Abstract

Background

   Polycystic ovary syndrome (PCOS) is the most common
   reproductive-endocrine disorder in women of reproductive age. The
   granulosa cell metabolism correlated with oocyte competence. However,
   the alteration of fatty acid in granulosa cell of PCOS patients is
   unclear. Besides, obesity per se is related to dysfunction of oocyte
   quality. Hence, this study was aimed to comprehensively investigate the
   fatty acid alteration of oocyte environment and their associations with
   embryo quality in normal weight women with PCOS.

Method

   Two independent prospective cohorts included women with PCOS and
   age-BMI-matched controls that underwent IVF treatment were recruited.
   DIA-mass spectrometry-based proteomics analysis and absolute
   quantification of fatty acids were conducted in granulosa cells and
   follicular fluid from women with PCOS and controls, and then the
   relationship between fatty acid level and embryo quality in PCOS
   patients was analysed.

Results

   Proteomic analysis in cohort 1 revealed 205 differentially expressed
   proteins in the granulosa cells of normal-weight PCOS patients compared
   with those of controls. Functional enrichment analysis and metabolism
   score calculation revealed that fatty acid metabolism pathways,
   especially the biosynthesis of unsaturated fatty acids, were
   downregulated. The hallmark fatty acid metabolism score was negatively
   correlated with the total testosterone level and the AMH level
   (R=-0.52, P = 0.004; R=-0.64, P = 0.00017, respectively).Compared with
   those in the control group, the levels of the SFAs myristic acid
   (C14:0) and MUFAs nervonic acid (C24:1) in the follicular fluid of PCOS
   patients in cohort 1 were greater, whereas the ratio of PUFAs LA: ALA
   was lower, which were validated by cohort 2. C14:0 was moderately
   positively correlated with the basal LH level and LH/FSH ratio, even
   after correction for multiple comparisons. Receiver operating
   characteristic (ROC) analysis combining the C24:1, C14:0, and LA: ALA
   ratios with the seven fatty acid metabolic-related proteins revealed
   that the predictive accuracy for PCOS was 0.86.

Conclusion

   In normal-weight women with PCOS, fatty acid disturbance occurs in
   granulosa cells and follicular fluid, and also suggesting a role for
   C14:0, C24:1 and LA: ALA in PCOS patients, independent of obesity.

Supplementary Information

   The online version contains supplementary material available at
   10.1186/s13048-025-01711-5.

   Keywords: PCOS, Fatty acid, Granulosa cell, Proteomics

Introduction

   Polycystic ovary syndrome (PCOS) is the most common endocrine‒metabolic
   disorder affecting 5–20% of women of reproductive age [[36]1]. It is
   generally characterised by two of three features: oligo/anovulation,
   biochemical or clinical manifestations of hyperandrogenaemia, and
   polycystic ovarian morphology [[37]2]. However, the aetiology of PCOS
   remains elusive because of its heterogeneity and complexity.

   PCOS patients have abnormally increased antral follicle counts and
   decreased oocyte quality, which affect reproductive outcomes [[38]3,
   [39]4]. The oocyte environment, which is composed of granulosa cells
   and follicular fluid, has a significant effect on oocyte health
   [[40]5]. As the surrounding cells of oocytes, granulosa cells play key
   roles in the maturation of oocytes and the development of follicles.
   Recently, Morimoto A el al. found that energy metabolism dynamic
   profiles in granulosa cells, including fatty acid oxidation, glycolysis
   and mitochondrial respiration, are related to fertilization and
   blastocyst development [[41]6]. In addition, alterations in follicular
   fluid composition are involved in cellular metabolic pathways related
   to oocyte quality and thus correspond with IVF outcomes [[42]7].
   Therefore, this study aimed to systematically investigate fatty acid
   disturbance in follicles and their impact on embryo quality in PCOS
   patients and to identify potential fatty acid markers in PCOS.

   Increasing evidence indicates that PCOS is frequently associated with
   obesity [[43]8]. In women undergoing IVF treatment, a decreased
   probability (risk ratio RR = 0.85) of live birth was observed in obese
   women compared with normal weight women (BMI 18.5–24.9 kg/m^2) [[44]9].
   Obesity is likely a confounding factor in the study of embryo quality
   in PCOS patients because obesity per se is related to hyperandrogenism,
   anovulation and infertility [[45]10]. PCOS women with normal weight,
   also called “lean PCOS”, provide an opportunity to elucidate the
   pathogenesis of PCOS independent of obesity.

   Proteomic analysis is a hypothesis-free approach that enables the
   pathogenesis of the disease to be unveiled by compressively identifying
   distinct protein expression profiles. Recently, proteomic analyses of
   the serum and follicular fluid of PCOS patients have been reported
   [[46]11–[47]14], but the granulosa cells of PCOS patients have rarely
   been assessed [[48]15, [49]16]. Given that obesity can alter the
   function of granulosa cells [[50]17], in this study, we used a
   data-independent acquisition (DIA)-mass spectrometry approach to
   investigate the proteomic signatures of granulosa cells isolated from
   thirty normal-weight PCOS patients and controls (named cohort 1).
   Notably, our results revealed that proteins that participate in fatty
   acid metabolism were down regulated in PCOS patients. Given that fatty
   acids within the granulosa cells are either taken up from follicular
   fluid or broken down from cellular lipids, we therefore conducted
   absolute quantification of fifty fatty acids of follicle fluid from
   cohort 1 using ultrahigh-performance liquid chromatography coupled with
   tandem mass spectrometry (UPLC-MS/MS) and 52 newly recruited normal
   weight cases (named cohort 2) for validation.

Materials and methods

Participants

   Cohort 1 consisted of 15 Han Chinese normal weight women with PCOS and
   15 BMI-matched women without PCOS who were collected prospectively from
   the Reproductive Centre, Tianjin Medical University General Hospital,
   from September 2022 to December 2022. Cohort 2 consisted of 27
   normal-weight women with PCOS and 25 matched controls who were
   recruited from the same centre as cohort 1 from March 2023 to December
   2023. The inclusion criteria for patients with PCOS included two of the
   following criteria: oligo-ovulation/anovulation (menstrual cycle
   length > 35 days), biochemical or clinical hyperandrogenism (total
   testosterone ≥ 57 ng/dl, Ferriman–Gallwey score ≥ 6, or severe acne),
   and PCO morphology on ultrasound (at least 12 follicles that are 2–9 mm
   in diameter in one ovary and/or ovarian volume > 10 mL). Patients with
   congenital adrenal hyperplasia, androgen-secreting tumours, Cushing’s
   syndrome, thyroid disease, or hyperprolactinaemia were excluded.
   Participants taking oral contraceptives or metformin during the last
   three months were also excluded. This study was approved by the Ethics
   Committee of Tianjin Medical University General Hospital
   (IRB2021-YX-223-01).

