Abstract

Objective:

Polycystic ovary syndrome (PCOS) is a complex endocrine disorder. In this study, we characterize serum bile acids (BAs) metabolic profiles in PCOS patients using targeted metabolomics and investigated their correlation with embryonic parameters, thereby elucidating the role of BAs in the pathogenesis of PCOS.

Methods:

We enrolled 20 PCOS patients undergoing in vitro fertilization-embryo transfer (IVF-ET) who met the Rotterdam criteria and 20 age-matched healthy controls. We recorded clinical baseline data and collected serum samples from all participants. The metabolic profiles of BAs were obtained by performing ultra-high-performance liquid chromatography–tandem mass spectrometry (UHPLC–MS/MS). Orthogonal partial least squares discriminant analysis (OPLS-DA) and receiver operating characteristic (ROC) curve analyses were performed to identify differential metabolites and evaluate their diagnostic value. Finally, we further analyzed the correlations between key differential metabolites and clinical indicators.

Results:

We identified 43 BA metabolites, including 22 upregulated and 21 downregulated species. We selected 11 key BA metabolites, of which six demonstrated diagnostic potential based on ROC curve analysis. We found negative correlations between these metabolites and embryonic parameters, although none of the correlations were statistically significant.

Conclusion:

Although targeted metabolomics is an exploratory tool, it is valuable for identifying potential diagnostic biomarkers in PCOS, offering preliminary novel insights into the pathophysiology of PCOS. The findings of this study suggest that targeted modulation of the metabolism of BA may represent an emerging and promising strategy for ameliorating metabolic and reproductive dysfunction in PCOS; however, these findings need to be validated in larger, independent cohorts.

1 Introduction

Polycystic ovary syndrome (PCOS) is a highly prevalent endocrine and metabolic disorder in women of reproductive age. PCOS is characterized by the dysregulation of gonadotropin secretion, hyperandrogenism, anovulation, and polycystic ovarian morphology, and its global prevalence is approximately 10% (Lizneva et al., 2016). PCOS is frequently accompanied by insulin resistance, obesity, and abnormalities in lipid metabolism (Anagnostis et al., 2018). These metabolic disturbances not only increase long-term risks associated with type-2 diabetes and cardiovascular diseases (Dapas and Dunaif, 2022) but also impair the reproductive health of affected women. Metabolic dysfunction may serve as an important link between reproductive abnormalities and long-term complications in PCOS patients.

The molecular mechanisms underlying metabolic dysfunction in PCOS have largely been elucidated. For example, studies on the amino acid metabolism have found significantly high plasma levels of branched-chain amino acids (BCAAs) in PCOS patients, with specific amino acids being correlated with greater risks of obesity, insulin resistance, and metabolic syndrome (Ye et al., 2022). Research on lipid metabolism has further identified BCAAs as prognostic factors for insulin resistance (Hajitarkhani et al., 2021).

Recent studies have found the novel role of bile acids (BAs) in modulating inflammatory responses, thereby contributing to the pathogenesis of PCOS (Ding et al., 2024). However, the precise mechanisms by which they play a role in PCOS remain poorly understood. BAs are terminal metabolites of cholesterol that not only facilitate the digestion and absorption of lipids but also regulate glucose and lipid metabolism, energy homeostasis, and immune-inflammatory responses via specific signaling pathways (Fuchs et al., 2025). Clinical studies have demonstrated significantly higher fasting serum BA levels in PCOS patients than in controls (Zhu et al., 2024). Additionally, high levels of circulating conjugated BAs have been positively correlated with hyperandrogenism in women with PCOS, manifesting in associated clinical symptoms (Zhang et al., 2019). By conducting a randomized controlled trial, researchers identified characteristic alterations in follicular fluid BA profiles in PCOS patients, and targeted metabolomics analysis revealed high levels of primary and conjugated BAs, suggesting that they may serve as biomarkers in the pathogenesis of PCOS (Yu et al., 2023). Moreover, high levels of BA transporter expression in ovarian tissue and the disrupted metabolism of BA may directly impair folliculogenesis and ovulation (Yang et al., 2021). Therefore, this study investigated BA metabolism to determine its pathological role in PCOS.

