Abstract

Background:

Estrogens have been proposed as modulators of gut microbiome (GM) composition, yet evidence from observational studies remains inconsistent.

Objective:

This meta-analysis aimed to systematically summarise existing evidence on GM alterations in hypoestrogenic women – post-menopausal or premature ovarian insufficiency (POI) – compared to euestrogenic pre-menopausal controls.

Methods:

PubMed, SCOPUS and Embase were searched through December 2024 for studies comparing GM characteristics between hypoestrogenic and pre-menopausal women. Primary outcome was α-diversity (Shannon index). Secondary outcomes included relative abundances of Bacteroidetes, Firmicutes, and the Bacteroidetes to Firmicutes ratio. Random-effects models were used for data synthesis.

Results:

Out of 1092 studies screened, 7 met the inclusion criteria (n = 45 women with POI, n = 1222 post-menopausal women, n = 463 eustrogenic controls). No significant differences were observed in α–diversity (p=0.990), Bacteroidetes (p=0.440), or Firmicutes abundance (p=0.110) between hypoestrogenic and euestrogenic groups, irrespective of POI or postmenopause. Similarly, the Bacteroidetes to Firmicutes ratio showed no significant difference between the groups (p=0.400). Study heterogeneity was high (I² 61-99%).

Conclusion:

Current evidence does not support consistent differences in GM diversity or major bacterial phyla between hypoestrogenic and euestrogenic women. Given the substantial heterogeneity, limited control of confounding factors, and variability in methodological quality, these findings should be interpreted with caution. High-quality, well-controlled studies are needed to better define the relationship between estrogen status and the GM.

1 Introduction

Interest in the gut microbiome (GM) and its potential influence on health has expanded rapidly in recent years (1–4). The GM is a complex and dynamic ecosystem shaped by host biology and environmental factors, including age, diet, lifestyle, and hormonal status (5–10). In women, reproductive ageing represents a major transition, characterized by profound changes in circulating estrogen levels. Because estrogens have immunomodulatory, metabolic, and epithelial effects, it has been hypothesised that shifts in estrogen status – particularly during menopause or premature ovarian insufficiency (POI) – may be reflected in alterations in GM composition.

Mechanistically, the relationship between estrogens and the GM has been attributed in part to the “estrobolome”, a collection of bacteria-driven enzymatic reactions involved in gut estrogen metabolism (11–14) (Figure 1). This process facilitates entero-hepatic recirculation of estrogens and has been proposed as a potential mechanism by which the GM may modulate estrogen availability. Conversely, estrogens may influence GM composition by influencing gut barrier integrity, immune response, and microbial niche conditions (15–17). These bidirectional interactions have prompted hypoestrogenic states may be associated with dysbiosis.

Overview of the Estrobolome. Endogenous estrogens are mainly secreted by the ovaries, adrenal glands, and adipose tissue. After entering systemic circulation, they reach the liver where they undergo first-pass metabolism and conjugation, forming glucuronidated estrogens. A portion is excreted in urine, while another portion enters the intestines, where some are deconjugated by β-glucuronidase-producing bacteria, resulting in unconjugated estrogens. These are reabsorbed into systemic circulation via enterohepatic circulation, thus increasing circulating estrogen levels. Created in BioRender. Sarav, K. (2024)https://BioRender.com/y23y384.

Despite this growing interest, studies directly comparing the GM of hypoestrogenic women (post-menopausal or POI) with euestrogenic pre-menopausal controls have produced inconsistent results. Some small-scale studies have reported reduced α-diversity (which includes the Shannon Index, reflecting species diversity) among post-menopausal women when compared to menstruating women, while others have demonstrated minimal differences (18–20). In POI, findings have also varied, with some reports suggesting specific taxonomic perturbations or decreased β-diversity (17, 21). Importantly, many of these investigations are limited by small sample sizes, heterogeneous populations, inconsistent exclusion of known GM disruptors (e.g., obesity, diabetes, antibiotics, probiotics, smoking), and variability in sequencing methods and analytic pipelines. The most commonly used parameter to assess differences between bacteria groups is α-diversity, which includes the Shannon Index as a metric and reflects species diversity within a specific ecosystem, combining species richness and evenness. Whereas β-diversity measures the change in species diversity between ecosystems and can be used for ecosystem comparison (22).

In the current literature, no systematic revisions with meta-analysis on GM in women with hypoestrogenism involving postmenopausal and women with POI are available. In this study, we aimed to systematically review and combine existing data to understand the alterations in GM changes in both postmenopausal and women with POI.

