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Metabonomics Reveals Plasma Metabolic Changes and Inflammatory Marker in Polycystic Ovary Syndrome Patients Liye Sun,†,‡,§ Weihong Hu,∥,§ Qiao Liu,† Qinfang Hao,∥ Bo Sun,† Qi Zhang,† Sha Mao,∥ Jie Qiao,*,⊥ and Xianzhong Yan*,‡ †

National Center of Biomedical Analysis, Beijing 100039, China Chinese PLA Postgraduate Medical School, Beijing 100853, China ∥ Department of Obstetrics and Gynecology, General Hospital of Chinese People’s Armed Police Forces, Beijing 100039, China ⊥ Department of Obstetrics and Gynecology, Peking University Third Hospital, Beijing 100191, China ‡

S Supporting Information *

ABSTRACT: Polycystic ovary syndrome (PCOS) is a common, clinically heterogeneous endocrine disorder affecting women of reproductive age, associated with endocrinopathy and metabolic abnormalities. Although some metabolic parameters have been investigated, very little information has been reported on the changes of small metabolites in biofluids. The aim of this study was to establish the metabolic profile of PCOS and compare it with that of controls. In this cross-sectional study of 34 women with PCOS and 36 controls, contents of small metabolites and lipids in plasma samples were measured using nuclear magnetic resonance (NMR)-based techniques and analyzed using multivariate statistical methods. Significant decrease (P < 0.05) in the levels of amino acids (leucine, isoleucine, methionine, glutamine, and arginine), citrate, choline, and glycerophosphocholine/phosphocholine (GPC/PC), and increase (P < 0.05) in the levels of lactate, dimethylamine (DMA), creatine, and N-acetyl glycoproteins were observed in PCOS patients compared with the controls. Subgroups of patients with obesity, metabolic syndrome, or hyperandrogenism exhibited greater metabolic deviations than their corresponding subgroups without these factors. PCOS patients have perturbations in amino acid metabolism, the tricarboxylic acid (TCA) cycle, and gut microflora, as well as mild disturbances in glucose and lipid metabolism. The elevated level of N-acetyl glycoproteins demonstrates the existence of low-grade chronic inflammation in PCOS patients. KEYWORDS: polycystic ovary syndrome, metabonomics, nuclear magnetic resonance, gut microflora, glycoprotein

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INTRODUCTION Polycystic ovary syndrome (PCOS) is a common, clinically heterogeneous endocrine disorder affecting approximately 4− 10%1 of women of reproductive age. It is associated with endocrinopathy and metabolic abnormalities, characterized by amenorrhea, hirsutism, obesity, hypertension, hyperinsulinism, insulin resistance, hyperandrogenism, and polycystic ovaries on ovarian ultrasonography.2,3 Some of these features, such as obesity, hyperinsulinism, and hyperandrogenism, are known to be risk factors for cardiovascular disease and type 2 diabetes mellitus.4−6 Consequently, women with PCOS have a higher risk of developing cardiovascular disease or type 2 diabetes mellitus than those without PCOS. It has also been reported that patients with impaired fasting plasma glucose, dyslipidemia, and hypertension may suffer metabolic syndrome (MS).7 There is a high prevalence of MS or its components in women with PCOS.8,9 There is a consensus that both environmental and genetic factors cause PCOS; however, the detailed etiology has not been identified. This is reflected by the existence of different diagnostic criteria and debates.10,11 The pathogenesis © 2012 American Chemical Society

of PCOS is complex and poorly understood, and the lack of a molecular diagnosis for PCOS means its therapies are poorly targeted and can be ineffective. Omics, including genetics, proteomics, and metabonomics, are hypothesis-free methods that may help us find useful biomarkers for the diagnosis of PCOS and studies of its mechanism.12−14 Genetic polymorphisms associated with PCOS have been reported,15−18 proteomic studies have been undertaken recently,19−21 and novel plasma markers have been identified.22 However, there are no published metabonomic investigations of PCOS. As the furthest downstream pool of biochemical events in biological systems, the metabolome reflects the final results of perturbations upstream or directly from the environment, revealing what actually occurs in the organism, whereas genetics and proteomics can only suggest what might occur.23 The profile of the metabolome is closely correlated with traditional biological and clinical end points and Received: January 11, 2012 Published: March 20, 2012 2937

dx.doi.org/10.1021/pr3000317 | J. Proteome Res. 2012, 11, 2937−2946

Journal of Proteome Research

Article

Sample Collection and Clinical Data Collection

is thus becoming a useful new tool for toxicology and diagnosis. There has been much research on the metabonomics of disease,24,25 especially in cancers and metabolic diseases such as diabetes,26−28 obesity,29,30 cardiovascular disease31−33 and MS.34 Very recently, the effects of Pio/Flu/Met polytherapy on the metabolic profile of plasma samples of nonobese PCOS patients was reported, with emphasis on the changes of lipoprotein profile, especially the reduced amount of oxidized lipoprotein and increased HDL and LDL subclasses in response to the treatment.35 Metabonomics is the systematic investigation of metabolic responses in biological systems to genetic or environmental stimuli.36 In systems biology, it is a research method for the analysis of low molecular weight compounds and is usually conducted on biofluids and tissues. Many studies have demonstrated that metabonomics is a potential tool for personalized medicine37 and analysis of disease processes.38 Knowledge of the metabolic differences between patients and healthy controls can aid the discovery of biomarkers for diagnosis and the evaluation of therapy and can also provide insights into the underlying pathology, which in turn can provide information for the development of new treatments. Various analytical methods are available for metabonomic research, among which high resolution proton nuclear magnetic resonance (NMR) spectroscopy is favored because of its minimal sample processing, non-invasiveness, and low cost.39 In the present study, we analyzed the metabolic differences between plasma samples from PCOS patients and healthy controls using NMR-based metabonomic techniques, identified characteristic metabolites, and attempted to interpret these changes in terms of metabolic pathways.