Metabolomics is a key component of systems biology and enables the comprehensive analysis of metabolic products to reflect overall metabolic status and functional changes. By integrating targeted metabolomics with bioinformatics, we elucidated the pathological mechanisms underlying BA metabolism in PCOS and its association with embryonic parameters, thereby providing novel therapeutic insights.

2 Materials and methods2.1 Participants

In this study, the patient cohort comprised 20 individuals with PCOS undergoing in vitro fertilization-embryo transfer (IVF-ET), and the control group comprised 20 randomly selected age-matched healthy women. The diagnosis of PCOS adhered to the 2003 Rotterdam criteria (Rotterdam ESHRE/ASRM-Sponsored PCOS Consensus Workshop Group, 2004), requiring at least two of the following: (1) oligo-ovulation or anovulation; (2) presence of clinical (hirsutism, acne) or biochemical hyperandrogenism; (3) polycystic ovarian morphology, as determined by ultrasound examinations (≥12 follicles 2–9 mm in diameter in one or both ovaries and/or ovarian volume ≥10 mL). The exclusion criteria were as follows: (1) coexisting endocrine disorders (hyperprolactinemia, congenital adrenal hyperplasia, Cushing’s syndrome, and androgen-secreting tumors); (2) use of medications affecting outcomes within 3 months (oral contraceptives, hormonal agents, insulin sensitizers, and lipid-lowering drugs); (3) severe cardiopulmonary, hepatic, renal, or psychiatric conditions precluding participation. The study was approved by the Ethics Committee of the First Affiliated Hospital of Heilongjiang University of Chinese Medicine (approval no. KY2023-018), and all participants provided written informed consent.

2.2 Clinical data collection

Baseline clinical data were systematically recorded for all participants, including their height, weight, waist circumference, hip circumference, body mass index (BMI), and waist-to-hip ratio (WHR), along with their menstrual cycle characteristics. Fasting venous blood samples were collected on days 2–4 of the menstrual cycle or during withdrawal bleeding, processed into aliquots, and analyzed. Serum levels of luteinizing hormone (LH), follicle-stimulating hormone (FSH), LH/FSH ratio, estradiol (E2), testosterone (T), progesterone (P), fasting plasma glucose (FPG), fasting insulin (INS), triglycerides (TG), and low-density lipoprotein (LDL) were measured. Insulin resistance was quantified via homeostasis model assessment (HOMA-IR = [INS (mIU/L) × FPG (mmol/L)]/22.5). For participants undergoing IVF, several parameters were documented, including total oocytes, metaphase II (M2) oocytes, 2-pronuclei (2 PN) embryos, transferable embryos, and high-quality embryos.

2.3 Blood sample collection and preprocessing

For metabolomic analysis, another set of blood samples was left undisturbed at room temperature for 30 min and then centrifuged at 3,000 r/min for 10 min. The separated serum was aliquoted into EP tubes and stored at −80 °C in an ultra-low temperature freezer.

For metabolomic detection, 100 µL of plasma sample was first measured into an EP tube, and then 400 µL of ice-cold organic extraction solvent (methanol: acetonitrile, 1:1, v/v) was added. The mixture was immediately vortexed for 30 s and sonicated for 10 min in an ice-water bath. Next, the solution was incubated at −40 °C for 1 h to allow precipitation. Subsequently, the mixture was centrifuged at 12,000 r/min for 15 min at 4 °C. Finally, the supernatant was carefully transferred to LC-MS vials for subsequent UHPLC–MS/MS analysis.

2.4 UHPLC-MS/MS analysis

Bile acids were separated using a Vanquish ultra-high-performance liquid chromatography system coupled with a Waters ACQUITY UPLC BEH C18 column (150 × 2.1 mm, 1.7 μm, Waters Corp.). The mobile phase consisted of solvent A (5 mmol/L ammonium acetate in water) and solvent B (pure acetonitrile). The chromatographic conditions used for the analysis were that column temperature was maintained at 45 °C, autosampler temperature was set to 4 °C, and injection volume was 1 µL. Mass spectrometry was conducted on an Orbitrap Exploris 120 High-Resolution Mass Spectrometer in the parallel reaction monitoring (PRM) mode. The optimized ionization parameters included a spray voltage of +3,500 V (positive mode) and −3,200 V (negative mode), nitrogen gas flow rates of 40 (sheath gas) and 15 (auxiliary gas) arbitrary units, an auxiliary gas heater temperature at 350 °C, and a capillary temperature at 320 °C. The sweep gas was disabled (flow rate = 0) in this experiment.

