addition to applying Delicate Softwash®+ and Delicate Softgelcream®+ at external genitalia once a day (14), [59.5], [4] | ↔ α-diversity (trend to ↓) | ↔ | ↓ Pathogenic flora | ↓ Vaginal pH ↔ GSM (trend to ↓) | |||||||| | + All products contain Lactobacillus rhamnosus vitaP1 and Lactobacillus plantarum KCTC3108] | |||||||||||| | Control (5), [60.8], [4] | ↔ α-diversity | ↔ | ↔ Pathogenic flora | ↔ Vaginal pH ↔ GSM | |||||||| | Postmenopausal women with genitourinary syndrome of menopause (43), [59 8] A probiotic capsule containing 3 × 109 CFU/g (1 × 109 CFU/g of each probiotic strain) Lactiplantibacillus plantarum PBS067 (DSM 24937), Bifidobacterium animalis subsp. Lactis BL050 (DSM 25566), and Lacticaseibacillus rhamnosus LRH020 (DSM25568) Oral once daily (43), [59 8], [4] | V3 to V4 region of 16S rRNA gene | ↔ α-diversity ↔ β-diversity | ↔ Lactobacillus | ↓ Staphylococcus | ↑ VHI ↓ Vaginal pH | The oral probiotic helped restore the vaginal microbiota, evidenced by a significant reduction in CST IV, particularly in Staphylococcus, and a tendency to increase the Lactobacillus-dominant community | [69] | ||||| | Pretreatment: CST IV > III > I > IVC3 Posttreatment: CST IV (↓) > III (↑) > I (↑) > IVC3 (↑) > V (↑) | |||||||||||| | Postmenopausal women with intermediate Nugent score (4–6) (14), [NA] | V6 region of 16S rRNA gene | ↔ α-diversity | ↑ Lactobacillus | ↑ Staphylococcus ↓ Atopobium ↔ Gardnerella (trend to ↓) ↔ Prevotella (trend to ↓) | ↔ Nugent score ↔ vaginal pH ↔ Lactate (trend to ↑) | Vaginal probiotics increased Lactobacillus and Staphylococcus, reduced pathogenic Atopobium, and had no significant impact on alpha diversity, the Nugent score, vaginal pH, or lactate levels | [70] | ||||| | L. rhamnosus GR-1/L. reuteri RC-14 probiotic (Chr. Hansen, minimum 2.5 × 109 CFU of each strain) intravaginally twice a day (7), [NA], [3 days] | |||||||||||| | Visually identical placebo (49 mg Gelatin and 1 mg titanium dioxide) intravaginally twice a day (7), [NA], [3 days] | - | - | ↑ Streptococcus ↑ Staphylococcus | ↔ Nugent score ↔ vaginal pH ↔ Lactate (trend to ↓) | |||||||| | Postmenopausal women with Nugent scores between 4 and 6 (72), [57.6] Probiotic capsules containing 2.5 × 109 CFU each of lyophilized L. rhamnosus GR-1 and L. rheuteri RC-14 as well as sodium alginate oral once daily (35), [NA], [2] | Gram-stained and microscopy analysis by using the Nugent scoring system | - | - | - | ↓ Nugent score | Oral probiotics improved the Nugent score, indicating improved vaginal microbiome health by decreasing signs of bacterial vaginosis and enhancing bacterial balance | [71] | ||||| | Oral lactose placebo once daily (37), [NA], [2] | |||||||||||| | Postmenopausal women with breast cancer receiving chemotherapy (22), [NA] | Gram-stained and microscopy analysis by using the Nugent scoring system | 1 day after the last capsule | Oral probiotics demonstrated the potential to improve the Nugent score and enhance the protective effect of vaginal microbiota in immunocompromised breast cancer patients, without causing adverse effects | [72] | |||||||| | Probiotic capsule containing 2.3 × 109 colony-forming unit each of lyophilized L. crispatus LbV88, L. rhamnosus LbV96, L. jensenii LbV 116 and L. gasseri LbV 150N twice daily oral intake | - | - | - | ↔ Nugent score ( trend to ↓) One 1 week after the last capsule ↔ Nugent score (trend to ↓ from baseline but ↑ from 1 day after the last capsule) | |||||||| | Placebo medication twice daily oral intake | - | - | - | ↑ Nugent score | |||||||| | 2. Vaginal energy-based therapies | |||||||||||| | Postmenopausal women (55), [59.8] | Gram-stained and microscopy analysis | ↑ Lactobacillus | - | ↓ Vaginal pH ↑ VMI ↑ VHI One adverse event: burning and redness lasted 2–3 days | Microablative fractional radiofrequency treatment increased Lactobacillus spp. Levels, potentially improving the VHI, VMI, and reducing vaginal pH | [116] | |||||| | Microablative fractional radiofrequency* (Loktal Medical Electronics, Sao Paulo, Brazil) applications in vagina/ vaginal introitus 3 times with intervals of 30 days | - | ||||||||||| | *Microablative fractional radiofrequency (MAFRF) Wavetonic 6000 Touch device used with Megapulse HF FRAXX system with a vaginal pen with 64 microneedles, 200µ in diameter and 1 mm in length | |||||||||||| | Postmenopausal women with vulvovaginal atrophy (VHI ≤ 15) (120), [59] Microablative fractional radiofrequency* (Loktal Medical Electronic) applications in vagina/ vaginal introitus 3 times with intervals of 30 days | Gram-stained and microscopy analysis | - | ↑ Lactobacillus | - | ↓ Nugent score ↑ VHI ↑ Sexual desire ↑ VMI | Radiofrequency treatment was as effective as local estrogen therapy in increasing Lactobacillus levels and improving VHI, VMI, sexual desire, and Nugent score | [56] | ||||| | Vaginal estrogen: 1 gm of Estradiol 3-propy 17β-methyl diether-based vaginal cream twice weekly for 3 months | ↑ Lactobacillus | - | ↓ Nugent score ↑ VHI ↑ Sexual desire ↑ VMI | ||||||||| | Control group (40), [61] | As control group | ||||||||||| | *Microablative fractional radiofrequency (MAFRF) Wavetonic 6000 Touch device used with Megapulse HF FRAXX system with a vaginal pen with 64 microneedles, 200µ in diameter and 1 mm in length | |||||||||||| | Postmenopausal women with breast cancer with genitourinary syndrome of menopause (20), [58.2] | V3 region of 16S rRNA gene | ↔ α-diversity ↔ relative abundance of bacteria taxonomic group | ↔ Lactobacillus | ↔ Prevotella ↔ Aerococcus ↔ Streptococcus ↔ Enterococcus ↔ Haemophilus ↔ Bacteroides | ↑ VHI ↑ FSFI ↔ FSDSr | Fractional CO2 laser treatment had no impact on alpha diversity or bacterial abundance but improved the Vaginal Health Index and Female Sexual Function Index | [117] | ||||| | Fractional CO2 laser* (SmartXide2 V2LR, Monalisa Touch, DEKA, Florence, Italy) vaginal applications 2 times with intervals of 30 day | |||||||||||| | *Fractional CO2 laser system with vaginal probe at the Service of Laser Surgery and Colposcopy Daimi, AOU Careggi, Florence Setting: 30-W power, dwell time 1000 µs, dot spacing 1000 µm and smart stack parameter of 1, total exposure time 3–5 min | |||||||||||| | Postmenopausal women with genitourinary syndrome of menopause (VHI ≤ 15, FSHI ≤ 26.55) (64), [51] CO2 laser* (Hunan Zexin Medical Equipment Co., Ltd., model: JZ-3D) vaginal application of 2–3 laser sessions spaced at intervals of 4 ± 1 weeks (21) | V3 to V4 region of 16S rRNA gene | ↓ α-diversity++ Difference β-diversity | ↑ Lactobacillus | ↑ IheB3_7 ↑ Trichoderma ↑ Rumex ↑ Rhodobacter ↓ Streptococcus ↓ Aerococcus ↓Bifidobacterium ↓Peptostrptococcus ↓Atopobium ↓ Veillonella ↓Mageibacillus ↓ Dilaster ↓Anaerococcus ↓ Fastidodipila ↓Faecalibacyerium ↓ Carboxylicivirga ↓ Bacillus ↓Candidatus_ Saccharimonas | Compared to the estrogen group ↔ Collagen type I ↑Collagen type III Compare to baseline At 3 months ↑ VHI score ↑ Elasticity score ↑ Moisture score ↓UDI-6 score | CO2 laser therapy matched vaginal estrogen therapy in modulating vaginal microbiota, reducing alpha diversity, increasing Lactobacillus species, and improving GSM symptoms, with longer-lasting effects At 1 year ↑ VHI score ↑ Elasticity score ↑ Moisture score ↓UDI-6 score ↑ Vaginal mucosal density | [55] | ||||| | Vaginal estriol suppositories daily with a dosage of 0.5 mg for 3 months (21) | ↓ α-diversity Difference β-diversity | ↑ Lactobacillus | ↑ Streptococcus | At 3 months ↑ VHI score ↑ Elasticity score ↑ Moisture score ↓UDI-6 score | At 1 year ↔ VHI score ↔ Elasticity score ↔ Moisture score ↔ UDI-6 score ↑ Vaginal mucosal density | ||||||| | Control group: not received any treatment (22) | As control | ||||||||||| | * CO2 laser, equipped with 360o probe scanning system Hiscan V2LR laser, power range of 35–40 W, dwell time of 800–1000 s, spot spacing of 800–1000 µm with an option for single or double stacking ++ ↓ Choa1 index, ACE index, Shannon index, Simpson index, OTU count | |||||||||||| | Postmenopausal women with vaginal atrophy symptoms (39) | Gram-stained and microscopy analysis | - | ↔ Lactobacillus | - | ↔ Vaginal atrophy symptoms ↓ Vaginal dryness ↓ Vaginal atrophy score ↔ Vaginal pH | The Er:YAG laser effectively reduced dryness symptoms and atrophy scores without altering Lactobacillus levels, total symptoms, or pH, compared to the sham operation, which only improved atrophy scores. Post-laser treatment, Lactobacillus levels increased, and atrophy symptoms and scores decreased, while pH remained stable | [118] | ||||| | Erbium in yttrium aluminum-garnet crystal (Er:YAG) laser* (Fotona Smooth™ XS, Fotona, Ljubjana, Slovenia) 2 times with interval of 4 weeks (20), [55.5] | |||||||||||| | Erbium in yttrium aluminum-garnet crystal (Er:YAG) laser | Compare posttreatment to baseline | ||||||||||| | ↑ Lactobacillus | - | ↓ Vaginal atrophy symptoms ↓ Vaginal atrophy score ↔ Vaginal pH | |||||||||| | Sham group as a control group (Same equipment but no energy was emitted) 2 times with an interval of 4 weeks (19), [60.4] | ↔ Lactobacillus | - | ↔ Vaginal atrophy symptoms ↓ Vaginal atrophy score ↔ Vaginal pH | ||||||||| | * Erbium in yttrium aluminum-garnet crystal (Er:YAG) laser at a wave length of 2940 nm, spot size of 7 