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Fasting blood samples were collected at 0800 h on the second to fourth days of the menstrual cycle or when ultrasound scanning showed no dominant follicles in patients with amenorrhea. Plasma samples were separated by centrifugation at 3000 rpm for 5 min and stored at −20 °C for later experiments. At the same time, we measured the subjects’ height and weight, and calculated their BMI (weight (kg)/ height (m2)). Plasma prolactin, luteinizing hormone (LH), and folliclestimulating hormone (FSH) were measured by chemiluminescence, and plasma testosterone was measured by radioimmunoassay. Plasma insulin and enzymatic oxidation were measured by chemiluminescence to determine the plasma glucose level. Biochemical assays were used to analyze plasma lipids, including total cholesterol, triglycerides (TG), high density lipoprotein cholesterol (HDL-C) and low density lipoprotein cholesterol (LDL-C). 1

H NMR Spectroscopy of Plasma Samples

Before NMR analysis, the plasma samples were thawed at room temperature and centrifuged at 14000 rpm for 10 min. Aliquots of 300 μL supernatant were transferred into 5 mm NMR tubes, and mixed with 200 μL of normal saline (0.9% w/v in 80% deionized water and 20% D 2 O) and 100 μL of 3(trimethylsilyl)-propionic acid-d4 sodium salt (TSP) (0.1% w/v in D2O). D2O was used for field frequency locking and normal saline was used to maintain normal plasma osmolarity. NMR measurements were performed on an NMR spectrometer (Varian INOVA 600) operating at a proton NMR frequency of 599.73 MHz, using a 5 mm triple resonance probe with a z-axis gradient at 27 °C. Both low molecular weight metabolites and high molecular weight macromolecules are present in plasma samples; therefore, NMR spectra of plasma samples comprise sharp peaks due to small molecules, broad peaks due to lipids or glycans, and very broad humps due to proteins. Consequently, a transverse relaxation-edited Carr− Purcell−Meiboom−Gill (CPMG) sequence (90-(τ-180-τ)nacquisition) and a diffusion-edited bipolar pulse pair longitudinal eddy current delay (BPP-LED) pulse sequence were used.42 CPMG spin−echo was used to attenuate the broad signals from proteins and lipoproteins, resulting in spectra showing signals only from small metabolites due to their longer transverse relaxation time. A total spin−spin relaxation delay (2nτ) of 320 ms was used for the 64 scans. BPP-LED was used to obtain spectra with signals only from lipids. The strength of the gradient pulses was 35 G cm−1 and the duration was 2.0 ms, with a 300 μs delay for the decay of the eddy current. A diffusion delay of 100 ms and an eddy current decay time of 5 ms were used. Water suppression was applied during the recycle delay and the delay after the first BPP.

EXPERIMENTAL SECTION

Subjects

Patients diagnosed with PCOS (n = 34) were recruited from outpatients of the General Hospital of the Chinese People’s Armed Police Forces. PCOS was diagnosed according to the criteria recommended by the European Society of Human Reproduction and Embryology and the American Society for Reproductive Medicine at the Rotterdam conference in 2003:3 (a) oligo- and/or anovulation; (b) clinical and/or biochemical hyperandrogenism; and (c) polycystic ovary with typical ultrasound features (presence of at least 12 follicles in each ovary measuring 2−9 mm in diameter, and/or increase in ovary size >10 mL). The presence of two of these three criteria is sufficient once all other diagnoses have been excluded. The control group comprised 36 healthy women of reproductive age who underwent physical examination in the same hospital, with matched ages and similar average body mass index (BMI). The subjects were further divided into subgroups according to clinical and biochemical phenotype, designated as follows: (a) obese (BMI ≥ 25 kg/m2) and nonobese (BMI < 25 kg/m2) patients and controls40 (OPO, obese patients (n = 19)); NOP, nonobese patients (n = 15); OCO, obese controls (n = 8); NOC, nonobese controls (n = 28)); (b) hyperandrogenic (testosterone ≥0.75 ng/mL) and normoandrogenic patients (HAP: hyperandrogenic patients (n = 16); NAP, normoandrogenic patients (n = 18)); and (c) patients with or without MS, based on the Chinese Diabetes Society criteria41 (MSP, patients with MS (n = 17); NMP, patients without MS (n = 17)). Informed consent was obtained from all subjects and the study was approved by the ethics committee of the hospital.

Analysis of 1H NMR Spectra

All 1H NMR data were processed using VNMR 6.1C software (Varian, Inc., Palo Alto, CA). Before Fourier transformation, the free induction decays were zero-filled by a factor of 2 and multiplied by an exponential line-broadening function of 0.5 Hz. All NMR spectra were manually phased and baseline corrected, and reduced to integrated segments of equal width.43 The CPMG spectrum was data-reduced to 2000 integrated segments of equal width (0.002 ppm) corresponding to the region of δ 4.40−0.41 and the BPP-LED spectrum was reduced to 599 integrated segments of equal width (0.01 ppm) corresponding to the region of δ 6.00−0.02. The integrated 2938



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also significantly different between nonobese patients and controls (Supplemental Table 1, Supporting Information). In patients without MS, only testosterone, LH, and LH/FSH differed significantly from those of the controls. However, in patients with MS, all parameters except age were significantly different from the controls (Supplemental Table 2, Supporting Information).These differences were similar to those between all PCOS patients and controls, demonstrating the contribution of MS to biochemical parameters. Within the PCOS patients, the MSP subgroup had significantly higher levels of TG and FSH and lower levels of HDL and LH/FSH than the NMP subgroup. Both normoandrogenic and hyperandrogenic PCOS patients had a significantly higher LH and LH/FSH than the controls. Normoandrogenic patients had significantly higher BMI and triglyceride and lower HDL-C than the controls, whereas hyperandrogenic patients had significantly higher levels of testosterone, TC and LDL-C. Other than testosterone level, no other significant difference was observed between the HAP and NAP subgroups, though LDL-C was higher in the HAP subgroup (Supplemental Table 3, Supporting Information).

data were normalized to the total integrals of each spectrum and then exported to text files for further multivariate statistical analysis. Multivariate Pattern Recognition Analysis

Multivariate pattern recognition analysis for NMR data was conducted using SIMCA-P Plus software (version 12.0; Umetrics, Umeå, Sweden). A nonsupervised principal component analysis (PCA) model was constructed to determine the distributions of and separations between different groups. Before PCA, all NMR data variables were mean-centered and Pareto-scaled. The NMR data were further processed using the supervised orthogonal projections to latent structures-discriminant analysis (OPLS-DA) method with unit variance scaling. All models were cross-validated using a 7-fold method by default; the validity of the models against overfitting was assessed by the parameters R2X and R2Y, and the predictive ability was described by Q2. Negative or very low Q2 values indicate that the differences between groups are not statistically significant. The OPLS-DA model removes variation in the X matrix that is not correlated to the Y matrix. Thus, normally only one predictive component is used for the discrimination between two classes.44,45 Coefficient plots were generated using MATLAB R2008a (The MathWorks, Inc., Natick, MA) and were color coded with the absolute value of the correlation coefficients (|r|). The loadings in the coefficient plots were calculated from the coefficients, combining the weight of the variables contributing to the sample clustering in the model.46

1

Representative CPMG 1H NMR spectra of plasma samples from a PCOS patient and a healthy control are shown in Figure 1A and B. Major metabolites are labeled in the spectra. Visual inspection of the spectra suggested that the most prominent changes in the PCOS group compared with the control group were the increases in lactate and DMA, and the decrease in citrate. Further analysis by simple PCA did not show any clear trend of group clustering between the PCOS and control group. To maximize the group separation and to identify discriminating metabolites, supervised orthogonal projections to latent structures-discriminant analysis (OPLS-DA) was applied to the data set to remove factors unrelated to group characters. This approach resulted in a marked improvement in group separation between the PCOS and control groups, as revealed in the scores plot with one predictive (Pp) and one orthogonal (Po) component (Figure 2A). The metabolic changes in PCOS patients are reflected in the color coded coefficient plot (Figure 2B). Metabolites exhibiting significant changes (P < 0.05) were identified based on the absolute cutoff value of Pearson product-moment correlation coefficients (|r|) and are listed in Table 2. The cutoff values (|r|c) were determined according to a test for significance based on the degree of freedom of the data sets. With a |r|c of 0.334, the plasma samples of PCOS patients showed significantly lower levels of amino acids (arginine, methionine, glutamine, leucine, isoleucine), citrate, and choline, and higher levels of lactate, DMA, and N-acetyl glycoprotein.