2.5 Data processing and statistical analysis

The raw dataset comprising sample information, peak names, and peak intensities underwent log10 transformation before it was imported into the R ropls package. Supervised orthogonal partial least squares discriminant analysis (OPLS-DA) was applied to processed data (mean-centered and unit-variance-scaled) to identify metabolic differences between experimental and control groups. Variable importance in projection (VIP) scores were calculated to rank the contribution of each variable to the OPLS-DA model, with VIP >1.0 considered discriminatory. The model used seven-fold cross-validation as the default and underwent 200 response permutation tests to generate R2 and Q2 values for assessing model robustness and overfitting risk. R2Y = (0, 0.39) and Q2 = (0, −0.64). Generally, a Q2 value > 0.5 indicated good predictive capability for the model. Differential metabolites were determined based on the VIP score (VIP > 1.0) derived from the OPLS-DA model.

2.6 Statistical methods

Clinical data and laboratory test results were recorded in spreadsheets (Microsoft Excel) and analyzed using SPSS (version 27.0). Continuous variables following a normal distribution were presented as the mean ± standard deviation (SD), and between-group comparisons were performed using independent-samples t-tests. Data that did not follow a normal distribution were expressed as median (interquartile range), and group differences were assessed via the Mann–Whitney U test. The association between bile acid levels and embryo parameters was investigated using the Spearman rank correlation method. In multivariate analyses, including orthogonal partial least squares-discriminant analysis (OPLS-DA), known metabolic confounders that differed significantly between groups—BMI, homeostasis model assessment of insulin resistance (HOMA-IR), and TG—were incorporated as covariates. This adjustment was made to isolate the specific metabolic signature associated with PCOS independent of these common metabolic confounders. Statistical significance was set at p < 0.05.

2.7 Metabolomic data processing and quality control

To ensure data quality and decrease technical variation, we implemented a standardized preprocessing pipeline. We filtered raw peak areas and imputed any missing values using a random value between 0.1 and 0.5 times the minimum observed value for the corresponding metabolite. Subsequently, the data were log10-transformed and scaled to unit variance so that they followed a normal distribution and to give equal weight to all variables before multivariate statistical analysis.

An important step to avoid batch effects involved all samples being analyzed within a single, continuous analytical batch. Although a pooled quality control (QC) sample was not included in the final study cohort, instrument performance and stability were rigorously monitored by the analytical platform vendor throughout the data acquisition period using standard QC protocols.

Bile acids were quantified in the PRM mode using a predefined, targeted panel of metabolites. This targeted approach increased sensitivity and specificity for the metabolites of interest.

3 Results3.1 Comparison of clinical characteristics

Significant differences were observed between the PCOS group and the normal control group in weight, BMI, waist circumference, and WHR (p < 0.05). The PCOS group had higher levels of LH, LH/FSH ratio, and T (p < 0.05), along with lower P (p < 0.05). In terms of glucose and lipid metabolism, FPG, INS, HOMA-IR, and TG were significantly higher in the PCOS group. Regarding embryo parameters, the PCOS group showed higher total oocyte retrieval and metaphase II (M2) oocytes (p < 0.05) (Table 1). These findings suggest that associations may be present, but the findings need validation in larger, independent cohorts.

