Diagnosis-dependent Metabolic Reprogramming of Follicular Fluid

Diagnosis-dependent Metabolic Reprogramming of Follicular Fluid | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Diagnosis-dependent Metabolic Reprogramming of Follicular Fluid Csilla Kurdi, Dávid Hesszenberger, Dávid Csabai, Anikó Lajtai, and 6 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-9609696/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 7 You are reading this latest preprint version Abstract Background The follicular fluid (FF) microenvironment plays a critical role in oocyte maturation and embryo development, reflecting local ovarian activity and systemic metabolic status. While metabolic alterations in FF have been described in different diseases, comparative analyses across different infertility-related disorders remain limited. Objective This study aimed to characterize and compare amino acid profiles in FF from patients undergoing in vitro fertilization (IVF) with insulin resistance (IR), endometriosis (EM), thyroid dysfunction (TD) and to identify disease-specific metabolic signatures. Methods We analyzed 171 FF samples using targeted ultra-high-performance liquid chromatography. The twenty proteogenic amino acids were quantified and analyzed using univariate and multivariate statistical analyses, including Kruskal-Wallis testing with post-hoc correction, generalized linear modeling adjusted for BMI, principal component analysis (PCA) and pathway enrichment analysis. Results PCA revealed that the global amino acid composition of FF was largely conserved across all groups. However, disease status had a statistically significant but moderate effect on the overall metabolic profile (PERMANOVA, R²=0.081, p = 0.001). After adjusting for BMI, IR was associated with decreased glycine and arginine levels and TD was associated with lower histidine, tryptophan and lysine concentrations. In contrast, EM was characterized by a selective increase in the histidine content. Pathway analysis revealed alterations in branched-chain amino acid (BCAA) metabolism in IR group and broader disruptions in central amino acid metabolism in TD group. Multivariate and ROC analyses indicated limited discriminative performance of individual amino acids (AUC ≤ 0.71), suggesting that metabolic alterations are subtle and distributed across multiple pathways. Conclusions FF amino acid composition is tightly regulated but distinct disease-specific metabolic alterations can be detected in IR, EM and TD. These changes are independent of BMI and reflect coordinated alterations in the metabolism of different amino acids rather than strong individual biomarkers. These results show the sensitivity of the follicular environment to overall metabolic health and support the idea of using multivariable metabolic patterns to better understand reproductive dysfunction. follicular fluid amino acids insulin resistance endometriosis thyroid dysfunction metabolomics in vitro fertilization Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 1. Introduction Successful oocyte maturation and embryo development depend on a finely tuned microenvironment within the ovarian follicle.[ 1 ] The follicular fluid (FF) surrounding the oocyte not only nourishes the gamete but also reflects the dynamic interplay between local ovarian activity and systemic physiological states.[ 2 ][ 3 ] Composed of metabolites secreted by granulosa and theca cells, as well as circulating components crossing the blood-follicle barrier, FF represents a picture of both the follicular and whole-body physiology at the time of oocyte retrieval.[ 3 ][ 4 ] Among its constituents, amino acids play multifaceted roles that extend far beyond serving as building blocks for protein synthesis. They provide energy, regulate osmotic balance and act as precursors for signaling molecules and antioxidants such as glutathione. [ 5 ] Emerging evidence indicates that alterations in FF amino acid composition can influence oocyte competence, fertilization rates and early embryo development, suggesting that the follicular amino acid milieu serves as a sensitive indicator of reproductive health. [ 6 ] Endocrine and metabolic disorders, such as IR and TD, and inflammatory conditions such as EM, are frequently associated with infertility and suboptimal assisted reproductive technology (ART) outcomes. [ 7 ] [ 8 ] [ 9 ] IR disrupts glucose and amino acid metabolism within the follicle, potentially limiting nutrient availability for developing oocytes. [ 10 ] Chronic inflammation in EM may elevate oxidative stress and alter metabolic signaling in FF. [ 11 ] Thyroid hormones, as central regulators of systemic metabolism, can profoundly affect ovarian function and the local follicular environment when imbalanced. [ 12 ] Despite the clinical significance of these conditions, the mechanism by which they uniquely reprogram the FF amino acid landscape remain unclear. Previous studies have often examined single disorders [ 13 ] [ 14 ] [ 15 ] or global metabolic trends, leaving a gap in our understanding of diagnosis-specific alterations. Recent advances in ultra-high-performance chromatography (UHPLC) have enabled the precise and comprehensive quantification of all proteogenic amino acids, offering an opportunity to map disease-associated metabolic shifts with high resolution. In this study, we aimed to characterize FF amino acid profiles in patients with IR, EM and TD compared with healthy controls. To the best of our knowledge, this is the first study to directly evaluate these conditions using a unified FF amino acid profiling framework. By integrating multivariate statistical modeling, pathway and biomarker analysis and correlation with clinical parameters, we aimed to uncover diagnosis-dependent metabolic signatures. Understanding these patterns may provide mechanistic information on ovarian dysfunction, highlight potential biomarkers for oocyte competence and ultimately inform personalized strategies to optimize ART outcomes. 2. Materials and Methods 2.1. Study population This study was performed between July 2025 and February 2026 at the Department of Obstetrics and Gynecology and at the Szentágothai Research Center, both at the University of Pécs, Hungary. All patients received detailed information of the study protocol, and a written informed consent was obtained prior to participation. Patients under 18 years and those who were unable to provide or withdrew the consent were excluded from the study. The study protocol was approved by the Regional Research Ethics Committee of the University of Pécs (No. 4327.316–2900/KK15/2011) in accordance with the 7th revision of the Declaration of Helsinki (2013). Follicular fluid samples were obtained from women undergoing oocyte retrieval as part of their routine assisted reproduction procedure. Based on clinical diagnosis and medical history, the patients were categorized into four groups: control, IR, EM and TD. The control group consisted of patients without known metabolic or endocrine disorders affecting reproductive function. The participants of the IR group were diagnosed by endocrinologists based on the homeostatic model assessment of insulin resistance (HOMA-IR) formula. The EM group comprised patients with clinically (by ultrasound) confirmed endometriosis. The TD group included patients with a documented history of hypothyroidism who were receiving thyroid hormone replacement therapy. Relevant clinical parameters, including age and body mass index (BMI) were recorded for all participants. These variables were considered in a subsequent statistical analysis to account for potential confounding effects. 2.2. Follicular fluid collection Follicular fluid samples were collected during transvaginal ultrasound-guided oocyte retrieval following controlled ovarian hyperstimulation as a part of the IVF procedure. For each patient, FF obtained from multiple follicles during the retrieval procedure was pooled to obtain a representative sample of the follicular environment. Aspirated FF was carefully inspected to avoid visible blood contamination. Samples were centrifuged (6700× g for 10 minutes at room temperature) to remove cellular debris and the supernatant was aliquoted and stored at -80°C until further analysis. 2.3. Amino acid quantification Targeted quantification of twenty proteinogenic amino acids in FF samples were performed using ultra-high-performance liquid chromatography (Shimadzu Nexera X2 UHPLC System) with fluorescence detection (RF-20A XS, both from Shimadzu Europa GmbH Duisburg, Germany), after protein precipitation and amino acid derivatization. Proteins were precipitated by adding 300 µL ice-cold acetonitrile to the samples, followed by centrifugation for 4 minutes at 6100 g (ScanSpeed Mini, Labogene, Allerod, Denmark). The supernatant was diluted with 600 µL phosphate buffer and filtered (Millex® GV 4 mm Durapore PVDF 0.22 µm, Merck KGaA, Darmstadt, Germany) prior to analysis. The amino acids were then derivatized using ortho-phtalaldehyde (OPA, ≥ 99% HPLC grade, Merck KGaA, Darmstadt, Germany) in the presence of 3-mercaptopropionic acid (MPA, ≥ 99.0%, HPLC grade, Merck KGaA, Darmstadt, Germany), while proline was derivatized using 9-fluorenylmethylcarbonyl chloride (FMOC, ≥ 99.0% (HPLC grade, Merck KGaA, Darmstadt, Germany). L-norvaline (250 µmol/L L-Norvaline, Merck, KgaA, Darmstadt, Germany) was used as an internal standard for quantitative analysis. Chromatographic separation was performed on a reverse-phase C18 column (100 x 3.0 mm Kinetex 2.6 µm EVO C18 100Å (Phenomenex, Torrance, CA, USA) using gradient mobile phase consisting of 20 mmol/L phosphate buffer (A) and a 40:45:15 acetonitrile: methanol: water solution (B). The flow rate was 1.3 mL/min and the column temperature was maintained at 27°C. The total running time was 15.1 min. All amino acids except proline were detected using a fluorescence detector (RF-20A XS, Shimadzu) at 350 nm excitation and 450 nm emission. Proline was measured separately at 266 nm excitation and 305 nm emission. The analysis was performed by Shimadzu LabSolutions 5.97 SP1 software. The amino acids were identified based on retention times and quantified using the peak area relative to the internal standard. All samples were analyzed in duplicate and final concentrations were calculated as the mean of the two parallel measurements. 2.4. Data preprocessing and statistical analysis Before the statistical analysis, amino acid concentration data were subjected to median normalization, followed by log10 transformation and auto-scaling to reduce systematic variation and enhance comparability across samples. Data distribution and normality were evaluated using the Shapiro-Wilk test. Most variables did not follow normal distribution so non-parametric statistical methods were applied. 2.4.1. Univariate and post-hoc analysis Kruskal-Wallis test was used to initially evaluate the differences in amino acid concentration in the four study groups. For those amino acids that showed significant global differences, post-hoc pairwise comparisons were performed using Dunn’s test. In order to control the family-wise error rate, p-values were adjusted using Bonferroni corrections. The magnitude and direction of metabolic differences between disease groups and the control group were expressed as log 2-fold change (log2FC). 2.4.2. Adjustment for potential confounding factors Given the observed differences in clinical characteristics among the study groups, particularly body mass index (BMI), generalized linear models (GZLM) were applied to evaluate whether the identified metabolic alterations remained significant after adjusting for BMI as a potential confounding factor. 2.4.3. Multivariate analysis Multivariate analyses were performed to explore global metabolic patterns and identify metabolites contributing to group separation. Unsupervised principal component analysis (PCA) was first applied to visualize overall variation in amino acid profiles across samples. Partial least squares-discriminant analysis (PLS-DA) was performed to identify amino acids contributing to group separation. Models were constructed using autoscaled data and the optimal number of components were determined by cross-validation. Model performance was evaluated using R 2 (explained variance) and Q 2 (predictive ability) metrics. To assess model robustness and avoid overfitting, permutation testing was conducted with 1000 iterations. Variable importance in projection (VIP) scores were calculated to estimate the contribution of each amino acid to the model. Metabolites with VIP > 1 were considered relevant contributors to group discrimination. 2.4.4. Random Forest Analysis Random Forest (RF) classification was applied using the MetaboAnalyst platform to rank amino acids according to their importance in distinguishing between disease groups and controls. The RF models were constructed using normalized and scaled dataset with disease group as the response variable and amino acid concentration as predictors. Model training was performed using an ensemble of decision trees (n = 500) and feature importance was assessed based on the mean decrease in accuracy. Model performance was internally evaluated using out-of-bag error estimation. The resulting importance score were used to identify key metabolites contributing to group classification. 2.4.5. Pathway enrichment analysis To investigate metabolic pathways potentially associated with the observed amino acid alterations, pathway enrichment analysis was performed using Metaboanalyst platform. Identified metabolites were mapped to metabolic pathways based on the Kyoto Encyclopedia of Genes and Genomes (KEGG) database. Enrichment significance was evaluated using over-representation analysis combined with pathway topology analysis and the results were reported as raw p-values, false discovery rate (FDR) and pathway impact scores. 2.4.6. Biomarker analysis Receiver operating characteristics (ROC) curve analysis was performed to evaluate the discriminatory ability of each amino acid. As a measure of diagnostic performance, the area under curve (AUC) was calculated. For multivariate ROC analysis, classification models were constructed using combinations of amino acids and model performance was assessed based on AUC values. Due to the relatively small metabolic differences between groups, ROC analyses were interpreted with caution, focusing on overall trends than single, high-performing biomarkers. 3. Results 3.1. Patient characteristics In this study 171 FF samples were measured and the 20 main proteogenic amino acids were determined. The samples were classified based on the existing diseases and four groups were identified namely control group (n = 98), EM group (n = 19), IR group (n = 21) and TD group (n = 33). The clinical characteristics of the study groups are summarized in Table 1 . The mean age of patients was comparable across all groups, ranging from 33.3 years in the IR group to 34.97 years in the TD group. Body mass index differed between groups, with the highest values observed in the IR group, while patients with EM showed the lowest BMI. These findings are consistent with current literature, which frequently reports a strong correlation between insulin resistance and elevated BMI [ 16 ] and a strong correlation between lower BMI and EM. [ 17 ] The mean number of retrieved oocytes was similar in the control, EM, and TD groups, whereas a higher number was observed in the IR group. A similar trend was observed for the number of mature (MII) oocytes and inseminated oocytes, which were also highest in the IR group. The number of fertilized mature oocytes ranged from 3.93 in the control group to 6.45 in the TD group. Fertilization rates showed variability among the groups, with the lowest mean value in the IR group and the highest in the TD group. The mean number of blastocysts formed per patient ranged from 1.67 in the EM group to 2.9 in the IR group. Table 1 General statistics of patients involved in the study Age Control group EM group IR group TD group 34.20 ± 5.26 34.28 ± 4.78 33.33 ± 5.78 34.97 ± 7.48 BMI 25.06 ± 5.22 22.09 ± 5.17 30.33 ± 5.08 24.47 ± 4.04 Number of oocytes retrieved 10.90 ± 7.12 11.39 ± 9.18 15.57 ± 6.80 11.21 ± 5.18 Number of mature oocytes (MII) 6.50 ± 5.33 6.00 ± 4.69 9.04 ± 5.56 7.30 ± 6.55 Number of inseminated oocytes 9.92 ± 6.80 10.11 ± 9.02 14.52 ± 6.82 10.49 ± 5.69 Number of fertilized mature oocytes 3.93 ± 3.53 4.17 ± 2.87 6.00 ± 4.49 6.45 ± 4.44 Fertility rate (%) 38.92 ± 27.55 48.03 ± 25.88 30.40 ± 28.01 57.54 ± 27.74 Number of blastocysts 1.75 ± 2.58 1.67 ± 2.81 2.90 ± 3.71 2.82 ± 3.07 3.2. Global amino acid profile of FF The overall amino acid composition of FF was evaluated across all samples and summarized in Table 2 . Among the 20 amino acids measured, glutamine showed the highest concentration, followed by alanine, glycine, proline and valine. Lower concentrations were detected for several amino acids including aspartate, methionine, cysteine and isoleucine. The relatively wide range of concentrations reflects the diverse metabolic roles of amino acids within the follicular microenvironment. Table 2 Mean concentrations of amino acids in FF samples. Aspartate Mean ± SD 6.67 ± 7.78 Glutamate 63.72 ± 27.12 Asparagine 30.28 ± 7.62 Serine 50.01 ± 15.73 Glutamine 347.70 ± 78.51 Histidine 48.93 ± 15.65 Glycine 151.61 ± 46.20 Threonine 103.04 ± 32.29 Arginine 31.37 ± 11.12 Alanine 230.81 ± 63.09 Tyrosine 28.80 ± 9.54 Cysteine 21.26 ± 8.22 Valine 127.61 ± 40.71 Methionine 16.34 ± 6.67 Tryptophan 41.56 ± 14.68 Phenylalanine 37.00 ± 8.66 Isoleucine 28.05 ± 10.10 Leucine 51.23 ± 18.48 Lysine 75.09 ± 31.67 Proline 143.37 ± 58.67 Values are presented as mean ± standard deviation (SD) and expressed in µmol/L. 3.3. Global metabolic differences between groups 3.3.1. Principal component analysis To explore global differences in FF amino acid composition among the study groups, PCA was performed using the concentrations of the 20 analyzed amino acids. The first two principal components explained 31.8% and 14.2% of the total variance. Together, the first two components explained 46% of the total variance. The PCA score plot (Fig. 1 ) revealed a substantial overlap between the four groups, suggesting that the overall amino acid composition of the FF is largely conserved across patients. Despite this overlap, subtle trends in group distribution were observed. Samples from the IR and TD groups showed a tendency to shift along the PC1 relative to the control group, suggesting coordinated alterations in amino acid metabolism associated with these conditions. In contrast, samples from the EM group largely overlapped with the control group, indicating the absence of a pronounced global metabolic shift in this dataset. Samples are grouped according to clinical diagnosis: control, IR, EM, and TD. To further interpret the PCA results, the contribution of individual amino acids to the first two principal components was examined (Supplementary Fig. 1). PC1 was driven by a broad range of amino acids, with the highest contributions from alanine (10.4%), glutamine (8.7%), phenylalanine (7.5%) and threonine (7.3%), indicating that this component reflects overall variation in amino acid abundance. In contrast, PC2 was strongly dominated by BCAAs, including leucine (23.5%), isoleucine (20.2%) and valine (17.1%) which together accounted for approximately 60% of the total contribution. This pattern suggests that coordinated variation in BCAA metabolism represents a major source of metabolic variability in the dataset. 