Potential biomarkers for clinical outcomes of IVF cycles in women with/without PCOS: Searching with metabolomics

Metabolomic Analysis of Follicular Fluid in Normal-Weight Patients with Polycystic Ovary Syndrome

BACKGROUND

This study aimed to examine the differential variations in the metabolic composition of follicular fluid (FF) among normal-weight patients with polycystic ovary syndrome (PCOS) and controls and to identify potential biomarkers that may offer insights into the early identification and management of these patients.

METHODS

We collected FF samples from 45 normal-weight women with PCOS and 36 normal-weight controls without PCOS who were undergoing in vitro fertilization-embryo transfer. An untargeted metabolomic study of collected FF from infertile women was performed using high-performance liquid chromatography-tandem spectrometry (LC-MS). The tendency of the two groups to separate was demonstrated through multivariate analysis. Univariate analysis and variable importance in projection were used to screen out differential metabolites. Metabolic pathway analysis was conducted using the Kyoto Encyclopedia of Genes and Genomes (KEGG), and a diagnostic model was established using the random forest algorithm.

RESULTS

The metabolomics analysis revealed an increase in the expression of 23 metabolites and a decrease in that of 10 metabolites in the FF of normal-weight women with PCOS. According to the KEGG pathway analysis, these differential metabolites primarily participated in the metabolism of glycerophospholipids and the biosynthesis of steroid hormones. Based on the biomarker combination of the top 10 metabolites, the area under the curve value was 0.805. The concentrations of prostaglandin E2 in the FF of individuals with PCOS exhibited an inverse association with the proportion of high-quality embryos (p < 0.05).

CONCLUSIONS

Our research identified a distinct metabolic profile of the FF from normal-weight women with PCOS. The results offer a broader comprehension of the pathogenesis and advancement of PCOS, and the detected differential metabolites could be potential biomarkers and targets for the treatment of PCOS.

Follicular Fluid Metabolomics: Tool for Predicting IVF Outcomes of Different Infertility Causes

Infertility affects approximately 15% of couples at child-bearing ages and assisted reproductive technologies (ART), especially in vitro fertilization and embryo transfer (IVF-ET), provided infertile patients with an effective solution. The current paradox is that multiple embryo transfer that may leads to severe obstetric and perinatal complications seems to be the most valid measure to secure high success rate in the majority of clinic centers. Therefore, to avoid multiple transfer of embryos, it is urgent to explore biomarkers for IVF prognosis to select high-quality oocytes and embryos. Follicular fluid (FF), a typical biofluid constituted of the plasma effusion and granulosa-cell secretion, provides essential intracellular substances for oocytes maturation and its variation in composition reflects oocyte developmental competence and embryo viability. With the advances in metabolomics methodology, metabolomics, as an accurate and sensitive analyzing method, has been utilized to explore predictors in FF for ART success. Although FF metabolomics has provided a great possibility for screening markers with diagnostic and predictive value, its effectiveness is still doubted by some researchers. This may be resulted from the ignorance of the impact of sterility causes on the FF metabolomic profiles and thus its predictive ability might not be rightly illustrated. Therefore, in this review, we categorically demonstrate the study of FF metabolomics according to specific infertility causes, expecting to reveal the predicting value of metabolomics for IVF outcomes.

Metabolic landscape and pathogenic insights: a comprehensive analysis of high ovarian response in infertile women undergoing in vitro fertilization

BACKGROUND

In the realm of assisted reproduction, a subset of infertile patients demonstrates high ovarian response following controlled ovarian stimulation (COS), with approximately 29.7% facing the risk of Ovarian Hyperstimulation Syndrome (OHSS). Management of OHSS risk often necessitates embryo transfer cancellation, leading to delayed prospects of successful pregnancy and significant psychological distress. Regrettably, these patients have received limited research attention, particularly regarding their metabolic profile. In this study, we aim to utilize gas chromatography-mass spectrometry (GC-MS) to reveal these patients' unique serum metabolic profiles and provide insights into the disease's pathogenesis.