ItemPCOS (n = 20)Normal (n = 20)p-valueAge (years)32.50 ± 4.1533.60 ± 3.550.373Weight (kg)65.03 ± 8.3758.40 ± 9.440.024Height (cm)164.40 ± 3.89164.35 ± 4.250.969BMI (kg/m2)24.00 ± 2.4321.62 ± 3.370.015Waist circumference (cm)100.32 ± 11.3690.23 ± 14.130.017WHR0.82 ± 0.010.80 ± 0.020.012LH (IU/mL)10.73 ± 3.294.64 ± 2.40<0.001FSH (IU/mL)5.82 ± 1.536.32 ± 1.910.368LH/FSH1.88 ± 0.440.78 ± 0.43<0.001E2 (pg/mL)47.3 ± 14.3653.9 ± 20.730.244T (ng/mL)0.42 ± 0.170.29 ± 0.160.008P (ng/mL)0.21 ± 0.090.33 ± 0.17−2.714FPG (mmol/L)5.48 ± 0.624.81 ± 0.43<0.001INS (µU/mL)16.07 ± 7.1610.87 ± 4.290.003HOMA-IR3.89 ± 1.652.31 ± 0.91<0.001TG (mmol/L)2.08 ± 0.941.36 ± 0.78<0.001LDL (mmol/L)3.28 ± 0.643.01 ± 0.440.126Total oocytes(n)16.00 (13.25–20.00)13.00 (9.00–17.75)0.0332 PN(n)12.00 (9.00–16.50)10.00 (5.50–12.75)0.063M2 oocytes(n)13.00 (11.25–17.75)11.00 (7.25–15.00)0.043Transferable embryos(n)5.50 (3.25–8.00)7.00 (4.25–7.00)0.237High-quality embryos(n)8.00 (6.00–13.25)7.00 (5.00–10.00)0.849

Clinical baseline characteristics of the participants.

3.2 Data analysis and metabolite identification of BAs

Principal component analysis (PCA) is an exploratory data analysis method, which was first used to construct an optimal projection plane that maps high-dimensional original data into a low-dimensional coordinate system with minimal loss of information, thereby concisely presenting the primary variance characteristics of the data (Figure 1A). Subsequently, OPLS-DA was applied for data modeling. Using this approach, we effectively separated orthogonal variations unrelated to classification variables while independently analyzing predictive variations, enabling a more accurate identification of differential metabolites associated with experimental groups and assessment of the strength of their correlations (Figure 1B). By conducting targeted metabolomics analysis, we identified 43 differential metabolites, including 21 downregulated and 22 upregulated species (Figure 2A). The top 10 upregulated and top 10 downregulated metabolites are illustrated in Figure 2B. As the key BA metabolites with VIP scores greater than 1, 11 compounds were obtained, which included isoursodeoxycholic acid (isoUDCA), murideoxycholic acid (MDCA), 3-epideoxycholic acid (βDCA), hyocholic acid (HCA), cholic acid (CA), glycoursodeoxycholic acid (GUDCA), glycocholic acid (GCA), deoxycholic acid-3-sulfate (DCA-3S), taurochenodeoxycholic acid (TCDCA), taurocholic acid (TCA), and taurolithocholic acid-3-sulfate (TLCA-3S) (Table 2).

PCA and OPLS-DA score plots derived from BA metabolomic profiles comparing the PCOS and normal control groups. PCA (A) and OPLS-DA (B) were performed for the PCOS patients (purple circle) and the healthy controls (blue circle).

Differential expression analysis of BA metabolites. Volcano plot (A) and VIP–weighted bar plot (B).

ParameterPCOS (n = 20)Normal (n = 20)VIP scorep-valueisoUDCA32.52 (20.51–60.01)62.98 (31.31–174.99)2.700.202MDCA53.14 (29.94–88.67)106.74 (43.54–234.61)2.590.209βDCA85.11 (55.24–108.07)87.53 (49.14–336.47)1.940.097HCA8.28 (3.92–17.53)4.28 (0.28–7.22)1.460.048*CA44.22 (20.81–123.32)20.89 (17.33–65.19)1.340.432GUDCA28.17 (7.82–51.11)21.91 (12.96–39.29)1.900.532GCA57.98 (33.70–259.77)124.48 (50.84–254.32)1.140.388DCA-3S4.34 (0.27–14.58)5.96 (0.23–8.08)1.730.106TCDCA37.18 (19.68–148.92)66.04 (27.31–138.28)1.090.689TCA6.42 (2.26–33.05)14.22 (7.29–50.13)1.050.509TLCA-3S23.25 (0.28–50.91)38.43 (20.44–64.37)1.060.281

Quantitative detection results of BAs in the PCOS and control group.