3.3.2. Permutational multivariate analysis of variance To statistically evaluate whether disease status has an influence on the overall amino acid profile of FF, a permutational multivariate analysis of variance (PERMANOVA) was performed. The analysis revealed that the disease status has a significant effect on the overall amino acid profile (F = 4.906, R 2 =0.081, p = 0.001), although it explains only 8.1% of the total variability, indicates a relatively small effect size. Since PERMANOVA test may be sensitive to differences in group dispersion this result should be interpreted in the context of the relatively small effect size and the substantial overlap observed in the PCA. Together with the PCA results, these findings indicate that disease-associated metabolic alterations in FF are relatively small and arise from coordinated changes across multiple amino acids rather than large shifts in individual metabolite concentrations. 3.4. Disease-associated changes in individual amino acids To identify individual amino acids that may contribute to the disease-associated metabolic differences, a Kruskal-Wallis test was performed to compare the concentrations of the 20 amino acids measured across the four study groups. Initially, several amino acids, including histidine, glycine, tryptophan and lysine showed significant differences (p < 0.05). To account for multiple comparisons and reduce the risk of false-positive findings, we applied a Bonferroni correction. Post-hoc pairwise comparisons using Dunn’s test revealed that these global differences were driven by specific metabolic shifts between patient groups. Notably, histidine (adjusted p = 0.008) and lysine (adjusted p = 0.027) levels remained significantly different between the TD and EM groups. Furthermore, glycine demonstrated a significant metabolic shift in the IR group compared with controls (adjusted p = 0.023). Several additional amino acids exhibited trends that approached statistical significance, such as arginine (adjusted p = 0.086) and tryptophan (adjusted p = 0.07), both observed in the comparison between TD and EM patients. These findings indicate that thyroid disorders and insulin resistance are associated with selected differences in FF amino acid composition. (Table 3 ). Table 3 Amino acids showing significant differences among the study groups. Amino acid Krustal-Wallis Dunn's test Adj.sign. Arg 0.107 TD-EM 0.086 Hist 0.012 TD-EM 0.008 Gly 0.022 IR-CTRL 0.023 Try 0.044 TD-EM 0.070 Lys 0.019 TD-EM 0.027 Global differences were evaluated using the Kruskal–Wallis test, followed by Dunn’s post-hoc pairwise comparisons with Bonferroni correction. Adjusted p-values indicate statistically significant differences between the specified groups 3.5. Multivariate adjustment for clinical confounders To evaluate the independent effect of each pathological condition on the FF amino acid profile, a Generalized Linear Model (GZLM) was used. This analysis was specifically designed to separate the metabolic influence of the diseases from the potential confounding effect of BMI with the healthy control group, serving as a reference category. The results are shown in Table 4 . Despite the statistical significance of this adjusted model, the overall magnitude of these effects remains modest. This suggests that the observed associations represent relatively small alterations in the amino acid profile. The adjusted models confirmed that the most prominent metabolic shifts identified in the study were robust and independent of the patients’ body mass. Regarding the IR group, the analysis revealed a significant disease- specific depletion of key amino acids, specifically, glycine levels were markedly lower compared to controls (p = 0.032, B=-25.77) and arginine concentrations also showed a significant independent decrease (p = 0.018, B=-6.725). In both cases, BMI did not act as a significant predictor (p = 0.555 and p = 0.737), suggesting that these alterations are intrinsic to the pathophysiology of IR. Patients in the TD group exhibited the most extensive metabolic reprogramming within the follicular environment. This group was characterized by a significant reduction in the concentrations of histidine (p = 0.030, B=-6.845), tryptophan (p = 0.019, B=-7.06) and lysine (p = 0.030, B=-13.935). The lack of association between these amino acids and BMI (all p > 0.30) suggests that the observed alterations are independent of BMI and may reflect TD-related changes in the follicular amino acid microenvironment. Patients in the TD group exhibited the most extensive metabolic reprogramming within the follicular environment. This group was characterized by a significant reduction in the concentrations of histidine (p = 0.030, B=-6.845), tryptophan (p = 0.019, B=-7.06) and lysine (p = 0.030, B=-13.935), which remained significant after adjustment for BMI. The lack of association between these amino acids and BMI (all p > 0.30) suggests that the observed alterations are independent of BMI and may reflect thyroid dysfunction–related changes in the follicular amino acid milieu. In contrast, the metabolic signature of the EM group was more specific, showing significant elevation in histidine levels compared to the controls (p = 0.039, B = 8.228). While other amino acids showed nominal variations in this group, only the shift in histidine remained statistically significant after adjusting for clinical confounders. Table 4 GZLM results for amino acids showing significant associations with disease groups after adjustment for BMI. Amino Acid Disease group effect (p) BMI effect Dominant group (vs. control) Effect (B) Arginine 0.018 0.737 IR -6.725 Histidine 0.030 0.587 TD -6.845 Histidine 0.039 0.587 EM 8.228 Glycine 0.032 0.555 IR -25.77 Tryptophan 0.019 0.994 TD -7.063 Lysine 0.030 0.342 TD -13.935 The model evaluated the effect of disease group and BMI on amino acid concentrations. The dominant group indicates the patient group showing the strongest deviation from the control group. Effect size is represented by the regression coefficient (B). 3.6. Multivariate discrimination analysis 3.6.1. Partial least squares-discriminant analysis To further explore the multivariate structure of the amino acid dataset and identify metabolites that contribute to the group separation, partial least squares-discriminant analysis (PLS-DA) was performed. As a supervised method, PLS-DA maximizes class separation and may find group structures that PCA did not captured, however, the results of this analysis require cautious interpretation. The PLS-DA score plot (presented on Fig. 2) showed a partial clustering of samples according to disease status, with the IR and TD groups displaying the most distinct tendencies, while the EM samples largely overlapped with the control group. These observations were consistent with the PCA results, suggesting that the metabolic alterations associated with IR and TD may contribute more strongly to the overall variability of the FF amino acid profile than those observed in EM. Model quality was assessed using R2 and Q2 statistics derived from cross-validation. Q2 values represent the predictive ability of the model and were used as the primary indicator of the robustness of the model. The first component showed a slightly positive Q 2 value, indicating limited predictive capacity for the model. To further assess model robustness, a permutation test with 1000 permutations was performed. The empirical p-value was not significant (p = 0.677), indicating that the observed group separation was not stronger than expected by chance. These results suggest that although the PLS-DA visualization showed mild clustering patterns, the overall discriminative power of the supervised model remains limited. Samples are colored according to diagnosis: CTRL, IR, EM, and TD. Ellipses indicate group clustering patterns. 3.6.2. Variable importance in projection scores The VIP scores were used to identify the metabolites contributing most strongly to group separation. The highest VIP scores were observed for threonine (VIP > 1.72), lysine (VIP > 1.47) and alanine (VIP > 1.4), suggesting that these amino acids contribute most strongly to the observed multivariate patterns. Importantly, several of the metabolites highlighted by the VIP analysis did not reach statistical significance in the univariate tests after multiple-testing correction. This finding further supports the interpretation that disease-associated metabolic differences in FF arise from coordinated shifts across multiple amino acids rather than large changes in individual metabolite concentrations (Fig. 3 ). Higher VIP scores indicate greater influence on the model. The heatmap on the right shows relative amino acid abundance across the study groups (CTRL, EM, IR, TD). Group order is consistent across all rows and relative differences can be interpreted by comparing color intensity within each row, where warmer colors indicate higher and cooler colors indicate lower relative levels. 3.7. Pathway analysis To explore the metabolic patterns underlying the differences observed between the study groups, pathway enrichment analysis was performed using quantified amino acids. In the comparison between the control and IR groups, pathways related to BCAA metabolism were prominently represented, including valine, leucine and isoleucine biosynthesis and degradation. Additional pathways associated with amino acid metabolism, such as cysteine and methionine metabolism and the one-carbon pool by folate pathway, were also identified, suggesting alterations in amino acid utilization and related metabolic processes in the follicular environment of IR patients. In the TD group, several pathways related to central amino acid metabolism were enriched, including alanine, aspartate and glutamate metabolism, arginine biosynthesis, and glycine, serine and threonine metabolism. Nitrogen metabolism and glyoxylate and dicarboxylate metabolism were also represented, indicating coordinated changes in pathways involved in nitrogen handling and interconnected amino acid metabolic networks. In contrast, no pathways remained statistically significant in the EM group after multiple testing correction, which is consistent with the PCA results showing substantial overlap between EM and control samples. Overall, these results suggest that the metabolic differences observed among the patient groups may involve coordinated alterations in amino acid metabolic pathways, particularly those related to BCAAs and central nitrogen metabolism (Table 5 ). Table 5 Metabolic pathway enrichment analysis of amino acid alterations in FF. Valine, leucine and isoleucine biosynthesis Total Compound Hits Raw p −log10(p) Holm adjust FDR Impact Disease 8 4 0.0013 2.8637 0.0451 0.0347 0 IR Valine, leucine and isoleucine degradation 40 3 0.0021 2.6767 0.0674 0.0347 0 IR Pantothenate and CoA biosynthesis 20 3 0.0116 1.9347 0.3603 0.0833 0 IR Cysteine and methionine metabolism 33 3 0.0119 1.925 0.3603 0.0833 0.2222 IR One carbon pool by folate 26 4 0.0127 1.8979 0.3669 0.0834 0.1071 IR Alanine, aspartate and glutamate metabolism 28 5 0.0064 2.196 0.1847 0.0420 0.5345 TD Arginine biosynthesis 14 4 0.0112 1.9499 0.3142 0.0498 0.2021 TD Nitrogen metabolism 6 2 0.0115 1.9411 0.3142 0.0498 0 TD Glycine, serine and threonine metabolism 33 4 0.0136 1.8667 0.3398 0.0498 0.4744 TD Glyoxylate and dicarboxylate metabolism 32 4 0.01605 1.7946 0.38509 0.05295 0.11 TD Pathways were identified using MetaboAnalyst based on detected amino acids and mapped to the KEGG database. The table shows pathway size (Total Compound), the number of matched metabolites (Hits), enrichment significance (raw p-value, Holm-adjusted p-value and FDR), and pathway impact values. 3.8. Group-specific patterns of FF amino acid profiles A hierarchical clustered heatmap was generated to visualize relative differences in FF amino acid levels across the study groups (CTRL, EM, IR, TD). The results of this analysis are presented in Fig. 4 . The control and EM group exhibited broadly similar profiles, clustering closely together which is consistent with the minimal separation observed in the PCA analysis. This suggests that amino acid metabolism remains relatively preserved in EM, with only a few deviations from physiological conditions. In contrast, the IR and TD groups showed more pronounced alterations in amino acid levels. Several amino acids, including BCAAS and glucogenic amino acids, displayed relatively higher abundance in these groups, particularly in TD. These findings indicate a coordinated shift in amino acid metabolism associated with metabolic and endocrine dysfunction. Hierarchical clustering further supported these observations, as IR and TD groups formed a distinct cluster separate from CTRL and EM, showing similarities in their metabolic profiles. Overall, the heatmap demonstrates that disease-associated changes in FF amino acid composition are subtle but structured, reflecting coordinated metabolic reprogramming rather than isolated alterations in individual metabolites. Rows represent individual amino acids, while columns represent study groups (CTRL, EM, IR, TD). The dendrogram on the left indicates metabolic similarities between amino acids. Color scale: Red (higher concentration), Green (lower concentration). Clustering highlights similarities in metabolic patterns across the groups. 3.9. Diagnostic potential of amino acid profiles ROC analysis was performed to evaluate the potential of individual amino acids to discriminate between disease groups and controls (Fig. 5 ). Among the analyzed metabolites, isoleucine demonstrated the highest diagnostic performance in comparison between the IR and control groups, with an area under the curve (AUC) exceeding (AUC = 0.71), indicating moderate discriminative ability. None of the other amino acids reached this threshold in the EM and TD comparisons. These findings are consistent with the pathway analysis results, which showed alterations in the BCAA metabolism in the IR group. Biomarker analysis of follicular fluid amino acids using ROC curve analysis. The boxplot shows group differences in isoleucine concentrations across the study groups (CTRL, EM, IR, TD). The ROC curve demonstrates the discriminatory performance of isoleucine, which exhibited the highest diagnostic potential among the analyzed amino acids (AUC > 0.7). To further explore the discriminative performance of the metabolic profile, a Random Forest-based feature ranking was performed. Isoleucine and valine emerged as the most influential variables in discriminating IR patients from controls, followed by glycine and arginine. Although multivariate ROC analysis yielded a moderate predictive accuracy (maximal AUC = 0.602 with 20 features), the high importance scores of these specific amino acids consistently support the findings of the adjusted regression models. This cross-validation between frequentist statistics (GZLM) and machine learning (Random Forest) reinforces the significance of the BCAA and urea cycle disruptions in the follicular environment of IR patients. In the EM group, the metabolic alterations were more subtle but highly specific. Both the GZLM and the Random Forest classification identified histidine as the primary marker of the disease, with a significant independent elevation compared to controls (p = 0.039). Although the multivariate ROC analysis showed limited diagnostic power (AUC range 0.52–0.54), the consistent selection of histidine as the top-ranking feature (importance score: 0.76) suggest a targeted disruption of the histidine-histamine pathway in the FF of women with endometriosis, rather than a broad amino acid dysregulation. Finally, patients with thyroid disorders demonstrated a complex metabolic shift in the follicular environment. The multivariate ROC analysis for the TD group yielded an AUC of 0.609, further suggesting that endocrine disruptions do not rely on a single biomarker but rather induce a systemic reconfiguration of the follicular amino acid pool. Across all disease groups (EM, IR and TD), the consistency between frequentist statistics and machine learning feature ranking confirms that these amino acid signatures are reliable indicators of the altered oocyte microenvironment, independent of maternal BMI. Overall, these analyses demonstrate that reproductive and endocrine disorders are associated with subtle but coordinated alterations in the amino acid composition of FF, reflecting disease-specific metabolic signatures within the follicular microenvironment. 