Lipidomic analysis of plasma samples from women with polycystic ovary syndrome

Circulatory lipid signature in response to short-term testosterone gel treatment of healthy young females

The impact of testosterone administration on the circulating lipidome in females remains unexplored, despite its relevance to understanding metabolic disorders like polycystic ovary syndrome (PCOS). This study addresses this gap by examining the effects of testosterone gel on the plasma lipidome of healthy women over three menstrual cycles. A cohort of 14 women aged 22-37 years with regular cycles was analyzed, with plasma samples collected at baseline, during peak testosterone levels (D45), and post-treatment (D59, D80). Testosterone gel treatment lasted 28 days, administered between day 29 and day 57 of the study. Using a deep-targeted lipidomic approach, 597 lipids were quantified to provide a detailed profile of the lipidome and capture subtle changes in lipid species and their associations with testosterone fluctuations. Extensive profiling revealed a significant decrease in 17 lipid species, especially ether- and ester-linked lysophosphatidylcholines (LPC), at peak testosterone. These lipid reductions were strongly negatively correlated with free and total testosterone, as well as dihydrotestosterone (DHT), and positively correlated with SHBG levels. Notably, intra-individual lipid variability was consistently lower than inter-individual variability, indicating a highly personalized lipidome regulation. Despite testosterone-induced changes, overall plasma lipidome alterations were minimal, suggesting mechanisms that maintain lipid homeostasis. This study highlights the complex interplay between testosterone and lipid metabolism in women. The minimal overall lipidome changes and high inter-individual variability point to the need for further research to assess the clinical relevance of these findings, particularly in hyperandrogenic conditions like PCOS. Clinical Trial Registration number: This study was registered on https://www.isrctn.com/ (ISRCTN10122130) on 09/01/2019.

The significant role of IL-15, IL-22, IL-37, and caspase 9 in polycystic ovary syndrome: A case-control study in a sample of Iraqi women

The study aims to evaluate the significant role of interleukin 15 (IL-15), IL-22, IL-37, and Caspase 9 gene expression in polycystic ovary syndrome (PCOS), focusing on the underlying mechanisms and potential diagnostic or therapeutic implications. Peripheral blood has been collected, and serum was separated for the evaluation of the serum IL-15, IL-22, and IL-37. The ELISA technique has been carried out to determine the serum levels of understudied factors mentioned above in Iraqi women patients diagnosed with PCOS (No. = 90) via a specialized gynecologist and healthy fertile women (No. = 48) as a control group. In addition, a genetic study on the expression of the caspase 9 gene in these patients had been performed. The data reveals statistically significant differences in interleukin levels in PCOS patients versus the control group. Specifically, the PCOS group exhibits significantly higher levels of IL-15 and IL-22 as compared to the control group. Conversely, the PCOS group shows significantly lower levels of IL-37 compared to the control group. The results showed no statistically significant difference in the mean expression of the Caspase 9 gene when comparing these fold graduations. However, it's worth noting that a higher fold frequency was observed in both the PCOS and control groups, with 57.1 % and 60 %, respectively, having folds less than 1. The distribution of folds varied across other categories was also addressed. Additionally, there was a notable difference in the frequency of 11.4 % in the PCOS group compared to 2 % in the control group for folds greater than 9. The findings suggest that interleukins, particularly IL-22 and IL-37, hold promise as diagnostic markers for distinguishing PCOS from healthy conditions. However, the potential diagnostic utility of the Caspase 9 gene expression was not confirmed in this study.

Insight into metabolic dysregulation of polycystic ovary syndrome utilizing metabolomic signatures: a narrative review

Polycystic ovary syndrome (PCOS) is a complex multifactorial endocrinopathy affecting reproductive aged women globally, whose presentation is strongly influenced by genetic makeup, ethnic, and geographic diversity leaving these affected women substantially predisposed to reproductive and metabolic perturbations. Sophisticated techniques spanning genomics, proteomics, epigenomics, and transcriptomics have been harnessed to comprehensively understand the enigmatic pathophysiology of PCOS, however, conclusive markers for PCOS are still lacking today. Metabolomics represents a paradigm shift in biotechnological advances enabling the simultaneous identification and quantification of metabolites and the use of this approach has added yet another dimension to help unravel the strong metabolic component of PCOS. Reports dissecting the metabolic signature of PCOS have revealed disparate levels of metabolites such as pyruvate, lactate, triglycerides, free fatty acids, carnitines, branched chain and essential amino acids, and steroid intermediates in major biological compartments. These metabolites have been shown to be altered in women with PCOS overall, after phenotypic subgrouping, in animal models of PCOS, and also following therapeutic intervention. This review seeks to supplement previous reviews by highlighting the aforementioned aspects and to provide easy, coherent and elementary access to significant findings and emerging trends. This will in turn help to delineate the metabolic plot in women with PCOS in various biological compartments including plasma, urine, follicular microenvironment, and gut. This may pave the way to design additional studies on the quest of unraveling the etiology of PCOS and delving into novel biomarkers for its diagnosis, prognosis and management.

Screening of serum biomarkers in patients with PCOS through lipid omics and ensemble machine learning

Polycystic ovary syndrome (PCOS) is a primary endocrine disorder affecting premenopausal women involving metabolic dysregulation. We aimed to screen serum biomarkers in PCOS patients using untargeted lipidomics and ensemble machine learning. Serum from PCOS patients and non-PCOS subjects were collected for untargeted lipidomics analysis. Through analyzing the classification of differential lipid metabolites and the association between differential lipid metabolites and clinical indexes, ensemble machine learning, data preprocessing, statistical test pre-screening, ensemble learning method secondary screening, biomarkers verification and evaluation, and diagnostic panel model construction and verification were performed on the data of untargeted lipidomics. Results indicated that different lipid metabolites not only differ between groups but also have close effects on different corresponding clinical indexes. PI (18:0/20:3)-H and PE (18:1p/22:6)-H were identified as candidate biomarkers. Three machine learning models, logistic regression, random forest, and support vector machine, showed that screened biomarkers had better classification ability and effect. In addition, the correlation of candidate biomarkers was low, indicating that the overlap between the selected biomarkers was low, and the combination of panels was more optimized. When the AUC value of the test set of the constructed diagnostic panel model was 0.815, the model's accuracy in the test set was 0.74, specificity was 0.88, and sensitivity was 0.7. This study demonstrated the applicability and robustness of machine learning algorithms to analyze lipid metabolism data for efficient and reliable biomarker screening. PI (18:0/20:3)-H and PE (18:1p/22:6)-H showed great potential in diagnosing PCOS.

High levels of fatty acid-binding protein 5 excessively enhances fatty acid synthesis and proliferation of granulosa cells in polycystic ovary syndrome

BACKGROUND

Polycystic ovary syndrome (PCOS) is one of the most complex endocrine disorders in women of reproductive age. Abnormal proliferation of granulosa cells (GCs) is an important cause of PCOS. This study aimed to explore the role of fatty acid-binding protein 5 (FABP5) in granulosa cell (GC) proliferation in polycystic ovary syndrome (PCOS) patients.