2 Materials and methods

The study was conducted according to a predesigned protocol, developed in conformance with the 2015 PRISMA (Preferred Reporting Items for Systematic reviews and MetaAnalyses) statement (Supplementary File 1) [52]. The meta-analysis was a priori registered in the international prospective register of systematic reviews (PROSPERO) database (ID CRD42024497630).

2.1 Search strategy and study selection

We did a comprehensive electronic search of the PubMed, SCOPUS and Embase library databases forstudies published from inception until December 23, 2024 using the following terms:(“premature ovarian insufficiency” OR “POI” OR “menopause” OR “post-menopause”) AND (“gut microbiome” OR “gut microbiota” OR estrobolome) (Supplementary File 2). Postmenopause and POI were defined as loss of ovarian function in women above or under 40 years of age, respectively. We included full-English-text, original, observational studies regarding GM composition in hypoestrogenic compared to euestrogenic women. We further searched bibliographies of included articles to identify any eligible studies that the electronic search may have missed. Studies reporting on hormonal replacement therapy (HRT) use or women with active infections, active intestinal diseases, or history of cancer were excluded.

Two reviewers (KS and FC) independently screened identified studies for eligibility. Conforming to the predefined inclusion criteria, they reviewed the titles and abstracts of identified studies in duplicate and removed all studies that did not fulfill the inclusion criteria at this stage. When reviewers disagreed, studies progressed to the next stage. In this phase, the same reviewers independently screened full-text articles to assess eligibility for final inclusion. When there was any conflict, it was solved by two co-agreeing investigators (LM and DS).

2.2 Data extraction and quality assessment

Data extracted from each study included first author, year of publication, country where the study was conducted, recruitment period, definition of hypoestrogenic status, inclusion/exclusion criteria, population recruited (including age at recruitment, age at menarche and menopause, years since menopause, body mass index [BMI], waist-hip circumference [WHR], smoking and alcohol drinking habits, number of previous pregnancies), potential confounders, GM profiling method, and relevant results (including GM characteristics, pituitary-gonadal axis hormone levels). Data extraction was performed by two reviewers, KS and FC. Studies were divided between the reviewers, with each reviewer independently extracting data from their assigned studies using a standardized data extraction form. Any uncertainties or ambiguous data were discussed between reviewers and resolved by consensus. When studies were considered eligible, but data were incomplete in the article or in the Supplementary Materials, corresponding authors were contacted via email to obtain missing data.

The primary endpoint was α-diversity, evaluated by the Shannon index, comparing hypoestrogenic women (study group) to euestrogenic pre-menopausal women (control group). The α-diversity index is considered as a closer proxy of intestinal dysbiosis and measures species heterogeneity in a single sample. When this index was not available, it was calculated at the species level using all the identified species reported in the Supplementary Materials with the formula H=−∑[(pi)×loge(pi)], where: H, Shannon diversity index; pi, proportion of individuals of one particular species in the whole microbiota community; ∑, sum (23). When studies reported the median and the interquartile range (IQR) of the index, the corresponding mean ± standard deviation (SD) was calculated [54, 55]; meanwhile, when articles reported the standard error of mean (σ), SD was calculated using the formula SEM = σ/√n; σ = SEM × √n (1), where n indicates the number of subjects. Secondary endpoints were β-diversity (species diversity between different samples), Firmicutes, Bacteroidetes and other phyla relative abundances, and Bacteroidetes to Firmicutes ratio.

Two reviewers (DS and FC) independently assessed the quality of each included study, using the Newcastle-Ottawa scale for observational studies. This scale relies on a 9-star system in which scores of 0–3, 4–6, and 7–9 are considered poor, moderate and good quality, respectively [56].

2.3 Data synthesis and statistical analysis

Heterogeneity among studies (I2) was considered as “low,” “moderate,” and “high” for values of 25, 50, and 75%, respectively [57]. Considering the high heterogeneity expected for the outcomes selected, the random effect model was applied to evaluate the mean difference (MD) among continuous data.

Subgroup analyses were performed considering if the hypoestrogenism was due to POI or to post-menopause, and based on the sequencing method, shotgun metagenomic sequencing or 16S rRNA gene sequencing. To address the potential confounding due to oral contraceptive use reported in premenopausal participants in one study (19), a subanalysis removing this study was conducted. If a significant difference was detected between post-menopausal/POI and pre-menopausal women, or between shotgun metagenomic sequencing or 16S rRNA gene sequencing, meta-regression analysis was performed, considering other endpoints extracted. Since meta-regression is typically recommended only when there are ≥10 studies per covariate, analyses including fewer studies were considered purely exploratory. The meta-regression analysis result was synthesized reporting both slope (S) and intercept (I) with appropriate lower and upper limits.