4. Discussion This study provides a comparative analysis of FF amino acid profiles across multiple infertility-associated conditions, such as IR, EM and TD, within a unified analytical framework. One of the key findings of this study is that, while the global amino acid composition of FF remains broadly conserved, disease-specific alterations emerge at the level of coordinated metabolic shifts rather than larger individual changes. Importantly, these differences are relatively small and distributed across multiple metabolites, rather than reflecting strong or clearly separated group-specific metabolic phenotypes. This pattern suggests that the follicular environment is not metabolically static but instead undergoes fine-tuned reprogramming in response to systemic pathophysiological states. Such small but structured alterations are consistent with the tightly regulated nature of the oocyte microenvironment,[ 18 ] where even small metabolic deviations may have functional consequences. Although the detected group differences showed statistical significance, their overall magnitude was modest, as also indicated by the PERMANOVA effect size and the substantial overlap observed in PCA. Because of this the identified alterations should be interpreted as moderate shifts in metabolic leaning rather than pronounced metabolic reprogramming. Despite this overall stability, disease-specific metabolic alterations were detected. Among the investigated conditions, IR exhibited a characteristic metabolic signature marked by significantly decreased glycine and arginine levels and enrichment of BCAA-related pathways. These findings align with known systemic metabolic features of IR[ 19 ] and extend them to the ovarian microenvironment.[ 20 ] The reduction in glycine, a key component of cellular redox balance,[ 21 ] may indicate an altered metabolic state that could influence oocyte quality through impaired antioxidant capacity.[ 22 ] In parallel, the involvement of BCAA metabolism suggests a broader reorganization of energy-related pathways within the follicles.[ 23 ] However, it is important to emphasize that these interpretations are based on associative metabolic patterns and do not provide direct mechanistic evidence. Thus, while IR appears to be linked to a distinct metabolic tendency in FF, the functional implications remain to be clarified in future studies. In contrast, TD was associated with a broader and more pronounced remodeling of the amino acid landscape, including decreased histidine, tryptophan and lysine concentrations. Given the central role of thyroid hormones in regulating systemic metabolism, [ 24 ] these findings suggest that thyroid-related endocrine disturbances may exert widespread effects on the biochemical composition of the FF. The observed changes in FF amino acid composition may reflect altered metabolic processes within the follicular microenvironment conditions of thyroid dysfunction. Tryptophan metabolism has been implicated in reproductive physiology due to its role in immune regulation and oxidative stress through pathways such as the kynurenine pathway. [ 25 ] [ 26 ] Similarly, histidine participates in antioxidant defense [ 27 ] and can serve as a precursor for histamine synthesis [ 28 ] which is involved in inflammatory and vascular responses within reproductive tissues. Together, these findings suggest that TD may induce a systemic metabolic shift that also affects the biochemical environment of ovarian follicle. In contrast to the broader metabolic changes observed in IR and TD patients, the metabolic alterations associated with EM appeared more selective and localized with histidine emerging as a key altered metabolite. EM is characterized by chronic inflammation and oxidative stress within the pelvic environment[ 29 ] and alterations in histidine metabolism may reflect inflammatory activity within the follicular compartment.[ 30 ] Histidine plays a role in immune regulation and vascular permeability, processes that are known to be dysregulated in EM.[ 31 ] Unlike IR and TD, which represent systemic metabolic conditions, EM may primarily influence the follicular microenvironment through localized, inflammation-driven mechanisms, resulting in a more targeted metabolic signature. Importantly, the consistency between univariate statistics, BMI-adjusted models and multivariate feature ranking strengthens the robustness of these findings. However, the supervised models (PLS-DA and Randon Forest) demonstrated limited predictive performance and should therefore be regarded primarily as exploratory tools rather than confirmatory evidence. Although individual metabolites showed only moderate discriminatory performance, their reproducible association with specific disease states supports their biological relevance. These results suggest that FF amino acid profiles reflect small, multi-metabolite patterns rather than robust diagnostic biomarkers. From a clinical perspective, these findings highlight the sensitivity of the follicular microenvironment to systemic metabolic and endocrine disturbances. Unfortunately the relatively modest effect sizes and limited discriminative power suggest that immediate clinical applicability is limited. The identification of diagnosis-dependent metabolic patterns may contribute to a more refined understanding of oocyte competence and but further validation and integration with other data types will be required before translational conclusions can be drawn Several limitations should be considered. The observational design precludes causal inference and the relatively smaller sample size in the EM group may limit the detection of additional subtle effects. Furthermore, the lack of complementary functional measurements restricts mechanistic interpretation. Nevertheless, the consistent patterns observed across multiple analytical approaches support the validity of the identified metabolic signatures. In conclusion, the study demonstrates that infertility-associated conditions are linked to small but coordinated alterations in the amino acid profile of the FF. These findings should be interpreted as associative and exploratory, highlighting trends in metabolic regulation rather than definitive mechanistic information. Future studies integrating multi-omics approaches and functional validation will be essential to further elucidate the role of these metabolic changes in reproductive outcomes. 5. Conclusion Infertility-associated conditions are linked to distinct, diagnosis-specific alterations in the amino acid composition of follicular fluid, reflecting coordinated metabolic reprogramming of the ovarian microenvironment. These changes are characterized by pathway-level patterns rather than single metabolite biomarkers. Our findings highlight the sensitivity of the follicular environment to systemic metabolic and endocrine disturbances and support the relevance of multi-metabolite signatures in understanding reproductive dysfunction. Further studies integrating multi-omics approaches are warranted to clarify their biological and clinical significance. Independent validation in larger and clinically more homogeneous cohorts will be necessary before translational conclusions can be drawn. Abbreviations AUC area under curve ART assisted reproductive technology BMI Body mass index BCAA branched-chain amino acids EM endometriosis FDR false discovery rate FF Follicular fluid GZLM generalized linear models HOMA-IR homeostatic model assessment of insulin resistance IR insulin resistance IVF in vitro fertilization KEGG Kyoto Encyclopedia of Genes and Genomes PLS-DA Partial least squares-discriminant analysis PERMANOVA permutational multivariate analysis of variance PCA principal component analysis RF Random Forest ROC Receiver operating characteristics TD thyroid dysfunction UHPLC ultra-high-performance chromatography VIP variable importance in projection Declarations Ethics approval and consent to participate The study protocol was approved by the Regional Research Ethics Committee of the University of Pécs (No. 4327.316–2900/KK15/2011, approved on 26 April 2012). Detailed information was given to all patients or their next-of-kin regarding our study protocol, while written consent was obtained from all. Consent for publication Not applicable Competing interests The authors declare that they have no competing interests. Funding National Laboratory on Human Reproduction, University of Pécs; Project no. RRF-2.3.1- 21-2022-00012, titled National Laboratory on Human Reproduction, has been implemented with the support provided by the Recovery and Resilience Facility of the European Union within the framework of Program Széchenyi Plan Plus. Author Contribution CK, TK, and GLK were responsible for the conceptualization and design of the study. CK developed the methodology, performed the formal analysis, and was responsible for software application, data curation, and visualization. CK, TK, and GLK participated in the validation of the metabolic data. DH, DC, AL, ÁL, ÁV, PM, KG, and TK provided essential resources and clinical samples for the study. CK conducted the investigations and prepared the original draft of the manuscript. CK, TK, GLK, and PM were major contributors in reviewing and editing the manuscript. TK and GLK provided supervision, managed project administration, and were responsible for funding acquisition. All authors read and approved the final manuscript. Acknowledgements Not applicable Data Availability The datasets used and/or analysed during the current study are available from the corresponding author on reasonable request. References Hsieh M, Zamah AM, Conti M. Epidermal growth factor-like growth factors in the follicular fluid:Role in oocyte development and maturation. Jan. 2009. 10.1055/s-0028-1108010 . Yang J, et al. Metabolic signatures in human follicular fluid identify lysophosphatidylcholine as a predictor of follicular development. Commun Biol. Dec. 2022;5(1). 10.1038/s42003-022-03710-4 . Pan Y, Pan C, Zhang C. Unraveling the complexity of follicular fluid: insights into its composition, function, and clinical implications. Dec 01 2024 BioMed Cent Ltd. 10.1186/s13048-024-01551-9 del Collado M, Andrade GM, Meirelles FV, da Silveira JC, Perecin F. Contributions from the ovarian follicular environment to oocyte function. Anim Reprod. 2018;15(3):261–70. 10.21451/1984-3143-AR2018-0082 . Chandel NS. Amino acid metabolism. Cold Spring Harb Perspect Biol. 2021;13(4). 10.1101/CSHPERSPECT.A040584 . Albeitawi S et al. Associations Between Follicular Fluid Biomarkers and IVF/ICSI Outcomes in Normo-Ovulatory Women—A Systematic Review, Mar. 01, 2025, Multidisciplinary Digital Publishing Institute (MDPI) . 10.3390/biom15030443 Ashraf N, et al. Metabolic Dysregulation and Female Infertility: A Systematic Review of Hormonal and Reproductive Outcomes From Recent Clinical Trials. Cureus Aug. 2025. 10.7759/cureus.90887 . Ebrahimpoor M, Firouzabadi RD, Javaheri A, Shamsi F, Dashti S. The Impact of Endometriosis on Reproductive Outcomes in ART Cycles, Adv. Biomed. Res. , vol. 13, no. 1, Sep. 2024, 10.4103/abr.abr_436_23 Cai YY, et al. Outcome of in vitro fertilization in women with subclinical hypothyroidism. Reproductive Biology Endocrinol. May 2017;15(1). 10.1186/s12958-017-0257-2 . Kurdi C et al. Aug., Amino Acid Profiling of Follicular Fluid in Assisted Reproduction Reveals Important Roles of Several Amino Acids in Patients with Insulin Resistance, Int. J. Mol. Sci. , vol. 24, no. 15, 2023, 10.3390/ijms241512458 Nasiri N et al. Apr., Oxidative Stress Statues in Serum and Follicular Fluid of Women with Endometriosis Citation, CELL JOURNAL(Yakhteh) , vol. 18, no. 4, pp. 582–587, 2016. Muderris II, Boztosun A, Oner G, Bayram F. Effect of thyroid hormone replacement therapy on ovarian volume and androgen hormones in patients with untreated primary hypothyroidism, Ann. Saudi Med. , vol. 31, no. 2, pp. 145–151, Mar. 2011, 10.4103/0256-4947.77500 Bastos DC et al. Dec., Metabolomic analysis of follicular fluid from women with Hashimoto thyroiditis, Sci. Rep. , vol. 13, no. 1, 2023, 10.1038/s41598-023-39514-7 Kurdi C, et al. Follicular Fluid Amino Acid Alterations in Endometriosis: Evidence for Oxidative Stress and Metabolic Dysregulation. Biomedicines. Nov. 2025;13(11). 10.3390/biomedicines13112634 . Yu L, et al. Alteration of the follicular fluid amino acid profile reveals the important roles of several amino acids in embryo quality in patients with polycystic ovary syndrome. Reproductive Biology Endocrinol. Dec. 2025;23(1). 10.1186/s12958-025-01460-6 . Jiang J, et al. Relationship of obesity to adipose tissue insulin resistance. BMJ Open Diabetes Res Care. Apr. 2020;8(1). 10.1136/bmjdrc-2019-000741 . Piriyev E, Mennicken C, Schiermeier S, Römer T. Does BMI Have an Impact on Endometriosis Symptoms and Endometriosis Types According to the #ENZIAN Classification? J Clin Med. Jun. 2025;14(12). 10.3390/jcm14124040 . Yang J, Feng T, Li S, Zhang X, Qian Y. Human follicular fluid shows diverse metabolic profiles at different follicle developmental stages. Reproductive Biology Endocrinol. Jul. 2020;18(1). 10.1186/s12958-020-00631-x . Guo S. Insulin signaling, resistance, and metabolic syndrome: Insights from mouse models into disease mechanisms. Feb. 2014. 10.1530/JOE-13-0327 . Purwar A, Nagpure S. Insulin Resistance in Polycystic Ovarian Syndrome. Cureus Oct. 2022. 10.7759/cureus.30351 . Adeva-Andany M, Souto-Adeva G, Ameneiros-Rodríguez E, Fernández-Fernández C, Donapetry-García C, Domínguez-Montero A. Insulin resistance and glycine metabolism in humans. Jan. 2018;01. 10.1007/s00726-017-2508-0 . Springer-Verlag Wien. Sasaki H et al. Impact of Oxidative Stress on Age-Associated Decline in Oocyte Developmental Competence, Nov. 22, 2019, Frontiers Media S.A. 10.3389/fendo.2019.00811 Choi BH, Hyun S, Koo SH. The role of BCAA metabolism in metabolic health and disease. Jul 01 2024 Springer Nature. 10.1038/s12276-024-01263-6 Rosales M, Nuñez M, Abdala A, Mesch V, Mendeluk G. Thyroid hormones in ovarian follicular fluid: Association with oocyte retrieval in women undergoing assisted fertilization procedures. J Bras Reprod Assist. 2020;24(3):245–9. 10.5935/1518-0557.20200004 . Badawy AAB. Tryptophan metabolism, disposition and utilization in pregnancy. Sep 17. 2015. 10.1042/BSR20150197 . Prescott S, et al. Tryptophan as a biomarker of pregnancy-related immune expression and modulation: an integrative review. Front Media SA. 2024. 10.3389/frph.2024.1453714 . Holeček M. Histidine in health and disease: Metabolism, physiological importance, and use as a supplement. Mar 01 2020 MDPI AG. 10.3390/nu12030848 Kraus FBT et al. Jun., Expression pattern and prognostic potential of histamine receptors in epithelial ovarian cancer, J. Cancer Res. Clin. Oncol. , vol. 149, no. 6, pp. 2501–2511, 2023, 10.1007/s00432-022-04114-x Clower L, Fleshman T, Geldenhuys WJ, Santanam N. Targeting Oxidative Stress Involved in Endometriosis and Its Pain, Aug. 01, 2022, MDPI . 10.3390/biom12081055 Adamyan L et al. Metabolomic biomarkers of endometriosis: A systematic review. Sep 01 2024 Elsevier Masson s r l 10.1016/j.jeud.2024.100077 Velho RV, Freitag JH, Brueckner AM, Thalmeier L, Pohl J, Mechsner S. The Histamine-Associated Inflammatory Landscape of Endometriosis: Molecular Profiling of HDC, HRH1-HRH4, and Cytokines Across Lesion Subtypes. Int J Mol Sci. Jan. 2026;27(1). 