METHODS

The FABP5 gene, which is related to lipid metabolism, was identified through data analysis of the gene expression profiles of GSE138518 from the Gene Expression Omnibus (GEO) database. The expression levels of FABP5 were measured by quantitative real-time PCR (qRT‒PCR) and western blotting. Cell proliferation was evaluated with a cell counting kit-8 (CCK-8) assay. Western blotting was used to assess the expression of the proliferation marker PCNA, and immunofluorescence microscopy was used to detect Ki67 expression. Moreover, lipid droplet formation was detected with Nile red staining, and qRT‒PCR was used to analyze fatty acid storage-related gene expression.

RESULTS

We found that FABP5 was upregulated in ovarian GCs obtained from PCOS patients and PCOS mice. FABP5 knockdown suppressed lipid droplet formation and proliferation in a human granulosa-like tumor cell line (KGN), whereas FABP5 overexpression significantly enhanced lipid droplet formation and KGN cell proliferation. Moreover, we determined that FABP5 knockdown inhibited PI3K-AKT signaling by suppressing AKT phosphorylation and that FABP5 overexpression activated PI3K-AKT signaling by facilitating AKT phosphorylation. Finally, we used the PI3K-AKT signaling pathway inhibitor LY294002 and found that the facilitation of KGN cell proliferation and lipid droplet formation induced by FABP5 overexpression was inhibited. In contrast, the PI3K-AKT signaling pathway agonist SC79 significantly rescued the suppression of KGN cell proliferation and lipid droplet formation caused by FABP5 knockdown.

CONCLUSIONS

FABP5 promotes active fatty acid synthesis and excessive proliferation of GCs by activating PI3K-AKT signaling, suggesting that abnormally high expression of FABP5 in GCs may be a novel biomarker or a research target for PCOS treatment.

Analysis of Methylome, Transcriptome, and Lipid Metabolites to Understand the Molecular Abnormalities in Polycystic Ovary Syndrome

Purpose

This study aimed to identify differentially methylated genes (DMGs) and differentially expressed genes (DEGs) to investigate new biomarkers for the diagnosis and treatment of polycystic ovary syndrome (PCOS).

Methods

To explore the potential biomarkers of PCOS diagnosis and treatment, we performed methyl-binding domain sequencing (MBD-seq) and RNA sequencing (RNA-seq) on ovarian granulosa cells (GCs) from PCOS patients and healthy controls. MBD-seq was also performed on the ovarian tissue of constructed prenatally androgenized (PNA) mice. Differential methylation and expression analysis were implemented to identify DMGs and DEGs, respectively. The identified gene was further verified by real-time quantitative PCR (RT-qPCR) and methylation-specific PCR (MSP) in clinical samples. Furthermore, ultra-performance liquid chromatography-mass spectrometry (UPLC-MS) was carried out on PCOS patients and healthy controls to identify differential lipid metabolites.

Results

Compared to the control group, 13,526 DMGs related to the promoter region and 2429 DEGs were found. The function analysis of DMGs and DEGs showed that they were mainly enriched in glycerophospholipid, ovarian steroidogenesis, and other lipid metabolic pathways. Moreover, 5753 genes in DMGs related to the promoter region were screened in the constructed PNA mice. Integrating the DMGs data from PCOS patients and PNA mice, we identified the following 8 genes: CDC42EP4, ERMN, EZR, PIK3R1, ARHGEF18, NECTIN2, TSC2, and TACSTD2. RT-qPCR and MSP verification results showed that the methylation and expression of TACSTD2 were consistent with sequencing data. Additionally, 15 differential lipid metabolites were shown in the serum of PCOS patients. The differential lipids were involved in glycerophospholipid and glycerolipid metabolism.

Conclusion

Using integration of methylome and lipid metabolites profiling we identified 8 potential epigenetic markers and 15 potential lipid metabolite markers for PCOS. Our results suggest that aberrant DNA methylation and lipid metabolite disorders may provide novel insights into the diagnosis and etiology of PCOS.

Lipid Spectrum Generator: A Simple Script for the Generation of Accurate In Silico Lipid Fragmentation Spectra

Due to the complexity of lipids in nature, the use of in silico generated spectral libraries to identify lipid species from mass spectral data has become an integral part of many lipidomic workflows. However, many in silico libraries are either limited in usability or their capacity to represent lipid species. Here, we introduce Lipid Spectrum Generator, an open-source in silico spectral library generator specifically designed to aid in the identification of lipids in liquid chromatography-tandem mass spectrometry analysis.

Multiomics Analysis-Based Biomarkers in Diagnosis of Polycystic Ovary Syndrome

Polycystic ovarian syndrome is an utmost communal endocrine, psychological, reproductive, and metabolic disorder that occurs in women of reproductive age with extensive range of clinical manifestations. This may even lead to long-term multiple morbidities including obesity, diabetes mellitus, insulin resistance, cardiovascular disease, infertility, cerebrovascular diseases, and ovarian and endometrial cancer. Women affliction from PCOS in midst assemblage of manifestations allied with menstrual dysfunction and androgen exorbitance, which considerably affects eminence of life. PCOS is recognized as a multifactorial disorder and systemic syndrome in first-degree family members; therefore, the etiology of PCOS syndrome has not been copiously interpreted. The disorder of PCOS comprehends numerous allied health conditions and has influenced various metabolic processes. Due to multifaceted pathophysiology engaging several pathways and proteins, single genetic diagnostic tests cannot be supportive to determine in straight way. Clarification of cellular and biochemical pathways and various genetic players underlying PCOS could upsurge our consideration of pathophysiology of this syndrome. It is requisite to know pathophysiological relationship between biomarker and their reflection towards PCOS disease. Biomarkers deliver vibrantly and potent ways to apprehend the spectrum of PCOS with applications in screening, diagnosis, characterization, and monitoring. This paper relies on the endeavor to point out many candidates as potential biomarkers based on omics technologies, thus highlighting correlation between PCOS disease with innovative technologies. Therefore, the objective of existing review is to encapsulate more findings towards cutting-edge advances in prospective use of biomarkers for PCOS disease. Discussed biomarkers may be fruitful in guiding therapies, addressing disease risk, and predicting clinical outcomes in future.

Serum fatty acid profiles associated with metabolic risk in women with polycystic ovary syndrome

PURPOSE

Dyslipidemia is a feature of polycystic ovary syndrome (PCOS) that may augment metabolic disturbances. Serum fatty acids are important biomedical indicators of dyslipidemia. The aim of this study was to determine the distinct serum fatty acids in various PCOS subtypes and their association with metabolic risk in women with PCOS.

METHODS

Fatty acids in the serum of 202 women with PCOS were measured using gas chromatography-mass spectrometry. Fatty acids were compared between PCOS subtypes and correlated with glycemic parameters, adipokines, homocysteine, sex hormones, and sex hormone-binding globulin (SHBG).