mm, frequency of 1.6 Hz, laser energy of 6.0 J/cm | |||||||||||| | 3. Non-medical intravaginal device / practice | |||||||||||| | Postmenopausal women (21), [64.2] Intravaginal practice (IVP) (the insertion of a substance (i.e., soaps, deodorizers, powers, and other products which is not medication) via finger(s), cloth, or specialized tool(s) into the vagina) at least one type since menopause (11), [NA], [NA] | V3–V4 region of 16S rRNA gene | ↔ α-diversity ↔ β-diversity | ↔ Relative abundance at community and individual genus level (Abundance: Lactobacillus spp. > Prevotella spp.) | - | Intravaginal practices had no impact on the vaginal microbiota's alpha diversity, beta diversity, or relative abundance at both the community and genus levels | [123] | |||||| | No intravaginal practice (10), [NA], [NA] | As control group | ||||||||||| | Postmenopausal women with POP stage ≥ II (40) Continuous group (Continuous placement of pessary in the vagina without removal of pessary during nighttime) (12), [69.9][4] | 16S rRNA gene | ↔ Number of species | ↔ Lactobacillus | - | ↔ Sexual activity ↑ Severe POP (POP stage IV) ↑ Pessary size ↑ Vaginal discharge ↑ WBC of discharge ↔ Abnormal vaginal pH (≥ 5) | The continuous pessary had no impact on vaginal microbiota species richness, Lactobacillus levels, or vaginal pH. However, it was associated with an increase in vaginal discharge and white blood cell count | [129] | ||||| | Self-replacement group (Insert the pessary in the morning and remove it during the nightly shower) (28), [62.1], [4] | As control group | ||||||||||| | POP patients with ring pessary (13), [4, 68] * - Postmenopausal women (12), [4, 69] - Premenopausal women (1), [4, 37] * No patients were prescribed estrogen | 16S rRNA gene | ↓ number of bacterial species | ↑ Lactobacillus | ↓ other anaerobic bacteria | ↓ Vaginal pH | Pessary may alter the vaginal microbiome, leading to lesser species richness and other anaerobes but increase Lactobacillus-dominated environment | [127] | ||||| | Women used pessary with vaginal erosion (10), [77], [ median 3 years] * | V4 region of 16S rRNA gene | ↑ α-diversity Difference β-diversity | ↓ Lactobacillus | ↑ Pathologic flora | ↑ Vaginal pH | The use of pessaries did not alter the vaginal microbiome, but when vaginal erosion occurred during pessary use, it was linked to increased microbial diversity, altered microbiome composition with reduced Lactobacillus and more pathogenic bacteria | [42] | ||||| | Women used pessary (10), [72], [ median 3 years] * | ↔ α-diversity ↔ β-diversity | ↔ Lactobacillus | ↔ | ↔ Vaginal pH | |||||||| | Women with did not use pessary (10), [77] * *All women were using vaginal estrogen cream | As control group (not all had POP) | ||||||||||| | Korean women with POP with pessary (17), [79.7], [> 6 months] | V3–V4 regions of 16S rRNA gene | ↔ α-diversity ↓↓ Species richness Difference β-diversity | - | ↑ Fusobacterium ↑ Sneathia | ↓ gut microbiota contamination | Pessary use and reconstructive surgery led to changes in the vaginal microbiome, characterized by decreased species richness and reduced gut contamination, although there was an increase in some pathogenic flora | [125] | ||||| | Only CST IV | |||||||||||| | Korean women with POP with reconstructive surgery (11), [69.6], [> 6 months] | ↔ α-diversity ↓ Species richness Difference β-diversity | - | ↑ Prevotella | ↓ gut microbiota contamination | |||||||| | Only CST IV | |||||||||||| | Korean women with POP (42), [68] | As control | - | ↑ Staphylococcus ↑ Faecalibacterium ↑ Fastidiosipila | As control | |||||||| | CST IV > III > I > II | The arrow symbols ↑, ↓, and ↔ denote a significant increase, decrease, and no significant change. The hyphen symbol (-) indicates that no information is available, mentioned, or described. Abbreviations: GSM, genitourinary syndrome of menopause; VVA, vulvovaginal atrophy; MBS, most bothersome symptom; VMI, vaginal maturation index; FSFI, Female Sexual Function Index; FSDSr, Female Sexual Distress Scale-Revised; VHI, Vaginal Health Index; POP, pelvic organ prolapse; WBC, white blood cell Probiotics Probiotics were believed to have beneficial effects on conditions associated with alterations in vaginal microbiota, such as POI [24–26], the GSM [15, 69], and bacterial vaginosis [70–72]. These benefits were mediated by a restoration of normal vaginal microflora. One of the fundamental mechanisms underlying the role of probiotics in disease pathogenesis is their ability to modulate immune responses, inhibit pathogen colonization, facilitate nutrient metabolism and detoxification, and regulate energy homeostasis [73]. Probiotic strains demonstrated unique characteristics that affected metabolic health in various ways [74]. While certain strains contributed to improved insulin sensitivity, cholesterol reduction, and weight regulation, others had little to no impact or, in some cases, unintended consequences. This underscored the importance of selecting probiotic strains based on their specific properties and interactions with an individual’s microbiome to optimize metabolic outcomes [74]. Probiotics containing Lactobacillus species, administered both vaginally [15, 70] and orally [69, 71, 72], did not appear to significantly affect overall vaginal microbiome diversity. However, they had the potential to decrease pathogenic facultative anaerobic bacteria [15, 69, 70] and to improve Nugent scores, indicating a shift of the vaginal microbiome toward a healthier state [71]. Nevertheless, one study did not observe a statistically significant change in Nugent scores following vaginal probiotic application [70]. Another study in breast cancer patients receiving chemotherapy treated with probiotics found potential improvements in Nugent scores, but these did not reach statistical significance [72]. Some discrepancies in findings might be attributed to differences in participant characteristics, probiotic strains, routes of administration, treatment durations, and small sample sizes. Intravaginal probiotics, including Lactobacillus rhamnosus vitaP1 and Lactobacillus plantarum KCTC3108, showed high adhesion ability to vaginal and cervical epithelial cells, exhibited antagonistic activity against the tested pathogens, produced hydrogen peroxide and lactic acid (with H2O2 production being one of the most important defense mechanisms against colonization by pathogenic or opportunistic microorganisms), and demonstrated moderate ability in biofilm formation in both in vivo and in vitro studies [15, 75, 76]. These strains positively influenced the vaginal microbiota by reducing pathogenic flora such as Atopobium and vaginal pH, together with a trend toward decreased microbial diversity and GSM symptoms [15]. Similarly, probiotics containing L. rhamnosus GR-1 and L. reuteri RC-14 showed antagonistic activity against the tested pathogens in in vitro studies [76] and adhered well to urogenital epithelial and vaginal cells in vitro [77, 78]. Their immunomodulatory effects included upregulation of complement receptor 1 and toll-like receptor 2, while downregulating IL-18 gene expression [70]. This paired study revealed that probiotic treatment affected IL-18 expression following probiotics, possibly indicating anti-viral activity or general innate immune system upregulation [70]. The upregulation of complement receptors 1 and 3a, as well as TLR2, suggested increased anti-bacterial immune activity, which may have helped clear BV-associated pathogens [70]. Microarray analysis showed upregulation of caspase 14, potentially improving vaginal epithelial strength and integrity [70, 79]. Overall, this short-duration probiotic therapy increased lactobacilli and modulated inflammatory markers in postmenopausal women [70]. These probiotics were associated with a decline in bacterial vaginosis-related bacteria, including Gardnerella and Prevotella, while also promoting an increase in Lactobacillus levels following intravaginal administration [70]. Nonetheless, another study did not find a significant change in Lactobacillus levels after treatment [15]. These inconsistencies might be attributed to the limited sample sizes, variations in probiotic strains used, and differences in treatment duration. Oral probiotics exert influence beyond the digestive tract through immune modulation, nervous system signaling, and microbial metabolites, demonstrating significant effects across multiple body systems [80]. They enhanced skin barrier function, reduced inflammation, and improved conditions like atopic dermatitis and eczema, with Lactobacillus and Bifidobacterium species showing benefits. Some probiotics, such as Lactobacillus rhamnosus and Streptococcus thermophilus, helped prevent airway inflammation and infections by regulating immune responses through the gut-lung axis. In cardiovascular health, they influenced cholesterol metabolism, lowered LDL and CRP levels, and reduced inflammation. The gut-brain axis linked probiotics to neurological benefits, with strains like Lactobacillus rhamnosus and Bifidobacterium longum modulating neurotransmitters to alleviate stress, anxiety, and depression. Additionally, probiotics improved metabolic health by enhancing