10.3390/ijms27010212 . Additional Declarations No competing interests reported. Cite Share Download PDF Status: Under Review Version 1 posted Reviewers agreed at journal 17 May, 2026 Reviews received at journal 17 May, 2026 Reviewers agreed at journal 11 May, 2026 Reviewers invited by journal 06 May, 2026 Editor assigned by journal 04 May, 2026 Submission checks completed at journal 04 May, 2026 First submitted to journal 04 May, 2026 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-9609696","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":638590607,"identity":"492fc659-fc91-43d3-80d1-397d7db93e37","order_by":0,"name":"Csilla Kurdi","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA2ElEQVRIiWNgGAWjYBACCRBOABJs7MwNDB9gQsRpYWZsYJzBYECkFjALqIWZhxgtkjNyH954wGBnz8fM2PjZdscfOQbpHgO8WqQl0o0tEhiSE9uYGZulc88YGDPInMGvRU4ijQ3olwMJIL9I57YZJDZI5BCnxR6opfm3ZZtBPUEt0lAtjECHtUkzthkkMBDSItnzjNkiwQDslzbL3jPGhm0SaQV4tUgcT2O8+aPCzl6+vfnwjZ875OT5JZI34NUCATCXMDYA45QI9UgApGUUjIJRMApGAToAAJCBN0DEOhIHAAAAAElFTkSuQmCC","orcid":"","institution":"University of Pecs","correspondingAuthor":true,"prefix":"","firstName":"Csilla","middleName":"","lastName":"Kurdi","suffix":""},{"id":638590608,"identity":"693a270c-71a0-4e8d-a227-a2b166cfc6fd","order_by":1,"name":"Dávid Hesszenberger","email":"","orcid":"","institution":"University of Pecs","correspondingAuthor":false,"prefix":"","firstName":"Dávid","middleName":"","lastName":"Hesszenberger","suffix":""},{"id":638590610,"identity":"4d6af00a-203f-4c6a-ba85-3f1249705b66","order_by":2,"name":"Dávid Csabai","email":"","orcid":"","institution":"University of Pecs","correspondingAuthor":false,"prefix":"","firstName":"Dávid","middleName":"","lastName":"Csabai","suffix":""},{"id":638590612,"identity":"56fa5076-aafd-40ba-a858-59da457ece40","order_by":3,"name":"Anikó Lajtai","email":"","orcid":"","institution":"University of Pecs","correspondingAuthor":false,"prefix":"","firstName":"Anikó","middleName":"","lastName":"Lajtai","suffix":""},{"id":638590615,"identity":"fd6b9537-2239-4f09-8d1c-8b0f5711ef61","order_by":4,"name":"Ágnes Lakatos","email":"","orcid":"","institution":"University of Pecs","correspondingAuthor":false,"prefix":"","firstName":"Ágnes","middleName":"","lastName":"Lakatos","suffix":""},{"id":638590616,"identity":"9ffd7dcf-6310-40b5-891c-6bd11a3aa29b","order_by":5,"name":"Krisztina Gödöny","email":"","orcid":"","institution":"University of Pecs","correspondingAuthor":false,"prefix":"","firstName":"Krisztina","middleName":"","lastName":"Gödöny","suffix":""},{"id":638590618,"identity":"127666b8-df96-4671-8469-ee62d1dd5124","order_by":6,"name":"Péter Mauchart","email":"","orcid":"","institution":"University of Pecs","correspondingAuthor":false,"prefix":"","firstName":"Péter","middleName":"","lastName":"Mauchart","suffix":""},{"id":638590620,"identity":"38d7621d-aca2-4f1a-98e6-5cb288a43e48","order_by":7,"name":"Ákos Várnagy","email":"","orcid":"","institution":"University of Pecs","correspondingAuthor":false,"prefix":"","firstName":"Ákos","middleName":"","lastName":"Várnagy","suffix":""},{"id":638590623,"identity":"b86e5384-cfce-4a84-8769-d3d8e88a6b5b","order_by":8,"name":"Gábor L. Kovács","email":"","orcid":"","institution":"University of Pecs","correspondingAuthor":false,"prefix":"","firstName":"Gábor","middleName":"L.","lastName":"Kovács","suffix":""},{"id":638590624,"identity":"95763f2e-97ce-426a-b794-40009dd30924","order_by":9,"name":"Tamás Kőszegi","email":"","orcid":"","institution":"University of Pecs","correspondingAuthor":false,"prefix":"","firstName":"Tamás","middleName":"","lastName":"Kőszegi","suffix":""}],"badges":[],"createdAt":"2026-05-04 14:54:36","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-9609696/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-9609696/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":109331814,"identity":"bf488e3d-470a-4358-bfc3-049afba69552","added_by":"auto","created_at":"2026-05-15 16:10:21","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":493441,"visible":true,"origin":"","legend":"\u003cp\u003ePCA score plot based on FF amino acid concentrations.\u003c/p\u003e","description":"","filename":"Figure1.png","url":"https://assets-eu.researchsquare.com/files/rs-9609696/v1/bd88dee86d97ecf7c9f58e57.png"},{"id":109331809,"identity":"33eafcc3-eb15-440b-8d99-892c89082644","added_by":"auto","created_at":"2026-05-15 16:10:18","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":487138,"visible":true,"origin":"","legend":"\u003cp\u003ePLS-DA score plot based on FF amino acid concentrations in the four study groups.\u003c/p\u003e","description":"","filename":"Figure2.png","url":"https://assets-eu.researchsquare.com/files/rs-9609696/v1/d19ada4086d12cfd313b7f84.png"},{"id":109331810,"identity":"034ef586-ebbe-4568-8fc5-a541dcb56e58","added_by":"auto","created_at":"2026-05-15 16:10:18","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":182106,"visible":true,"origin":"","legend":"\u003cp\u003eVIP scores from the PLS-DA model presenting amino acids contributing to group separation.\u003c/p\u003e","description":"","filename":"Figure3.png","url":"https://assets-eu.researchsquare.com/files/rs-9609696/v1/1c132c8a0a719feaef1be66d.png"},{"id":109331812,"identity":"46379f7f-f7b4-4af7-9a33-c280efe88214","added_by":"auto","created_at":"2026-05-15 16:10:19","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":99916,"visible":true,"origin":"","legend":"\u003cp\u003eHierarchical clustered heatmap of group-averaged amino acid concentrations in FF.\u003c/p\u003e","description":"","filename":"Figure4.png","url":"https://assets-eu.researchsquare.com/files/rs-9609696/v1/66704b3ec1b68b2b4d4215a2.png"},{"id":109331811,"identity":"fc7fb89f-fa97-491f-b9f7-29da0a52df2a","added_by":"auto","created_at":"2026-05-15 16:10:18","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":166256,"visible":true,"origin":"","legend":"\u003cp\u003eHierarchical clustered heatmap of group-averaged amino acid concentrations in FF.\u003c/p\u003e","description":"","filename":"Figure5.png","url":"https://assets-eu.researchsquare.com/files/rs-9609696/v1/58bc228c2dd9451b73a95d4f.png"},{"id":109331821,"identity":"39fae628-a445-47dc-86f0-16a9b64b2d27","added_by":"auto","created_at":"2026-05-15 16:10:27","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1606934,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-9609696/v1/a3401a6a-2500-4989-9e93-1b8db46bc25b.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Diagnosis-dependent Metabolic Reprogramming of Follicular Fluid","fulltext":[{"header":"1. Introduction","content":"\u003cp\u003eSuccessful oocyte maturation and embryo development depend on a finely tuned microenvironment within the ovarian follicle.[\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e] The follicular fluid (FF) surrounding the oocyte not only nourishes the gamete but also reflects the dynamic interplay between local ovarian activity and systemic physiological states.[\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e][\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e] Composed of metabolites secreted by granulosa and theca cells, as well as circulating components crossing the blood-follicle barrier, FF represents a picture of both the follicular and whole-body physiology at the time of oocyte retrieval.[\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e][\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e]\u003c/p\u003e \u003cp\u003eAmong its constituents, amino acids play multifaceted roles that extend far beyond serving as building blocks for protein synthesis. They provide energy, regulate osmotic balance and act as precursors for signaling molecules and antioxidants such as glutathione. [\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e] Emerging evidence indicates that alterations in FF amino acid composition can influence oocyte competence, fertilization rates and early embryo development, suggesting that the follicular amino acid milieu serves as a sensitive indicator of reproductive health. [\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e]\u003c/p\u003e \u003cp\u003eEndocrine and metabolic disorders, such as IR and TD, and inflammatory conditions such as EM, are frequently associated with infertility and suboptimal assisted reproductive technology (ART) outcomes. [\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e] [\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e] [\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e] IR disrupts glucose and amino acid metabolism within the follicle, potentially limiting nutrient availability for developing oocytes. [\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e] Chronic inflammation in EM may elevate oxidative stress and alter metabolic signaling in FF. [\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e] Thyroid hormones, as central regulators of systemic metabolism, can profoundly affect ovarian function and the local follicular environment when imbalanced. [\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e] Despite the clinical significance of these conditions, the mechanism by which they uniquely reprogram the FF amino acid landscape remain unclear. Previous studies have often examined single disorders [\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e] [\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e] [\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e] or global metabolic trends, leaving a gap in our understanding of diagnosis-specific alterations. Recent advances in ultra-high-performance chromatography (UHPLC) have enabled the precise and comprehensive quantification of all proteogenic amino acids, offering an opportunity to map disease-associated metabolic shifts with high resolution.\u003c/p\u003e \u003cp\u003eIn this study, we aimed to characterize FF amino acid profiles in patients with IR, EM and TD compared with healthy controls. To the best of our knowledge, this is the first study to directly evaluate these conditions using a unified FF amino acid profiling framework. By integrating multivariate statistical modeling, pathway and biomarker analysis and correlation with clinical parameters, we aimed to uncover diagnosis-dependent metabolic signatures. Understanding these patterns may provide mechanistic information on ovarian dysfunction, highlight potential biomarkers for oocyte competence and ultimately inform personalized strategies to optimize ART outcomes.\u003c/p\u003e"},{"header":"2. Materials and Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003e2.1. Study population\u003c/h2\u003e \u003cp\u003eThis study was performed between July 2025 and February 2026 at the Department of Obstetrics and Gynecology and at the Szent\u0026aacute;gothai Research Center, both at the University of P\u0026eacute;cs, Hungary. All patients received detailed information of the study protocol, and a written informed consent was obtained prior to participation. Patients under 18 years and those who were unable to provide or withdrew the consent were excluded from the study. The study protocol was approved by the Regional Research Ethics Committee of the University of P\u0026eacute;cs (No. 4327.316\u0026ndash;2900/KK15/2011) in accordance with the 7th revision of the Declaration of Helsinki (2013). Follicular fluid samples were obtained from women undergoing oocyte retrieval as part of their routine assisted reproduction procedure. Based on clinical diagnosis and medical history, the patients were categorized into four groups: control, IR, EM and TD.\u003c/p\u003e \u003cp\u003eThe control group consisted of patients without known metabolic or endocrine disorders affecting reproductive function. The participants of the IR group were diagnosed by endocrinologists based on the homeostatic model assessment of insulin resistance (HOMA-IR) formula. The EM group comprised patients with clinically (by ultrasound) confirmed endometriosis. The TD group included patients with a documented history of hypothyroidism who were receiving thyroid hormone replacement therapy.\u003c/p\u003e \u003cp\u003eRelevant clinical parameters, including age and body mass index (BMI) were recorded for all participants. These variables were considered in a subsequent statistical analysis to account for potential confounding effects.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003e2.2. Follicular fluid collection\u003c/h2\u003e \u003cp\u003eFollicular fluid samples were collected during transvaginal ultrasound-guided oocyte retrieval following controlled ovarian hyperstimulation as a part of the IVF procedure. For each patient, FF obtained from multiple follicles during the retrieval procedure was pooled to obtain a representative sample of the follicular environment. Aspirated FF was carefully inspected to avoid visible blood contamination. Samples were centrifuged (6700\u0026times; g for 10 minutes at room temperature) to remove cellular debris and the supernatant was aliquoted and stored at -80\u0026deg;C until further analysis.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec5\" class=\"Section2\"\u003e \u003ch2\u003e2.3. Amino acid quantification\u003c/h2\u003e \u003cp\u003eTargeted quantification of twenty proteinogenic amino acids in FF samples were performed using ultra-high-performance liquid chromatography (Shimadzu Nexera X2 UHPLC System) with fluorescence detection (RF-20A XS, both from Shimadzu Europa GmbH Duisburg, Germany), after protein precipitation and amino acid derivatization.\u003c/p\u003e \u003cp\u003eProteins were precipitated by adding 300 \u0026micro;L ice-cold acetonitrile to the samples, followed by centrifugation for 4 minutes at 6100 g (ScanSpeed Mini, Labogene, Allerod, Denmark). The supernatant was diluted with 600 \u0026micro;L phosphate buffer and filtered (Millex\u0026reg; GV 4 mm Durapore PVDF 0.22 \u0026micro;m, Merck KGaA, Darmstadt, Germany) prior to analysis. The amino acids were then derivatized using ortho-phtalaldehyde (OPA, \u0026ge;\u0026thinsp;99% HPLC grade, Merck KGaA, Darmstadt, Germany) in the presence of 3-mercaptopropionic acid (MPA, \u0026ge;\u0026thinsp;99.0%, HPLC grade, Merck KGaA, Darmstadt, Germany), while proline was derivatized using 9-fluorenylmethylcarbonyl chloride (FMOC, \u0026ge;\u0026thinsp;99.0% (HPLC grade, Merck KGaA, Darmstadt, Germany). L-norvaline (250 \u0026micro;mol/L L-Norvaline, Merck, KgaA, Darmstadt, Germany) was used as an internal standard for quantitative analysis.\u003c/p\u003e \u003cp\u003eChromatographic separation was performed on a reverse-phase C18 column (100 x 3.0 mm Kinetex 2.6 \u0026micro;m EVO C18 100\u0026Aring; (Phenomenex, Torrance, CA, USA) using gradient mobile phase consisting of 20 mmol/L phosphate buffer (A) and a 40:45:15 acetonitrile: methanol: water solution (B). The flow rate was 1.3 mL/min and the column temperature was maintained at 27\u0026deg;C. The total running time was 15.1 min. All amino acids except proline were detected using a fluorescence detector (RF-20A XS, Shimadzu) at 350 nm excitation and 450 nm emission. Proline was measured separately at 266 nm excitation and 305 nm emission. The analysis was performed by Shimadzu LabSolutions 5.97 SP1 software. The amino acids were identified based on retention times and quantified using the peak area relative to the internal standard. All samples were analyzed in duplicate and final concentrations were calculated as the mean of the two parallel measurements.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec6\" class=\"Section2\"\u003e \u003ch2\u003e2.4. Data preprocessing and statistical analysis\u003c/h2\u003e \u003cp\u003e Before the statistical analysis, amino acid concentration data were subjected to median normalization, followed by log10 transformation and auto-scaling to reduce systematic variation and enhance comparability across samples. Data distribution and normality were evaluated using the Shapiro-Wilk test. Most variables did not follow normal distribution so non-parametric statistical methods were applied.\u003c/p\u003e \u003cdiv id=\"Sec7\" class=\"Section3\"\u003e \u003ch2\u003e2.4.1. Univariate and post-hoc analysis\u003c/h2\u003e \u003cp\u003eKruskal-Wallis test was used to initially evaluate the differences in amino acid concentration in the four study groups. For those amino acids that showed significant global differences, post-hoc pairwise comparisons were performed using Dunn\u0026rsquo;s test. In order to control the family-wise error rate, p-values were adjusted using Bonferroni corrections. The magnitude and direction of metabolic differences between disease groups and the control group were expressed as log 2-fold change (log2FC).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec8\" class=\"Section3\"\u003e \u003ch2\u003e2.4.2. Adjustment for potential confounding factors\u003c/h2\u003e \u003cp\u003eGiven the observed differences in clinical characteristics among the study groups, particularly body mass index (BMI), generalized linear models (GZLM) were applied to evaluate whether the identified metabolic alterations remained significant after adjusting for BMI as a potential confounding factor.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec9\" class=\"Section3\"\u003e \u003ch2\u003e2.4.3. Multivariate analysis\u003c/h2\u003e \u003cp\u003eMultivariate analyses were performed to explore global metabolic patterns and identify metabolites contributing to group separation. Unsupervised principal component analysis (PCA) was first applied to visualize overall variation in amino acid profiles across samples. Partial least squares-discriminant analysis (PLS-DA) was performed to identify amino acids contributing to group separation. Models were constructed using autoscaled data and the optimal number of components were determined by cross-validation. Model performance was evaluated using R\u003csup\u003e2\u003c/sup\u003e (explained variance) and Q\u003csup\u003e2\u003c/sup\u003e (predictive ability) metrics. To assess model robustness and avoid overfitting, permutation testing was conducted with 1000 iterations.