RESULTS

The levels of total monounsaturated fatty acids (MUFAs) and polyunsaturated fatty acids (PUFAs) in the reproductive subtype of PCOS were lower than those in the metabolic subtype. Docosahexaenoic acid, a PUFA, was associated with higher SHBG after correction for multiple comparisons. Eighteen species of fatty acids emerged as potential biomarkers associated with the metabolic risk factors measured, independent of body mass index (BMI). Among them, myristic acid (C14:0), palmitoleic acid (C16:1), oleic acid (C18:1n-9C), cis-vaccenic acid (C18:1n-7), and homo-gamma-linolenic acid (C20:3n-6) were the strongest lipid species that were consistently associated with metabolic risk factors, particularly insulin-related parameters in women with PCOS. As for adipokines, 16 fatty acids were positively associated with serum leptin. Among them, C16:1 and C20:3n-6were significantly associated with leptin levels.

CONCLUSION

Our data demonstrated that a distinct fatty acid profile comprising high C14:0, C16:1, C18:1n-9C, C18:1n-7, and C20:3n-6levels is associated with metabolic risk in women with PCOS, independent of BMI.

Alterations of bacteriome, mycobiome and metabolome characteristics in PCOS patients with normal/overweight individuals

To characterize the gut bacteriome, mycobiome and serum metabolome profiles in polycystic ovary syndrome (PCOS) patients with normal/overweight individuals and evaluate a potential microbiota-related diagnostic method development for PCOS, 16S rRNA and ITS2 gene sequencing using 88 fecal samples and 87 metabolome analysis from serum samples are conducted and PCOS classifiers based on multiomics markers are constructed. There are significant bacterial, fungal community and metabolite differences among PCOS patients and healthy volunteers with normal/overweight individuals. Healthy individuals with overweight/obesity display less abnormal metabolism than PCOS patients and uniquely higher abundance of the fungal genus Mortierella. Nine bacterial genera, 4 predicted pathways, 11 fungal genera and top 30 metabolites are screened out which distinguish PCOS from healthy controls, with AUCs of 0.84, 0.64, 0.85 and 1, respectively. The metabolite-derived model is more accurate than the microbe-based model in discriminating normal BMI PCOS (PCOS-LB) from normal BMI healthy (Healthy-LB), PCOS-HB from Healthy-HB. Featured bacteria, fungi, predicted pathways and serum metabolites display higher associations with free androgen index (FAI) in the cooccurrence network. In conclusion, our data reveal that hyperandrogenemia plays a central role in the dysbiosis of intestinal microecology and the change in metabolic status in patients with PCOS and that its effect exceeds the role of BMI. Healthy women with high BMI showed unique microbiota and metabolic features.The priority of predictive models in discriminating PCOS from healthy status in this study were serum metabolites, fungal taxa and bacterial taxa.

Novel Lipidomic Signature Associated With Metabolic Risk in Women With and Without Polycystic Ovary Syndrome

CONTEXT

Dyslipidemia is a feature of polycystic ovary syndrome (PCOS) and may augment metabolic dysfunction in this population.

OBJECTIVE

Using comprehensive lipidomic profiling and gold-standard metabolic measures, we examined whether distinct lipid biomarkers were associated with metabolic risk in women with and without PCOS.

METHODS

Using preexisting data and biobanked samples from 76 women (n = 42 with PCOS), we profiled > 700 lipid species by mass spectrometry. Lipids were compared between women with and without PCOS and correlated with direct measures of adiposity (dual x-ray absorptiometry and computed tomography) and insulin sensitivity (hyperinsulinemic-euglycemic clamp), as well as fasting insulin, HbA1c, and hormonal parameters (luteinizing and follicle-stimulating hormones; total and free testosterone; sex hormone-binding globulin [SHBG]; and free androgen index [FAI]). Multivariable linear regression was used with correction for multiple testing.

RESULTS

Despite finding no differences by PCOS status, lysophosphatidylinositol (LPI) species esterified with an 18:0 fatty acid were the strongest lipid species associated with all the metabolic risk factors measured in women with and without PCOS. Across the cohort, higher concentrations of LPI(18:0) and lower concentrations of lipids containing docosahexaenoic acid (DHA, 22:6) n-3 polyunsaturated fatty acids were associated with higher adiposity, insulin resistance, fasting insulin, HbA1c and FAI, and lower SHBG.

CONCLUSION

Our data indicate that a distinct lipidomic signature comprising high LPI(18:0) and low DHA-containing lipids are associated with key metabolic risk factors that cluster in PCOS, independent of PCOS status. Prospective studies are needed to corroborate these findings in larger cohorts of women with varying PCOS phenotypes.

Urinary metabolomics identified metabolic disturbance associated with polycystic ovary syndrome

Polycystic ovary syndrome (PCOS) is a common endocrine and metabolic disorder. Nevertheless, its accurate mechanisms remain unclear. Metabolomics is a powerful technique to identify small molecules that could be used to discover pathogenesis and therapeutical targets of disease. In the present study, a urinary untargeted metabolomics combined with targeted quantification analysis was performed to uncover metabolic disturbance associated with PCOS. A total of thirty-eight metabolites were obtained between PCOS patients and healthy controls, which were mainly involved in lipids (39.5%), organic acids and derivatives (23.7%), and organic oxygen compounds (18.4%). Based on enrichment analysis, fourteen metabolic pathways were found to be perturbed in PCOS, particularly glycerophospholipid metabolism and tryptophan metabolism. Targeted quantification profiling of tryptophan metabolism demonstrated that seven compounds (tryptophan, kynurenine, kynurenic acid, quinolinic acid, xanthurenic acid, 3-hydroxyanthranilic acid and 3-hydroxykynurenine) were up-regulated in PCOS. And these tryptophan-kynurenine metabolites showed significant correlations with PCOS clinical features, such as positively associated with testosterone, free androgen index, and the ratio of luteinizing hormone to follicle stimulating hormone. Thus, this study disclosed urinary metabolome changes associated with PCOS, and might provide new insights into PCOS pathogenesis elucidation and therapeutical target development.

Follicular fluid lipidomic profiling reveals potential biomarkers of polycystic ovary syndrome: A pilot study

BACKGROUND

Polycystic ovary syndrome (PCOS) is a heterogeneous endocrine disorder associated with multiple metabolic conditions including obesity, insulin resistance, and dyslipidemia. PCOS is the most common cause of anovulatory infertility; however, the molecular diversity of the ovarian follicle microenvironment is not fully understood. This study aimed to investigate the follicular fluid (FF) lipidomic profiles in different phenotypes of PCOS and to explore novel lipid biomarkers.

METHODS

A total of 25 women with PCOS and 12 women without PCOS who underwent in vitro fertilization and embryo transfer were recruited, and their FF samples were collected for the lipidomic study. Liquid chromatography-tandem mass spectrometry was used to compare the differential abundance of FF lipids between patients with different PCOS phenotypes and controls. Subsequently, correlations between specific lipid concentrations in FF and high-quality embryo rate (HQER) were analyzed to further evaluate the potential interferences of lipid levels with oocyte quality in PCOS. Candidate biomarkers were then compared via receiver operating characteristic (ROC) curve analysis.