* indicates that the P-value is less than 0.05, which represents a statistically significant difference.

3.3 ROC curve analysis and expression levels of key BAs

The predictive sensitivity of the screened key metabolites for distinguishing PCOS from control groups was evaluated using the area under the receiver operating characteristic curve (AUC), with an AUC > 0.6 considered to suggest the presence of diagnostic value. Among the upregulated BAs, isoUDCA (AUC = 0.69; 95% CI: 0.523–0.875), MDCA (AUC = 0.66; 95% CI: 0.484–0.836), TCA (AUC = 0.62; 95% CI: 0.438–0.802), and TLCA-3S (AUC = 0.60; 95% CI: 0.414–0.776) demonstrated significant discriminatory ability. For downregulated BAs, DCA-3S (AUC = 0.68; 95% CI: 0.505–0.85) and HCA (AUC = 0.67; 95% CI: 0.498–0.847) met the threshold (Figure 3). Subsequent analysis of the expression levels of these six key BAs revealed that, compared to the control group, the PCOS group exhibited a significantly lower serum concentration of HCA (p < 0.05) (Figure 4). These findings indicate that BA metabolism may play a role in PCOS, but further investigation is needed to confirm this speculation.

ROC curves of the six key metabolites in the PCOS group. (A) Isoursodeoxycholic acid. (B) Murideoxycholic acid. (C) Taurocholic acid. (D) Taurolithocholic acid 3-sulfate. (E) Deoxycholic acid 3-sulfate. (F) Hyocholic acid.

Box plots showing differences in BA levels between the PCOS and control groups. (A) Isoursodeoxycholic acid. (B) Murideoxycholic acid. (C) Taurocholic acid. (D) Taurolithocholic acid 3-Sulfate. (E) Deoxycholic acid 3-Sulfate. (F) Hyocholic acid.

3.4 Correlation between key BA metabolites and clinical parameters

Based on the ROC curve analysis, six key BA metabolites were identified. To investigate their associations with embryonic parameters, Spearman’s correlation analysis was conducted. The results indicated that most of these BAs exhibited negative correlations with embryonic parameters, although none of the correlations were statistically significant (Table 3).

BAEmbryo parameterPearson’s rp-valueisoUDCATotal oocytes−0.2010.3972 PN−0.2970.203M2 oocytes−0.3100.184Transferable embryos−0.2970.204High-quality embryos−0.2780.235MDCATotal oocytes−0.1420.5502 PN−0.2490.289M2 oocytes−0.2570.273Transferable embryos−0.2550.278High-quality embryos−0.2300.329TCATotal oocytes−0.1640.4902 PN−0.0790.740M2 oocytes−0.1300.586Transferable embryos0.2900.215High-quality embryos0.0720.762TLCA-3STotal oocytes−0.0390.8692 PN−0.1590.502M2 oocytes−0.0230.924Transferable embryos−0.0530.824High-quality embryos−0.2010.395DCA-3STotal oocytes−0.2790.2332 PN−0.2420.304M2 oocytes−0.1930.415Transferable embryos−0.1550.515High-quality embryos−0.3280.158HCATotal oocytes−0.0720.7642 PN−0.1980.403M2 oocytes−0.2440.301Transferable embryos−0.4250.062High-quality embryos−0.2300.329

Association between BA levels and embryo parameters in IVF cycles.

4 Discussion

The etiology of PCOS is extremely complex. We investigated serum BA metabolic profiles in a cohort of PCOS patients undergoing IVF-ET and compared these profiles to those of healthy controls using targeted metabolomics. As the sample size was small (n = 40), the observed differences and associations described below are preliminary and exploratory; therefore, larger-scale studies are needed to validate our findings. Consequently, no mechanistic conclusions can be drawn from this study. Our analysis suggests potential alterations in the BA profile of PCOS patients relative to the control group. PCOS patients exhibited alterations in their BA profile, with significantly lower serum levels of isoUDCA, MDCA, TCA, TLCA-3S, GUDCA, GCA, and TCDCA, while levels of DCA-3S, CA, and HCA were significantly higher. Further ROC analysis demonstrated diagnostic value for isoUDCA, MDCA, TCA, TLCA-3S, DCA-3S, and HCA in distinguishing PCOS patients from healthy individuals, indicating their value as candidate biomarkers.