\u003c/p\u003e \u003cp\u003eVariable importance in projection (VIP) scores were calculated to estimate the contribution of each amino acid to the model. Metabolites with VIP\u0026thinsp;\u0026gt;\u0026thinsp;1 were considered relevant contributors to group discrimination.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec10\" class=\"Section3\"\u003e \u003ch2\u003e2.4.4. Random Forest Analysis\u003c/h2\u003e \u003cp\u003eRandom Forest (RF) classification was applied using the MetaboAnalyst platform to rank amino acids according to their importance in distinguishing between disease groups and controls. The RF models were constructed using normalized and scaled dataset with disease group as the response variable and amino acid concentration as predictors.\u003c/p\u003e \u003cp\u003eModel training was performed using an ensemble of decision trees (n\u0026thinsp;=\u0026thinsp;500) and feature importance was assessed based on the mean decrease in accuracy. Model performance was internally evaluated using out-of-bag error estimation. The resulting importance score were used to identify key metabolites contributing to group classification.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec11\" class=\"Section3\"\u003e \u003ch2\u003e2.4.5. Pathway enrichment analysis\u003c/h2\u003e \u003cp\u003eTo investigate metabolic pathways potentially associated with the observed amino acid alterations, pathway enrichment analysis was performed using Metaboanalyst platform. Identified metabolites were mapped to metabolic pathways based on the Kyoto Encyclopedia of Genes and Genomes (KEGG) database. Enrichment significance was evaluated using over-representation analysis combined with pathway topology analysis and the results were reported as raw p-values, false discovery rate (FDR) and pathway impact scores.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec12\" class=\"Section3\"\u003e \u003ch2\u003e2.4.6. Biomarker analysis\u003c/h2\u003e \u003cp\u003eReceiver operating characteristics (ROC) curve analysis was performed to evaluate the discriminatory ability of each amino acid. As a measure of diagnostic performance, the area under curve (AUC) was calculated. For multivariate ROC analysis, classification models were constructed using combinations of amino acids and model performance was assessed based on AUC values. Due to the relatively small metabolic differences between groups, ROC analyses were interpreted with caution, focusing on overall trends than single, high-performing biomarkers.\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e"},{"header":"3. Results","content":"\u003cdiv id=\"Sec14\" class=\"Section2\"\u003e\n \u003ch2\u003e3.1. Patient characteristics\u003c/h2\u003e\n \u003cp\u003eIn this study 171 FF samples were measured and the 20 main proteogenic amino acids were determined. The samples were classified based on the existing diseases and four groups were identified namely control group (n\u0026thinsp;=\u0026thinsp;98), EM group (n\u0026thinsp;=\u0026thinsp;19), IR group (n\u0026thinsp;=\u0026thinsp;21) and TD group (n\u0026thinsp;=\u0026thinsp;33).\u003c/p\u003e\n \u003cp\u003eThe clinical characteristics of the study groups are summarized in Table \u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003e. The mean age of patients was comparable across all groups, ranging from 33.3 years in the IR group to 34.97 years in the TD group. Body mass index differed between groups, with the highest values observed in the IR group, while patients with EM showed the lowest BMI. These findings are consistent with current literature, which frequently reports a strong correlation between insulin resistance and elevated BMI [\u003cspan class=\"CitationRef\"\u003e16\u003c/span\u003e] and a strong correlation between lower BMI and EM. [\u003cspan class=\"CitationRef\"\u003e17\u003c/span\u003e]\u003c/p\u003e\n \u003cp\u003eThe mean number of retrieved oocytes was similar in the control, EM, and TD groups, whereas a higher number was observed in the IR group. A similar trend was observed for the number of mature (MII) oocytes and inseminated oocytes, which were also highest in the IR group. The number of fertilized mature oocytes ranged from 3.93 in the control group to 6.45 in the TD group. Fertilization rates showed variability among the groups, with the lowest mean value in the IR group and the highest in the TD group. The mean number of blastocysts formed per patient ranged from 1.67 in the EM group to 2.9 in the IR group.\u003c/p\u003e\n \u003cdiv class=\"gridtable\"\u003e\n \u003cdiv class=\"colspec\" align=\"left\"\u003e\u0026nbsp;\u003c/div\u003e\n \u003cdiv class=\"colspec\" align=\"left\"\u003e\u0026nbsp;\u003c/div\u003e\n \u003ctable id=\"Tab1\" border=\"1\"\u003e\n \u003ccaption\u003e\n \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e\n \u003cdiv class=\"CaptionContent\"\u003e\n \u003cp\u003eGeneral statistics of patients involved in the study\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth rowspan=\"2\" align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eAge\u003c/strong\u003e\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eControl group\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eEM group\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eIR group\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eTD group\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003e34.20\u0026thinsp;\u0026plusmn;\u0026thinsp;5.26\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003e34.28\u0026thinsp;\u0026plusmn;\u0026thinsp;4.78\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003e33.33\u0026thinsp;\u0026plusmn;\u0026thinsp;5.78\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003e34.97\u0026thinsp;\u0026plusmn;\u0026thinsp;7.48\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eBMI\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e25.06\u0026thinsp;\u0026plusmn;\u0026thinsp;5.22\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e22.09\u0026thinsp;\u0026plusmn;\u0026thinsp;5.17\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e30.33\u0026thinsp;\u0026plusmn;\u0026thinsp;5.08\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e24.47\u0026thinsp;\u0026plusmn;\u0026thinsp;4.04\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cdiv class=\"gridtable\"\u003e\n \u003cdiv class=\"colspec\" align=\"left\"\u003e\u0026nbsp;\u003c/div\u003e\u003cstrong\u003eNumber of oocytes retrieved\u003c/strong\u003e\n \u003c/div\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e10.90\u0026thinsp;\u0026plusmn;\u0026thinsp;7.12\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e11.39\u0026thinsp;\u0026plusmn;\u0026thinsp;9.18\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e15.57\u0026thinsp;\u0026plusmn;\u0026thinsp;6.80\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e11.21\u0026thinsp;\u0026plusmn;\u0026thinsp;5.18\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eNumber of mature oocytes (MII)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e6.50\u0026thinsp;\u0026plusmn;\u0026thinsp;5.33\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e6.00\u0026thinsp;\u0026plusmn;\u0026thinsp;4.69\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e9.04\u0026thinsp;\u0026plusmn;\u0026thinsp;5.56\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e7.30\u0026thinsp;\u0026plusmn;\u0026thinsp;6.55\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eNumber of inseminated oocytes\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e9.92\u0026thinsp;\u0026plusmn;\u0026thinsp;6.80\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e10.11\u0026thinsp;\u0026plusmn;\u0026thinsp;9.02\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e14.52\u0026thinsp;\u0026plusmn;\u0026thinsp;6.82\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e10.49\u0026thinsp;\u0026plusmn;\u0026thinsp;5.69\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eNumber of fertilized mature oocytes\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3.93\u0026thinsp;\u0026plusmn;\u0026thinsp;3.53\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e4.17\u0026thinsp;\u0026plusmn;\u0026thinsp;2.87\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e6.00\u0026thinsp;\u0026plusmn;\u0026thinsp;4.49\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e6.45\u0026thinsp;\u0026plusmn;\u0026thinsp;4.44\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eFertility rate (%)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e38.92\u0026thinsp;\u0026plusmn;\u0026thinsp;27.55\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e48.03\u0026thinsp;\u0026plusmn;\u0026thinsp;25.88\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e30.40\u0026thinsp;\u0026plusmn;\u0026thinsp;28.01\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e57.54\u0026thinsp;\u0026plusmn;\u0026thinsp;27.74\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eNumber of blastocysts\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.75\u0026thinsp;\u0026plusmn;\u0026thinsp;2.58\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.67\u0026thinsp;\u0026plusmn;\u0026thinsp;2.81\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2.90\u0026thinsp;\u0026plusmn;\u0026thinsp;3.71\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2.82\u0026thinsp;\u0026plusmn;\u0026thinsp;3.07\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n \u003c/div\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec15\" class=\"Section2\"\u003e\n \u003ch2\u003e3.2. Global amino acid profile of FF\u003c/h2\u003e\n \u003cp\u003eThe overall amino acid composition of FF was evaluated across all samples and summarized in Table\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003e. Among the 20 amino acids measured, glutamine showed the highest concentration, followed by alanine, glycine, proline and valine. Lower concentrations were detected for several amino acids including aspartate, methionine, cysteine and isoleucine. The relatively wide range of concentrations reflects the diverse metabolic roles of amino acids within the follicular microenvironment.\u003c/p\u003e\n \u003cdiv class=\"gridtable\"\u003e\n \u003cdiv class=\"colspec\" align=\"left\"\u003e\u0026nbsp;\u003c/div\u003e\n \u003ctable id=\"Tab2\" border=\"1\"\u003e\n \u003ccaption\u003e\n \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e\n \u003cdiv class=\"CaptionContent\"\u003e\n \u003cp\u003eMean concentrations of amino acids in FF samples.\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth rowspan=\"2\" align=\"left\"\u003e\n \u003cp\u003eAspartate\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eMean\u0026thinsp;\u0026plusmn;\u0026thinsp;SD\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003e6.67\u0026thinsp;\u0026plusmn;\u0026thinsp;7.78\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eGlutamate\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\"±\"\u003e\n \u003cp\u003e63.72\u0026thinsp;\u0026plusmn;\u0026thinsp;27.12\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eAsparagine\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\"±\"\u003e\n \u003cp\u003e30.28\u0026thinsp;\u0026plusmn;\u0026thinsp;7.62\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eSerine\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\"±\"\u003e\n \u003cp\u003e50.01\u0026thinsp;\u0026plusmn;\u0026thinsp;15.73\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eGlutamine\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\"±\"\u003e\n \u003cp\u003e347.70\u0026thinsp;\u0026plusmn;\u0026thinsp;78.51\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eHistidine\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\"±\"\u003e\n \u003cp\u003e48.93\u0026thinsp;\u0026plusmn;\u0026thinsp;15.65\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eGlycine\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\"±\"\u003e\n \u003cp\u003e151.61\u0026thinsp;\u0026plusmn;\u0026thinsp;46.20\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eThreonine\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\"±\"\u003e\n \u003cp\u003e103.04\u0026thinsp;\u0026plusmn;\u0026thinsp;32.29\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eArginine\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\"±\"\u003e\n \u003cp\u003e31.37\u0026thinsp;\u0026plusmn;\u0026thinsp;11.12\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eAlanine\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\"±\"\u003e\n \u003cp\u003e230.81\u0026thinsp;\u0026plusmn;\u0026thinsp;63.09\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eTyrosine\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\"±\"\u003e\n \u003cp\u003e28.80\u0026thinsp;\u0026plusmn;\u0026thinsp;9.54\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eCysteine\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\"±\"\u003e\n \u003cp\u003e21.26\u0026thinsp;\u0026plusmn;\u0026thinsp;8.22\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eValine\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\"±\"\u003e\n \u003cp\u003e127.61\u0026thinsp;\u0026plusmn;\u0026thinsp;40.71\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eMethionine\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\"±\"\u003e\n \u003cp\u003e16.34\u0026thinsp;\u0026plusmn;\u0026thinsp;6.67\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eTryptophan\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\"±\"\u003e\n \u003cp\u003e41.56\u0026thinsp;\u0026plusmn;\u0026thinsp;14.68\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ePhenylalanine\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\"±\"\u003e\n \u003cp\u003e37.00\u0026thinsp;\u0026plusmn;\u0026thinsp;8.66\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eIsoleucine\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\"±\"\u003e\n \u003cp\u003e28.05\u0026thinsp;\u0026plusmn;\u0026thinsp;10.10\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eLeucine\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\"±\"\u003e\n \u003cp\u003e51.23\u0026thinsp;\u0026plusmn;\u0026thinsp;18.48\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eLysine\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\"±\"\u003e\n \u003cp\u003e75.09\u0026thinsp;\u0026plusmn;\u0026thinsp;31.67\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eProline\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\"±\"\u003e\n \u003cp\u003e143.37\u0026thinsp;\u0026plusmn;\u0026thinsp;58.67\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n \u003c/div\u003e\n \u003cp\u003e\u003cem\u003eValues are presented as mean\u0026thinsp;\u0026plusmn;\u0026thinsp;standard deviation (SD) and expressed in \u0026micro;mol/L.\u003c/em\u003e\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec16\" class=\"Section2\"\u003e\n \u003ch2\u003e3.3. Global metabolic differences between groups\u003c/h2\u003e\n \u003cdiv id=\"Sec17\" class=\"Section3\"\u003e\n \u003ch2\u003e3.3.1. Principal component analysis\u003c/h2\u003e\n \u003cp\u003eTo explore global differences in FF amino acid composition among the study groups, PCA was performed using the concentrations of the 20 analyzed amino acids. The first two principal components explained 31.8% and 14.2% of the total variance. Together, the first two components explained 46% of the total variance. The PCA score plot (Fig.\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003e) revealed a substantial overlap between the four groups, suggesting that the overall amino acid composition of the FF is largely conserved across patients.\u003c/p\u003e\n \u003cp\u003eDespite this overlap, subtle trends in group distribution were observed. Samples from the IR and TD groups showed a tendency to shift along the PC1 relative to the control group, suggesting coordinated alterations in amino acid metabolism associated with these conditions. In contrast, samples from the EM group largely overlapped with the control group, indicating the absence of a pronounced global metabolic shift in this dataset.\u003c/p\u003e\n \u003cp\u003e\u003cem\u003eSamples are grouped according to clinical diagnosis: control, IR, EM, and TD.\u003c/em\u003e\u003c/p\u003e\n \u003cp\u003eTo further interpret the PCA results, the contribution of individual amino acids to the first two principal components was examined (Supplementary Fig.\u0026nbsp;1). PC1 was driven by a broad range of amino acids, with the highest contributions from alanine (10.4%), glutamine (8.7%), phenylalanine (7.5%) and threonine (7.3%), indicating that this component reflects overall variation in amino acid abundance. In contrast, PC2 was strongly dominated by BCAAs, including leucine (23.5%), isoleucine (20.2%) and valine (17.1%) which together accounted for approximately 60% of the total contribution. This pattern suggests that coordinated variation in BCAA metabolism represents a major source of metabolic variability in the dataset.