glucose metabolism and insulin sensitivity, with Akkermansia muciniphila and Faecalibacterium prausnitzii associated with reduced obesity and prediabetes risk [80]. Beyond these systemic effects, oral probiotics were believed to restore the vaginal microbiome through their influence on the gut microbiota. There appeared to be a connection between the Lactobacillus colonization of the gastrointestinal and genitourinary tract, suggesting that the gut might serve as a reservoir for vaginal colonization, contributing to the maintenance of normal vaginal microbiota [72]. Therefore, this gut-vaginal axis was thought to play a crucial role in maintaining vaginal health, and oral probiotics targeting the gut microbiome might have indirectly supported a healthy vaginal microbiome [69, 71, 72]. This relationship reflected a dynamic and complex interplay between the host and microbiota, particularly within the gut and vaginal environments, which significantly impacted health, disease susceptibility, and therapeutic outcomes [81]. Inflammation served as a key mediator in this bidirectional communication, influencing the stability and functional capacity of host-microbiota interactions. A deeper understanding of these systemic interactions was essential for developing microbiota-targeted interventions, including probiotics, to optimize disease prevention and treatment strategies in both primary and secondary healthcare settings [81]. Oral probiotics were formulated with various probiotic strains, including the following: - A. Multi-strain probiotic capsules containing Lactiplantibacillus plantarum PBS067, Bifidobacterium animalis subsp. lactis BL050, and Lacticaseibacillus rhamnosus LRH020 exhibited remarkable effectiveness against various urogenital pathogens through several distinct mechanisms [69, 82]. Research demonstrated that this probiotic combination fully suppressed Trichomonas vaginalis while substantially diminishing Candida glabrata and Neisseria gonorrhoeae populations in human vaginal epithelium models [82]. The individual strains displayed unique properties: B. animalis subsp. lactis BL050 successfully eliminated free-floating E. coli and decreased tissue-attached bacteria [82]. Other strains of Bifidobacterium animalis such as Bifidobacterium animalis VKL and Bifidobacterium animalis VKB demonstrated high adhesion to epithelial cells and remarkable resistance to biological fluids such as gastric juice, bile, and pancreatic enzymes [73]. L. plantarum PBS067 eradicated E. coli from tissue samples and effectively prevented bacterial adhesion, while L. rhamnosus LRH020 showed complete inhibition of E. coli attachment and superior co-aggregation capabilities with various pathogens [82]. The multi-strain probiotics functioned by creating protective barriers through monolayer formation, generating pathogen-inhibiting acidic environments, competing for epithelial binding sites, and forming aggregates with pathogens including G. vaginalis, E. coli, and C. albicans [82]. Clinical observations revealed reductions in Staphylococcus populations and trends toward increased Lactobacillus and Bifidobacterium abundance posttreatment. Patients experienced improved vaginal health index scores, lower pH levels, and notably reduced inflammatory markers, with IL-6 decreasing by 87.80%, TNF-α by 57.60%, and IL-1β by 40.80%. These findings suggest this probiotic combination effectively addresses postmenopausal vaginal microbiome imbalances and associated discomfort [69]. - B. Probiotic capsules containing 2.5 × 109 CFU each of lyophilized L. rhamnosus GR-1 and L. reuteri RC-14, along with sodium alginate as a stomach acid protectant [65], yielded notable clinical outcomes. Earlier research had established these specific probiotic strains’ capacity to inhibit pathogen growth in vitro [76] and their strong adherence properties to urogenital and vaginal epithelial tissues in vitro [77, 78]. Previous investigations had documented that L. rhamnosus GR-1 and L. reuteri RC-14 successfully colonized vaginal environments after oral administration [71, 83]. Other strains of Lactobacillus rhamnosus, such as LB-3 and VK6, previously showed potential for resistance to biological fluids, suggesting their suitability for oral preparations. These strains exhibited moderate gastric juice resistance, high bile resistance, and full resistance to pancreatic enzymes [73]. The study revealed substantial improvements in vaginal bacterial communities among postmenopausal participants receiving oral probiotics, as measured through Nugent scoring assessments. Regular intake of the lactobacilli formulation successfully normalized intermediate vaginal flora in 60% of women in the treatment group, despite their low-estrogen state—a result that substantially surpassed the mere 16% improvement observed in the control cohort. This marked disparity between groups, which reached statistical significance, pointed to oral probiotic therapy as a viable hormone-free alternative for reestablishing healthy vaginal microecology and potentially decreasing susceptibility to urogenital infectious conditions in women after menopause [65]. - C.Probiotic capsules containing 2.3 × 109 CFU each of lyophilized L. crispatus LbV88, L. rhamnosus LbV96, L. jensenii LbV116, and L. gasseri LbV150N showed promising therapeutic potential [72]. These strains contribute to vaginal health through distinct mechanisms. - Lactobacillus crispatus supported vaginal health via its distinct immunomodulatory properties, primarily mediated by surface layer proteins (SLPs). Unlike problematic vaginal bacteria that triggered inflammation, L. crispatus avoided activating pro-inflammatory TLR2/TLR4 pathways while selectively engaging with anti-inflammatory receptors such as DC-SIGN. Research demonstrated that SLPs served dual functions: they shielded inflammatory TLR ligands in the bacterial cell wall while actively interacting with DC-SIGN to promote an anti-inflammatory environment [84]. Clinical evidence supported these laboratory findings, as women with L. crispatus-dominated vaginal microbiomes who had detectable SLPs in their cervicovaginal fluid showed reduced inflammatory markers. This mechanism helped explain why L. crispatus dominance correlated with protection against conditions like bacterial vaginosis, STIs, and preterm birth, offering potential pathways for future therapeutic interventions targeting vaginal health [84]. - Lactobacillus rhamnosus exerted its antimicrobial effects primarily through the production of organic acids. Acetic acid and lactic acid were the main substances in the cell-free supernatant (CFS) of L. rhamnosus that inhibited the growth of G. vaginalis [85]. Other in vitro experiments with the L. rhamnosus TOM 22.8 strain exhibited a strong ability to adhere to both vaginal VK2/E6E7 and intestinal Caco-2 epithelial cells, indicating its potential for colonization in these environments [86]. This finding is supported by previous studies on Lactobacillus rhamnosus LB-3 and VK6 strains, which showed resistance to biological fluids, including gastric fluid, bile, and pancreatic enzymes, indicating their suitability for oral preparations [73]. This strain also demonstrated antimicrobial activity against multiple vaginal pathogens, including Escherichia coli, Gardnerella vaginalis, and Candida species. The antagonistic effect was attributed primarily to the production of organic acids and hydrogen peroxide rather than bacteriocins. The strain showed significant anti-inflammatory effects, reducing COX-2 gene expression and increasing IL-10 expression in human macrophages. Additionally, it demonstrated antioxidant activity similar to α-tocopherol, indicating its role in mitigating oxidative stress and inflammation [86]. - Lactobacillus jensenii and Lactobacillus gasseri demonstrated strong adherence to epithelial cells through various surface molecules including proteins, glycoproteins, and carbohydrates, which enhanced their ability to colonize and effectively compete against pathogenic microorganisms [87]. These lactobacilli species exhibited self-aggregating properties that supported biofilm development, while simultaneously producing various antimicrobial compounds—including lactic acid, hydrogen peroxide, and bacteriocins—that effectively suppressed pathogens like E. coli, G. vaginalis, and C. albicans [87]. Additionally, their ability to form aggregates with pathogenic microorganisms played a significant role in modifying immune responses and decreasing inflammatory processes. This unique property, combined with their other beneficial characteristics, positioned these lactobacilli strains as especially valuable candidates for incorporation into probiotic therapies designed to preserve vaginal microbial equilibrium and provide protection against various urogenital infections [87]. Clinical evidence supports the efficacy of this probiotic combination, particularly in breast cancer patients undergoing chemotherapy. In a study, 63% of participants in the probiotic group exhibited positive changes in their vaginal microbiota compared to 36% in the placebo group. Additionally, Nugent scores improved significantly in the probiotic group