Statistical Analysis

SPSS 11.0 (SPSS Inc., Armonk, NY) was used for statistical analysis of the clinical parameters of the PCOS patients and healthy controls. Values were expressed as the mean ± SD. The two-tailed Welch’s t-test was used. P < 0.05 was considered to be statistically significant.

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RESULTS

Clinical and Biochemical Characteristics

There were no significant differences in age or BMI between PCOS patients and controls (P > 0.05). The PCOS patients had significantly higher concentrations of testosterone, LH, LH/FSH, TG, LDL-C, and total cholesterol than the controls (Table 1). Only LH and LH/FSH were significantly different between obese patients and obese controls; testosterone was Table 1. Clinical Indicators and Plasma Sex Hormone Levels in PCOS Patients and Controls Age (years) BMI (kg/m2) Testosterone (ng/mL) FSH (U/L) LH (U/L) LH/FSH TG (mmol/L) TC (mmol/L) HDL-C (mmol/L) LDL-C (mmol/L)

controls (n = 36)

PCOS (n = 34)

P value

26.9 ± 4.1 22.6 ± 3.6 0.5 ± 0.1 7.3 ± 2.5 4.0 ± 2.8 0.6 ± 0.4 0.9 ± 0.5 4.0 ± 0.7 1.3 ± 0.2 2.3 ± 0.3

27.2 ± 4.1 24.5 ± 4.5 0.7 ± 0.2 7.9 ± 2.4 15.6 ± 10.2 2.0 ± 0.9 1.2 ± 0.7 4.5 ± 1.0 1.2 ± 0.2 2.7 ± 0.8

0.74 0.07 4.9 × 10−5 0.30 2.0 × 10−6 5.5 × 10−9 0.04 0.03 0.26 0.02

H NMR CPMG Spectra and General Comparison

Metabolic Changes Correlated with Obesity and Other Factors

OPLS-DA was also applied to the metabolic features of PCOS patients considering obesity, MS, and hyperandrogenism (Supplemental Figure 1, Supporting Information). When both PCOS patients and controls were separated into subgroups according to whether they were or were not obese, the OPLS-DA model still demonstrated a clear separation between patients and controls, although the differences between the NOP and NOC subgroups were slightly larger than those between the OPO and OCO subgroups. The PCOS patients were also divided into subgroups with or without MS. In this case, the OPLS-DA showed clear differences between three groups. This may

Data are means ± SD. BMI, body mass index; FSH, Folliclestimulating hormone; LH, Luteinizing hormone; TG, triglyceride; TC, total cholesterol; HDL-C, high-density lipoprotein cholesterol; LDLC, low-density lipoprotein cholesterol. 2939



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Figure 1. Typical 600 MHz CPMG 1H NMR spectra of the plasma samples from a PCOS patient (A) and a control (B). Key: 1, lipoprotein; 2, leucine; 3, isoleucine; 4, valine 5, 3-hydroxybutyric acid; 6, threonine; 7, lactate; 8, alanine; 9, arginine; 10, acetate; 11, glutamate/glutamine; 12, methionine; 13, acetone; 14, glutamate; 15, glutamine; 16, citrate; 17, dimethylamine; 18, creatine; 19, creatinine; 20, choline; 21, glycerophosphocholine.phosphocholine; 22, glucose; 23, N-acetylglycoprotein; 24, trimethylamine N-oxide; 25, glycerol.

Figure 2. OPLS-DA results of CPMG NMR data of patients (black square) and controls (red triangle). (A) scores plot and (B) coefficients plot. Keys are the same as listed in Figure 1.

Table 2. Summary of the Differential Metabolites Represented by the Correlation Coefficientsa metabolites

PCOS vs CON (0.334)

NOP vs NOC (0.444)

+0.47 −0.38 −0.51 −0.45 −0.41 +0.52 +0.64 −0.62 −0.45 −0.58 −0.50

+0.49

OPO vs OCO (0.666)

NMP vs CON (0.468)

MSP vs CON (0.468)

NAP vs CON (0.456)

HAP vs CON (0.482)

CPMG Lactate TMAO GPC/PC Choline Creatinine Creatine DMA Citrate Glutamine Methionine Arginine Isoleucine Leucine

−0.48

−0.46 −0.54 +0.58 +0.57 −0.59 −0.56 −0.54 −0.55 −0.52

+0.57 −0.68

−0.71

+0.66 +0.63 −0.56

−0.68

−0.50 −0.50 −0.50 −0.52

+0.52 −0.59 −0.62

−0.46

−0.56 −0.50

+0.48 +0.65 −0.65 −0.61 −0.52 −0.59

+0.62 +0.57 −0.65

+0.70 −0.57

−0.57

−0.53

−0.56

−0.60

BPP−LED PtdCho NAc HDL LDL

−0.38 +0.45

−0.63 +0.65

+0.51

+0.56

+0.91

The cutoff values of coefficients (|rc|) were shown in parentheses. + and − represent relatively higher or lower levels of the metabolites, respectively. PCOS, polycystic ovrary syndrome group; CON, control group; NOC, non-obese control; OCO, obese control; NOP, non-obese PCOS; OPO, obese PCOS; MSP; PCOS with metabolic syndrome; NMP, PCOS without metabolic syndrome; HAP, PCOS with hyperandrogenism; NAP, PCOS without hyperandrogenism; DMA, dimethylamine; TMAO, trimethylamine N-oxide; GPC/PC, glycerophosphocholine/phosphocholine; PtdCho, phosphatydilcholine; NAc, N-acetylglycoprotein; HDL, high-density lipoprotein; LDL, low-density lipoprotein. a

DA model showed that the PCOS subgroups (HAP and NAP) were clearly separated from the control group, but there was no segregation between the HAP and NAP subgroups, suggesting

indicate that MS contribute to the metabolic changes observed in PCOS. When the PCOS patients were classified into two subgroups based on plasma testosterone, the resulting OPLS2940