Clinical and biochemical measurements

   All the subjects were assessed for age, height and weight. Body mass
   index (BMI) was calculated as weight (kg)/height (m)^2. Based on the
   classification of the World Health Organization (WHO),
   18.5 kg/m^2 ≤ BMI < 25 kg/m^2 was defined as normal weight [[51]18].
   Fasting blood samples were obtained on Days 2–4 of the menstrual cycle
   to examine the serum levels of hormones, including follicle-stimulating
   hormone (FSH), luteinizing hormone (LH), and total testosterone. Serum
   anti-Mullerian hormone (AMH) was measured by a chemiluminescence
   immunoassay, and fasting glucose was measured by the glucose oxidase
   method. The insulin level in the follicle fluid was measured via
   Luminex xMAP technology via a Luminex 200 instrument.

IVF treatment

   All participants underwent the regular controlled ovarian stimulation
   protocol with individual doses of exogenous gonadotropins. Ovarian
   stimulation was monitored by the concentrations of hormones and the
   numbers and diameters of follicles on ultrasound. Ovulation was induced
   using human chorionic gonadotropin and/or a gonadotropin-releasing
   hormone (GnRH) agonist when two leading follicles were ≥ 18 mm in
   diameter or three leading follicles were ≥ 17 mm in diameter. After
   36 h, the oocytes were retrieved under the guidance of transvaginal
   ultrasound. Oocytes were fertilised by IVF, and the fertilization
   status was evaluated on Day 1 after fertilization on the basis of the
   presence of two pronuclei (2PN). Embryo morphology was evaluated on Day
   3 according to the Istanbul consensus, mainly by assessing the embryo
   cell number and the percentage of fragmentation [[52]19]. Rate of 2PN
   fertilization (*100%) = number of 2PN fertilized oocytes/number of MII
   oocytes. Rate of D3 good-quality embryos (*100%) = number of D3
   good-quality embryos/number of cleaved embryos. Rate of D3 available
   embryos (*100%) = number of D3 available embryos/number of cleaved
   embryos. Rate of oocyte utilization (*100%) = number of D3 available
   embryos/number of MII oocytes.

Follicular fluid and granulosa cell collection

   The procedures of sample collection were described in detail in our
   previous study [[53]20]. Briefly, follicular fluid samples were
   collected from the first one or two punctured mature follicles, were
   cleared of blood contamination, and then stored in liquid nitrogen in a
   2-ml cryopreservation tube. All the granulosa cells were isolated after
   the oocytes were retrieved. In brief, red blood cells were depleted
   using blood cell lysis buffer (Solarbio^® Science & Technology Co.,
   Beijing, China), and leukocytes were removed via the use of the
   lymphocyte separation solution Ficoll (Cytiva, Sweden, USA). Isolated
   granulosa cells were washed with 1X Dulbecco’s phosphate-buffered
   saline (DPBS) and stored in liquid nitrogen.

Protein extraction and digestion

   The protein extraction procedures were performed as described in our
   previous study [[54]20]. Briefly, RIPA buffer with protease inhibitors
   was added to cryopreservation tubes containing granulosa cells (GCs).
   After the protein concentration was detected via the BCA method, we
   prepared a protein mixture with an aliquot of 100 µg of protein and
   precipitated the protein by adding 50% TCA solution. The protein
   pellets were then thoroughly washed twice with acetone at -20 °C. The
   pellets were subsequently resuspended in 100 mM ammonium bicarbonate
   (pH 8.0) and subjected to tryptic digestion at 37 °C for 16 h.
   Dithiothreitol (DTT; 5 mM) was added to the mixture, which was
   subsequently incubated at 56 °C for 30 min. Iodoacetamide (IAA) was
   subsequently added to a final concentration of 15 mM to alkylate the
   proteins, followed by a 30-minute incubation period in the dark at room
   temperature. The alkylation reaction was then quenched by the addition
   of 30 mM cysteine, and the reaction was maintained at room temperature
   for an additional 30 min. Trypsin was reintroduced at a
   trypsin-to-protein ratio of 1:100 (w/w) for a 4-hour digestion at
   37 °C. The digestion was terminated with 1% TFA. The resulting peptides
   were purified using C18 ZipTips (Millipore) prior to analysis via
   liquid chromatography‒tandem mass spectrometry (LC‒MS/MS).

LC‒MS/MS analysis of proteomes

   Peptides were dissolved in 0.1% FA and directly loaded onto a
   reversed-phase precolumn (Acclaim PepMap 100, Thermo Scientific). The
   enriched peptides were separated using a reversed-phase analytical
   column (Acclaim PepMap RSLC, Thermo Scientific). The standard
   parameters commonly used in HPLC‒MS/MS proteomics were used.
   Specifically, HPLC gradient elution for sample separation was performed
   by increasing the volume from 5 to 22% solvent B (0.1% FA in 98% ACN)
   for 41 min, increasing it to 22–38% for 23 min, increasing it to 100%
   solvent B in 3 min and then holding it at 100% for 8 min, all at a
   constant flow rate of 280 nl/min, on an EASY-nLC 1000 UPLC system. The
   resulting peptides were injected into an NSI source, followed by tandem
   MS/MS analysis in an Eclipse Orbitrap mass spectrometer (Thermo Fisher
   Scientific) at Tianjin Medical University. Intact peptides were
   detected in the Orbitrap at a resolution of 60,000. For MS scans, the
   m/z scan range was 400–1200. A DIA procedure was performed with the
   setting parameters (Table [55]S1).

Database search

   The resulting MS/MS data were processed using Spectronaut17 in direct
   DIA mode on the basis of the SwissProt human protein sequence database
   downloaded from UniProt. Trypsin was selected as the digestion enzyme,
   and a maximum of two missed tryptic cleavages were allowed.
   Carbamidomethylation of cysteine was specified as a fixed modification,
   and oxidation of Met and acetylation of the protein N-terminus were
   specified as variable modifications. The false discovery rate (FDR)
   thresholds for proteins and peptides were specified at 1%. All the
   other parameters in Spectronaut17 were set to default values.

Bioinformatic analysis

   The bioinformatics analysis was performed using R. The output file
   generated by Spectronaut was stringently filtered to keep proteins with
   a maximum of 8 missing values in at least one condition and normalised
   to the median. Missing values were imputed via the R package Impute
   (1.74.1) before statistical testing. Differential protein expression
   analysis was performed using the limma R package. Principal component
   analysis (PCA) was performed through the R packages FactoMineR (v.2.8)
   and factoextra (v.1.0.7). Gene Ontology (GO) analysis and Kyoto
   Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analysis
   were performed to better understand the functions of the differentially
   expressed proteins via the ClusterProfiler (v.4.8.3) R package.
   Single-sample gene set enrichment analysis (ssGSEA) was conducted using
   the GSVA (v.1.52.2) R package to obtain a hallmark gene set score. The
   hallmark gene sets were obtained from the Molecular Signatures Database
   (MSigDB). The Wilcoxon test was conducted to evaluate the differences
   in the FATTY_ACID_METABOLISM score.