while worsening in the control group. These findings suggest that oral Lactobacillus probiotics provide a safe and effective alternative for restoring vaginal health, especially in cases where estrogen therapy is contraindicated [72]. Bacillus strains represented a promising alternative probiotic approach for normalizing vaginal microbiome in postmenopausal women. These spore-forming, Gram-positive, lactic acid-producing bacteria were distinguished by their remarkable resilience, particularly their ability to form spores. This characteristic allowed them to persist longer at specific epithelial sites and withstand harsh environmental conditions, making them significantly more resistant to environmental changes than non-spore-forming probiotics [88, 89]. Although clinical data on their use in postmenopausal women remain limited, Bacillus probiotics have demonstrated potential for normalizing vaginal dysbiosis. An in vivo study using female BALB/c mice with experimental bacterial vaginitis showed that treatment with a probiotic composition of Bacillus subtilis and B. megatherium led to gradual symptom resolution, restoration of normal vaginal microbiota, reduced Staphylococcus populations, and increased beneficial Lactobacillus and Bifidobacterium. Immunological benefits included enhanced phagocytic activity of peritoneal exudate macrophages, normalized oxygen-dependent bactericidal activity, and reduced anti-Staphylococcus antibody titers in blood serum [88]. Further research identified Bacillus velezensis ON116948, isolated from a healthy woman’s cervicovaginal microbiota, as another potential probiotic candidate. This strain exhibited remarkable resilience, tolerating low pH, 12.5% NaCl concentrations, and 0.3% bile salts. It also demonstrated strong adherence to stainless steel surfaces and antimicrobial activity against Candida species (C. albicans, C. glabrata, C. tropicalis) and Staphylococcus strains (S. condimenti, S. epidermidis), though it had no effect on E. coli or P. mirabilis [90]. The therapeutic potential of Bacillus probiotics extends beyond theoretical applications, as evidenced by clinical studies on Bacillus coagulans for vulvovaginitis. Intravaginal administration significantly alleviated symptoms such as pruritus, burning, irritation, and abnormal discharge while lowering vaginal pH, suggesting microbiota restoration. Notably, the most substantial improvements occurred within the first 4 days of treatment. Despite these promising findings, further large-scale clinical studies are needed before Bacillus-based probiotics can be formally integrated into clinical guidelines for managing vaginal dysbiosis in postmenopausal women [89]. No adverse events were reported in clinical studies that reviewed the safety profile of probiotic administration [15, 69–72]. In summary, probiotics containing Lactobacillus species exerted the potential to positively influence the postmenopausal vaginal microbiome when administered vaginally or orally and emerged as a potential preventive strategy. This non-hormonal approach offered a safe alternative for women seeking to maintain vaginal health without hormonal intervention. Current microbiome research suggested that each probiotic strain was uniquely suited for specific conditions, clinical presentations, and patient phenotypes [73, 91]. Personalized probiotic interventions, tailored to an individual’s microbiota composition and specific health conditions, tended to be more effective in managing certain health issues than generalized probiotic treatments. While research on personalized probiotics for vaginal conditions was limited, evidence from gut microbiome studies supported their efficacy in alleviating symptom severity, particularly in patients with diarrhea-predominant (IBS-D) and constipation-predominant (IBS-C) subtypes [92], as well as in regulating distinct stool patterns, such as constipation and diarrhea [93]. Future investigations should explore how strain selection can be tailored based on each patient’s baseline microbiome composition to maximize individualized therapeutic outcomes and deliver truly personalized probiotic interventions. Personalized medicine approach, a predictive, preventive, and personalized medical (3P) strategy, was crucial for tailoring probiotic and prebiotic therapies to individuals based on biomarkers, microbiome composition, and metabolic phenotype [74]. Further comprehensive research with larger cohorts and standardized protocols was needed to determine optimal probiotic strains, route, treatment duration, and long-term efficacy for maintaining a healthy postmenopausal vaginal microbiome. Additionally, long-term follow-up studies comparing different probiotic strains and administration routes were warranted to establish evidence-based preventive protocols with particular attention to how strain-specific properties correlated with clinical outcomes in diverse patient populations. Another potential role of probiotics that gained attention was their ability to treat infertility-related conditions such as POI, polycystic ovary syndrome (PCOS), and endometriosis. These conditions were often associated with gut or vaginal microbiota imbalances, which might have contributed to reproductive dysfunction [94, 95]. Endometriosis had been linked to alterations in gut microbiota, including a reduction in Lactobacillus spp. and an imbalanced Firmicutes:Bacteroidetes ratio [94, 96, 97]. Additionally, changes in the genital tract microbiome, such as a decrease in Lactobacillus species and an increase in pathogenic bacteria, had been observed [95]. These microbial shifts might have disrupted the hormonal balance and promoted inflammation, negatively affecting fertility [94, 95]. One proposed mechanism involved neuroactive metabolites stimulating GnRH neurons, leading to increased ovarian estrogen secretion and further hormonal dysregulation [94, 97]. PCOS had also been associated with gut dysbiosis, characterized by an altered Escherichia:Shigella ratio and an excess of Bacteroides [94, 98, 99]. These changes had contributed to insulin resistance and metabolic dysfunction, exacerbating infertility [94, 96, 97, 99]. PCOS-related reproductive tract dysbiosis, including reduced Lactobacillus and increased pathogenic microbes, had further impacted fertility [95]. While the gut-gonadal connection had not been fully understood, gut microbiota appeared to influence steroid hormones, insulin sensitivity, immune function, and reproductive tract microbiota [94, 95]. Disruptions in gut microbiota might have also led to imbalances in the uterine and vaginal microbiomes, affecting endometrial receptivity and implantation [95]. The female genital tract (FGT) microbiome had played a critical role in reproductive health. A Lactobacillus-dominant vaginal and endometrial microbiome had been associated with improved fertility outcomes, while decreased Lactobacillus levels and increased microbial diversity had correlated with lower pregnancy success rates [95, 100–103]. Infertile individuals had often exhibited microbial imbalances, with a higher prevalence of Ureaplasma and Gardnerella vaginalis [95, 104]. A meta-analysis had found that women with vaginal dysbiosis had a 1.4-fold lower likelihood of conception following in vitro fertilization (IVF) compared to those with a balanced microbiome [95, 105]. The gut-liver axis had also influenced reproductive health through estrogen metabolism via the estrobolome, a collection of gut microorganisms involved in estrogen processing [106–108]. Disruptions in this axis could have led to hormonal imbalances, chronic inflammation, and reproductive disorders. The gut microbiota had also regulated enterohepatic estrogen circulation, impacting systemic hormone levels and fertility [107, 108]. Additionally, short-chain fatty acids (SCFAs) produced by gut microbes had modulated immune responses, including vaginal immunity. Although SCFAs had helped maintain gut barrier function, they had also been linked to vaginal dysbiosis and inflammation, highlighting a systemic metabolic connection between the gut and reproductive tract [108]. Maternal gut microbiota-derived SCFAs had further influenced fetal metabolic programming, underscoring the broader relevance of gut-reproductive interactions [100, 101, 108]. Given the complexity of the gut-microbiome-liver-gonadal network, the potential benefits of probiotics and prebiotics in enhancing fertility had warranted further exploration. Large-scale, multicenter studies had been needed to clarify these interactions. Modulating gut and reproductive tract microbiota through probiotics and prebiotics had presented a promising strategy for improving reproductive health. Diet, antibiotic exposure, and lifestyle choices influenced the composition of gut microbiota. Consuming foods high in prebiotics and fiber supported beneficial bacterial growth, whereas diets high in fat or fructose were linked to microbial imbalances [74]. Prebiotics, substances specifically utilized by beneficial microorganisms in the host to positively impact health [109], showed promising potential for vaginal microbiome normalization, despite limited data in postmenopausal