RESULTS

In total, 19 lipids were identified in ovarian FF. Of these, the concentrations of ceramide (Cer) and free fatty acids (FFA) in FF were significantly increased, whereas those of lysophosphatidylglycerol (LPG) were reduced in women with PCOS compared to controls, especially in obese and insulin-resistant groups. In addition, six subclasses of ceramide, FFA, and LPG were correlated with oocyte quality. Twenty-three lipid subclasses were identified as potential biomarkers of PCOS, and ROC analysis indicated the prognostic value of Cer,36:1;2, FFA C14:1, and LPG,18:0 on HQER in patients with PCOS.

CONCLUSIONS

Our study showed the unique lipidomic profiles in FF from women with PCOS. Moreover, it provided metabolic signatures as well as candidate biomarkers that help to better understand the pathogenesis of PCOS.

Lipidomics analysis of human follicular fluid form normal-weight patients with polycystic ovary syndrome: a pilot study

Background

The polycystic ovary syndrome (PCOS) is the most common endocrine associated with insulin resistance, even in the absence of overweight. The global lipid profile of the follicular fluid in PCOS with normal weight as yet has not been investigated. The objection of this pilot study was to explore the changes of lipids in the follicular fluid of PCOS with normal weight.

Methods

Follicular fluid samples were collected from patients who underwent IVF, including normal-weight women without PCOS (control group, n = 10) and normal-weight women with PCOS (PCOS group, n = 8). A lipidomic analysis was performed by high performance liquid chromatography/ mass spectrometry (HPLC-MS). Multidimensional statistical analysis was performed to disclose the global differences between the two groups. Further, differential lipid analysis between the two groups was performed by Fold Change Analysis (FC Analysis) and T-test to screen potential markers.

Results

All 812 species of 32 subclasses of lipids were identified by lipidomics analysis. 108 kinds of lipids were considered as the potential candidate differential metabolites with the score of variable importance in the project (VIP) more than 1 by the orthogonal partial least squares discriminant analysis. 32 lipids were significantly different between the PCOS group and the control group simultaneously with FC > 1.5 or FC < 0.67, p-value < 0.05 and VIP value > 1. These differential species of lipid belong to lipid subclasses including triglycerides (TG), phosphatidylethanolamines (PE) and phosphatidylinositols (PI).

Conclusion

The identified differential lipids in the follicular fluid may be considered as candidate biomarkers as well as therapeutic targets of PCOS with normal-weight.

Supplementary Information

The online version contains supplementary material available at 10.1186/s13048-021-00885-y.

The comparative effectiveness of 55 interventions in obese patients with polycystic ovary syndrome: A network meta-analysis of 101 randomized trials

BACKGROUND

Polycystic ovary syndrome (PCOS) affects up to 18% of reproductive-age females. The prevalence of obesity in PCOS patients reaches up to 80%, which is 2-fold higher than the general population.

OBJECTIVE

The present study aimed to compare the effectiveness of 55 pharmacological interventions across 17 different outcomes in overweight/obese PCOS patients with hyperandrogenism manifestations for both short- and long-term follow-ups. A comprehensive literature search was performed on PubMed, Scopus, Embase, Science Direct, Web of Science, and Cochrane CENTRAL for randomized controlled trials comparing any conventional pharmacological intervention as a monotherapy or a combination in overweight/obese patients with polycystic ovary syndrome and hyperandrogenism manifestations. Extracted data included three main parameters; I. Anthropometric parameters (BMI, Waist and Hip circumferences, and Waist/HIP ratio), II. Hormonal parameters (FSH, LH, FSG, SHBG, Estradiol, Total Testosterone, Free testosterone, DHEAS, Androstenedione), and III. Metabolic parameters (Total Cholesterol, LDL-C, HDL-C, Triglycerides, Fasting glucose, Fasting glucose, HOMA-IR). Critical appraisal and risk of bias assessments were performed using the modified Jadad scale, and the overall quality of this network meta-analysis was evaluated according to the CINeMA framework. We performed both a pairwise meta-analysis and a network meta-analysis to evaluate the effect sizes with 95% CI, and we calculated the surface under the cumulative ranking curve (SUCRA) for each intervention.

RESULTS

Our final search on May 15th 2021 retrieved 23,305 unique citations from searching six electronic databases. Eventually, 101 RCTs of 108 reports with a total of 8,765 patients were included in our systematic review and multi-treatments meta-analysis. 55 different interventions were included: 22 monotherapies, and 33 combinations. The two-dimensional cluster ranking of the average SUCRA values for metabolic and hormonal parameters with significant estimates revealed flutamide (77.5%, 70%; respectively) as the highest and rosiglitazone (38.2%, 26.3%; respectively) as the lowest, in terms of the overall efficacy in reducing weight and hyperandrogenism. However, cyproterone-acetate+ethinylestradiol exhibited a higher ranking in improving hormonal parameters (71.1%), but even a lower-ranking regarding metabolic parameters (34.5%).

CONCLUSIONS AND RELEVANCE

Current evidence demonstrated the superiority of flutamide in improving both metabolic and hormonal parameters, and the higher efficacy of cyproterone-acetate+ethinylestradiol only in improving hormonal parameters. Nearly all interventions were comparable in female hormones, FGS, HDL, glucose, and insulin levels improvements.

Serum Metabolomics in PCOS Women with Different Body Mass Index

Polycystic ovary syndrome (PCOS) is the most prevalent endocrine and metabolic disorder, affecting 5–10% of women of reproductive age. It results from complex environmental factors, genetic predisposition, hyperinsulinemia, hormonal imbalance, neuroendocrine abnormalities, chronic inflammation, and autoimmune disorders. PCOS impacts menstrual regularities, fertility, and dermatological complications, and may induce metabolic disturbances, diabetes, and coronary heart disease. Comprehensive metabolic profiling of patients with PCOS may be a big step in understanding and treating the disease. The study aimed to search for potential differences in metabolites concentrations among women with PCOS according to different body mass index (BMI) in comparison to healthy controls. We used broad-spectrum targeted metabolomics to evaluate metabolites’ serum concentrations in PCOS patients and compared them with healthy controls. The measurements were performed using high-performance liquid chromatography coupled with the triple quadrupole tandem mass spectrometry technique, which has highly selective multiple reaction monitoring modes. The main differences were found in glycerophospholipid concentrations, with no specific tendency to up-or down-regulation. Insulin resistance and elevated body weight influence acylcarnitine C2 levels more than PCOS itself. Sphingomyelin (SM) C18:1 should be more intensively observed and examined in future studies and maybe serve as one of the PCOS biomarkers. No significant correlations were observed between anthropometric and hormonal parameters and metabolome results.