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Figure 3. OPLS-DA cross-validated scores (left column) and coefficient-coded loadings plots (right column) for CPMG NMR spectra of plasma from the subgroups of PCOS patients (black square) and the controls (red triangle). (A) Nonobese PCOS vs nonobese control; (B) obese PCOS vs obese control; (C) PCOS without metabolic syndrome vs control; (D) PCOS with metabolic syndrome vs control; (E) nonhyperandrogenic PCOS vs control; (F) hyperandrogenic PCOS vs control. Metabolites are labeled as in Figure 1.

that androgen level does not have much effect on plasma metabolic profiles. To investigate the impact of these risk factors on metabolic changes in details, pairwise comparison between multiple subgroups was conducted using OPLS-DA models (Figure 3). Very clear differentiation between nonobese PCOS patients and controls was observed in scores plots, with significantly decreased levels of arginine, methionine, citrate, glutamine, choline, and GPC/PC, and increased levels of DMA in the nonobese PCOS subgroup. The differences between the obese subgroups were not as large as those between the nonobese subgroups, with only three metabolites (citrate, leucine, and GPC/PC) being decreased significantly in the obese PCOS subgroup. As mentioned previously, MS was associated with large metabolic changes in PCOS patients. Detailed comparison showed that patients without MS (NMP subgroup) had

significantly lower levels of leucine, isoleucine, arginine, methionine, and citrate, and increased levels of creatine, DMA, and lactate compared with the controls. In patients with MS (MSP subgroup), the concentrations of glutamine, choline and GPC/PC were also decreased significantly but without significant increase in lactate. Figure 3E and F shows the comparison between the NAP or HAP subgroups and the controls. Arginine, leucine, citrate and GPC/PC were significantly decreased in the NAP subgroup compared with the controls, and a decrease in choline was also observed in the HAP subgroup. Increased DMA was observed in both NAP and HAP subgroups, while creatine and lactate were also increased in NAP subgroup compared with the controls. When the HAP and NAP subgroups were compared, OPLS-DA showed a negative Q2 value, indicating that the model was not significant. Other comparisons between different subgroups were demon2941



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Figure 4. Typical 600 MHz BPP-LED 1H NMR spectra of plasma samples from PCOS patients (A) and controls (B). Keys: 1, high-density lipoprotein; 2, low-density lipoprotein; 3, lipid: CH2CH2CO; 4, N-acetylglycoprotein peak 1; 5, N-acetylglycoprotein peak 2; 6, lipid: CH2CO; 7, lipid: CHCH2CH; 8, phosphatydilcholine; 9, lipid: HCCH.

Figure 5. OPLS-DA comparison between LED 1H NMR spectra of plasma from PCOS patients (black square) and controls (red triangle) represented as score plot (A) and coefficient-coded loadings plot (B). Metabolites are labeled as in Figure 4.

between the obese subgroups showed a significant increase in LDL in patients. No significant differences were observed between patients without MS and controls, whereas significantly increased N-acetyl glycoproteins and decreased phosphatidylcholine were observed in patients with MS. Taking hyperandrogenism into consideration, the only significant change in a metabolite was the increased N-acetyl glycoprotein observed in the HAP subgroup of patients compared with the controls. A minor increase in the low frequency part of the peak from PtdCho (δ 3.2) was observed in PCOS patients. This part of the signal has been reported to be contributed by sphingomyelin,47 the second most abundant component of phospholipid. Therefore, the observed change shows that PCOS patients had a higher plasma level of sphingomyelin.

strated in Supplemental Figure 2 in Supporting Information. The marker metabolites were summarized in Table 2 and were demonstrated in Supplemental Figure 3−5 in box-and-whisker plots (Supporting Information). The above results also demonstrated the main differences between PCOS patients and control subjects are due to the PCO syndrome itself, whereas other factors such as MS and obesity contribute to the severity of the metabolic changes. Diffusion-edited 1H NMR Spectra and Lipid Variations in PCOS Patients

Figure 4A and B shows representative BPP-LED 1H NMR spectra of plasma samples from a PCOS patient and a healthy control. These spectra comprise resonant peaks mainly from lipids, including TG, cholesterol and phospholipids from lipoproteins, and signals of N-acetyl methyl (NAc) groups from glycoproteins such as α-1 acid glycoprotein, an acute phase protein with inflammatory properties. Visual inspection of the spectra failed to reveal clear differences between patients and controls. Therefore, multivariate analysis was used to obtain more information. General comparison between patients and controls using an OPLS-DA model resulted in a clear separation (Figure 5A). The coefficient plot (Figure 5B) shows different concentrations of HDL, LDL, phosphatidylcholine (PtdCho), and glycoproteins in the two groups, with significantly increased N-acetyl glycoproteins and significantly decreased phosphatidylcholine in patients, together with increased LDL and decreased HDL, although these were not statistically significant. Detailed comparisons between subgroups taking obesity, MS and hyperandrogenism into consideration revealed further details on differences in lipid metabolism (Figure 6 and Supplemental Figure 6 in Supporting Information). Comparison between nonobese patients and controls showed a significant increase in N-acetyl glycoproteins in patients, whereas comparison

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DISCUSSION PCOS is a heterogeneous disorder of uncertain etiology, with different clinical and biochemical phenotypes in affected individuals. Changes in the levels of plasma metabolites reflect the various metabolic pathways that participate in physiological functions. In this study, we applied a NMR-based metabonomics approach to investigate PCOS. Our results show metabolic variations between PCOS patients and controls, including significant decreases in the concentrations of some amino acids (leucine, isoleucine, arginine, glutamine, and methionine), citrate, choline, and GPC/PC, and increases in the levels of DMA, lactate, and Nacetyl glycoproteins. Different metabolic profiles were observed in subgroups of PCOS patients based on obesity, MS and hyperandrogenism, which may reflect the heterogeneity of the pathogenesis of PCOS. General comparison between PCOS patients and controls showed a slightly significant increase in lactate accompanied by a minor decrease in glucose in the patients, suggesting 2942



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Figure 6. OPLS-DA cross-validated scores (left column) and coefficient-coded loadings plots (right column) for BPP-LED NMR spectra of plasma from the subgroups of PCOS patients (black square) and the controls (red triangle). (A) Nonobese PCOS vs nonobese control; (B) obese PCOS vs obese control; (C) PCOS without metabolic syndrome vs control; (D) PCOS with metabolic syndrome vs control; (E) nonhyperandrogenic PCOS vs control; (F) hyperandrogenic PCOS vs control. Metabolites are labeled as in Figure 4.