women. Research identified several prebiotic candidates with varying effectiveness. Lactulose demonstrated strong potential by enhancing beneficial Lactobacillus growth, lowering vaginal pH, and being selectively metabolized by Lactobacillus, but not by BV-associated bacteria or Candida albicans. Lactitol and raffinose exhibited some Lactobacillus-stimulating effects but were less effective than lactulose, while oligofructose proved unsuitable as it supported Candida albicans growth [110]. A triple-blind randomized controlled trial evaluated a prebiotic vaginal gel containing 2% red clover extract, 10% inulin, and 10% fructo-oligosaccharide (FOS). When used alongside oral metronidazole for bacterial vaginosis, the gel significantly enhanced treatment efficacy, reduced BV recurrence, decreased vaginal malodor and discharge, normalized pH, increased Lactobacillus counts, and reduced pathogenic bacteria [111]. Similarly, glucomannan hydrolysates (GMH) from Amorphophallus konjac showed promise by serving as a carbon source for lactic acid bacteria, demonstrating immunomodulatory effects, promoting Lactobacillus growth, and offering advantages over antibiotics by restoring normal flora, enhancing immune regulation, and inducing an anti-inflammatory response [112]. Research on tailored nanoceria combined with specific probiotic strains highlighted the importance of matching prebiotics with appropriate probiotics. Lactobacillus casei IMV B-7280 with nanoceria (0.01 mol/L) demonstrated the most significant cholesterol reduction in obese mice compared to Bifidobacterium animalis strains with nanoceria [113]. These findings suggested that future research should focus on developing tailored prebiotic-probiotic combinations and further investigating promising compounds like lactulose for vaginal microbiome modulation. Vaginal energy base devices Energy-based therapies provided a non-hormonal approach for treating GSM and potentially modulating the vaginal microbiome. These interventions were particularly valuable for hormone-restricted patients like breast cancer survivors, as they helped alleviate vaginal dryness, a common GSM symptom, through tissue rejuvenation. However, as of a 2018 Safety Communication, the FDA has not approved any devices for treating GSM and has issued warnings about their use for vaginal cosmetic procedures due to unestablished safety and effectiveness [3]. The primary energy-based devices for GSM treatment included radiofrequency and laser systems such as CO2-fractional and Er:Yag (erbium-doped yttrium aluminum garnet) lasers. Laser devices delivered precise beams to the vaginal wall through specialized probes, where water absorption caused controlled heating and tissue modification. This process stimulated collagen production, neovascularization, and mucosal remodeling. Er:YAG lasers offered reduced thermal effects compared to CO2-fractional systems. Radiofrequency devices utilized electromagnetic fields to generate deep tissue heating without surface damage, promoting collagen synthesis and tissue remodeling [114, 115]. -Microablative fractional radiofrequency The impact of microablative fractional radiofrequency devices on vaginal microbiota includes increased levels of Lactobacillus species, a decreased Nugent score, and lower vaginal pH, as well as the improvement of VHI, VMI, and sexual desire. However, changes in other vaginal microbiota were not reported due to limitations in bacterial identification techniques: Gram staining and microscopy analysis [56, 116]. Interestingly, radiofrequency therapy showed comparable effects to those of estrogen therapy in improving Lactobacillus abundance and GSM symptoms, as indicated by improved VHI, VMI, sexual desire, and Nugent score [56]. - CO2 laser therapy The impact of CO2 laser therapy on vaginal microbiota included a decrease in alpha diversity and changes in composition, reflected in differences in beta diversity [55]. These alterations included increased Lactobacillus species and decreased facultative anaerobic bacteria, potentially associated with the severity of GSM [55]. The improvement of VHI, elasticity scores, moisture scores, and Urogenital Distress Inventory (UDI-6) scores was observed at 3 months posttreatment and persisted for up to 1 year, alongside the increase in vaginal mucosal density [55]. CO2 laser and vaginal estrogen therapies had comparable short-term effects in treating GSM [55]. However, the long-term efficacy of CO2 laser therapy was superior, with caused sustained improvements in vaginal health and urinary function at the 1-year follow-up [55]. In contrast, the effects of vaginal estrogen therapy diminished over time, with only increased mucosal density and elevated levels of type I and type III collagen persisting 1 year after treatment [55]. Additionally, CO2 laser therapy led to increased collagen type III, when compared to estrogen therapy [55]. Another study did not observe significant alterations in vaginal microbiota but still reported increases in VHI and Female Sexual Function Index (FSFI) following laser treatment [117]. The discrepancies in vaginal microbiota changes between the two studies could be attributed to differences in methodology, particularly the bacterial identification techniques used and the inclusion of an untreated control group. Despite these differences, both studies found improvements in GSM symptoms and vaginal health following CO2 laser therapy [55, 117], suggesting that the treatment’s efficacy may involve mechanisms beyond modulating the microbiota, such as stimulating collagen production, neovascularization, and remodeling of the vaginal mucosa [114, 115]. - Erbium in yttrium aluminum-garnet crystal (Er:YAG) laser The impact on vaginal microbiota following Er:Yag laser treatment included increased Lactobacillus species. However, when compared to a sham control, there were no significant differences in Lactobacillus abundance between the groups. The improvement of vaginal atrophy symptoms and vaginal atrophy scores was also observed only in the treatment group, but the effect on vaginal pH was not exhibited [118]. Energy-based therapies have shown potential for modulating vaginal microbiota, particularly by prominently increasing Lactobacillus dominance and reducing dysbiosis-associated markers. While their efficacy in microbiome restoration varies, they contribute to symptom relief and vaginal tissue regeneration, making them a promising alternative for GSM management, especially in non-hormonal candidates. Further studies are needed to establish long-term effects and optimize protocols for microbiota-targeted interventions. Intravaginal practice Intravaginal practices, commonly known as vaginal douching, were believed to be potential risk factors for developing vaginal dysbiosis. This disruption in the natural balance of vaginal microbiota can increase susceptibility to various infections, including pelvic inflammatory disease, bacterial vaginosis, sexually transmitted diseases, and even HIV [119–122]. However, one study investigating the effects of intravaginal practices, including the insertion of substances (e.g., soaps, deodorizers, powders, and other non-medical products) via fingers, cloth, or specialized tools into the vagina, did not observe significant alterations in the vaginal microbiota [123]. The lack of significant findings might be attributed to the diverse materials used in intravaginal practices and the variability in timing and frequency of insertion among participants. While these findings suggested minimal immediate microbiome impact, the practice offered no documented health benefits and was generally unnecessary for maintaining vaginal health. Current medical evidence recommended against routine intravaginal practices as they could disrupt the complex protective systems including microbiota, pH balance, and skin barriers that helped prevent infection and maintain health [124]. Vaginal pessary Pelvic organ prolapse is a condition characterized by the descent of pelvic organs into the vaginal canal, resulting in a vaginal bulge, pelvic pressure, and dysfunction in either voiding or defecation [125]. Following the prolapse of the vagina, which causes its exposure to the external environment, it was revealed that there was an increase in some CST IV bacteria such as Staphylococcus, Faecalibacterium, and Fastidiosipila, along with an increase in gut microbiota contamination when compared to prolapse patients undergoing treatment, whether pessary or reconstructive surgery [125]. Vaginal pessaries are commonly used to treat pelvic organ prolapse, and patients are typically advised to remove and reinsert the pessary daily to reduce the risk of abnormal vaginal discharge or ulcers. The insertion of a pessary introduces a foreign body into the vagina, which can lead to increased vaginal discharge and potentially abnormal discharge or ulcers, likely due to an imbalance in vaginal microflora [126–128]. Following vaginal pessary use, the vaginal microbiota had decreased species richness [125, 127] and decreased gut microbiota contamination [125]. However, the changes in vaginal microbiome were somewhat discrepant among