The Review Manager (RevMan) 5.3 software (Version 5.3.1 Copenhagen: The Nordic Cochrane Centre, The Cochrane Collaboration, 2014) was used to perform meta-analyses. Meta-regression analyses were performed using Comprehensive Meta-analysis Version 2, Biostat (Englewood, NJ, USA). Statistical significance was considered for p values < 0.05.

3 Results

Among 1092 abstracts screened, 21 studies were assessed for eligibility (Figure 2). Fourteen studies were excluded, ten because the primary endpoint was not reported, one because the hypoestrogenism was not defined, one because the participants had chronic infection, one because the participants had an oncological history and one since it involved dataset published in a more recent article (Figure 2, Supplementary File 3). Seven studies were included in the meta-analysis (Figure 2). Four studies assessed the GM using shotgun metagenomic sequencing (18, 19, 24, 25), whereas three studies used 16s rRNA gene sequencing method (17, 21, 26). The characteristics of the included papers are summarized in Table 1 (17–19,21, 24–26). The articles were published between 2018 and 2024 and had sample sizes in a range of 30 to 1322 patients. All studies were cross-sectional (17–19, 21, 24–26). Four were conducted in China (17, 18, 21, 24), two in the USA (19, 25), and one in Japan (26). The participants were pre-menopausal women (n = 463 in total) (17–19, 21, 24–26), post-menopausal women (n = 1222 in total) (18, 19, 24–26), and women with POI (n = 45 in total) (17, 21). Three studies were assessed as moderate risk of bias and four as low risk (Supplementary File 4).

PRISMA flow diagram.

Author (year)CountryPeriodPopulation included in the meta-analysisDefinition of hypoestrogenic statesExclusion criteriaConfoundersEstrogen serum levels (pmol/L), Mean ± SDSampling and SequencingSampleSequencing methodSequencing platformZhu et al. (2018) (24)ChinaNA25 pre-menopausal women
46 post-menopausal womenNABoth groups:
1) Diarrhea, diabetes mellitus, ulcerative colitis, Crohn’s disease, or infectious diseases
2) Usage of antibiotics in the past 3 months
3) Usage of steroid hormones
4) Usage of probiotics or Chinese herbal medicine 3 months beforeNANAFecesShotgun metagenomic sequencingIllumina HiSeqZhao et al. (2019)
(18)ChinaNA24 pre-menopausal women
24 post-menopausal womenNABoth groups:
1)History of chronic serious infection, any current infection and any type of malignant cancer
2) Usage of antibiotic treatment within 1 month before participating in the study
Post-menopausal group:
1) other than natural menopauseHypertension, Diabetes, Hyperlipidemia, Smoking, Alcohol intake, Diarrhea, Gastritis, Hyperthyreosis
Fatty liver diseaseNAFecesShotgun metagenomic sequencingBGISEQ-500 platformWu et al. (2021)
(21)ChinaFrom August 2019 to September
201918 pre-menopausal women
35 POI patientsPOI:
- primary or secondary amenorrhea for at least 4 months
- at least two determinations of serum FSH > 40 IU/L with an interval of 4–6 weeks
- before 40 yearsBoth groups:
1) Pregnancy
2) Tumor, chronic diarrhea, autoimmune diseases, gastrointestinal disease, active infections
3) Use of antibiotics/medications in the preceding 3 months
4) Chemo/radiotherapy
5) BMI< 18.5 or > 23.9 kg/m2
6) Smoking
Pre-menopausal group:
1) normal ovarian function,
2) no history of menstrual dysfunction and infertility;
3)regular menstruation
4) FSH < 10 IU/LPre-menopausal group:
200.31 ± 33.04
POI group:
112.75 + 42.95Feecs16S rRNA (V3-V4)Illumina NovaSeqJiang et al. (2021)
(17)ChinaNA10 pre-menopausal women
10 POI patients
10 POI patients taking HRTPOI:
- oligo/amenorrhea for at least 4 months,
- FSH > 25 IU/L detected at two intervals more than four weeks apart
- before 40 years old.Both groups:
1) Infections, malignant tumors, intestinal diseases, obesity or other metabolism-related diseases
2) drug or alcohol use, and antibiotics, probiotics, or prebiotics use in the past three monthsNAPre-menopausal group:
410.09 ± 72.62
POI group:
102.03 ± 24.86Feecs16S rRNA (V3-V4)Illumina HiSeqYoshikata et al. (2022)
(26)JapanFrom May 2021 to
July 202135 pre-menopausal women
35 post-menopausal womenPost-Menopause:
1) No menstruation for 12 months;
2) FSH >25 mIU/mL;
3) E2 <20 pg/mL.Both groups:
1) Having or being treated for genitourinary symptoms such as vaginitis and cystitis
2) those with unstable ovarian functionNAPre-menopausal women:
617.15 ± 968.44
Post-menopausal group:
36.7 + 0.00Feces16S rRNA (V1-V2)NAPeters et al. (2022)
(19)USAFrom 2014 to 2017295 premenopausal women
1027 postmenopausal womenPost.Menopause:
1) answered no to the question “Have your natural periods stopped permanently?”Both groups:
1) Cancer history
Postmenopausal group:
1) HRT or hormonal birth control
2) Other than natural menopause
3) <35 years old
Premenopausal group:
1) > 55 years old
2) Did not have a period
within 90 days prior to the visit
3) > 45 years old at the study visit with stool sample collected >2 years after the study visit
4) > 45 years at the study visit with stool sample collected <2 years after the study visit but did not have a period within 60 days prior to the visitObesity, Hypertension, Diabetes, Hyperlipidemia, Smoking, Alcohol intake, Antibiotics
Oral contraceptive pills in the premenopausal groupNAFecesShotgun metagenomics sequencingIllumina NovaSeqWang et al. (2024)
(25)USAFrom 2015 to 201956 pre-menopausal women
90 Post-menopausal womenPost-Menopause:
1) no periods for ≥12 months; not due to pregnancy or medication use
3) bilateral ovariectomy
4) uncertain status but age ≥55 yearsPost-menopausal group:
1) taking hormonal contraceptives or HRTObesity, Hypertension, Diabetes, Hyperlipidemia, Smoking, Alcohol intake, Recreational drug useNAFecesShotgun metagenomics sequencingIllumina NovaSeq