contribute to fatty liver,50 obesity,52 diabetes53 and cardiovascular disease.54 The changes of gut produced metabolites from choline metabolism in the present study are similar to those found in diabetes53 and cardiovascular disease,54 suggesting a possible relationship of PCOS with these diseases. Obese patients showed greater changes than nonobese patients in the concentrations of many metabolites, supporting the view that obesity is a key factor in metabolism in PCOS.55,56 Hyperandrogenemia and MS had similar influences on metabolism in PCOS patients. Dyslipidemia is a typical symptom of PCOS, normally with an increase in LDL-C and a decrease in HDL-C and HDL phospholipids. The results of the present study were consistent with this. Plasma samples from PCOS patients contained slightly increased levels of LDL lipids and decreased levels of

disturbed glucose metabolism with increased glycolytic activity in PCOS. Plasma citrate was significantly decreased in the PCOS patients, indicating impairment of the TCA cycle. This type of metabolic perturbation is consistent with reported findings in Zucker rats48 and PPAR-α null mice.34 No significant difference was reported in fasting glucose level between nonobese PCOS patients and controls.9,49 In this study, several amino acids were also observed to be decreased in PCOS patients, suggesting a disturbance of amino acid metabolism. Plasma DMA was significantly increased in PCOS patients, with a concomitant significant decrease in choline and GPC/PC. DMA is the product of choline metabolism by gut microflora.50,51 These results demonstrate that the activity of gut microflora was increased in the PCOS patients. Gut microflora have been considered to make an important 2943



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Notes

HDL lipids, as well as significantly decreased phosphatidylcholine, a major component of phospholipids.57 This decrease was due to not only the reduced HDL level, but also the reduced phospholipid content in the HDL.58 Lower HDL has been found in females with features of MS.59,60 Another finding of this study was the possible increase in plasma sphingomyelin in PCOS patients. Sphingomyelin is the second most abundant component of phospholipid, with a relatively higher content in LDL than in HDL.57 Plasma sphingomyelin level has been found to be a risk factor for coronary artery disease61,62 and has been related to subclinical atherosclerosis.63 Reduction of plasma membrane sphingomyelin level can increase insulin sensitivity.64 The relationship between sphingolipids and insulin resistance and metabolic disease was reviewed recently. 65 The present findings imply that increased sphingomyelin may contribute to the increased risk of cardiovascular disease in PCOS patients. The most prominent and consistent change observed in diffusion-edited NMR spectra was the increased signal intensity of the N-acetyl group from glycoproteins, mainly from acute phase α-1 acid glycoproteins.66 This glycoprotein has been found to be increased in inflammation and may be useful in the diagnosis and prognosis of acute and chronic inflammatory disorders.67 Glycoproteins are correlated with the development and progression of many conditions, including infection, swelling, cardiovascular disorders, and diabetes.68,69 In a recent report, this protein (also called orosomucoid) was induced in adipose tissue in obese mice to suppress inflammation caused by insulin, high glucose, and other metabolic signals, as well as by the proinflammatory cytokine tumor necrosis factor α.70 Our recent work has demonstrated elevated C-reactive protein in plasma of PCOS patients.71 The present observations provide confirmatory evidence for the reported low-grade inflammation associated with PCOS.72,73 The present study investigated metabolic variations in patients with PCOS using NMR-based metabonomics. Clear metabolic differences were observed between PCOS patients and healthy controls. These variations involved significant perturbations in amino acid metabolism, gut microflora, the TCA cycle, and lipid metabolism. The metabolic deviations were increased when obesity, MS and hyperandrogenism were taken into account. The increased level of acute phase glycoprotein in the patients confirmed the presence of inflammation in PCOS. The present study also demonstrated that metabonomics is a powerful tool in the study of PCOS, providing information on changes in metabolites and endocrine pathways that will aid investigation of the complex causes and pathogenesis of this condition and the development of new methods for clinical diagnosis and treatment.

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The authors declare no competing financial interest.

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ACKNOWLEDGMENTS This research was supported by grants from Chinese Key Project for the Infectious Diseases (2008ZX10002-016), National Key Technologies R & D Program for New Drugs (2009XZ09301-002), National Natural Science Foundation of China (30973676, 81001419) and National Natural Science Funds for Distinguished Young Scholar (No. 30825038).

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(1) Eisenhardt, S.; Schwarzmann, N.; Henschel, V.; Germeyer, A.; von Wolff, M.; Hamann, A.; Strowitzki, T. Early effects of metformin in women with polycystic ovary syndrome: a prospective randomized, double-blind, placebo-controlled trial. J. Clin. Endocrinol. Metab. 2006, 91 (3), 946−952. (2) Ehrmann, D. A. Polycystic ovary syndrome. New Engl. J. Med. 2005, 352 (12), 1223−1236. (3) The Rotterdam ESHRF/ASRM2 sponsored PCOS consensus workshop group.. Revised 2003 consensus on diagnostic criteria and longterm heath risks related to polycystic ovary syndrome (PCOS). Hum. Reprod. 2004, 19 (1), 41−47. (4) Teede, H. J.; Hutchison, S.; Zoungas, S.; Meyer, C. Insulin resistance, the metabolic syndrome, diabetes, and cardiovascular disease risk in women with PCOS. Endocrine 2006, 30 (1), 45−53. (5) Muneyyirci-Delale, O.; Nacharaju, V. L.; Dalloul, M.; Jalou, S.; Rahman, M.; Altura, B. M.; Altura, B. T. Divalent cations in women with PCOS: implications for cardiovascular disease. Gynecol. Endocrinol. 2001, 15 (3), 198−201. (6) Sharma, S. T.; Nestler, J. E. Prevention of diabetes and cardiovascular disease in women with PCOS: treatment with insulin sensitizers. Best Pract. Res. Clin. Endocrinol. Metab. 2006, 20 (2), 245− 260. (7) Bhattacharya, S. M. Metabolic syndrome in females with polycystic ovary syndrome and International Diabetes Federation criteria. J. Obstet. Gynaecol. Res. 2008, 34 (1), 62−66. (8) Ehrmann, D. A.; Liljenquist, D. R.; Kasza, K.; Azziz, R.; Legro, R. S.; Ghazzi, M. N. Prevalence and predictors of the metabolic syndrome in women with polycystic ovary syndrome. J. Clin. Endocrinol. Metab. 2006, 91 (1), 48−53. (9) Vural, B.; Caliskan, E.; Turkoz, E.; Kilic, T.; Demirci, A. Evaluation of metabolic syndrome frequency and premature carotid atherosclerosis in young women with polycystic ovary syndrome. Hum. Reprod. 2005, 20 (9), 2409−2413. (10) Azziz, R. Controversy in clinical endocrinology: diagnosis of polycystic ovarian syndrome: the Rotterdam criteria are premature. J. Clin. Endocrinol. Metab. 2006, 91 (3), 781−785. (11) Franks, S. Controversy in clinical endocrinology: diagnosis of polycystic ovarian syndrome: in defense of the Rotterdam criteria. J. Clin. Endocrinol. Metab. 2006, 91 (3), 786−789. (12) Escobar-Morreale, H. F.; Insenser, M.; Corton, M.; Millan, J. L.; Peral, B. Proteomics and genomics: A hypothesis-free approach to the study of the role of visceral adiposity in the pathogenesis of the polycystic ovary syndrome. Proteomics Clin. Appl. 2008, 2 (3), 444− 455. (13) Escobar-Morreale, H. F.; Luque-Ramirez, M.; San Millan, J. L. The molecular-genetic basis of functional hyperandrogenism and the polycystic ovary syndrome. Endocr. Rev. 2005, 26 (2), 251−282. (14) Hughes, C.; Elgasim, M.; Layfield, R.; Atiomo, W. Genomic and post-genomic approaches to polycystic ovary syndrome--progress so far: Mini Review. Hum. Reprod. 2006, 21 (11), 2766−2775. (15) Ewens, K. G.; Stewart, D. R.; Ankener, W.; Urbanek, M.; McAllister, J. M.; Chen, C.; Baig, K. M.; Parker, S. C.; Margulies, E. H.; Legro, R. S.; Dunaif, A.; Strauss, J. F., 3rd; Spielman, R. S. Familybased analysis of candidate genes for polycystic ovary syndrome. J. Clin. Endocrinol. Metab. 2010, 95 (5), 2306−2315.