Standard solution Preparation for fatty acid quantification

   A stock solution of individual fatty acids was prepared in a fatty
   acid-free matrix to obtain a series of fatty acid calibrators at
   concentrations of 40,000, 20,000, 10,000, 4000, 2000, 1000, 400, 200,
   100, 40, 20 or 10 ng/mL. The internal standard (IS) was a mixture of
   certain concentrations of decanoic acid-d19, myristic acid-d2,
   octadecanoic acid-d35, eicosanoic acid-d39 and lignoceric acid-d4. The
   stock solution and working solution were stored at -20 °C.

Metabolite extraction and UPLC‒MS/MS

   One hundred microlitres of each diluted sample was homogenised with 300
   µL of isopropanol/acetonitrile (1:1), which contained mixed internal
   standards. The mixture was placed on ice for 30 min and centrifuged at
   12,000 rpm for 10 min, after which the supernatant was analysed via
   LC‒MS. The supernatant was subsequently diluted 20 times with
   isopropanol/acetonitrile (1:1), which contained mixed internal
   standards, and the mixture was subsequently centrifuged at 12,000 rpm
   for 10 min, after which the supernatant was analysed.

   An ultrahigh-performance liquid chromatography coupled with tandem mass
   spectrometry (UHPLC-MS/MS) system (ExionLC™ AD UHPLC-QTRAP 6500+, AB
   SCIEX Corp., Boston, MA, USA) was used to quantify fatty acids at
   Novogene Co., Ltd. (Beijing, China). Separation was performed on a
   Waters ACQUITY UPLC BEH C18 column (2.1 × 100 mm, 1.7 μm), which was
   maintained at 50 °C. The mobile phase, consisting of 0.05% formic acid
   in water (solvent A) and isopropanol/acetonitrile (1:1) (solvent B),
   was delivered at a flow rate of 0.30 mL/min. The solvent gradient was
   set as follows: initial 30% B, 1 min; 30–65% B, 2 min; 65–100% B,
   11 min; 100% B, 13.5 min; 100–30% B, 14 min; and 30% B, 15 min.

   The mass spectrometer was operated in negative multiple reaction mode
   (MRM). The parameters used were as follows: ion spray voltage
   (-4500 V), curtain gas (35 psi), ion source temperature (550 °C), and
   ion source gases of 1 and 2 (60 psi).

   A total of 40 and 41 individual fatty acids were quantified in cohort 1
   and cohort 2, respectively. In this study, we evaluated the 37 major
   individual fatty acids that were detected in two cohorts and grouped
   them into even-chain saturated fatty acids (SFAs), odd-chain SFAs,
   very-long-chain SFAs, cis-long-chain monounsaturated fatty acids
   (MUFAs), very-long-chain MUFAs, trans-long-chain-MUFAs, omega-3
   polyunsaturated fatty acids (n-3 PUFAs) and n-6 PUFAs.

Statistical analysis

   The normality of the variables was assessed using the Shapiro‒Wilk
   test. The quantitative variables of the clinical characteristics of the
   PCOS patients are displayed as the means ± standard errors (SDs) or
   medians (interquartile ranges), as appropriate. The values for each
   fatty acid were log10 transformed because most had a naturally skewed
   distribution. Statistical analysis was performed via SPSS 25.0 for
   Windows (SPSS Inc., Chicago, IL, USA) or R (4.3.2). Differences between
   the PCOS and control groups were assessed using independent t tests for
   normally distributed quantitative data or Mann‒Whitney U tests for
   nonparametric data. Logistic regression analyses were used to determine
   the associations between fatty acids and PCOS and between adjusted
   forage and BMI. The odds ratios (ORs) were used to analyse risk factors
   for PCOS, and 95% confidence intervals (95% CIs) are presented. P
   values were corrected for multiple testing using the Benjamini–Hochberg
   method, the false discovery rate (FDR)-adjusted P values were
   calculated, and statistical significance was set at P < 0.05.

Results

Clinical features of PCOS patients

   The clinical and embryo-related parameters of the PCOS and control
   groups are summarised in Table [56]1. All PCOS patients in this study
   presented with oligo- or ovulation. To ensure the objectivity of
   comparative research, the mean age, BMI, basal FSH level and duration
   of infertility were similar between groups in the collected samples.
   Among the two cohorts, women with PCOS were more likely to have
   significantly higher basal LH levels, LH/FSH ratios, total testosterone
   levels and AMH levels (P < 0.05). No obvious differences were observed
   in the serum fasting level or insulin level of the follicular fluid in
   cohort 1, whereas a slight increase was observed in the PCOS group of
   cohort 2. The numbers of metaphase II (MII) oocytes, two pronuclear
   (2PN) fertilised oocytes and cleaved embryos were significantly greater
   in the PCOS group than in the control group (all P < 0.05), whereas the
   numbers of Day 3 good-quality embryos and Day 3 available embryos were
   not significantly different.

Table 1.