Metabolomic Biomarkers in Polycystic Ovary Syndrome: A Review of the Evidence

Polycystic ovary syndrome (PCOS) is an endocrinologic condition affecting one in five women of reproductive age. PCOS is often characterized by disruptions to the menstrual cycle, development of male-pattern hair growth (hirsutism), and polycystic ovary morphology. Recently, PCOS has been linked to metabolic dysfunction, with 40 to 80% of women characterized as overweight or obese. Despite these well-known negative health effects of PCOS, 75% of sufferers remain undiagnosed. This is most likely due to the variability in symptom presentation and the lack of a definitive test for the condition. Metabolomics, which is a platform used to analyze and characterize a large number of metabolites, has recently been proposed as a potential tool for investigating the metabolic pathways that could be involved in the pathophysiology of PCOS. In doing so, novel biomarkers could be identified to improve diagnosis and treatment of PCOS. This review aims to summarize the findings of recent metabolomic studies that highlight metabolic-specific molecules which are deranged in PCOS, to identify potential biomarkers for the condition. Current limitations for metabolomic studies are discussed, as well as future directions to progress the field toward further validation and integration into clinical practice.

Reliability of LipidSearch software identification and its application to assess the effect of dry salting on the long-chain free fatty acid profile of tilapia muscles

Dry salting has important effects on food lipids. In this work, the reliability of LipidSearch software identification and its application to assess the effect of dry salting on the long-chain free fatty acid profile of tilapia muscles were studied by ultra-high-performance liquid chromatography-Q-Extractive Orbitrap mass spectrometry and LipidSearch software. Compared with the standard reference identification method, the LipidSearch software identification method was suggested to be a reliable identification method for long-chain free fatty acid identification. During the dry salting process, tilapia muscles with low muscle-to-salt mass ratios of 3-8 might have stable and similar free fatty acid profile changes, and the free fatty acid amounts decreased and then increased with time. This work could provide useful information to evaluate the development and application of LipidSearch software as well as a way to analyze the effect of dry salting on the free fatty acids change of aquatic products.

Metabolomic biomarkers of polycystic ovary syndrome related-obesity: a review of the literature

Background and objectives: Polycystic ovary syndrome (PCOS) displays a phenotype-dependent cardio-metabolic risk. By performing a systematic search of the literature, we aimed to summarize metabolomic signatures associated with obesity in PCOS women.

Data sources and study eligibility criteria: We conducted a comprehensive search including: Embase, PubMed, and Web of Science until 31st of May 2019. We used the terms: metabolomics and polycystic ovary syndrome. We excluded the following papers: animal studies, studies that included only lean PCOS women, reviews, meta-analyses, results of interventional studies, those that did not apply metabolomic techniques.

Results: The lipid signature in obese women with PCOS showed increased levels of free fatty acids (carnitine, adipic acid, linoleic acid, oleic acid) and lower levels of lysophosphatidylcholines and glycerolphosphocholine compared with non-obese PCOS women. Regarding carbohydrate metabolism, a decrease in citric and lactic acid levels characterized obese PCOS women. Decreased lactic acid in obese PCOS women suggests augmented insulin stimulated glucose muscle use in lean, but not in obese women. Considering amino acid metabolomic markers, valine, glycine, serine, threonine, isoleucine and lysine were higher in obese PCOS women. Patients with visceral obesity presented a diminished uptake of essential amino acids, BCAA, leucine and serine in the skeletal muscle. α-ketoglutarate was significantly higher in obese women with PCOS in comparison with lean women with PCOS, distinguishing these 2 subgroups of PCOS with high ‘predictive accuracy’.

Limitations: Overall, a small number of studies have focused on the impact of obesity on the metabolic fingerprints of PCOS women. There is need for properly controlled, high-quality studies.

Conclusions: There is compelling evidence of significant alterations in carbohydrate, lipid, and amino acid metabolism in women with PCOS and obesity. Metabolomics may identify new metabolic pathways involved in PCOS and improve our understanding of the complex relation between PCOS and obesity in order to personalize PCOS therapy.

Metabolomic Insight into Polycystic Ovary Syndrome-An Overview

Searching for the mechanisms of the polycystic ovary syndrome (PCOS) pathophysiology has become a crucial aspect of research performed in the last decades. However, the pathogenesis of this complex and heterogeneous endocrinopathy remains unknown. Thus, there is a need to investigate the metabolic pathways, which could be involved in the pathophysiology of PCOS and to find the metabolic markers of this disorder. The application of metabolomics gives a promising insight into the research on PCOS. It is a valuable and rapidly expanding tool, enabling the discovery of novel metabolites, which may be the potential biomarkers of several metabolic and endocrine disorders. The utilization of this approach could also improve the process of diagnosis and therefore, make treatment more effective. This review article aims to summarize actual and meaningful metabolomic studies in PCOS and point to the potential biomarkers detected in serum, urine, and follicular fluid of the affected women.

Lipidomic Biomarkers in Polycystic Ovary Syndrome and Endometrial Cancer

Women with polycystic ovary syndrome (PCOS) are more likely to develop endometrial cancer (EC). The molecular mechanisms which increase the risk of EC in PCOS are unclear. Derangements in lipid metabolism are associated with EC, but there have been no studies, investigating if this might increase the risk of EC in PCOS. This was a cross-sectional study of 102 women in three groups of 34 (PCOS, EC and controls) at Nottingham University Hospital, UK. All participants had clinical assessments, followed by obtaining plasma and endometrial tissue samples. Lipidomic analyses were performed using liquid chromatography (LC) coupled with high resolution mass spectrometry (HRMS) and the obtained lipid datasets were screened using standard software and databases. Using multivariate data analysis, there were no common markers found for EC and PCOS. However, on univariate analyses, both PCOS and EC endometrial tissue samples showed a significant decrease in monoacylglycerol 24:0 and capric acid compared to controls. Further studies are required to validate these findings and investigate the potential role of monoacylglycerol 24:0 and capric acid in the link between PCOS with EC.

Ceramide subclasses identified as novel lipid biomarker elevated in women with polycystic ovary syndrome: a pilot study employing shotgun lipidomics

This study aimed to identify potential lipid biomarkers in women with polycystic ovary syndrome (PCOS) and determine their predictive value for PCOS. Eighteen women with PCOS and 17 healthy controls were enrolled. A multi-dimensional mass spectrometry-based shotgun lipidomics approach was employed to analyze serum lipid profiles. Shotgun lipidomics revealed that the concentrations of ceramide (Cer) and phosphatidylcholine (PC) were higher (PC: 831.6 ± 217.4 vs. 605.2 ± 164.2 μmol/l; Cer: 3,387.6 ± 829.9 vs. 2,552.2 ± 679.4 nmol/l, respectively), whereas that of lysophosphatidylcholine was lower, in PCOS women than in healthy controls (82.02 ± 39.49 vs. 133.62 ± 65.36 μmol/l, respectively). Receiver operating characteristic analysis showed that the combination of Cer (OH_N16:0/N18:0) and Cer (N22:0) had the greatest discriminatory power to differentiate between women with and without PCOS (area under the curve: 0.889, 95% confidence interval: 0.784-0.994). These results indicate that the combination of Cer (OH_N16:0/N18:0) and Cer (N22:0) may represent a novel lipid predictor of PCOS.