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S Supporting Information *

Supplemental figures and tables. This material is available free of charge via the Internet at http://pubs.acs.org.

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REFERENCES

AUTHOR INFORMATION

Corresponding Author

*Xianzhong Yan, e-mail: [email protected]. Phone: +86-1068186281. Fax: +86-10-68186281. Jie Qiao, e-mail: jie.qiao@ 263.net. Phone: +86-10-62017691. Fax: +86-10-82266849. Author Contributions §

These authors contributed equally to this work. 2944



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(16) Li, C.; Qiao, B.; Zhan, Y.; Qi, W.; Chen, Z. J. First Evidence of Genetic Association Between the MIF-173G/C Single-Nucleotide Polymorphisms and Polycystic Ovary Syndrome. Am. J. Reprod. Immunol. 2011, 66 (5), 416−422. (17) Ramos Cirilo, P. D.; Rosa, F. E.; Moreira Ferraz, M. F.; Rainho, C. A.; Pontes, A.; Regina Rogatto, S. Genetic polymorphisms associated with steroids metabolism and insulin action in polycystic ovary syndrome. Gynecol. Endocrinol. 2012, 28 (3), 190−194. (18) Deligeoroglou, E.; Kouskouti, C.; Christopoulos, P. The role of genes in the polycystic ovary syndrome: predisposition and mechanisms. Gynecol. Endocrinol. 2009, 25 (9), 603−609. (19) Atiomo, W.; Khalid, S.; Parameshweran, S.; Houda, M.; Layfield, R. Proteomic biomarkers for the diagnosis and risk stratification of polycystic ovary syndrome: a systematic review. Br. J. Gynecol. 2009, 116 (2), 137−143. (20) Corton, M.; Botella-Carretero, J. I.; Lopez, J. A.; Camafeita, E.; San Millan, J. L.; Escobar-Morreale, H. F.; Peral, B. Proteomic analysis of human omental adipose tissue in the polycystic ovary syndrome using two-dimensional difference gel electrophoresis and mass spectrometry. Hum. Reprod. 2008, 23 (3), 651−661. (21) Ma, X.; Fan, L.; Meng, Y.; Hou, Z.; Mao, Y. D.; Wang, W.; Ding, W.; Liu, J. Y. Proteomic analysis of human ovaries from normal and polycystic ovarian syndrome. Mol. Hum. Reprod. 2007, 13 (8), 527− 535. (22) Insenser, M.; Martinez-Garcia, M. A.; Montes, R.; San-Millan, J. L.; Escobar-Morreale, H. F. Proteomic analysis of plasma in the polycystic ovary syndrome identifies novel markers involved in iron metabolism, acute-phase response, and inflammation. J. Clin. Endocrinol. Metab. 2010, 95 (8), 3863−3870. (23) Schmidt, C. W. Metabolomics:What’s Happening Downstreamof DNA. Environ. Health Perspect. 2004, 112 (7), A412−A415. (24) Salih, S.; Xu, X.; Veenstra, T. D.; Duleba, A. J.; Fouad, H.; Nagamani, M.; Al-Hendy, A. Lower levels of urinary 2-hydroxyestrogens in polycystic ovary syndrome. J. Clin. Endocrinol. Metab. 2007, 92 (8), 3285−3291. (25) Stener-Victorin, E.; Holm, G.; Labrie, F.; Nilsson, L.; Janson, P. O.; Ohlsson, C. Are there any sensitive and specific sex steroid markers for polycystic ovary syndrome? J. Clin. Endocrinol. Metab. 2010, 95 (2), 810−819. (26) Hodavance, M. S.; Ralston, S. L.; Pelczer, I. Beyond blood sugar: the potential of NMR-based metabonomics for type 2 human diabetes, and the horse as a possible model. Anal. Bioanal. Chem. 2007, 387 (2), 533−537. (27) Makinen, V. P.; Soininen, P.; Forsblom, C.; Parkkonen, M.; Ingman, P.; Kaski, K.; Groop, P. H.; Ala-Korpela, M. Diagnosing diabetic nephropathy by 1H NMR metabonomics of serum. Magn. Reson. Mater. Phys., Biol. Med. 2006, 19 (6), 281−296. (28) Messana, I.; Forni, F.; Ferrari, F.; Rossi, C.; Giardina, B.; Zuppi, C. Proton nuclear magnetic resonance spectral profiles of urine in type II diabetic patients. Clin. Chem. 1998, 44 (7), 1529−1534. (29) Griffin, J. L.; Nicholls, A. W. Metabolomics as a functional genomic tool for understanding lipid dysfunction in diabetes, obesity and related disorders. Pharmacogenomics 2006, 7 (7), 1095−1107. (30) Gulston, M. K.; Titman, C. M.; Griffin, J. L. Applications of metabolomics to understanding obesity in mouse and man. Biomarkers Med. 2007, 1 (4), 575−582. (31) Lewis, G. D.; Gerszten, R. E. Toward metabolomic signatures of cardiovascular disease. Circ. Cardiovasc. Genet. 2010, 3 (2), 119−121. (32) Shah, S. H.; Hauser, E. R.; Bain, J. R.; Muehlbauer, M. J.; Haynes, C.; Stevens, R. D.; Wenner, B. R.; Dowdy, Z. E.; Granger, C. B.; Ginsburg, G. S.; Newgard, C. B.; Kraus, W. E. High heritability of metabolomic profiles in families burdened with premature cardiovascular disease. Mol. Syst. Biol. 2009, 5, 258. (33) Waterman, C. L.; Kian-Kai, C.; Griffin, J. L. Metabolomic strategies to study lipotoxicity in cardiovascular disease. Biochim. Biophys. Acta 2010, 1801 (3), 230−234. (34) Atherton, H. J.; Bailey, N. J.; Zhang, W.; Taylor, J.; Major, H.; Shockcor, J.; Clarke, K.; Griffin, J. L. A combined 1H-NMR spectroscopy- and mass spectrometry-based metabolomic study of