   Clinical and biochemical characteristics of normal-weight women with
   polycystic ovary syndrome
   Cohort 1 Cohort 2
   PCOS cases Controls P PCOS cases Controls P
   (n = 15) (n = 15) (n = 27) (n = 25)
   Age (year) 30.40 ± 3.46 32.33 ± 2.09 0.075 30.26 ± 3.28 30.84 ± 2.67
   0.49
   BMI (kg/m2) 22.49 ± 1.68 21.93 ± 1.78 0.388 21.52 ± 1.94 21.79 ± 1.80
   0.599
   Duration of Infertility (years) 2(1–3) 2(1–4) 0.862 3(1–5) 2(1–3) 0.318
   Menstrual cycle (days) 50(38–60) 30(27–30) < 0.001 60(40–120) 30(28–30)
   < 0.001
   Antral follicle count 33.0 ± 16.60 16.40 ± 4.09 0.002 38.41 ± 12.26
   19.88 ± 6.55 < 0.001
   Basal FSH(IU/l) 5.55 ± 1.54 5.98 ± 0.88 0.359 5.55 ± 1.24 6.18 ± 1.53
   0.109
   Basal LH(IU/l) 7.05 ± 4.96 3.32 ± 1.10 0.012 6.96 ± 3.99 4.53 ± 2.45
   0.013
   LH/FSH 0.91(0.69–1.93) 0.59(0.42–0.70) 0.003 1.05(0.78–1.51)
   0.62(0.40–1.06) 0.002
   T(ng/dl) 44.36 ± 16.64 27.03 ± 9.58 0.002 48.77 ± 19.4 32.61 ± 12.71
   0.001
   AMH(ng/ml) 7.71 ± 3.34 3.28 ± 1.37 < 0.001 10.03 ± 3.80 4.26 ± 2.18
   < 0.001
   Fasting glucose (mmol/l) 4.84 ± 0.41 4.83 ± 0.48 0.922 5.20 ± 0.81
   4.80 ± 0.38 0.037
   Insulin of follicular fluid (pg/ml) 2318.07 ± 267.32 2333.87 ± 252.73
   0.871 2310.88 ± 512.10 1920.78 ± 261.35 0.045
   No. of retrieved oocytes 28.93 ± 12.05 18 ± 7.28 0.006 28.41 ± 14.68
   18.08 ± 7.82 0.003
   No. of MII oocytes 24 ± 9.18 15.47 ± 6.66 0.007 24.15 ± 12.47
   15.84 ± 7.10 0.005
   No. of 2PN fertilized oocytes 17.73 ± 7.56 10.6 ± 5.25 0.006
   17.33 ± 9.35 10.92 ± 5.94 0.005
   No. of cleavage embryos 17.53 ± 7.83 10.27 ± 5.22 0.006 16.93 ± 9.15
   10.72 ± 5.83 0.006
   No. of D3 good quality embryos 8(4–10) 4(2–9) 0.197 6(4–10) 5(2-7.5)
   0.065
   No. of D3 available embryos 9(6–13) 6(4–9) 0.07 9(5–15) 6(3.5–9.5)
   0.051
   Rate of 2PN fertilization (*100%) 0.61 ± 0.12 0.6 ± 0.16 0.745
   0.63 ± 0.13 0.57 ± 0.18 0.19
   Rate of D3 good quality embryo (*100%) 0.40 ± 0.19 0.52 ± 0.22 0.144
   0.43 ± 0.21 0.42 ± 0.25 0.906
   Rate of D3 available embryos(*100%) 0.56 ± 0.17 0.68 ± 0.24 0.136
   0.62 ± 0.22 0.67 ± 0.26 0.444
   Rate of oocyte utilization ( *100%) 0.40 ± 0.13 0.44 ± 0.16 0.426
   0.45 ± 0.21 0.44 ± 0.20 0.816
   [57]Open in a new tab

   * BMI: body mass index; FSH: follicle-stimulating hormone; LH:
   luteinizing hormone; T: testosterone; AMH, anti-mullerian hormone; MII
   oocytes: metaphase II oo cytes; 2 PN fertilized oocytes: two-pronuclear
   fertilized oocyte

Identifcation of deps and pathways associated with PCOS

   To identify the abnormally expressed proteins in PCOS patients, we
   applied mass spectrometry (MS) based on the DIA strategy to analyse the
   proteomes of soluble granulosa cells from PCOS patients and controls
   from cohort 1. As shown in Fig. [58]1A, PCA revealed two significant
   clusters, indicating distinct protein expression between the PCOS group
   and the control group. Among the 6474 identified proteins, 205
   differentially expressed proteins (DEPs) were observed (119 upregulated
   and 86 downregulated, Fig. [59]1B) in the PCOS group. To understand the
   distribution characteristics of protein expression in the two groups,
   we generated unsupervised hierarchical clustering heatmaps to display
   the DEPs (Fig. [60]1C) and the results revealed differential proteome
   expression between the PCOS and control groups. To characterise the
   abnormally expressed proteins in PCOS, we subsequently performed GO
   enrichment analysis, which revealed that the upregulated proteins in
   the biological process classification were involved mainly in DNA
   replication, whereas the downregulated proteins were involved primarily
   in metabolism-related processes, including steroid metabolic processes,
   cholesterol metabolic processes, ribonucleoside diphosphate metabolic
   processes and fatty acid metabolic processes (Figure [61]S1). KEGG
   pathway enrichment analysis revealed that the downregulated DEPs were
   enriched mainly in fatty acid metabolism, glycolysis, biosynthesis of
   unsaturated fatty acids, and the PPAR pathway (Fig. [62]1D). Notably,
   peroxisome proliferator-activated receptors (PPARs), as nuclear hormone
   receptors, mediate the effects of fatty acids on gene expression and
   cellular processes [[63]21]. Hence, this study focused on the
   disturbance of fatty acid metabolism in PCOS patients.

Fig. 1.

   [64]Fig. 1
   [65]Open in a new tab

   Comparison of the DIA-based quantitative proteomic landscape in the
   PCOS and control groups. (A) Principal component analysis showing
   moderate separation between the two groups. (B) Volcano plot depicting
   differentially expressed proteins between the PCOS group and the
   control group, with red circles indicating upregulated proteins
   (FDR < 0.05, fold change > 1.2) and blue circles denoting downregulated
   proteins (FDR < 0.05, fold change < 0.833). (C) Heatmap illustrating
   the differential protein abundance between the PCOS group and the
   control group. (D) Bar plot displaying the results of the KEGG pathway
   enrichment analysis of the downregulated proteins in the PCOS group

Fatty acid metabolism related proteins and associations with clinical
parameters

   As shown in Fig. [66]2, the heatmap depicting significantly different
   proteins involved in fatty acid metabolism pathways. The hallmark fatty
   acid metabolism score calculated via the ssGSEA method via the MSigDB
   database was significantly lower in the PCOS group than in the control
   group (PCOS: -0.24 (-0.28~-0.10); control: 0.21 (0.04–0.38);
   P = 5.6e-5). The hallmark fatty acid metabolism score was negatively
   correlated with the total testosterone level and the AMH level
   (R=-0.52, P = 0.004; R=-0.64, P = 0.00017, respectively). Additionally,
   the steroid metabolic and steroid biosynthetic process scores
   calculated by ssGSEA were significantly lower in the PCOS group
   (P < 0.001) and weakly related to the D3 good-quality embryo rate
   (R = 0.36, P = 0.049; R = 0.38, P = 0.041, respectively; see
   supplementary figure [67]S1).

Fig. 2.