Lipidomic identification of plasma lipids associated with pain behaviour and pathology in a mouse model of osteoarthritis

INTRODUCTION

Osteoarthritis (OA) is the most common form of joint disease, causing pain and disability. Previous studies have demonstrated the role of lipid mediators in OA pathogenesis.

OBJECTIVES

To explore potential alterations in the plasma lipidomic profile in an established mouse model of OA, with a view to identification of potential biomarkers of pain and/or pathology.

METHODS

Pain behaviour was assessed following destabilisation of the medial meniscus (DMM) model of OA (n = 8 mice) and compared to sham controls (n = 7). Plasma and knee joints were collected at 16 weeks post-surgery. Plasma samples were analysed using ultra-high performance liquid chromatography accurate mass high resolution mass spectrometry (UHPLC-HR-MS) to identify potential differences in the lipidome, using multivariate and univariate statistical analyses. Correlations between pain behaviour, joint pathology and levels of lipids were investigated.

RESULTS

24 lipids, predominantly from the lipid classes of cholesterol esters (CE), fatty acids (FA), phosphatidylcholines (PC), N-acylethanolamines (NAE) and sphingomyelins (SM), were differentially expressed in DMM plasma compared to sham plasma. Six of these lipids which were increased in the DMM model were identified as CE(18:2), CE(20:4), CE(22:6), PC(18:0/18:2), PC(38:7) and SM(d34:1). CEs were positively correlated with pain behaviour and all six lipid species were positively correlated with cartilage damage. Pathways shown to be involved in altered lipid homeostasis in OA were steroid biosynthesis and sphingolipid metabolism.

CONCLUSION

We identify plasma lipid species associated with pain and/or pathology in a DMM model of OA.

Current Progress of Lipid Analysis in Metabolic Diseases by Mass Spectrometry Methods

BACKGROUND

Obesity, insulin resistance, diabetes, and metabolic syndrome are associated with lipid alterations, and they affect the risk of long-term cardiovascular disease. A reliable analytical instrument to detect changes in the composition or structures of lipids and the tools allowing to connect changes in a specific group of lipids with a specific disease and its progress, is constantly lacking. Lipidomics is a new field of medicine based on the research and identification of lipids and lipid metabolites present in human organism. The primary aim of lipidomics is to search for new biomarkers of different diseases, mainly civilization diseases.

OBJECTIVE

We aimed to review studies reporting the application of mass spectrometry for lipid analysis in metabolic diseases.

METHOD

Following an extensive search of peer-reviewed articles on the mass spectrometry analysis of lipids the literature has been discussed in this review article.

RESULTS

The lipid group contains around 1.7 million species; they are totally different, in terms of the length of aliphatic chain, amount of rings, additional functional groups. Some of them are so complex that their complex analyses are a challenge for analysts. Their qualitative and quantitative analysis of is based mainly on mass spectrometry.

CONCLUSION

Mass spectrometry techniques are excellent tools for lipid profiling in complex biological samples and the combination with multivariate statistical analysis enables the identification of potential diagnostic biomarkers.

Alterations of Sphingolipid Metabolism in Different Types of Polycystic Ovary Syndrome

The roles of sphingolipids in polycystic ovary syndrome (PCOS) are still unknown. This study aimed to investigate the sphingolipid characteristics for different types of PCOS using liquid chromatography-mass spectrometry (LC-MS). A total of 107 women with PCOS and 37 healthy women as normal controls were studied. PCOS patients were further classified into non-obesity with insulin resistance (IR) (NOIR), obesity with IR (OIR), and non-obesity and non-IR (NIR) subgroups. A total of 87 serum sphingolipids, including 9 sphingosines, 3 sphinganines, 1 sphingosine-1-phosphate (S1P), 19 ceramides (Cers), 1 ceramide-1-phosphate, 44 sphingomyelins (SMs), 4 hexosylceramides, and 6 lactosylceramides (LacCers) were analyzed using an improved sphingolipidomic approach based on LC-MS. Notable elevations in the levels of S1P, Cer, and SM were observed in PCOS patients when compared with healthy women, and SM species with long saturated acyl chains showed potential as novel biomarkers of PCOS. In addition, the level of LacCer was only elevated in NIR, and there was almost no change in NOIR and OIR. This study is the first to report the comprehensive sphingolipidomic profiling of different subgroups of PCOS with or without IR or obesity and suggests that serum sphingolipids might be useful as diagnostic biomarkers for different types of PCOS.

Identification of the metabolic fingerprints in women with polycystic ovary syndrome using the multiplatform metabolomics technique

In addition to chronic anovulation and clinical signs of hyperandrogenism women with polycystic ovary syndrome (PCOS) are insulin resistant and therefore, develop central obesity with its long term consequences such as dyslipidaemia, hypertension, atherosclerosis and type 2 diabetes mellitus (T2DM), which all lead to the development of cardiovascular disease (CVD). Due to the polysymptomatic nature of this syndrome and lack of consensus on its diagnostic criteria there is a strong need of finding a reliable biochemical or molecular marker, which would facilitate making the accurate diagnosis of PCOS. Therefore, the aim of our study was to perform a metabolomics analysis with the use of two complementary techniques: gas chromatography and liquid chromatography coupled with mass spectrometry, of the serum samples from women with PCOS (n = 30) and to compare them with healthy age and BMI matched controls (n = 30). Obtained results were subjected to one-dimensional statistical analysis (student's t-test or its non-parametric equivalent U Mann-Whitney test) and multivariate statistical analysis (the principal component analysis [PCA], variable importance into projection [VIP] and selectivity ratio [SR]). The results of our study showed that women with PCOS are characterised by metabolic disorders of the amino acids, carbohydrates, steroid hormones, lipids and purines. Compared to control subjects, women with PCOS had increased serum levels of phospholipids, aromatic amino acids, organic acids, hormones and sphinganine and decreased total cholesterol. Among the identified compounds, total cholesterol, phenylalanine and dehydroepiandrosterone sulfate, uric and lactic acid were the compounds with the strongest discriminating power.

Menstrual cycle rhythmicity: metabolic patterns in healthy women

The menstrual cycle is an essential life rhythm governed by interacting levels of progesterone, estradiol, follicular stimulating, and luteinizing hormones. To study metabolic changes, biofluids were collected at four timepoints in the menstrual cycle from 34 healthy, premenopausal women. Serum hormones, urinary luteinizing hormone and self-reported menstrual cycle timing were used for a 5-phase cycle classification. Plasma and urine were analyzed using LC-MS and GC-MS for metabolomics and lipidomics; serum for clinical chemistries; and plasma for B vitamins using HPLC-FLD. Of 397 metabolites and micronutrients tested, 208 were significantly (p < 0.05) changed and 71 reached the FDR 0.20 threshold showing rhythmicity in neurotransmitter precursors, glutathione metabolism, the urea cycle, 4-pyridoxic acid, and 25-OH vitamin D. In total, 39 amino acids and derivatives and 18 lipid species decreased (FDR < 0.20) in the luteal phase, possibly indicative of an anabolic state during the progesterone peak and recovery during menstruation and the follicular phase. The reduced metabolite levels observed may represent a time of vulnerability to hormone related health issues such as PMS and PMDD, in the setting of a healthy, rhythmic state. These results provide a foundation for further research on cyclic differences in nutrient-related metabolites and may form the basis of novel nutrition strategies for women.