BAs function as signaling molecules that activate multiple nuclear and membrane receptor-mediated pathways in various tissues, thereby regulating glucose and lipid homeostasis, inflammatory responses, and energy expenditure (Fiorucci et al., 2021). The levels of BAs in PCOS patients, including those of cholic acid and ursodeoxycholic acid, are negatively correlated with androgen concentrations, which may aggravate the clinical manifestations of hyperandrogenism (Qi et al., 2019). Altered BA metabolism in PCOS, particularly aberrant synthesis and metabolic pathways in the liver and intestine, may be closely associated with the development of insulin resistance (Staels and Fonseca, 2009). In our study, although the results were not statistically significant, we observed that isoUDCA, an epimer of ursodeoxycholic acid (UDCA) with hydrophilic and cytoprotective properties (Purucker et al., 2001), was present at lower levels in PCOS patients than in healthy controls, suggesting that it may be involved in the pathogenesis of PCOS; however, further investigation is needed to confirm this speculation. Its downregulation may imply a weakened cytoprotective effect on liver, intestinal, or ovarian tissues, rendering these more susceptible to damage from metabolic disturbances or inflammatory stress; this could be a potential factor involved in the pathogenesis of PCOS.

As shown in animal studies, the intraduodenal administration of TCA markedly inhibits dietary lipid absorption and attenuates postprandial triglyceride fluctuations, thereby improving lipid metabolism (Farr et al., 2020). Xu et al. (2022) consistently reported that TCA alters the composition of gut microbiota and BA profiles while activating TGR5 and FXR signaling pathways to enhance the transport and reabsorption of BAs. The lower (though not significant) TCA levels observed in our PCOS patients align with this regulatory axis. The reduction in TCA may impair the regulation of lipid metabolism and maintenance of intestinal microecological homeostasis mediated via the TGR5/FXR pathways, which could partially explain the common abnormalities in lipid metabolism and propensity for insulin resistance in PCOS patients. This association requires further assessment in larger-scale studies.

Glycocholic acid (GCA) is a potent suppressor of hyperactive immune responses. Its mechanism involves the activation of the FXR receptor, which inhibits pro-inflammatory cytokine secretion in lipopolysaccharide-stimulated murine macrophages. This demonstrates that GCA can serve as a naturally derived anti-inflammatory agent capable of modulating immune function (Ge et al., 2023). Yamamura et al. (2025) revealed that fecal GCA levels exhibit a significant positive correlation with BMI, with high GCA groups showing substantially greater obesity risk. Genes encoding BA deconjugation enzymes were downregulated in these high-GCA cohorts, suggesting that GCA may influence the development of obesity by altering the composition of the gut microbiota. GCA supplementation can also alleviate hepatic cholestasis, hepatic steatosis, and intestinal damage induced by high-pectin diets. This effect is characterized by an increase in hepatic total BA concentrations and significantly upregulated expression of the FXR gene. The GCA-mediated activation of FXR promotes the expression of BA synthesis genes such as CYP7A1 and CYP27A1 and enhances the activity of BA transporters such as BSEP and NTCP, thereby restoring BA homeostasis (Yao et al., 2024). The non-significant trend toward lower GCA levels in our PCOS patients suggests a potential disturbance in this anti-inflammatory or metabolic regulatory pathway.