METHODS

We categorized 145 infertile women into two main groups: the CON infertility group from tubal infertility patients and the Polycystic Ovary Syndrome (PCOS) infertility group. Within these groups, we further subdivided them into four categories: patients with normal ovarian response (CON-NOR group), patients with high ovarian response and at risk for OHSS (CON-HOR group) within the CON group, as well as patients with normal ovarian response (PCOS-NOR group) and patients with high ovarian response and at risk for OHSS (PCOS-HOR group) within the PCOS group. Serum metabolic profiles were analyzed using GC-MS. The risk criteria for OHSS were: the number of developing follicles > 20, peak Estradiol (E2) > 4000pg/mL, and Anti-Müllerian Hormone (AMH) levels > 4.5ng/mL.

RESULTS

The serum metabolomics analysis revealed four different metabolites within the CON group and 14 within the PCOS group. Remarkably, 10-pentadecenoic acid emerged as a discernible risk metabolite for the CON-HOR, also found to be a differential metabolite between CON-NOR and PCOS groups. cysteine and 5-methoxytryptamine were also identified as risk metabolites for the PCOS-HOR. Furthermore, KEGG analysis unveiled significant enrichment of the aminoacyl-tRNA biosynthesis pathway among the metabolites differing between PCOS-NOR and PCOS-HOR.

CONCLUSION

Our study highlights significant metabolite differences between patients with normal ovarian response and those with high ovarian response and at risk for OHSS within both the tubal infertility control group and PCOS infertility group. Importantly, we observe metabolic similarities between patients with PCOS and those with a high ovarian response but without PCOS, suggesting potential parallels in their underlying causes.

Biomarkers identification in follicular fluid in relation to live birth in in vitro fertilization of women with polycystic ovary syndrome in different subtypes by using UPLC-MS method

BACKGROUND

Polycystic ovary syndrome (PCOS) is a common infertility disorder which affects reproductive-aged women. However, metabolic change profiles of follicular fluid (FF) in lean and obese women diagnosed with and without PCOS remains unclear.

METHODS

95 infertile women were divided into four subgroups: LC (lean control), OC (overweight control), LP (lean PCOS), and OP (overweight PCOS). The FF samples were collected during oocyte retrieval and assayed by ultra-performance liquid chromatography coupled with mass spectrometry (UPLC-MS) metabolomics.

RESULTS

A total of 236 metabolites were identified by metabolic analysis. The pathway enrichment analysis revealed that the glycerophospholipid metabolism (impact = 0.11182), ether lipid metabolism (impact = 0.14458), and primary bile acid biosynthesis (impact = 0.03267) were related to metabolic pathway between PCOS and control. Correlation analyses showed that epitestosterone sulfate was found positively correlated with fertilization rate in PCOS, while falcarindione, lucidone C. and notoginsenoside I was found to be negatively correlated. The combined four biomarkers including lucidone C, epitestosterone sulfate, falcarindione, and notoginsenoside I was better in predicting live birth rate, with AUC of 0.779.

CONCLUSION

The follicular fluid of women with PCOS showed unique metabolic characteristics. Our study provides better identification of PCOS follicular fluid metabolic dynamics, which may serve as potential biomarkers of live birth.

Exploring the mechanism of clomiphene citrate to improve ovulation disorder in PCOS rats based on follicular fluid metabolomics

To examine the effects of clomiphene citrate (CC) on follicular fluid metabolites and related metabolic pathways in rats with polycystic ovary syndrome (PCOS) using non-targeted metabolomics and determine how CC treats ovulation disorder in PCOS. The Sprague Dawley rats were randomly divided into control, model, and CC groups. A PCOS model was established with letrozole. Body weight, ovarian weight, estrus cycles, serum hormone levels, and ovary histopathology of the rats were collected for further evaluation. Moreover, through ultra-performance liquid chromatography-mass spectrometry, the study of follicular fluid metabolites revealed the mechanism of action of CC. CC reduced ovarian weight and regulated estrous cycles and serum hormone levels in PCOS rats but did not affect their body weight. Moreover, the metabolomic results showed that CC adjusted 153 metabolites, among which 16 cross metabolites like testosterone, androstenedione, 17α-hydroxyprogesterone, and cholic acid were considered as potential biomarkers for CC to improve ovulation disorders in PCOS rats. Kyoto Encyclopedia of Genes and Genomes pathway enrichment also showed that the CC group mainly engaged in tryptophan metabolism and steroid hormone biosynthesis. CC can improve ovulation disorders in rats, and its mechanism is related to the regulation of the secretion of serum hormone and follicular fluid metabolites and the amelioration of multi-metabolic pathways.