Identification of Potential Biomarkers for Urine Metabolomics of Polycystic Ovary Syndrome Based on Gas Chromatography-Mass Spectrometry

Background:

Polycystic ovary syndrome (PCOS) is a complex endocrine and metabolic disorder, and it's diagnosis is difficult. The aim of this study was to investigate the metabolic profiles of PCOS patients by analyzing urine samples and identify useful biomarkers for diagnosis of PCOS.

Methods:

This study was carried out in the Department of Obstetrics and Gynecology of the Maternal and Child Health Hospital of Hunan Province from December 2014 to July 2016. In this study, the urine samples of 21 women with PCOS and 16 healthy controls were assessed through gas chromatography-mass spectrometry to investigate the urine metabolite characteristics of PCOS and identify useful biomarkers for the diagnosis of this disorder. The Student's t-test and rank sum test were applied to validate the statistical significance of the between the two groups.

Results:

In total, 35 urine metabolites were found to be significantly different between the PCOS patients and the controls. In particular, a significant increase in the levels of lactose (10.01 [0,13.99] mmol/mol creatinine vs. 2.35 [0.16, 3.26] mmol/mol creatinine, P = 0.042), stearic acid (2.35 [1.47, 3.14] mmol/mol creatinine vs. 0.05 [0, 0.14] mmol/mol creatinine, P < 0.001), and palmitic acid (2.13 [1.07, 2.79] mmol/mol creatinine vs. 0 [0, 0] mmol/mol creatinine, P < 0.001) and a decrease in the levels of succinic acid (0 [0, 0] mmol/mol creatinine vs. 38.94 [4.16, 51.30] mmol/mol creatinine, P < 0.001) were found in the PCOS patients compared with the controls. It was possible to cluster the PCOS patients and the healthy controls into two distinct regions based on a principal component analysis model. Of the differentially expressed metabolites, four compounds, including stearic acid, palmitic acid, benzoylglycine, and threonine, were selected as potential biomarkers.

Conclusions:

This study offers new insight into the pathogenesis of PCOS, and the discriminating urine metabolites may provide a prospect for the diagnosis of PCOS.

Lipidomics reveals altered biosynthetic pathways of glycerophospholipids and cell signaling as biomarkers of the polycystic ovary syndrome

PURPOSE

In this work, a non-targeted approach was used to unravel changes in the plasma lipidome of PCOS patients. The aim is to offer new insights in PCOS patients strictly selected in order to avoid confounding factors such as dyslipemia, obesity, altered glucose/insulin metabolism, cardiovascular disease, or cancer.

RESULTS

Multivariate statistics revealed a specific lipidomic signature for PCOS patients without associated pathologies. This signature implies changes, mainly by down-regulation, in glycerolipid, glycerophospholipid and sphingolipid metabolism suggesting an altered biosynthetic pathway of glycerophospholipids and cell signaling as second messengers in women with PCOS.

CONCLUSIONS

Our study confirms that a lipidomic approach discriminates a specific phenotype from PCOS women without associated pathologies from healthy controls.

METHODS

In a cross-sectional pilot study, data were obtained from 34 subjects, allocated to one of two groups: a) lean, healthy controls (n = 20), b) PCOS patients (n = 14) with diagnosis based on hyperandrogenaemia, oligo-anovulation and abnormal ovaries with small follicular cysts. A detailed biochemical characterization was made and lipidomic profiling was performed via an untargeted approach using LC-ESI-QTOF MS/MS.

Serum metabolomics of Indian women with polycystic ovary syndrome using 1H NMR coupled with a pattern recognition approach

Polycystic ovary syndrome (PCOS) is one of the most commonly occurring metabolic and endocrinological disorders affecting women of reproductive age. Metabolomics is an emerging field that holds promise in understanding disease pathophysiology. Recently, a few metabolomics based studies have been attempted in PCOS patients; however, none of them have included patients from the Indian population. The main objective of this study was to investigate the serum metabolomic profile of Indian women with PCOS and compare them with controls. Proton nuclear magnetic resonance (¹H NMR) was used to first identify the differentially expressed metabolites among women with PCOS from the Eastern region of India during the discovery phase and further validated in a separate cohort of PCOS and control subjects. Multivariate analysis of the binned spectra indicated 16 dysregulated bins in the sera of these women with PCOS. Out of these 16 bins, 13 identified bins corresponded to 12 metabolites including 8 amino acids and 4 energy metabolites. Amongst the amino acids, alanine, valine, leucine and threonine and amongst the energy metabolites, lactate and acetate were observed to be significantly up-regulated in women with PCOS when compared with controls. The remaining 4 amino acids, l-glutamine, proline, glutamate and histidine were down-regulated along with 2 energy metabolites: glucose and 3-hydroxybutyric acid. Our findings showed dysregulations in the expression of different metabolites in the serum of women with PCOS suggesting the involvement of multiple pathways including amino acid metabolism, carbohydrate/lipid metabolism, purine and pyrimidine metabolism and protein synthesis.

The association of the lipidomic profile with features of polycystic ovary syndrome

Polycystic ovary syndrome (PCOS) affects up to 18% of reproductive-aged women with reproductive and metabolic complications. While lipidomics can identify associations between lipid species and metabolic diseases, no research has examined the association of lipid species with the pathophysiological features of PCOS. The aim of this study was to examine the lipidomic profile in women with and without PCOS. This study was a cross-sectional study in 156 age-matched pre-menopausal women (18-45 years, BMI >20 kg/m²; n = 92 with PCOS, n = 64 without PCOS). Outcomes included the association between the plasma lipidomic profile (325 lipid species (24 classes) using liquid chromatography mass spectrometry) and PCOS, adiposity, homeostasis assessment of insulin resistance (HOMA), sex hormone-binding globulin (SHBG) and free androgen index (FAI). There were no associations of the lipidomic profile with PCOS or testosterone. HOMA was positively associated with 2 classes (dihydroceramide and triacylglycerol), SHBG was inversely associated with 2 classes (diacylglycerol and triacylglycerol), FAI was positively associated with 8 classes (ceramide, phosphatidylcholine, lysophosphatidylcholine, phosphatidylethanolamine, lysophosphatidylethanolamine, phosphatidylinositol, diacylglycerol and triacylglycerol) and waist circumference was associated with 8 classes (4 positively (dihydroceramide, phosphatidylglycerol, diacylglycerol and triacylglycerol) and 4 inversely (trihexosylceramide, G_M3 ganglioside, alkenylphosphatidylcholine and alkylphosphatidylethanolamine)). The lipidomic profile was primarily related to central adiposity and FAI in women with or without PCOS. This supports prior findings that adiposity is a key driver of dyslipidaemia in PCOS and highlights the need for weight management through lifestyle interventions.