Study population characteristics.

POI, Premature ovarian insufficiency; FSH, Follicle stimulating hormone; E2, Estradiol; NA, Not available in the original article.

The α-diversity index was not significantly different between hypoestrogenic and euestrogenic women (p=0.990, I2 = 73%) (Figure 3). Subgroup analysis also showed a lack of statistical significance (POI vs. eustrogenic women, p=0.070, I2 = 0%; post-menopausal vs. eustrogenic women, p=0.570, I2 = 80%). This result remained also when the work by Peters et al. has been removed (mean difference -0.01; 95%CI: -0.21, 0.20, p=0.950) (Figure 4). Similarly, no consistent differences were found when dividing studies using shotgun metagenomic sequencing (mean difference -0.06; 95%CI: -0.25, 0.13, p=0.520) and 16S rRNA gene sequencing (mean difference 0.09; 95%CI: -0.07, 0.26, p=0.250) (Figure 5).

Forest plot showing the comparison of α–diversity index (Shannon index) between hypoestrogenic and euestrogenic women. SD, standard deviation; CI, confidence interval.

Forest plot showing the comparison of α–diversity index (Shannon index) between hypoestrogenic and euestrogenic women after removal of the Peters study. SD, standard deviation; CI, confidence interval.

Forest plot showing the comparison of α–diversity index (Shannon index) between hypoestrogenic and euestrogenic women based on the sequencing method. SD, standard deviation; CI, confidence interval.

No significant differences were seen in Bacteroidetes (Figure 6A) and Firmicutes components (Figure 6B) between hypoestrogenic and eustrogenic women (p=0.440, I2 = 68% and p=0.110, I2 = 77%, respectively).

Forest plot showing the comparison of Bacteroidetes abundance (A) and Firmicutes abundance (B) between hypoestrogenic and euestrogenic women. SD, standard deviation; CI, confidence interval.

Considering Bacteroidetes, the lack of difference remained also dividing studies using shotgun metagenomic sequencing (mean difference 0.02; 95%CI: -0.04, 0.08, p=0.550) and 16S rRNA gene sequencing (mean difference 0.02; 95%CI: -0.06, 0.09, p=0.640) (Figure 7A). Similar results were obtained for Firmicutes analysis (shotgun metagenomic sequencing - mean difference -0.04; 95%CI: -0.11, 0.02, p=0.210) and 16S rRNA gene sequencing (mean difference -0.04; 95%CI: -0.12, 0.05, p=0.400) (Figure 7B). This lack of significant difference was confirmed also in subgroup analyses, when Peters et al. was removed in both Bacteroidetes (mean difference 0.03; 95%CI: -0.03, 0.09, p=0.270) (Figure 8A) and Firmicutes analysis (mean difference -0.06; 95%CI: -0.12, 0.01, p=0.080) (Figure 8B).