   [68]Fig. 2
   [69]Open in a new tab

   Fatty acid metabolism is suppressed in the granulosa cells of
   individuals with PCOS. (A) Heatmap depicting significantly different
   proteins involved in fatty acid metabolism and biosynthesis pathways.
   Protein expression values are presented as z scores. (B) Violin plot
   illustrating the difference in the hallmark fatty acid metabolism score
   between the PCOS and control groups. The P value was determined using
   the Wilcoxon test. (C, D) The correlation between the fatty acid
   metabolism score and anti-Mullerian hormone (AMH) or testosterone (T)
   is shown, with translucent bands around the regression line
   representing the 95% confidence interval. The correlation coefficient
   was calculated using the Spearman method

Free fatty acid quantification in follicular fluid

   Given that fatty acids in the granulose cells were partly taken up from
   follicular fluid, we conducted a quantification analysis of 50 free
   fatty acids in the follicular fluidof 30 women from cohort 1. As shown
   in Table [70]2, the levels of the even-chain SFA myristic acid (C14:0),
   the very long-chain MUFA nervonic acid (C24:1), the cis-long-chain-MUFA
   C18:1(n-7), the trans-long-chain-MUFA C14:1T, and the n-3 PUFA
   salpha-linolenic acid (ALA, C18:3n-3) were greater in normal-weight
   PCOS patients, whereas the ratio of the PUFA LA: ALA (C18:2n-6:
   C18:3n-3) was lower than that in the control group. After correction
   for multiple comparisons, the level of nervonic acid (C24:1) was still
   significantly different between PCOS patients and controls (P = 0.039).
   In validation cohort 2, the associations between PCOS and fatty acids,
   including SFA C14:0, MUFA C24:1 and the ratio of LA: ALA (C18:2n-6:
   C18:3n-3), remained robust (P = 0.004, FDR-adjusted P = 0.039;
   P = 0.013, FDR-adjusted P = 0.072; P = 0.004, FDR-adjusted P = 0.039,
   respectively). The results pooled across the two cohorts revealed that
   increased levels of SFA C14:0 and MUFA C24:1 and a decreased ratio of
   LA: ALA (C18:2n-6: C18:3n-3) were associated with increased odds of
   PCOS even after adjustment for age and BMI (P-adj = 0.035;
   P-adj = 0.022; P-adj = 0.035, respectively; see Table [71]2;
   Fig. [72]3A). In addition, the level of the MUFA C20:1 was also
   significantly greater in the PCOS patients in the two pooled cohorts;
   the trans-long-chain-MUFA C15:1T and n-3 PUFA DHA were significantly
   greater only in the PCOS patients in cohort 2, even after FDR
   correction for multiple comparisons.

Table 2.