The effect of β-cell dysfunction on reproductive outcomes of PCOS undergoing IVF or ICSI embryo transfer cycles: a retrospective cohort study

Objective

To investigate the effects of β-cell dysfunction on IVF outcomes in women with PCOS.

Methods

This retrospective cohort study includes 1,212 women with PCOS undergoing their first IVF cycle between September 2010 and December 2019. Beta-cell dysfunction was measured by homeostasis model assessment of β-cell function (HOMA-β) index.

Results

In quartiles of HOMA-β, the incidence of miscarriage dramatically increased from 10.2% (Q1) to 31.1% (Q4) (P for trend <0.001). Likewise, the incidence of miscarriage in quartiles of HOMA-β also showed a similar trend (P for trend <0.001). After adjusting for confounding factors, logistic regression analyses showed that high HOMA-IR values were independently associated with a high risk of miscarriage, with the odds ratios (OR) and 95% confidence intervals for quartiles 2-4 versus quartile 1 were 1.30 (0.69-2.46), 1.82 (0.97-3.43), and 3.57 (1.86-6.85), respectively (P for trend <0.001). When analyzed jointly, women in the highest HOMA-IR and highest HOMA-β group exhibited the highest risk for miscarriage compared with all other groups. Furthermore, higher HOMA-IR values were associated with higher risks of miscarriage among PCOS women regardless of HOMA-β values.

Conclusions

β-cell dysfunction is independently associated with increased miscarriage rate and decreased live birth rate in women with PCOS. It also plays a synergistic role with IR in terms of the reproductive outcomes, while the influence of IR overweighs that of β-cell dysfunction.

Causal association between 637 human metabolites and ovarian cancer: a mendelian randomization study

BACKGROUND

Current evidence suggests a significant association between metabolites and ovarian cancer (OC); however, the causal relationship between the two remains unclear. This study employs Mendelian randomization (MR) to investigate the causal effects between different metabolites and OC.

METHODS

In this study, a total of 637 metabolites were selected as the exposure variables from the Genome-wide Association Study (GWAS) database ( http://gwas.mrcieu.ac.uk/datasets/ ). The OC related GWAS dataset (ieu-b-4963) was chosen as the outcome variable. R software and the TwoSampleMR package were utilized for the analysis in this study. MR analysis employed the inverse variance-weighted method (IVW), MR-Egger and weighted median (WM) for regression fitting, taking into consideration potential biases caused by linkage disequilibrium and weak instrument variables. Metabolites that did not pass the tests for heterogeneity and horizontal pleiotropy were considered to have no significant causal effect on the outcome. Steiger's upstream test was used to determine the causal direction between the exposure and outcome variables.

RESULTS

The results from IVW analysis revealed that a total of 31 human metabolites showed a significant causal effect on OC (P < 0.05). Among them, 9 metabolites exhibited consistent and stable causal effects, which were confirmed by Steiger's upstream test (P < 0.05). Among these 9 metabolites, Androsterone sulfate, Propionylcarnitine, 5alpha-androstan-3beta,17beta-diol disulfate, Total lipids in medium VLDL and Concentration of medium VLDL particles demonstrated a significant positive causal effect on OC, indicating that these metabolites promote the occurrence of OC. On the other hand, X-12,093, Octanoylcarnitine, N2,N2-dimethylguanosine, and Cis-4-decenoyl carnitine showed a significant negative causal association with OC, suggesting that these metabolites can inhibit the occurrence of OC.

CONCLUSIONS

The study revealed the complex effect of metabolites on OC through Mendelian randomization. As promising biomarkers, these metabolites are worthy of further clinical validation.