the PPAR-alpha null mutant mouse defines profound systemic changes in metabolism linked to the metabolic syndrome. Physiol. Genomics 2006, 27 (2), 178−186. (35) Vinaixa, M.; Rodriguez, M. A.; Samino, S.; Díaz, M.; Beltran, A.; Mallol, R.; Bladé, C.; Ibañez, L.; Correig, X.; Yanes, O. Metabolomics reveals reduction of metabolic oxidation in women with polycystic ovary syndrome after pioglitazone-flutamide-metformin polytherapy. PLoS One 2011, 6 (12), e29052. (36) Nicholson, J. K.; Lindon, J. C.; Holmes, E. ’Metabonomics’: understanding the metabolic responses of living systems to pathophysiological stimuli via multivariate statistical analysis of biological NMR spectroscopic data. Xenobiotica 1999, 29 (11), 1181−1189. (37) Woodcock, J. The prospects for ″personalized medicine″ in drug development and drug therapy. Clin. Pharmacol. Ther. 2007, 81 (2), 164−169. (38) Nebert, D. W.; Vesell, E. S. Can personalized drug therapy be achieved? A closer look at pharmaco-metabonomics. Trends Pharmacol. Sci. 2006, 27 (11), 580−586. (39) Lindon, J. C.; Holmes, E.; Bollard, M. E.; Stanley, E. G.; Nicholson, J. K. Metabonomics technologies and their applications in physiological monitoring, drug safety assessment and disease diagnosis. Biomarkers 2004, 9 (1), 1−31. (40) WHO.. Obesity: preventing and managing the global epidemic. Report of a WHO consultation. WHO Tech. Rep. Ser. 2000, 894 (i− xii), 1−253. (41) Metabolic syndrome study cooperation group of Chinese diabetes society. Suggestions about metabolic syndrome of Chinese diabetes society. Chin. J. Diab. 2004, 12, 156−161. (42) Wu, D.; Chen, A.; Johnson, C. S. An improved diffusion-ordered spectroscopy experiment incorporating bipolar-gradient pulses. J. Magn. Reson. A 1995, 115, 260−264. (43) Holmes, E.; Nicholls, A. W.; Lindon, J. C.; Connor, S. C.; Connelly, J. C.; Haselden, J. N.; Damment, S. J.; Spraul, M.; Neidig, P.; Nicholson, J. K. Chemometric models for toxicity classification based on NMR spectra of biofluids. Chem. Res. Toxicol. 2000, 13 (6), 471− 478. (44) Trygg, J.; Wold, S. Orthogonal projections to latent structures (OPLS). J. Chemometrics 2002, 16, 119−128. (45) Bylesjo, M.; Eriksson, D.; Sjodin, A.; Jansson, S.; Moritz, T.; Trygg, J. Orthogonal projections to latent structures as a strategy for microarray data normalization. BMC Bioinform. 2007, 8, 207. (46) Cloarec, O.; Dumas, M. E.; Trygg, J.; Craig, A.; Barton, R. H.; Lindon, J. C.; Nicholson, J. K.; Holmes, E. Evaluation of the orthogonal projection on latent structure model limitations caused by chemical shift variability and improved visualization of biomarker changes in 1H NMR spectroscopic metabonomic studies. Anal. Chem. 2005, 77 (2), 517−526. (47) Soininen, P.; Oorni, K.; Maaheimo, H.; Laatikainen, R.; Kovanen, P. T.; Kaski, K.; Ala-Korpela, M. 1H NMR at 800 MHz facilitates detailed phospholipid follow-up during atherogenic modifications in low density lipoproteins. Biochem. Biophys. Res. Commun. 2007, 360, 290−294. (48) Williams, R.; Lenz, E. M.; Wilson, A. J.; Granger, J.; Wilson, I.; Major, H.; Stumpf, C.; Plumb, R. A multi-analytical platform approach to the metabonomic analysis of plasma from normal and zucker (fa/fa) obese rats. Mol. Biosyst. 2006, 2, 174−183. (49) Panidis, D.; Kourtis, A.; Farmakiotis, D.; Mouslech, T.; Rousso, D.; Koliakos, G. Serum adiponectin levels in women with polycystic ovary syndrome. Hum. Reprod. 2003, 18 (9), 1790−1796. (50) Dumas, M. E.; Barton, R. H.; Toye, A.; Cloarec, O.; Blancher, C.; Rothwell, A.; Fearnside, J.; Tatoud, R.; Blanc, V.; Lindon, J. C.; Mitchell, S. C.; Holmes, E.; McCarthy, M. I.; Scott, J.; Gauguier, D.; Nicholson, J. K. Metabolic profiling reveals a contribution of gut microbiota to fatty liver phenotype in insulin-resistant mice. Proc. Natl. Acad. Sci. U.S.A. 2006, 103 (33), 12511−12516. (51) Martin, F. P.; Wang, Y.; Sprenger, N. Y.; I., K.; Lundstedt, T.; Lek, P.; Rezzi, S.; Ramadan, Z.; van Bladeren, P.; Fay, L. B.; Kochhar, S.; Lindon, J. C.; Holmes, E.; Nicholson, J. K. Probiotic modulation of 2945