\u003c/p\u003e\n \u003c/div\u003e\n \u003cdiv id=\"Sec18\" class=\"Section3\"\u003e\n \u003ch2\u003e3.3.2. Permutational multivariate analysis of variance\u003c/h2\u003e\n \u003cp\u003eTo statistically evaluate whether disease status has an influence on the overall amino acid profile of FF, a permutational multivariate analysis of variance (PERMANOVA) was performed. The analysis revealed that the disease status has a significant effect on the overall amino acid profile (F\u0026thinsp;=\u0026thinsp;4.906, R\u003csup\u003e2\u003c/sup\u003e =0.081, p\u0026thinsp;=\u0026thinsp;0.001), although it explains only 8.1% of the total variability, indicates a relatively small effect size. Since PERMANOVA test may be sensitive to differences in group dispersion this result should be interpreted in the context of the relatively small effect size and the substantial overlap observed in the PCA.\u003c/p\u003e\n \u003cp\u003eTogether with the PCA results, these findings indicate that disease-associated metabolic alterations in FF are relatively small and arise from coordinated changes across multiple amino acids rather than large shifts in individual metabolite concentrations.\u003c/p\u003e\n \u003c/div\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec19\" class=\"Section2\"\u003e\n \u003ch2\u003e3.4. Disease-associated changes in individual amino acids\u003c/h2\u003e\n \u003cp\u003eTo identify individual amino acids that may contribute to the disease-associated metabolic differences, a Kruskal-Wallis test was performed to compare the concentrations of the 20 amino acids measured across the four study groups. Initially, several amino acids, including histidine, glycine, tryptophan and lysine showed significant differences (p\u0026thinsp;\u0026lt;\u0026thinsp;0.05). To account for multiple comparisons and reduce the risk of false-positive findings, we applied a Bonferroni correction. Post-hoc pairwise comparisons using Dunn\u0026rsquo;s test revealed that these global differences were driven by specific metabolic shifts between patient groups. Notably, histidine (adjusted p\u0026thinsp;=\u0026thinsp;0.008) and lysine (adjusted p\u0026thinsp;=\u0026thinsp;0.027) levels remained significantly different between the TD and EM groups. Furthermore, glycine demonstrated a significant metabolic shift in the IR group compared with controls (adjusted p\u0026thinsp;=\u0026thinsp;0.023).\u003c/p\u003e\n \u003cp\u003eSeveral additional amino acids exhibited trends that approached statistical significance, such as arginine (adjusted p\u0026thinsp;=\u0026thinsp;0.086) and tryptophan (adjusted p\u0026thinsp;=\u0026thinsp;0.07), both observed in the comparison between TD and EM patients. These findings indicate that thyroid disorders and insulin resistance are associated with selected differences in FF amino acid composition. (Table\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e3\u003c/span\u003e).\u003c/p\u003e\n \u003cdiv class=\"gridtable\"\u003e\n \u003cdiv class=\"colspec\" align=\"left\"\u003e\u0026nbsp;\u003c/div\u003e\n \u003ctable id=\"Tab3\" border=\"1\"\u003e\n \u003ccaption\u003e\n \u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e\n \u003cdiv class=\"CaptionContent\"\u003e\n \u003cp\u003eAmino acids showing significant differences among the study groups.\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eAmino acid\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eKrustal-Wallis\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eDunn\u0026apos;s test\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eAdj.sign.\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eArg\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\"\u003e\n \u003cp\u003e0.107\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eTD-EM\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\"\u003e\n \u003cp\u003e0.086\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eHist\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\"\u003e\n \u003cp\u003e0.012\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eTD-EM\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\"\u003e\n \u003cp\u003e0.008\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eGly\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\"\u003e\n \u003cp\u003e0.022\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eIR-CTRL\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\"\u003e\n \u003cp\u003e0.023\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eTry\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\"\u003e\n \u003cp\u003e0.044\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eTD-EM\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\"\u003e\n \u003cp\u003e0.070\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eLys\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\"\u003e\n \u003cp\u003e0.019\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eTD-EM\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\"\u003e\n \u003cp\u003e0.027\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003ctfoot\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"4\"\u003e\u003cem\u003eGlobal differences were evaluated using the Kruskal\u0026ndash;Wallis test, followed by Dunn\u0026rsquo;s post-hoc pairwise comparisons with Bonferroni correction. Adjusted p-values indicate statistically significant differences between the specified groups\u003c/em\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tfoot\u003e\n \u003c/table\u003e\n \u003c/div\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec20\" class=\"Section2\"\u003e\n \u003ch2\u003e3.5. Multivariate adjustment for clinical confounders\u003c/h2\u003e\n \u003cp\u003eTo evaluate the independent effect of each pathological condition on the FF amino acid profile, a Generalized Linear Model (GZLM) was used. This analysis was specifically designed to separate the metabolic influence of the diseases from the potential confounding effect of BMI with the healthy control group, serving as a reference category. The results are shown in Table\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e4\u003c/span\u003e. Despite the statistical significance of this adjusted model, the overall magnitude of these effects remains modest. This suggests that the observed associations represent relatively small alterations in the amino acid profile.\u003c/p\u003e\n \u003cp\u003eThe adjusted models confirmed that the most prominent metabolic shifts identified in the study were robust and independent of the patients\u0026rsquo; body mass. Regarding the IR group, the analysis revealed a significant disease- specific depletion of key amino acids, specifically, glycine levels were markedly lower compared to controls (p\u0026thinsp;=\u0026thinsp;0.032, B=-25.77) and arginine concentrations also showed a significant independent decrease (p\u0026thinsp;=\u0026thinsp;0.018, B=-6.725). In both cases, BMI did not act as a significant predictor (p\u0026thinsp;=\u0026thinsp;0.555 and p\u0026thinsp;=\u0026thinsp;0.737), suggesting that these alterations are intrinsic to the pathophysiology of IR.\u003c/p\u003e\n \u003cp\u003ePatients in the TD group exhibited the most extensive metabolic reprogramming within the follicular environment. This group was characterized by a significant reduction in the concentrations of histidine (p\u0026thinsp;=\u0026thinsp;0.030, B=-6.845), tryptophan (p\u0026thinsp;=\u0026thinsp;0.019, B=-7.06) and lysine (p\u0026thinsp;=\u0026thinsp;0.030, B=-13.935). The lack of association between these amino acids and BMI (all p\u0026thinsp;\u0026gt;\u0026thinsp;0.30) suggests that the observed alterations are independent of BMI and may reflect TD-related changes in the follicular amino acid microenvironment.\u003c/p\u003e\n \u003cp\u003ePatients in the TD group exhibited the most extensive metabolic reprogramming within the follicular environment. This group was characterized by a significant reduction in the concentrations of histidine (p\u0026thinsp;=\u0026thinsp;0.030, B=-6.845), tryptophan (p\u0026thinsp;=\u0026thinsp;0.019, B=-7.06) and lysine (p\u0026thinsp;=\u0026thinsp;0.030, B=-13.935), which remained significant after adjustment for BMI. The lack of association between these amino acids and BMI (all p\u0026thinsp;\u0026gt;\u0026thinsp;0.30) suggests that the observed alterations are independent of BMI and may reflect thyroid dysfunction\u0026ndash;related changes in the follicular amino acid milieu.\u003c/p\u003e\n \u003cp\u003eIn contrast, the metabolic signature of the EM group was more specific, showing significant elevation in histidine levels compared to the controls (p\u0026thinsp;=\u0026thinsp;0.039, B\u0026thinsp;=\u0026thinsp;8.228). While other amino acids showed nominal variations in this group, only the shift in histidine remained statistically significant after adjusting for clinical confounders.\u003c/p\u003e\n \u003cdiv class=\"gridtable\"\u003e\n \u003cdiv class=\"colspec\" align=\"left\"\u003e\u0026nbsp;\u003c/div\u003e\n \u003cdiv class=\"colspec\" align=\"char\"\u003e\u0026nbsp;\u003c/div\u003e\n \u003ctable id=\"Tab4\" border=\"1\"\u003e\n \u003ccaption\u003e\n \u003cdiv class=\"CaptionNumber\"\u003eTable 4\u003c/div\u003e\n \u003cdiv class=\"CaptionContent\"\u003e\n \u003cp\u003eGZLM results for amino acids showing significant associations with disease groups after adjustment for BMI.\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eAmino Acid\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eDisease group effect (p)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eBMI effect\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eDominant group (vs. control)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eEffect (B)\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eArginine\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\"\u003e\n \u003cp\u003e0.018\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\"\u003e\n \u003cp\u003e0.737\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eIR\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\"\u003e\n \u003cp\u003e-6.725\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eHistidine\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\"\u003e\n \u003cp\u003e0.030\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\"\u003e\n \u003cp\u003e0.587\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eTD\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\"\u003e\n \u003cp\u003e-6.845\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eHistidine\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\"\u003e\n \u003cp\u003e0.039\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\"\u003e\n \u003cp\u003e0.587\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eEM\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\"\u003e\n \u003cp\u003e8.228\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eGlycine\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\"\u003e\n \u003cp\u003e0.032\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\"\u003e\n \u003cp\u003e0.555\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eIR\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\"\u003e\n \u003cp\u003e-25.77\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eTryptophan\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\"\u003e\n \u003cp\u003e0.019\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\"\u003e\n \u003cp\u003e0.994\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eTD\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\"\u003e\n \u003cp\u003e-7.063\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eLysine\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\"\u003e\n \u003cp\u003e0.030\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\"\u003e\n \u003cp\u003e0.342\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eTD\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\"\u003e\n \u003cp\u003e-13.935\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n \u003c/div\u003e\n \u003cp\u003e\u003cem\u003eThe model evaluated the effect of disease group and BMI on amino acid concentrations. The dominant group indicates the patient group showing the strongest deviation from the control group. Effect size is represented by the regression coefficient (B).\u003c/em\u003e\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec21\" class=\"Section2\"\u003e\n \u003ch2\u003e3.6. Multivariate discrimination analysis\u003c/h2\u003e\n \u003cdiv id=\"Sec22\" class=\"Section3\"\u003e\n \u003ch2\u003e3.6.1. Partial least squares-discriminant analysis\u003c/h2\u003e\n \u003cp\u003eTo further explore the multivariate structure of the amino acid dataset and identify metabolites that contribute to the group separation, partial least squares-discriminant analysis (PLS-DA) was performed. As a supervised method, PLS-DA maximizes class separation and may find group structures that PCA did not captured, however, the results of this analysis require cautious interpretation. The PLS-DA score plot (presented on Fig.\u0026nbsp;2) showed a partial clustering of samples according to disease status, with the IR and TD groups displaying the most distinct tendencies, while the EM samples largely overlapped with the control group. These observations were consistent with the PCA results, suggesting that the metabolic alterations associated with IR and TD may contribute more strongly to the overall variability of the FF amino acid profile than those observed in EM.\u003c/p\u003e\n \u003cp\u003eModel quality was assessed using R2 and Q2 statistics derived from cross-validation. Q2 values represent the predictive ability of the model and were used as the primary indicator of the robustness of the model. The first component showed a slightly positive Q\u003csup\u003e2\u003c/sup\u003e value, indicating limited predictive capacity for the model. To further assess model robustness, a permutation test with 1000 permutations was performed. The empirical p-value was not significant (p\u0026thinsp;=\u0026thinsp;0.677), indicating that the observed group separation was not stronger than expected by chance. These results suggest that although the PLS-DA visualization showed mild clustering patterns, the overall discriminative power of the supervised model remains limited.\u003c/p\u003e\n \u003cp\u003e\u003cem\u003eSamples are colored according to diagnosis: CTRL, IR, EM, and TD. Ellipses indicate group clustering patterns.\u003c/em\u003e\u003c/p\u003e\n \u003c/div\u003e\n \u003cdiv id=\"Sec23\" class=\"Section3\"\u003e\n \u003ch2\u003e3.6.2. Variable importance in projection scores\u003c/h2\u003e\n \u003cp\u003eThe VIP scores were used to identify the metabolites contributing most strongly to group separation. The highest VIP scores were observed for threonine (VIP\u0026thinsp;\u0026gt;\u0026thinsp;1.72), lysine (VIP\u0026thinsp;\u0026gt;\u0026thinsp;1.47) and alanine (VIP\u0026thinsp;\u0026gt;\u0026thinsp;1.4), suggesting that these amino acids contribute most strongly to the observed multivariate patterns.\u003c/p\u003e\n \u003cp\u003eImportantly, several of the metabolites highlighted by the VIP analysis did not reach statistical significance in the univariate tests after multiple-testing correction. This finding further supports the interpretation that disease-associated metabolic differences in FF arise from coordinated shifts across multiple amino acids rather than large changes in individual metabolite concentrations (Fig.\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e3\u003c/span\u003e).\u003c/p\u003e\n \u003cp\u003e\u003cem\u003eHigher VIP scores indicate greater influence on the model. The heatmap on the right shows relative amino acid abundance across the study groups (CTRL, EM, IR, TD). Group order is consistent across all rows and relative differences can be interpreted by comparing color intensity within each row, where warmer colors indicate higher and cooler colors indicate lower relative levels.\u003c/em\u003e\u003c/p\u003e\n \u003c/div\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec24\" class=\"Section2\"\u003e\n \u003ch2\u003e3.7. Pathway analysis\u003c/h2\u003e\n \u003cp\u003eTo explore the metabolic patterns underlying the differences observed between the study groups, pathway enrichment analysis was performed using quantified amino acids.\u003c/p\u003e\n \u003cp\u003eIn the comparison between the control and IR groups, pathways related to BCAA metabolism were prominently represented, including valine, leucine and isoleucine biosynthesis and degradation. Additional pathways associated with amino acid metabolism, such as cysteine and methionine metabolism and the one-carbon pool by folate pathway, were also identified, suggesting alterations in amino acid utilization and related metabolic processes in the follicular environment of IR patients.\u003c/p\u003e\n \u003cp\u003eIn the TD group, several pathways related to central amino acid metabolism were enriched, including alanine, aspartate and glutamate metabolism, arginine biosynthesis, and glycine, serine and threonine metabolism. Nitrogen metabolism and glyoxylate and dicarboxylate metabolism were also represented, indicating coordinated changes in pathways involved in nitrogen handling and interconnected amino acid metabolic networks.\u003c/p\u003e\n \u003cp\u003eIn contrast, no pathways remained statistically significant in the EM group after multiple testing correction, which is consistent with the PCA results showing substantial overlap between EM and control samples.