Transcriptome and Metabolome Analyses Reveal the Mechanism of Corpus Luteum Cyst Formation in Pigs

Corpus luteum cysts are a serious reproductive disorder that affects the reproductive performance of sows. In this study, transcriptome and metabolome datasets of porcine normal and cyst luteal granulosa cells were generated to explore the molecular mechanism of luteal cyst formation. We obtained 28.9 Gb of high-quality transcriptome data from luteum tissue samples and identified 1048 significantly differentially expressed genes between the cyst and normal corpus luteum samples. Most of the differentially expressed genes were involved in cancer and immune signaling pathways. Furthermore, 22,622 information-containing positive and negative ions were obtained through gas chromatography-mass spectrometry, and 1106 metabolites were successfully annotated. Important differentially abundant metabolites and pathways were identified, among which abnormal lipid and choline metabolism were involved in the formation of luteal cysts. The relationships between granulosa cells of luteal cysts and cancer, immune-related signaling pathways, and abnormalities of lipid and choline metabolism were elaborated, providing new entry points for studying the pathogenesis of porcine luteal cysts.

Artificial intelligence in cardiovascular diseases: diagnostic and therapeutic perspectives

Artificial intelligence (AI), the technique of extracting information from complex database using sophisticated computer algorithms, has incorporated itself in medical field. AI techniques have shown the potential to accelerate the progression of diagnosis and treatment of cardiovascular diseases (CVDs), including heart failure, atrial fibrillation, valvular heart disease, hypertrophic cardiomyopathy, congenital heart disease and so on. In clinical scenario, AI have been proved to apply well in CVD diagnosis, enhance effectiveness of auxiliary tools, disease stratification and typing, and outcome prediction. Deeply developed to capture subtle connections from massive amounts of healthcare data, recent AI algorithms are expected to handle even more complex tasks than traditional methods. The aim of this review is to introduce current applications of AI in CVDs, which may allow clinicians who have limited expertise of computer science to better understand the frontier of the subject and put AI algorithms into clinical practice.

Small molecule metabolites: discovery of biomarkers and therapeutic targets

Metabolic abnormalities lead to the dysfunction of metabolic pathways and metabolite accumulation or deficiency which is well-recognized hallmarks of diseases. Metabolite signatures that have close proximity to subject's phenotypic informative dimension, are useful for predicting diagnosis and prognosis of diseases as well as monitoring treatments. The lack of early biomarkers could lead to poor diagnosis and serious outcomes. Therefore, noninvasive diagnosis and monitoring methods with high specificity and selectivity are desperately needed. Small molecule metabolites-based metabolomics has become a specialized tool for metabolic biomarker and pathway analysis, for revealing possible mechanisms of human various diseases and deciphering therapeutic potentials. It could help identify functional biomarkers related to phenotypic variation and delineate biochemical pathways changes as early indicators of pathological dysfunction and damage prior to disease development. Recently, scientists have established a large number of metabolic profiles to reveal the underlying mechanisms and metabolic networks for therapeutic target exploration in biomedicine. This review summarized the metabolic analysis on the potential value of small-molecule candidate metabolites as biomarkers with clinical events, which may lead to better diagnosis, prognosis, drug screening and treatment. We also discuss challenges that need to be addressed to fuel the next wave of breakthroughs.

Intrafollicular fluid metabolic abnormalities in relation to ovarian hyperstimulation syndrome: Follicular fluid metabolomics via gas chromatography-mass spectrometry

INTRODUCTION

Ovarian hyperstimulation syndrome (OHSS) is the most serious iatrogenic complication of ovulation stimulation during assisted reproductive technology. The main objective of this study was to investigate intrafollicular fluid metabolic change profiles of OHSS in non-ovarian etiologic infertility women (CON) and polycystic ovarian syndrome patients (PCOS).

METHODS

87 infertile women were divided into four subgroups: CON-Norm (CON with normal ovarian response), CON-OHSS (CON with OHSS), PCOS-Norm (PCOS with normal ovarian response), and PCOS-OHSS (PCOS with OHSS). The intrafollicular fluid metabolic profiles were analyzed with gas chromatography-mass spectrometry. The multivariable-adjusted conditional logistic regression was applied to assess the association of metabolites with OHSS risk.

RESULTS

We identified 17 and 3 metabolites that related to OHSS risk in CON and PCOS, respectively. 13 OHSS risk-related metabolites in CON were unsaturated fatty acids, 8 of which were also the significantly altered metabolites between all PCOS and CON-Norm.

CONCLUSION

Our study may shed light on the role of intrafollicular fluid metabolic abnormalities in the pathophysiology of OHSS. The findings suggested that there might be some metabolic heterogeneities underlying the development of OHSS in CON and PCOS women and indicated possible shared etiological factors in the development of PCOS and OHSS.