   Distributions of fatty acid in follicular fluid of normal-weight women
   with polycystic ovary syndrome. OR: odds ratio; FDR: false discovery
   rate; SFA: saturated fatty acid; MUFA: monounsaturated fatty acid;
   PUFA: polyunsaturated fatty acid; ALA: alpha-Linolenic acid; EPA:
   eicosapentaenoic acid; DPA: docosapentaenoic acid; DHA: docosahexaenoic
   acid; LA: Linoleic acid; DGLA: homo-gamma-Linolenic ac-id; AA:
   arachidonic acid. *: p value < 0.05
   Fatty acid(log10, ng/mL) Cohort 1(n = 30) Cohort 2(n = 52)
   Pooled(n = 82)
   PCOS cases Controls P adj P PCOS cases Controls P adj P OR(95%CI) adj P
   SFAs
   Even-chain SFAs
   Decanoic acid C10:0 2.36 ± 0.14 2.32 ± 0.18 0.611 0.745 2.03 ± 0.21
   2.04 ± 0.17 0.97 0.97 0.999(0.993–1.006) 0.92
   Dodecanoic acid C12:0 2.60 ± 0.26 2.57 ± 0.29 0.769 0.882 2.42 ± 0.19
   2.38 ± 0.25 0.565 0.629 1(0.999–1.001) 0.909
   Myristic acid C14:0 2.77 ± 0.11 2.68 ± 0.10 0.03* 0.279 2.86 ± 0.11
   2.73 ± 0.18 0.004* 0.039* 1.004(1.002–1.007) 0.035*
   Hexadecanoic acid C16:0 4.35 ± 0.10 4.29 ± 0.09 0.102 0.375 4.31 ± 0.31
   4.29 ± 0.12 0.521 0.598 1(1–1) 0.316
   Octadecanoic acid C18:0 3.86 ± 0.12 3.82 ± 0.11 0.301 0.531 3.74 ± 0.15
   3.78 ± 0.14 0.434 0.584 1(1–1) 0.92
   Odd-chain SFAs
   Pentadecanoic acid C15:0 1.77 ± 0.25 1.61 ± 0.32 0.146 0.375
   1.75 ± 0.23 1.62 ± 0.21 0.068 0.185 1.016(0.999–1.032) 0.173
   Heptadecanoic acid C17:0 2.19 ± 0.11 2.13 ± 0.15 0.173 0.375
   2.05 ± 0.20 2.01 ± 0.22 0.467 0.588 1.004(0.997–1.012) 0.345
   Very-long-chain SFAs
   Arachidic acid C20:0 2.05 ± 0.21 1.99 ± 0.19 0.355 0.577 1.71 ± 0.29
   1.72 ± 0.28 0.927 0.951 1.004(0.995–1.013) 0.529
   Docosanoic acid C22:0 2.14 ± 0.25 2.02 ± 0.21 0.157 0.375 1.89 ± 0.28
   1.84 ± 0.27 0.517 0.598 1.005(0.998–1.011) 0.309
   MUFAs
   Cis-long-chain-MUFAs
   Myristoleic acid C14:1 2.27 ± 0.09 2.32 ± 0.15 0.303 0.531 1.72 ± 0.23
   1.92 ± 0.35 0.02* 0.087 0.995(0.99-1) 0.158
   Palmitoleic acid C16:1 3.50 ± 0.11 3.44 ± 0.14 0.147 0.375 3.08 ± 0.23
   3.14 ± 0.27 0.402 0.56 1(1–1) 0.987
   cis-10-Heptadecenoic acid C17:1 2.31 ± 0.14 2.26 ± 0.18 0.379 0.591
   2.16 ± 0.26 2.08 ± 0.24 0.255 0.432 1.003(0.998–1.008) 0.316
   Petroselinic acid C18:1(n-12) 1.86 ± 0.09 1.89 ± 0.09 0.459 0.663
   1.38 ± 0.25 1.43 ± 0.34 0.613 0.646 0.989(0.972–1.006) 0.316
   Oleic acid C18:1(n-9) 4.23 ± 0.12 4.15 ± 0.14 0.089 0.375 4.11 ± 0.17
   4.06 ± 0.17 0.333 0.519 1(1–1) 0.275
   cis-Vaccenic acid C18:1(n-7) 4.26 ± 0.11 4.17 ± 0.12 0.043* 0.279
   4.09 ± 0.18 4.06 ± 0.18 0.485 0.591 1(1–1) 0.275
   Very long chain MUFAs
   cis-11-Eicosenoic acid C20:1 2.74 ± 0.13 2.66 ± 0.13 0.125 0.375
   2.76 ± 0.15 2.64 ± 0.14 0.006* 0.039* 1.004(1.001–1.007) 0.039*
   Erucic acid C22:1 2.11 ± 0.12 2.00 ± 0.20 0.11 0.375 2.01 ± 0.15
   1.93 ± 0.17 0.057 0.171 1.013(1.001–1.025) 0.155
   Nervonic acid C24:1 2.63 ± 0.14 2.44 ± 0.15 0.001* 0.039* 2.59 ± 0.20
   2.45 ± 0.45 0.013* 0.072 1.006(1.003–1.01) 0.022*
   Trans-long-chain-MUFAs
   Myristelaidic acid C14:1T 1.85 ± 0.16 1.80 ± 0.19 0.042* 0.279
   1.65 ± 0.26 1.62 ± 0.21 0.583 0.632 1.007(0.993–1.022) 0.418
   trans-10-Pentadecenoic acid C15:1T 1.61 ± 0.40 1.64 ± 0.21 0.812 0.893
   1.14 ± 0.18 1.46 ± 0.37 0.001* 0.02* 0.987(0.97–1.003) 0.275
   Palmitelaidic acid C16:1T 2.26 ± 0.13 2.11 ± 0.36 0.149 0.375
   2.10 ± 0.20 2.04 ± 0.24 0.401 0.56 1.002(0.997–1.007) 0.527
   trans-10-Heptadecenoic acid C17:1T 1.52 ± 0.21 1.52 ± 0.19 0.988 0.988
   1.12 ± 0.28 1.27 ± 0.35 0.141 0.321 0.989(0.965–1.014) 0.509
   trans-7-Nonadecenoic acid C19:1(n-12)T 2.04 ± 0.19 1.90 ± 0.22 0.08
   0.375 1.45 ± 0.25 1.57 ± 0.42 0.226 0.401 1(0.992–1.009) 0.979
   trans-10-Nonadecenoic acid C19:1(n-9)T 1.97 ± 0.15 1.89 ± 0.17 0.264
   0.515 1.44 ± 0.15 1.55 ± 0.32 0.299 0.486 1.003(0.989–1.018) 0.74
   PUFA
   n-3 PUFA
   ALA C18:3(n-3) 2.92 ± 0.12 2.76 ± 0.23 0.029* 0.279 2.76 ± 0.27
   2.70 ± 0.22 0.395 0.56 1.001(1-1.002) 0.275
   cis-11,14,17-Eicosatrienoic acid C20:3(n-3) 2.93 ± 0.12 2.90 ± 0.15
   0.586 0.737 2.89 ± 0.22 2.82 ± 0.14 0.197 0.384 1.001(1-1.003) 0.285
   cis-5,8,11,14,17- EPA C20:5(n-3) 2.34 ± 0.19 2.29 ± 0.34 0.563 0.737
   2.32 ± 0.26 2.19 ± 0.18 0.032* 0.113 1.003(0.999–1.006) 0.275
   cis-7,10,13,16,19 -DPA C22:5(n-3) 2.78 ± 0.18 2.73 ± 0.21 0.575 0.737
   2.80 ± 0.28 2.64 ± 0.19 0.029* 0.113 1.002(1-1.003) 0.158
   cis-4,7,10,13,16,19-DHA C22:6(n-3) 3.75 ± 0.16 3.72 ± 0.17 0.663 0.784
   3.90 ± 0.21 3.75 ± 0.17 0.006* 0.039* 1(1–1) 0.121
   n-6 PUFA
   LA C18:2(n-6) 4.33 ± 0.08 4.28 ± 0.11 0.188 0.386 4.10 ± 0.14
   4.17 ± 0.19 0.148 0.321 1(1–1) 0.545
   gamma-Linolenic acid C18:3(n-6) 2.84 ± 0.12 2.76 ± 0.17 0.172 0.375
   2.73 ± 0.26 2.68 ± 0.20 0.451 0.586 1.001(0.999–1.003) 0.362
   cis-11,14-Eicosadienoic acid C20:2(n-6) 3.28 ± 0.14 3.22 ± 0.16 0.313
   0.531 3.18 ± 0.16 3.12 ± 0.17 0.21 0.39 1.001(1-1.001) 0.293
   DGLA C20:3(n-6) 3.03 ± 0.19 3.03 ± 0.26 0.981 0.988 3.07 ± 0.25
   2.96 ± 0.17 0.071 0.185 1.001(1-1.001) 0.316
   AA C20:4(n-6) 3.47 ± 0.16 3.39 ± 0.14 0.142 0.375 3.49 ± 0.24
   3.42 ± 0.25 0.197 0.384 1(1-1.001) 0.158
   cis-13,16-Docosadienoic acid C22:2(n-6) 2.75 ± 0.16 2.72 ± 0.13 0.577
   0.737 2.77 ± 0.15 2.68 ± 0.15 0.038* 0.124 1.002(1-1.005) 0.158
   cis-7,10,13,16-Docosic acidtraenoic acid C22:4(n-6) 3.53 ± 0.20
   3.52 ± 0.15 0.824 0.893 3.53 ± 0.27 3.41 ± 0.20 0.097 0.236 1(1–1)
   0.275
   cis-4,7,10,13,16- Docosapentaenoic acid C22:5(n-6) 2.78 ± 0.20
   2.72 ± 0.16 0.406 0.609 2.84 ± 0.25 2.69 ± 0.16 0.016* 0.078
   1.002(1-1.004) 0.121
   n-6:n-3 4.13 ± 0.81 4.14 ± 1.38 0.963 0.988 2.42 ± 0.66 3.43 ± 1.20
   0.001* 0.02 0.627(0.42–0.936) 0.124
   LA: ALA 26.60 ± 6.84 35.41 ± 1.28 0.029* 0.279 23.73 ± 8.22
   31.40 ± 10.23 0.004* 0.039 0.914(0.866–0.965) 0.022*
   [73]Open in a new tab

Fig. 3.

   [74]Fig. 3
   [75]Open in a new tab

   Fatty acid disturbance in follicular fluid and its correlation with
   DEPs in granulosa cells from patients with PCOS. (A) The violin plots
   illustrate the distribution of Myristic acid, Nervonic acid, and the
   LA: ALA ratio between PCOS and control groups. Statistical significance
   is indicated by asterisks (* P < 0.05, ** P < 0.01), based on unpaired
   t-tests. (B) Heatmap depicting correlations between significantly
   different fatty acids and proteins involved in fatty acid metabolism
   and biosynthesis pathways. The analysis utilized Spearman’s correlation
   coefficient, with statistical significance defined as a P value < 0.05.
   (C) Receiver operating characteristic analysis of Myristic acid,
   Nervonic acid, the LA: ALA ratio and seven proteins combined to
   discriminate the PCOS and control groups

Relationship between proteomics and fatty acid level

   To determine their intrinsic connection and regularity, the
   correlations between proteomic DEPs in granulosa cells and fatty acids
   in follicular fluid were further analysed using Spearman’s correlation
   analysis. As shown in Fig. [76]3B, the expression of ACOT2, ELOVL5,
   FADS2, FASN, HSD17B12, HSD17B4 and SCD was negatively correlated with
   C24:1 and C14:0 but positively correlated with the ratio of PUFA LA:
   ALA. Receiver operating characteristic (ROC) analysis combining the
   C24:1, C14:0, and LA: ALA ratios with the seven proteins revealed that
   the predictive accuracy for PCOS, as demonstrated by the AUC, was 0.86
   (Fig. [77]3C).