Article

symbiotic gut microbial-host metabolic interactions in a humanized microbiome mouse model. Mol. Syst. Biol. 2008, 4, 157. (52) Calvani, R.; Miccheli, A.; Capuani, G.; Tomassini Miccheli, A.; Puccetti, C.; Delfini, M.; Iaconelli, A.; Nanni, G.; Mingrone, G. Gut microbiome-derived metabolites characterize a peculiar obese urinary metabotype. Int. J. Obes. (London) 2010, 34 (6), 1095−1098. (53) Dumas, M. E.; Wilder, S. P.; Bihoreau, M. T.; Barton, R. H.; Fearnside, J. F.; Argoud, K.; D’Amato, L.; Wallis, R. H.; Blancher, C.; Keun, H. C.; Baunsgaard, D.; Scott, J.; Sidelmann, U. G.; Nicholson, J. K.; Gauguier, D. Direct quantitative trait locus mapping of mammalian metabolic phenotypes in diabetic and normoglycemic rat models. Nat. Genet. 2007, 39 (5), 666−672. (54) Wang, Z.; Klipfell, E.; Bennett, B. J.; Koeth, R.; Levison, B. S.; Dugar, B.; Feldstein, A. E.; Britt, E. B.; Fu, X.; Chung, Y. M.; Wu, Y.; Schauer, P.; Smith, J. D.; Allayee, H.; Tang, W. H.; DiDonato, J. A.; Lusis, A. J.; Hazen, S. L. Gut flora metabolism of phosphatidylcholine promotes cardiovascular disease. Nature 2011, 472 (7341), 57−63. (55) Castelo-Branco, C.; Steinvarcel, F.; Osorio, A.; Ros, C.; Balasch, J. Atherogenic metabolic profile in PCOS patients: role of obesity and hyperandrogenism. Gynecol. Endocrinol. 2010, 26 (10), 736−742. (56) Carmina, E.; Orio, F.; Palomba, S.; Longo, R. A.; Cascella, T.; Colao, A.; Lombardi, G.; Rini, G. B.; Lobo, R. A. Endothelial dysfunction in PCOS: role of obesity and adipose hormones. Am. J. Med. 2006, 119 (4), e1−6. (57) Skipski, V. P.; Barclay, M.; Barclay, R. K.; Fetzer, V. A.; Good, J. J.; Archibald, F. M. Lipid composition of human serum lipoproteins. Biochem. J. 1967, 104 (2), 340−352. (58) Rajkhowa, M.; Neary, R. H.; Kumpatla, P.; Game, F. L.; Jones, P. W.; Obhrai, M. S.; Clayton, R. N. Altered composition of high density lipoproteins in women with the polycystic ovary syndrome. J. Clin. Endocrinol. Metab. 1997, 82 (10), 3389−3394. (59) Cussons, A. J.; Stuckey, B. G.; Watts, G. F. Metabolic syndrome and cardiometabolic risk in PCOS. Curr. Diab. Rep. 2007, 7 (1), 66− 73. (60) Carmina, E.; Napoli, N.; Longo, R. A.; Rini, G. B.; Lobo, R. A. Metabolic syndrome in polycystic ovary syndrome (PCOS): lower prevalence in southern Italy than in the USA and the influence of criteria for the diagnosis of PCOS. Eur. J. Endocrinol. 2006, 154 (1), 141−145. (61) Jiang, X. C.; Paultre, F.; Pearson, T. A.; Reed, R. G.; Francis, C. K.; Lin, M.; Berglund, L.; Tall, A. R. Plasma sphingomyelin level as a risk factor for coronary artery disease. Atertio. Thromb. Vasc. Biol. 2000, 20 (12), 2614−2618. (62) Schlitt, A.; Blankenberg, S.; Yan, D.; von Gizycki, H.; Buerke, M.; Werdan, K.; Bickel, C.; Lackner, K. J.; Meyer, J.; Rupprecht, H. J.; Jiang, X. C. Further evaluation of plasma sphingomyelin levels as a risk factor for coronary artery disease. Nutr. Metab. (London) 2006, 3, 5. (63) Nelson, J. C.; Jiang, X. C.; Tabas, I.; Tall, A.; Shea, S. Plasma sphingomyelin and subclinical atherosclerosis: findings from the multiethnic study of atherosclerosis. Am. J. Epidemiol. 2006, 163 (10), 903− 912. (64) Li, Z.; Zhang, H.; Liu, J.; Liang, C. P.; Li, Y.; Li, Y.; Teitelman, G.; Beyer, T.; Bui, H. H.; Peake, D. A.; Zhang, Y.; Sanders, P. E.; Kuo, M. S.; Park, T. S.; Cao, G.; Jiang, X. C. Reducing plasma membrane sphingomyelin increases insulin sensitivity. Mol. Cell. Biol. 2011, 31 (20), 4205−4218. (65) Holland, W. L.; Summers, S. A. Sphingolipids, Insulin Resistance, and Metabolic Disease: New Insights from in Vivo Manipulation of Sphingolipid Metabolism. Endocr. Rev. 2008, 29, 381−402. (66) Bell, J. D.; Brown, J. C.; Nicholson, J. K.; Sadler, P. J. Assignment of resonances for ’acute-phase’ glycoproteins in high resolution proton NMR spectra of human blood plasma. FEBS Lett. 1987, 215 (2), 311−315. (67) Schultz, D. R.; Arnold, P. I. Properties of four acute phase proteins: C-reactive protein, serum amyloid a protein, α1-acid glycoprotein, and fibrinogen. Semin. Arthritis Rheum. 1990, 20 (3), 129−147.

(68) Itoh, N.; Sakaue, S.; Nakagawa, H.; Kurogochi, M.; Ohira, H.; Deguchi, K.; Nishimura, S.; Nishimura, M. Analysis of N-glycan in serum glycoproteins from db/db mice and humans with type 2 diabetes. Am. J. Physiol. Endocrinol. Metabol. 2007, 293 (4), E1069− 1077. (69) Nishida, K.; Nagamura, Y.; Ogitsu, N.; Ishiguro, I.; Ito, M. [Change in levels of specific terminal sugar residues of glycoproteins in sera of patients with complication of diabetes and liver injury]. Rinsho Byori 1990, 38 (7), 835−837. (70) Lee, Y. S.; Choi, J. W.; Hwang, I.; Lee, J. W.; Lee, J. H.; Kim, A. Y.; Huh, J. Y.; Koh, Y. J.; Koh, G. Y.; Son, H. J.; Masuzaki, H.; Hotta, K.; Alfadda, A. A.; Kim, J. B. Adipocytokine orosomucoid integrates inflammatory and metabolic signals to preserve energy homeostasis by resolving immoderate inflammation. J. Biol. Chem. 2010, 285 (29), 22174−22185. (71) Hu, W.; Qiao, J.; Yang, Y.; Wang, L.; Li, R. Elevated C-reactive protein and monocyte chemoattractant protein-1 in patients with polycystic ovary syndrome. Eur. J. Obestet. Gynecol. Reprod. Biol. 2011, 157 (1), 53−56. (72) Diamanti-Kandarakis, E.; Paterakis, T.; Kandarakis, H. A. Indices of low-grade inflammation in polycystic ovary syndrome. Ann. N.Y. Acad. Sci. 2006, 1092, 175−186. (73) Kelly, C. C.; Lyall, H.; Petrie, J. R.; Gould, G. W.; Connell, J. M.; Sattar, N. Low grade chronic inflammation in women with polycystic ovarian syndrome. J. Clin. Endocrinol. Metab. 2001, 86 (6), 2453− 2455.

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