Forest plot showing the comparison of Bacteroidetes abundance (A) and Firmicutes abundance (B) between hypoestrogenic and euestrogenic women based on the sequencing method. SD, standard deviation; CI, confidence interval.

Forest plot showing the comparison of Bacteroidetes abundance (A) and Firmicutes abundance (B) between hypoestrogenic and euestrogenic women after removal of the Peters study. SD, standard deviation; CI, confidence interval.

Finally, considering the Bacteroidetes to Firmicutes ratio, no consistent differences were observed comparing hypoestrogenic to euestrogenic women (p=0.400, I2 = 99%), irrespective of POI and post-menopause (Figure 9).

Forest plot showing the comparison of Bacteroidetes to Firmicutes ratio between hypoestrogenic and euestrogenic women. SD, standard deviation; CI, confidence interval.

4 Discussion

In this systematic review and meta-analysis, we found no significant differences in α-diversity, relative abundances of Bacteroidetes and Firmicutes, or the Bacteroidetes to Firmicutes ratio between hypoestrogenic and euestrogenic women. These results were consistent across subgroup analyses of post-menopausal women and women with POI, and across both shotgun metagenomic and 16S rRNA gene sequencing platforms.

The absence of clear differences contrasts with the hypothesis that declining estrogen levels contribute significantly to altered GM composition through the estrobolome. Although β-glucuronidase activity and enterohepatic recirculation of estrogens remain biologically plausible mechanisms of host–microbe interaction, the aggregated data suggest that these processes may not translate into reproducible, large-scale compositional changes detectable at the level of α-diversity or dominant phyla. Estrogen-related effects may be subtle, strain-specific, or functionally relevant without producing broad taxonomic shifts.

Similarly, while individual studies have reported diminished abundance of certain β-glucuronidase-expressing species (e.g., Parabacteroides johnsonii, Clostridium lactatifermentans, Akkermansia muciniphila) (19) and short-chain-fatty-acid-producing genera (Roseburia) in hypoestrogenic states (18, 19, 27), these findings have not been consistent across studies. Women with POI were found to have increased levels of Eggerthella in their feces, indicating a possible shift towards gut dysbiosis, which was reversed by stroprogestagen therapy (17). Supporting a possible causal role, mice administered Eggerthella showed signs of ovarian fibrosis and inflammation, which were ameliorated after introducing estradiol (17).

Differences in sequencing depth, taxonomic assignment, and statistical correction further complicate cross-study comparisons. Our results emphasise that proposed estrogen–microbiome interactions may not manifest through broad microbial diversity metrics but may instead involve narrower, functionally relevant pathways that require more refined analysis. Mouse models showed different caecal microbial flora based on sex hormone level, and estrogen receptor stimulation in male mice resulted in a significant reduction in the Shannon index and in the abundance of bacterial species known to influence insulin sensitivity (28), as well as a decline of Proteobacteria and a higher abundance of Akkermansia (29). All these findings suggest a bidirectional relationship between the steroid sex-hormone levels and the gut microbial ecosystem, where the diversity and abundance of GM depend on the level of sex hormones, while the activity of certain microbiome species modulates hormonal levels through deconjugation and entero-hepatic recycling.

The high degree of heterogeneity observed across analyses suggests that the lack of standardisation and variable population characteristics have likely obscured possible associations. Some of the included studies enrolled participants with conditions known to alter the GM – such as obesity (25), hypertension (18, 25), diabetes (18, 25), dyslipidemia (18), gastrointestinal disorders (18), alcohol consumption (18), and smoking (18) – yet did not consistently exclude or adjust for these factors. Given the established impact of these conditions on GM diversity and composition, they may represent stronger determinants of GM structure than estrogen status itself. Similarly, dietary intake and probiotic use, both major modulators of the GM, were insufficiently reported in most studies. Antibiotic exposure was inconsistently addressed (25, 26), and in some cohorts (24), the exclusion window may not have been adequate to fully mitigate its effects (30). These issues introduce substantial residual confounding and restrict the ability to attribute differences – or lack thereof – to estrogen status.

Geographical clustering also limits generalisability. Most studies were conducted in East Asia or North America, with relatively homogenous dietary patterns, genetic backgrounds, and lifestyle factors within cohorts. Given the profound effect of geography and diet on the GM, the evidence base lacks broader representation from other regions, limiting the generalisability of our findings (