studies. Indeed, one study revealed that pessaries did not impact vaginal microbiota composition [42], while another revealed an increase in some bacteria in CST IV such as Fusobacterium and Sneathia [125]. A similar trend was also observed following reconstructive surgery in this study [125]. On the other hand, another study found that vaginal microbiota had a trend of increased Lactobacillus abundance with lower abundances of other anaerobic bacteria, but still had an increase and variation of some bacteria in CST IV [127]. These discrepancies might be attributed to variations in pessary use duration, study methodologies, limited follow-up periods in some studies, inconsistent follow-up timelines across studies, individual patient variability in vaginal microbiota response, the use of vaginal estrogen, and the descriptive nature of the final study [127], which lacked statistical comparison between pre- and post-pessary treatment. All of these results suggested that although pessary insertion or even reconstructive surgery for pelvic organ prolapse could lead to resolution of structural discomfort, these treatments might not fully restore the balance of the vaginal microbiome. Therefore, it is important to address any imbalances in the vaginal microbiome as part of the overall treatment plan to prevent additional gynecological diseases [125]. Moreover, the anaerobic environment created by the pessary seemed to support the persistence of some anaerobic species, albeit at lower levels of diversity. The response varied considerably between individual patients, with some maintaining a more complex anaerobic flora than others after therapy [127]. Prolonged placement of a vaginal pessary is sometimes necessary for bedridden or disabled patients who require assistance with insertion and removal. This prolonged placement is associated with an increased risk of vaginal discharge and concerns about ulcers and infections [127]. A study comparing prolonged pessary placement to daily self-replacement found no significant differences in the vaginal microbiota between the two groups. The continuous use of a pessary neither impacted species richness, Lactobacillus abundance, nor vaginal pH. However, it was associated with an increase in vaginal discharge and white blood cell count, which might be the vagina’s mechanism for preventing ulcers by releasing more lubricant as a response to the presence of a foreign body [129]. Interestingly, a significant shift in the vaginal microbiota was observed in patients using pessaries with vaginal erosion or ulceration. This shift included increased alpha diversity, altered beta diversity with reduced Lactobacillus abundance, increased pathogenic flora, and elevated vaginal pH, indicating a disturbed vaginal microbiota pattern [42]. The underlying mechanism was found to involve inflammation [42] rather than necrosis due to ischemia from tissue compression by the pessary, as previously hypothesized [130]. Pathologic reports from vaginal erosion biopsies revealed signs of inflammation, including multiple leukocytic infiltrations, edema, and loss of epithelium, with no evidence of necrosis or ischemia, and blood vessels were free of thrombosis [42]. In addition, inflammation was further supported by significant increases in inflammatory cytokines such as IFN-γ, IL-1β, IL-4, IL-10, IL-12p70, and TNF-α following pessary use [125]. Consequently, this shift in vaginal microbiota appeared to be associated with vaginal erosion after pessary use and could potentially be a cause of the inflammation-induced vaginal erosion [42]. Accordingly, changes in vaginal microbiota composition may serve as potential markers for vaginal erosion, a complication in pessary users. Further research is warranted to explore this correlation, which may lead to improved management and prevention strategies for vaginal erosion in pessary users [42]. Vaginal pessaries and reconstructive surgery modulated the vaginal microbiota in pelvic organ prolapse by reducing gut contamination and certain CST IV bacteria. As a foreign body, pessaries affected the microbiome by increasing Lactobacillus trends, while also elevating some CST IV bacteria. However, neither pessaries nor reconstructive surgery fully restored vaginal microbiome balance. Prolonged pessary use did not significantly impact the overall vaginal microbiota. In cases of vaginal erosion from pessary use, the microbiota underwent significant changes, including increased diversity, reduced Lactobacillus, elevated pathogenic flora, and higher inflammatory cytokine levels. These changes may serve as potential markers for erosion complications, warranting further research to develop improved management strategies and personalized treatment approaches. Modulating vaginal microbiome as preventive strategies Previous research demonstrated that vaginal microbiota underwent substantial changes in postmenopausal women, with these alterations showing significant correlations with ovarian function indicators. The modifications in vaginal microbiota were particularly pronounced in postmenopausal women experiencing genitourinary syndrome of menopause (GSM) compared to asymptomatic counterparts, suggesting a potential mechanistic role in GSM development. Various interventions have emerged for modulating the vaginal microbiome, offering both hormonal and non-hormonal approaches to normalize microbial communities. These interventions served as secondary preventive strategies in patients showing early microbiome changes who were at risk for GSM, as well as tertiary prevention in those who had already developed symptoms, enabling treatment optimization based on individual patient characteristics and needs. Hormonal intervention, specifically estrogen replacement therapy, emerged as an effective strategy for normalizing the vaginal microbiome, which correlated with improvements in GSM symptoms. Emerging evidence supported the role of vaginal microbiota in advancing targeted approaches for managing GSM. From a predictive standpoint, alterations in microbiota composition—specifically decreased alpha diversity, increased Lactobacillus abundance, and a decrease in non-Lactobacillus-dominant communities—served as valuable markers for therapeutic response [33, 49, 50, 53, 54] to hormone therapy (both local [45, 55] and systemic routes [33, 49]), tibolone (a hormone-mimicking compound) [53], and ospemifene [54]. The potential for prevention was demonstrated by these interventions’ capacity to normalize vaginal microbiota composition [33, 45–48, 51–53, 55, 56] and improve key vaginal health parameters, including VMI, VHI, and vaginal pH [18, 33, 45–48, 53, 54]. This suggested the potential for microbiota-based strategies in individuals whose baseline profiles indicated a predisposition to GSM symptoms, allowing for early intervention before symptom manifestation. These microbial signatures could potentially identify patients most likely to benefit from specific interventions, enabling more precise treatment selection. Regarding individualized approaches, postmenopausal women demonstrated variable responses to treatment, with distinct differences in both vaginal health indicators and microbiota composition following intervention [45–48, 55, 56]. Treatment response appeared to be significantly influenced by several baseline characteristics. Patients with higher initial alpha diversity [61], elevated baseline serum estradiol, and higher vaginal glycogen levels [63] showed better therapeutic outcomes. These findings highlighted the potential value of using microbiota profiles alongside vaginal and serum markers for patient stratification before treatment initiation. Such pretreatment assessment of microbial communities, combined with hormonal and metabolic markers, could serve as predictive indicators of therapeutic response, enabling clinicians to select and optimize treatments based on individual patient profiles. This evidence-based approach to patient stratification opens the door to more targeted therapeutic strategies, potentially improving treatment outcomes through personalized intervention plans. For patients with contraindications to hormonal therapy, including those with breast cancer or thromboembolic conditions, several non-hormonal approaches have emerged as viable preventive strategies for maintaining vaginal microbiome health. Probiotics administered either orally or vaginally have shown promising results in modulating vaginal microbiota composition and improving vaginal health indices [15, 69–72]. These probiotic interventions can help restore beneficial bacteria, particularly in women at risk for vaginal dysbiosis. Each strain exhibited specific effects, showing promise for tailored treatment in postmenopausal women [89]. Clinical studies reviewing the safety profile of probiotic administration reported no adverse events [15, 69–72], and strains were uniquely suited to specific conditions and patient phenotypes [73]. Energy-based therapies, such as CO2 laser, Er:YAG laser, and radiofrequency treatments, have demonstrated dual benefits by both beneficially modifying the vaginal microbiome and alleviating GSM symptoms [55, 56, 116–118], offering