Free fatty acid and embryo quality of PCOS

   To further investigate the correlations of fatty acid levels in the
   follicular fluid with the clinical characteristics and embryo quality
   of PCOS patients, we analysed the fatty acids C14:0, C20:1, C24:1 and
   LA: ALA in the combined two cohorts. As shown in Table [78]3, C14:0 was
   moderately positively correlated with the basal LH level and LH/FSH
   ratio and weakly positively correlated with AMH through Spearman
   correlation analysis in women with normal-weight PCOS (P = 0.007,
   FDR-adjusted P = 0.049; P = 0.006, FDR-adjusted P = 0.049; P = 0.022,
   FDR-adjusted P = 0.102, respectively). C20:1 was slightly positively
   correlated with the total testosterone level, C24:1 was slightly
   positively correlated with the D3 good-quality embryo rate, and LA: ALA
   was slightly negatively correlated with the fertilization rate;
   however, this correlation was not robust after correction for multiple
   comparisons (all FDR-adjusted P > 0.05).

Table 3.

   The correlation between four fatty acid level and clinical
   characteristics of normal-weight women with PCOS
   Clinical characteristics C14:0 C20:1 C24:1 LA: ALA
   r P adj P r P adj P r P adj P r P adj P
   Basal LH(IU/l) 0.408 0.007* 0.049* 0.258 0.1 0.583 0.241 0.124 0.334
   -0.04 0.804 0.919
   LH/FSH 0.415 0.006* 0.049* 0.142 0.37 0.854 0.214 0.173 0.346 0.003
   0.987 0.987
   T(ng/dl) 0.145 0.358 0.835 0.317 0.041* 0.574 0.202 0.199 0.348 -0.049
   0.758 0.919
   AMH(ng/ml) 0.354 0.022* 0.102 0.072 0.65 0.988 0.23 0.143 0.334 -0.132
   0.406 0.742
   Fasting glucose (mmol/l) -0.09 0.557 0.946 -0.164 0.3 0.84 -0.239 0.127
   0.334 0.229 0.145 0.406
   Antral follicle count 0.149 0.345 0.835 0.01 0.95 0.988 0.008 0.962
   0.962 -0.048 0.762 0.919
   No. of retrieved oocytes -0.02 0.906 0.946 0.002 0.99 0.988 0.162 0.306
   0.389 -0.029 0.854 0.919
   No. of 2PN fertilized oocytes -0.01 0.936 0.946 0.11 0.49 0.854 0.261
   0.096 0.334 -0.113 0.477 0.742
   No. of D3 good quality embryos 0.011 0.946 0.946 0.119 0.45 0.854 0.296
   0.057 0.334 -0.263 0.092 0.322
   No. of D3 available embryos -0.07 0.645 0.946 0.009 0.96 0.988 0.168
   0.289 0.389 -0.197 0.211 0.492
   Rate of 2PN fertilization (*100%) 0.051 0.749 0.946 0.24 0.13 0.583
   0.17 0.283 0.389 -0.305 0.049* 0.322
   Rate of D3 good quality embryo (*100%) 0.177 0.262 0.835 0.208 0.19
   0.651 0.336 0.03* 0.334 -0.303 0.051 0.322
   Rate of D3 available embryos (*100%) -0.01 0.933 0.946 -0.037 0.82
   0.988 0.018 0.911 0.911 -0.121 0.447 0.742
   Rate of oocyte utilization (*100%) 0.078 0.624 0.946 0.035 0.82 0.988
   0.096 0.545 0.636 -0.271 0.083 0.322
   [79]Open in a new tab

   r, Pearson’s coefficient; *: P < 0.05

Discussion

   Currently, overweight or obese women with PCOS exhibit lipid metabolism
   disorders. However, the energy metabolism in the follicles of
   normal-weight women with PCOS remains unclear. Thus, in the present
   study, proteomic analysis was performed on granulosa cells derived from
   normal-weight PCOS patients and controls, and a profile of proteins
   involved in the biosynthesis of unsaturated fatty acids was identified
   as being downregulated in PCOS patients. Additionally, by quantitative
   analysis of follicular fluid derived from normal weight women, we found
   that higher levels of the SFA myristic acid (C14:0) and MUFA nervonic
   acid (C24:1) and a lower PUFA LA: ALA ratio may indicate a distinct
   fatty acid signature denoting increased PCOS risk, independent of
   obesity.

   Accumulating evidence suggests that cumulus cells and follicular fluid
   can influence oocyte competence through bidirectional dynamic somatic
   cell–oocyte signalling. Free fatty acids can undergo conversion into
   acetylcoenzyme A (acetyl-CoA), which is a central metabolic
   intermediate that powers the TCA cycle and electron transport chain in
   mitochondria and then produces ATP through beta-oxidation [[80]22].
   Compared with glycolysis, mitochondrial oxidation of free fatty acids
   is a more efficient source of ATP for the maturation of oocytes during
   ovulation [[81]5, [82]23]. In this study, proteomic analysis revealed
   that proteins that participate in energy metabolism (fatty acid
   metabolism as well as glycolysis) were all downregulated, indicating
   that energy supplementation is abnormal in the follicle environment in
   PCOS patients and may influence cellular processes such as
   folliculogenesis. Notably, the profiles of proteins involved in the
   biosynthesis of unsaturated fatty acids, such as SCD, FADS2 and ELOVL5,
   were reduced in PCOS patients.

   Evidence indicates that the SCD1 gene encodes delta-9 stearoyl-CoA
   desaturase, which is responsible for the formation of MUFAs. SCD1 is
   required for the in vitro development of eight-cell embryos into
   blastocysts, mainly through the regulation of unsaturated fatty acids
   [[83]24]. The activity of SCD in the serum, calculated by the ratio of
   C18:1/C18:0 through a metabolomics approach, was increased in lean PCOS
   patients compared with controls [[84]25]. In this study, the ratio of
   C18:1/C18:0 in follicular fluid was also greater in women with
   normal-weight PCOS. The underlying mechanism of the increased activity
   of SCD is unclear, and it is likely because of the compensatory
   response to the disturbance of fatty acids.

   The FADS2 gene encodes delta-6 desaturase enzymes, and the ELOVL5 gene
   encodes elongates that are responsible for generating PUFAs. In our
   previous study, the FADS2 gene was identified as a susceptibility gene
   for PCOS through a genome-wide association study (GWAS), a case‒control
   validated study and a family-based study [[85]26]. Consistent with the
   findings of the present study, the expression of FADS2 at the mRNA
   level was decreased in the peripheral blood of PCOS patients and in the
   granulosa cells of dehydroepiandrosterone (DHEA)-induced PCOS mice
   [[86]26, [87]27]. However, the activity of FADS2 in follicular fluid,
   calculated as the ratio of C18:3n-6 to C18:2n-6, is increased in
   normal-weight PCOS patients, and the role of FADS2 in the pathogenesis
   of PCOS needs further study.