Hyocholic acid (HCA), a primary bile acid, exhibits unique receptor effects by simultaneously activating both TGR5 and FXR. This dual agonism increases the production and secretion of glucagon-like peptide-1 (GLP-1) in intestinal endocrine cells, thereby improving insulin sensitivity (Sun et al., 2021). A cohort study demonstrated that HCA levels can predict the future risk of diabetes as diabetic patients have significantly lower concentrations of fecal HCA (Wang et al., 2023). The glucose-lowering mechanism of HCA involves the activation of TGR5 and inhibition of FXR, thus increasing the secretion of GLP-1 from enteroendocrine cells. Additionally, the HCA/TGR5 signaling pathway in the ileum regulates postprandial GLP-1 expression through interactions between the gut microbiota and host metabolism (Anhê et al., 2019). Our data showed significantly higher serum HCA levels in PCOS patients (P = 0.048). This observed increase in HCA in PCOS may represent a compensatory adjustment or dysregulation that disrupts FXR-mediated suppression of hepatic gluconeogenesis and TGR5-dependent stimulation of GLP-1 secretion, thereby exacerbating insulin resistance (IR). The level of DCA-3S showed a non-significant reduction in PCOS patients in our study, which differs from some previous observations and requires further validation. Following the activation of FXR, NF-κB/Nrf2 signaling is inhibited, which attenuates the release of pro-inflammatory cytokines (Wang et al., 2024). FXR further regulates BA transporters such as Bsep, Ntcp, and Ugt1a1, along with metabolic enzymes such as Cyp7a1, thus alleviating inflammation and oxidative stress (Liu et al., 2022). Similarly, HCA suppresses NF-κB phosphorylation, nuclear translocation, and transcriptional activity, downregulating inflammatory genes (IL-1β, IL-6, and TNF-α) and inhibiting the LPS-induced activation of AKT, which exert anti-inflammatory effects (Kuang et al., 2023).

In summary, this preliminary investigation describes potential perturbations in the BA profile of PCOS patients. The changes observed in specific BAs, such as isoUDCA, TCA, GCA, and HCA, offer exploratory hypotheses regarding their involvement in PCOS-related metabolic and inflammatory dysregulation. However, the absence of statistically significant correlations indicates that the results should be interpreted with caution. Future studies with larger sample sizes are needed to validate these alterations in the BA profile, determine their clinical relevance, and identify any causal role in the pathophysiology of PCOS.

5 Limitations

Although this study preliminarily revealed the potential role of BA metabolism in diseases through targeted metabolomics analysis, it had certain limitations. First, only the change in the level of HCA was statistically significant. Although another five types of BAs (UDCA, MDCA, TCA, TLCA-3S, and DCA-3S) showed diagnostic value through the ROC curve, the differences in their expressions were not significant. This may be related to insufficient statistical power due to a small sample size, and the statistical power may also be limited by the sensitivity of serum metabolite detection or biological variation. Although these five types of BA did not show significant differences, they have potential associations with key receptors (such as FXR and TGR5) in metabolic pathways, and they may participate in the disease process by regulating glycolipid metabolism or inflammatory signals in a coordinate way. For example, the hydrophilic cytoprotective effect of UDCA and the regulatory effect of taurocholic acid on the intestinal flora suggest that these metabolites may serve as markers or regulatory targets for disease progression.

Second, as this was an observational and cross-sectional study, causality cannot be inferred. The observed associations between altered BA profiles and PCOS phenotype could not establish whether these changes were a cause or a consequence of the metabolic disturbances of the syndrome. Third, the lack of measurement of the key BA synthesis biomarker 7α-hydroxy-4-cholesten-3-one (C4) limits the ability to delineate whether the observed serum BA profile stems primarily from dysregulated hepatic synthesis or altered enterohepatic circulation. Finally, a single serum metabolomic snapshot cannot accurately capture potential diurnal or physiological fluctuations in BA levels, and the targeted approach may have missed other relevant metabolites.

In this study, the statistical analysis had several limitations. Multiple comparison correction was applied using the Benjamini–Hochberg procedure to control the false discovery rate (FDR) for the 43 BA metabolites measured. However, after adjustment, most of the resulting Q-values did not reach the conventional threshold for statistical significance (FDR <0.05), with the reported adjusted Q-value being 0.99. Therefore, the interpretation of differential metabolites relied primarily on VIP scores from OPLS-DA and unadjusted nominal p-values.

This outcome may be attributed to insufficient statistical power, given that the sample size was relatively small (n = 20 per group). Strict correction for multiple testing further reduced the ability to detect metabolites with modest but biologically me