a particularly valuable option for hormone-restricted patients. For postmenopausal women with pelvic organ prolapse, pessary use and reconstructive surgery have shown potential in modulating vaginal microbiota [42, 125, 127, 129]. However, these interventions require careful monitoring, as changes in the vaginal microbiome can predict treatment-related complications. For instance, increased microbial diversity and reduced Lactobacillus abundance have been identified as potential early markers for pessary-related vaginal erosion [42], highlighting the importance of regular microbiome assessment in preventing adverse outcomes. The effectiveness of these non-hormonal interventions underscores their value as preventive strategies, particularly for women at risk of developing GSM symptoms or vaginal dysbiosis. The ability to monitor microbiome changes during treatment enables a personalized medicine approach, allowing for early intervention and adjustment of therapeutic strategies based on individual patient responses. This preventive framework, supported by regular assessment of vaginal microbiota, represents a promising direction in managing postmenopausal vaginal health. Future directions in preventive strategies should focus on three critical areas that will advance personalized vaginal health management in postmenopausal women. First, the identification and validation of baseline microbiota signatures as predictive biomarkers for GSM development and treatment response will enable early risk assessment and guide preventive interventions. Second, establishing optimal screening protocols and monitoring intervals will facilitate timely detection of adverse changes in the vaginal microbiome. Third, developing targeted therapeutic approaches based on individual microbiota profiles will enable more precise preventive interventions. Implementation of these strategies should be tailored to individual patient characteristics, with treatment selection between hormonal and non-hormonal interventions guided by baseline microbiota profiles, underlying medical conditions, and specific symptom patterns. Critical research priorities include validating causative relationships between microbiome changes and clinical outcomes, establishing clinically relevant thresholds for preventive intervention, and developing predictive models to identify at-risk patients before symptom onset or complications develop. These advancements would ultimately enable more effective and personalized preventive approaches in managing postmenopausal vaginal health through early intervention and careful monitoring. Recommendations in the framework of 3P medicine Predictive medical approach: microbiota-based biomarkers for menopause and GSM Recent studies identified microbial signatures as potential predictive biomarkers for menopausal status, primary ovarian insufficiency (POI), and the development of genitourinary syndrome of menopause (GSM). Integrating microbial and hormonal indicators supports microbiota-based screening tools for predicting menopausal transition, POI, and GSM risk, as described in the following topics. Alpha diversity as a predictive marker: Increased Shannon diversity index was observed in postmenopausal and POI women compared to premenopausal individuals, indicating higher vaginal microbiota diversity as a predictive biomarker for ovarian function decline [10–15, 24, 25]. This association was stronger with complete ovarian cessation (menopause or POF with FSH > 40) than with perimenopause [11] or early POI (FSH > 25) [26]. Higher alpha diversity emerged as a potential predictor for vaginal dryness symptoms [11, 34]. Bacterial taxa as diagnostic indicators: A shift from Lactobacillus-dominated communities (CST I-III) to CST IV was associated with ovarian insufficiency [10–16, 18, 20–22] and GSM severity [11, 19, 33–35]. Key microbial markers for postmenopausal women included Lactobacillus, Atopobium, Pseudomonas, Escherichia, Proteus, uncultured Lactobacillaceae, and Prevotella [10, 20]. Key microbial markers for POI included Streptococcus, Atopobium, Gardnerella, Prevotella, Bifidobacterium, Snethia, Porphyromonas, Escherichia, Bacteroides, Dialister, Veillonella, Campylobacter, and Lactobacillus [24–26]. Atopobium and Gardnerella were highlighted in ROC curve analyses as potential diagnostic indicators for POI [26]. A decrease in Lactobacillus and an increased presence of Gardnerella, Prevotella, Porphyromonas Peptoniphilus, and Bacillus served as potential indicator for GSM symptoms [11, 19, 33–35]. Increased diversity, reduced Lactobacillus, and elevated pathogenic flora may serve as early markers for pessary-related vaginal erosion in vaginal pessary user [42]. Correlations between vaginal microbiota and hormonal markers: FSH and LH levels positively correlated with pathogenic anaerobic bacteria, particularly Gardnerella, Prevotella, and Atopobium, while estradiol levels correlated positively with Lactobacillus, Bifidobacterium, Brevundimonas, and Odoribacter [24–26]. AMH, a key marker of ovarian reserve, exhibited a significant correlation with beneficial bacteria such as Lactobacillus, Bifidobacterium, Brevundimonas, and Odoribacter, while showing an inverse relationship with Gardnerella and Atopobium [24–26]. The potential targeted prevention strategies The vaginal microbiota composition offered a valuable tool for targeted primary, secondary, and tertiary prevention strategies in postmenopausal health. Primary prevention: early risk stratification Routine microbiota profiling for perimenopausal women may help to detect early microbial shifts toward dysbiosis before GSM symptoms develop. Women exhibiting low-Lactobacillus dominance and increased anaerobes (CST IV patterns) may benefit from early probiotic intervention to maintain vaginal health. Secondary prevention: modulating microbiota to prevent GSM progression Hormonal interventions (vaginal estrogen, systemic hormone therapy) and non-hormonal alternatives (probiotics, energy-based devices) have been shown to restore Lactobacillus dominance and reduce pathogenic bacteria, preventing symptom progression. Tertiary prevention: personalized interventions for GSM In women with established GSM, targeted interventions such as vaginal estrogen therapy, tibolone, ospemifene, systemic hormonal therapy, probiotics, and energy-based treatments (CO2 laser, Er:YAG laser, and radiofrequency therapy) showed efficacy in restoring vaginal microbiota balance and improving clinical outcomes. These interventions should be personalized based on specific conditions, clinical presentations, and patient phenotypes to optimize treatment outcomes. Higher baseline alpha diversity was associated with better responses to vaginal estrogen therapy [61], while serum estradiol and vaginal glycogen levels were proposed as predictive markers of systematic hormonal treatment response [63]. This finding suggested that microbiome profiling could help guide treatment selection more effectively. Potential of microbiota-guided personalized treatment approaches The vaginal microbiome showed significant promise for advancing personalized medicine approaches in postmenopausal women’s health management through several key applications. First, baseline vaginal microbiota profiles could guide individualized GSM treatment selection, as demonstrated by studies showing different therapeutic responses based on initial microbiome composition; for example, higher baseline alpha diversity predicted better response to local vaginal hormonal therapy [61], while serum estradiol and vaginal glycogen levels were predictive markers for systemic hormone therapy response [63]. Second, microbiota signatures could serve as predictive biomarkers of treatment outcomes, with specific patterns of Lactobacillus dominance and CST distributions correlating with therapeutic success across different interventions. Third, microbiome analysis has emerged as a valuable tool for preventing complications, particularly in pessary users where changes in microbial diversity and composition could serve as early warning signs of vaginal erosion, including increased alpha diversity, reduced Lactobacillus abundance, and elevated pathogenic flora [73]. However, translation of these findings into clinical practice requires further research to establish standardized screening protocols that can identify at-risk patients and determine optimal timing for preventive interventions. The development of such protocols would enable truly personalized therapeutic approaches based on individual microbiome profiles, potentially improving treatment outcomes through early intervention and careful monitoring. Key priorities include validating predictive microbiota signatures, establishing clinically relevant thresholds for biomarkers, and determining appropriate screening intervals to enable timely preventive measures. The potential strategies for microbiota-based interventions in postmenopausal women are summarized in Fig. 1. - 1.1. Patient stratification based on microbiome profiles Microbiome data enabled refined patient categorization beyond traditional menopausal status, including (1) microbial community types as