\u003c/p\u003e\n \u003cp\u003eOverall, these results suggest that the metabolic differences observed among the patient groups may involve coordinated alterations in amino acid metabolic pathways, particularly those related to BCAAs and central nitrogen metabolism (Table\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e5\u003c/span\u003e).\u003c/p\u003e\n \u003cdiv class=\"gridtable\"\u003e\n \u003cdiv class=\"colspec\" align=\"left\"\u003e\u0026nbsp;\u003c/div\u003e\n \u003cdiv class=\"colspec\" align=\"left\"\u003e\u0026nbsp;\u003c/div\u003e\n \u003ctable id=\"Tab5\" border=\"1\"\u003e\n \u003ccaption\u003e\n \u003cdiv class=\"CaptionNumber\"\u003eTable 5\u003c/div\u003e\n \u003cdiv class=\"CaptionContent\"\u003e\n \u003cp\u003eMetabolic pathway enrichment analysis of amino acid alterations in FF.\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth rowspan=\"2\" align=\"left\"\u003e\n \u003cp\u003eValine, leucine and isoleucine biosynthesis\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eTotal Compound\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eHits\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eRaw p\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003e\u0026minus;log10(p)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eHolm adjust\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eFDR\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eImpact\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eDisease\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003e8\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003e4\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003e0.0013\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003e2.8637\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003e0.0451\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003e0.0347\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eIR\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eValine, leucine and isoleucine degradation\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\"\u003e\n \u003cp\u003e40\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\"\u003e\n \u003cp\u003e3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\"\u003e\n \u003cp\u003e0.0021\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\"\u003e\n \u003cp\u003e2.6767\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\"\u003e\n \u003cp\u003e0.0674\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\"\u003e\n \u003cp\u003e0.0347\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eIR\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ePantothenate and CoA biosynthesis\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\"\u003e\n \u003cp\u003e20\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\"\u003e\n \u003cp\u003e3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\"\u003e\n \u003cp\u003e0.0116\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\"\u003e\n \u003cp\u003e1.9347\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\"\u003e\n \u003cp\u003e0.3603\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\"\u003e\n \u003cp\u003e0.0833\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eIR\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eCysteine and methionine metabolism\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\"\u003e\n \u003cp\u003e33\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\"\u003e\n \u003cp\u003e3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\"\u003e\n \u003cp\u003e0.0119\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\"\u003e\n \u003cp\u003e1.925\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\"\u003e\n \u003cp\u003e0.3603\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\"\u003e\n \u003cp\u003e0.0833\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.2222\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eIR\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eOne carbon pool by folate\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\"\u003e\n \u003cp\u003e26\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\"\u003e\n \u003cp\u003e4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\"\u003e\n \u003cp\u003e0.0127\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\"\u003e\n \u003cp\u003e1.8979\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\"\u003e\n \u003cp\u003e0.3669\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\"\u003e\n \u003cp\u003e0.0834\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.1071\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eIR\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eAlanine, aspartate and glutamate metabolism\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\"\u003e\n \u003cp\u003e28\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\"\u003e\n \u003cp\u003e5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\"\u003e\n \u003cp\u003e0.0064\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\"\u003e\n \u003cp\u003e2.196\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\"\u003e\n \u003cp\u003e0.1847\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\"\u003e\n \u003cp\u003e0.0420\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.5345\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eTD\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eArginine biosynthesis\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\"\u003e\n \u003cp\u003e14\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\"\u003e\n \u003cp\u003e4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\"\u003e\n \u003cp\u003e0.0112\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\"\u003e\n \u003cp\u003e1.9499\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\"\u003e\n \u003cp\u003e0.3142\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\"\u003e\n \u003cp\u003e0.0498\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.2021\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eTD\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNitrogen metabolism\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\"\u003e\n \u003cp\u003e6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\"\u003e\n \u003cp\u003e0.0115\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\"\u003e\n \u003cp\u003e1.9411\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\"\u003e\n \u003cp\u003e0.3142\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\"\u003e\n \u003cp\u003e0.0498\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eTD\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eGlycine, serine and threonine metabolism\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\"\u003e\n \u003cp\u003e33\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\"\u003e\n \u003cp\u003e4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\"\u003e\n \u003cp\u003e0.0136\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\"\u003e\n \u003cp\u003e1.8667\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\"\u003e\n \u003cp\u003e0.3398\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\"\u003e\n \u003cp\u003e0.0498\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.4744\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eTD\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eGlyoxylate and dicarboxylate metabolism\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\"\u003e\n \u003cp\u003e32\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\"\u003e\n \u003cp\u003e4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\"\u003e\n \u003cp\u003e0.01605\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\"\u003e\n \u003cp\u003e1.7946\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\"\u003e\n \u003cp\u003e0.38509\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\"\u003e\n \u003cp\u003e0.05295\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.11\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eTD\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n \u003c/div\u003e\n \u003cp\u003e\u003cem\u003ePathways were identified using MetaboAnalyst based on detected amino acids and mapped to the KEGG database. The table shows pathway size (Total Compound), the number of matched metabolites (Hits), enrichment significance (raw p-value, Holm-adjusted p-value and FDR), and pathway impact values.\u003c/em\u003e\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec25\" class=\"Section2\"\u003e\n \u003ch2\u003e3.8. Group-specific patterns of FF amino acid profiles\u003c/h2\u003e\n \u003cp\u003eA hierarchical clustered heatmap was generated to visualize relative differences in FF amino acid levels across the study groups (CTRL, EM, IR, TD). The results of this analysis are presented in Fig.\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e4\u003c/span\u003e.\u003c/p\u003e\n \u003cp\u003eThe control and EM group exhibited broadly similar profiles, clustering closely together which is consistent with the minimal separation observed in the PCA analysis. This suggests that amino acid metabolism remains relatively preserved in EM, with only a few deviations from physiological conditions.\u003c/p\u003e\n \u003cp\u003eIn contrast, the IR and TD groups showed more pronounced alterations in amino acid levels. Several amino acids, including BCAAS and glucogenic amino acids, displayed relatively higher abundance in these groups, particularly in TD. These findings indicate a coordinated shift in amino acid metabolism associated with metabolic and endocrine dysfunction.\u003c/p\u003e\n \u003cp\u003eHierarchical clustering further supported these observations, as IR and TD groups formed a distinct cluster separate from CTRL and EM, showing similarities in their metabolic profiles. Overall, the heatmap demonstrates that disease-associated changes in FF amino acid composition are subtle but structured, reflecting coordinated metabolic reprogramming rather than isolated alterations in individual metabolites.\u003c/p\u003e\n \u003cp\u003e\u003cem\u003eRows represent individual amino acids, while columns represent study groups (CTRL, EM, IR, TD). The dendrogram on the left indicates metabolic similarities between amino acids. Color scale: Red (higher concentration), Green (lower concentration). Clustering highlights similarities in metabolic patterns across the groups.\u003c/em\u003e\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec26\" class=\"Section2\"\u003e\n \u003ch2\u003e3.9. Diagnostic potential of amino acid profiles\u003c/h2\u003e\n \u003cp\u003eROC analysis was performed to evaluate the potential of individual amino acids to discriminate between disease groups and controls (Fig.\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e5\u003c/span\u003e). Among the analyzed metabolites, isoleucine demonstrated the highest diagnostic performance in comparison between the IR and control groups, with an area under the curve (AUC) exceeding (AUC\u0026thinsp;=\u0026thinsp;0.71), indicating moderate discriminative ability. None of the other amino acids reached this threshold in the EM and TD comparisons. These findings are consistent with the pathway analysis results, which showed alterations in the BCAA metabolism in the IR group.\u003c/p\u003e\n \u003cp\u003e\u003cem\u003eBiomarker analysis of follicular fluid amino acids using ROC curve analysis. The boxplot shows group differences in isoleucine concentrations across the study groups (CTRL, EM, IR, TD). The ROC curve demonstrates the discriminatory performance of isoleucine, which exhibited the highest diagnostic potential among the analyzed amino acids (AUC\u0026thinsp;\u0026gt;\u0026thinsp;0.7).\u003c/em\u003e\u003c/p\u003e\n \u003cp\u003eTo further explore the discriminative performance of the metabolic profile, a Random Forest-based feature ranking was performed. Isoleucine and valine emerged as the most influential variables in discriminating IR patients from controls, followed by glycine and arginine. Although multivariate ROC analysis yielded a moderate predictive accuracy (maximal AUC\u0026thinsp;=\u0026thinsp;0.602 with 20 features), the high importance scores of these specific amino acids consistently support the findings of the adjusted regression models. This cross-validation between frequentist statistics (GZLM) and machine learning (Random Forest) reinforces the significance of the BCAA and urea cycle disruptions in the follicular environment of IR patients.\u003c/p\u003e\n \u003cp\u003eIn the EM group, the metabolic alterations were more subtle but highly specific. Both the GZLM and the Random Forest classification identified histidine as the primary marker of the disease, with a significant independent elevation compared to controls (p\u0026thinsp;=\u0026thinsp;0.039). Although the multivariate ROC analysis showed limited diagnostic power (AUC range 0.52\u0026ndash;0.54), the consistent selection of histidine as the top-ranking feature (importance score: 0.76) suggest a targeted disruption of the histidine-histamine pathway in the FF of women with endometriosis, rather than a broad amino acid dysregulation.\u003c/p\u003e\n \u003cp\u003eFinally, patients with thyroid disorders demonstrated a complex metabolic shift in the follicular environment. The multivariate ROC analysis for the TD group yielded an AUC of 0.609, further suggesting that endocrine disruptions do not rely on a single biomarker but rather induce a systemic reconfiguration of the follicular amino acid pool. Across all disease groups (EM, IR and TD), the consistency between frequentist statistics and machine learning feature ranking confirms that these amino acid signatures are reliable indicators of the altered oocyte microenvironment, independent of maternal BMI.\u003c/p\u003e\n \u003cp\u003eOverall, these analyses demonstrate that reproductive and endocrine disorders are associated with subtle but coordinated alterations in the amino acid composition of FF, reflecting disease-specific metabolic signatures within the follicular microenvironment.\u003c/p\u003e\n\u003c/div\u003e"},{"header":"4. Discussion","content":"\u003cp\u003eThis study provides a comparative analysis of FF amino acid profiles across multiple infertility-associated conditions, such as IR, EM and TD, within a unified analytical framework. One of the key findings of this study is that, while the global amino acid composition of FF remains broadly conserved, disease-specific alterations emerge at the level of coordinated metabolic shifts rather than larger individual changes. Importantly, these differences are relatively small and distributed across multiple metabolites, rather than reflecting strong or clearly separated group-specific metabolic phenotypes. This pattern suggests that the follicular environment is not metabolically static but instead undergoes fine-tuned reprogramming in response to systemic pathophysiological states. Such small but structured alterations are consistent with the tightly regulated nature of the oocyte microenvironment,[\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e] where even small metabolic deviations may have functional consequences.\u003c/p\u003e \u003cp\u003eAlthough the detected group differences showed statistical significance, their overall magnitude was modest, as also indicated by the PERMANOVA effect size and the substantial overlap observed in PCA. Because of this the identified alterations should be interpreted as moderate shifts in metabolic leaning rather than pronounced metabolic reprogramming.\u003c/p\u003e \u003cp\u003eDespite this overall stability, disease-specific metabolic alterations were detected. Among the investigated conditions, IR exhibited a characteristic metabolic signature marked by significantly decreased glycine and arginine levels and enrichment of BCAA-related pathways. These findings align with known systemic metabolic features of IR[\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e] and extend them to the ovarian microenvironment.[\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e] The reduction in glycine, a key component of cellular redox balance,[\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e] may indicate an altered metabolic state that could influence oocyte quality through impaired antioxidant capacity.[\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e] In parallel, the involvement of BCAA metabolism suggests a broader reorganization of energy-related pathways within the follicles.[\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e] However, it is important to emphasize that these interpretations are based on associative metabolic patterns and do not provide direct mechanistic evidence. Thus, while IR appears to be linked to a distinct metabolic tendency in FF, the functional implications remain to be clarified in future studies.