Combining a nontargeted and targeted metabolomics approach to identify metabolic pathways significantly altered in polycystic ovary syndrome

OBJECTIVE

Polycystic ovary syndrome (PCOS) is a condition of androgen excess and chronic anovulation frequently associated with insulin resistance. We combined a nontargeted and targeted metabolomics approach to identify pathways and metabolites that distinguished PCOS from metabolic syndrome (MetS).

METHODS

Twenty obese women with PCOS were compared with 18 obese women without PCOS. Both groups met criteria for MetS but could not have diabetes mellitus or take medications that treat PCOS or affect lipids or insulin sensitivity. Insulin sensitivity was derived from the frequently sampled intravenous glucose tolerance test. A nontargeted metabolomics approach was performed on fasting plasma samples to identify differentially expressed metabolites, which were further evaluated by principal component and pathway enrichment analysis. Quantitative targeted metabolomics was then applied on candidate metabolites. Measured metabolites were tested for associations with PCOS and clinical variables by logistic and linear regression analyses.

RESULTS

This multiethnic, obese sample was matched by age (PCOS, 37±6; MetS, 40±6years) and body mass index (BMI) (PCOS, 34.6±5.1; MetS, 33.7±5.2kg/m²). Principal component analysis of the nontargeted metabolomics data showed distinct group separation of PCOS from MetS controls. From the subset of 385 differentially expressed metabolites, 22% were identified by accurate mass, resulting in 19 canonical pathways significantly altered in PCOS, including amino acid, lipid, steroid, carbohydrate, and vitamin D metabolism. Targeted metabolomics identified many essential amino acids, including branched-chain amino acids (BCAA) that were elevated in PCOS compared with MetS. PCOS was most associated with BCAA (P=.02), essential amino acids (P=.03), the essential amino acid lysine (P=.02), and the lysine metabolite α-aminoadipic acid (P=.02) in models adjusted for surrogate variables representing technical variation in metabolites. No significant differences between groups were observed in concentrations of free fatty acids or vitamin D metabolites. Evaluation of the relationship of metabolites with clinical characteristics showed 1) negative associations of essential and BCAA with insulin sensitivity and sex hormone-binding globulin and 2) positive associations with homeostasis model of insulin resistance and free testosterone; metabolites were not associated with BMI or percent body fat.

CONCLUSIONS

PCOS was associated with significant metabolic alterations not attributed exclusively to androgen-related pathways, obesity, or MetS. Concentrations of essential amino acids and BCAA are increased in PCOS, which might result from or contribute to their insulin resistance.

Metabolic profiling of polycystic ovary syndrome reveals interactions with abdominal obesity

Background:

Polycystic ovary syndrome (PCOS) is a common reproductive disorder associated with metabolic disturbances including obesity, insulin resistance and diabetes mellitus. Here we investigate whether changes in the metabolic profile of PCOS women are driven by increased tendency to obesity or are specific features of PCOS related to increased testosterone levels.

Design and methods:

We conducted an NMR metabolomics association study of PCOS cases (n=145) and controls (n=687) nested in a population-based birth cohort (n=3127). Subjects were 31 years old at examination. The main analyses were adjusted for waist circumference (WC) as a proxy measure of central obesity. Subsequently, metabolite concentrations were compared between cases and controls within pre-defined WC strata. In each stratum, additional metabolomics association analyses with testosterone levels were conducted separately among cases and controls.

Results:

Overall, women with PCOS showed more adverse metabolite profiles than the controls. Four lipid fractions in different subclasses of very low density lipoprotein (VLDL) were associated with PCOS, after adjusting for WC and correction for multiple testing (P<0.002). In stratified analysis the PCOS women within large WC strata (⩾98 cm) had significantly lower high density lipoprotein (HDL) levels, Apo A1 and albumin values compared with the controls. Testosterone levels were significantly associated with VLDL and serum lipids in PCOS cases with large WC but not in the controls. The higher testosterone levels, adjusted for WC, associated adversely with insulin levels and HOMA IR in cases but not in the controls.

Conclusions:

Our findings show that both abdominal obesity and hyperandrogenism contribute to the dyslipidaemia and other metabolic traits of PCOS which all may negatively contribute to the long-term health of women with PCOS.

LipidFinder: A computational workflow for discovery of lipids identifies eicosanoid-phosphoinositides in platelets

Accurate and high-quality curation of lipidomic datasets generated from plasma, cells, or tissues is becoming essential for cell biology investigations and biomarker discovery for personalized medicine. However, a major challenge lies in removing artifacts otherwise mistakenly interpreted as real lipids from large mass spectrometry files (>60 K features), while retaining genuine ions in the dataset. This requires powerful informatics tools; however, available workflows have not been tailored specifically for lipidomics, particularly discovery research. We designed LipidFinder, an open-source Python workflow. An algorithm is included that optimizes analysis based on users' own data, and outputs are screened against online databases and categorized into LIPID MAPS classes. LipidFinder outperformed three widely used metabolomics packages using data from human platelets. We show a family of three 12-hydroxyeicosatetraenoic acid phosphoinositides (16:0/, 18:1/, 18:0/12-HETE-PI) generated by thrombin-activated platelets, indicating crosstalk between eicosanoid and phosphoinositide pathways in human cells. The software is available on GitHub (https://github.com/cjbrasher/LipidFinder), with full userguides.

Lipidomic analyses in epidemiology

Clinical lipid measurements have been the mainstay of risk assessment for chronic disease since the Framingham study commenced over 60 years ago. Thousands of subsequent epidemiological studies have provided much insight into the relationship between plasma lipid profiles, health and disease. However, the human lipidome consists of thousands of individual lipid species, and current lipidomic technology presents us with an unprecedented opportunity to measure lipid phenotypes, representing genomic, metabolic, diet and lifestyle-related exposures, in large epidemiological studies. The number of epidemiological studies using lipidomic profiling is increasing and has the potential to provide improved biological and clinical insight into human disease. In this review, we discuss current lipidomic technologies, epidemiological studies using these technologies and the statistical approaches used in the analysis of the resulting data. We highlight the potential of integrating genomic and lipidomic datasets and discuss the future opportunities and challenges in this emerging field.

Recent advances in lipidomics for disease research

Lipidomics is an important branch of metabolomics, which aims at the detailed analysis of lipid species and their multiple roles in the living system. In recent years, the development of various analytical methods for effective identification and characterization of lipids has greatly promoted the process of lipidomics. Meanwhile, as many diseases demonstrate a remarkable alteration in lipid profiles compared with that of healthy people, lipidomics has been extensively introduced to disease research. The comprehensive lipid profiling provides a chance to discover novel biomarkers for specific disease. In addition, it plays a crucial role in the study of lipid metabolism, which could illuminate the pathogenesis of diseases. In this review, after brief discussion of analytical methods for lipidomics in clinical research, we focus on the recent advances of lipidomics related to four types of diseases, including cancer, atherosclerosis, diabetes mellitus, and Alzheimer's disease.