the basis for treatment selection; (2) combined hormonal-microbial profiles for personalized intervention timing; and (3) symptom-microbiome correlations for targeted therapy approaches. - 2.2. Individualized treatment selection Microbiome profiling could guide personalized therapeutic decisions, including (1) hormone therapy responsiveness predicted by baseline microbiome composition, with higher baseline alpha diversity associated with better responses to vaginal estrogen therapy [61]; (2) additionally, higher baseline serum estradiol and vaginal glycogen levels were proposed as predictive markers of systematic hormonal treatment response [63]; (3) non-hormonal alternatives may select based on specific microbial dysbiosis patterns; and (4) combination approaches tailored to individual microbial and hormonal profiles. - 3.3. Long-term microbiota monitoring for treatment optimization Longitudinal microbiome assessment offers opportunities for therapy optimization, including (1) routine assessment of vaginal microbiota composition and metabolic markers which may provide real-time insights into treatment efficacy, allowing for dynamic modifications in therapeutic strategies; (2) response markers based on microbial community restoration; (3) early detection of suboptimal response through microbial indicators; (4) adaptive treatment protocols guided by microbiome dynamics; (5) future developments in vaginal microbiome sequencing could enable the creation of precision medicine algorithms to predict individual treatment responses.

All of the information from this review article is summarized in Fig. 2. Postmenopausal women and individuals with POI exhibited significant changes in their vaginal microbiota, mostly characterized by increased alpha diversity, a higher presence of pathogenic bacteria, and a reduction in Lactobacilli. Patients with GSM also demonstrated a higher prevalence of pathogenic bacteria, particularly CST IV, along with decreased Lactobacillus and increased alpha diversity. Systemic hormonal treatments appeared to effectively restore the vaginal microbiota of postmenopausal women to be comparable to that of premenopausal women. This effect was also notable with local hormonal therapies, tibolone, and ospemifene, but not with isoflavones. Non-hormonal interventions, such as probiotics and energy-based devices, seemed to benefit the vaginal microbiota composition, improve overall vaginal health, and potentially serve as alternative treatments for menopausal symptoms or vaginal dysbiosis. In contrast, intravaginal practices and devices did not show a definitive impact on the vaginal microbiome. The analysis of vaginal microbiota revealed significant potential for advancing Predictive, Preventive, and Personalized Medicine (PPPM/3PM) in women’s health, particularly regarding menopausal transitions and related conditions. From a predictive standpoint, specific markers such as Shannon alpha diversity, shifts in Lactobacillus dominance, and the presence of key bacterial taxa serve as valuable indicators for the menopausal state, POI, and GSM symptoms. These predictive capabilities enable preventive interventions through early detection and treatment, with both hormonal and non-hormonal approaches showing effectiveness in normalizing vaginal microbiota composition. Furthermore, systemic hormonal treatments demonstrated the ability to effectively restore vaginal microbiota to be comparable to that of premenopausal women, with similar effects observed with local hormonal therapies, tibolone, and ospemifene. Non-hormonal interventions, including probiotics and energy-based devices, showed promise in beneficially modulating vaginal microbiota composition, improving overall vaginal health, and potentially serving as alternative treatments for menopausal symptoms or vaginal dysbiosis. However, each treatment modality for vaginal health in postmenopausal women presented distinct advantages and limitations. Hormone therapy remained the gold standard with the strongest evidence base but had important contraindications. Energy-based devices offered promising non-hormonal alternatives, promoting structural improvements such as increased collagen type III, with effects lasting over a year. These treatments might be suitable for patients who could not use hormones, had mild structural defects like vaginal laxity, or required vaginal strengthening. However, further validation and standardization were needed. Probiotics represented the safest and most accessible option but demonstrated inconsistent efficacy. The selection of tailored probiotic strains based on individual profiles remained a key gap in knowledge. The optimal approach likely involved individualized treatment selection based on patient characteristics, preferences, contraindications, and baseline microbiome profiles. Future research needs to focus on developing predictive tools to match patients with their most likely beneficial treatment, standardizing interventions to improve the consistency of outcomes, and exploring combination therapies that might have leveraged the strengths of multiple approaches while mitigating their individual limitations. The personalized medicine aspect was evidenced by the varying treatment responses based on individual microbiota profiles, suggesting that patient stratification and treatment selection could be optimized through microbiota-based approaches. Moving forward, priorities should focus on validating microbiota signatures as predictive biomarkers, establishing optimal screening protocols, and developing targeted therapeutic approaches based on individual profiles, ultimately advancing toward more precise and personalized clinical care in women’s health management. This paradigm shifts toward PPPM/3PM in menopausal healthcare shows promise for optimizing long-term health outcomes through precisely tailored interventions based on individual microbiota profiles and clinical presentations. This comprehensive study transformed menopausal healthcare by applying the PPPM/3PM approach to vaginal microbiota research. It significantly advanced the shift from reactive to precision medicine by identifying specific vaginal microbiota signatures as early biomarkers for menopausal transition, POI, and GSM, enabling risk detection before symptom onset. Going beyond conventional approaches, the research integrated microbiota-based risk stratification with hormonal and clinical markers, established correlations between specific microbiota patterns and GSM symptoms, and evaluated diverse interventions for normalizing microbiome composition. By validating both hormonal and non-hormonal treatments and discovering baseline characteristics that predicted therapeutic responses, the study facilitated personalized treatment selection. This multifaceted framework supported the transition toward proactive, personalized gynecological care through precisely tailored interventions based on individual microbiome profiles, ultimately enhancing health outcomes for postmenopausal women and representing a transformative step toward precision medicine in menopausal healthcare. Abbreviations - GSM Genitourinary syndrome of menopause - FSH Follicle-stimulating hormone - LH Luteinizing hormone - POI Primary ovarian insufficiency - POF Premature ovarian failure - VSM Vasomotor symptoms - NAMS North American Menopause Society - VMI Vaginal maturation index - VHI Vaginal health index - CST Community state types - HPV Human papillomavirus - ROC Receiver operating characteristic curve - AUC Area under the curve - LDA Linear discriminant analysis - AMH Anti-Müllerian hormone - DHEA Dehydroepiandrosterone - CEE Conjugated equine estrogens - UDI-6 Urogenital Distress Inventory scores - FSFI Female Sexual Function Index - Er:YAG Erbium in yttrium aluminum-garnet crystal laser Author contribution All authors contributed to the study conception and design. Material preparation, data collection, and analysis were performed by Panchita Pongsupasamit, Chanisa Thonusin, and Suchaya Luewan. The first draft of the manuscript was written by Panchita Pongsupasamit, and all authors commented on and revised previous versions of the manuscript. Nipon Chattipakorn and Siriporn C. Chattipakorn provided funding acquisition and supervision. All authors read and approved the final manuscript. Funding This study was supported by a Distinguished Research Professor Grant from the National Research Council of Thailand (N42A660301: S.C.C.); the Research Chair Grant from the National Research Council of Thailand (N42A670594: N.C.); and a Chiang Mai University Center of Excellence Award (N.C.). Data availability No datasets were generated or analysed during the current study. Declarations Competing interests The authors declare no competing interests. Footnotes Publisher's Note Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

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[DOI] [PubMed] [Google Scholar] Associated Data This section collects any data citations, data availability statements, or supplementary materials included in this article. Data Availability Statement No datasets were generated or analysed during the current study.