\u003c/p\u003e \u003cp\u003eIn contrast, TD was associated with a broader and more pronounced remodeling of the amino acid landscape, including decreased histidine, tryptophan and lysine concentrations. Given the central role of thyroid hormones in regulating systemic metabolism, [\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e] these findings suggest that thyroid-related endocrine disturbances may exert widespread effects on the biochemical composition of the FF. The observed changes in FF amino acid composition may reflect altered metabolic processes within the follicular microenvironment conditions of thyroid dysfunction. Tryptophan metabolism has been implicated in reproductive physiology due to its role in immune regulation and oxidative stress through pathways such as the kynurenine pathway. [\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e] [\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e] Similarly, histidine participates in antioxidant defense [\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e] and can serve as a precursor for histamine synthesis [\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e] which is involved in inflammatory and vascular responses within reproductive tissues. Together, these findings suggest that TD may induce a systemic metabolic shift that also affects the biochemical environment of ovarian follicle.\u003c/p\u003e \u003cp\u003eIn contrast to the broader metabolic changes observed in IR and TD patients, the metabolic alterations associated with EM appeared more selective and localized with histidine emerging as a key altered metabolite. EM is characterized by chronic inflammation and oxidative stress within the pelvic environment[\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e] and alterations in histidine metabolism may reflect inflammatory activity within the follicular compartment.[\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e] Histidine plays a role in immune regulation and vascular permeability, processes that are known to be dysregulated in EM.[\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e] Unlike IR and TD, which represent systemic metabolic conditions, EM may primarily influence the follicular microenvironment through localized, inflammation-driven mechanisms, resulting in a more targeted metabolic signature.\u003c/p\u003e \u003cp\u003eImportantly, the consistency between univariate statistics, BMI-adjusted models and multivariate feature ranking strengthens the robustness of these findings. However, the supervised models (PLS-DA and Randon Forest) demonstrated limited predictive performance and should therefore be regarded primarily as exploratory tools rather than confirmatory evidence. Although individual metabolites showed only moderate discriminatory performance, their reproducible association with specific disease states supports their biological relevance. These results suggest that FF amino acid profiles reflect small, multi-metabolite patterns rather than robust diagnostic biomarkers.\u003c/p\u003e \u003cp\u003eFrom a clinical perspective, these findings highlight the sensitivity of the follicular microenvironment to systemic metabolic and endocrine disturbances. Unfortunately the relatively modest effect sizes and limited discriminative power suggest that immediate clinical applicability is limited. The identification of diagnosis-dependent metabolic patterns may contribute to a more refined understanding of oocyte competence and but further validation and integration with other data types will be required before translational conclusions can be drawn\u003c/p\u003e \u003cp\u003eSeveral limitations should be considered. The observational design precludes causal inference and the relatively smaller sample size in the EM group may limit the detection of additional subtle effects. Furthermore, the lack of complementary functional measurements restricts mechanistic interpretation. Nevertheless, the consistent patterns observed across multiple analytical approaches support the validity of the identified metabolic signatures.\u003c/p\u003e \u003cp\u003eIn conclusion, the study demonstrates that infertility-associated conditions are linked to small but coordinated alterations in the amino acid profile of the FF. These findings should be interpreted as associative and exploratory, highlighting trends in metabolic regulation rather than definitive mechanistic information. Future studies integrating multi-omics approaches and functional validation will be essential to further elucidate the role of these metabolic changes in reproductive outcomes.\u003c/p\u003e"},{"header":"5. Conclusion","content":"\u003cp\u003eInfertility-associated conditions are linked to distinct, diagnosis-specific alterations in the amino acid composition of follicular fluid, reflecting coordinated metabolic reprogramming of the ovarian microenvironment. These changes are characterized by pathway-level patterns rather than single metabolite biomarkers. Our findings highlight the sensitivity of the follicular environment to systemic metabolic and endocrine disturbances and support the relevance of multi-metabolite signatures in understanding reproductive dysfunction. Further studies integrating multi-omics approaches are warranted to clarify their biological and clinical significance. Independent validation in larger and clinically more homogeneous cohorts will be necessary before translational conclusions can be drawn.\u003c/p\u003e"},{"header":"Abbreviations","content":"\u003cdiv class=\"DefinitionList\"\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eAUC\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003earea under curve\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eART\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eassisted reproductive technology\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eBMI\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eBody mass index\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eBCAA\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003ebranched-chain amino acids\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eEM\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eendometriosis\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eFDR\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003efalse discovery rate\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eFF\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eFollicular fluid\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eGZLM\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003egeneralized linear models\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eHOMA-IR\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003ehomeostatic model assessment of insulin resistance\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eIR\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003einsulin resistance\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eIVF\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003ein vitro fertilization\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eKEGG\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eKyoto Encyclopedia of Genes and Genomes\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003ePLS-DA\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003ePartial least squares-discriminant analysis\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003ePERMANOVA\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003epermutational multivariate analysis of variance\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003ePCA\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eprincipal component analysis\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eRF\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eRandom Forest\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eROC\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eReceiver operating characteristics\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eTD\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003ethyroid dysfunction\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eUHPLC\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eultra-high-performance chromatography\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eVIP\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003evariable importance in projection\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003c/div\u003e"},{"header":"Declarations","content":" \u003cp\u003e \u003cstrong\u003eEthics approval and consent to participate\u003c/strong\u003e \u003cp\u003eThe study protocol was approved by the Regional Research Ethics Committee of the University of P\u0026eacute;cs (No. 4327.316\u0026ndash;2900/KK15/2011, approved on 26 April 2012). Detailed information was given to all patients or their next-of-kin regarding our study protocol, while written consent was obtained from all.\u003c/p\u003e \u003c/p\u003e \u003cp\u003e \u003cstrong\u003eConsent for publication\u003c/strong\u003e \u003cp\u003eNot applicable\u003c/p\u003e \u003c/p\u003e\u003cp\u003e \u003ch2\u003eCompeting interests\u003c/h2\u003e \u003cp\u003eThe authors declare that they have no competing interests.\u003c/p\u003e \u003c/p\u003e\u003ch2\u003eFunding\u003c/h2\u003e \u003cp\u003eNational Laboratory on Human Reproduction, University of P\u0026eacute;cs; Project no. RRF-2.3.1- 21-2022-00012, titled National Laboratory on Human Reproduction, has been implemented with the support provided by the Recovery and Resilience Facility of the European Union within the framework of Program Sz\u0026eacute;chenyi Plan Plus.\u003c/p\u003e\u003ch2\u003eAuthor Contribution\u003c/h2\u003e\u003cp\u003eCK, TK, and GLK were responsible for the conceptualization and design of the study. CK developed the methodology, performed the formal analysis, and was responsible for software application, data curation, and visualization. CK, TK, and GLK participated in the validation of the metabolic data. DH, DC, AL, \u0026Aacute;L, \u0026Aacute;V, PM, KG, and TK provided essential resources and clinical samples for the study. CK conducted the investigations and prepared the original draft of the manuscript. CK, TK, GLK, and PM were major contributors in reviewing and editing the manuscript. TK and GLK provided supervision, managed project administration, and were responsible for funding acquisition. All authors read and approved the final manuscript.\u003c/p\u003e\u003ch2\u003eAcknowledgements\u003c/h2\u003e \u003cp\u003eNot applicable\u003c/p\u003e\u003ch2\u003eData Availability\u003c/h2\u003e\u003cp\u003eThe datasets used and/or analysed during the current study are available from the corresponding author on reasonable request.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eHsieh M, Zamah AM, Conti M. Epidermal growth factor-like growth factors in the follicular fluid:Role in oocyte development and maturation. 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Sep 01 2024 Elsevier Masson s r l \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1016/j.jeud.2024.100077\u003c/span\u003e\u003cspan address=\"10.1016/j.jeud.2024.100077\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eVelho RV, Freitag JH, Brueckner AM, Thalmeier L, Pohl J, Mechsner S. The Histamine-Associated Inflammatory Landscape of Endometriosis: Molecular Profiling of HDC, HRH1-HRH4, and Cytokines Across Lesion Subtypes. Int J Mol Sci. Jan. 2026;27(1). \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.3390/ijms27010212\u003c/span\u003e\u003cspan address=\"10.3390/ijms27010212\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"reproductive-biology-and-endocrinology","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"rbej","sideBox":"Learn more about [Reproductive Biology and Endocrinology](http://rbej.biomedcentral.com)","snPcode":"12958","submissionUrl":"https://submission.nature.com/new-submission/12958/3","title":"Reproductive Biology and Endocrinology","twitterHandle":"@BioMedCentral","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"BMC/SO AJ","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"follicular fluid, amino acids, insulin resistance, endometriosis, thyroid dysfunction, metabolomics, in vitro fertilization","lastPublishedDoi":"10.21203/rs.3.rs-9609696/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-9609696/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eBackground\u003c/h2\u003e \u003cp\u003eThe follicular fluid (FF) microenvironment plays a critical role in oocyte maturation and embryo development, reflecting local ovarian activity and systemic metabolic status. While metabolic alterations in FF have been described in different diseases, comparative analyses across different infertility-related disorders remain limited.\u003c/p\u003e\u003ch2\u003eObjective\u003c/h2\u003e \u003cp\u003eThis study aimed to characterize and compare amino acid profiles in FF from patients undergoing in vitro fertilization (IVF) with insulin resistance (IR), endometriosis (EM), thyroid dysfunction (TD) and to identify disease-specific metabolic signatures.\u003c/p\u003e\u003ch2\u003eMethods\u003c/h2\u003e \u003cp\u003eWe analyzed 171 FF samples using targeted ultra-high-performance liquid chromatography. The twenty proteogenic amino acids were quantified and analyzed using univariate and multivariate statistical analyses, including Kruskal-Wallis testing with post-hoc correction, generalized linear modeling adjusted for BMI, principal component analysis (PCA) and pathway enrichment analysis.\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e \u003cp\u003ePCA revealed that the global amino acid composition of FF was largely conserved across all groups. However, disease status had a statistically significant but moderate effect on the overall metabolic profile (PERMANOVA, R\u0026sup2;=0.081, p\u0026thinsp;=\u0026thinsp;0.001). After adjusting for BMI, IR was associated with decreased glycine and arginine levels and TD was associated with lower histidine, tryptophan and lysine concentrations. In contrast, EM was characterized by a selective increase in the histidine content. Pathway analysis revealed alterations in branched-chain amino acid (BCAA) metabolism in IR group and broader disruptions in central amino acid metabolism in TD group. Multivariate and ROC analyses indicated limited discriminative performance of individual amino acids (AUC\u0026thinsp;\u0026le;\u0026thinsp;0.71), suggesting that metabolic alterations are subtle and distributed across multiple pathways.\u003c/p\u003e\u003ch2\u003eConclusions\u003c/h2\u003e \u003cp\u003eFF amino acid composition is tightly regulated but distinct disease-specific metabolic alterations can be detected in IR, EM and TD. These changes are independent of BMI and reflect coordinated alterations in the metabolism of different amino acids rather than strong individual biomarkers. These results show the sensitivity of the follicular environment to overall metabolic health and support the idea of using multivariable metabolic patterns to better understand reproductive dysfunction.\u003c/p\u003e","manuscriptTitle":"Diagnosis-dependent Metabolic Reprogramming of Follicular Fluid","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2026-05-15 16:10:11","doi":"10.21203/rs.3.rs-9609696/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"reviewerAgreed","content":"120756766483864004067073929635750042617","date":"2026-05-18T02:18:28+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2026-05-17T09:20:15+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"317575900758581346460339139157759369296","date":"2026-05-11T23:44:06+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2026-05-06T12:52:21+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2026-05-04T23:32:00+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2026-05-04T23:31:26+00:00","index":"","fulltext":""},{"type":"submitted","content":"Reproductive Biology and Endocrinology","date":"2026-05-04T14:43:55+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"reproductive-biology-and-endocrinology","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"rbej","sideBox":"Learn more about [Reproductive Biology and Endocrinology](http://rbej.biomedcentral.com)","snPcode":"12958","submissionUrl":"https://submission.nature.com/new-submission/12958/3","title":"Reproductive Biology and Endocrinology","twitterHandle":"@BioMedCentral","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"BMC/SO AJ","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"77ef30a8-d0d3-44ca-a6aa-8d102ff10d9f","owner":[],"postedDate":"May 15th, 2026","published":true,"recentEditorialEvents":[{"type":"reviewerAgreed","content":"120756766483864004067073929635750042617","date":"2026-05-18T02:18:28+00:00","index":33,"fulltext":""},{"type":"editorInvitedReview","content":"","date":"2026-05-17T09:20:15+00:00","index":19,"fulltext":""},{"type":"reviewerAgreed","content":"317575900758581346460339139157759369296","date":"2026-05-11T23:44:06+00:00","index":18,"fulltext":""},{"type":"reviewersInvited","content":"22","date":"2026-05-06T12:52:21+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2026-05-04T23:32:00+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2026-05-04T23:31:26+00:00","index":"","fulltext":""},{"type":"submitted","content":"Reproductive Biology and Endocrinology","date":"2026-05-04T14:43:55+00:00","index":"","fulltext":""}],"rejectedJournal":[],"revision":"","amendment":"","status":"under-review","subjectAreas":[],"tags":[],"updatedAt":"2026-05-15T16:10:12+00:00","versionOfRecord":[],"versionCreatedAt":"2026-05-15 16:10:11","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-9609696","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-9609696","identity":"rs-9609696","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}