   Free fatty acids within cumulus oocyte complexes are either taken up
   from follicular fluid or broken down from cellular lipids [[88]5];
   therefore, the downregulated fatty acid metabolism process may
   influence the fatty acid level in follicular fluid and vice versa. In
   this work, increased levels of the SFA myristic acid (C14:0) and MUFA
   nervonic acid (C24:1) as well as a decreased ratio of PUFA LA: ALA in
   PCOS patients were identified in cohort 1 and validated in cohort 2,
   suggesting that these fatty acids are potential biomarkers of
   normal-weight PCOS patients. A serum metabolomics study revealed that
   myristic acid was significantly increased in the serum of PCOS
   patients, particularly in individuals with insulin resistance [[89]28].
   Our previous study also suggested that myristic acid is one of the most
   robust fatty acids associated with metabolic risk factors, especially
   insulin-related parameters, in PCOS patients [[90]29]. Excess myristic
   acid may be related to incomplete mitochondrial β-oxidation, which
   contributes to IR [[91]30]. Consistent with the results of the serum
   analysis, myristic acid levels were increased in follicular fluid in
   the present study, which may reflect energy metabolism disturbances in
   follicles. In addition, the patients in this study were all
   normal-weight PCOS women who had similar levels of insulin to the
   controls in the follicular fluid of the combined two cohorts, and the
   data showed that myristic acid levels were moderately positively
   correlated with basal LH levels and LH/FSH ratios in PCOS patients.
   Considering that LH can stimulate an increase in androgen levels, which
   accelerates preferential intra-abdominal fat deposition in
   normal-weight women with PCOS [[92]31, [93]32], the association between
   LH levels and myristic acid is interesting and needs further
   exploration in the future.

   In addition, our data and other research results indicate that nervonic
   acid is a potential biomarker for PCOS patients [[94]33]. Szczuko et
   al. reported that the level of nervonic acid in the serum was several
   times greater in PCOS patients with normal androgen levels than in the
   control group [[95]34]. Our previous work investigated serum fatty
   acids and pregnancy outcomes in PCOS women undergoing ovulation
   induction and reported an inverse association between nervonic acid and
   pregnancy outcomes [[96]35]. In this study, nervonic acid was greater
   in the follicular fluid of PCOS patients and was weakly correlated with
   the D3 good-quality embryo rate; however, this correlation was not
   significant after FDR correction. In consistent with our study,
   abnormal accumulation of nervonic acid in ovary was found to be
   associated with low embryo survival rates in sows through inducing
   mitochondrial oxidative stress to activate NLRP3/IL-1beta pathway
   [[97]36]. Additional investigations are needed to further define the
   role of nervonic acid in PCOS as well as in embryo quality.

   The most common n-3 PUFAs are alpha-linolenic acid (ALA),
   docosahexaenoic acid (DHA) and eicosapentaenoic acid (EPA), and the
   most common n-6 PUFAs are linoleic acid (LA), arachidonic acid (AA) and
   dihom-gamma-linolenic acid (DGLA) [[98]37]. The evidence indicates that
   n-3 PUFAs compete for the enzymes involved in the n-6 PUFA pathway;
   hence, the LA: ALA ratio is associated with the risk of chronic disease
   as well as human reproduction [[99]38, [100]39]. Generally, reducing
   n-6 PUFA intake and increasing n-3 PUFA intake seem to promote better
   health, and the recommended ratio of n-6 to n-3 PUFA in the diet is 4:1
   [[101]40]. The n-6:n-3 ratio in the follicular fluid was similar in
   cohort 1 (PCOS vs. control = 4.13 vs. 4.14, P > 0.05) but lower in the
   PCOS group in cohort 2 (PCOS vs. control = 2.42 vs. 3.14, P < 0.05). In
   addition, LA: ALA was only slightly associated with the fertilization
   rate but was not related to D3 good-quality embryos or the oocyte
   utilization rate. Stanhiser et al. also did not find any association
   between the serum levels of n-3 or n-6 PUFAs and the probability of
   conceiving naturally [[102]37]. Interestingly, two years later, data
   from the same group revealed that n-3 PUFA supplementation was
   associated with an increased probability of conceiving [[103]39]. The
   results concerning PUFA concentrations and reproductive outcomes in IVF
   treatment are incongruous. Jungheim et al. reported that a higher n-3
   PUFA content and LA: ALA ratio in the serum were associated with higher
   implantation and pregnancy rates [[104]41]. Additionally, the
   concentration of total PUFAs in follicular fluid is positively
   associated with embryo cleavage [[105]42]. Conversely, Ruiz-Sanz
   reported that n-3 PUFA levels in large follicles were negatively
   associated with the number of mature oocytes, the fertilization rate
   and the number of top-quality embryos [[106]43]. Recently, dietary n-3
   PUFA supplements were shown to potentially exert beneficial effects on
   PCOS through anti-inflammatory effects, the regulation of
   steroidogenesis and adipokine production [[107]44, [108]45]. Our
   present study is a preliminary analysis of the association between the
   LA: ALA ratio in follicular fluid and oocyte competence, providing
   clues for further exploration of the role of a balanced n-3/n-6 PUFA
   ratio in determining oocyte quality in PCOS patients.

   This study has several limitations. First, although this study is
   limited by the sample size, our validation in cohort 2 enhanced the
   reliability of our results, which need further validation in large
   cohort. Second, this study analyzed luteinized granulosa cells, which
   were collected after hyperstimulated and cannot fully reflect the
   physiological state in natural menstrual cycles. Finally, the findings
   from our normal-weight Han Chinese women with PCOS without records of
   nutrition data may not fully extrapolate to other PCOS women of varying
   diet habit, ethnicity and adiposity.

   In conclusion, in normal-weight women with PCOS, proteomics analysis
   revealed that fatty acid metabolism-related proteins were
   downregulated, and metabolite quantitative assessments revealed
   increased levels of SFA C14:0 and MUFA C24:1 and unbalanced levels of
   PUFA LA/ALA, reflecting disturbances in fatty acid metabolism in the
   oocyte microenvironment. Our results revealed abnormal fatty acid
   metabolism in normal-weight PCOS women, suggesting potential markers in
   granulosa cells and follicular fluid for these patients. Future
   investigations are needed in multi-ethnic cohorts to confirm our
   results and explore regulation mechanism between granulosa cells and
   follicular fluid.

Electronic supplementary material

   Below is the link to the electronic supplementary material.
   [109]Supplementary Material 1^ (13.8KB, xlsx)
   [110]Supplementary Material 2^ (4.4MB, docx)

Acknowledgements